Proceedings of the 10th International Conference on Intellectual Capital, Knowledge Management vol 1 by ACPIL

Proceedings of the 10th International Conference on Intellectual Capital, Knowledge Management and Organisational Learning The George Washington University Washington, DC, USA 24-25 October 2013

Edited by

Dr Annie Green Volum Volume One A conference managed by ACPI, UK www.academic-conferences.org

The Proceedings of The 10th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning ICICKM-2013 The George Washington University Washington, DC, USA 24-25 October 2013 Edited by Dr Annie Green

Volume one

Copyright The Authors, 2013. All Rights Reserved. No reproduction, copy or transmission may be made without written permission from the individual authors. Papers have been double-blind peer reviewed before final submission to the conference. Initially, paper abstracts were read and selected by the conference panel for submission as possible papers for the conference. Many thanks to the reviewers who helped ensure the quality of the full papers. These Conference Proceedings have been submitted to Thomson ISI for indexing. Please note that the process of indexing can take up to a year to complete. Further copies of this book and previous yearâ&#x20AC;&#x2122;s proceedings can be purchased from http://academic-bookshop.com E-Book ISBN: 978-1-909507-79-1 E-Book ISSN: 2048-9811 Book version ISBN: 978-1-909507-77-7 Book Version ISSN: 2048-9803 CD Version ISBN: 978-1-909507-80-7 CD Version ISSN: 2048-982X The Electronic version of the Proceedings is available to download at ISSUU.com. You will need to sign up to become an ISSUU user (no cost involved) and follow the link to http://issuu.com Published by Academic Conferences and Publishing International Limited Reading UK 44-118-972-4148 www.academic-publishing.org

Contents Paper Title

Author(s)

Page No.

Preface

Committee

vii

Biographies

Volume One Knowledge Management Strategies Balanced Systems in Public Sector

Salwa Alhamoudi

The Linkages Among Intellectual Capital, Corporate Governance and Corporate Social Responsibility

Doğan Altuner, Şaban Çelik and Tuna Can Güleç

Knowledge Management in Support of Collaborative Innovation Community Capacity Building

Xiaomi An, Hepu Deng and Lemen Chao

Knowledge Management Systems for Attrition Control Activities in Private Higher Learning Institutions

Muthukkaruppan Annamalai, Kalsom Salleh and Salmiah Amin

Knowledge Management and Development of Entrepreneurial Skills Among Students in Vocational Technical Institutions in Nigeria

Stella Ify Anumnu

An Individual-Centred Model of Intellectual Capital

Teresita Arenas, Paul Griffiths and Alejandro Freraut

HEALTHQUAL International All Country Learning Network (ACLN): A Peer-Driven Knowledge Management Strategy and Community of Practice to Build Capacity for Sustainable National Quality Management Programs in Low- and Middle-Income Countries

Joshua Bardfield, Margaret Palumbo, Richard Birchard, Michelle Geis and Bruce Agins

Business Capability Modeling as a Foundation for Intellectual Capital Audits

Denise Bedford

Research Management at the Brazilian Agricultural Research Corporation (Embrapa): Development of an Information Management System

Marcelo Moreira Campos, Juarez Barbosa Tomé Júnior and Elizabeth Cristina de Oliveira Ferreira

Intellectual Capital and Its Influence on the Financial Performance of Companies in Under Developed Capital Markets – the Case of the Caribbean

Donley Carrington

Human Resource Practices and Knowledge Sharing: The Mediator Role of Culture

Delio Castaneda and Paul Toulson

Organizational Antecedents Shaping Knowledge Sharing Behaviors: Empirical Evidence From Innovative Manufacturing Sectors

Vincenzo Cavaliere and Sara Lombardi

Teaching Cases for Capturing, Capitalizing and Re-Using Knowledge: A Case Study in Senology Knowledge Sharing and Innovation: An Empirical Study in Iraqi Private Higher Education Institutions

Souad Demigha and Corinne Balleyguier

104

The Influence of ICT on the Communication of Knowledge in Academia

Natalia Dneprovskaya and Irina Koretskaya

114

The Learning Journey of IC Missionaries: A Staged Approach

John Dumay and Mary Adams

122

Knowledge Sharing and Innovation: An Empirical Study in Iraqi Private Higher Education Institutions

Sawasn Al-husseini and Ibrahim Elbeltagi

129

Big Data and Intellectual Capital: Conceptual Foundations

Scott Erickson and Helen Rothberg

139

Paper Title

Author(s)

Page No.

A Risk and Benefits Behavioral Model to Assess Intentions to Adopt Big Data

José Esteves and José Curto

147

Bridging Knowledge Management Life Cycle Theory and Practice

Max Evans and Natasha Ali

156

Intellectual Capital Disclosure in IPO Prospectuses: Evidence From Technology Companies Listed on NASDAQ

Tatiana Garanina and Alexandra Manuilova

166

Organisational Learning and Problem Solving Through Cross-firm Networking of Professionals

Mahmood Ghaznavi, Paul Toulson, Martin Perry and Keri Logan

177

Knowledge Orientation in Information Intensive Organisations: Is There a Change in Paradigm?

Paul Griffiths and Teresita Arenas

186

The Impact of HRM Practices on Knowledge Sharing Behaviour: Unexpected Results From Knowledge Intensive Firms

Salman Iqbal, Paul Toulson and David Tweed

195

Looking Further Into Externalization Phase of Organizational Learning: Questions and Some Answers

Palmira Juceviciene and Ramune Mazaliauskiene

205

Smart Development: A Conceptual Framework

Robertas Jucevicius and Laura Liugailaite – Radzvickiene

212

Big Data, Tacit Knowledge and Organizational Competitiveness

Nowshade Kabir and Elias Carayannis

220

The ADIIEA Cycle: Creating an Integrated Framework for Business Processes and Organizational Learning

John Lewis

228

Working Meetings as a Tool for Knowledge Management and Trust Building

Palmira Lopez-Fresno and Taina Savolainen

236

Knowledge Management: A Business Plan Approach

Elizandra Machado, Ana Maria Bencciveni Franzoni, Helio Aisenberg Ferenhof and Paulo Mauricio Selig

243

Relationship Between Knowledge Management and SME´s Performance in México

Gonzalo Maldonado Guzmán, Gabriela Citlalli López Torres and María del Carmen Martínez Serna

252

Knowledge Sharing and Intellectual Liabilities in a Global Perspective

Maurizio Massaro, Roland Bardy and Michael Pitts

259

Innovating Corporate Management: Introducing Environmental Aspects to Design Activities

Eunika Mercier-Laurent

267

Examining the Transfer of Academic Knowledge to Business Practitioners: Doctoral Program Graduates as Intermediaries

Madora Moshonsky, Alexander Serenko and Nick Bontis

272

The Influence of Cultural Factors on Creation of Organization’s Knowing

Vyda Mozuriuniene, Palmira Juceviciene and Kestutis Mozuriunas

282

A Structural Equation Model of Organizational Learning Based on Leadership Style in Universities

Fattah Nazem, Mona Omidi and Omalbanine Sadeghi

290

The Compilation of a Structural Model for Organizational Learning Based on Social Capital in Universities

Fattah Nazem, Omalbanine Sadeghi and Mona Omidi

298

Structural Equation Modeling of Intellectual Capital Based on Organizational Learning in Iran's General Inspections Organization

Faezeh Norozi, Fattah Nazem and Mina Mozaiini

304

The Construction of an Operational-Level Knowledge Management Framework

Jamie O’Brien

310

Facilitators, Inhibitors, and Obstacles – a Refined Categorization Regarding Barriers for Knowledge Transfer, Sharing and Flow

Dan Paulin and Mats Winroth

320

Paper Title

Author(s)

Page No.

Volume Two The Influence of Intellectual Capital on Firm Performance Among Slovak SMEs

Anna Pilková, Jana Volná, Ján Papula and Marián Holienka

329

Indicators for Assessment of Innovation Related Intellectual Capital

Agnieta Pretorius

339

Voluntary Sector Organisations: Untapped Sources of Lessons for Knowledge Management

Gillian Ragsdell

349

Proposal of Indicators for Reporting on Intellectual Capital in Universities

Yolanda Ramírez, Ángel Tejada and Agustín Baidez

355

10 Years of IC and KM Research – a Content and Citation Analysis

Vincent Ribiere and Christian Walter

367

Towards an Anthropological-Based Knowledge Management

Francis Rousseaux and Jean Petit

377

Intelligence in the Oil Patch: Knowledge Management and Competitive Intelligence Insights

Helen Rothberg and Scott Erickson

387

To Study the Relationship Between Knowledge Utilization and Learning Capability in a Team

Manasi Shukla

394

Competency-Based HRM and Lifelong Learning in Poland

Lukasz Sienkiewicz, Agnieszka ChłońDomińczak and Katarzyna Trawińska-Konador

401

Following Traces of Collective Intelligence in Social Networks: Case of Lithuania

Aelita Skarzauskiene, Birute PitrenaiteZileniene and Edgaras Leichteris

411

Relational Capital and Social Capital: One or two Fields of Research?

Kaisa Still, Jukka Huhtamäki and Martha Russell

420

The Personalised Computer Support of Knowledge Management

Stefan Svetsky, Oliver Moravcik and Jana Stefankova

429

The Moscow State University of Economics, Statistics and Informatics (MESI) on the way to Smart Education

Vladimir Tikhomirov

434

The Management of the Intellectual Capital in the Russian Industrial Networks

Elena Tkachenko and Sergey Bodrunov

440

Intellectual Capital Practices of SMEs and MNCs: A Knowledge Management Perspective

Mariza Tsakalerou and Rongbin Lee

447

Managing Knowledge and Overcoming Resistance to Change: A Case Study at Firat University

Nurhayat Varol and Serkan Varol

452

Organizational Learning Rate Dependence on National Wealth: Case Study of Business Schools

Karen Voolaid and Üllas Ehrlich

457

Ready For Open Innovation or not? An Open Innovation Readiness Assessment Model (OIRAM)

Naphunsakorn Waiyawuththanapoom, Thierry Isckia and Farhad Danesghar

465

PHD Research Papers

473

Cultural Influences on Knowledge Sharing Behaviours Through Open System Vs. Closed System Cultures: The Impact of Organisational Culture on Knowledge Sharing

Hanan Abdulla Al Mehairi

475

Knowledge Management as a Competitive Advantage of Contemporary Companies

Andrijana Bogdanovska Gjurovikj

482

The Importance of Knowledge Waste for Intellectual Capital Management and Enterprise Performance

Helio Aisenberg Ferenhof and Paulo Mauricio Selig

489

Dissemination of Professional Routines, a Case Study in the Automotive Industry

Johanna Frances, Stéphane Robin and JeanLouis Ermine

499

iii

Paper Title

Author(s)

Page No.

Cluster Analysis of the European Countries: The Europe 2020 Point of View

Adela Anca Fucec and Corina Marinescu (Pirlogea)

507

Literature Review: The Role of Intangible Resources in Improving Quality of Care in Hospitals: A Framework to Evaluate Technical and Functional Quality

Hussain Hamed and Simon De Lusignan

514

Organizational Employee Seen as Environmental Knowledge Fractal Agents as a Consequence of the Certification With ISO 14001

Ionut Viorel Herghiligiu, Luminita Mihaela Lupu, Cristina Maria Paius, Christian Robledo and Abdessamad Kobi

524

Career and Knowledge Management Practices and Occupational Self Efficacy of Elderly Employees

Chandana Jayawardena and Ales Gregar

533

Easy, Economic, Expedient – an Effective Training Evaluation Model for SMEs

Sajid Khan and Phil Ramsey

540

An Exploratory Study of Knowledge Management in the UK Local Government Planning System for Improved Efficiency and Effectiveness

Nasrullah Khilji and Stephen Roberts

551

Characterization of Knowledge Sharing Practices in a Project Based Organization

Irene Kitimbo and Kimiz Dalkir

561

Person-Organisation fit as an Organisational Learning Tool in Employee Selection

Jana Makraiova, Paul Woolliscroft, Dagmar Caganova and Milos Cambal

569

Models for Describing Knowledge Sharing Practices in the Healthcare Industry

Negar Monazam Tabrizi

576

The Systemic “Learning by Sharing” Diamond: How to Implant it Concretely in Private Organization?

Alexandru-Ionuţ Pohonţu, Camille Baulan and Costache Rusu

585

A Leadership Framework for Organizational Knowledge Sharing

Hong Quach

596

Project Context and its Effect on Individual Competencies and Project Team Performance

Mikhail Rozhkov, Benny Cheung and Eric Tsui

602

The Influence of Emotional Intelligence on Employees’ Knowledge Sharing Attitude in Organizations in Thailand

Chanthawan Sathitsemakul and Francesco Calabrese

612

Quality of Higher Education Institutions as a Factor of Students’ Decision-Making Process

Petr Svoboda and Jan Cerny

622

Business Clusters and Knowledge Management: Information Flows and Network Concepts

Mariza Tsakalerou and Stefanos Katsavounis

632

An Analysis of Mobile Applications for the Purpose of Facilitating Knowledge Management

Serkan Varol and Ryan Underdown

638

The Implications of Tacit Knowledge Utilisation Within Project Management Risk Assessment

Paul Woolliscroft, Marcin Relich, Dagmar Caganova, Milos Cambal, Jana Sujanova and Jana Makraiova

645

Non Academic Paper

653

Managing Learning Style Across Generation in Workplace

Juli Purwanti, Fadillah Rizky and Wahyudi Handriyanto

Masters Research Papers

655 665

A Critical Analysis of Intellectual Capital Reports in Banking Industry from 1994 to 2011

Linlin Cai, Eric. Tsui and Benny Cheung

667

Research on Intellectual Capital Elements Synergy in Research Organizations

Li Ya-nan, Xiao Jian-hua, Cao Liu and Zhu Linlin

674

Preface These proceedings represent the work of researchers participating in the 10th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning - ICICKM 2013, which this year is being held at The George Washington University. The Conference Chair is Dr Michael Stankosky and the Programme Chair is Dr Annie Green, both from The George Washington University, Washington, DC, USA. The conference sessions are being held at the The George Washington University and the conference dinner is being held at the Smith & Wollensky restaurant in Washington. The conference will open with keynote speaker Debra Amidon, from ENTOVATION International, Ltd. Wilmington, Massachusetts, USA who will address the topic of The IC Bretton Woods: A Global Innovation Frontier. The second keynote presentation will be by Jonathan Low, from PREDICTIV, USA on the topic of Competitive Advantage in the Age of Intangibles. The ICICKM Conference constitutes a valuable platform for individuals to present their research findings, display their work in progress and discuss conceptual advances in many different branches of intellectual capital, knowledge management and organisational learning. At the same time, it provides an important opportunity for members of the KM community to come together with peers, share knowledge and exchange ideas. ICICKM has evolved and developed over the past nine years, and the range of papers accepted in this year's conference ensures an interesting two-day event. Following an initial submission of 219 abstracts that have undergone a double blind peer review process, 57 research papers, 21 PhD research papers, 2 Master's research papers, and 1 non-academic papers are published in the ICICKM 2013 Conference Proceedings, representing work from Australia, Barbados, Brazil, Canada, Chile, Colombia, Czech Republic, Denmark, Estonia, Ethiopia, Finland, France, Greece, Hong Kong, India, Indonesia, Iran, Kazakhstan, Lithuania, Macedonia, Malaysia, Mexico, New Zealand, Nigeria, Poland, Romania, Russia, Russian Federation, Saudi Arabia, Slovakia, South Africa, Spain, Sweden, Thailand, Turkey UK, United Arab Emirates and the USA. I hope that you have an enjoyable conference. Dr Annie Green Programme Chair October 2013

Conference Executive Dr Michael Stankosky, The George Washington University, USA Dr Annie Green, The George Washington University, USA Vincent Ribiere, Bangkok University, Bangkok, Thailand Kevin O’Sullivan, New York Institute of Technology, New York, USA Mark Addleson, George Mason University, USA Denise Bedford, Kent State University, USA Dr. Sebastián Díaz, West Virginia University, USA Dr. William “Bill” E. Halal, The George Washington University, USA Patrice Jackson, Lockheed Martin, USA Jim Lee, Knowledge Management, APQC, USA Dr. John Lewis, Kent State University, USA Dr. Arthur J. Murray, CEO, Applied Knowledge Sciences Inc., USA Dr. Alfredo Revilak, The George Washington University, USA Dr. Anthony J. Rhem, A.J. Rhem & Associates, Inc, USA Douglas Weidner, International Knowledge Management Institute, USA Ellen Ensel, Information Services, United States Institute of Peace Dr Anne L. Washington, George Mason University, USA Mary Adams, Trek Consulting, USA Verna Allee, President, ValueNet Works, USA Mini track chairs Mary Adams, Smarter-Companies, USA John Dumay, University of Sydney, Australia Dr G. Scott Erickson, Ithaca College, Ithaca, New York, USA Dr Helen N. Rothberg, Marist College, Poughkeepsie, NY, USA Professor Eunika Mercier-Laurent, IAE Lyon, France Camilo Augusto Sequeira, Institute of Energy of PUC-Rio, Brazil Dr Susanne Gretzinger, University of Southern Denmark, Sønderborg Dr Kalsom Salleh, Universiti Teknologi MARA, Shah Alam, Malaysia. Dr Anthony J Rhem, Knowledge Systems Institute (KSI), Illinois, USA Dr Mark Addleson, George Mason University, Virginia, USA Dr. John Lewis, Kent State University, USA Conference Committee The conference programme committee consists of key people in the intellectual capital, knowledge management and organisational learning communities; the list includes leading academics, researchers, and practitioners from around the world. The following people have confirmed their participation: Mohd Helmy Abd Wahab (Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia); Prof Marie-Hélène Abel (Compiegne University of Technology, France); PROF. PHD. Maria-Madela Abrudan (University of Oradea, faculty of Economics, Romania); Dr. Bulent Acma (Anadolu University, Turkey); Mary Adams (Trek Consulting, USA); Mark Addleson (George Mason University, USA); Faisal Ahmed (FORE School of Management, New Delhi, India); Prof. Dr. Dr. Ruth Alas (Estonian Business School, Tallin, Estonia); Dr Joao Pedro Albino (UNESP, , Brazil); Mulhim Al-Doori (American University in Dubai, United Arab Emirates); Tahseen Al-Doori (American University in Dubai, United Arab Emirates); Dr Alex Alexandropoulos (American University in Dubai, United Arab Emirates); Verna Allee (ValueNet Works, USA); Prof. Mohammed Allehaibi (Umm Alqura University, Makkah, Saudi Arabia); Dr Luis Alvarado (Universidad Catolica del Norte , Chile); Dr/Prof Xiaomi An (Renmin University of China, China); Dr. Gil Ariely (School of Government, Interdisciplinary Center Herzliya, Israel);Dr Fátima Armas (CISUC, Coimbra University , Portugal); Dr Yousif Asfour (Injazat Data Systems, Abu Dhabi, United Arab Emirates); Derek Asoh (Ministry of Government Services, Ontario, Canada); Bijan Azad (AUB school of Business, Lebanon); Mahjabin Banu (Jayoti Vidyapeeth Women's University, India);Professor Michael Banutu-Gomez (Rowan University, USA); Dr. Neeta Baporikar (Sultanate of Oman Ministry of Higher Education, Oman); Dr Bob Barrett(American Public University, USA,); Dr. Tomas Gabriel Bas (Pontificia Universidad Catolica de Chile, Chile); Dr Belghis Bavarsad (Shahid chamran University, Ahvaz, Iran); Abdullah Bayat (University of the Western Cape, Bellville, South Africa); Prof. Dr. Aurilla Bechina Arnzten (Hedmark University College, Norway);DR Denise Bedford (Kent state University, USA); Esra Bektas (TU Delft, The Netherlands); Diana Belohlavek (The Unicist Research Institute, Argentina); Dr David Benmahdi (Laboratoire Paragraphe EA349, Paris, France); Prof. Galiya Berdykulova (International IT university, Kazakhstan); Prof Constantin Bratianu(Academy of Economic Studies, Bucharest, Romania, Romania); Jean Pierre Briffaut (UTT, Université de Technologie de Troyes, Troyes, France); Sheryl Buckley (Unisa, South Africa); vii

Dr Acma Bulent (Anadolu University, Eskisehir, Turkey); Dr Francesco Calabrese (Institute for Knowledge and Innovation (GWU) - USA, USA); Dr. Delio Castaneda (Pontificia Universidad Javeriana, Colombia,); Saban Celik (Yasar University, Turkey); Eric Chan (Knowledge Management Development Centre, Hong Kong , Hong Kong); Fernando Chaparro (Universidad del Rosario, Bogotá, Colombia); Prof David Chapinski (Rutgers, The State University of New Jersey: Newark, United States) Daniele Chauvel (SKEMA Business School , France); Prof Phaik Kin Cheah (Universiti Tunku Abdul Rahman, Malaysia); Dr Benny Cheung (The Hong Kong Polytechnic University, Hong Kong); Dr. Vikas Choudhary (National Institute of Technology,Kurukshetra, India); Rashid Chowdhury (Independent University, Bangladesh, Chittagong, Bangladesh); Dr Reet Cronk (Harding University, USA); Prof. Marina Dabic(University of Zagreb, Croatia); Professor Pablo da Silveira (Catholic University of Uruguay, Uruguay); Raymond D'Amore (The Mitre Corporation, McLean, VA , USA); Geoffrey Darnton (Bournemouth University, UK); Dr. Kandy Dayaram (Curtin University of Technology, Perth, Australia); John Deary (Independent Consultant, UK & Italy); Prof Paola Demartini (University of Rome 3, Faculty of Economics, department of Management and Law, Italy); Dr Michael D'Eredita(Syracuse University, New York, USA); Dr Charles Despres (Skema Business School, Sophia-Antipolis, Nice , France); Dr Sebastián Díaz (Learning & Culture at West Virginia University, USA); Dr John Dumay (University of Sydney, Australia); Dr Neeraj Dwivedi (Indian Institute of Management Lucknow, India); Jamal El Den (Charles Darwin University, Australia); Ellen Ensel (United States Institute of Peace, USA); Dr. Scott Erickson (Ithaca College, USA); Jean-Louis Ermine(Institut National des telecommunications, Evry, France); Geoff Erwin (Independent Consultant, South Africa); Mercy Escalante (Sao Paulo University, Brazil);Dr. Ibrahim Fahmi (Glasgow Caledonian University, UK); Nima Fallah (University of Strasbourg, France); Tony Feghali (AUB school of Business, Lebanon);Prof Liliana Feleaga (Academy of Economic Studies, Romania,); Dr Silvia Florea (Lucian Blaga University, Romania,); Dr Ines Friss de Kereki (ORT Uruguay University, Montevideo, Uruguay); Stan Garfield (Global Consulting Knowledge Management Group, USA); Dr Liza M. Gernal (American Collegeof Dubai, United Arab Emirates); Dr. Nasim Ghanbartehrani (IMI, Iran,); Dr John Girard (Minot State University, , USA); Dr Marco Giuliani (University of The Marche, Ancona, Italy); Prof. Dr. Adriana Giurgiu (University of Oradea, Faculty of Economic Sciences, Romania); Dr Andrew Goh (International Management Journals, Singapore); Gerald Goh (Multimedia University, Melaka, Malaysia); Dr. Sayed Mahdi Golestan Hashemi (Faculty of Industrial Engineering - MA University & center for Creatology & triz & innovation Manage, Iran); Farshid Golzadeh Kermani (University of Sheffield, UK); Liliana Gomez (Universidad del Rosario, Bogotá, Colombia); Ken Grant (Ryerson University, Toronto, Canada); Dr Annie Green (The George Washington University, Washington, DC, USA); Prof. Dr. Susanne Gretzinger (Department for Border Region Studies, Denmark); Paul Griffiths (Director, IBM, Santiago, Chile); Prof Veronica Grosu (Stefan Cel Mare University Of Suceava,Romania); Michel Grundstein (Lamsade Paris Dauphine University, France); Dr Daniel Guevara (KM-IC Research, Mexico); Giora Hadar (Federal Aviation Administration, USA); Dr Anne Hakansson (Uppsala University, Sweden); Dr William Halal (George Washington University, USA); Dr Leila Halawi (American University in Dubai, United Arab Emirates); Igor Hawryszkiewycz (University of Technology, Sydney, Australia); Dr Ilona Heldal(University of Skovde, Sweden); Liaquat Hossain (Syracuse University, USA); Dr. Yassaman Imani (University of Hertfordshire, UK); Patrice Jackson(Lockheed Martin, UK); Prof. Brigita Janiunaite (Kaunas University of Technology, Lithuania); Dawn Jutla (University of Halifax, Canada); Prof Konstantinos Kalemis (National Centre of Local Goverment and Administration, Greece); Dr Amrizah Kamaluddin (Universiti Teknologi MARA, Malaysia); Dr SilvaKarkoulian (Lebanese American University Beirut Campus, Lebanon); Dr. Jalil Khavandkar (Zanjan Science & Technology Park, Iran); Dr Prof Aino Kianto(Lappeenranta University of Technology, Finland); Hans-Peter Knudsen (Universidad del Rosario, Bogotá, Colombia); Dr Andrew Kok (University of Johannesburg, South Africa); Eric Kong (University of Southern Queensland, Australia); Prof. Dr. Richard Lackes (Institute of Business Informatics, TU Dortmund, Germany ); Jim Lee (APQC, USA); Prof. Rongbin W.B. Lee (The Hong kong polytechnic university, Hong Kong); Rene Leveaux (University of Technology, Sydney, Australia); Dr John Lewis (Kent State University, USA); Dr Antti Lönnqvist (Tampere University of Technology, Finland); Professor Ilidio Lopes ( Polytechnic Institute of Santarém; University of Coimbra, Portugal, Portugal); Dr. Palmira Lopez-Fresno (Unniversity of East of Finland, Finland); Dr Fergal McGrath (University of Limerick, Ireland); Prof Eunika Mercier-Laurent (University Jean Moulin Lyon, France,); Kostas Metaxiotis (National Technical University Athens, Greece); Dr Marina Meza (Universidad Simón Bolívar, Venezuela,); Dr Ian Michael (Zayed University, Dubai, United Arab Emirates);Associate Prof. Ludmila Ml dkov (University of Econimics Prague, Czech Republic); Dr Sandra Moffett (University of Ulster, UK); Dr Kavoos Mohannak (Queensland University of Technology, Australia); Muhammad Izwan Mohd Badrillah (UITM, Malaysia); Dr. Alunica Morariu, (“Stefan cel Mare" University of Suceava, Faculty of Economics and Public Administration, Romania); Maria Cristina Morariu (The Academy of Economic Studies, Romania); Elaine Mosconi (Université Laval, Quebec, Canada); Dr Claudia Mueller (Innsbruck University School of Management, Austria); Hafizi Muhamad Ali (Yanbu University College, Saudi Arabia); Aroop Mukherjee (King Saud University, Saudi Arabia); Dr Arthur Murray (Applied Knowledge Sciences, Inc., USA); Maria Mylopoulos (University of Toronto, Canada); Prof. Nader Nada (College of Computing, AAST, Egypt); Dr Atulya Nagar (Liverpool Hope University, UK); Tasawar Nawaz (Kozminski University, Poland); Dr Artie Ng (The Hong Kong Polytechnic University, Hong Kong); Prof Emanuela Alisa Nica (Petre Andrei University from Iasi, Romania); Dr Chetsada Noknoi (Thaksin University, Songkhla, Thailand); Dr. Kevin O’Sullivan (New York Institute of Technology, USA); Reese Olger (USMC, USA); Dr Abdelnaser Omran (School of Housing, Building and Planning, Universiti Sains Malaysia, Malaysia); Professor Ibrahim Osman (American University of Beirut, Lebanon); Kevin O'Sullivan (School of Management, USA); Dr. Jayanth Paraki (Omega Associates, Bangalore, India); Prof Robert Parent (Université de Sherbrooke, Quebec, Canada); Dr Shaun Pather (Cape Peninsula University of Technology, , South Africa); Dan Paulin (Chalmers University of Technology, Göteborg, Sweden); Dr Parag Pendharkar (Pennsylvania State University at Harrisburg, USA); Pramuk Perera (Aviareps FZ LLC , Dubai, UAE); Milly Perry(The Open University of Israel, Isviii

rael); Dr. Monika Petraite (Kaunas University of Technology, Lithuania ); Dr Prapon Phasukyud (The Knowledge Management Institute (KMI) - Thailand, Thailand); Rajiv Phougat (IBM Corporation, USA); Dr. V. Nguyen Phuc (Asian Institute of Technology and Management, Vietnam,); Dr John Politis (Neapolis University, Pafos, Cyprus); Dr Siwarit Pongsakornrungsilp (Walailak University, Thailand); Dr Agnieta Pretorius (Tshwane University of Technology, Witbank, South Africa); Dr. Devendra Punia (Wipro Consulting Services, New Delhi, India); Dr Mohamed Rabhi (Saudi Basic Industries Corporation (SABIC), Saudi Arabia); Dr Bilba Radu (George Bacovia University, Romania); Dr Gillian Ragsdell (Information Science, Loughborough University, UK); Azlina Rahim (Universiti Teknologi MARA, Malaysia,); Dr Lila Rajabion (Penn State University, Mont Alto , USA); Prof Subashini Rajagopal (VIT University, India);Dr Siriwan Ratanakarn (Bangkok University, Thailand, Thailand); Dr Alfredo Revilak (George Washington University Institute for Knowledge and Innovation (IKI), USA); Dr Anthony Rhem (A.J. Rhem & Associates, Inc, USA); Dr Vincent Ribière (The Institute for Knowledge and Innovation Southeast Asia (IKI-SEA) of Bangkok University, Bangkok, , Thailand); Waltraut Ritter (Asia Pacific Intellectual Capital Centre, Hong Kong ); Eduardo Rodriguez (IQ Analytics, Ottawa, Canada); Prof Goran Roos (Cranfield University, UK); Mustafa Sagsan (Near East University, Nicosia, Northern Cyprus, Cyprus); Randa Salamoun Sioufi(American University of Beirut, Lebanon, Lebanon); Dr Kalsom Salleh (University Technology MARA, Malaysia); Dr. Antonio Sandu (Mihail Kogalniceanu University, Romania); Assoc.Prof.Dr. Tulen Saner (Near East University, North Cyprus); Prof. Taina Savolainen (Universiy of Eastern Finland, Dpt. of Business, Finland); Professor Giovanni Schiuma (Universita dela Basilicata, Matera, Italy); Camilo Augusto Sequeira (Catholic University, Rio De Janeiro, Brazil); Dr. Enric Serradell-Lopez (Open University of Catalonia, Barcelona, Spain); Amanuddin Shamsuddin (Universiti Tenaga Nasional, Malaysia); Dr Mehdi Shariatmadari (Islamic Azad University, Central Tehran Branch, Iran,); Dr Michael Shoukat (UMUC, USA); Dr Sharad Sinha (R.B.S. College of Education, Rewari, India); Guy St.Clair (SMR Intel, USA); Dr Michael Stankosky (The George Washington University, Washington, DC, USA); Michael Stelzer (Knowledge Management Services & Associates, USA); PhD. Jukka Surakka (Arcada-University of Applied Science, Helsinki, Finland); Dr Marzena Swigon (University of Warmia and Mazury, Poland); Dr Cheng Ling Tan (Universiti Sains Malaysia, Malaysia); Nya Ling Christine Tan (Multimedia University, Malaysia); Paul Toulson (Massey University , New Zealand); Ana Treviño (ITESM, Mexico); Dr Nachiketa Tripathi (Indian Institute of Technology Guwahati, India); Dr Edward Truch (Lancaster University Management School, UK); Prof Eric Tsui (Knowledge Management Research Centre ,The Hong Kong Polytechnic University, Hong Kong); Dr Geoff Turner (University of Nicosia, Cyprus); Ass.Prof.Dr. Lucian Unita (University of Oradea, Faculty of Medicine and Pharmacy, Romania,); Mathias Uslar (OFFIS, Oldenburg, Germany); Dr Herman van Niekerk (Suritec Pty Ltd, Cape Town, South Africa); Prof Asaf Varol (Firat Univeristy, Elazig, Turkey); Nurhayat Varol (Firat University, Turkey); Francisco Vasquez (Universidad Jorge Tadeo Lozano, Bogotá, Colombia); Jeannette Vélez (Universidad del Rosario, Bogotá, , Colombia); Professor Jose Maria Viedma (Polytechnic University of Catalonia, Spain); Dr Anne L Washington (George Mason University, USA); Douglas Weidner (International Knowledge Management Institute, USA); Dr Ismail Wekke (State College of Sorong, West Papua); LaNae Wheeler ( Johnson Controls, UK); Tanakorn Wichaiwong (Kasetsart University, Thailand); Dr Roy Williams (University of Portsmouth, UK); Dr Tiparatana Wongcharoen (Bangkok University, Thailand, Thailand); Dr Lugkana Worasinchai (The Institute for Knowledge and Innovation Southeast Asia (IKI-SEA) of Bangkok University, Bangkok , Thailand); Dr Les Worrall (University of Coventry, UK); Dr An Xiaomi (Renmin University of China, China); Dr Mohammad Hossein Yarmohammadian (Health Management and Economic Research Center, Isfahan University of Medical Sciences, Iran); Dr. Pitipong Yodmongkon (College of Arts Media and Technology, Chiang Mai University, Thailand); Aw Yoke Cheng (The Asia Pacific University of Technology and Innovation(A.P.U) UNITAR International University, Malaysia); Dr Malgorzata Zieba (Gdansk University of Technology,Poland); Philip Zgheib (American University of Beirut , Lebanon); Prof. Qinglong Zhan (Tianjin University of Technology and Education, China) Dr Suzanne Zyngier (Latrobe University, France)

Biographies Conference Chair Dr Michael Stankosky obtained his doctorate from George Washington University (GW) by researching organizational effectiveness. His subsequent research focuses on how to engineer and manage a global enterprise in a knowledge-based economy. He created the theoretical constructs required for the master’s and doctorate in knowledge management (KM) – a first in academia. He is Editor Emeritus of VINE: The Journal of Information and Knowledge Management Systems - part of Emerald Group Ltd.

Programme Chair Dr Annie Green is a Knowledge Strategist/Architect and has led several KM initiatives. Her initial research efforts were focused on intangible asset valuation. Her subsequent research efforts are focused on the development of two methodologies: 1) PLANT (Plan, Layout, Actualize, Nourish, Transition) a performance based Knowledge Management methodology, and 2) BRAIN (Business Reasoning, Analytics and Intelligence Network) an intangible asset valuation methodology and measurement tool.

Keynote Speakers Debra Amidon is a global innovation strategist and founder of ENTOVATION International Ltd, and is considered an architect of the Knowledge Economy demonstrating how theories can be applied for practical results. An international author and thought leader, she has published 8 books in foreign translations, including a trilogy on Knowledge Economics and The Innovation Superhighway – acclaimed as the “Innovation Book of the Decade”. She has 45 years of experience in academic administration, serving as Assistant Secretary of Education for the Commonwealth of Massachusetts, Dean at Babson College and as a corporate executive in the Office of the President. Debra has delivered hundreds of articles and keynote presentations in 38 countries on 6 continents. Her seminal research has focused on intellectual capital, stakeholder innovation, knowledge innovation zones and collaborative advantage. She advised the first student entrepreneur association [1972], established the first corporate office of technology transfer [1982]; and last year hosted the World Summit on Innovation and Entrepreneurship in Boston [WSIE 2012]. With a Network of 200+ across 68 countries, her clients include Fortune 50 companies, government agencies and enterprises such as the EU, OECD, IADB, Confederation of Indian Industries, Arab Knowledge Economy Association, UN and The World Bank. She has received several honors including Outstanding Young Professional of New England, Pi Lambda Theta Scholar, the Sigma Kappa Colby Award, selected for the Festival of Thinkers, and IC2 Institute Global Fellow for the University of Texas at Austin. She’s taught courses at IPADE, Tartu University, the Banff Center and Tilburg University. Debra holds degrees from Boston University, Columbia University and the MIT where she was an Alfred P. Sloan Fellow. Jonathan Low is a Partner and Co-founder of Predictiv Consulting and PredictivA sia. Predictiv assists corporations, government agencies, family-owned businesses and not-for-profits improve management performance, organizational effectiveness, marketing and strategy. Predictiv has particular expertise in evaluating the impact on financial results of factors such as strategy execution, reputation, organizational effectiveness, brand, innovation and post-merger integration. Clients have included Southwest Air, Pfizer, Major League Baseball, Petrobras, General Motors, UPS, United Technologies, Trump Holdings, the US Joint Chiefs of Staff, Novartis and Visa. Jon has served in a number of positions related to his work such as Co-Chair for Strategic Organizational Issues of The Brookings Institution’s Task Force on Intangible Sources of Value. He has presented his findings to the US Securities and Exchange Commission, the Financial Accounting Standards Board, the European Commission, Chinese Ministry of Technology and the New York Federal Reserve Bank. His work has appeared in Forbes, the Wall Street Journal, Harvard Management Update, New York Times and Business Week. Jon has appeared on ABC, CNN, CNBC, PBS and other electronic media. He was co-editor of Enterprise Value in the Knowledge Economy, a joint publication of the OECD and Ernst & Young in 1997. He co-authored the book Invisible Advantage, published by Perseus Press in 2002. He has contributed chapters to Business Power; Creating New Wealth from IP Assets (Wiley, 2007) and From Assets to Profits (Wiley 2009). He blogs for The Low-Down. Jon currently serves on the Board of the Center for International Understanding at Dartmouth College whose Nominating Committee he chairs; is a Director of the Athena Alliance, a Washington, DC-based policy research organization; Chairman of the Board of Classical South Florida, an NPR affiliate radio station; a Director of IPTI, a research and innovation NGO in Brazil; a Faculty member of the Reputation Institute Management Training Program; an Executive Committee member of the Palm Beach County Ethics Initiative and a member of the Advisory Committee to the Baccalaureate Degree Program at Palm Beach State College. He is a graduate of Dartmouth College and Yale University’s School of Management. x

Mini Track Chairs Mary Adams is the co-author of Intangible Capital: Putting Knowledge to Work in the 21st Century Organization and the founder of Trek Consulting, a firm that helps private companies improve their performance and value. Mary is also the author of the Smarter Companies blog and creator of the IC Knowledge Center, a global community of 350+ IC thought leaders. Prior to starting Trek in 1999, she spent fifteen years as a high-risk lender at Citicorp and Sanwa Business Credit. Dr Mark Addleson is on the faculty at George Mason University, Virginia, USA. He has taught the knowledge management course in the OD and KM Masters program for nearly 20 years and consults in the area of organizational change. His book, Beyond Management: Taking Charge at Work, about organizing knowledge-work, was published by Palgrave in 2011. Dr John Dumay is a Senior Lecturer at the University of Sydney Business School and a leading international scholar and academic author on the topic of intellectual capital. His research questions and critiques IC theory by focussing on understanding the impact of IC in practice and whether it “makes a difference Dr Scott Erickson is Professor in the Marketing/Law Department in the School of Business at Ithaca College, Ithaca, NY. He holds a PhD from Lehigh University and Masters degrees from Thunderbird and SMU. He served as Fulbright Visiting Chair at The Monieson Centre for the Study of Knowledge-Based Enterprise at Queen’sSchool of Business, Kingston, ON in 2010/2011. He has published widely on intellectual property, intellectual capital, and competitive intelligence. Dr Susanne Gretzinger is Associate Professor (PhD) at the University of Southern Denmark, Sønderborg. Her research interest is in the areas of: Social Capital, Innovation-Management, Cooperative Networks, Value Adding Webs. Susanne Gretzinger is teaching Strategic Marketing Management, Business Marketing and Consumer Behaviour. Susanne Gretzinger took her PhD from University of Paderborn (Germany) and was studying one Semester as PhD student at the IllinoisState University at Urbana/Champaign (USA). She was appointed as Marketing Manager at the BWIBau GmbH, Düsseldorf, Germany before she was appointed to the University of Southern Denmark. Dr John Lewis is an accomplished leader, author, and consultant in Knowledge Management, Strategic Management, and Performance Improvement, within multiple industries, education, and the government. He frequently presents at conferences, and has been a Masters Series speaker at ISPI and a Thought Leader speaker at CSTD. He holds a Doctoral degree in Educational Psychology from the University of Southern California, with a dissertation focus on mental models and decision making. John teaches Organizational Learning as a Knowledge Management Faculty Associate at Kent State University, and is the founder and president of Explanation Age LLC, a management consulting company that focuses on knowledge management and strategic planning. John is a proven leader with business results, and was acknowledged by Gartner with a “Best Practice” paper for a knowledge management implementation. Professor Eunika Mercier-Laurent is Global Innovation Strategist and President of Innovation 3D, researcher with IAE Lyon and professor of "knowledge innovation". Her previous positions include: research in computer architecture at INRIA, computer designer, artificial intelligence methods and tools and innovative applications with Groupe Bull. She holds degrees from Politechnika Warszawska (electronic engineer), PhD in Computer Science (Paris Diderot University) and HDR (University Jean Moulin, Lyon). She's author of over 80 publications, her last book: Innovation Ecosystems was published by Wiley2011. She is member of Institut F.R. Bull, multidisciplinary group working on influence of IT on various fields, of New Club of Paris, on board of French Association for Artificial Intelligence, expert for ANR and European Commission and Chairman of IFIP group devoted to Knowledge Management. Dr Anthony J Rhem, PhD. is an Information Systems professional with thirty (30) years of experience and currently serves as the CEO/Chief Scientist of Tacit Ware, Inc., a Knowledge Management Software company located in Chicago, Illinois. As a Knowledge Management (KM) consultant and software engineer Dr. Rhem has worked with fortune 500 corporations in retail, communications, financial, Insurance and the military in implementing KM programs, policies and KM software solutions. Dr. Rhem serves on the Board of Trustees at the Knowledge Systems Institute (KSI), where he also teaches and heads the Rexi

search Department within KSI’s Computer Science Masters program. Dr Helen Rothberg is Professor of Strategy in the School of Management at Marist College, Poughkeepsie, NY. She holds a PhD and MPhil from City University GraduateCenter, and an MBA from Baruch College, CUNY. She is on the faculty of the Academy of Competitive Intelligence and principal of HNR Associates. She has published extensively on topics including competitive intelligence and knowledge management. Helen & Scott’s latest book is Intelligence in Action: Strategically Managing Knowledge Assets, published by Palgrave Macmillan in 2012. Dr Kalsom Salleh is Associate Professor (PhD) is a senior lecturer at the Faculty of Accountancy, Universiti Teknologi MARA, Shah Alam, Malaysia. Her research areas of interest include Knowledge Management, Intellectual Capital, Accounting and Auditing. She has published many of her research papers in international refereed journals, conference proceedings and book chapters as well as sitting on the editorial board and reviewing committee members of several journals. Camilo Augusto Sequeira has a Master’s degree in Electronic Engineering from Catholic University, Rio de Janeiro, and has taught in both undergraduate and graduate programs. He has an MBA from Salford University, England. Sequeira has been top executive for multinational companies and international lecturer. He is currently a consultant and a researcher for the Institute of Energy of PUC-Rio.

Biographies of Presenting Authors Natasha Ali BA, MA is a PhD student at the Faculty of Information at the University of Toronto. Her research interests include organizational behavior, information-seeking and the strategic management of information and knowledge. Salwa Alhamoudi is an Assistant Professor and High Level Programs Coordinatora in Institute of Public Administration in Saudi Arabia. Salwa is serving as a Lecturer and Consultant specializing in Public Administration, Strategic Management, knowledge management, Balance Scorecard and Electronic Governments. She got her Ph.D from University of Portsmouth, UK. She had Msc double major in Public Administration and Research Methods. Sawasn Al-Husseini Is currently a PhD candidate at Plymouth University School of Management in the UK and a lecturer at Foundation Technical Education, Baghdad, Iraq. Shehas published five journal papers in innovation, leadership style, organizational loyalty, knowledge management, and sharing in Iraq. Recently she published three papers on knowledge sharing in proceedings of the 10th European conference on E-learning in the UK, leadership and knowledge sharing in proceedings of the 4th and 5th European conference in intellectual capital in Finland and Spain. Hanan Abdulla Al Mehairi is currently doing her PhD at Wollongong University in Dubai. She acquired a Distinction with honours Bachelor degree in applied sciences and in applied media communications from Dubai Women's College in June 2008. Hanan earned her Master's in strategic human resources management at Wollongong University in Dubai. Mrs Al Mehairi has participated in many academic conferences in different parts of the globe as she is progressing in her PhD such as Chicago, Greece, Turkey, and Las Vegas. Doğan Altuner is a professor of finance who has been working as a head of department of international trade and finance in Yaşar University. Previously, he worked as a tenured professor in East Carolina University and head of financial analyst in NATO. His primary research interests are corporate finance, behavioral finance, investment analysis. Xiaomi An is a professor of records and knowledge management at School of Information Resources Management, Renmin University of China (RUC). She is leader of Knowledge Management Team at Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education at RUC. She is Fulbright Research Scholar of University of California, Los Angeles, at California. Stella Anumnu is a lecturer in the Department of Educational Foundations, Federal College of Education (Technical) Akoka, Lagos, Nigeria. She teaches Educational Management and Research. Her areas of interest are Human Resource management,

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Entrepreneurship and Gender studies. She has published articles in professional journals and authored books. She belongs to various professional associations. Teresita Arenas Is a Professor at the Department of Industry, at the Technical University Federico Santa Maria (UTFSM), Finance and Accounting courses. She has had senior positions in the academic administration of the University, as Head of Career and Academic Director of Campus Santiago. Her research has focused on knowledge management and intellectual capital. has developed some studies in regions, particularly in the V Region of Valparaiso, along with other authors wrote the book "Towards a new concept of Cluster "and has participated in several international conferences related to the subject. Joshua Bardfield, MPH has over a decade of public health communications, research and writing experience. He currently leads communications and knowledge management strategies for HEALTHQUAL, a capacity building initiative to build sustainable national and local quality management programs to improve population health in low- and middle-incomes countries. Denise Bedford is currently the Goodyear Professor of Knowledge Management at the College of Communication and Information, Kent State University. She is currently engaged in expanding the M.A. program and the future Ph.D. program in Knowledge Management, as well as outreach and support to the national and international knowledge management communities. Kiranmai Bhamidi is a Masters student in the Department of Management Studies, IIT Madras. She received her Bachelorâ&#x20AC;&#x2122;s from Osmania University, Hyderabad in Textile Engineering. Her other research interests include Strategic Management, Technology and Innovation management and Organizational Behaviour. Andrijana Bogdanovska Djurovic is a Sheffield MBA graduate. Andrijana is a Researcher in the area of Strategic Management. Mrs. Djurovic has more than 5 years of work experience in managing and administering international donor projects, 3 years in lecturing at a University level and 3 years in leading research projects. Donley Carrington Lecturer in Accounting and Coordinator MSc. Investment and Wealth Management programme at the University of West Indies (UWI), Cavehill Campus, Barbados. He is a Graduate of UWI, Iowa State University, USA, Institute of Management Accountants USA and University of Hull, UK. Has published articles on Intellectual Capital in the Caribbean and is co-author of two books on cost accounting. Linlin Cai graduated from Jilin University in July of 2011. Her major is archive science. When she was a junior, she got a good opportunity to go to Taiwan as an exchange student. This trip has greatly broadened her horizons. Now she is a master of Philosophy of HK PolyU. Her areas of interest are intellectual capital. Delio I Castaneda is an Associate Professor of HRM and KM, School of Management, Pontificia Universidad Javeriana, Colombia. Vincenzo Francesco Cavaliere is an Associate Professor of Business Organization at Department of Business Administration, University of Florence. His research interests include entrepreneurship and organization learning in SMEs, knowledge sharing and strategic human resource management. He is member of AIDEA (Accademia Italiana di Economia Aziendale). He served as a referee for The International Journal of Knowledge, Culture & Change Management and for Nonprofit and Voluntary Sector Quarterly. Agnieszka Chlon-Dominczak, Ph. D. is an Assistant Professor in the Educational Research Institute in Warsaw as well as at Institute for Statistics and Demography in Warsaw School of Economics. Previously she was a Deputy Minister and Head of Department of Economic Analyses and Forecasting in the Ministry of Labour and Social Policy. Her research interest include: demography, pension systems, labour markets, social policy, health and education. Souad Demigha. Has a PhD in computer science from the Sorbonne-University. Souad is a Lecturer in computer science at the University of Paris XI and researcher at the CRI (Research Department of Computer Science) of the Sorbonne University. Her research lies in the area of information systems, educational systems, medical imaging and data warehousing systems. Dagmar Caganova, assoc. prof. in the field of Industrial Engineering, is the Vice director for Foreign Affairs and International Projects of the Institute of Industrial Engineering, Management and Quality at the Slovak University of Technology, Bratislava, Slovakia. Her professional interests lie in Human Resource Management, Intercultural Management and Gender Diversity. She is a tutor on PhD study programmes in Intercultural Management and Professional Language Communication and has participated in EU research programmes. Her numerous publication activities are closely connected to her professional as well as research interests. xiii

Marcelo Moreira Campos holds a Master´s Degree in Information Science in the field of knowledge management. His professional experience encompasses information and knowledge management in government organizations in Brasília, strategic management of information, and research project management. Since 2005, he is in charge of information management and organization at the R&D Department of the Brazilian Agricultural Research Corporation (Embrapa). Natalia Dnerpovskaya, PhD, Associate Professor and Head of Knowledge Management Department of Moscow State University of Economics, Statistics and Informatics (MESI). Professional activities include the academic knowledge management, online training courses design and E-learning development. José Esteves is an Associate professor of Information Systems at IE Businss School. José Holds a Ph.D. in Information systems, a Diploma in Business Administration, MSc. and engineer degrees in Information systems. He has been an Author of many published articles about ERP systems. Interests focus on the implementation and use of enterprise systems, ERP systems, and impact of information systems on organizations, benefits of information systems, knowledge management and its use at organizational level. Max Evans BSc, MI, PhD is an Adjunct at the University of Toronto (Faculty of Information and Institute of Communication, Culture and Information Technology (Digital Enterprise Management program)) where he teaches strategy, innovation, and information systems/technology. Max’s is also an Affiliated Researcher at the Knowledge Media Design Institute (KMDI) in the area of knowledge management. Helio Aisenberg Ferenhof, M. Eng., MBA, PMP PhD candidate in Production Engineering (UFSC). Has Master degree in Knowledge Management from UFSC (2011). MBA in E-Business from FGV / RJ (2001); Specialist in Ditatics for Higher Education from SENAC/SC (2012); Bachelor's degree in computer science from UNESA (1999); Johanna Frances has worked 10 years in the fields of computer science before going back to school in 2004. She did a Master 1 of History and Ethnology and a Master 2 of Sociology. Today she is a PhD in Knowledge Management in partnership with the PSA (French car manufacturer) and Telecom Ecole de Management (France). nd

Adela Anca Fucec Adela is a 2 year PhD Student at the Management Doctoral School of the Bucharest University of Economic Studies, Romania. The author’s main focus of research is the knowledge economy and its effects on micro and macroeconomic level, especially from the point of view of the quantitative and qualitative managerial efficiency. Tatiana A. Garanina Currently Tatiana A. Garanina is Senior Lecturer, Department of Finance and Accounting and Associate Director of Master in Management Programs at Graduate School of Management, St.Petersburg University. Tatiana got her Specialist Degree and Ph.D. from the same University in 2009. She also participated in Executive Programs at Harvard Business School (2011, 2012). Mahmood Ghaznavi Has 12 years of professional experience in the field of Banking and Information Technology (IT). Mahmood was involved in planning and managing IT initiatives/investments of a bank and played a key role in the development of National Credit Information System of Pakistan. Mahmood is currently pursuing his PhD degree in knowledge management (KM) in Massey University, New Zealand. Ales Gregar, Ph.D. is a Vice-Rector at Tomas Bata University in Zlín, Czech Republic. He teaches in the Faculty of Management and Economics for master and doctoral degree courses in Human Resource Management and Operating Systems. He has led many international research projects focused at strategic HRM for competitiveness, Knowledge management, and managing the Careers of elderly employees. Hussain Hamed PhD student in health care management. My research area is focused on improving performance and quality of care in acute hospitals. The focus of my PhD thesis is on the role of intangible resources (IR)in improving quality of care in hospitals. I am also interested in service improvements in health care settings. Ionut Viorel Herghiligiu is currently a PhD student in the last year at “Gheorghe Asachi” Technical University from Iasi, Romania and in the first year at University of Angers, ISTIA, and France. The title of Ionut PhD thesis is “Research on the Environmental Management System as a Complex Process at Organizations Level” Jukka Huhtamäki, M.Sc. (Hypermedia) is a researcher and a teacher at the Intelligent Information Systems Laboratory at Tampere University of Technology and a founding member of Stanford’s Innovation Ecosystems Network. His research is focused on developing methods and processes of data-driven visual analytics for insights on the structure and dynamics of business and innovation ecosystems.

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Chandana Jayawardena Doctoral researcher attached to the Department of Management and Marketing, Faculty of Management and Economics, Tomas Bata University in Zlin, Czech Republic. His research interests focus on Career Development, Managerial Competencies, and Employees Behaviour at work. He is an academic staff member of the University of Peradeniya, Sri Lanka Palmira Juceviciene Ph. D., Habil. Dr., full professor at Kaunas University of Technology, Lithuania. Research interests are individual and organizational learning, knowledge creation and management, learning organizations and regions, human resource development, higher education. Palmira has published more than 200 scholarly articles and 10 books, and is a consultant in individual and organizational learning, learning organizations and regions, and human resource development. Robertas Jucevicius is a Professor and Director of the Business Strategy Institute at Kaunas University of Technology, Lithuania. Robertas holds a PhD in Economics and Habilitated Doctor in Management, a visiting fellow at the University of Cambridge (UK), as well as Fulbright (USA) and Wallenberg (Sweden) fellow and the member of the Council for National Progress of Lithuania. Nowshade Kabir CEO of Knolee Group, a Canadian investment and consulting company focused on technology investment. Has M. Sc. in Computer Science, MBA and Ph. D. in Information Technology. Present interests are Big Data, Innovation, Knowledge Management, Semantic Technologies, Entrepreneurship and Strategic Management. Sajid Khan is doing PhD in Management at Massey University New Zealand. His doctoral research aims to identify the characteristics of mental models of flexible educators developing innovative approaches to instruction. Sajid has earned a Master degree in Human Resource Development and Post Graduate Diploma in Management from IMSciences, Pakistan and Massey University, New Zealand respectively. Nasrullah Khilji is an MBA graduate from Cardiff University of Wales, UK and currently pursuing his doctorate research at University of West London. He is actively involved in the technology driven strategies for innovative business processes at Cranfield University Innovation Centre. He also has senior level management experience in training and development. Irene Kitimbo is a doctoral candidate in the School of Information Studies at McGill University. She holds a Masters in Information Studies from McGill with an emphasis in Knowledge Management. Irene currently studies learning processes in project based organizations, such as knowledge sharing, lessons learned and the effects of institutional memory loss. Palmira López-Fresno is specialized in service quality, management and leadership abilities. Palmira is a Visiting professor at the University of East of Finland, and a President of the Service Quality Committee – Spanish Association for Quality (AEC) and Vice President of AEC. Palmira is also Author of several books on the topics of service quality and leadership abilities. Gabriela Citlalli López Torres Graduated from the Manchester Business School, in 2010, PhD in Operations Management. Her work experience includes working at the London Business School, as program developer of the Master in Business Administration (MBA). She taught at the University of Manchester. She has participated in ISO 9000 certifications at the Economical Ministry of Aguascalientes. Her industrial experience is in the automotive and electronic sector. She is researcher at the Universidad de Aguascalientes. Sarah McNabb is a Research and Dissemination Associate at Futures Group, where she supports corporate knowledge management initiatives and the USAID-funded Health Policy Project. She holds a BS in International Health from Georgetown University. Her research interests include knowledge management, health promotion, behavior change communications, health informatics, and data demand and use. Tarryn Mason is the General Manager of Progression, based in Johannesburg, South Africa, which offers end to end disability equity solutions. Tarryn has a finance degree and an MBA and has over 6 years of experience in disability solutions. She is also the project sponsor of The Core Programme – a strategic knowledge leadership intervention in Progression. Negar Monazam Tabrizi is a Doctoral candidate at the School of Environment and Development at the University of Manchester. Her research interests are knowledge management, information technology communications, and healthcare. Prior to entering academia she worked as an industrial engineer in the field of software engineering. Her academic background includes industrial engineering and management of information systems. Oliver Moravcik from 1972-1976 he was at Technische Hochschule Ilmenau/Germany, Dipl.-Ing. in Automation. 1978-1982 Technische Hochschule Ilmenau/Germany, Dr.-Ing. in Computer Science, 1990 Slovak University of Technology, assoc. Professor/ Applied Informatics and Automation, visiting profesor in Koethen and Darmstadt/Germany, 1998 Professor/Applied In-

formatics and Automation at Slovak University of Technology Bratislava, 2006 Dean of Faculty of Materials Science and Technology in Trnava, Slovak University of Technology Bratislava Vyda Mozuriuniene from Comfort Heat Ltd is the Managing Director, and has a Ph.D. in Management. Research interests – knowledge creation and management, process management, strategic management. Vyda is a Consultant in the areas of organization’s knowledge management, process management, and franchise. Dr. Mozuriuniene has published 5 scholarly articles. Li Ya Nan is a master student from University of Chinese Academy of Sciences (UCAS). Fattah Nazem is an Associate Professor. He has been vice-president of the research department for the last five years. His research interests are in the field of Higher Education Management. He has written 2 books and 97 articles. He is Chief Executive of the Quarterly Journal of Educational Science. Faezeh Norouzi has lived in Tehran, Iran for 10 years. Faezeh accomplished his master of Art in research training and has taught statistic and research approach within his study in master degree. Faezeh is now in charge of bank accountant in City bank-syyed khandan street, Tehran, Iran. Jamie O'Brien teaches at St. Norbert College in De Pere, Wisconsin, in the Business Administration Department. His areas of teaching include Management, Organizational Theory, Organizational Behavior and Strategy. He earned his Ph.D. from the University Of Limerick, Ireland, in May 2012. His research interests fall predominantly within the management discipline in the knowledge management field. Dan Paulin holds a PhD in Technology Management and Economics from Chalmers University of Technology in Gothenburg, Sweden. Currently he holds positions as Lecturer, Program Director for executive education programs, and Vice Head of Department. His research is focused on knowledge dissemination in multinational settings, with publications in scientific journals such as the EJKM. Michael Pitts is an Associate Professor of Strategic Management at Virginia Commonwealth University. He has also held a Fulbright Scholar position in Bratislava, Slovakia. He has used the ‘live-case’ to assist nearly 100 companies and he is honored to be twice selected as a School of Business “Distinguished Teacher”. Internationally, He has presented research and/or participated in grants in the European Union, Africa and the Middle East as well as North America. Alexandru-Ionut Pohontu PhD student within “Gheorghe Asachi” Technical University from Iasi, Romania focused on knowledge sharing, and organizational learning process. The title of my PhD thesis is „Promoting of synergistic processes to knowledge sharing within organizations.”. Agnieta Beatrijs Pretorius is the Academic Manager, ICT, at the eMalahleni campus of the Tshwane University of Technology, South Africa. Prior to this she was a software developer. Her current domains of research and teaching include knowledge management, assessment of intellectual capital, performance management, decision support systems, software engineering and technical programming. Juli Purwanti graduated in Mathematical Science, University of GadjahMada Yogyakarta, and getting Magister of Management, Institute of Technology Bandung. Is Leadership Facilitator in Telkom Corporate University. Juli has experience in designing Human capital Policy for 11 years. Juli is ery interested on Knowledge Management and Learning Organization practices. Hong Quach pursued her Doctor of Science in Engineering Management at the George Washington University. She has extensive professional experience in leadership and management, engineering, information technology, strategy planning, and change management. Her research interests include Engineering and Technology Management, Knowledge and Information Management. Gillian Ragsdell is a Senior Lecturer in Knowledge Management and Director of Research Degree Programme in the Department of Information Science at Loughborough University. Her interest in knowledge management practices has taken her into a wide variety of organisations; recent examples are from the voluntary sector and the energy industry. Yolanda Ramírez is an Assistant Professor of the Faculty of Economics and Business Administration at the University of Castilla-La Mancha, Spain. Her current research interests include intellectual capital, knowledge management, non-profit management and quality management. Her research work is focused on methods and techniques for building models of measuring and management intellectual capital in the universities. xvi

Vincent Ribière is a Co-Founder and the Managing Director of the Institute for Knowledge and Innovation (IKI-SEA) Southeast Asia hosted by Bangkok University. He is an Associate Professor at Bangkok University and the Program Director of the Ph.D. program in Knowledge and Innovation (KIM). Vincent consults, teaches, and conducts research in the areas of knowledge management, innovation management and creativity. Mochamad Fadillah Rizky earned a Bachelor’s Degree in Management of Telecommunication and Informatics Business (S.MB) at Sekolah Tinggi Manajemen Bisnis Telkom in 2007 and a Master‘s Degree in Business Administration (MBA) at James Cook University in 2009. He is currently working in PT Telekomunikasi Indonesia, Tbk. as an officer in Leadership and Global Talent Academy, Telkom Corporate University. Francis Rousseaux is a full professor in computer science at University, coordinator of several European R&D projects for Ircam-CNRS, a research laboratory dedicated to computer music. Engineer in informatics, he first worked within the software industry before becoming a researcher in artificial intelligence. Jean Petit is one of his students, working on cultural heritage problems. Mikhail Rozhkov PhD candidate of the Knowledge Management and Innovation Research Centre of the Department of Industrial and Systems Engineering of The Hong Kong Polytechnic University. Associate Head of the KM Research Institute of the Moscow State University of Economics, Statistics and Informatics (MESI), Moscow, Russia. Mikhail graduated from the Amur State University, Blagoveshchensk, Russia. Kalsom Salleh is a Senior lecturer at the Faculty of Accountancy, Universiti Teknologi MARA, Shah Alam, Malaysia. Research areas include Knowledge Management, Intellectual Capital, Accounting and Auditing. Published many research papers in international refereed journals, conference proceedings and book chapters as well as sitting on the editorial board and reviewing committee members of several journals and conference proceedings. Chanthawan Sathitsemakul is a Ph.D. candidate in Knowledge Management and Innovation Management and is working at Kasikornbank, the leading Thai financial institute. She is interested in how to sustain KM in the knowledge sensitive organization. She is currently doing her research on the topic “The influence of emotional intelligence on employees’ knowledge sharing attitude in organizations in Thailand”. Taina Savolainen is a Professorship of Management and Leadership at the University of Eastern Finland, Dpt. of Business. Prof. Savolainen is specialized in trust within organizations, leadership, and organizational change, and global competitiveness management. Her academic achievements have been recognized in Who’s Who in the World with over 100 international academic publications. Alexander Serenko is an Associate Professor of Management Information Systems in the Faculty of Business Administration at Lakehead University, Canada. Dr. Serenko holds a Ph.D. in Business Administration from McMaster University. Alexander has published over 60 articles in refereed journals, including MIS Quarterly, Information & Management, Communications of the ACM, and Journal of Knowledge Management. Manasi Shukla, obtained her MBA (FMS: from top five B-schools in India), PhD in Knowledge management services industries (Delhi University) has around seven years each of industry and academic experience. In total, she has around 9 publications and 17 conference acceptances. She is currently an Assistant Professor and KM Strategist at IKI-SEA, Bangkok University, Thailand. Łukasz Sienkiewicz holds a Ph.D. in human capital management from Warsaw School of Economics, where he is currently employed as an Associate Professor at the Department of Human Capital Development. He specialises in human capital management and labour market issues. Lukasz is an Expert in European Employment Observatory of the European Commission and national skills forecasting expert at CEDEFOP. Aelita Skaržauskienė was the couch in the Self-managing teams building project in European Parliament together with DEMOS Group Belgium (www.demosgroup.com). In her work dr. A. Skaržauskienė applies both knowledge of management and modern leadership-correlated disciplines such as Business dynamics, Systems thinking, Chaos and complexity theories. Adam Soder is an Applications Project Engineer at Sumitomo Drive Technologies in Chesapeake, Virginia; and is the lead managing engineer for the company’s “smart” gearbox technology. He received his Bachelors Degree in Mechanical Engineering Technology from Old Dominion University in Norfolk, Virginia in 2009; and will soon be pursuing graduate studies in engineering management.

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Melanie Sutton is the owner of i-innovate, a strategic intangible capital consulting company in Johannesburg, South Africa. With a Masters Degree in Philosophy - Knowledge Management, 13 years of hands-on practical experience as a knowledge management practitioner and an ICountant accreditation from Smarter Companies, Melanie focuses primarily on designing and developing intangible strategy programmes for organisations. Petr Svoboda received both B.Sc. and M.Sc. degrees in Management and Economics from the Faculty of Management, University of Economics in Prague, Czech Republic, in 2009 and 2011. He is currently studying for his Ph.D. degree at the same university. His research interests include marketing strategies and management. Elena Tkachenko Anatolyevna Doctor of Economics, the professor of the Department of the Enterprise Economics and Industrial Management (St. Petersburg State University Of Economics). Author of more than 120 scientific and methodical works, including 10 textbooks and 7 monographs. The sphere of scientific interests â&#x20AC;&#x201C;innovations, investments, management of the intellectual capital, Industrial development. Katarzyna TrawiĹ&#x201E;ska-Konador Studied at the University of Leuven in Belgium, the Freie UniversitĂ¤t in Berlin and the University of Vienna. Katarzyna accuired extensive hands-on professional experience in education working as director for studies at private continuing education institutions. Main fields of professional interest include vocational education and training, continuing, non-formal and informal education, and distance education. Nurhayat Varol is an Instructor in the field of Information Technology at Firat University/Turkey since 1992. Teaches IT courses based on student and project centered learning methods using distance education. Research interests are in multimedia, computer aided learning and computer aided design, e-pedagogy, distance education, knowledge management, and technical communication. Nurhayat has published more than 40 journal and conference publications. Serkan Varol He is currently pursuing Doctorate degree at Lamar University. He earned his Bachelor of Science in Industrial Engineering at West Virginia University and Master of Science in Engineering Management at Wilkes University. His research interests are in knowledge management and engineering management. Jana Volna is a Ph.D. student. She currently works on the research project VEGA 1/0920/11 called "Intellectual capital management as part of the strategic management of the company's value" with the duration of the project: January 2011 - December 2013. Karen Voolaid, graduated from Estonian Business School in 2001, defended her PhD in business administration at Tallinn University of Technology in 2013. She is working as Head of International programs and Director of Dean`s office at Tallinn School of Economics and Business Administration of TUT. Field of Research: organizational learning and development. Naphunsakorn Waiyawuththanapoom (Ronnie) is a researcher in IKI-SEA, with an extensive background and expertise in the energy economic, knowledge management and innovation management. He is currently a PhD candidate in the PhD program in Knowledge and Innovation management (PhD KIM) at Bangkok University, focused on the Open Innovation readiness framework. Paul Woolliscroft is a PhD student and researcher at the Slovak University of Technology, Faculty of Materials Science and Technology in Trnava. His PhD study area is Industrial Management and his current research interests focus upon knowledge management and the application of tacit knowledge. He holds a Masters Degree in Marketing from Staffordshire University, England.

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Knowledge Management Strategies Balanced Systems in Public Sector Salwa Alhamoudi Institute of Public Administration, Riyadh, Saudi Arabia salwa@ipa.edu.sa Abstract: In an era of sweeping technological and economic change, interest in Knowledge Management (KM) and the Balanced Scorecard (BSC) has grown among public administrators because these address issues of change, innovation, and environmental adaptation, all of which have been major concerns in organisation theory and practice for decades and are clearly important now as public organisations are being reinvented and reengineered. This study aims to investigate how do Knowledge Management Strategies influence the development of an organisation’s strategies, and Could BSC be used to develop Strategic Knowledge Management Balanced System (KMBS) for strategic management. The research provides a theoretical theory through linking research and literature on Strategic Management (SM), Knowledge Management (KM), and Balance Scorecard (BSC). This study examines the underappreciated influence of strategic Knowledge Management on performance management by using the Balanced Scorecard in the Public sector. Keywords: knowledge management, balance scorecard, strategic management; information technology, organizational learning

1. Introduction The relation between Knowledge Management and the Balanced Scorecard has been widely embraced by many organisations during the last decades. After several years of development, many organisations such as AT&T, BMW, DuPont, Mellon, and UPS, have shown an excellent performance based on the BSC which allows them to use resources effectively based on the implementation of a strategy (Wu, 2005). Despite some scepticism, it has become a significant force for organisational improvement and change, and is hailed as a critical weapon for competitiveness in the modern competitive market, particularly in developed countries (Yahya, 2009). Public sectors are finding it difficult to cope with the complexity, dynamism and inevitable crises and difficulties of such rapid development. Finding a way for organisations to develop in response to their changing environment culminated in the development of the theory of Knowledge Management. Research on these issues, however, is concentrated mostly in advanced countries (Pedler et al., 1997). Theoretical adaptations of other topics are common and beneficial to raise the level of theoretical development. Knowledge management and Balance Scorecard are two areas of knowledge of great interest that grows continuously, but still have a lack of research concerning its interrelations, so due to the importance of both topics, the aims of this paper are:

To present a perspective‐based approach to knowledge management and Balance scorecard, which intended to bridge the gap between the disciplines involved in this study.

To show how the Balance scorecard perspectives can be used to derive a definition for KMS Usable for investigations into the application of such systems in organisations.

To present a framework to analyse the state of strategic KM from different perspectives of BSC.

2. Knowledge management Despite the voluminous literature on KM, there is no readily accepted definition of the concept (Earl, 2001). The term knowledge management (KM) is just as difficult to define as knowledge itself (Bhatt, 2001). Bhatt (2001) defined KM as the processes and procedures that govern the creation, dissemination and utilisation of knowledge by merging organisational structures and people with technology in order to better leverage resources within an organisation. Davenport et al. (1998) have argued that knowledge management is concerned with the exploitation and development of the knowledge assets of an organisation with a view to furthering the organisation’s objectives. The knowledge to be managed includes both explicit, documented knowledge, and tacit, subjective knowledge. They also suggest that management entails all of those processes associated with the identification, sharing and creation of knowledge. The definition of KM adopted in this paper integrates various approaches to KM. A researcher advocates the holistic approach to KM definition and believes the KM can encompass any or all the following items: IT,

Salwa Alhamoudi business process; human/individual dimension and competitive advantage. These dimensions allow the organisation to develop, transfer, transmit, store and applies knowledge. The following definition was adopted in this research: KM is the strategic application of integrated managerial strategy, which combines the explicit (IT) and tacit (people) knowledge with organisational process to create, store, share, and apply knowledge assets from the different sources (internal and external) of knowledge to make the right decisions in order to gain the strategic objectives.

3. Knowledge management in public sector It is only in recent years that knowledge management has begun to be discussed in the context of public sector organisations, which are in dire need of more efficient and innovative product and service delivery. One considerable problem of this research is the demographic shift in the work place, whereby a large percentage of the working population will retire in the coming five to ten years. This calls for public sector organisations to institutionalize the tacit knowledge of the experienced civil servants who will soon be retiring, and passing that knowledge on to new staff through various training and mentoring programs. This problem is further compounded by high turnover rates in the public sector (OECD, 2003, 6). This recognition of the importance of knowledge management is reflected in a survey of public sector organisations in The Organization for Economic Co‐operation and Development (OECD) countries in 2003. About 44 percent of organisations surveyed indicated that managing knowledge was a strategic priority (see figure1).

Source: OECD (2003) Figure 1: Knowledge management as priority Interestingly, even for those departments where knowledge management has been articulated as a strategic priority, this has often neither been communicated across departments nor translated into actions. Only 40% of organisations questioned by the OECD (2003) who have a KM strategy have actually communicated these widely across stakeholders. In a sample of 27 mostly non‐OECD countries in Asia, (Middle East and North Africa) MENA and Europe, over two thirds are evaluating the need for knowledge Management but less than a third have or are currently setting up a knowledge management program (Yahya, 2009). Ultimately, unless public sector organisations start becoming aware of the benefits of setting organisation‐wide knowledge management goals and strategies, which involve viewing knowledge as a “significant competitive differentiator and resource of wealth and value‐creation” they will risk falling behind the dominant practices in the private sector (Riege and Lindsay, 2006, p.25).

4. The challenge face KM in public sector Milner (2000) suggests that the lack of enthusiasm to adopt KM in public services is directly linked to the required achievement of innovative and creative outcomes through the sharing of tacit knowledge “knowledge‐rich open and creative operating cultures” (Milner, 2000, p74).

Salwa Alhamoudi The connection between KM implementation and organisational form has been explored by Zack (1999) who concluded that the greatest barrier to implementing KM was the lack of fit between an organisations strategy, its structure and culture. Oliver and Roos (2000) have noted how knowledge can be seen to grow at individual, community and organisational level. The lower levels of that hierarchy together create the upper level or organisational knowledge landscape. They note “much of today’s wealth is created by knowledge workers who require vastly different management context from employees of the bureaucratic organisations of the industrial era” (Oliver and Roos, 2000, p.24).

5. Balanced scorecard The BSC as a strategic management technique has achieved much popularity amongst managers, since it has given corporate management a structured approach to measuring and managing business performance in four key areas, namely customers, financials, internal processes, and organisational learning and improvement (Kaplan and Norton, 1996a). Research has shown that a growing number of firms are replacing their financial PM and compensation systems with BSC, incorporating multiple financial and non‐financial indicators (Kaplan and Norton, 1996a; 2004). Niven (2008, P. 13) describe the Balance Scorecard as “A carefully selected set of measures derived from an organisation’s strategy. The measures selected for the Scorecard represent a tool for leaders to use in communicating to employees and external stakeholders the outcomes and performance drivers by which the organisation will achieve its mission and strategic objectives” According to the literature review, some studies attempted to examine BSC from different role perspectives, first in a role of performance measurement and second in a strategy implementation role (Geuser et al., 2009; Wu, 2005). Geuser et al. (2009), in their empirical studies, claim that BSC plays dominant function not only as a strategic performance measurement system but also as the core component of strategic processes. Chavan (2009) emphasises that the reason for adapting the BSC system is to guide, control and challenge an entire organisation towards realising a shared conception of the future.

6. Balance scorecard in public sector The BSC’s acclaimed merits and prescribed design seem to be identical for both the business and the pubic management contexts. The literature is full with examples of the successful implementation of the Balanced Scorecard approach, especially in private organizations. In Public sector, companies have recognized that financial metrics by themselves are inadequate for measuring and managing their performances (Kaplan 2001). Kaplan and Norton (1996) complemented the financial perspective with the other three perspectives: the customer, the internal process, and learning and growth. For‐profit seeking corporations, the financial perspective provides clear long‐run objectives (Kaplan 2001). On the other hand, in the not‐profit sector, the financial perspective provides a constraint rather than an objective. While the not‐profits monitor spending and adhere to financial budgets, their success or failure is not measured by spending in relationship to budgeted amounts. According to Kaplan, the typical not‐profit has had difficulty placing the financial perspective at the top of the Balanced Scorecard. He suggests that the not‐ profits consider placing a mission objective at the top of their scorecard as the mission represents the accountability between the not‐profit and society. He also suggests the not‐profits expand the definition of who their customer is. As noted by Kaplan, a growing number of not‐profits have begun using the Balanced Scorecard model (Kaplan 2001).

7. The challenges of implementing BSC in public sector A move towards managing by measures may have profound effects on the culture and working practices of the organisation. The following some challenges of implementing BSC in public sector (Aslani, 2009):

Designing and implementing the strategy require the constant support and backing from top management. Management support and their approach to create a cultural change is the required foundation to encourage employees and departments to engage in this process.

Salwa Alhamoudi

A comprehensive strategic plan such as BSC needs participation and active engagement of the employees that carry out the momentum of cultural change. Incentive programs and employee rewards for those aligned with the organisation performance should be planned and executed so that the cultural change becomes effective and be a part of the core competency of the organisation.

A channel of Communication which is normally overcomes through training sessions for management and stakeholders along with a specialized website addressing the BSC progress. Easy to use along with capabilities in displaying information according to the customer preference in a short time should be of great importance.

8. Theoretical framework for knowledge management strategies balanced systems (KMSBS) The following analyses the Strategic Knowledge Management System by reviewing the relevant literature on both soft and hard factors that are said to contribute to success of KM efforts.

8.1 Strategic management and knowledge strategies within strategic BSC systems As previously stated, KM is a new phenomenon within management systems, and thus implementation methodologies are still developing with experience (Chong and Choi, 2005). In this section, the relation between Knowledge management and strategic management will be examined within the concept of BSC strategic system. To ensure the success of KM strategies, a KM strategic plan to confirm that the KM goals are in congruence with the strategic goals of the firm or the enterprise business strategy should be developed based on the overall business strategy (Davenport et al., 1998; Wong and Aspinwall, 2005). The evolving definition of the function of knowledge management has stressed its increasingly strategic orientation. The KM activities must effectively be linked to the strategy of the organisation to ensure effective BSC incorporation as the guiding method for the enhancement of the knowledge function’s strategic role. Figure (2) shows the strategic knowledge management system.

Produced for the purpose of this research Figure 2: The strategic knowledge management system Strategic knowledge management, by utilising the BSC strategic system, can only be effectively implemented for strategic performance enhancement if the organisation is aligned to strategy. Therefore it is imperative that the strategy and related aspects be studied and understood. In Figure(1), the relation between strategic management, strategic knowledge management, and the strategic business plan within the organisation is illustrated. The following explains how these three concepts interact and knowledge is exchanged between them. Strategic Management (SM) is developed and deployed with the mission and vision of helping organisations to close the gap between what firms can do and what firms must do to be competitive (Zack, 1999, 2002). Strategic management provides the input to formulate a vision and objectives for competitive strategy based on the firm’s knowledge assets. He stated that the executives who develop the strategy need input from people throughout the organisation to be able to develop a competitive strategy.

Salwa Alhamoudi Strategic Knowledge Management(SKM) uses the output from SM to define the actions necessary to ensure the organisation’s available knowledge assets meet the organisational vision objectives and support its strategies. This involves the process required to manage the gaps between the knowledge available from internal processes and the knowledge needed from external environments. The knowledge audit is an attempt to find out what knowledge exists and what is missing, where and how it is being created, and who owns it (Lee, et al., 2010). The results from SKM show that knowledge assets, resources and capabilities have two types of output, which provide strategic competitive management, and input, which is a prime strategic resource for drawing up the strategic business plan. The Strategic Business Plan (SBP), using the output from SKM, will influence the strategic options developed in the next stage of the strategic planning process, as will the external market environment of customers and competitors. The performance and results of the SBP provide input into the firm’s strategic management and knowledge management strategies through feedback so that the firm can learn about knowledge needed and can improve strategy (Callahan, 2002; Kaplan and Norton, 2001a). 8.1.1 Proposed taxonomy of CSFs in knowledge management strategies Successful KM programmes are able to address competitive challenges not because they excel at one thing, but because they effectively integrate all the parts of the process into a strategic whole. From the previous discussion, it can be seen that the potential use of the BSC for strategic knowledge management can now be examined from the different perspectives it is used to highlight. This research develops the holistic approach to the knowledge management strategic system of the four types of strategies for managing knowledge: knowledge management resources, ICT internal knowledge, learning organisation based knowledge, and beneficiaries’ external knowledge. In addition, it discusses the critical factors that affect KM strategies based on a comprehensive analysis of KM literature. The four strategies listed below are distilled from various articles and empirical research from different perspectives from the KM literature review. They were categorised into a number of subgroups representing various dimensions of critical factors related to KM implementation. These dimensions were used to build a proposed framework for Strategic KM from different perspectives, see figure (3) The dimensions with their factors are listed below:

Level two: KM Strategies Level one: SKM

Within SM (Alhamoudi, 2010) Figure 3: Proposal model for strategic knowledge management balanced system As Figure (3) shows, the model has been divided into two levels. The first level is made up of the dominant strategic knowledge management critical factors related to Strategic Management which play a more overriding strategic role in KM projects. These factors are KM strategy and vision, communicating and linking

Salwa Alhamoudi existing knowledge, the strategic plan, and feedback and learning about knowledge needs. The second level is made up of the main Knowledge Management Strategies and has been divided into four strategies, namely KM Resources, KM Technology, KM Learning and Innovation, KM Beneficiaries strategies. These strategies are not independent of each other, and each strategy should be used in interaction with the others. Each strategy contains a number of critical factors. The dimensions with their factors are listed in and discussed in the following sections.

9. Strategic knowledge management A theme that guides and defines a firm’s knowledge management efforts which include: KM strategy provides the foundation for an organisation to deploy its resources and capabilities to achieve its strategic goals and objectives. KM vision defines the core values, purpose and goals of knowledge. It focuses on the identification of the sources of sustainable competitive advantage and how managing knowledge might contribute value to the enterprise and its members. Knowledge Resource Organisation Strategy: An emphasis on knowledge resources from people, structures, and codifying organisational knowledge for storage in repositories, and on protecting organisational knowledge from leakages and misappropriation which include:

Top management support and commitment

Flat and flexible organisational structures, supporting, facilitating and encouraging people to create and share.

Internal resource knowledge in organisations, Often available as codified knowledge stored in the repositories.

Knowledge Management Information Technology Strategy: An emphasis on knowledge processes and providing basic IT infrastructure and creating visible KM roles for sharing and transferring knowledge between employees in friendly culture which include:

KM processes provide the organisation with knowledge needed in systematic way to enable employees to access and reuse it.

Information and communication technology.

Knowledge‐friendly culture that is open and built upon trust, cooperation and collaboration among employees.

Knowledge Management Learning and Innovation Strategy: An emphasis on organisational learning that occurs through two‐way interaction between individuals, groups and organisations in a climate of learning that encourage employees to learn and develop in ways of producing and sharing knowledge which include:

Facilitates the learning of its individuals; requires open channels and free flow of information and knowledge between colleagues, departments, organisations.

Creates the climate that encourages individual to learn and to participate in work groups.

Each individual is responsible for and can plan their own development through education and training

Knowledge Management Beneficiaries Strategy: An emphasis on the external knowledge resources that give the organisation a competitive edge derived from external knowledge, typically focussing on customer‐related knowledge including:

Analyse the customer markets needs and requirements to develop products and services.

Careful performance measurement and assessment is the key to success for organisations.

The KM strategy may use benchmarking on knowledge to set targets, and the firms should stretch the targets according to best practices inside and outside the organisation.

Salwa Alhamoudi

10. Conclusion This research provided a wide overview of the literature related to strategic management, knowledge management strategies, and the balanced scorecard. It covered in depth the suggested strategic knowledge management systems and the critical success factors found in the literature related to the KM program. Balance scorecard activities require an appropriated knowledge management at all stages of return of the product to solve problems that must be addressed in all these process and factors. Knowledge management and Balance scorecard have common theoretical foundation. The strategic role of knowledge as an intangible that achieves sustainable competitive advantage for the organization fully connected with the theory of Resources and Capabilities. Moreover, this theory can be applied to important areas of research of Balance scorecard as the tangible and intangible resources can be developed inside the company. Therefore, both variables have a leading strategic role in business management. It can be concluded that putting into effect a KM project is not an easy task and has the potential for failure if the organization does not consider these factors. In addition, a small attempt was made to integrate all the KM strategies with the success factors proposed by the KM researchers. The strategic KM framework was proposed in this research. This framework aims to cover the important features of KM synthesis and consequently can provide organizations with a guideline for implementation.

References Alhamoudi, S. (2010).Strategic Knowledge Management System in Public Sector in Saudi Arabia, An adaptation of the Balanced Scorecard. Ph.D theses, Business school, University of Portsmouth. Bhatt, G. (2001). Knowledge management in organizations: examining the interaction between technologies, techniques, and people. Journal of Knowledge Management 5(1), p.68‐75. Callahan, S. 2002, Crafting a Knowledge Strategy. Paper presented at ACT Knowledge Management Forum (ActKM) Conference, Canberra. Chavan, M. (2009). The balanced scorecard: a new challenge. Journal of Management Development, 28(5), 339‐406. Chong, S. C. and Choi, Y. S., (2005). Critical Factors in the Successful Implementation of Knowledge Management. Journal of Knowledge Management Practice [electronic source] June. Davenport, T. De Long, D. and M. Beers (1998). Successful Knowledge Management Projects. Sloan Management Review, 39(2), pp. 43‐57 Earl, M. (2001). Knowledge Management Strategies: Toward a Taxonomy. Journal of Management Information Systems, 18(1), p.215. Geuser, F., Mooraj, S., and Oyon, D. (2009). Does the Balanced Scorecard Add Value? Empirical Evidence on its Effect on Performance. EUROPEAN ACCOUNTING REVIEW (THE), 18(1), 93‐122. Kaplan, R. S and Norton, D. P. (1996). Using the Balanced Scorecard as a strategic management system. Harvard Business Review, Boston, 74(1), 75. Kaplan, R. S. and Norton, D.P. (2001). Transforming the Balanced Scorecard from Performance Measurement to Strategic Management: Part1. Accounting Horizons, 14(1), 87‐104. Kaplan, R.S. and Norton, D.P. (2004). Measuring the strategic readiness of intangible assets. Harvard Business Review, 82(2), 52‐63 Lee, R. Cheung, B. And Wang Y. (2010). Managing Unstructured Information and Knowledge Flow in Knowledge Work nd Team. Proceeding of The 2 European Conference on Intellectual Capital, ECIC, Italy.355‐361 Milner, E. (2000). Managing Information and Knowledge in Public Sector. Routledge. London. Moullin, M. (2002), Delivering Excellence in Health and Social Care, Open University Press,Buckingham. Niven, P. (2008). Balanced Scorecard step‐by‐step for government and non‐profit agencies. John Wiley and Sons, New Jersey. OECD (2003). Conclusions from the Results of the Survey of Knowledge Management Practices for Ministries/Departments/Agencies of Central Government in OECD Member countries, 3‐4 Feb. Oliver, D., Roos, J. (2000). Striking a Balance: Complexity and Knowledge Landscapes, McGraw Hill. Pedlar M., Burgoyne, J. and Boydell, T. (1997). The learning company. A strategy for sustainable development. London, McGraw Hill Riege, A. and Lindsay, N. (2006). knowledge Management in the Public Sector: Stakeholder Partnerships in the Public Policy Development. Journal of Knowledge Management, 10(3), 24‐39. Wong, K.and Aspinwall, E.(2005). An empirical study of the important factors for knowledge management adoption in the SME sector. Journal of Knowledge Management, 9(3), 64‐82 Wu, A. (2005) The integration between Balanced Scorecard and intellectual capital, Journal of Intellectual Capital, 6, 2. Yahya, K. (2009), Power‐Influence in Decision Making, Competence, and Organizationa Culture in Public Organizations. Journal of Public Administration Research and Theory 19 (2), 385‐408 Zack, M. (1999). Managing codified knowledge. Sloan Management Review, 40(4), 45‐58. Zack, M. (2002). Epilogue: developing a knowledge strategy, in Bontis, N. and Choo, C.W. The Strategic Management of Intellectual Capital and Organizational Knowledge, Oxford University Press, Oxford.

The Linkages Among Intellectual Capital, Corporate Governance and Corporate Social Responsibility Doğan Altuner, Şaban Çelik and Tuna Can Güleç Yaşar University, Department Of International Trade and Finance, Turkey Dogan.Altuner@Yasar.Edu.Tr saban.celik@yasar.edu.tr tuna.gulec@yasar.edu.tr Abstract: The purpose of present study is to explore the linkages among Intellectual Capital (IC), Corporate Governance (CG) and Corporate Social Responsibility (CSR) through direct and indirect empirical inquiry. In related literature, IC has been conceptualized within different perspective of assessments. Empirical studies conducted on IC have previously analyzed IC in terms of its measurability and linkage with firm performance. On the other hand, CG and CSR have been evaluated by using their rating value such as CG and/or CSR Index. Since their importance for all stakeholders became more and more necessary for preventing organization from any types of chaotic environment, there is a need to comprehend their interrelated dynamics. Empirical investigation is conducted on manufacturing firms listed in Istanbul Stock Exchange from 2007 to 2011. Empirical results do support a positive relationship among these important constructs. Keywords: intellectual capital, corporate governance, corporate social responsibility, manufacturing industry, Turkey

1. Introduction The present study examines the interrelated linkages among three concepts: Corporate Governance (hereafter CG), Intellectual Capital (hereafter IC) and Corporate Social Responsibility (hereafter CSR). There is an ongoing debate about each of these concepts for exploring their impact on value, measurability and relevancy. In addition, there is a considerable research interest to document the determinants of value addition process for an organization. Therefore, CG, IC and/or CSR became challenging phenomena to study their linkages with value. In the same manner, their linkages with relevancy in the organization have been a difficult task to accomplish due to their unobservable characteristics in terms of measurability. In the last two decades, there have been many attempts to decompose the dynamics of each of these concepts for developing indices or scales that reflect their level for each organization. Since their importance for all stakeholders became more and more necessary for preventing organization from any types of chaotic environment, there is a need to comprehend their interrelated dynamics. We aim to analyze this challenging research interest at the micro level within manufacturing listed firms. The research setting is designed for exploring the relationship among IC, CG and CSR. These three constructs are examined directly in which their statistical relation is evaluated among themselves and indirectly in which their possible effects are examined onto firms’ unsystematic factors such as cash flow, short‐term solvency, long‐term solvency, profitability and asset utilization. Empirical investigation is conducted on manufacturing listed firms in Istanbul Stock Exchange within the period from 2007 to 2011 based on annual data. Variables are designed in both continuous and categorical structure for purpose of use in research setting. We applied three methods to investigate the linkages among proposed constructs. Pearson (linear) Correlation, Independent Sample T‐Test and ANOVA (analysis of variance) are proposed to test these linkages because of their purpose of use in research model. In this manner two approaches are structured into two paths: (i) looking at the linkages among these three constructs (IC, CG and CSR) directly and (ii) looking at the linkages between each of these three concepts (IC, CG and CSR) and firms’ unsystematic factors. The structure of present study is as follows: section two gives a relatively short literature review for IC, CG and CSR; Section three explains raw data, variable structure and methods applied for exploring the linkages among these constructs; Section four reports the findings of estimated research model developed for the study; Section five gives a short summary.

2. Literature review IC is a relatively new academic endeavor that is coming originally from practice and consultancy and is far away from having a consensus on a common definition. Svieby (1997) proposed a simple definition for IC as a difference of market value and book value. In last two decades, IC has been conceptualized (Ross, et.al, 1997;

Doğan Altuner, Şaban Çelik and Tuna Can Güleç Petrash, 1996; Lowendahl, 1997; Sullivan, 1998; Edvinsson and Malone, 1997; Brooking, 1996) within different assessment perspectives. Empirical studies on IC have previously focused on the assessment of the degree or the ranking value of intellectual assets in a given firm(s). However those well known methods such as Tobin’s Q, Market to book ratio, Accounting Based Measures (Lee and Guthrie, 2010; Maditinos, et.al, 2011) have provided much knowledge to better understand and interpret the importance of assessment of IC. There are some studies (Leibowitz and Suen, 2000; Marr and Chatzkel, 2004; Chen, Zhu and Xie, 2004) that attempt to propose the metrics for measuring IC whereas there is no attempt found the link among intellectual capital, corporate governance and corporate social responsibility. The term governance should be clearly defined in order to understand its role within structure of research model. Governance is defined as ‘the structure and function of a corporation in relation to its stakeholders generally, and its shareholders specifically’ (Banks, 2004:3). The importance of corporate governance around the world rises significantly due to its possible impact on all stakeholders. Banks (2004) underlines two facts: (i) the first one is about the impact of corporate governance on stakeholders and (ii) the second one is about the risk that may take place if corporate governance is not effectively designed. There are many examples that show how corporate governance affects the firms’ operations around the world. Some of these are Enron, Tyco, Andersen and WorldCom from USA; Swissair from Switzerland; Kirch Media from Germany; Daiwa Bank and Sumitomo Corporation from Japan and many others (see Banks (2004) for an extensive list). Governance assumes various forms in modern corporate systems. These elements of governance are centered on both internal and external mechanisms. Internal governance is based on specific mechanisms and actions taken by individual firms to enforce control and accountability. These can vary by company, industry, and country, but broadly speaking include (Banks, 2004:24): (i) establishing a capable and unbiased board of directors; (ii) creating appropriate responsibilities and norms within the ranks of executive management; (iii) developing independent control groups, including finance/accounting, legal, risk management and internal 1 audit; (iv) creating and promulgating a code of conduct . Supplementing internal governance processes are external forces that establish overarching frameworks which define, or operate with, internal mechanisms. Again, although specific external elements vary by country and economic system (depending on law, custom, and behavior), key forces include (Banks, 2004:25): (i) establishing appropriate regulatory oversight; (ii) creating proper legal and bankruptcy regimes; (iii) ensuring efficient capital markets access; (iv) encouraging corporate control activities (such as mergers and buyouts); (v) permitting block holder monitoring of corporate activities; (vi) encouraging the participation of activist institutional investors; (vii) requiring thorough and comprehensive external audits; and (viii) facilitating credit rating agency reviews. There is a strong belief that the primary goal of firms’ management is to maximize shareholders’ wealth whereas it is no longer true that the role of other stakeholders can be ignored. It is proposed first time within the proposed model that the primary goal of firms’ management is to maximize stakeholders’ wealth. Despite the fact that the importance of CSR has not been recognized yet in the last two decades, firms are becoming more eager to promote themselves as more friendly to environment, recycling or energy savers. These arguments provide such a solid base that, no one can ignore the importance of CRS in explaining rate of return. Most theories on the relationship between corporate social/environmental performances (CSP) and corporate financial performance (CFP) assumes that the current evidence is too fractured or too variable to draw any generalizable conclusions (Orliztky, Schmidt and Rynes, 2003). Orliztky, Schmidt and Rynes, (2003) conducted a meta‐analysis of 52 studies (which represent the population of the prior quantitative inquiry) yielding a total sample size number of 33,878 observations. The meta‐analytic findings suggest that corporate virtue in the form of social responsibility and, to a lesser extent, environmental responsibility is likely to pay off, although the operationalizations of CSP and CFP also moderate the positive association. There is an increasing trend for measuring the level and impact of CSR in the world. One of these sings is to develop stock market indices to observe their return and performance structure. The Domini 400 Social Index (DS400), as an example of this kind, is a float‐adjusted, market capitalization‐weighted, common stock index of U.S. equities. Launched by KLD in May 1990, the DS400 is the first benchmark index constructed using environmental, social and governance (ESG) factors. It is a widely recognized benchmark for measuring the impact of social and environmental screening on investment portfolios. DS400 holds at approximately 250 S&P 500 companies, 100 1

This is also known as code of ethic.

Doğan Altuner, Şaban Çelik and Tuna Can Güleç additional large and mid cap companies chosen for sector diversification, and 50 smaller companies with exemplary social and environmental records. Companies engaged beyond specific levels of involvement in certain industries are not eligible for the Index. These include: Tobacco, alcohol, gambling, firearms, military weapons and nuclear power (FactSet Research Systems and Standard & Poor’s, 2009:2). KLD selects companies for the DS400 that have positive environmental, social and governance (ESG) performance. KLD recognizes that many companies will have some ESG concerns and gives careful consideration to how companies address the risks and opportunities they face in the context of their sector or industry and relative to their peers. The ESG performance evaluation is based on overall company performance using the following indicators: Table 1: Social responsibility indicators Environment

Social

Governance

Alternative Energy Climate Change Liabilities Management Systems

Community Relations Workforce Diversity Employee Relations Human Rights

Accounting Executive Compensation Political Accountability Transparency

Regulatory Problems

Product Quality and Innovation

Ownership

Source: FactSet Research Systems and Standard & Poor’s (2009:3). IC, CG and CSR are three demanding research areas to study whereas this is the first attempt here to examine their possible linkages based on so called direct and indirect empirical inquiries. The primary reason behind this attempt is that these concepts are assumed to be important for all stakeholders.

3. Data and methodology 3.1 Research model The research model developed for the present paper is depicted in Figure 1. The main setting is designed for exploring the relationship among IC, CG and CSR. Therefore, these three constructs are examined directly in which their statistical relation is evaluated among themselves and indirectly in which their possible effects are examined onto firms’ unsystematic factors such as cash flow, short‐term solvency, long‐term solvency, profitability and asset utilization.

Figure 1: Research model

3.2 Variable set and data structure In the context of research model, several variables have been used in different forms including continuous and dummy as depicted in Table 2. The first column indicates the name of constructs; the second column shows the proxy variable that best represent each construct; the third column indicates type of the variable and

Doğan Altuner, Şaban Çelik and Tuna Can Güleç fourth column gives the formula of each variable. There are three main constructs as depicted in research model: IC, CG and CSR. CG measure is structured according to its availability and suitability to the aim of the study. Firms that are actively traded in Istanbul Stock Exchange (ISE 2 ) are graded by independent corporate governance rating firms licensed by Turkish Capital Market Board. These rating are considered by ISE to determine Corporate Governance Index (XKURY) which has developed since 2007 for the purpose of measuring the price and return performance of companies with a corporate governance rating of minimum 7 to 10. There are four dimensions of corporate governance principles including shareholders (25%), public disclosure (35%), stakeholders (15%) and board of directors and executives (25%). Each of these dimensions has several sub‐elements. However, as depicted, these four main dimensions have different weights in calculating the rate. Since there are only a few manufacturing firms in the Corporate Governance Index, corporate governance indicator was considered as a dummy variable. It takes one if the firm is included in the index and zero otherwise. There are no indicators that can be used to measure the CSR of the firms in Turkey. However, Corporate Social Responsibility Association of Turkey is developing a scale for rating the firms. This initiation has not been activated and widespread in Turkey. That is why there is no available data for the firms analyzed in the study. In order to eliminate this problem, the mentioned scale is conducted through a sort of content analysis. The scale consists of five constructs including Corporate Strategy, Management and Processes (10%), Economic (30%), Social (30%), Environmental (25%) and Corporate Social Responsibility Report (5%). The content analysis is conducted based on the scale and the availability of information about the firms. The information was collected by searching the annual reports and web sites of the firms and then decide whether a firm has a project within the scope of CSR or not. The rate is calculated based on the scale weights. If the firm has a project, it takes the value of one and zero otherwise. Then, the sum‐product of resulted from construct determined the final rate. As a result, a continuous variable was obtained. This continuous variable is used in direct examination of the possible linkages with CG and IC. In addition we structure a categorical variable based on this rate such as very intensive (a rate higher than 66.6%), moderate (a rate between 33.3% and 66.6%) and low intensive (a rate lower than 33.3%) in order to evaluate its impact on firms’ unsystematic factor in the form of indirect inquiry. IC, on the other hand, is calculated by two variables: market value to book value and Tobin Q. Both variables are assumed to be an indicator for excess value of the firms that is not reported within financial statements. If the values of these two variables are higher than one, then there is a positive sign for IC. We used this variable in both continuous and dummy form in order to examine its linkages with CG and CSR. Firms’ unsystematic factors are determined as Cash Flow Short‐Term Solvency, Long‐Term Solvency, Profitability and Asset Utilization. We choose one variable that is frequently used per each construct in continuous form. The primary reason here is that whether or not IC, CG and CSR have differentiating effect on firms’ unsystematic factors. Table 2: Variable set Construct Corporate Governance (CG)

Proxy Variable Corporate Governance Rating Market Value to Book Value

Intellectual Capital (IC)

Tobin Q

Corporate Social Responsibility (CSR)

Corporate Social Responsibility Rating

Type of Variable Dummy form: 1 if firm is rated; 0 otherwise Continuous Form: ratio Dummy Form: 2 if the ratio is higher than 1; 1 otherwise Continuous Form: ratio Dummy Form: 2 if the ratio is higher than 1; 1 otherwise Continuous Form: rating Dummy Form: 1 if the ratio is higher than 66.6 %; 2 if the ratio is between 33.3 % and 66.6 %; and 3 if the ratio is lower than 33.3 %.

Formula Independent rating agencies methodology Market Value / Book Value

(Book Value of Debt + Market Value of Common Stock) / Total Assets Authors calculation via content analysis conducted on firms’ publicly available information including annual reports, footnotes of financial statements and websites based on a

For companies included in ISE Corporate Governance Index, the annual listing/registration fee is applied as 50% of the tariff for the first two years; 75% of the tariff for the following two years and then continue as 90% of the tariff (www.ise.gov.tr).

Doğan Altuner, Şaban Çelik and Tuna Can Güleç Construct

Proxy Variable

Type of Variable

Cash Flow

Cash Flow From Operations to Total Liabilities Current Asset to Current Liabilities Total Liabilities to Total Assets Net Income to Total Assets Sales to Total Assets

Continuous Form: ratio

Short‐Term Solvency Long‐Term Solvency Profitability Asset Utilization

Continuous Form: ratio Continuous Form: ratio Continuous Form: ratio Continuous Form: ratio

Formula scale developed for measuring CSR. Cash Flow From Operations / Total Liabilities Current Asset / Current Liabilities Total Liabilities / Total Assets Net Income / Total Assets Sales / Total Assets

The analysis period takes place between 2007 and 2011 in the form of annual data. We used CG as a dummy variable in the analyses. In this structure, firms that are rated by independent rating agencies take a value of one and zero otherwise. Despite the fact that we could use corporate governance rates as a continuous variable, the limited number of rated firms restrict us from making any statistical examination with the rates. Therefore, we categorize firms into rated and non‐rated groups and explore whether any statistical difference exist with respect to IC, CSR and firms’ unsystematic factors. According to statistics given, the numbers of rated firms are 17, 15, 11, 8 and 4 in 2011, 2010, 2009, 2008 and 2007 respectively. As observed, there is an increasing tendency of being rated by independent rating agencies in Turkey. One of the most important figures is that the existence of mean differences among firms’ unsystematic risk based on the categorization of rated and non‐rated firms. The formal statistical examination will take place in forthcoming sections. The proxy variable of CSR is the index value of CSR Scale which is used in both continuous and categorical variables. While we were using the index rate as a continuous variable within the framework of indirect tests, we structured the index into three categorical dummy variables as very intensive, moderate and non‐intensive for the purpose of representing the intensity of firms in CSR. There is an increasing tendency for the firms to be more intensive in CSR. The numbers of very intensive firms in CSR are 55, 45, 47, 41 and 38 in 2011, 2010, 2009, 2008 and 2007 respectively. In the same manner, the numbers of non‐intensive firms have decreased from 79 in 2007 to 69 in 2011. The statistical examination will take place in exploring the statistically significant differences based on the categorization CSR with respect to IC, CG and firms’ unsystematic factors. Tobin Q as a proxy variable of IC has been used in both continuous and categorical variables within the framework of direct and indirect tests. We structured categorical variable into two groups: The first group contains the firms that have Tobin Q ratio lower than 1 and the second group contains the firms that have Tobin Q higher than 1. The logic behind this categorization is that a higher value of Tobin Q may reflect positive signals in terms of intellectual capital. It is observed that there are more firms taking a higher value of Tobin Q over the threshold value than those of having lower value except for 2008 in which 89 and 45 firms take a lower and a higher value respectively. It is aimed to test whether there are statistically significant differences based on the categorization IC with respect to CG, CSR and firms’ unsystematic factors. Market to Book Ratio (MV/BV) as a proxy variable of IC has been used in both continuous and categorical variables within the framework of direct and indirect tests. We structured categorical variable into two groups: The first group contains the firms that have MV/BV ratio lower than 1 and the second group contains the firms that have MV/BV higher than 1. The logic behind this categorization is that a higher value of MV/BV may indicate positive signals in terms of intellectual capital. It is observed that there are more firms taking a higher value of MV/BV over the threshold value than those of having lower value except for 2008 in which 93 and 42 firms take a lower and a higher value respectively. It is aimed to test whether there are statistically significant differences based on the categorization IC with respect to CG, CSR and firms’ unsystematic factors.

3.3 Methodology In the present study, we applied three methods to investigate the linkages among proposed constructs. Pearson (linear) Correlation, Independent Sample T‐Test and ANOVA (analysis of variance) are proposed to test these linkages due to their purpose of use in research model. We used each of these techniques by having their assumptions in mind. Therefore, we simply explained these three techniques in a way to describe them with their main features. The linear correlation coefficient (r) measures the strength of the linear relationship between the paired values of two variables in a sample. This analysis is conducted for exploring the direct linkages between IC and CSR due to the fact that these two variables are continuous. Independent Sample T‐ test is another statistical technique to test the mean difference between two constructs. This is a parametric

Doğan Altuner, Şaban Çelik and Tuna Can Güleç statistical test that requires continuous, normally distributed and equal group variances. In case of two group comparison, independent sample t‐tests are applied whereas if there are more than two group comparisons, then ANOVA is an appropriate statistical technique to conduct. Independent sample t‐tests are applied for exploring the direct linkages among IC, CG and CSR for the fact that CG has initially a categorical variable and IC and CSR have continuous variables. In addition, independent sample t‐tests are applied in exploring indirect linkages among the constructs where applicable. In case of examining the mean differences among more than two groups, we determine to use ANOVA for its applicability. In case of exploring indirect linkages between CSR and firms’ unsystematic factors, this technique is applied since there are three categories within CSR.

4. Empirical findings Empirical findings are documented based on two approaches. The first approach is about looking at the linkages among the three constructs (IC, CG and CSR) directly. In this case, we reported the results coming from correlation and independent sample t‐tests. The second approach is about the linkages between each of these three concepts (IC, CG and CSR) and firms’ unsystematic factors. The question here is that whether or not firms’ unsystematic factors do show statistically significant differences based on categorization of IC, CG and CSR.

4.1 Direct linkages among corporate governance, intellectual capital and corporate social responsibility This section gives the findings coming from exploring direct linkages among IC, CG and CSR. Figure 3 depicts the structure of the proxy variables for each construct and the proposed statistical tests employed. CG is represented by one dummy variable: if a firm is rated by independent rating agency, then it takes value of one and zero otherwise. As a result, this structure classifies CG into two categories. On the other hand, IC and CSR have continuous variables which allow us to apply independent sample t‐test to figure out a possible relationship. We used two proxy variables for IC as market to book ratio and Tobin Q ratio. In addition, we used CSR rates as a proxy variable representing CSR which is also continuous. In case of looking at the relationship between IC and CSR, we interpret correlation between these two continuous variables.

Figure 2: Direct tests of research model The first examination for direct test takes place between IC and CSR. Table 3 demonstrates summary statistics and Pearson correlation among CSR Index, MV/BV and Tobin Q ratio. We used five‐year annual data for the analysis. Therefore, N represents observations within this period. Correlations among CSR Index and two proxy variables for IC show that there is statistically significant positive relation between CSR Index and Tobin Q

Doğan Altuner, Şaban Çelik and Tuna Can Güleç whereas there is no statistically significant relation between CSR Index and MV/BV. Despite the fact that there is no purpose to test relation between MV/BV and Tobin Q, a statistically significant positive relation is observed between two proxy variables of CSR as expected since they were chosen for the same purpose. Table 3: Summary statistics and correlations CSR Index MV/BV

Mean

Std. Deviation

647 647

,4548 1,6611

,38111 1,73651

TOBIN Q

647

1,2770

,96484

Pearson Correlation CSR Index MV/BV ,010 ,100*

,809**

Note: *. Correlation is significant at the 0.05 level (2‐tailed); **. Correlation is significant at the 0.01 level (2‐ tailed). The second examination of direct test takes place among CG, IC and CSR. Table 4 gives summary statistics and t‐test results for the constructs. In this case, number of firms that are rated by independent rating agencies is 55 within the sample analysis period. The second category of CG in which firms are not rated includes 592 firms (observations). As depicted, the mean value of rated firms for CSR is higher than that of Non‐rated firms which is also confirmed by independent sample t‐test. However, independent sample t‐test does not reject the hypotheses that mean value of both categories are the same for CG. Table 4: Independent sample T‐Tests for corporate governance (full and subsample) CSR Index

Corporate Full Sub Full Governance Sample Sample Sample N N Mean

Rated Non‐rated Rated MV/BV Non‐rated Rated TOBIN Q Non‐rated

55 592 55 592 55 592

53 80 53 80 53 80

,7418 ,4281 1,5722 1,6694 1,2322 1,2812

Sub Sample Mean ,7491 ,2744 1,5909 2,3519 1,2411 1,2026

Full Sample

Sub Full Sample Sub Sample Sample Std. Std. Deviation t‐test t‐test Deviation Sig. (2‐tailed) Sig. (2‐tailed) ,29686 ,29750 ,000 ,000 ,37727 ,33366 ,87821 ,88760 ,692 ,021 1,79578 2,70258 ,37639 ,38002 ,719 ,593 1,00220 ,42068

Note: T‐Test significance level takes Levene's Test for Equality of Variances into account. In this examination, unequal size of categories may create a disadvantage for the test. The main reason is that there might be many firms in the second category (non‐rated category) which shows high financial performance. In order to eliminate this problem, we have constructed a diagnosis testing strategy by choosing a sub‐sample from non‐rated categories. In this sub‐category, we selected a sample of distressed firms based on several criteria ((i) Included into Watchlist Companies Market; (ii) Had Total Debt greater than Total Asset; (iii) Prepared Financial Statement based on Turkish Bankruptcy Code of 324; (iv)Announced Loss for Three consecutive year; (v) Had execution for debt). Table 4 demonstrates the results of this step. As depicted, the mean difference of CSR is statistically significant as it is the same in the full sample. However, we proved that the mean difference of MV/BV is also statistically significant at 5% level in this sub‐sample examination. On the other hand, Tobin Q, another proxy for IC, is not proved that the mean difference is statistically significant among categories of CG. Results of direct tests among IC, CG and CSR show that there are statistically significant differences among these constructs. At the first step, we show that there is a positive relationship observed between CSR and IC by using their continuous proxy variables. At the second stage, we applied independent sample t‐test among CG, IC and CSR. Since CG has a categorical variable, we tested whether there is statistically significant difference between these categories. As a result of conducting the test, we proved that there is a statistically significant mean difference between IC, CG and CSR by using two different samples.

Doğan Altuner, Şaban Çelik and Tuna Can Güleç

4.2 Indirect linkages among corporate governance, intellectual capital and corporate social responsibility This section gives the findings coming from exploring indirect linkages among IC, CG and CSR. Figure 4 depicts the structure of the proxy variables for each construct and the proposed statistical tests employed. In conducting indirect tests, we change the structure of IC and CSR from continuous variables to categorical variables as it is the case for CG. The reason is simply to test the mean difference of firms’ unsystematic factors based on categorization of IC, CG and CSR. Categories of CG remain the same as rated firms and non‐rated firms. In case of categorizing IC, we use threshold value of 1 for both proxies of MV/BV and Tobin Q ratios. Conceptually, threshold value of 1 is interpreted as a limit for a firm to show positive expectation for unexplained components of intellectual value. Therefore, we categorize MV/BV and Tobin Q into two categories: the first category takes a value below 1 and the second category takes a value above 1. In case of categorizing CRS, we developed three categories: the first category, so called non‐intensive firm in CSR, takes a value less than 33.3%; the second category, so called moderate firm in CSR, takes a value between 33.3% and 66.6% and the third category, so called very intensive firm in CSR, takes a value higher than 66.6%.

Figure 3: Indirect tests for research model The primary logic behind indirect testing methodology is to decide that whether there is a statistical significant difference in firms’ unsystematic factors based on the categorization of IC, CG and CSR. Firms’ unsystematic factors are chosen as Cash Flow (CF), Short‐Term Solvency (SS), Long‐Term Solvency (LS), Profitability (PR) and Asset Unitization (AU). We chose one proxy variable per each firms’ unsystematic factors. These proxies are selected based on a review of frequently used financial ratios derived from the literature. The first indirect test is conducted between CG and firms’ unsystematic factors. Since there are two categories of CG, independent sample t‐test is applied to explore the linkages between CG and firms’ unsystematic factors. Table 5 demonstrates summary statistics and t‐test results for the full sample. The hypotheses that there are statistical significant mean differences between rated and non‐rated firms cannot be rejected for CF, SS and LS whereas the hypotheses are rejected for PR and AU. Even though these results are quite significant for firms’ unsystematic factors, we conduct a diagnosis analysis developed in previous section in which a distressed sample is derived from non‐rated firms for comparing with rated firms. The results of independent sample t‐test conducted on sub‐sample are also depicted in Table 5. In this case, we compare rated firms with distressed firms in order to see the mean differences. The results as depicted are more supportive than full sample comparison. The mean differences for CF, LS, PR and AU cannot be rejected whereas there is no enough evidence to reject the mean difference for SS based on the categorization of rated and distressed firms. Despite the fact that there is high value of SS for rated firms than that of distressed firms

Doğan Altuner, Şaban Çelik and Tuna Can Güleç as expected, it is not confirmed by t‐test. The main reason can be hidden in the manipulation of the proxy for SS. The argument here is that construct of SS is measured by a proxy of current ratio which is a well‐known indicator of short‐term solvency. Firms are thought to be eager to manipulate this ratio in order to persuade creditors whereas there is no statistical test to prove this argument within the context of present paper. Table 5: Independent sample T‐Tests for corporate governance (full and subsample‐indirect test)

CF SS LS PR AU

Corporate Governance Rated Nonrated Rated Nonrated Rated Nonrated Rated Nonrated Rated Nonrated

Full Sub Full Sample Sample Sample N N Mean

55 592 55 592 55 592 55 592 55 592

53 80 53 80 53 80 53 80 53 80

,0969 ,3011 1,7533 2,3952 ,5516 ,4346 ,0492 ,0421 1,1075 ,9723

Sub Sample Mean

,0968 ‐,0385 1,7836 1,4771 ,5508 ,5842 ,0536 ‐,0466 1,1493 ,8592

Full Sub Full Sample Sub Sample Sample Sample Std. Std. t‐test t‐test Deviation Deviation Sig. (2‐ Sig. (2‐tailed) tailed) ,21380 ,21784 ,001 ,004 1,33815 ,32125 1,45029 1,46913 ,004 ,344 2,42401 2,25337 ,17061 ,17333 ,004 ,000 ,21553 ,22036 ,06007 ,05624 ,442 ,000 ,10155 ,13261 ,74162 ,72263 ,193 ,014 ,56576 ,54262

Notes: T‐Test significance level takes Levene's Test for Equality of Variances into account; N: Number of Firms; CF: Cash Flow from Operations / Total Liabilities; SS: Current Assets / Current Liabilities; LS: Total Liabilities / Total Assets; PR: Net Income / Total Assets; AU: Sales / Total Assets. The second indirect test is conducted between IC (Tobin Q and MV/BV) and firms’ unsystematic factors. Since categorization of Tobin Q (denoted as DTOBINQ in Table 6) includes two groups as firms below and above the value of 1, independent sample t‐tests are applied. Table 6 depicts the results of the methods. As depicted, numbers of firms below 1 and above 1 are 278 and 369 respectively. The hypotheses that there are statistically significant mean differences between these two groups cannot be rejected for LS, PR and AU whereas the hypotheses are rejected for CF and SS. The inference can be derived from these findings is about which of these factors lead a differential in such categorization of Tobin Q. Long‐term solvency, profitability and asset utilization seem to be important factors to differentiate firms based on Tobin Q whereas cash flow and short‐ term solvency do not imply a statistical significant contribution to this differential within five‐year period of analysis. In addition, cash flow of firms that have a value of Tobin Q above 1 is still higher than that of firms having a value of lower 1. However, a contradicting finding exists for short‐term solvency (current ratio) as it was the case in direct test. It seems that firms that have a lower Tobin Q ratio produce higher current ratio than those of having higher Tobin Q ratio. Table 6: Independent sample T‐Tests for Tobin Q and Market to Book Value (indirect test)

CF SS LS PR AU

DTOBINQ and Tobin Q (MV/BV) DMVBV N N (MV/BV)

Tobin Q (MV/BV) Mean Mean

BELOW ONE (1) ABOVE ONE (1) BELOW ONE (1) ABOVE ONE (1) BELOW ONE (1) ABOVE ONE (1) BELOW ONE (1) ABOVE ONE (1) BELOW ONE (1) ABOVE ONE (1)

,2537 ,3064 2,4003 2,2958 ,3948 ,4820 ,0327 ,0503 ,8644 1,0738

278 369 278 369 278 369 278 369 278 369

260 387 260 387 260 387 260 387 260 387

,2360 ,3159 2,3987 2,3017 ,4005 ,4742 ,0295 ,0516 ,8819 1,0523

Tobin Q (MV/BV) Tobin Q (MV/BV) Std. Std. t‐test t‐test Deviation Deviation Sig. Sig. (2‐tailed) (2‐tailed) ,96750 ,96260 ,585 ,402 1,47728 1,45916 2,13912 2,19418 ,578 ,609 2,52040 2,47187 ,18588 ,18645 ,000 ,000 ,22686 ,22686 ,08792 ,08843 ,021 ,005 ,10558 ,10420 ,55451 ,56784 ,000 ,000 ,58899 ,58424

Notes: T‐Test significance level takes Levene's Test for Equality of Variances into account; DTOBINQ: Tobin Q in dummy form; N: Number of Firms; CF: Cash Flow from Operations / Total Liabilities; SS: Current Assets / Current Liabilities; LS: Total Liabilities / Total Assets; PR: Net Income / Total Assets; AU: Sales / Total Assets.

Doğan Altuner, Şaban Çelik and Tuna Can Güleç MV/BV, as a second proxy of IC, is categorized into two categories the same as Tobin Q. Since categorization of MV/BV (denoted as DMVBV in Table 6) includes two groups as firms below and above the value of 1, independent sample t‐tests are applied. Table 6 depicts the results of the methods. As depicted, numbers of firms below one and above one are 260 and 387 respectively. The hypotheses that there are statistical significant mean differences between these two groups cannot be rejected for LS, PR and AU whereas the hypotheses are rejected for CF and SS. The inference can be derived from these findings is about which of these factors lead a differential in such categorization of MV/BV. Long‐term solvency, profitability and asset utilization seem to be important factors to differentiate firms based on MV/BV whereas cash flow and short‐ term solvency do not imply a statistical significant contribution to this differential within five‐year period of analysis. In addition, cash flow of firms that have a value of MV/BV above one is still higher than that of firms having a value of below one. The same contradicting finding exists for short‐term solvency (current ratio) as it was the case in direct test. It seems that firms that have a lower MV/BV ratio produce higher current ratio than those of having higher MV/BV ratio. The third indirect test takes place between CSR and firms’ unsystematic factors. Since CSR is categorized into three categories (very intensive, moderate and non‐intensive firms in CSR), ANOVA is more appropriate method to test equality of these three sample means by analyzing their sample variances. Table 7 documents summary statistics and ANOVA results. As depicted, numbers of very intensive, moderate and non‐intensive firms in CSR are 212, 121 and 314 respectively. The hypotheses that there are statistical significant mean differences among these three groups cannot be rejected for CF, LS, PR and AU whereas the hypothesis is rejected for SS. There is sufficient evidence to support the claim that the three group means are not all the same except for the construct of SS. The mean values of these three groups for the construct of SS are quite close to each other whereas the value for very intensive firms in CSR is still higher than those of non‐intensive firms in CSR. This does not change the reality that short‐term solvency does not contribute the statistically significant differential among categorization of CSR. As a result of indirect tests, it was aimed to test the possible effect of categorizations of IC, CG and CSR on the firms’ unsystematic factors. We found a strong effect of these three constructs on firms’ unsystematic factors. Table 73: ANOVA for corporate social responsibility (indirect test)

Corporate Social Responsibility

Mean

Std. Deviation

very intensive moderate non intensive Total very intensive moderate non intensive Total very intensive moderate non intensive Total very intensive moderate non intensive Total very intensive moderate non intensive Total

212 121 314 647 212 121 314 647 212 121 314 647 212 121 314 647 212 121 314 647

,5209 ,2511 ,1362 ,2838 2,4272 2,4557 2,2379 2,3407 ,4137 ,4151 ,4768 ,4446 ,0715 ,0384 ,0250 ,0427 1,1031 ,9299 ,9241 ,9838

1,63217 ,57019 1,18946 1,28267 2,29925 1,68486 2,61740 2,36294 ,21014 ,18355 ,22411 ,21447 ,10174 ,07316 ,10093 ,09869 ,67096 ,52815 ,52671 ,58329

ANOVA (sig)

,003 ,559 ,001 ,000 ,001

Doğan Altuner, Şaban Çelik and Tuna Can Güleç

5. Concluding remarks Present study was aimed to explore the linkages among IC, CG and CSR through direct and indirect statistical examination. Results of direct tests among IC, CG and CSR show that there are statistical significant differences among these constructs. Firstly, we showed that there is positive relationship observed between CSR and IC by using their continuous proxy variables. Secondly, we applied independent sample t‐test among CG, IC and CSR. Since CG has a categorical variable, we tested whether there is statistically significant difference between these categories. In conducting the test, we documented that there is statistically significance mean difference between IC, CG and CSR by using two different samples. As a result of indirect tests, it was aimed to test the possible effects of categorizations of IC, CG and CSR on the firms’ unsystematic factors. The results indicate that there is a strong effect of these three constructs on firms’ unsystematic factors. However, we cannot tell the sensitivity of these effects among the constructs which needs additional multivariate statistical examination. We left this issue for future implications.

References Banks, E. (2004). The Insider's View on Corporate Governance. New York. Palgrave Macmillan. Brooking, A. (1996), “Intellectual Capital”, Thomson Business Press, London. Chen, J., Zhu, Z. and Xie, H.Y. (2004), “ Measuring intellectual capital: a new model and empirical study” Journal of Intellectual Capital, Vol. 5 No. 1, pp. 195‐212. Dimitrios Maditinos, D., Chatzoudes, D., Tsairidis, C. and Theriou, G. (2011), “The impact of intellectual capital on firms’ market value and financial performance” Journal of Intellectual Capital Vol. 12 No. 1, pp. 132‐151. Edvinsson, L. and Malone, M.S. (1997), “Intellectual Capital: Realizing Your Company’s True Value by Finding its Hidden Brainpower”, Judy Piatkus (Publishers) Ltd, London. Leibowitz, J. and Suen, C.Y. (2000), “Developing knowledge management metrics for measuring intellectual capital” Journal of Intellectual Capital, Vol. 1 No. 1, pp. 54‐67. Lee, L.L. and Guthrie, J. (2010), “Visualising and measuring intellectual capital in capital markets: a research method” Journal of Intellectual Capital, Vol. 11 No. 1, pp. 4‐22. Lowendahl, B. (1997), “Strategic Management of Professional Service Firms” Handelshojskolens Forlag, Copenhagen. Marr, B. and Chatzkel, J. (2004), “Intellectual capital at the crossroads: managing, measuring, and reporting of IC” Journal of Intellectual Capital, Vol. 5 No. 2, pp. 224‐229. Maditinos, D., Chatzoudes, D., Tsairidis, C and Theriou, G. (2011), “The impact of intellectual capital on firms’ market value and financial performance” Journal of Intellectual Capital, Vol. 12 No. 1, pp. 132‐151. Petrash, G. (1996), “Dow’s journay to a knowledge value management culture”, European Management Journal, Vol.14 No.4, pp.365‐73. Ross, J., Ross, G., Edvinsson, L. and Dragonetti, N.C. (1997), “Intellectual Capital: Navigating in the New Business Landscape”, Macmillan Business, Houndsmills. Orliztky, M., Schmidt, F.L., and Rynes, S.L. (2003), “Corporate Social and Financial Performance: A Meta Analysis”, Organization Studies, Vol. 24, No: 3, pp. 403‐441. Sullivan, P.H.(1998), “Profiting from Intellectual Capital: Extracting Value from Innovation”, Wiley, London. Svieby, K.(1997), “Organizational Wealth: Managing and Measuring Knowledge based Assets”, Berrett‐Koehler, San Francisco. The Domini 400 SocialSM Index (DS400) Structure (2008), KLD Research & Analytics, Inc, FactSet Research Systems and Standard & Poor’s, www.KLDIndexes.com.

Knowledge Management in Support of Collaborative Innovation Community Capacity Building Xiaomi An1, 2, Hepu Deng3 and Lemen Chao1 1 Key Laboratory of Data Engineering and Knowledge Engineering of the Ministry of Education, Renmin University of China, Beijing, China 2 School of Information Resources Management, Renmin University of China, Beijing, China 3School of Business Information Technology and Logistics, RMIT University, Victoria, Australia anxiaomi@ruc.edu.cn hepu.deng@rmit.edu.au chaolemen@yahoo.cn Abstract: Collaborative innovation is a trans‐disciplinary approach for developing the wholeness synergy through holistic, competitive and complementary interactions between and among innovation participants in a specific environment. It is a formal process of collaborative innovation community capacity building (CICCB). Much research has been done on how to make collaborative innovation work effectively, efficiently and economically. While collaborative innovation is increasingly gaining attention in both theory and practice, it is still under‐explored from a trans‐disciplinary perspective of knowledge management (KM) and community capacity building. This paper aims to fill in this gap by addressing the following two questions: (a) what are the roles of KM in collaborative innovation, and (b) what are the KM approaches for supporting CICCB? To effectively answer these two questions, this paper presents a comprehensive review of the related literature that identifies three demands for CICCB including (a) trust building for enhancing the effectiveness; (b) sustainability building for improving the efficiency, and (c) extensibility building for developing better economy. It identifies three roles of KM in support of CICCB including (a) reformation of KM for convergence in collaboration; (b) remediation of knowledge activities for synergy in communication; (c) reconfiguration of knowledge artifacts for integration in connectivity. To adequately meet the three demands, this paper suggests a holistic approach for effective CICCB including (a) multi‐dimensional convergence and trust building in collaboration; (b) multi‐directional synergy and sustainability building in communication; and (c) multi‐layer integration and extensibility building in connectivity. The contribution of this paper is mainly reflected in three ways. Firstly, it provides insights into the way in which the collaborative innovation literature currently lacks attention but crucial for its success. Secondly, the paper identifies the demand for CICCB and the roles of KM in support of collaborative innovation. Thirdly, this paper proposes a holistic approach for effective CICCB. This paper is the first step of a comprehensive study on the role of KM in support of CICCB. It is therefore conceptual on the definition of the basic concept and the identification of the KM approaches in support of CICCB. Keywords: knowledge management; collaborative innovation; collaborative innovation community capacity building; community capacity building; community capacity

1. Introduction Collaborative innovation is a trans‐disciplinary approach for developing the wholeness synergy effect through holistic, competitive and complementary interactions between and among innovation participants in a specific environment (Cheng and Yang, 2012). It is a formal process of collaborative innovation community capacity building (CICCB). Collaborative innovation is becoming increasingly popular among governments, enterprises, universities and research institutions in their active search for various ways to improve the effectiveness, efficiency and economy of their operations (Gloor, 2006; Karlsson, 2008; Niosi, 2010). The popularity of collaborative innovation is due to the main benefits that it can bring with for individual organizations including obtaining more resources, recognition and reward when facing competition for limited resources (Wagner, 2004). CICCB promotes a social construct to achieve a wholeness synergy effect on social innovation, science and technology innovation, economical and political reform and social changes through the engagement of participants in collaborative problem solving and collective visions building (Ministry of Education of China, 2012). Much research has been done on CICCB including its mechanisms, theoretical models and strategies for collaborative innovation, demands for improving collaborative innovation. There is, however, a lack of a systematic study of the mechanisms, models and strategies and their interactions and a lack of empirical studies in CICCB (Xiong, et al., 2011).

Xiaomi An, Hepu Deng and Lemen Chao

Effective knowledge management (KM) is one of the main approaches for improving the performance of collaborative innovation through KM processes, KM stages and knowledge platforms (Chen and Wei, 2008; Füller, et al, 2012; Kong, Wu and Zhang 2012; Luo, Zhang, Du, 2012). Existing studies show that successful KM encourages and enhances collaboration between employees. KM, however, does not automatically increase collaboration in organizations. This is because KM is a collaborative activity that depends on the creation of ‘shared context’ between participants. (Clarke, Cooper, 2000). The process of innovation depends heavily on knowledge. As a result, the management of knowledge should be an essential element of running any types of business (Gloet and Terziovski, 2004; Plessis, 2007). There is, however, a lack of in‐depth studies of the roles of KM in and the KM approach for collaborative innovation. This shows the need for a holistic approach to effective CICCB. This paper aims to fill in this gap by addressing the following two questions: (a) what are the roles of KM in collaborative innovation, and (b) what are the KM approaches for supporting CICCB? To effectively answer the two questions, this paper presents a comprehensive review of the related literature from a trans‐disciplinary perspective of both KM and community capacity building (CCB) for adequately exploring the possible interactions between KM and CICCB. In what follows, the types of CICCB in practice and their demands are examined first. This is followed by an analysis of the roles of KM in supporting of CICCB. Such an analysis leads to the proposition of a holistic approach for effective CICCB. Finally, the applicability of the proposed approach in effective CICCB is discussed.

2. Demands for collaborative innovation community capacity building Community capacity is the “interaction of human capital, organizational resources, and social capital within a given community that can be leveraged to solve collective problems and improve or maintain the well‐being of that community” (Chaskin, Brown,Verkatesh, and Vidal, 2001 ). It may operate through informal social processes and/or organized efforts by individuals, organizations, and social networks that exist among them and between them and the larger systems of which the community is a part (Deorah, 1997, 2007). There are four characteristics of community capacity (Chaskin, Brown,Verkatesh, and Vidal, 2001) that provide a foundation for collective actions including:

The degree of connectedness among members and a recognition of the mutuality of the circumstance including a threshold level on collectively held values, norms and visions for sharing and togetherness,

The commitment to the community among its members, which describes the responsibility that particular individuals, groups or organizations take for what happens in the community, the obligations and the willingness of community members as stakeholders,

The ability to solve problems, which refers to the ability to take charge of and make decisions as a collective group, to be able to endure or adapt over time, responding to or compensating for the impact of community change, and

An access to resources, which refers to the capacity to access economic, human, physical, and political resources within and beyond the community, at different levels (e.g. municipality, region, nation) and from different types of external actors (e.g. private foundation or government).

Community capacity building (CCB) is “the process of developing and strengthening the skills, instincts, abilities, processes and resources that organizations and communities need to survive, adapt, and thrive in the fast‐changing world” (Philbin, 1996). It is a conceptual approach that focuses on understanding the obstacles that inhibit people, governments, organizations and non‐governmental organizations from realizing their developmental goals while enhancing their abilities that allow them to achieve measureable and sustainable results, and to improve or build their own collective commitments, resources and skills (Kenny and Clarke, 2010). CICCB refers to a social construct of CCB for achieving shared collaborative innovation goals and visions. It is a participative model focusing on consensus building, sustaining multiple and diverse networks and relationships, identifying and celebrating community strengths and assets, generating broad‐based community involvement toward mutual gains, developing whole community visions for the future, and identifying steps that can be taken to make such visions real (Miller, 2010, p.21). Such a way of thinking is important for increasing the success of innovation as the participants engage in collaborative problem solving with shared knowledge for

Xiaomi An, Hepu Deng and Lemen Chao mutual benefits and complementary advantages while sharing difficulties and obtaining greater resources, recognition and reward when facing competition for finite resources (Wagner, 2004). Such an achievement depends only on the interaction with others, indispensable for the realization of one particularly important value, necessary for the realization of various goods (Mason, 2000). Understanding the change and response of innovation interactions, and the obstacles that inhibit the interactions from realizing their collaborative development goals, three demands for improving and building collective commitments, resources and skills are identified as follows:

Trust building for enhancing the effectiveness among different innovation participants. With multiple innovation participants from multiple disciplinary fields and sectors, collective vision building faces great challenges, intellectual property protection and knowledge sharing depends on trust building for shared values in collaboration for the enhancement of effectiveness (Fawcett, Jone, Fawcett, 2011; Kong, Wu, and Zhang, 2012; Luo, Zhang and Du,2012).

Sustainability building for improving the efficiency in different innovation practices. with multi‐directional dynamic changes of key influential factors in innovation processes, maintaining a sustainable results becomes difficult, ways for consensus and participation in a shared way of life than controls in communication are important for the improvement of the efficiency (Bueno, Balestrin, 2012; Coming, 1998; Zhang, Cheng , Xu, 1997).

Extensibility building for developing green economy along the life of different innovation projects. With multi‐layers of systems and platforms in innovation architecture, cutting cost and sharing resources through networks are the mutual benefits for all the innovation programs, building all‐round connections across people, process and technology, inside and outside, identification with the group and mutual reorganization in connectivity are important for the promotion of the green economy (Fuller, et al, 2012; He, 2012; Kong, Wu, and Zhang, 2012).

3. Knowledge management for collaborative innovation capacity building KM is a systematic process of managing knowledge assets, processes, and organizational environments to facilitate the creation, organization, sharing, and utilization of knowledge for achieving the strategic aim of an organization (Deng, 2010; Song and Deng, 2005). It is a formal process that engages an organization’s people, processes, and technologies in a solution that captures knowledge and delivers it to the right people at the right time (Duff, 2001). It is an effective learning process with the exploration, exploitation and sharing of knowledge using appropriate technologies in a specific cultural environment for enhancing an organization's intellectual capital and learning capabilities (Japshapara, 2010). KM is a multidisciplinary approach that takes a comprehensive and systematic view of the knowledge assets of an organization by identifying, capturing, collecting, organizing, indexing, storing, integrating, retrieving, and sharing them. KM helps an organization gain insights and understanding from its own experiences (Geisler, Wickramasinghe, 2009). KM is increasingly gaining recognition as the determinant for improving the performance, competitive advantages and innovation through the sharing of lessons learned, integration and continuous improvement of an organization (Geisler, Wickramasinghe, 2009; Xiong and Deng, 2008). In recent years, the significance of KM for organizational competitiveness and better performance has been widely recognized around the world. Important KM strategies and practices that need to be used in organizations to identify, create, represent, distribute, and enable the adoption of insights and experiences which either embodies in individuals or embedded in organizations as processes or practices are recommended ( BS PAS‐2001; CWA14924‐ 2004; AS 5037‐2005, GB/T 23703‐2011). From the perspective of innovation, KM is the formalization of and access to experience, knowledge, and expertise that create new capabilities, enable superior performance, encourage innovation, and enhance customer value (Gloet and Terziovsk, 2004). From the perspective of collaborative innovation, KM has provided a common language set for multidisciplinary projects that support people access, create and share knowledge and leverage the knowledge for competitive advantages. It provides organizations with networked architecture, models for community development and collaboration. Knowing how, when, why to collaborate involves community participants in the process of innovation (Yahia, Bellamine, Ghezala, 2012). A critical analysis of existing literature on KM in CICCB leads to the identification of the three roles of KM in CICCB.

Xiaomi An, Hepu Deng and Lemen Chao

The reformation of KM for convergence in collaboration is to reformat the knowledge arrangement for convergence of different knowledge stakeholders and knowledge innovation participants to build an alliance system to achieve collective vision and common goals in order to enhance the effectiveness of innovation. The knowledge center of smart city projects is a good example of such a role. The mechanism behind such a role is a social arrangement of multi‐dimensional knowledge stakeholder partnerships to play leaderships roles in harnessing complementary capacities and the fair share of the costs and benefits of managing resources (An, 2013). The remediation of knowledge activities for synergy in communication is to remediate knowledge activities for synergy between and among different core components of practice and degree of cohesiveness of knowledge in support of common interest, needs, risks, resources, preferences by collective governance and interactions for improving efficiency of innovation. It is as a response to complexity and multiplicity of concerned innovation factors. The SECI knowledge conversion and creation models proposed by Nonaka and Takeuchi in terms of tacit knowledge to tacit knowledge (socialization), tacit knowledge to explicit knowledge (externalization), explicit knowledge to explicit knowledge (combination) and explicit knowledge to tacit knowledge (internalization) has provided a framework for management of the relevant processes, appreciation the dynamic nature of knowledge and knowledge creation (Noaka & Takeuchi, 1995; Noaka, Toyama, Konno, 2000). It has been used for examining knowledge activities during university‐industry collaboration, which shows that flexible structure works better and encourages the easy transfer of knowledge than traditional structure (Cheng, Wei, 2008). The reconfiguration of knowledge artifacts for integration in connectivity is to reconfigurate knowledge artifacts for integration of knowledge innovation actors, actions and their interactions together by web 2.0 networks, platforms and infrastructure in support of shared ideas, information and work. Such a way can help promote green economy, and enhance effectiveness and improve efficiency. A good example of this is virtual worlds as collaborative innovation and knowledge platform. Existing studies show that web 2.0 applications and virtual worlds bear the potential to connect organizational members as they provide media richness and facilitates social interactions, which enables gathering insights and knowledge from different departments and organizational backgrounds to engage in the generation of new innovative ideas and the access to distributed knowledge (Füller, et al, 2012).

4. A holistic approach for collaborative innovation community capacity building The above studies show that there are demands for a holistic approach to make CICCB work effectively, efficiently and economically. By ‘holistic’, this paper recommends a trans‐disciplinary perspective of KM and CCB for better CICCB performance rather than the analysis of, treatment of or dissection them into parts or assessment of them in isolation. To enable CICCB work effectively, efficiently and economically adaptable to complex collaborative innovation domains, three KM approaches as show in Table 3 and Figure 1 are considered as complementary with each other and as interacting wholes for effective CICCB. Such convergence, synergy and integration of a variety of roles of KM and embedding them into diverse CICCB processes can not only bring complementary capacities, but also can take comparative advantages of diversity of CICCB in management, thus can profitably harness them together. The first KM approach is the multi‐dimensional convergence and trust building in collaboration. Multi‐dimensional knowledge stakeholder partnership and a new alliance system are suggested to be built up for the enhancement of effectiveness of collaborative innovation. Such a social arrangement enhances knowledge asset reuse and knowledge transferring among different knowledge creators, owners, producers and users and the trust building (Cowan, Jonard, 2004; Wang, Deng, 2007). Such a reformation can explore the advantages of each participant and create a complementary new whole to benefit each other (Baldwin and Von, 2011; Xiong, et al, 2011; Cheng and Yang, 2012), e.g. smart city ‐traveling plans of Smart Transportation Center. The second KM approach is the multi‐directional synergy and sustainability building in communication. The multi‐directional collective governance mechanism is suggested to be built up in conformity with different types and different levels of collaborative innovation demands and requirements for efficiency of collaborative

Xiaomi An, Hepu Deng and Lemen Chao innovation. Such remediation of knowledge activities and practices provides a knowledge continuum regime to improve the sustainability building (Zhang and Deng, 2008). Remediation of knowledge accumulation, sharing and transferring processes to interact with each other can produce a harmonious new whole to support each other (Henttonen, Jauhiainen, Blomqvist, 2004; Zong, 2007; Xiong, et al, 2011), e.g. Smart collective decision making of Rio De Janeiro Intelligent Operation Center. The third KM approach is the multi‐layer integration and extensibility building in connectivity. Multi‐layer ubiquitous knowledge networks are suggested to be built up to connect people, process and technology and environment for green economy. Such reconfiguration of knowledge art facts e.g. knowledge resources, systems, platforms, infrastructure and architecture provides integration approaches to promote extensibility building (Geisler, Wickramasinghe, 2009; Li, 2011). Reconfiguration of knowledge networks can enable actors, activities and their interactions working as an integral and green new whole (Berasated, Arana and Castellano, 2011; Serrano and Fishcher, 2007; Swink, 2006; Tomas, 2009; Wang and Deng, 2007; Xiong, et al, 2011), e.g. Smart Cities and the Future Internet, Living Lab of Amsterdam Smart City Knowledge Center.

5. Conclusion Collaborative innovation is an emerging research topic in literature. A few studies have been done in KM field. Examining the domains of collaborative innovation and their patterns in research and practice. Demands for CICCB are identified as (a) trust building for enhancing the effectiveness; (b) sustainability building for improving the efficiency, and (c) extensibility building for developing green economy. An analysis of current literature reveals about positive social changes can be made by KM for CICCB. Three roles of KM in support of CICCB are identified as: (a) reformation of knowledge arrangement for convergence in collaboration; (b) remediation of knowledge activities for synergy in communication; (c) reconfiguration of knowledge art facts for integration in connectivity. To enable CICCB work effectively, efficiently and economically adaptable to complex collaborative domains, three KM approaches are suggested as: (a) multi‐dimensional convergence and trust building in collaboration; (b) multi‐directional synergy and sustainability building in communication; and (c) multi‐layer integration and extensibility building in connectivity, they shall be regarded as interacting wholes rather than separate ones towards a holistic approach for effective CICCB. The paper contributes to the literature in three ways. First, it provides insight into the ways as in which the collaborative innovation literature currently lacks attention towards aspects what are crucial for successfully performance of collaborative innovation. These elements are provided by incorporating roles of the KM and CCB thinking. Secondly, the paper identifies demands for CICCB in support of collaborative innovation actions and roles of KM in their support. Thirdly, a holistic approach for effective CICCB is suggested. This paper is the first step of a more comprehensive study on the role of KM in support of CICCB. As a result the paper is currently limited to define the basic concepts and identify the basic approaches of KM in support of CICCB.

Acknowledgements This work is partly supported by the National Social Science Foundation of China Major Program (Project number: 12&ZD220), the China‐US Fulbright Program, the National Natural Science Major Project (71133006/ G0314) and Natural Science Foundation of China (Project number: 71103020).

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Knowledge Management Systems for Attrition Control Activities in Private Higher Learning Institutions Muthukkaruppan Annamalai1, Kalsom Salleh1 and Salmiah Amin2 1 Universiti Teknologi MARA, Malaysia, 2 Asia Pacific Institute of Information Technology, Malaysia mk@tmsk.uitm.edu.my kalsom816@salam.uitm.edu.my salmiah@apiit.edu.my Abstract: Private Higher Learning Institutions (PHLI) are concerned about student attrition because they are in competitive stage gaining numbers of students recruited into the institutions. While mentoring programmes are a useful approach for attrition control, such systems are often not well established in PHLI due to higher turnover of lecturers who play the role of mentors and also attributed to weak institutional follow‐through process. Apropos Knowledge Management System (KMS) can help to equip the mentors with the requisite capabilities, as well as, facilitate the reinforcement of the institutional networks and commitments to effectively deal with student attrition. Following a structured path of KM, the paper recommends a KMS arrangement for attrition control in the context of a mentoring program in PHLI, and outlines an analytical approach for implementing KMS through KM mechanisms and KM technologies. The core of the idea in this paper is based on case studies involving two PHLI in Malaysia, which were analysed according to exemplar student integration and attrition models and espouses intervention strategies through a mentoring program. The structured analysis focuses on the knowledge and information needed by mentors and students, and the KM processes and the sub‐ processes involved to identify suitable KM mechanisms. The latter set the ground for the proposed KMS availed through a KM portal. Keywords: knowledge management system, attrition control strategies, analytical approach, higher learning institution

1. Introduction Student attrition occurs when a student leaves an institution citing financial, personal or academic matters, lack of support from the institution. For an individual learner, attrition means a lost opportunity to enhance his level of personal and career development. Involuntary withdrawal because of academic failure or inability to cope with the demands of the educational system lowers self‐confidence and self‐esteem and likely represents a negative impact to the students. For an institution, attrition is frequently cited as a critical factor in assessing the cost‐effectiveness of the learning institutions (Tyler‐Smith, 2006). Attrition is considered a waste of university resources, especially in an environment of limited financial and general resources. It also represents a loss of revenue as well as potentially damages its image and reputation, creating long‐term implications for attracting new students (Ozga and Sukhnandan, 1998). Accordingly, it is far more cost effective for a Higher Learning Institutions (HLI) to retain students that have been admitted than to recruit new ones (Taylor, 2005). This matter is of particular interest to the Private HLI (PHLI) in Malaysia encompass private universities, university colleges, colleges, foreign university branch campuses and distance learning centres (Ministry of Higher Education, 2007), which are in competitive state to gain numbers of students recruited into their institutions. Studies have shown that student retention can be increased and academic achievement can be upheld through the setting up of a mentoring program in HLI (Leung and Bush, 2003). However, the mentoring systems are often not well set up in PHLI due to the high turnover of lecturers who play the role of mentors (Suraya and Azah, 2005). The ineffective establishment of the mentoring systems is also attributed to weak institutional follow‐through. New mentors have little knowledge as to how to carry out attrition control, and often need to self‐learn, whether rightly or wrongly. Therefore, we propose to adopt the Knowledge Management (KM) approach and recommend apropos KM mechanisms supported by KM systems (KMS) to equip the mentors and students with the requisite capabilities, as well as to reinforce the institutional network and commitment to effectively deal with attrition. KM mechanisms are social and structural means to facilitate the knowledge activities (e.g. attrition control) in

Muthukkaruppan Annamalai, Kalsom Salleh and Salmiah Amin an organisation (Becerra‐Fernandez et al, 2004) and to implement KMS through various KM technologies for the creation and maintenance of organisational memories like portals, repositories and collaborative systems. These KM products for knowledge exploitation are the most vital KM category in terms of sustainable competitive for KM in academic institutions (Sherif, 2006). Consequently, the paper outlines the analytical approach for implementing KMS through KM mechanisms and KM technologies for attrition control activities in PHLI. The core of the idea in this research proposal is based on case studies involving two PHLI in Malaysia, which are analysed according to exemplar student integration and attrition models. The proposal espouses intervention strategies through a mentoring programme via KMS in the PHLI in Malaysia. A triangulation research approach is used for the case studies for which the primary data is based on observation and interviews and secondary data is obtained from literature reviews and document reviews. The structure of data analysis for this study is to achieve the following purposive steps:

Determine the responsibilities of mentors performing the attrition control activities.

Recognise the knowledge and information needed by students and mentors in the course of their responsible actions in step 1.

Identify the KM processes and sub‐processes that can bring about the knowledge conversion based on the knowledge and information needs in step 2.

Recommend KM mechanisms to support the identified KM processes and sub‐processes in step 3.

Propose specific KMS based on the generally applicable KMS to support the KM mechanisms in step 4.

The paper is organised in six sections. In the following section two, we discuss the literature reviews and related works of study. The attrition control models and strategies that serve as the basis for analysing the mentors’ responsibilities and the attrition control tasks are explained. The relevant KM concepts are also briefly described in here. In section three, we mention about the case studies that served as the source of our primary data and secondary data to describe the research method that we employed to gather and analyse the data. We also discuss the case study findings. In section four, we present the recommended KM mechanisms and the supporting KM processes and the sub‐processes based on the analyses on the knowledge needed to support the attrition control tasks and mentors’ responsibilities. In section five, we propose the relevant KMS and finally, we conclude in the last section.

2. Literature review and related works 2.1 Attrition control models and strategies Tinto’s (1993) Student Integration Model (SIM) is the dominant model amongst the reviews in attrition research. The model extends his principles of attrition control (Tinto, 1975) that backs the three rites of passage stages namely, a) Separation (from family support), b) Transition (recognition of new values and behaviours), and c) Incorporation (adaption to a new set of values and behaviours). Tinto posits that attrition occurs when a student’s rites of passage is incomplete. Tinto’s SIM underscores that the academic and social experiences are integrated and advocates that a student’s intentions, his goals, and his institutional and external commitments ought to be revisited once the student has gone through the academic and social experience in the learning institution. The model, which can serve as the basis for institutional action to overcome the attrition problem, urges institutional commitment to attrition control activities through a mentoring program. Upholding Tinto’s principles of attrition control, Bean and Metzner’s proposes a Student Attrition Model (SAM) (Bean and Metzner, 1985) that attributes attrition to the following factors that affect a student’s grades, commitments and fitness: a) Academic factors (academic performances and readiness), b) Social‐psychological factors (student’s goals setting and fit into social and academic expectations), c) Environmental factors (financial, external influences and opportunities). This attrition model contributed to the improvement of Tinto’s SIM, which assigns the causes of student attrition summarised in Table 1.

Muthukkaruppan Annamalai, Kalsom Salleh and Salmiah Amin Table 1: Attrition causes (Tinto, 1993; Bean and Metzner, 1985)

Based on these attrition models, Gabb et al. (2006) proposed the following nine intervention strategies aimed at improving retention in HLI.

Improving academic advisory services,

Recruiting with integrity,

Paying attention to the early stages of courses offered to the students,

Tutoring focused on student progress,

Monitoring and following up poor attendance,

Identifying under‐performing students or students who are at risk,

Diagnosing student requirements for necessary skills,

Improving student motivation, and

Improving teaching.

We regard the above intervention strategies that aim to improve students’ retention as fitting attrition control activities. The attrition control activities served as the basis for our primary data gathering and analyses, which will be explained later.

2.2 KM processes, KM mechanisms KM technologies and KMS Explicit knowledge is recordable and easily communicated, while tacit knowledge is personal and is a rich source of experience. It is impossible to separate the tacit knowledge from the action (context). The challenge is with the management on how to use the tacit and explicit knowledge effectively. The KM processes contribute to this end through knowledge conversion sub‐processes. The four key organisational KM processes are Creation, Organisation, Sharing and Application of knowledge (Becerra‐Fernandez et al., 2004), involving seven KM sub‐processes: 1) Socialisation, 2) Externalisation, 3) Combination, 4) Internalisation, 5) Exchange, 6) Direction and 7) Routine (Nonaka, 1994). The idea is for an organisation to utilise the KM processes and sub‐processes to create and build up its knowledge sources and resources and make them accessible and/or available wherever and whenever they are needed through a variety of KM mechanisms. Note: In general, from this point on, when we speak of KM process, it should be understood that the term also includes its knowledge conversion sub‐processes, unless we specifically refer to a KM sub‐process. KM mechanisms are organisational or structural means used to support KM processes (Becerra‐Fernandez et al., 2004). KM mechanisms are social and structural means to facilitate the knowledge activities involving the KM processes in order to get the most out of knowledge sources and resources. Examples of KM mechanisms facilitating the socialisation sub‐process that relates to knowledge creation and sharing are on‐the‐job training, learning by observation and face‐to‐face meeting. New mentors may gain important attrition control knowledge from experienced mentors via mechanisms such as training and consultation. Similarly, mechanisms established by the institution to exchange documents could benefit the mentors in providing quick and easy access to the knowledge needed for relevant student care in attrition control.

Muthukkaruppan Annamalai, Kalsom Salleh and Salmiah Amin KM technologies include the combinations of data, information, hardware, software and process that facilitate KM mechanisms. For example, the Combination and Externalisation sub‐processes have shown significant influence on explicit knowledge satisfaction in terms of knowledge organisation and sharing (Becerra and Sabherwal, 2001). The KM mechanisms supporting these processes can greatly leverage on Information Communication and Technology (ICT). Consequently, the adoption of apropos KM technologies serve as enabling platform for KM processes promoting knowledge creation, organisation, sharing and application in organisations (Prusak, 2001; Alavi and Leidner, 2001). Typical KM technologies are document management, web conferencing, groupware and workflow. KMS generally refer to the technology set in place to support KM processes and improve KM in organisation. The KMS are distinguished among three levels of KM technologies (Gallupe, 2001). Level 1 consists of KMS components such as database and document languages that provide the basic building blocks for a KMS. At level 2, there are KMS generators such as Lotus Notes that can be used to build a variety of specific KMS. KM generators are usually self‐contained, and consist of a number of technological tools such as document management software and groupware that help groups or teams to communicate and collaborate. Level 3 comprises the specialised KMS themselves that provide an idealised representation of KMS with some features and processes such as inputs, review and evaluation, structured knowledge repository, hovering external websites, outputs, and so on. We intend to supplement the mentoring effort with suitable KM processes, KM mechanisms and KM technologies to support apropos KMS for controlling attrition in PHLI. The adoption of KMS in the attrition control activity and mentoring program can help students to have more confident towards the PHLI’s learning environment.

3. Data gathering and analysis We followed a mixed method illustrated in Figure 1 to gather and analyse the research data. Such triangulation approach is generally used for gathering qualitative data. The primary data is based on observation and semi‐structured interviews carried out at two PHLI; which we call INST_A and INST_B. The institutions will remain unnamed for the sake of confidentiality. These institutions were selected because they acknowledge the need to overcome the problem of institutional attrition, i.e., their students are leaving to another institution to continue their study. Moreover, one of the authors of this paper has access to the sources of knowledge in these institutions. The secondary data is obtained from documents in the case study institutions and literature reviews. Literature reviews. We reviewed the literature to learn about attrition control and the issues related to it. In the process, we identified the relevant attrition models and the intervention activities in the context of a mentoring program in PHLI. The models and strategies (discussed in section III) helped us to support the organisation of the case study findings. Observation. Data is gained by observing the attrition control tasks carried out by mentors in the PHLI. We also took note of the existing attrition control strategies employed in these institutions and the knowledge sources and resources referred to by the mentors. Semi‐structured interviews. Three different groups of mentors, namely the new mentors, the experienced mentors and the academic administrators were interviewed to distinguish the attrition control tasks of the mentors in the different groups. Document reviews. Resources related to the attrition control tasks such as meeting notes and activity forms were reviewed to understand the nature of knowledge shared in the institutions to support the mentors carrying out the attrition control tasks. Strauss and Corbin (1990) pointed that the qualitative data gathering and data analysis are tightly interwoven, and occur alternately; the gathered data are analysed, and the analysis directs the further sampling of data. Therefore, our primary data gathering was conducted in two phases.

Muthukkaruppan Annamalai, Kalsom Salleh and Salmiah Amin

Figure 1: Mixed methods used for data gathering and analysis In the first phase, the attrition control tasks of experienced mentors in INST_A were observed. The mentors in the institutions were later interviewed to understand how different groups of mentors in INST_A dealt with attrition control. The preliminary study helps to uncover some of the mentoring issues related to attrition control. The observations, interviews and documents review gathered in this phase directed us to look for additional information. We embarked on a second phase of data gathering in INST_B. The data collected from INST_B helped to enrich the data gathered earlier from INST_A. We determined the attrition control tasks and mentors’ responsibilities detailed in Table 2) by analysing the similarity and the differences in the data gathered from INST_A and INST_B, and supported by literature reviews. Next, we analysed the knowledge sources and resources that can be exploited to support the attrition control tasks. Finally, we reflected on the KM processes associated with these tasks to recommend suitable KM mechanisms that can facilitate the knowledge conversion involving those processes. The recommended KM mechanisms for attrition control are detailed in Table 2. The ensuing paragraphs discuss the key findings of the study. Both institutions are concerned about attrition reduction and have set up strategic means to control attrition. They aim to achieve positive academic and social environment for their students. INST_A supports its students on track towards achieving their academic goals. Its Course Appraisal system allows the institution to retrieve the course appraisal reports completed by students apart from the feedback given to the course tutors. The Feedback system and the Online Helpdesk provide alternative ways for students in this institution to reach for help when required. Mentors are engaged to deal with attrition when needs arise. Using these support systems, the institution becomes aware about the students needs and areas for improvement. INST_B provides pastoral cares apart from student services. Students are assigned mentors from the time of their enrolment. The mentors follow up with necessary actions to deal with the academic related issues that affected their students under their care. Three attrition control activities are enacted in this institution, namely, 1) Monitoring and following up poor attendance, 2) Identifying underperforming students and 3) Tutoring on students’ progress. Activity forms are filled by the mentors after meeting the students to write down the academic advisory provided to the students, feedback obtained from them and their progressing status. The tutored students are classified under one of three various categories, and those identified to have withdrawal potentials are followed up by the institution’s administration. It is commonly observed that attrition occurs when students are under‐prepared or incur financial problem. The under‐prepared students also tend to lack the necessary communication skills. As a result, they do not seem to know on how to seek for the right assistance when faced with adjustment difficulties. Similarly, students who faced financial inadequacies tend to have negative attitude about their own future and are more likely to withdraw from the institution. KM mechanisms are needed to connect these students with academic support and/ or with financial facilitators. Both PHLI acknowledge that new mentors can learn much from the experienced mentors to effectively deal with attrition control. KM mechanisms that allow mentors to gain the

Muthukkaruppan Annamalai, Kalsom Salleh and Salmiah Amin requisite experiential knowledge are necessary. Suitable KM mechanisms and supporting KMS are needed that can guide the appropriate use of the shared knowledge.

4. Recommended KM mechanisms and the supporting KM processes From the analysis of the case study findings, a model arrangement for attrition control in the context of a mentoring program in PHLI can be envisaged. In the first column of Table 2, we summarise the attrition control tasks that constitute the attrition control activities and the responsibilities of mentors; the latter is detailed in the second column. In listing the tasks, we also drew ideas from numerous studies that explored the issues affecting student attrition (Lettman, 1986; Picklesimer and Miller, 1998; Wilson et al, 1999, Cuseo, 2003; Fry et al, 2003; Grayson and Grayson, 2003; Pascarella and Terenzini, 2005; Tinto and Pusser, 2006; Morda et al, 2007; Sweeny, 2008). The attrition control tasks targeted at students are listed under the heading STUDENTS, while those targeted at mentors and institution are listed under the heading MENTORS/ INSTITUTION. Next, we reflected on the knowledge or information needed by the students and mentors and the KM processes related to the knowledge conversion involved in the performance of the attrition control tasks. The crucial information that students and mentors need to be equipped with are highlighted in bold within the description of the attrition control tasks (second column). Based on these, an array of KM mechanisms and supporting KMS are proposed. The KM mechanisms to deal with an attrition control activity are given in the last column of Table 2. The KM sub‐process or sub‐processes supporting a mechanism is specified in a square bracket at the end of each description. The proposed KMS and their organisation are discussed in the next section. Table 2: Summary of attrition control tasks, mentors’ responsibilities, recommended mechanisms and supporting KM sub‐processes for the attrition control activities

Attrition Control Activity 1. Improving academic advisory services

Attrition Control Task and Mentors’ Responsibility STUDENTS Introduce the students to institution’s academic and social environment. Inform the students about institution’s policies, procedures and expectations. Inform the students about their study plans and advise on course selection. Help the students to set their academic goal. MENTORS/ INSTITUTION Provide institution’s information. Provide mentoring guidelines; improve listening skills and self esteem (Miller, 2002; Cuseo, 2003). Help the institution to formulate academic and social policies and regulations. Encourage mentors to establish memberships in the social communities of their institution (Grayson and Grayson, 2003).

Recommended KM Mechanism and the supporting KM Subprocesses: [S]ocialisation, [E]xternalisation, [C]ombination, [I]nternalisation, E[X]change, [D]irection and [R]outine STUDENTS Student academic handbook [E, X] Student orientation kit (that includes academic policies and procedures) [E, X, R] Briefing sessions for students [S] Academic counselling – discussion/face‐to‐face meetings [S, I] MENTORS/ INSTITUTION Staff orientation modules [E, X] Mentor meetings and discussion [S, I] Experience sharing sessions ‐ mentoring/ advising skills [S, I]

Muthukkaruppan Annamalai, Kalsom Salleh and Salmiah Amin

Attrition Control Activity 2. Recruiting with integrity

3. Paying attention to the early stages of courses offered to the students

Attrition Control Task and Mentors’ Responsibility STUDENTS Introduce students to the academic related services and facilities in the institution. Assist students to select suitable academic and social transition workshops to attend. Direct needy students to relevant persons who are responsible for financial consult (advice on financial preparations, scholarships, loan, instalments, etc). MENTORS/ INSTITUTION Obtain information about students’ academic and family background to better understand the students’ needs. Obtain information about the institution’s financial policies and support facilities. Obtain information about transition programs and workshops. Provide support for academic adjustments (Morda et al, 2007). STUDENTS Solicit students’ views on their expectation during the beginning of the course. Solicit students’ views/feedback about the courses lectures, tutorials, transition workshops and seminars. Advise students on course selection, transfer, withdrawal and also the procedures (Pascarella and Terenzini, 2005). Check up on student’s adaptation to college life in terms of academic, social and personal‐emotional adjustment. Provide strategic ideas to students for academic progress. Promote and encourage the students to involve in academic workshops organised, competitions, seminars and study skills program organised by the institution (Morda et al, 2007) MENTORS/ INSTITUTION Identify students’ needs. Provide information about program study plan. Provide updated information about institution’s policies, procedures and academic expectations.

Recommended KM Mechanism and the supporting KM Subprocesses: [S]ocialisation, [E]xternalisation, [C]ombination, [I]nternalisation, E[X]change, [D]irection and [R]outine STUDENTS Banners/ Electronic boards [D] Student orientation kit (which includes documents about program leadership, academic advice services and academic expectation) [E, X] Campus tour [S, I] Briefing sessions ‐ information about transition programs and workshops [X, S] Information booth [D] MENTORS/ INSTITUTION Student portfolios [X, C] Financial policies and support facilities handbook [E, X, R] Staff orientation modules [E, X]

STUDENTS Student orientation kit (which includes program study plans) [E, X] Course modules [E, X] Ice‐breaking sessions [S] Academic workshops for students [X, S] MENTORS/ INSTITUTION Students’ feedback forms [E, C] Entrance/ exit survey forms [E, C] Inventories to assess students’ needs (Picklesimer and Miller, 1998) [E, C] Exchange of documents/reports/memos [X] Academic seminars for mentors [X, S, I] Field visits (INST_B) [S, I]

Muthukkaruppan Annamalai, Kalsom Salleh and Salmiah Amin

Attrition Control Activity 4. Tutoring focused on student progress

5. Monitoring and following up poor attendance

6. Identifying under‐ performing students or students who are at risk

Attrition Control Task and Mentors’ Responsibility STUDENTS Provide strategic ideas for students to achieve their academic goals based on their academic progress. MENTORS/ INSTITUTION Solicit students’ views/feedback about the courses lectures, tutorials, transition workshops and seminars. Monitor students’ academic performance progress. Seek opportunities for extra assistance. STUDENTS Enquire students for possible problem faced that lead to absenteeism. Remind students on attendance policy. MENTORS/ INSTITUTION Monitor student attendance and be aware of repeated absenteeism. Analyse students’ absenteeism patterns (Wilson et al, 1999). Report students’ repeated absenteeism (Tinto and Pusser, 2006). Dealing with problem students. STUDENTS Enquire reasons for lag in progress. Provide feedback on students’ performances and participation in class, assessments and mid‐term evaluations. Help students to recognise their weaknesses and provide assistance to improve. Help student to analyse and evaluate ideas. Help student to revisit the academic goal. MENTORS/ INSTITUTION Monitoring of students’ continuous evaluation and assessment results. Help to identify underperforming students. Appropriate actions to assist underperforming students. Provide assessment results and performance reports.

Recommended KM Mechanism and the supporting KM Subprocesses: [S]ocialisation, [E]xternalisation, [C]ombination, [I]nternalisation, E[X]change, [D]irection and [R]outine STUDENTS Tutorial session [X, S, I] Face‐to‐face meeting [S, I] MENTORS/ INSTITUTION Continuous evaluation and assessment reports [E, C] Appraisal and feedback forms [E, C] Activity forms [E, C] Exchange of documents/reports/memos [E]

STUDENTS Warning letters [D] Group discussions [S, I] Consultation/Face‐to‐face meeting [S, I] MENTORS/ INSTITUTION Students’ performance reports [E, C] Students’ absenteeism reports [E, C] Exchange of documents/reports/memos [X] Absenteeism patterns/Intervention strategies [C]

STUDENTS Re‐orientation programs (Morda et al, 2007) [S] Tutoring workshops [S, I] Face‐to‐face meeting [S, I] MENTORS/ INSTITUTION Students’ performance reports [E, C] Activity forms [E, C] Knowledge sharing on the appropriate actions to assist needy students [E, X, C] Exchange of documents/reports/ memos [X]

Muthukkaruppan Annamalai, Kalsom Salleh and Salmiah Amin

Attrition Control Activity 7. Diagnosing student requirements for necessary skills

8. Improving student motivation

9. Improving teaching

Attrition Control Task and Mentors’ Responsibility STUDENTS Brief students on learning skills. Help students to identify the learning skills they required. Help students to locate needed resources, equipment and services. Coach students to develop their interpersonal and communication skills. MENTORS/ INSTITUTION Assess students’ needs and provide information about students’ requirements to management. STUDENTS Guide students to do self assessment and build confidence in learning. Encouraging students to be socially active/ involve in extracurricular activities. Motivate students to achieve their academic goal. Share and positive experiences with students. MENTORS/ INSTITUTION Promote academic excellence (INST_A). STUDENTS Encourage students to provide honest feedback on lecturers/ tutors performance MENTORS/ INSTITUTION Participate in peer observations. Conduct discussion on current, relevant educational research and theories (Sweeny, 2008). Suggest improvements to the course content and the mode of delivery.

Recommended KM Mechanism and the supporting KM Subprocesses: [S]ocialisation, [E]xternalisation, [C]ombination, [I]nternalisation, E[X]change, [D]irection and [R]outine STUDENTS Face‐to‐face meeting [S, I] MENTORS/ INSTITUTION Students’ performance reports [E, C] Student needs assessment reports [E, C] Activity forms [E, C] Exchange of documents/ reports/ memos [X]

STUDENTS Open discussion sessions [S] Face‐to‐face meeting [S, I] Transition programs and workshops [S] Extracurricular activities [S] MENTORS/ INSTITUTION Motivation skill courses for mentors [S, I] Rewards for excelling mentors [D]

MENTORS/ INSTITUTION Student appraisal and feedback reports [E, C] Peer observation/ evaluation reports (INST_A) [E, C] Exchange of documents/ reports/ memos [X] Knowledge sharing sessions to discuss students’ feedback on teaching standard/ improvements (Fry et al, 2003); Techniques and strategies to increase the effectiveness of teaching and tutoring (Lettman, 1986; Grayson and Grayson, 2003) [S, E] Incentives and opportunities for excelling academic staffs [D]

5. Proposed KMs for the attrition control activities KMS refer to a broad class of information technologies for knowledge acquisition, creation, integration, transfer and application. Alavi and Leidner (2001) summarised three functions of KMS, i.e., 1) To build a knowledge infrastructure, 2) To proactively seek and offer knowledge, and 3) To make knowledge visible and show the role of knowledge in organisations. In addition, KMS can be divided into three levels of KM technologies (Gallupe, 2001). Level 1 consists of KM tools such as databases and document languages that provide the basic building blocks for a KMS. At level 2, there are KMS generators such as Lotus Notes that can be used to build a variety of specific KMS. KM generators are usually self‐contained technologies that consist of a number of tools such as document management software, intelligent agents and groupware that helps groups to communicate and collaborate. Level 3 KMS comprises the specialised KMS themselves that provides an idealised representation of KMS with some features and processes such as inputs, review and evaluation, structured knowledge repository, hovering external websites, outputs, and so on. Table 3 provides some contextual ideas for computer based KMS supporting the KM mechanisms to deal with attrition control tasks and activities.

Muthukkaruppan Annamalai, Kalsom Salleh and Salmiah Amin Table 3: Proposed computer based KMS to support KM mechanisms that target students and mentors in the attrition control activities Structured information repository campus resources manuals FAQs Electronic discussion groups with integrated instant messaging tools and online social networking services (e.g. facebook) for discussion and knowledge exchange on academic and social experience social adjustments mentor‐mentee relationships Best practices database contained recorded videos on training and briefing good teaching/ tutoring approaches positive counselling successful mentoring Lesson learned databases mentoring failures recovery programmes Secured shared database systems that generate report to track student attendance student performance mentor performance Secured communication to follow up and exchange information about student requirements student feedback student appraisal underperforming students Advisory services that provide for basic skills and appropriate support to needy students student centred learning support service links: Personal, Financial and General welfare matters

A portal is a suitable platform to integrate these KMS. Portals can provide users with interactive ICT tools such as electronic discussion group, email, instant messaging, video conferencing, database management systems, search engines and web based systems. KM portal is an extension of the portal concept with the purpose of adding superior knowledge representation and semantic search capabilities. They provide tools to extract, analyse, categorise both structured and unstructured information and reveal the relationship between content, people, topics and users’ activities in the organisation (Wagner, et al., 2003). In this regard, we think a KM portal is useful in making service delivery happen in attrition control. It can provide a means to personalise, sort and filter information, and therefore can be used as the focal points for knowledge and information exchange, supporting mentors and students in a PHLI.

6. Conclusion Student attrition is a real concern for PHLI in Malaysia. Following the path of KM and case study research, we proposed a systematic approach for implementing KMS based on the organised array of KM mechanisms for attrition control activities and mentoring programs in PHLI. Triangulation method of research is adopted to gather qualitative data from two PHLI in Malaysia. Critical review of literature was conducted to learn about the attrition control models and strategies. The structure of data analysis is carried out by the following purposive steps: 1) Data obtained through the observations and interviews are analysed, and the relevant attrition control tasks and activities and the responsibilities of the mentors are determined; 2) Knowledge and information needed by students and mentors in the course of their responsible actions are recognised; 3) KM processes that can bring about the knowledge conversion based on the knowledge and information are identified; 4) Suitable KM mechanisms to support the identified KM processes are recommended; 5) Attrition control activities and the KM mechanisms are then analysed in terms of KM processes, and specific KMS are proposed. We suggest a knowledge portal as an implementation platform for the computer based KMS. We hope the proposed KMS can help the PHLI and their mentors/mentees to foster innovation with the requisite

Muthukkaruppan Annamalai, Kalsom Salleh and Salmiah Amin capabilities, as well as, to aid in the reinforcement of the institutional networks and commitments to effectively deal with students’ attrition control measures.

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Knowledge Management and Development of Entrepreneurial Skills Among Students in Vocational Technical Institutions in Nigeria Stella Ify Anumnu Federal College of Education (Technical), Nigeria anumnustella@yahoo.com

Abstract: This study examined the extent to which learners in vocational technical institutions, capture, distribute, network and effectively use information made available to them during and after lectures that are entrepreneurially and skill oriented in other order to furnish the labour market with relevant school products. Development of an entrepreneurial skill is capable of equipping the Nigeria students to fit into different aspects of the economy after graduation. The study adopted a descriptive design. A twenty‐item unstructured questionnaire was used to assess 150 randomly selected final year students’ capacity to transform ideas gained in class into creative problem‐solving strategies in three vocational technical colleges in Lagos in south west Nigeria. Three research questions and hypotheses were used as guides to the study. Data obtained were analyzed using descriptive statistics, Pearson Moment Correlation. The major finding revealed that there is a significant relationship between knowledge sharing and networking and students’ capacity to translate some curriculum elements into the world of work. Given the importance of knowledge sharing, innovations and connectivity through networking in today’s competitive world of work, it was recommended that students be linked up with several entrepreneurs who serve as mentors to students during and after training. Students should be made to participate in seminars and workshops that are entrepreneurially oriented. There should be regular visits of students to small cottage industries in the form of field trips. There should be a collaborative effort between vocational institutions and some government agencies, for example’ Small and Medium Entrepreneurial Development Agency of Nigeria (SMEDAN) Keywords: knowledge, knowledge sharing, networking, entrepreneurial skills, vocational, technical, innovation and creativity

1. Introduction What makes the difference among academics all over the world is the ability of one to use acquired knowledge to solve real life problems in the society; hence, the importance of knowledge management is more on applicability or utilization of knowledge. Knowledge management involves a holistic amalgamation of an organization’s information assets towards the attainment of the organization’s objectives and goals.

2. Literature review What is knowledge management? According to Davenport (1994), knowledge management is the process of capturing, distributing and effectively using knowledge. But Duhon (1998) in his broader definition notes that knowledge management is a discipline that promotes an integrated approach to identifying, capturing, evaluating, retrieving, and sharing all of an enterprise’s information assets, which include databases, documents, policies, procedures, and previously un‐captured expertise and experience in individual workers. The above definitions of knowledge management have three major things in common about knowledge. These include knowledge identification and integration, knowledge acquisition and knowledge application or use. To Afrazah (2010) KM as the process of discovery, achievement, development and creation, maintenance, assessment and appropriate usage of knowledge in appropriate time by the fit person in the organization, which is done by having the joint between human source, information technology communications and the suitable organization schedule in order to achieve the goal. In this study therefore, KM is defined as students’ ability to gather information, share, analyze and apply it to develop their entrepreneurial skill so as to fit into the labour market. Knowledge is an indispensable asset to any organization that when it is properly harnessed, managed and utilized, will not only bring about increased productivity, but also expansion, growth and sustained profitability to the organization. Tertiary institutions are pioneers in knowledge‐based societies for information development, they are considered as centers and sources of consuming and producing information and most raw materials used in academic trainings are allocated to information exchange process. Therefore is need to

Stella Ify Anumnu harness and manage holistically the available wealth of knowledge assets (databases, policies, procedures, lecturers’ expertise, curriculum and course contents, lectures and training lessons, libraries, etc) towards producing students that on graduation would either become entrepreneurs or professionals and consultants in their areas of specialization. Koenig (2012) proposed three categories of knowledge: explicit, implicit and tacit. Explicit is the information or knowledge that is set out in tangible form; implicit is the information or knowledge that is not set out in tangible form but could be made explicit; while tacit is the information or knowledge that one would have extreme difficulty operationally setting out in tangible form.

3. Entrepreneurship Dana (2001, 405) highlights the fact that ‘there is no universally‐accepted definition of entrepreneurs or of entrepreneurship’ in the literature. This evident lack of a comprehensive and widely accepted definition does not detract from the reality that entrepreneurs possess distinctive features including ‘a capacity for innovation’. It refers to an individual’s ability to turn ideas into action and helping young people to be more creative and self‐confident in whatever they undertake. According to Sheetal, (2012), entrepreneurship is the engine fuelling innovation, employment generation and economic growth. The General Assembly of the United Nations, during its 48th session, adopted a resolution ‐ Entrepreneurship and Privatization for Economic Growth and Sustainable Development ‐ encouraging members to promote and facilitate the growth of entrepreneurship and the support to local entrepreneurs. This resolution according to Sheetal, is a reflection of the growing international realization that “Lighting the flame of the entrepreneurial spirit empowers nations and peoples, with ‘the knowledge and ability to fish, rather than just giving them a fish’ [Timmons, in Sheetal, 2011].” Entrepreneurship education has been embedded into the Nigerian system of education on the basis of peculiar need of unemployment and deficient self reliant skills. One of the major objectives of the Nigeria’s National Policy on Education (2004) on Vocational Technical Education is to produce students who will not only be equipped with functional contemporary technological knowledge, but also, will either be self‐employed on graduation or entrepreneurs and employers of labour and not job seekers. This objective was occasioned by Nigerian government’s enthusiasm for increasing the manpower stock of the country for industrial growth and development as well as for the reduction of unemployment through the production of student entrepreneurs. Entrepreneurs according to Ilo (1995) are individuals with entrepreneurship ability; and entrepreneurship ability as defined by Schumpeter (1994) is the ability to perceive and undertake business opportunities, taking advantage of scarce resource utilization. It involves direct willingness and ability to seek out investment opportunities and to run an enterprise for profit (Schumpeter, 1994).

4. Entrepreneurial skills Entrepreneurial skill refers to entrepreneurial competencies which enable an entrepreneur to be successful in his or her field. Entrepreneurship ability is a function of seven skills, which must be acquired for one to qualify as an entrepreneur. These skills according to Idowu (2004) and Adepoju & Adedeji (2012) include the following: creative skill (ability to visualize and identify new problem areas in the society and try to generate new ideas or concepts in that line); innovative skill (ability to generate and apply creative ideas in some specific content to solve identified problem for the benefit of society); managerial skill (ability to define goals and objectives, plan and stipulate strategies to organize, motivate, direct and control resources to attain stated objectives); analytical skill (ability of numeracy, generation and analysis of data for relevant decision making); marketing skill (ability of book keeping and accounting, integrating business logistics to increase sale of goods and services); communicative skill (ability to use relevant language to negotiate, persuade and convince) and career skill (ability to assess self, plan techniques and self‐directed learning knowledge, such as computer literacy, etc).

5. Developing entrepreneurial skills Ascertaining what needs to be taught in terms of entrepreneurship education is no easy task as no formula exists for what constitutes entrepreneurship to begin with (Dana 2001, . 414). Taatila (2010, . 56‐57) highlights the need for learning to take place in a relevant school or business environment, while also detailing the need for real‐life case studies based around student‐centred and pragmatic pedagogical approaches. Similarly, Plumy et al (2008) stated that ‘reality‐based pedagogies’ embedded in courses anchored to skill‐building are better suited to entrepreneurship education than more traditional methodologies that focus on knowledge

Stella Ify Anumnu building, such as in accounting or management. Therefore bringing together the student and learning, while ‘integrating theory and practice’, are key to implementing effective entrepreneurship education. Given that entrepreneurship represents an ongoing dynamic cycle of learning, it is surprising that there is somewhat of a gap in the school learning literature on this topic (Franco & Haase 2009). Nevertheless, Plumly et al (2008, p. 19) detail a range of key skill building areas that they recommended such areas as communication, leadership, teamwork, negotiation, strategic planning, basic business law, innovation and technology, and product life‐cycle and development. Today, Nigeria and many other developing countries have shown immense interest in the development of student entrepreneurs through entrepreneurship education and training in vocational institutions and universities, using it as a new strategy in tackling the high rate of unemployment bedeviling their countries. However, for Nigerian vocational institutions, especially the technical ones and universities to successfully produce the desired quality of student entrepreneurs, there is need for the effective management of the knowledge assets available to those institutions. This is because the development of the multidimensional entrepreneurial skills explained above could start with the process of identifying, capturing, evaluating, retrieving, and sharing of knowledge. It is against this background that this paper examines the relationship between knowledge management and entrepreneurial skill development among students in vocational technical institutions in Nigeria.

6. Institutional culture and knowledge management process DeLong and Fahey (2000) identify four comprehensive ways in which culture influences the behaviours central to knowledge creation, sharing, and use. According to DeLong et al, culture shapes assumptions about what knowledge is and which knowledge is worth managing. Secondly, culture defines relationships between individual and organizational knowledge, determining who is expected to control specific knowledge, as well as who must share it and who can board it. Thirdly, culture creates the context for social interaction that determines how knowledge will be used in particular situations and finally, culture shapes the processes by which new knowledge with its accompanying uncertainties is created, legitimated, and distributed in organizations. Similarly, Turban and Aronson (2001 . 355) also conclude that ‘the ability of an organization to learn, develop memory, and share knowledge is dependent on culture’. School should establish an appropriate culture that encourages students to create and share knowledge within the school (Holsapple & Joshi 2001). Therefore to stimulate the development and application of knowledge, the key elements of a knowledge culture are required, that is a climate of trust, confidence, and openness in an environment where constant learning and experimentation are highly valued, appreciated and supported (Moffett, et al, 2003). Knowledge management creates a culture where every student continuously assesses his/her learning habit and lesson learned and the school, looking for ways to improve. After every class activity student’s teams may review assignments, identify successes and failures and seek ways to perform better the next time. This approach to capture learning from experience builds knowledge that can be used to develop entrepreneurial skills operation and improve teaching and learning processes. Actively managing student’s knowledge can stimulate cultural change and innovation and creativity by encouraging the free flow of ideas.

7. Relationship between knowledge management and entrepreneurial skills Studies have shown that there is link between knowledge management and entrepreneurship. For example, the results of Liebowitz (1999) shows there is relationship between knowledge based strategies and entrepreneurship. He believes that one of the important factors for successful knowledge management is having a clear strategy and planned program. Similarly Akhavan et al (2006) in his study concluded that organizational culture has a positive effect on creativity and entrepreneurship. And also Wong and Chin (2007) Study results showed that the beliefs and culture of organizations is one of the key factors in entrepreneurship development. Nazem et al study (2010) also confirms the relationship between knowledge management and entrepreneurship. They achieved these results in their study that there is a relationship between knowledge management and entrepreneurship in employees of insurance organization of Tehran. In the same vein, Taleghani (2011) study revealed that there is meaningful relationship between knowledge management with an organizational entrepreneurship. Similarly, Mahmmood et al (2012) study results revealed that there is

Stella Ify Anumnu significant relationship between knowledge management and individual entrepreneurship and that knowledge creation and teamwork have a linear relationship with individual development.

8. Statement of problem It is well established that successful courses of formal education tend to be those that are relevant and meaningful to the lives of the students. But how to encourage prospective students into such study when their formal schooling is not considered sufficient to gain direct entry into the world of work is a great challenge to all stakeholders in education. This study is premised on the view of employers that large numbers of graduates of tertiary institutions are jobless because majority of them learn through lectures and academic textbooks and are academically sound but they have limited opportunity of acquiring practical experience by using machinery, equipments and practical techniques associated with a profession. This is a serious problem. Graduates of different levels of education go about looking for jobs that are non‐existent. Consequently, government has recently directed its policy actions towards initiating entrepreneurship education and training in Nigerian universities, technical and vocational institutions to enable them to start producing students with entrepreneurial skills. The achievement of this new educational objective calls for the effective management of the knowledge assets available to the universities and vocational technical institutions in the country. Therefore, ascertaining the extent to which the development of entrepreneurial skills among vocational technical students is susceptible to knowledge management summarizes the entire business of this study.

9. Conceptual framework Conceptual frameworks and theory‐based knowledge are essential to ground the practical learning activities (Fiet, in Sheetal, 2011). A teaching concept that integrates the major requirements for effective entrepreneurship education is the pedagogical concept of project‐based learning. In project‐based learning approaches, students have to take responsibility and complete a realistic task by independently gathering information and by building up, transforming, and constructing knowledge (Ahunanya et al, 2011). However for better understanding of the link between knowledge management and, development of entrepreneurial skill, this study adopted and modified Zahra and Geoge 2002, in Danijela (2011) knowledge management process as shown below. Entrepreneurial Skill Ability to acquire knowledge Knowledge Management process

Ability to use knowledge

Lesson learned based on the curriculum of courses taught, teamwork, brainstorming interaction and communication, Networking. Insight, sharing of ideas appropriate to what is learned, measurable project based on lesson learned.

Ability to assimilate knowledge Teacher delivery style, conducive classroom disposition or friendly, effective use of ICT Ability to transform knowledge Practical application of knowledge to real life situation and decision making.

Source: Adapted from Zahra and George, in Danijela (2011) and modified. Figure 1: The main abilities in the knowledge management process In the school, students faced with data and information (based on lesson learned) need to have a knowledge management process to make useful decision. In figure 1 the acquisition of knowledge involves group or team work, brainstorming (knowledge sharing), interaction between teacher and students, students to students and effective communication.

Stella Ify Anumnu The purpose of use of knowledge is the ability of students to embark on measurable project based on what is learned. That is, making use of their psychomotor domain and transferring the knowledge acquired into real life situation. Knowledge assimilation is using knowledge in the right way (entrepreneurship) in order to achieve their desire or benefits. This ultimately reduces the gap between knowledge that is needed (entrepreneurial skill) and the knowledge that is currently available. The transformation of knowledge includes the ability of a student to combine elements of knowledge in new ways. That is, ability to create wealth through entrepreneurship. This process equips a student to create an entirely new knowledge, providing him or her fresh ideas for further application of knowledge and new solutions to meet market needs. Research has shown that the connection between knowledge assimilation and transformation is particularly important because the greater the students’ assimilation of knowledge, their ability to transform knowledge becomes more effective (Danijela (2011). The ability to use knowledge includes student’s ability to incorporate knowledge gained into their learning processes and it is reflected into the development of ideas on how, when and where knowledge can be used to meet market needs (entrepreneurial skills)

10. Justification of the study This study attempts to provide concrete data that would enable students, teachers, educational managers, policy makers and other stake holders in education with a better understanding of knowledge management processes toward the development of entrepreneurial skills for wealth creation.

11. Research questions The following research questions were raised to guide this study.

What is the perception of students about the entrepreneurial skill development provided in their schools?

How do students perceive the relevance of knowledge management to entrepreneurial kill development?

To what extent has your exposure to all the knowledge assets of your department and school equipped you with the skills (creative, innovative, managerial, analytical, marketing, communicative and career skills) of an entrepreneur?

12. Research hypotheses The research hypothesis below was stated and tested during the study. Ho1: There is no significant relationship between knowledge management and the development of entrepreneurial skills among vocational technical students in Nigeria.

13. Methodology This study is out to establish the relationship between knowledge management and development of entrepreneurial skills. The study is descriptive survey. The sample of 150 students was selected randomly from final year students from three vocational technical institutions in Lagos, Nigeria. The institutions used were Federal College of Education (Technical) Akoka; Yaba College of Science and Technology and Lagos State Polytechnic. The students are studying vocational and technical courses such as Agriculture, Home Economics, Fine and Applied Arts, Accounting, Elect/Elect, Building, Automobile, Metal Work, Architecture among others in 2012‐2013 academic sessions. Final year students were selected because they are on the verge of going into the labour market. The instrument used for data collection was a self‐designed questionnaire titled: the Knowledge Management and Development of Entrepreneurial Skill Questionnaire (KMADESQ). The questionnaire comprises two sections. Section A solicits for responses on name of institutions and personal data of the respondents while section B contains items that elicit information from learners about knowledge management and development of entrepreneurial skills using a 4 point likert‐type response formula (4 = strongly agree, 3 agree, 2 disagree and strongly disagree 1). Data collected were analyzed using descriptive and inferential statistics.

14. Results of data analysis Research Question 1: What is the perception of students about the entrepreneurial skills development provided in their schools? Table 1, shows that on aggregate, majority of the respondents (80.5%) that participated in the study affirmed that through the entrepreneurial skill development provided to students in vocational technical institutions,

Stella Ify Anumnu they are: exposed to workshops and seminars that enhance their entrepreneurial skills; provided with skills to identify business opportunities; equipped with skills to test their creativity in business ideas; engaged in industrial work experience scheme that exposes them to the practical challenges of owning a business and ways of handling them; provided with knowledge of how to get financial support to a start a business, as well as the strong desire to start their own businesses immediately after graduation; but a few of the respondents (19.5%) expressed a contrary opinion to the above submissions. Table 1: Perception of students of the entrepreneurial skill development provided in their schools Statement items

Students’ are exposed to workshops and seminars that enhance their entrepreneurial skills. The school’s entrepreneurial training provides students with the skills to identify business opportunities. I have the skill now to test my creativity in business ideas. The school’s student industrial work experience scheme (SIWES) exposes the students to the practical challenges of owning a business venture and ways of handling them. The experience gathered from SIWES also equips students with the skills of managing business successfully. I learnt how to get financial support to start a business. From the training acquired, I can boast of starting my own business on graduation. Total = Aggregate Percent =

56 58

59 63

17 16

18 13

55 53

68 65

10 18

17 14

390 455 845(80.5%)

101 104 205(19.5%)

Key: SA = (Strongly agree), A = (Agree), D = (Disagree) and SD = (Strongly disagree) N =150 Research Question 2: How do students perceive the relevance of knowledge management to entrepreneurial skill development? Table 2, shows that on aggregate, majority of the respondents (80.3%) admitted that: sharing of information among students helps in knowledge dissemination among them; sharing of knowledge and information on business formation among students help them in developing creative and innovative business ideas; students acquire a lot of entrepreneurial ideas through knowledge and information sharing among themselves; students are able to clarify their entrepreneurial thought through sharing of knowledge and information among themselves; and through networking and group brainstorming students’ entrepreneurial thought and skill are nurtured; but a small number of the respondents (19.7%) indicated a contrary opinion to the above submissions. Table 2: Perception of students of the relevance of knowledge and information sharing to entrepreneurial skill development Statement items

Sharing of information with other students helps in knowledge dissemination. Sharing of knowledge and information on business formation with other students help them in developing creative and innovative business ideas. Students have acquired a lot of entrepreneurial ideas through knowledge and information sharing among themselves. I always clarify my entrepreneurial thought through sharing knowledge and information with other students.

Stella Ify Anumnu Statement items

Students’ entrepreneurial thought and skill are nurtured through networking and group brainstorming. Total = Aggregate Percent =

248 354 602(80.3%)

76 72 148(19.7%)

Key: SA = (Strongly agree), A = (Agree), D = (Disagree) and SD = (Strongly disagree) N =150 Research Question 3: To what extent has your exposure to all the knowledge assets of your department and school equipped you with the skills (creative, innovative, managerial, analytical, marketing, communicative and career skills) of an entrepreneur? Table 3, shows that out of the responses of students on their perception of the extent to which their exposure to all the knowledge assets of their departments and schools has equipped them with the different skills of an entrepreneur, 89.8% affirmed that their exposure to their departments and schools’ knowledge assets have immensely equipped them with all the skills to become an entrepreneur, while 10.2% of the responses expressed a contrary opinion to the submission. With this result, we infer that knowledge management enhances greatly the development of entrepreneurial skills. Table 3: Students’ perception of the extent to which knowledge management has equipped them with the different skills of an entrepreneur To what extent has your exposure to all the knowledge assets of your department and school equipped you with the following skills: Creative skill (ability to visualize and identify new problem areas in the society and try to generate new ideas or concepts in that line); Innovative skill (ability to generate and apply creative ideas in some specific content to solve identified problem for the benefit of society);l Managerial skill (ability to define goals and objectives, plan and stipulate strategies to organize, motivate, direct and control resources to attain stated objectives) Analytical skill (ability of numeracy, generation and analysis of data for relevant decision making) Marketing skill (ability of book keeping and accounting, integrating business logistics to increase sale of goods and services) Communicative skill (ability to use relevant language to negotiate, persuade and convince) career skill (ability to assess self, plan techniques and self‐directed learning knowledge, such computer literacy, etc) Total/Percent =

High extent

Low extent

138

129

133

124

141

143

135

943(89.8%)

107(10.2%)

N =150

15. Hypotheses testing and results Ho1: There is no significant relationship between knowledge management and the development of entrepreneurial skills among vocational technical students in Nigeria. Table 4 shows that the calculated r‐value of 0.614 is greater than the critical r‐value of 0.1946 given 148 degree of freedom at 5% level of significance. This result supports the rejection of null hypothesis 3 in acceptance of its alternative that a significant relationship exists between knowledge capturing and sharing (knowledge management) and entrepreneurial skill development among the students in vocational technical institutions.

Stella Ify Anumnu Table 4: Knowledge management and entrepreneurial skill development among students of vocational technical institutions Variables Students’ Entrepreneurial Skill Development Knowledge Management

N 150

Mean 20.55 15.44

SD 2.357 2.673

Df=(n‐2) 148

P‐value 0.05

r‐calculated 0.614

r‐critical 0.1946

***α = 5% significance level

16. Discussion of findings The study investigated the effect of knowledge management on the development of entrepreneurial skills among students of vocational and technical institutions. The test of the hypothesis posited for this study showed that there is a significant relationship between knowledge management and the development of entrepreneurial skills among vocational technical students in Nigeria. This finding supports those of DeLong and Fahey (2000), as well as those of Turban and Aronson (2001), which had earlier established that knowledge management impacts positively on entrepreneurship development. The implication of this finding is that if immense effort is directed at making knowledge management in Nigerian vocational technical institutions to be more effective, Nigerian vocational technical students will turnout great entrepreneurs in future.

17. Conclusion Encouraging the youth to become entrepreneurs has a lot of advantages for them and the economy as well as preparing the way for innovation and growth of the country. The ability to promote entrepreneurship requires an enquiring mind that is able to make connections between theory and practice. Action oriented teaching is required for creating entrepreneur awareness and designing a learning environment that is very close to reality.

18. Recommendations

There is need to recreate real life situations in the classroom with the help of examples and illustrations so that students are able to get a ‘concrete feel’ of various aspects of the outside world. For example, regular visits to small and medium scale enterprises, having mentors in their areas of specialization, going on field trips and networking on how to access microcredit facilities.

There should be a collaborative effort between vocational institutions and some government agencies, for example’ small and Medium Entrepreneurial Development Agency of Nigeria (SMEDAN)

Entrepreneurship should be closely linked with practice. Teachers should be encouraged to reach out to the business community and integrate it into the learning process.

To give students the hands‐on experience, some activities and learning tools should be used inside the classroom as well as outside the classroom like: workshop on business plans and business model.

According to education literature, active learning approaches create a stimulating atmosphere by encouraging interaction among students and thus promoting so‐called soft skills as problem‐solving, the ability to work in teams, decision taking, and conflict management and communication skills. These approaches should be encouraged in schools.

There is an urgent call for more action‐orientation in entrepreneurship education. In order to overcome the limitations of traditional theory‐based learning approaches, creating entrepreneurial awareness among the students, and designing a learning environment that is close to reality is a priority.

References Adepoju, O.O & Adedeji A.O (2012) Entrepreneurship Training and Self Employment Intention of Graduating Student of Tertiary Institutions in Ondo State. Journal of Educational Review (5) 474 – 479. Serials Publication NewDelhi (India). Ahunanya, S, Ibiam, N & Okere, R (2011) capacity building in knowledge creation for University lecturers: cost challenges Journal of Educational Review. (4) 4, 489‐509 Serials publication NewDelhi (India).

Stella Ify Anumnu Akhava, P., M. Jafari & M. Fathian, (2006). Critical success factors of knowledge management systems: a multi‐case analysis, European Business Review. 18(2):97‐113. Dana, L.P (2001) ‘The Education and Training of Entrepreneurship in Asia’, Education + Training, 43, 8/9, 405‐415. Sourced 6/12/2012. Danijela Jelenic (2011). The importance of Knowledge Management in Organizations with Emphasis on the Balanced Scorecard Learning and Growth Perspective. Management, Knowledge and Learning International Conference. Sourced 7/12/2013. Davenport, T.H. (1994), Saving IT’s Soul: Human Centered Information Management Harvard business Review, march‐April, 72 (2) . 119‐131. Duhon, Bryant (1998), it’s All in our heads. Inform, September, 12 (8). DeLong, D.W. & Fahey, L. (2000) ‘Diagnosing Culture Barriers to knowledge Management’, Academy of Management Executive, 14, (4) pp. 113‐28. th Federal Republic of Nigeria (2004). National Policy on Education (4 Edition) Lagos: Nigeria Research Development Council (NERDC) Press. Holsapple, C.W & Joshi, K.D. (2001) ‘Organizational Knowledge Resources’, Decision Support Systems, 31, (1) pp, 123‐41. Idowu, Sowunmi, (2004): Developing Entrepreneurial skills in Students" THISDAY December 14, 2004. ILO, N.H. (1995). School‐industry Linkage in the Training of Vocational Technical Teacher in Anambra State, Research and Publication Unit, F.C.E.(T), Umunze. Koenig, M.E.D. (2012). What is KM? Management Explained. Sourced 04/04/2013. Liebowitz, Jay, (1999). Key ingredients to the success of an organization’s knowledge management, John Wiley and Sons, Inc., 37‐40. Mahmmiid, G. Abedian, H. & Vahid, S. (2012). Relationship between Knowledge Management and Development in Individual Entrepreneurship at Industrial Estates World Applied Science Journal 18 (6) 745‐753. Moffett, S. McAdam, R & Parkinson, S. (2003), ‘Developing a Model for Technology and cultural Factors in Knowledge Management: A Factor Analysis’, Journal f Knowledge Management, 7, 3. Nazem, F. karimzadeh S. and Qaderi E. (2010). Examine the relationship between, knowledge management and organizational health with entrepreneurship employees in the organization of Insurance, social research, the third year No.9 Plumly, L.W, Marshall, L.L, Eastman, J, Iyer, R, Stanley, KL & Boatright, J. (2008), ‘Developing Entrepreneurial Competencies: a Student Business’, Journal of Entrepreneurship Education 11, 17‐118 Serials Publication NewDelhi (India). Schumpeter, P. (1994). Tough Times Never Last. But Tough People Do, U.S.A.: Thomas Nelson Inc. Sheetal, Munndra (2012) Interweaving of Entrepreneurial skills Development and Management Education. Sourced 5/5/2013. Taatila, V.P. (2010), ‘Learning entrepreneurship in higher education’, Education + Training (52) 1, 48‐61. th Turban, E. & Aronson, H.E. (2001), Decision Support Systems and Intelligent Systems, 6 Edn, Prentice Hall. Wong, S. & C., K. (2007). Organizational innovation management: An organization‐wide perspective, industrial management Data Systems, 107(9): 1290‐1315.

An Individual‐Centred Model of Intellectual Capital Teresita Arenas1, Paul Griffiths2 and Alejandro Freraut3 1 Departamento de Industrias, USM University, Santiago, Chile 2 Birchman Consultores, Santiago, Chile 3 Sixbell, Santiago, Chile teresita.arena@usm.cl paul.griffiths@vtr.net alejandro.freraut@sixbell.com Abstract: A detailed search of the Intellectual Capital (IC) literature has revealed that in the last few years much energy has been devoted to developing models on how to manage IC in organisations, and how IC impacts the organisation’s performance. Significant progress has also been made on how to identify and manage IC in Regions, and how this impacts their growth and benefits their inhabitants. However, surprisingly little has been found on people‐centred or employee‐ centred studies on IC. All individual members of an organisation have their own knowledge, skills, competencies, networks and potential for innovation so, on the one hand, it is hypothesised that there must be a way to define an individual’s IC. On the other hand, if an individual identifies his or her IC and manages it effectively, will this lead to personal benefits? This research is exploratory and aims to address these issues by departing from the present IC models for organisations to propose an IC model for individuals and possible links to the individual’s performance and benefits. It applies a quantitative approach based on structural equation modelling and arrives at some reassuring findings. Keywords: intellectual capital, employee performance, structural equations, happiness

1. Background The history of management shows that the route to competitive advantage has changed over time in accordance with the stage of the economy. In the Industrial age the key factors were access to capital and energy, while today, in the information society, people are key to generating sustainable competitive advantages for an organisation, and assuring permanent growth by applying knowledge to add value to its products and services (Viedma & Cabrita, 2012; Wiig, 1997) We have also witnessed a gradual change in the way managers view or conceive employees. The first visions, established in 1776 by Adam Smith in his Inquiry into the Nature and Causes of the Wealth of Nations state that an employee is one of an organization’s resources while nowadays it is more common to view an employee as an organisational asset. This paradigm shift also demonstrates a change in the employees’ own view, seeing themselves as valuable to an organization and thus prompting them to be more flexible and mobile when seeking material rewards for their own personal achievements. This new behaviour (high mobility and flexibility) helps to understand the new employee profile, and to identify the variables that generate a greater wellbeing and make employees more profitable and competitive, a phenomenon which can be likened to the behaviour of businesses (Campbell, Coff, & Kryscynski, 2012). Along the same lines, in the information society the intellectual capital (IC) of businesses, taken in its simplest form as knowledge that generates value for the organisation, is a relevant variable when it comes to profitability, a subject that has been the object of academic research since the 1990’s. Nonetheless, we do not find evidence of studies of IC as applied to employees, and whether there are links between this individual IC and outcomes such as impact on the remuneration that they receive. An exception is McCloy et al., (1994) that links employee performance to declarative knowledge, procedural knowledge, skills and motivation and concludes that the first three variables are relevant, but not so motivation. Having said this, a limitation of that study is that it does not tackle IC holistically but only through isolated components. Departing from the premise that all individual members of an organisation have their own knowledge, skills, competencies, networks and potential for innovation, this study aims at tackling the following research questions:

How do you define an individual’s IC; and

If an individual identifies his or her IC and manages it effectively, will this lead to personal benefits?

Teresita Arenas, Paul Griffiths and Alejandro Freraut This research is exploratory and aims to address these issues by departing from the present IC models for organisations to propose an IC model for individuals and possible links to the individual’s performance and benefits. Section 2 will define IC and present the literature on IC at company level; based on this, it will propose a model for IC at an individual level. Section 3 will describe the Methodology, and Section 4 will present the results. Section 5 discusses the implications of the results, and Section 6 arrives at the conclusions and limitations of the research.

2. Designing the model 2.1 Intellectual capital and its definition The origins of IC go back to eras as ancient as the creation of organizations. “…It has been present since the moment that the first vendor established a good relationship with a client…” (Brooking, 1997). However the desire to research it began in the 1980’s. The driving force behind this has been the technological and information revolution and the emerging conviction that IC is a key factor in what is called the information age (Drucker, 2002) or the knowledge economy (Viedma & Cabrita, 2012.) The first writings on the subject are attributed to Itami, who in 1980 published his book Mobilizing Invisible Assets in Japanese (Itami & Roehl, 1987). Nonetheless investigators such as Sveiby, Stewart, Saint‐Onge, Edvinsson, Brooking, Bontis, Sulliven, Viedma, Bueno and Andriessen have dedicated the past 20 years to a deeper investigation of the subject, establishing methodologies on how organizations can profit from the measurement and management of intellectual capital. The year 2010 was the most productive in terms of the publishing of books and papers; since 1999 there have been more than 20 annual publications, concentrated around the International Symposium on Measuring and Reporting Intellectual Capital, the European Conference on Knowledge Management (ECKM), the European Conference on Intellectual Capital (ECIC), the International Conference on Intellectual Capital, Knowledge Management and Organisational Learning (ICICKM) (Arenas, 2013) Concerning the definition of IC, we have found that there are as many definitions as there are definers, in the sense of those who have dedicated themselves to developing this theme. However, there are certain common themes when it comes to the elements they comprise and the way they are managed. From the start, IC has been defined as the sum of all the knowledge possessed by all the employees of a business (Stewart, 1998); (Brooking, 1997); (Sullivan, 2000 ), and which gives the business a competitive advantage. In other words it is intellectual material such as knowledge, information, intellectual property, experience (Bontis, 1998); (Meritum, 2001)that can be exploited to create wealth. Simultaneously but from a different perspective, other researchers have stressed that IC is a combination of intangible assets (Sveiby, 2001); (Allee, 1999) or non‐material assets (Lev, 2001). That is to say, they are not on the balance sheet (Ross, Edvinsson, Ross, & Dragonetti, 2001) (Club Intelect , 1998) and when they are well managed they enable a business to gain competitive and lasting advantages, thus generating value. Subsequently, researchers started to look more closely at certain elements of the definition of IC. They point out for example that organizations must create the conditions in which tacit understanding can be converted into explicit knowledge (Saint‐Onge, 1996), or emphasise knowledge in action (Danish Agency for trade and Industry., 2000). They also point out that the identifying and managing of IC must be guided by a vision of the business and based on its core activities and core competence (Andriessen, 2001); together with its collective talent (i.e., abilities, commitment and action) which take shape in a successful business recipe (Viedma, 2001) If we analyse these papers we can distinguish certain key words which form part of the definition of IC: “knowledge”, “intangibles”, “creation of value or benefits”. Taking these concepts into account, we can construct the following definition of IC that will be our working definition for this research: IC is a recipe for personal business success that the individual designs through leveraging his knowledge, skills, networks and sense for innovation to enable him to achieve sustainable benefits.

Teresita Arenas, Paul Griffiths and Alejandro Freraut

2.2 Methods of intellectual capital Just as there are many different definitions for IC, researchers have identified various methodologies for its measurement (Brooking, 1997), (Edvinsson & Malone, 1999), (Sveiby, 2001), (Bontis, 1998) or its management (Andriessen, 2001), (Viedma, 2001). In spite of methodological differences, most authors agree on that IC generates wealth and that it can be broken down into three sub‐categories for its analysis, namely, human capital, structural capital and relational capital. Human capital is taken to mean collective skills, creative capacity, problem‐solving abilities, leadership and those entrepreneurial and management capacities that are possessed by individuals of an organization (Brooking, 1997), (Edvinsson & Malone, 1999), (Viedma, 2003) It is centred on the value created by the capacity of employees to act in different circumstances and scenarios (Sveiby, 2001), making them intellectually agile, committed individuals (Ross, Edvinsson, Ross, & Dragonetti, 2001) and therefore hard to replace (Stewart, 1998). Structural capital is considered as a group of assets belonging to an organization’s infrastructure. It is the skeleton beneath aspects such as corporate culture, leadership methods, databases, procedural manuals, information systems (Brooking, 1997), patents, ideas and software, (Edvinsson & Malone, 1999), (Sveiby, 2000), (Ross, Edvinsson, Ross, & Dragonetti, 2001).It can also be interpreted as the way in which the general knowledge created by human capital is contained and retained, in such a way as to become the property of an organization. In other words it is knowledge ‘that does not go home in the evening…’ (Stewart, 1998). Structural capital can be represented through degrees of tangibility, the highest degree being legally protected knowledge and the lowest when knowledge is shared on an informal level, becoming ‘the way an organisation does things’ (Club Intellect, 1998), (Roos, 2001). In other words it also includes renovation and development. Finally, relational capital is centred on knowing clients and how to elicit their loyalty; brands and distribution channels (Brooking, 1997), (Sveiby, 2000), (Stewart, 1998); and the relationship with suppliers (Sveiby, 2001), (Bontis, 2004). Although relationships with clients and suppliers are key for success in most organisations, from the sustainability literature we can extend this concept to include relationship with all stakeholders, in different degrees according to their importance to the success of the organisation. Clearly clients and suppliers are at the centre, but there will be other key elements such as knowledge of regulations and relationship with regulators, knowledge of sources of finance and relationship with actual and potential financiers; knowledge of foreign markets and relationship with enablers in those markets (Griffiths, 2008)

2.3 Designing a model of intellectual capital for an employee In the traditional “homo economicus” approach to economics both employees and businesses seek to maximise their wealth, i.e. the cash flow they generate. In this paradigm it appears logical, therefore, to make an analogy between a business’ IC and that of an employee. This way we can construct a model of the IC of the employee, based on the elements that generally explain the IC of a business, namely human, structural and relational capital. We now know that homo economicus is a “rare species” (McFadden, 2013). It ignores the “endowment effect” and the effects of “loss aversion” and the role of memory and experience in determining choices (Kahneman, 2011). Economists usually incorporate the effects of “trust” in their models, but what about the “herd effect”, that is that what people like is influenced by others. And what about such human attributes as “altruism” and “kindness”? However, in this piece of exploratory research these considerations are left aside. It is quite understood that this is a limitation, but if it can be shown that the concept of IC can be extended to the individual in the simple “homo economicus” paradigm, then it will prove worth incorporating the mentioned complexities into the model in a future study. Employees’ individual human capital would be expressed by their capabilities, attitude and intellectual agility (Ross, Edvinsson, Ross, & Dragonetti, 2001), (Club Intelect , 1998). Their structural capital would be represented only by their renovation and development capital, which is defined as a person’s capacity to create, investigate and develop, in such a way as to generate value in the person’s future: in other words the capacity for improvement, the return gained from knowledge, by means of innovation. The following sub‐ dimensions can be defined: internal, as related to the search for changes in the individual’s life via training

Teresita Arenas, Paul Griffiths and Alejandro Freraut courses and a disposition to change jobs if that renders an advantage; personal deals, related to a disposition towards furthering the individual’s own negotiations as well as development in the current post; and finally the disposition to propose new products and services and change ways of doing the job. Employees’ individual relational capital would be defined as the set of relations which an individual maintains with the outside world. In other words, the wealth a person possesses by virtue of friends, family, acquaintances, fellow members at work and sports teams, alumni networks and all those people to whom the individual can relate and with whom he or she can do business, transact and make contacts in order to promote wellbeing and generate wealth. We shall consider the sustainability and quality of these relations and their potential for engendering new contacts, via the capacity for interpersonal, social and workplace communication. In addition to the latter it is believed that in the case of employees another variable to be considered is happiness, which is defined as an affective state of full satisfaction which an individual subjectively experiences when in possession of something that has been strongly desired (Alarcón, 2006). However, beyond Alarcon this goes further than simply attaining a material thing as these only produce temporary happiness; durable happiness derives from higher order elements such as the pursuit of knowledge (Cook, 2007). There will be a connection with satisfying the Me, with family, profession and society. A summary of this is shown in figure 1.

Figure 1: Model of intellectual capital centred on the individual

3. Methodology Using structural equation modelling and based on previously established theories, the hypothetical model that represents IC centred on the individual can be seen in figure 2. In order to test the model a structural equation tool was chosen as it enables working with both latent and observable variables, particularly in order to analyse the data model. The software tool applied was Amos version 21. The data was obtained from a survey of a sample of 800 individuals working in Chilean business, mostly in the telecommunications industry. The majority of those sampled are male, married, and with a completed university education and/or doctorate, with a remuneration in excess of Chilean $ 1.100.000. (US$ 2,200) per month. The survey was performed in Q1 2013. The instrument was developed in Spanish language and calibrated by working with a group of 12 executives on who it was applied as a pilot. The final version of the instrument comprises evaluation questions on a 7‐ point Likert scale where 7 represented total agreement and 1 total disagreement. Nineteen questions were asked on human capital, 7 on renovation and development capital, 18 on relational capital, 6 on happiness and 2 on remuneration. The survey was carried out via e‐mail containing a link to the instrument. There were 345 responses of which 197 were complete and valid and thus considered apt for the study.

Teresita Arenas, Paul Griffiths and Alejandro Freraut The data was analysed in its original language and only the final write‐up was done in English.

Figure 2: Theoretical model of individual‐centred intellectual capital

4. Results After running the model, the best adjustment is represented in figure 3. Following the steps suggested in the methodology for the validation stage of structural equations, first came the measuring model. From analysis of this, in terms of human capital, the use of four variables instead of three was adopted, separating adaptability to work from adaptability to a variable attitude. The other latent variables remain as posited by theory. It is also worth mentioning that the Cronbach alpha for each one of the latent variables under analysis exceeded 60%, suggesting that adjustment was good. Continuing with the structural model, the base model was tested: latent variables represented by human, relational and renovation and development capital and its relation to IC. This model adjusted itself well, but with some values above the recommendable, such as the mean square root of the error (RMR) of 0,116, where the values recommended are less than 0,1 (Kaplan, 2000). Afterwards, and as suggested in the theoretical model, happiness was incorporated as a latent variable that affects IC. Thus the final model shown in figure 3 does not contain infractory estimates, the Cronbach alfa for each one of the groups exceeds 60%, and the adjustment is fairly adequate when measured by: residual mean root (RMR) of 0,106; goodness fixing index(GFI) of 0,95 and relative chi square (CMIN/gl) of 3,04. (Hair, Andreson, Tatham, & Black, 1998) (Mejía & Cornejo, 2010) The following tables show these results:

Teresita Arenas, Paul Griffiths and Alejandro Freraut

Source: Amos version 21, based on survey data Figure 3: Final model of individual‐centred intellectual capital

Model Default model Saturatedmodel Independence model Zero model

NPAR 42 153 17 0

CMIN 337,563 ,000 2575,927 7420,072

Model Default model Saturatedmodel Independence model Zero model

RMR ,106 ,000 ,294 ,641

GFI ,955 1,000 ,653 ,000

AGFI ,937 ,609 ,000

PGFI ,692 ,580 ,000

5. Discussion There have been studies correlating individuals’ income in Chile with their level of education (MIDEPLAN, 2002), (Brunner & Elacqua, 2003) which, not surprisingly, conclude that the number of years spent in school influence the level of workers’ pay. In the present study many more variables are added since it is not only a question of education but of a broader concept: that of human capital, which this study shows to be closely related to the happiness of individuals and the IC they generate. Although in this research human capital is connected to an individual’s knowledge (years of study), it is most closely related to her talents, attitude to work and adaptability to changes and work. A further factor is relational capital, itself strongly linked to interpersonal communication more than to the attitude towards social and work networks. Yet another is renovation and development capital, more connected to creativity at work, for example through the proposal of new products as opposed to the attitude towards personal deals or internal change. IC, taken at its simplest manifestation as a resource that generates value, in a similar way to business models, was expressed through differing levels of remuneration and work satisfaction. The correlation was stronger with work and salary satisfaction than with the level of income.

Teresita Arenas, Paul Griffiths and Alejandro Freraut As far as happiness is concerned, it is unsurprising that the connections are stronger with personal and family well‐being than with social. It is also noteworthy that the base model, which emerged as an analogy with models of IC in businesses, indicates that the IC of an individual is expressed via capitals that are human, relational and derived from renovation and development. It needs to be pointed out that there are positive relationships between human capital and IC, not unexpected in the light of the theory. What is surprising is that from this study it emerges that the relationship between relationship capital and IC at an individual level, is negative. That is that the higher the relationship capital, the lower the IC. This phenomenon is aligned with a prior study on the tourism industry in Chile’s Region V that found that at an organisational level relationship capital and IC are also negatively correlated (Arenas, 2013). In that case it was attributed to the fact that there was no relational capital or that it is very weak or incipient. In the light of this new result it can be interpreted as that individuals in Chile do not use their personal networks lo leverage their organisation’s performance; relationship capital is more a disturbance than a resource to achieve results. Finally it is worth mentioning that adding the variable of happiness notably improved the adjustment of the model: It was established that human capital had a positive correlation with happiness and IC, and that happiness and IC were related. In other words human capital contributes to the generation of IC both directly and indirectly via the happiness of the individual.

6. Conclusions and future investigations This is the first investigation to examine and model IC centred on the individual, meaning that the objectives of the study have been accomplished. This modelling comes from making an analogy with business models of IC. The relations found tend to bear out the literature, but it is a fact that the major impacts on individual‐centred IC come from abilities and adaptation, interpersonal communication and creativity at work, and that this IC, which is also influenced by happiness, translates itself principally as a component of satisfaction, both work and income wise. These findings reaffirm the importance of satisfaction in employee productivity, but also show that a no lesser group of variables intervene in the individual’s generation of value. It is also clear that the generation of value for an individual does not only depend on income as it was thought to be in the scientific administration, but accords with modern administrative theory: that producing more effective employees requires not only a greater level of remuneration but also that the individual considers the remuneration to be appropriate, meaning that it is satisfying and that the employee is also satisfied with the tasks that she carries out. A surprising result is that it appears that Chilean individuals do not convert relationship capital into IC; it is as if they do not use their relationship capital to improve their performance at work. This finding is aligned with similar results at company level performed in the tourism industry, where it appears that organisations do not convert relationship capital into IC. This is certainly an area of interest for future research. Finally, it should be mentioned that researchers hope they will in future be able to expand the survey sample, trying out the model in different sectors and cultures in order to contrast findings in different scenarios. It is also important to reiterate that this model is based on the “homo economicus” paradigm and that future research in this area should delve into the connection of other variables with happiness as indicated in section 2.3 above.

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HEALTHQUAL International All Country Learning Network (ACLN): A Peer‐Driven Knowledge Management Strategy and Community of Practice to Build Capacity for Sustainable National Quality Management Programs in Low‐ and Middle‐Income Countries Joshua Bardfield, Margaret Palumbo, Richard Birchard, Michelle Geis and Bruce Agins New York State Department of Health AIDS Institute, HEALTHQUAL International, USA jeb16@health.state.ny.us map06@health.state.ny.us reb08@health.state.ny.us mrg07@health.state.ny.us bda01@health.state.ny.us Abstract: BACKGROUND: Peer communication about quality management (QM) between governments occurs rarely, if ever. The HEALTHQUAL All Country Learning Network (ACLN) is a knowledge management strategy providing a forum for peer exchange among 17+ countries to reinforce institutional improvement and QM. Panel and expert presentations together with participant‐driven discussion sessions advance quality improvement (QI) knowledge and build countries’ capacities to achieve sustainable national performance measurement strategies, improvement techniques and quality management frameworks. ACLN promotes knowledge exchange between Ministry of Health leaders and managers, with changing annual themes focused on programmatic priorities aligned with President’s Emergency Plan for AIDS Relief (PEPFAR) goals. METHODS: ACLN joins national delegations of Ministry of Health and US‐supported technical staff who are directly involved with HEALTHQUAL implementation. Plenary speakers provide expert technical information on improvement implementation emphasizing impact on specified health outcomes. Panels and country presentations feature Ministry of Health teams, allowing country representatives to share improvement challenges and successes. Open space, a meeting format of participant‐chosen and led discussion sessions, facilitates further investigation on improvement topics. Workshops, case studies and a storyboard competition encourage peer exchange and motivate reflection about implementation. Learning and knowledge exchange activities include dedicated time for peer‐to‐peer discussion and informal networking opportunities which continue throughout the week and beyond the formal schedule, further reinforcing important south‐to‐south links between participants. RESULTS: ACLN fosters cross‐country peer exchange between Ministry of Health leadership, managers and data analysts, where subgroups based on professional roles exchange knowledge and expertise. ACLN supports the development of sustainable national improvement programs through the sharing of strategies for practical implementation of quality management capacity building; highlights accomplishments in improvement across countries, including alignment of improvement activities with other national initiatives and priorities; fosters exchange about performance measurement strategies, including indicator development and data collection, data quality and analysis techniques while accelerating understanding of QI knowledge, skills and strategies in core technical areas; and promotes communication of national improvement work through country presentations and a formal storyboard competition. In year four of the ACLN, country teams will be undertaking various leadership roles and responsibilities for key components of the agenda, including coaching of other country teams in core technical areas and management of country presentations. CONCLUSION: Peer exchange facilitated by the ACLN is fundamental to building sustainability through shared experience, knowledge and expertise, and in establishing an international community of practice to reinforce effective improvement strategies and spread. Keywords: improvement, performance measurement, south‐to‐south, country ownership

1. Background Communication and knowledge exchange for improvement in health care within and across national governments in low‐ and middle‐income countries is highly uncommon, and characterized by multiple barriers including geography, need for logistical coordination, network gaps and opportunities for collaboration, human resources for health, internet connectivity, and technical skills. HEALTHQUAL International is a capacity building initiative to facilitate sustainable national and clinic‐based quality management programs with the goal of enhanced patient outcomes and improved population health in low‐ and middle‐income countries. With the goal of country ownership of a sustainable national quality management program, HEALTHQUAL is engaged in a dynamic multidisciplinary approach to the scale‐up and spread of improvement methodology

Joshua Bardfield et al. with the goal of creating a regional network and community of practice for improvement among President’s Emergency Plan for AIDS Relief (PEPFAR) focus countries. Development of networks and systems for dissemination and spread of knowledge and skills are fundamental to achieving this goal. The literature on dissemination dates to the early 1960’s and the seminal work on this topic, ‘Diffusion of Innovations,’ by Everett Rogers (1995), with many since reaffirming his central premises. Multiple factors and components contribute to an effective dissemination strategy. Nevertheless, a strong network or communications channel must exist to facilitate adoption, with success of adoption primarily reliant on the strength of these channels or networks (Greenhalgh et al, 2004). Moreover, Rogers’ work does not reflect the challenges of implementation within organizational systems nor within the complex public health domain of an entire country. Evidence suggests that workshops focused on targeted educational goals and strategies, including peer exchange have the capacity to not only improve individual knowledge and skills but also strengthen national health information systems and the broader health delivery system (Braa, Heywood & Sahay, 2012). Additional research demonstrates the importance of workshop‐based interpersonal communication for knowledge exchange and learning, with implementation strategies that reflect problem solving in one country shared with and applied in others (Sylla et al, 2012; Panisset et al, 2012; Kazanjian, Smillie & Stephen, 2012). The All Country Learning Network is not a formal meeting or an educational conference as traditionally conceived. Rather, the ACLN is a bi‐directional, evidence‐based strategy to promote sustainable country ownership of national quality management programs by creating and strengthening interpersonal communication channels, south‐to‐south social networks and opportunities for collaboration between Ministries of Health, US government and implementing partners where bi‐directional communication for QM in the health sector rarely occurs. Efforts to build and sustain this community of practice focus on shared interest in and commitment to collaborative learning and knowledge sharing, and draw upon clinical learning models defined in the literature, with a focus on translating ‘tacit’ or implied knowledge in the field of improvement into explicit knowledge for implementation through the process of face‐to‐face engagement and interaction (Parboosingh, 2002; Berry, 2011; White, 2008). Beginning in 2010 with 65 participants from 12 countries, the ACLN has expanded rapidly to include new country‐based Ministry of Health program staff and new HEALTHQUAL implementing countries. The 2012 ACLN was attended by over 200 participants from 16 countries, evidence of in‐country interest and support for this critical activity. The 2011 and 2012 ACLN were hosted by HEALTHQUAL countries and incorporated Ministry of Health leadership and staff, as well as facility‐level personnel responsible for improvement implementation within HIV clinics across the host countries. The ACLN represents the evolution of HEALTHQUAL programmatically, allowing countries at various stages of improvement knowledge and implementation to learn from one another while optimizing opportunities to strengthen networks based on professional roles and technical topics linked to improvement.

2. Methods In the context of QI in public health, the All Country Learning Network employs a unique multimodal and bidirectional learning and knowledge exchange strategy designed to maximize opportunities for peer‐to‐peer sharing and south‐to‐south network development. This includes close attention to the distinctive challenge and need to integrate technical knowledge about health with programmatic skills to successfully guide national quality management implementation. Expert plenary presentations (Table 1) Participants are engaged through presentations delivered by recognized experts in core technical areas linked to public health and improvement programming relevant to national quality management infrastructure development and/or acceleration of current knowledge in relevant content areas. Plenary speakers deliver targeted presentations, lead question and answer sessions with participants and are available for country‐ specific coaching to advance improvement implementation. Previous plenary presentations have included: prevention indicators for HIV care and treatment; use of the nutrition assessment, counseling and support (NACS) platform to support prevention of mother‐to‐child transmission (PMTCT); health information

Joshua Bardfield et al. technology and quality improvement; TB infection control; and retention in global HIV/AIDS programs, among others. Data workshop Building on each year’s content and curriculum, an intensive data workshop is conducted with an evolving and tailored focus on improving country systems and processes for collecting, analyzing, reporting and communicating performance measurement data and ensuring data quality and integrity. Data managers and in‐country program leads explore the process of transforming raw data into useful and meaningful information by mapping processes of data collection, storage and management. Particular attention is given to navigating common challenges. Working together, country teams contribute to the development of a sustainable and adaptable system of clinical performance data management through presentations, peer exchange, hands‐on exercises and case studies drawn from real country experiences. Country team presentations Each participating country team prepares a presentation directly linked to the theme of each ACLN, which varies by year – including use of clinical performance measurement data for improvement, country ownership and building a sustainable national quality management program. Country presentations offer teams an opportunity to evaluate progress, successes and challenges during the process of preparing their talks. These presentations offer national strategies and implementation barriers to building national quality management programs, addressing common themes and unique strategies such as: transitioning beyond HIV to other public health programs and areas; use of an electronic medical record for improvement; food security and nutrition in a national HIV quality management program; integration of quality assurance and quality improvement; and retention and linkage to care, among others. The presentation serves as a powerful catalyst to group discussion and often drives other ACLN‐related activities and post‐ACLN follow‐up. Coaching sessions Country‐specific, tailored coaching sessions provide targeted technical assistance to country teams in key programmatic areas. Coaching is conducted by HEALTHQUAL staff and technical experts who are matched with countries based on current needs and national priorities. These sessions are designed to focus on advancing implementation of the program model in areas of clinical performance data measurement, improvement project implementation, building a national framework for sustainable quality management, coaching for improvement and developing national improvement communication and dissemination plans. Open Space (Table 2) Open Space technology or Open Space is a participant‐driven and self‐organizing model for group learning. At the ACLN, Open space has repeatedly fostered diverse and highly relevant participant‐led discussions on wide‐ ranging improvement topics. Sessions have included topics of data quality and validation, indicator development, health care worker motivation, patient flow, retention in care, improving HIV quality of care for pediatric patients, and data literacy among many others. Open space discussions are recorded, transcribed and distilled into a document of key points, strategies and follow‐up questions for all ACLN participants. Participants interested in continuing their conversations are invited to meet in the evenings during the gathering and provided an online discussion platform to remain connected virtually. Case studies A case studies exercise is facilitated; based on real world implementation scenarios for participants to review and discuss solution strategies. Participants are divided into multinational and multidisciplinary teams to encourage varied solutions and strategies which can later be applied and adapted to country context. This activity fosters intensive peer discussion with a focus on contextual response to the given topic.

Joshua Bardfield et al. Storyboard competition Each country team prepares a national or clinic‐level improvement project to be presented at the ACLN as a storyboard. The presentation asks participants to consider a compelling improvement example and reinforces skills in storytelling, narrative development and visual presentation of data for these purposes. The winning teams – best improvement project and peer selection awards – are judged by a QI coaches panel and participants, respectively, and officially recognized by the entire group at closing. Learning and knowledge exchange activities included dedicated time for peer‐to‐peer discussion and informal networking opportunities which continue throughout the week and beyond the formal schedule, further reinforcing important links between participants. Ongoing communication in‐between face‐to‐face interaction is facilitated using the HEALTHQUAL improvement exchange, an email‐based program to advance work among technical subgroups based on their functional roles and focused on knowledge sharing and exchange to advance skills and expertise.

3. Results The All Country Learning Network encourages and accelerates development of cross country networks for peer exchange and knowledge sharing among Ministry of Health leadership, managers and data analysts within and across countries where opportunities for communication and learning do not occur. These personnel include directors of national HIV care and treatment programs, directors of quality departments, program managers, data analysts and clinical staff. Expansion from 65 participants from 12 countries in 2010, to over 200 participants from 16 countries in 2012, evidences country interest and support for this critical activity, which is particularly evident given resource constraints in the nations represented. Further, to demonstrate achievement toward PEPFAR goals of country ownership, the 2011 and 2012 ACLN were hosted by Namibia and Uganda, respectively, and included high‐ level participation by Ministry of Health leadership and staff in both countries, as well as inclusion of facility‐ level personnel responsible for improvement implementation within HIV clinics in those countries. Country teams depart ACLN with executable country‐specific work plans and next steps for improved patient care. Outcomes of ACLN include: Outcome Enhanced technical assistance by HEALTHQUAL to participating countries prompted by targeted coaching sessions led by recognized technical experts and workshops Accelerated knowledge and skills in advanced improvement methodology in core technical areas of quality management, including data collection, analysis and reporting; improved systems and processes of care through use of data to implement QI projects Wider access to critical performance measurement and improvement implementation tools and resources for daily use among clinic, regional and national program staff

Method of achievement Country focused, tailored coaching sessions including Ministry of Health quality program managers, data analysts and participating senior leadership with support from HEALTHQUAL staff and QI experts Intensive technical workshops Coaching sessions Open space Case studies Distribution of key materials and publications, including QI coaching and training toolkit, QI Operations Manual and current scholarship on QI in public health (print and electronic) Implementation of the online Improvement Exchange to facilitate resource distribution and email‐based communication between country‐based teams, with technical support from HEALTHQUAL staff (designed to be accessible in low‐bandwidth settings) Convening of country teams to develop and refine workplans, with HEALTHQUAL staff, to advance program implementation. This includes development of workplans in a variety of core areas; QI implementation at the national and clinic levels; performance measurement data collection, reporting and communication to inform national priorities, and for funders and implementing partners; organizational

Amplified knowledge and agreement on action steps among key stakeholders to effectively advance national improvement implementation across Ministry of Health leadership and staff, CDC country offices, US government and implementing partners

Joshua Bardfield et al. Outcome

Method of achievement assessment planning; regional/national QI coaching, etc.

Creation of an international improvement community of practice, driven by a robust network of experienced QI implementers invested in collaboration and exchange of knowledge to advance this work

Relationship building and professional network development begins during the ACLN and continues through the HEALTHQUAL Improvement Exchange, where Ministry of Health staff from the various countries, for example, engage one another in how workplan objectives have been advanced, processes to achieve goals, implementation obstacles and other technical areas related to this work. These conversations are facilitated by HQI staff and QI experts. Summaries of discussions are written and distributed. Resources relevant to specific technical areas are posted to the Exchange. As noted above, and through HEALTHQUAL facilitated development of country‐based websites, newsletters and through individualized use of the Improvement Exchange for country‐specific communication.

Regular communication between senior Ministry of Health leadership and improvement program staff across PEPFAR countries

The ACLN fosters development of a roadmap for country ownership; expansion and sustainability, wherever possible, targeted throughout the public health system at province and district levels to build capacity for supporting clinics in their jurisdictions. Finally, ACLN promotes the importance of communication for improvement and implementation of country‐ specific national quality management communications planning. This is accomplished through identification of existing networks and creation of new modalities for program communication and outreach within and beyond Ministries of Health to staff physicians, nurses, managers and other health care workers who are part of the core team.

4. Conclusions The All Country Learning Network offers Ministry of Health staff and leadership, US government colleagues and implementing partners a unique forum for peer exchange to build knowledge and skills for sustainable national quality management programs. With emphasis on shared experience and communication of strategies, successes and challenges – ACLN advances country‐led implementation and advances the goals of a global improvement community. It is our hope that this process will lead to the accelerated adoption of national policies and priorities through effective communications channels and strategies. In this context, areas for further investigation remain. Continuity of communication to sustain this community of practice in between face‐to‐face meetings is critical and remains a challenge given limited resources and information technology infrastructure. Further, although KM indicators for global health have been discussed, there are no widely agreed‐upon indicators to measure the success of KM interventions in this context, an area for further research and action. Nevertheless, ACLN is bolstered by the diversity of implementation experience which enriches peer learning along the spectrum of programs represented – from fledgling, to early implementers and those more advanced in quality management implementation, both in HIV and other public health areas. Table 1: Sample plenary presentations Collection of early warning indicators for HIV drug resistance Strategic ways of HIVQUAL‐T: sustainability in Thailand A public health approach to TB elimination Update on the 3 I’s from Namibia Evaluation of capacity building efforts: lessons from the development of the HQI evaluation Towards an MTCT free Kenya HIVQUAL, the Haitian model

Joshua Bardfield et al. HIV prevention for people living with HIV/AIDS What’s new in TB? Measuring for improvement: can QI facilitate retention in HIV care Putting improvements into practice at national scale How to implement a national quality improvement campaign Creating a partnership for HIV‐free survival Improving HIV‐free survival of infants born to HIV‐infected mothers Retention in care in global HIV/AIDS programs Health information technology and quality improvement How HIVQUAL‐T is moving forward from clinical care to humanized care Communities if practice: transcending boundaries to improve quality

Table 2: Open space topics chosen and led by participants Consumer involvement Data management Health systems strengthening Retention in care Improving cervical cancer screening Pediatric antiretroviral adherence Quality improvement and information technology Integrating QI into other systems and programs Health care worker attitudes and motivation Health care financing and quality improvement Integration of QI into primary care Developing non‐HIV indicators Data literacy Prevention indicators Monitoring and improving the quality of prevention of mother‐to‐child transmission care Coaching for improvement Implementation a national QM program Ensuring sustainability of QI in high volume clinics

References Berry L. (2011) Creating Community: Strengthening Education and Practice Partnerships through Communities of Practice. International Journal of Nursing Education Scholarship, Volume 8, Issue 1, pp 1‐18. Braa J, Heywood A and Sahay S. (2012) Improving Quality and Use of Data Through Data‐use Workshops: Zanzibar, United Republic of Tanzania. Bulletin of the World Health Organization, Vol 90, pp 379‐84. Greenhalgh et al. (2004) Diffusion of Innovations in Service Organizations: Systematic Review and Recommendations. The Milbank Quarterly, Vol 82, No. 4, pp 581‐629. Kazanjian A, Smillie K, and Stephen J. (2012) Evaluating a Knowledge Exchange Intervention in Cancer Survivorship Care: A Workshop to Foster Implementation of Online Support Groups. Support Care Cancer DOI 10.1007/s00520‐012‐1686‐ 2. Panisset U, et al. (2012) Implementation Research Evidence Uptake and Use for Policy‐making. Health Research Policy and Systems, Vol10, pp 20. Parboosingh J. (2002) Physician Communities of Practice: Where Learning and Practice Are Inseparable. The Journal of Continuing Education in the health profession, Volume 22. pp. 230‐236. Rogers, E.M. (1995) Diffusion of Innovations, Free Press, New York. Sylla A.H. et al. (2012) Qualitative Study of Health Information Needs, Flow and Use in Senegal. J Health Commun, Vol 17, Suppl 2, pp 46‐63. White D et al. (2008). Communities of practice: creating opportunities to enhance quality of care and safe practices. Healthcare Quarterly, Volume 11 Special Issue, pp 80‐84.

Business Capability Modeling as a Foundation for Intellectual Capital Audits Denise Bedford Kent State University, Kent, USA Dbedfor3@kent.edu Abstract: This paper presents a conceptual model for identifying and aligning intellectual capital assets with business capabilities. The methodology draws from the disciplines of business architecture, knowledge management, and knowledge economics. Universities are presumed to be knowledge organizations with high levels of intellectual capital assets. Universities are also presumed to manage those intellectual capital assets well by definition. The research leverages a use case grounded in a university to illustrate how a capability base approach, coupled with a traditional intellectual capital structure, can (1) brings into question the perception that universities are by definition knowledge organizations; (2) provide a more robust approach to asset valuation at the organizational level; (3) offer a context for identifying asset liabilities; (4) make explicit intellectual capital assets which are not now considered for valuation; and (5) support a conversation among business, stakeholders, intellectual capital managers, knowledge managers and business architects. Keywords: intellectual capital assets, universities, business capability modeling, business on a page, bottom‐up asset valuation, aggregated intellectual capital asset valuation

1. Introduction This paper discusses a new approach to assessing the value and liabilities of intellectual capital assets. Intellectual capital is the intangible value contained in the heads and relationships of employees, management staff, customers and other stakeholders. Intellectual capital encompasses not only the contents of employees’ minds but also the complex intangible structure that surrounds them and makes the organisations function. (Falzagic 2007). The approach derives from the convergence of three perspectives of intellectual capital assets: business architecture, knowledge economics, and knowledge management audits. Knowledge economy is described by OECD as the economic activities and systems which are directly established by creation, circulation and application of knowledge and information (Chen 2008). Intellectual capital has been widely recognized as a major resource of organisations in the knowledge economy .Knowledge economics considers how intellectual capital contributes to the generation of intangibles to improve outcomes and an organization’s performance, valuation and reputation. Typically, the term intellectual capital refers to all knowledge resources that determine the competitiveness of an organization (Edvinsson and Malone 1997) (Guthrie 2001) (Harvey and Lusch 1999) (Robinson and Kleiner 1996) (Stewart 1997) (Vallejo‐Alonso et al 2013) (Winter 1987). According to Sveiby (1997) it is the difference between the market and book value of the company (Handzic and Zhou 2005). It has also been widely recognized that the success of the knowledge economy or society depends on the effective utilization of its intangible assets (Economic and Social Research Council, 2007). Knowledge management focuses on how to more effectively manage knowledge and intellectual capital to increase value and minimize liabilities. The main point of convergence across these three views is the representation of an organization as a Business on a Page (BOAP) and its business capabilities. A Business on a Page allows organizations to identify and distinguish core business capabilities, and to assign value to those capabilities. In business architecture, business capabilities provide a focal point for identifying the use or misuse of intellectual capital assets. Value and degree of contribution of assets can be estimated through the use of business capability modeling. Business architecture practices consider how an organization leverages all of its assets, including intellectual capital, to achieve business goals. Intellectual capital has gained the increased prominence as a business and research topic principally due to the rise of the knowledge economy (Bullen et al. 2006) (Farrell 2001) (Roberts 2001) (Roberts 2009) (Rooney and Mckenna 2005). Business capability modeling is a technique that enables an organization to concisely represent what it does, to align that view with strategic goals and objectives, and supports heat mapping of capabilities to discover business criticality. Business capability models represent what an organization does in a way that speaks to business leaders, analysts, talent managers, economists, solutions developers and technologists. The practice of business capability modeling is gaining ground across

Denise Bedford organization adopting enterprise architecture practice. In the past thirty years, business architecture is an aspect of enterprise architecture that has been neglected in favor of a focus on applications and technology. Today, business architecture is maturing into the critical integration layer of enterprise architecture. Business capabilities are the core component of business architecture, providing a context against which an organization’s assets are assessed for their alignment with business goals and objectives. Enterprise architects have long viewed People as things that interact with applications and technology. This view is changing. Business architects have realized that it is as important to align “people assets” ‐ intellectual capital and knowledge assets – with capabilities as financial and physical assets.

2. Research goals and questions The exploratory research described in this paper proposes a novel method for using business capability modeling to identify, assess value and improve the management of intellectual capital assets. The conceptual model and methodology is illustrated in the context of an academic university. The research answers two questions:

Does capability modeling provide a context against which organizations, specifically academic organizations, can more effectively identify, value, and manage their intellectual capital assets?

Does capability modeling support an aggregate view of an academic institutions intellectual capital value and liabilities?

A business capability is what the organization does to perform or produce something of value to the organization’s stakeholders. A capability is expressed in terms of outcomes and services that provide value to stakeholders. Capabilities describe what the organization delivers as opposed to how the organization works or how (business process) it delivers the what. An organization’s full repertoire of business capabilities takes the form of a Business on a Page (BOAP). A Business on a Page organizes capabilities into three categories, including (1) strategic capabilities; (2) operational or core capabilities; and (3) enabling capabilities. Strategic capabilities are those dedicated to setting the future direction and driving the organization. The university used as an example has three strategic capability areas – learning, innovation, and governance. Enabling capabilities are those which support the day to day functioning of the organization. These capabilities and their use of intellectual capital add value indirectly. For this university there are fourteen enabling capabilities. In five years working with the Business on a Page methodology, we have found that there is consistency in strategic and enabling capabilities. It is core or operational capabilities that distinguish one organization from another. Operational or core capabilities are those that define the essential business of the organization. These capabilities must be – valuable, rare, costly to imitate, non‐substitutable. Capabilities that help a firm to exploit opportunities to create value for customers or to neutralize threats in the environment (Valuable). Capabilities that are possessed by few current or potential competitors, are difficult to imitate, result from complex relationships that cannot be replicated ‐ trust, teamwork, informal relationships, causal ambiguity (Rare). Capabilities that other firms cannot develop easily because of unique historical conditions, causal ambiguity or social complexity (Costly to Imitate). Capabilities that do not have strategic equivalents, such as firm‐specific knowledge or trust‐based relationship (Non‐substitutable). This speaks directly to the use of intellectual capital assets.

3. Research methods Higher education institutions, as knowledge‐intensive organizations, have been in the knowledge business for a long time (Rowley 2000). The application of knowledge management in higher education constitutes a recent research field (Benitez et al 2011) (Cranfield and Taylor 2008) (Cheng 2009) (Goh and Sandha 2013) (Hellstrom and Husted 2004) (Leitner 2002) (Oliver Handzic and Toom 2003) (Petrides and Nodine 2003) (Ramirez Loreduy and Rojas 2007) (Sanchez Elena and Castrillo 2009) (Sandhez and Elena 2006). Williams (2007) and Warhurst (2008) have suggested that intellectual capital is a university’s most valuable and strategic capital asset. Attracting and retaining qualified intellectual capital is vital to the university’s ability to achieve its goals and outcomes (Handzic and Ozlen 2011). It is an input, an output and an outcome. Universities are an excellent source for investigating the value, use and misuse of intellectual capital assets.

Denise Bedford We assume that a university efficiently and effectively manages its intellectual capital because learning and knowledge creation are its core capabilities (Falzagic 2007). As Falzagic suggests, today’s universities are slow to innovate. High‐tech startups and high performance private and public sector organizations may generate intellectual capital that is equal or superior to universities. The intellectual capital of universities should be measured for the following reasons:

A growing shift to evaluate higher education based on outcomes for all of the university’s business capabilities;

Focus on intellectual capital assets without regard for intellectual capital liabilities generated through mismanagement, misuse or poor judgment;

The growing awareness of the depth of intellectual capital assets and the need to manage them more effectively;

The need for universities to develop and apply new methods for evaluating learning (Sveiby 2000). This includes the need to shift the overall evaluation of universities from ‘press ranking lists’ to the measurement of learning outcomes.

This paper presents an exploratory model of a university represented as a set of business capabilities, intellectual capital assets and liabilities it applies the research methodology to a university to address some of these issues raised by Falzagic. In business architecture practice, the first step in aligning assets to a capability involves the creation of a Business on a Page. Figure 2 represents the full set of business capabilities for a typical university. This view was developed by Jennilyn Wiley, a graduate student in Kent State University’s Knowledge Management program. The university modeled has five high level operational capability areas including: (1) Teaching; (2) Research and Development; (3) Advising; (4) Advocacy; and (5) Convening. These high level operational capability areas break down into 28 capabilities. For illustration purposes, we select a Teaching capability, in particular Provide a Top Quality Undergraduate Education, to walk through the exploratory methodology.

Figure 1: University represented as a business on a page Resources are aligned to individual capabilities, and ideally at a level that allows us to observe the day to day use of intellectual capital assets and knowledge transactions. Figure 2 illustrates the breakdown of Provide a Top Quality Undergraduate Education into four subcapabilities.

Denise Bedford

Figure 2: Subcapability breakdown of provide a top quality undergraduate education The capability breakdown surfaced four subcapabilities, including:

Develop Top Quality Curriculum – Curriculum is developed through professional processes, is aligned with domain expectations, is designed to suit the profession, and reflects current knowledge of the field

Recruit Top Quality Faculty – University realizes that its faculty are its primary source of intellectual capital, recruits and retains top faculty, faculty maintain their expertise and are top teachers and learners

Recruit a Motivated Student Population – University recruits a student population who engage in learning and contribute to the learning of others

Provide a Productive Learning Environment – University provides an effective and modern learning environment which leverages state of the art forms of instructional design, the expertise of instructional designers, and appropriate technologies

Figure 2 also illustrates the level at which we would identify, assess value and liabilities of intellectual capital assets. For the purpose of this research, in teaching business capability modeling in our Enterprise Architecture concentration, and in teaching knowledge auditing, we leverage Andriessen’s Intellectual Capital Asset model. Applying Andriessen’s (2004) intellectual capital asset structure to capabilities allows us to see which and how assets are used, to assess their value in relation to the value of the capability, and also to identify intellectual capital liabilities. Per Andriessen’s model, we identify (1) Human Capital – aggregated tacit knowledge and skills, and the attitudes of all the people who support that capability; (2) Structural Capital – the general culture associated with the capability, its procedural knowledge and its explicit knowledge; and (3) Relational Capital – the communication, knowledge and social networks that support the capability as well as the capabilities overall reputation and brand. In order to assess the value and the liabilities of intellectual capital in a university environment, we need to work through each of the business capabilities. Tables 1‐4 describe the intellectual capital assets associated with each of the four subcapabilities. Table 1: Intellectual capital assets supporting “provide top quality curriculum” Asset Type Tacit Knowledge

Skills Attitude

Asset Examples subject matter knowledge, curriculum design knowledge, knowledge of metacognition, knowledge of learning processes and styles, knowledge of current trends, teaching experience, feedback evaluation, knowledge sharing, open access, continuous learning, situational learning approach, learning outcomes and activities, collaborative design

Denise Bedford Asset Type Explicit Knowledge Procedural Knowledge

Asset Examples good practice syllabus development, information resources, commercial databases, assignments, curriculum guidelines and standards curriculum development processes, knowledge of institutional policies

Culture

teaching philosophy, learning culture of the institution, reward and recognition in the classroom, monitoring and coaching culture

Networks

references to other thought leaders in the field, citations to other works, teaching teams, domain networks

Reputation

rigorous and comprehensive nature of the curriculum

Table 2: Intellectual capital assets supporting “recruit top faculty” Asset Type Tacit Knowledge Skills Attitude Explicit Knowledge Procedural Knowledge Culture Networks Reputation

Asset Examples subject matter expertise, situational knowledge, narrative intelligence, social intelligence, emotional intelligence, teaching experience, knowledge of teaching methods, technical capabilities and experience, course management application skills mentoring, coaching, engagement, self‐learning, critical thinking, self‐reflection and review publications, credentials, teaching experience, research experience, teaching models, teaching protocols, subject area “know how”, teaching ethics collaboration, risk preferred, community‐oriented extensive internal networks, extensive external networks, invisible college participation, teaching reputation, evaluation by students

Table 3: Intellectual capital assets supporting “provide a motivated student community” Asset Type Tacit Knowledge Skills Attitude Explicit Knowledge Procedural Knowledge Culture Networks Reputation

Asset Examples rudimentary knowledge of chosen course of study technical skills, different types of literacies, communication skills, emotional intelligence, social intelligence eager to learn, willingness to work in teams, pro‐learning vs. pro‐grade attitude assignments, projects, personal libraries how to learn, how to study, how to succeed in the education environment, educational ethics, how to “behave” in education environment risk preferring attitude, open mindedness, open to experimentation, open to different types of learning experiences family, social networks, college/school community, spiritual support network evaluations, grades, feedback

Table 4: Intellectual capital assets supporting “provide productive learning environment for students Asset Type Tacit Knowledge Skills Attitude Explicit Knowledge Procedural Knowledge Culture Networks Reputation

Asset Examples instructional methods, assignment design, teaching experience, technical skills, strong organizational skills, narrative intelligence, coaching and mentoring skills adaptability, visioning, creativity, emotional intelligence, social intelligence successfully developed courses, models for online learning, knowledge of online learning design principles and guidelines course delivery, assignment evaluation and feedback processes strong student‐faculty community culture, fair rewards and recognitions student‐faculty, faculty‐family, student‐family networks, student social networks student feedback, student evaluations, course design peer reviews

In business capability modelling, evaluation of the use of assets takes place at the Subcapability level. Ideally, each of the four subcapabilities should be evaluated in terms of their available stock and management of intellectual capital assets. A business capability evaluation would be conducted by those who are engaged in producing the subcapability’s outputs and outcomes, those who are responsible for acquiring the

Denise Bedford subcapablity’s assets, and both internal and external stakeholders, are involved in evaluating. Valuation of the assets is always in relation to the value of the capability to the organization. In business architecture, a distinction is made between value and cost. If the university is a four year college and high quality teaching is one of its core capabilities, a higher value might be assigned to knowledge of and experience with mentoring, teaching models, teaching protocols and teaching ethics than to deep subject area knowledge. Higher values might be assigned to a capability’s collaborative and community‐oriented culture. If the university is a Tier 1 research university, it will assign a higher value to Research and Development capabilities. By default, the intellectual capital assets assigned to those resources would be more highly valued. The intellectual capital assets needed to support the capability, Provide a Top‐Quality Undergraduate Education (Table 5) are extensive. Consider what a full list of intellectual capital assets would include if we were to model each of the 28 business capabilities in the operational core. Table 5: Provide top quality undergraduate education capability Intellectual Capital Asset Tacit Knowledge

Skills

Attitude

Explicit Knowledge

Procedural Knowledge

Culture

Networks

Reputation

Asset Examples assignment design, curriculum design knowledge, emotional intelligence, instructional methods, knowledge of current trends, knowledge of domain, learning resources, narrative intelligence, rudimentary knowledge of chosen course of study, situational knowledge, social intelligence, subject matter expertise, subject matter knowledge, teaching experience coaching and mentoring skills, communication skills, course management application skills, different types of literacies, emotional intelligence, feedback evaluation, knowledge of teaching methods, narrative intelligence, social intelligence, strong organizational skills, teaching experience, technical capabilities and experience, technical skills adaptability, coaching, continuous learning, creativity, critical thinking, eager to learn, emotional intelligence, engagement, knowledge sharing, mentoring, open access, pro‐ learning vs. pro‐grade attitude, self‐learning, self‐reflection and review, situational learning approach, social intelligence, visioning, willingness to work in teams assignments, commercial databases, credentials, curriculum guidelines and standards, good practice syllabus development, information resources, knowledge of online learning design principles and guidelines, models for online learning, personal libraries, projects, publications, research experience, successfully developed courses, teaching experience assignment evaluation and feedback processes,, course delivery, curriculum development processes, educational ethics, encroachment policies, how to “behave” in education environment, how to learn, how to study, how to succeed in the education environment, subject area “know how”, teaching ethics, teaching models, teaching protocols collaborative, community‐oriented, fair rewards and recognitions, learning culture of the institution, mentoring and coaching culture, open mindedness, open to different types of learning experiences, open to experimentation, reward and recognition in the classroom, risk preferred, risk preferring attitude, strong student‐faculty community culture, teaching philosophy citations to other works, college/school community, domain networks, extensive external networks, faculty‐family networks, family members, invisible college participation, references to other thought leaders in the field, social networks, spiritual support network, student social networks, student‐faculty, student‐family networks, teaching teams course design peer reviews, evaluation by students, evaluations, feedback, student feedback, grades, rigorous and comprehensive nature of the curriculum, student evaluations, teaching reputation

4. Exploratory results and observations We offer six observations on what we have learned from this exploratory research: Observation 1: We believe the use case model of a university provides a new perspective on the intensity and depth of intellectual capital assets required to sustain an academic institution. Where we typically count more tangible assets such as degrees, publications, research funds, and patents, this methodology surfaces the true nature of intellectual capital required to sustain an academic institution.

Denise Bedford Observation 2: When calculated, we believe the greater value of intellectual capital assets compared to financial and physical assets will be easier to demonstration using this method (Anton and Yao 2005) (Arora 1995) (Arora 1996). Observation 3: The capability‐focused methodology provides richer opportunities to discovery asset liabilities and gaps, to assess the goodness of assets through assessments that are pertinent to the asset‐type, to determine whether adjustments are cost effective based on the value of the capability to the organization, and to state the organization’s intellectual capital value based on assets that are relevant to the knowledge economy. Evaluating assets in relation to their capabilities allows us to discover impaired assets and write‐ downs (Saint‐Onge 1996) (Caddy ) (Harvey and Lusch 1999)), to discover asset gaps or asset management actions that have the potential to bring a university down, to identify and correct core rigidities – intellectual capital assets that are creating organizational inertia and preventing the university from changing in response to its external environment. Observation 4: A capability‐focused approach returns value to each of the three contributing disciplines. To Business Architecture the methodology provides a more comprehensive approach to identifying assets – extending the typical physical asset alignment to include intellectual capital. To knowledge economics, the methodology more completely elaborates characteristics and attributes that align with the traditional three asset categories of Human Capital, Structural Capital, and Relational Capital. To knowledge management, the methodology offers a strategy for aligning knowledge management with the business. While we read extensively of this need in the literature, the challenge of action persists. Observation 5: The most important aspect of this methodology, though, is its ability to support a common conversation between the business, stakeholders, intellectual capital managers, knowledge managers, and business architects on the valuation of the organization’s core capabilities. Where this dialog takes place around the Business on a Page, we have a starting point for establishing the value of intellectual capital assets. Observation 6: This methodology provides a bottom‐up aggregated view of an organization’s intellectual capital assets and liabilities. We believe that a bottom‐up approach to accounting for intellectual capital assets is a far more accurate picture than the top down approach which targets intellectual capital assets out of their business context.

References Agrawal, A., 2001, “University‐to‐industry knowledge transfer: Literature review and unanswered questions”. International Journal of Management Reviews, 3: 285–302. Andriessen, D. (2004). Making sense of intellectual capital. Elsevier Butterworth‐Heineman, Burlington MA. Anton, J. and D. Yao, 2005, “Markets for partially contractible knowledge: Bootstrapping versus bundling”. Journal of the European Economic Association, 3: 745–754. Arora, A., 1995, “Licensing tacit knowledge: Intellectual property rights and the market for know‐how”. Economics of Innovation and New Technology, 4: 41–60. Arora, A., 1996, “Contracting for tacit knowledge: The provision of technical services in technology licensing contracts”. Journal of Development Economics, 50: 233–256. Benítez, D., Pérez, D. Zhu, C. and Questier, F. (2011). “Experiential knowledge creation processes in the higher education teaching‐learning process”, Proceedings of 12th European Conference on Knowledge Management, ECKM 2011, Germany, September 2011, 88‐96, Bullen, E., Fahey, J. and Kenway, J. (2006) ”The knowledge economy and innovation: certain uncertainty and the risk economy”, Discourse: studies in the cultural politics of education, Vol.27,No.1.,pp 53‐68. Caddy, I. (2000). “Intellectual capital: recognizing both assets and liabilities”, Journal of Intellectual Capital, Vol. 1 Issue: 2, 129 – 146 Cearns, K. (1999). “Accounting for the intangible”, Accountancy Vol. 124, No. 1271, 82‐83 Cheng, M.‐Y. (2009). “Knowledge sharing among Malaysian academics: influence of affective commitment and trust,” Electronic Journal of Knowledge Management, Vol. 7, Issue 3, 297 ‐ 397 Cranfield, D. J. and J. Taylor (2008). "Knowledge management and higher education : A UK Case Study. “Journal of Knowledge Management 6: 85 ‐ 100. Dumay, J.C. (2009),”Intellectual capital measurement: a critical approach” Journal of Intellectual Capital, Vol.10, No.2, pp 190‐210 Edvinsson, L. and Malone, M.S. (1997) Intellectual Capital: Realising Your Company’s True Value by Finding Its Hidden Brainpower, Judy Piatkus (Publishers), Ltd., London.

Denise Bedford Fazlagic, A. (2007) “Measuring the Intellectual capital of a university” in Conference on Trends in the Management of Human Resources in Higher Education. (Accessed online on May 1,2013 at: http://www.oecd.org/edu/imhe/35322785.pdf ) Goh, S. K. and Sing Sandhu, M. (2013) “knowledge sharing among malaysian academics: influence of affective commitment and trust” Electronic Journal of Knowledge Management Vol. 11, Issue 1, 38‐48 Guthrie, J. (2001) “The management, measurement and the reporting of intellectual capital”, Journal of Intellectual Capital, Vol.2, No. 1, 27‐41. Handzic, M. and Ozlen, K. (2009) Intellectual capital in universities: faculty and student perceptions, in Proceedings of the th 12 International Conference on Knowledge Management, University of Passau Germany Sept. 1‐2, 2011. Harvey, M. G. and Lusch, R. F. (1999). “Balancing the intellectual capital books: intangible liabilities”, European Management Journal Vol. 17, No. 1, 85‐92. Hellstrom, T. and Husted, K. (2004) ”Mapping knowledge and intellectual capital in academic environments”,Journal of Intellectual Capital, Vol.5, No. 1, 165‐180. Leitner K.H (2002). “ Intellectual capital reporting for universities: coneptual background and applications within the recognition of Austrian universities”, in The Transparent Enterprise. The Value of Intangibles, November 25‐26 2002, Madrid, Spain. Oliver, G. R. Handzic, M. and Toom, C. V. (2003). “ Towards understanding km practices in the academic environment: the shoemaker’s paradox”, Electronic Journal of Knowledge Management Vol. 1 Iss. 2, 67‐74 Petrides, L. A. and T. R. Nodine (2003). Knowledge Management in Education: Defining the Landscape. Half Moon Bay, California, Institute for the Study of Knowledge Management in Education Ramirez, Y., Lorduy, C. and Rojas, A.J. (2007) ”Intellectual capital management in Spanish universities”, Journal of Intellectual Capital, Vol.8, No.4, 732‐748 Robinson, G. and Kleiner, B.H. (1996) “How to measure an organization’s intellectual capital“, Managerial Auditing Journal, Vol.11, No.8, 36‐39. Rowley, J. (2000). "Is higher education ready for knowledge management ?" International Journal of Educational Development 14: 325‐333. Sanchez, P.M. and Elena, S. (2006) ”Intellectual capital in universities”, Journal of Intellectual Capital, Vol.7, No.4, 529‐548. Sanchez, P.M., Elena, S. and Castrillo, R. (2009) ”Intellectual capital dynamics in universities: A reporting model”, Journal of Intellectual Capital, Vol.10, No.2, 307‐324. Stewart, T.A. (1997) Intellectual Capital: The New Wealth of Organisations, Doubleday/Currency, New York. Sveiby, K. (1997) The new organizational wealth: managing and measuring knowledge‐based assets, Berrett‐Koehler, San Francisco. Vallejo‐Alonso, B., Gerardo‐Arregui‐Ayastuy, G., Rodriguez‐Castellanos, A. and Garcia‐Merino, D. (2013) ”Real options in the valuation of intangibles: managers perception”, Electronic Journal of Knowledge Management Vol. 11 No. (2), 168‐182 Warhurst, C. (2008) “The knowledge economy, skills and government labour market intervention”, Policy Studies, Vol. 29, No.1, 71‐86. Williams, P.J. (2007) Valid Knowledge: The Economy and the Academy, Springer Science + Business Media B.V. Winter, S. G., 1987, “Knowledge and competence as strategic assets”. In D. J. Teece (ed.), The competitive challenge: Strategies for industrial innovation and renewal. Cambridge, MA: Ballinger, 159–184.

Research Management at the Brazilian Agricultural Research Corporation (Embrapa): Development of an Information Management System Marcelo Moreira Campos, Juarez Barbosa Tomé Júnior and Elizabeth Cristina de Oliveira Ferreira Brazilian Agricultural Research Corporation ‐ Embrapa, Brasília, Brazil marcelo.campos@embrapa.br juarez.tome@embrapa.br elizabeth.ferreira@embrapa.br Abstract: This paper addresses the deployment of a web‐based system for information management – IDEARE – of the Brazilian Agricultural Research Corporation (Embrapa), in the context of its knowledge management (KM) process. Embrapa is a Brazilian government corporation for agricultural research recognized worldwide, whose mission is to provide feasible solutions for the sustainable development of Brazilian agribusiness through knowledge and technology generation and transfer. Embrapa is comprised of 42 research centers scattered in all Brazilian regions, and laboratories established abroad, besides the headquarters at the capital of the country. The strategy used in this report was a single case study that supports the whole process of planning, preparation and submission of projects on research, development and innovation (RD&I) to the Embrapa Management System (EMS), with a focus on research management. The qualitative nature of the study covered the strategic, human and technological factors of KM, highlighting the technological factor. Two distinct non‐ exclusive phases were considered for deployment: the planning phase, based primarily on the Process Analysis and Improvement methodology, using its conceptual framework and some of its techniques; and the development phase that was based on the development patterns of Embrapa´s computerized systems, following the most reliable standards established worldwide. The results revealed a robust web‐based management information system with highly complex development patterns, but strongly flexible and adherent to Embrapa’s management strategy. Furthermore, the system is available full time for all Embrapa´s employees and external partners, and its use has proved that this tool is essential for all research activities. The conclusions suggest that the implementation of IDEARE promotes transdisciplinary team organization and collaborative work encouragement. The use of technology, contextualized in a research institution such as Embrapa, also showed that the implementation of this system is consistent with the Embrapa´s KM Model. Keywords: Embrapa; IDEARE; RD&I; knowledge management; information management; information management system

1. Introduction The Brazilian Agricultural Research Corporation (Embrapa), linked to the Ministry of Agriculture, Livestock and Food Supply, was created on April 26th, 1973, with the purpose of consolidating the Brazilian leadership in tropical agriculture. Embrapa is composed of 9,804 personnel, being 2,431 research scientists ‐ of these, 16% hold master's degrees, 74% hold doctorate degrees and 10% hold post‐doctorate experiences (data from the Human Resource Department of Embrapa, up to December 31st, 2012). Present in all regions of Brazil, it has built a network of 42 Research Centers, 5 Service Centers, 14 Central Divisions, and its Board of Directors and Administrative Council (Figure 1). In the field of international cooperation, Embrapa maintains research laboratories abroad, referred to as Labex (Laboratórios no Exterior, in Portuguese), in North America, Europe and Asia, as well as projects in Africa, Central and South America (Les Dossiers… 2012). Embrapa holds 78 bilateral agreements with 56 countries and 89 foreign institutions, mainly those involved in joint agricultural research and technology transfer (Embrapa 2013). Embrapa also coordinates the National Agricultural Research System, which includes most public and private institutions that undertake coordinated research amongst different geographical areas and fields of scientific knowledge (Embrapa 2013). Embrapa's mission is to provide research, development and innovation (RD&I) solutions for sustainable agriculture to benefit Brazilian society (Embrapa 2008a). Corporate strategy is a key element for Embrapa´s management, indicating the knowledge, core competences and systems in which to concentrate actions and efforts to fulfill its mission. In 2007, Embrapa began a new cycle of strategic planning described at the V Embrapa Master Plan (V EMP) ‐ a document which outlines its strategic objectives and guidelines covering the th period from 2008 to 2023, year of the 50 anniversary of Embrapa´s foundation (Embrapa 2008a). Knowledge management is contemplated in the V EMP at the Strategic Guideline #4 “To promote the management and preservation of knowledge”, and Associated Strategy #15 “To improve the process of mapping, organization,

Marcelo Moreira Campos, Juarez Barbosa Tomé Júnior and Elizabeth Cristina de Oliveira Ferreira management, and preservation of information and knowledge generated by Embrapa and to strengthen the necessary competences and tools” (Embrapa 2008a).

Figure 1: Organizational Structure of Embrapa. Source:http://www.embrapa.br/a_embrapa/Organograma‐ Embrapa Access: April 9th, 2013 The process of knowledge management comprises three basic factors in its essence ‐ the strategic factor, the human factor and the technological factor ‐ that together provide the basis for the theoretical and practical applications of this process (Campos 2003). As explained by Campos (2003), the strategic factor refers to initiatives related to the strategic planning of the organization, functioning as a means to achieve organizational objectives and provide a basis for decision‐making. The human factor refers to the essence of the process of knowledge management in any organization: its human resources. The organization cannot create knowledge without the initiative and interaction of people (Nonaka and Takeuchi 1997). Regarding the technological factor, technology is recognized as one of the pillars of knowledge management (Mac Morrow 2001) and knowledge as those objects which can be identified and manipulated by information systems (Sveiby 2001). According to Batista (2012), “technology provides a platform for retaining organizational knowledge through repositories of knowledge”. The technological factor is focused in this study, but always taking into consideration its connection to the human and strategic factors. Characterized as a research institution, Embrapa features in the roster of corporations which generates knowledge and is based on it. Thus knowledge management should be considered a strategic process because it promotes competitive advantage, adding value, and a basis for the decision‐making processes. According to Garvin (1993), a knowledge‐based organization is always learning and considers knowledge as a strategic

Marcelo Moreira Campos, Juarez Barbosa Tomé Júnior and Elizabeth Cristina de Oliveira Ferreira resource. These organizations, by means of their intellectual capital, create and use their knowledge internally and externally. Thus, Embrapa, by means of its employees, certainly creates and uses knowledge essential to the fulfillment of its mission using methodologies, tools, and management models able to handle all the knowledge generated. Thus, for example, the development of an information management system, beyond providing a strategic basis for decision‐making, becomes essential for the management, organization, and operation of any corporation, taking into account the basic purpose of the information, which is to enable the organization to accomplish its mission through efficient use of available information resources (Rezende and Abreu 2011).

2. Research management at Embrapa Research management at Embrapa is essentially based on a model called Embrapa Management System (EMS), which supports the management pillars of the Corporation ‐ Research and Development, Technology Transfer, Communication, and Institutional Development (Embrapa 2002). This model promotes a systematic, integrated, and transparent overview of the Corporation´s activities (Embrapa 2008b) in all levels ‐ strategic, tactical, and operational (Embrapa 2002) ‐ and provides support to the complete management cycle of research projects: planning, implementation, monitoring, evaluation, and administration of results (Figure 2). The EMS is operationalized through six structures, referred to as Macroprograms (Embrapa 2004), which have specific characteristics regarding their focuses and activities. They encompass the main areas of the Corporation’s performance and are managed to enable the research projects to achieve the results set out in their technical goals (Embrapa 2002). The Macroprograms are (Embrapa 2002, 2004):

acroprogram 1 (MP1) ‐ Major National Challenges;

Macroprogram 2 (MP2) ‐ Competitiveness and Sectoral Sustainability;

Macroprogram 3 (MP3) ‐ Incremental Technological Development of Agribusiness;

Macroprogram 4 (MP4) ‐ Technology Transfer and Business Communication;

Macroprogram 5 (MP5) ‐ Institutional Development;

Macroprogram 6 (MP6) ‐ Support for the Development of Family Agriculture and Rural Sustainability.

The operationalization of the EMS through Macroprograms enables greater flexibility and effectiveness in the management of research projects in order to align them with the strategic objectives of the Corporation, as set forth in V EMP.

Figure 2: Embrapa management system (EMS) model. Source: Embrapa, 2004

Marcelo Moreira Campos, Juarez Barbosa Tomé Júnior and Elizabeth Cristina de Oliveira Ferreira Research management at Embrapa is a dynamic process and projects are submitted to the EMS in response to public calls for proposals, which are widely disseminated in the Corporation. Beginning in 2012, Embrapa added new management instruments to its set of research projects: Portfolios and Project Arrays. Organized according to strategic topics, they are intended to ensure sustained improvement in research programming and to enable greater coordination of activities and capabilities in RD&I at Embrapa. Another feature of the research management at Embrapa, which seeks to improve the coordination of efforts and the efficient use of available resources, is the establishment of partnerships with researchers from other organizations, in order to provide additional expertise and infrastructure for the development of strategic research for agribusiness worldwide (Unicamp 2011). These partnerships have an interdisciplinary and multi‐institutional nature, and are mainly undertaken by means of large network projects (Unicamp 2011). The structure of the EMS meets the needs of the entire research management macroprocess at Embrapa (Figure 3), particularly the process of planning, preparation and presentation of RD&I projects, referred to as the "submission process". This process is essential not only for the research management, but for Embrapa as a whole, because it directly impacts the instrument that operationalizes the entire RD&I programming, which are the research projects (Tomé Júnior et.al. 2010). It was identified as a priority for constant monitoring, evaluation and improvement after the implementation of the prioritization matrix (Table 1), regarding the research management macroprocess at Embrapa (Tomé Júnior et.al. 2010). The prioritization matrix is one of the techniques of the Process Analysis and Improvement methodology, which is used by Embrapa (Embrapa 2009). Taking into account that research is the essential mission of Embrapa and that research is accomplished by means of projects, it is necessary that the Corporation pay particular attention to the process of RD&I project management, in order to make it increasingly agile, streamlined and efficient. For that purpose, Embrapa has a Department of Research and Development, responsible for RD&I programming management, technical articulation and integration, and information management. Regarding the latter, this Department has a team whose main activities include the constant improvement of instruments and information management of RD&I. The large volume of information modern organizations deal with requires a concerted effort of management and control for its effective use. In this context, Rezende and Abreu (2011) show the importance of well‐ structured information systems within organizations. They emphasize the importance of a socio‐technical approach to these systems, from the point of view of the use of technology in the context of the organization's environment (Rezende and Abreu 2011). In a more comprehensive approach regarding information interrelation within an information system, Tarapanoff (1995) highlights the application of a structured socio‐ technical approach that considers, in addition to internal factors, external factors which exert influence on the organization. In the context of research management, the main advantage provided by a computerized information management system is the ability to process and organize a gigantic volume of data simultaneously, making information available reliably in real time. McAfee and Brynjolfsson (2012) explain that using the vast amount of information currently available to organizations in a systematic manner can quite improve the results of such organizations, since management decisions are based on evidence rather than intuition. They also explain that what cannot be measured cannot be managed (McAfee and Brynjolfsson 2012). This is the modern organizational phenomenon called “Big Data” (McAfee and Brynjolfsson 2012). However, it must be observed that the business processes of organizations have their own evolving dynamics. The very availability of information, introducing new features and improving management tools, generates new requirements. Managers’ perceptions change, again creating new requirements. Hence the need for flexibility in technology and in the development patterns, for the constant improvement of information management systems. Embrapa has a consolidated model of knowledge management (Figure 4), which sustains the development of information systems in the Corporation. This model is made up of four axes: the strategy axis, which reflects the Corporation's philosophy regarding knowledge and strategies; the environment axis, which provides the enabling environment in the context of building capabilities; the tool box axis, regarding the practices of knowledge management and tools to assist this process, and the results and evaluation axis, which takes into account the tangible and intangible assets of the Corporation (Alvarenga Neto and Vieira 2011).

Marcelo Moreira Campos, Juarez Barbosa Tomé Júnior and Elizabeth Cristina de Oliveira Ferreira

3. Methodology A single case study on the development of an information management system at Embrapa is presented in this paper and it encompassed two distinct non‐exclusive phases: the planning phase and development phase.

3.1 Planning phase At this stage, the clients were identified as well as the primary information sources for the planning, such as document analysis, the application of questionnaires to assess the process, and application of semi‐structured interviews with the stakeholders, for process diagnosis. The concepts and some techniques from the Process Analysis and Improvement methodology were partially used. This methodology basically consists of leveling the degrees of the hierarchy (more horizontal and less vertical), with a focus on the final user, the recipient of the service or product, as well as periodic cycles of planning, implementation, evaluation, and corrective actions in the process (Embrapa 2009). The application of the SUT (Severity, Urgency, Tendency) matrix permitted the identification of the primary problem in the development of the system, which was the lack of efficiency in the information technology (IT) mechanisms for the submission of research projects and management of the database of Embrapa’s RD&I projects. The SUT matrix is one of the techniques of the Process Analysis and Improvement methodology, which permits to prioritize analysis and to propose solutions to the main problems identified in the process (Embrapa 2009). The application of the technique of brainstorming (Embrapa 2009) with the team involved, helped to identify and prioritize solutions for system development. The creation of the plan for process improvement, using the 5W2H technique (Embrapa 2009) enabled the team to establish improvement goals to be achieved for each of the prioritized solutions.

Figure 3: Embrapa´s Management Research Macroprocess Flowchart, in the context of EMS. Source: developed by the authors Table 1: Process importance vs. performance Source: Tomé Júnior et.al., 2010

Marcelo Moreira Campos, Juarez Barbosa Tomé Júnior and Elizabeth Cristina de Oliveira Ferreira

Figure 4: Embrapa´s knowledge management model. Source: Alvarenga Neto and Vieira, 2011 At the end of this phase, the document containing the qualitative analysis of the process was created, containing the details of the shortcomings and of the improvements to be implemented, as well as detailed flowcharts and documents outlining the basic guidelines for the implementation of the system’s development patterns.

3.2 Development phase The system was developed in partnership with Embrapa’s Department of Information Technology, which is responsible for its technical management. The development followed the standard process for systems development of this Department, based on the NBR ISO / IEC 12207:2009 standard (ABNT 2009), the Reference Model for the Process of Improvement of Brazilian Software (Softex 2009), the CMMI ‐ for Development, V1.2. (SEI 2006), and the PMBOK (PMI 2008), which is the global standard reference in project management. The measurement of the construction of the new system used the size estimation yardstick "Use Case Points" (UCP), as proposed by Karner (1993). Both in the planning phase and in the development phase, a measure called productivity, expressed in UCP/hour (Karner 1993) was used. This measure sought to adjust the

Marcelo Moreira Campos, Juarez Barbosa Tomé Júnior and Elizabeth Cristina de Oliveira Ferreira deadline to a reasonable productivity value, for better monitoring of system development, in order to meet quality standards. The development was documented, formalized in the project plan and in the requirement sheets to preserve the technical memory and the development patterns of the system. During the development phase, all system modules have been validated by various stakeholders through personal meetings at Embrapa, videoconferences, and the application of user satisfaction questionnaires.

4. Results Taking into account the basic factors of knowledge management, the technological context of Embrapa´s knowledge management model, the analysis of documents and the application of system development methodologies, Embrapa’s RD&I information management system was developed and named IDEARE (Figure 5). Embrapa already had an information management system for its research management macroprocess. However, the implementation of the IDEARE system allowed a more complete and less fragmented fulfillment of the requirements of this macroprocess as well as the adjustments regarding to the evolution in the structure of the EMS, arising from its use. The implementation of IDEARE, in a positive manner, also redesigned the connection between technology and research management at Embrapa, making it more flexible and integrated into the structure of the EMS.

Figure 5: Embrapa´s IDEARE System. Available at: https://sistemas.sede.embrapa.br/ideare/ Access: April 29th, 2013 IDEARE stores information about research projects at Embrapa over the last 10 years since the establishment of the EMS in 2002. It comprises information on 3,298 completed research projects (up to March 2013). Thus, it preserves valuable information regarding the history of research at Embrapa, allowing it to be accessed quickly and reliably. Beyond this historical database, it allows the management of 798 research projects in progress at Embrapa (up to March 2013), most of which in the fields of agriculture, rural development, biotechnology, livestock nutrition and feeding, food safety, genetic resources, information, and technology transfer. The main stakeholders involved in these projects are research and educational institutions, small and family‐based farmers, government agencies, agroindustry, the machinery, equipment and processing industries, as well as planning, technology transfer, and extension institutions, and national and international organizations for research promotion. The past and present undertaking of research projects at Embrapa are strategic factors that may assist the Corporation in the planning of activities and in the identification of current, potential and future areas to converge their efforts in RD&I. All Embrapa employees have free access to IDEARE, as do external users participating in research projects with the Corporation. Several innovations were introduced by IDEARE and one important feature is that it has

Marcelo Moreira Campos, Juarez Barbosa Tomé Júnior and Elizabeth Cristina de Oliveira Ferreira become a web‐based system, meaning that it can be accessed anytime from any Internet‐connected computer. The collaborative system nature of IDEARE is fully aligned with the goals of the managers of Embrapa, which are to encourage and promote the training of interdisciplinary teams, seeking to generate technological innovations and improvement of RD&I processes. In this sense, IDEARE was developed to foster the cooperation of those taking part in research projects throughout the various stages of the project’s life cycle ‐ from the collective creation of proposals by the stakeholders, to the monitoring of those projects which were approved. Therefore, the proposal does not contain only the project leader´s point of view. The development of systems of a cooperative nature creates new ways of working, which are more interactive and shared, without a rigid hierarchy, fostering creativity (Pimentel and Fuks 2011). In fact, IDEARE provides a cooperative working environment, involving stakeholders such as project leaders, those responsible for operational plans and those responsible for any other activities. In the context of management, noteworthy features include greater speed in the creation and dissemination of new calls for proposals as well as the simplified control of evaluation of these project proposals. In the same context, another IDEARE highlight concerns the performance of the technical leadership of Research Centers. Through IDEARE, they authorize the participation of the employees of that Research Center in the proposals, as well as approving or rejecting the proposed project before its final evaluation. A range of previously non‐ existent management reports was also made available. In the context of the human factor in knowledge management, this allows greater management control of the intellectual capital of the Corporation, contributing to a more efficient mapping of their organizational capabilities. Additionally, managers now have at their disposal full and immediate availability of information regarding the financial resources of the research projects in progress, facilitating the management of these resources. Finally, the results show an information management system with highly complex rules and modules, which are nevertheless flexible and adherent to Embrapa´s management strategy. The main technical and qualitative characteristics of this system (Embrapa 2008c) are: Alignment with the strategies of Embrapa: development patterns are aligned with the V EMP and the EMS. Efficiency: IDEARE is available via web, twenty‐four hours a day, seven days a week. At critical times, such as the timeframe for submitting proposals and drawing up progress reports on research projects, the system is able to accommodate at least two thousand concurrent users, with the smallest possible lag response. Flexibility: IDEARE was developed to support evolutionary changes in workflows, already predicted in the planning phase. Interoperability: IDEARE enables sharing and integration of specific data with other corporate information systems in use at Embrapa, avoiding or reducing information redundancy and inconsistency in databases. Maintainability: the source code of the system is well structured, clear, documented, and complies with Embrapa coding standards, facilitating any future corrective, adaptive or evolutionary changes. Reliability: the technological infrastructure adopted is capable of recovering data and maintaining a level of performance that prevents a shutdown of the system. Security: IDEARE is safeguarded against attacks and violations of content. It is integrated with Embrapa´s Directory System, which is a system of authentication and access control. The access control to system operation is accomplished by means of the Embrapa´s Access Control System. Usability: the system interface is highly interactive and offers online help on every screen accessed, which facilitates the understanding of each action to be performed by the user.

5. Conclusions The primary purpose of this paper was to present the development and the main features of the web‐based system of RD&I information management at Embrapa, in the context of knowledge management. It was shown

Marcelo Moreira Campos, Juarez Barbosa Tomé Júnior and Elizabeth Cristina de Oliveira Ferreira that not only the guidelines contained in V EMP, but also the complexity of the EMS itself, including the stakeholders, processes and activities involved, justified the development of IDEARE, resulting in a system that enables the submission, evaluation, implementation, and monitoring of RD&I projects in a manner which is integrated and controlled by the various stakeholders involved. The development of an information management system showed that proper management of the process requires a detailed description, rigorous documentation, and the verification of performance through indicators. These indicators and other management initiatives contribute to the identification of problems that require corrective actions, as well as identifying improvements that demand actions of an evolutionary nature in the system (Embrapa 2009). Embrapa, making strategic use of the process of knowledge management, by utilizing modern management tools, such as IDEARE, allows the improvement of its organizational model, continually improving it. IDEARE, as an information management system, provides the means for continuous evaluation of research management at Embrapa, guiding adjustments and supporting the integration and harmonization of research activities in the Corporation. The use of IDEARE is proving that cooperation in RD&I projects throughout a geographically dispersed organization such as Embrapa, adds greater value to research by involving interdisciplinary and multi‐ institutional teams. Thus, the use of technology, as embodied in the form of a RD&I information management system in the Corporation, positively impacts the work of its employees and managers, as well as offering new resources for increased efficiency in working with information.

Acknowledgements The authors wish to thank Alba Chiesse da Silva, PhD. Research Scientist at Embrapa, for her opinions and review of this paper.

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Marcelo Moreira Campos, Juarez Barbosa Tomé Júnior and Elizabeth Cristina de Oliveira Ferreira PMI (2008) Project Management Institute, Um Guia do Conhecimento em Gerenciamento de Projetos, Guia PMBOK, 4ª. Ed., Newtown Square, Pennsylvania, Estados Unidos da América. Rezende, D.A. and Abreu, A.F. (2011) Tecnologia da Informação Aplicada a Sistemas de Informação Empresariais: o Papel Estratégico da Informação e dos Sistemas de Informação nas Empresas, Atlas, São Paulo. SEI (2006) Software Engineering Institute, “CMMI para Desenvolvimento ‐ Versão 1.2”, [online], th http://www.sei.cmu.edu/library/assets/whitepapers/cmmi‐dev_1‐2_portuguese.pdf, viewed 26 February, 2013. Sveiby, K.E. (2001) “What is Knowledge Management?”, [online], http://www.sveiby.com/articles/KnowledgeManagement.html, viewed 28th March, 2013. Sofitex (2009) Associação para a Promoção da Excelência do Software Brasileiro, “MPS.BR”, [online], http://www.softex.br/mpsBr/_guias/guias/MPS.BR_Guia_Geral_2009.pdf, viewed 29th March, 2013. Tarapanoff, K. (1995) Técnicas para a Tomada de Decisão nos Sistemas de Informação, Thesaurus, Brasília. Tomé Júnior, J.B., Campos, M.M. and Silva, C.P. (2010) Relatório da Meta Qualitativa: Análise e Melhoria de Processos. Processo melhorado: Planejamento, Elaboração e Apresentação de Projetos de PD&I ao Sistema Embrapa de Gestão (SEG), Embrapa, Brasília, Distrito Federal. Unicamp (2011) Universidade Estadual de Campinas, Avaliação do Sistema Embrapa de Gestão (SEG): Relatório Final, Documento elaborado pelo Grupo de Estudos Sobre Organização da Pesquisa e Inovação, Departamento de Política Científica e Tecnológica, Instituto de Geociências, Unicamp, Campinas, São Paulo.

Intellectual Capital and Its Influence on the Financial Performance of Companies in Under Developed Capital Markets – the Case of the Caribbean Donley Carrington Department of Management Studies, University of the West Indies, Cave Hill Campus, St. Michael, Barbados donley.carrington@cavehill.uwi.edu Abstract: Intangible assets play an important role in determining the value of a company in this knowledge based economy. The extent to which lack of information on intellectual capital may affect the performance of companies in inefficient capital markets is yet to be determined. This study investigates the impact of IC efficiency measures on financial performance of companies in inefficient capital markets, using Pulic (2000) VAIC methodology. The empirical data for this study were drawn from the annual reports of 70 companies listed across the five stock exchanges in the region for the years 2006 to 2011. The independent variables were IC efficiency scores VAIC, HCE, CEE and SCE. The dependent variables were performance indicators (ROA, ROE, EPS, ECIN, MB). Results of the regression analysis demonstrate that there is significant positive relationship between HCE and three financial ratios (ROA, ECIN and EPS). CEE has a significant and positive relationship with ROA, MB and ROE. There was no significant relationship among SCE and the five independent variables. The only significant relationship between composite VAIC and the independent variables was ECIN. This study extends the efforts of previous researchers to empirically validate the VAIC methodology to different settings. The originality of this study rests upon the use of the VAIC model in capital markets that can be considered inefficient and undeveloped. Keywords: VAIC, Caribbean, measurement of IC, inefficient capital markets

1. Introduction The globalization of financial markets has provided investors with ready access to investment opportunities worldwide. These investment opportunities are also found in areas which where once categorized as undeveloped or underdeveloped. These Emerging Capital Markets (ECMs) have captured the interest of investors as they provide excellent opportunities for increase returns but one of their most notable features is share price volatility. This contrasts with developed markets where there has been a consistent increase in the gap between market value and book value of many companies. This created a catalyst for the investigation of the value missing from financial statements. According to various scholars that missing value represents the intellectual capital (IC) of the firm and its source of competitive advantage (Lev 2001; Chen et al, 2005; Maditinos et al, 2011). Although IC has been recognized as a major asset in creating superior financial performance and a source of competitive advantage finding an appropriate measure for IC is still difficult. However, measuring the efficiency of IC in value creation terms is quite possible. A widely used methodology for the measurement of IC efficiency is based on the value added intellectual capital coefficient (VAIC) developed by professor Ante Pulic (2000, 2004). This methodology allows an empirical assessment of the separate effects of capital employed efficiency, human capital efficiency and structural capital efficiency on market value and financial performance. Wang and Chang (2005) study provided the catalyst as researchers extended the model to examine the relationship among VAIC elements and firm’s performance. VAIC has been widely applied and its application proved the applicability, effectiveness, and credibility in measuring IC efficiency. However, despite the large number of empirical studies conducted using the VAIC methodology, no study was conducted on its use in capital markets that can be considered inefficient such as the Caribbean capital markets. Therefore the main objective of this paper is to extend the extant literature and provide an empirical examination of the relationship among IC, market value and financial performance using the VAIC methodology in these ECMs. This assessment is critical in determining if there is missing value on financial statements in these emerging capital markets. High price volatility is a characteristic of these markets and the extent to which IC impacts shares prices is unknown. Saudagaran (1997) argues that well functioning markets allow individuals to have a stake in enterprises in their respective countries to help change cultural attitudes with respect to participating in economic development activities and monitoring the socioeconomic contributions of enterprises. ECMs can

Donley Carrington assist in channeling funds to the most efficient and productive enterprises. But before they can fulfill their development role it is essential to have in place a set of corporate reporting policies and procedures geared towards supplying information necessary for making investment decisions.

2. Literature review The concept of intellectual capital emerged from the discussion of goodwill and the difference between book value and purchase value of assets (Lynn, 1998). According to Roos (2005), the intellectual capital perspective was initially developed as a framework for analyzing the value contribution of intangible assets in an organisation. The initial research focused on defining IC and its components, however, to date there is still a lack of consensus on an agreed taxonomy. IC may be defined from an asset perspective as the hidden assets of the company not fully captured on the balance sheet (Roos and Roos 1997); or as intangibles such as patents, intellectual property rights, copyrights and franchises (Brennan 2001). Another perspective is to define IC as a residual being the difference between book value of the firm and its market value (Dzinkowski 2000). Other early writers defined IC as the resources of an organisation that have been formalized, captured and leveraged to create assets of a higher value (Bontis 1999). Although this debate may be considered tautological, authors have agreed on its basis parameters by creating a three factor conceptual framework deconstructing IC into human capital, relational capital and structural capital to further our understanding. Human capital is perhaps the most important element of IC because people are primarily responsible for the firm’s structural and relational capitals. Human capital is not a physical asset of the organisation measured by the number of employees but it relates to employees’ education, skills, training, experience, attitudes about life and business, genetic inheritance and values (Edvinsson and Malone 1997; Litschker et al., 2006). Human capital is the source of innovation and renewal within organisations and the firm’s collective capability to extract the best solutions from the knowledge of its individuals (Bontis 1998). Structural capital is the supportive infrastructure that enables human capital to function within organization and includes all aspects that are related with the organisation of the company and its decision making process. Structural capital encapsulates the organisational culture, structural design, coordinating mechanisms, organisational routines, planning and control systems. SC also incorporates databases, patents, trademarks, legal parameters, research and development and anything whose value to the company is higher than its tangible value (Bontis, 1999; Roos et al. 1997). The third component relational capital refers to either the relationships that exist between employees and external economic actors (Stewart, 1997), or relationships that exist among employees and other departments within the organisation (Tsai and Ghoshal 1998). Measurement of IC has attracted significant empirical research over the years but to date there has been no agreement on an acceptable measurement system. The measurement of IC debate continues and various authors have outlined the merits and demerits of the various models developed thus far. Pike and Roos (2004) provided an early assessment of the various techniques and concluded that none of the methods identified was incompliance with the tenants of measurement theory. Sveiby (2010) identified 42 such measurement techniques categorized into direct intellectual capital methods, scorecard methods, market capitalization methods and return on asset methods. A methodology which has been included in Sveiby (2010) categorization of return on assets methods is Pulic (2000) VAIC. This methodology deviates from directly measuring the firm’s IC as the case of the direct intellectual capital and scorecard methods to providing a measure of the efficiency of the value added by intellectual ability. The VAIC methodology assesses the extent to which a firm produces added value based on efficient use of both its tangible (capital employed) and intangible (human and structural capitals) resources (Maditinos et al 2011). According to Stahle et al (2011) this was one of the first methods to focus on the connection between IC and economic performance based solely on analyses from a company balance sheet. The VAIC technique has been widely used to assess the efficiency of IC with organizations. This approach measures the efficiency of firm’s three types of input: physical and financial capital, human capital and structural capital. The aggregate of these three measures is the value of VAIC, with a resulting higher value suggesting better management utilization of companies’ value creation potential. The seminal work on this approach used data from 30 randomly selected companies from the London FTSE 250 for 1992 – 1998 with the results showing that the average values of VAIC and firm’s market value exhibit a high degree of correspondence (Pulic 2000). This study was replicated in South Africa by Firer and Williams (2003) investigating the relationship between IC and traditional measures of corporate performance using the data

Donley Carrington from 2001 annual reports of 75 public traded firms. The VAIC methodology has been used in a number of countries and industries since its inception in 2000. Despite the growing body of literature and published work on VAIC undertaken in various geographical areas, Greece (Mavridis and Kyrmizoglou 2005) Taiwan (Chen et al 2005; Shiu 2006), Japan (Mavridis 2004), Malaysia (Goh 2005), Finland (Kujansivu and Lonnqvist 2007), Singapore (Chu et at 2011), Iran (Fathi et al 2013), Australia (Joshi 2010) and Serbia (Komnenic et al (2012), no study have been identified as relating to the Caribbean. Researchers have been grappling with the representation and valuation problems linked with IC. If the Caribbean is to succeed in providing ready markets for investors then the system of reporting must provide them with the requisite information needed for decision making. Bearing this in mind how can the value of the entity be measured in a meaningful way and what is the impact of the new intangible economy where knowledge is increasingly seen as an asset in the organisation. The efficient functioning of capital markets is highly dependent upon information flows between companies and investors. It has been argued that adequate information helps investors to reduce their perceived risks when predicting the company’s future. In addition to the globalization of financial markets, which has provided investors with ready access to investment opportunities worldwide, a new phase of economic development where IC is critical has emerged. It is therefore imperative that research be conducted to assess the impact of IC efficiency in markets that can be considered undeveloped or underdeveloped. Two research questions have been formulated to guide this study R1 Is there any interrelationship between IC and a firm’s performance? R2 Is there any interrelationship between each of the three components measured in the VAIC model and firm’s performance? A number of empirical studies have shown a relationship between IC and firm performance. Chen et al (2005) using the VAIC methodology found a significant positive relationship between IC and firm’s profitability. Therefore in an attempt to answer the first research question a number of hypotheses have been developed to guide this research. H1. Intellectual capital (VAIC) positively affects firm’s performance H1a. Intellectual capital VAIC positively affects return on assets (ROA) H1b. Intellectual capital VAIC positively affects return on equity (ROE) H1c. Intellectual capital VAIC positively affects earnings per share (EPS) H1d. Intellectual capital VAIC positively affects economic income (ECIN) H1e. Intellectual capital VAIC positively affects market value to book value per share (MB) In addition, research has shown a relationship between the components of IC as measured by the VAIC methodology and firm’s profitability. Since different significance may be placed on Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE) and Capital Employed Efficiency (CEE), the three components of VAIC, it would be interesting to examine the separate effect of each on financial performance indicators. Clarke et al (2011) in using this approach found a positive relationship between HCE and SCE and performance in Australian companies. Therefore to investigate the explanatory power of these components in the model, it is hypothesized H2. Human capital efficiency (HCE) positively affects firm’s performance H2a. HCE positively affects ROA H2b. HCE positively affects ROE H2c. HCE positively affects EPS H2d. HCE positively affects ECIN H2e. HCE positively affects MB H3. Structural capital efficiency (SCE) positively affects firm’s performance H3a. SCE positively affects ROA H3b. SCE positively affects ROE

Donley Carrington H3c. SCE positively affects EPS H3d. SCE positively affects ECIN H3e. SCE positively affects MB H4. Capital employed efficiency (CEE) positively affects firm’s performance H4a. CEE positively affects ROA H4b. CEE positively affects ROE H4c. CEE positively affects EPS H4d. CEE positively affects ECIN H4e. CE pEositively affects MB

3. Methodology The empirical data for this study were drawn from the annual reports of 70 companies listed across the five stock exchanges in the region (Barbados, Bahamas, Eastern Caribbean, Jamaica and Trinidad and Tobago) for the years 2006 to 2011, yielding a total sample of 406 observations. Two types of variables are used in this research, one for measuring intellectual capital and the other for measuring financial performance.

4. Measurement of variables The independent variables for the present study includes the four IC efficiency scores as used in previous studies focusing on the work of Ante Pulic (2000). Zeghal and Maaoul (2010) and Shiu (2006) also follow Pulic’s methodology to calculate VAIC. This approach requires the calculation of the value added (VA), Human capital (HC), Structural capital (SC) and Capital employed (CE). The value added (VA)represents the excess of the firm’s output in terms of total income from the sales of products and services over the total expenses excluding employee costs, interest, dividends and depreciation. According to Chen et al (2005) the following formula can be used to calculate the VA of a firm. VA = NI + EC + D + T + I Where NI = Net income; EC = employee costs; D = depreciation; T = taxes; and I = interest. HC in the VAIC model represents the total remuneration paid to employees in terms of wages, salaries and other fringe benefits. SC is calculated by deducting the HC from the VA (VA–HC). CE is the total assets minus the intangible assets. The next step is to calculate the efficiency variables.

Human Capital Efficiency (HCE), is calculated by dividing the value added (VA) by the Human capital (VA÷HC). This indicator shows how much VA is created by each dollar spent on human capital. Clarke et al (2011) argues that firms with higher HCE demonstrate that they have more effectively utilized their HC to add value through operating profit.

Structural Capital Efficiency (SCE) is calculated by dividing the SC by VA. This efficiency indicator demonstrates the dollar value of structural capital within the firm. According to Laing et al (2010) the sum of the HCE and SCE is the intellectual capital efficiency of the firm which reflects the valued created by IC employed.

Capital Employed Efficiency (CEE) is calculated by dividing the VA by CE and according to Clarke et al (2011) it encompasses the efficiency within the firm that is not captured by the SCE or HCE.

VAIC is a composite of the three separate indicators the HCE, SCE and CEE.

The dependent variables were financial performance indicators return on assets (ROA), return on equity (ROE), earnings per share (EPS), and economic income (ECIN); and the market price to book value per share ratio (MB). Previous studies examined the relationship among the components of VAIC and these variables identified (Firer and Williams 2003; Chen et al 2005; Clarke et al 2011; and Maditinos et al 2011). These variables are calculated as follows:

ROA = [(net income ‐ preference dividends) / total assets]

ROE = [(net income ‐ preference dividends) / average stockholders’ equity].

Donley Carrington

EPS = [(net income ‐ preferred dividends) / weighted average number of shares]

ECIN = [operating income / total revenue]

The market to book value ratio (MB) is calculated by dividing the market value (MV) by the book value (BV) of common stocks. (MV÷BV) where MV = Number of shares x stock price at year end BV = stockholders’ equity – paid in capital of preferred stocks The adjustment to the raw data for stock splits was similar to that made by Robinson (2005). The adjusted market value used in a bonus issue was Pjt* = rPjt /m where r is the number of shares after the bonus for every m shares held by the stock holder. The book value was also adjusted for the stock split by dividing the stockholders equity by S*. S* = rS/m where r is the number of shares after the bonus issue for every m shares held by the stock holder and S is the number of shares outstanding. The data were entered in SPSSv19 and subjected to both univariate and multivariate analyses. The univariate analysis assessed the means and standard deviations of the data set. In order to examine the hypotheses of the study, various regression models have been evaluated. Models 1 to 5 examine the relationship between VAIC and financial performance (ROA, ROE, EPS, ECIN, MB), models 6 to 10 examine the relationship between HCE, SCE and CEE and financial performance (ROA, ROE, EPS, ECIN, MB). (1) ROA = β0 + β1VAIC + ε (2) ROE = β0 + β1VAIC + ε (3) EPS = β0 + β1VAIC + ε (4) ECIN = β0 + β1VAIC + ε (5) MB = β0 + β1VAIC + ε ROA = β0 + β1HCE + β2SCE +β3CEE + ε (6) ROE = β0 + β1HCE + β2SCE +β3CEE + ε (7) EPS = β0 + β1HCE + β2SCE +β3CEE + ε (8) ECIN = β0 + β1HCE + β2SCE +β3CEE + ε (9) MB = β0 + β1HCE + β2SCE +β3CEE + ε (10)

5. Results and discussion Descriptive Statistics The descriptive statistics for all study variables are presented in Table 1. As shown in Table I the mean of value added intellectual coefficient (VAIC) is 1.2559 and its standard deviation is 67.2845. Human Capital Efficiency (HCE) with mean 3.7537 has highest mean among other VAIC components and Earning per share (EPS) indicator with mean 1.5768 has highest mean among other financial performance indicators. SCE in this study reports a negative mean of ‐2.6607. Table 1: Descriptive statistics for all study variables ROA MB ROE EPS ECIN HCE SCE CEE VAIC Valid N (listwise)

N Statistic 408 408 408 408 408 408 408 408 408 408

Minimum Statistic ‐1.2289 .1363 ‐1.9940 ‐15.2732 ‐2.4607 ‐20.6743 ‐350.2277 ‐1.0133 ‐350.2268

Maximum Statistic .3345 17.1631 4.8053 23.7527 3.9435 50.8900 12.9754 .7566 52.1611

Mean Statistic .0480 1.4695 .1472 1.5768 .2034 3.7537 ‐2.6607 .1629 1.2559

Std. Deviation Statistic .0973 1.3543 .3079 3.2766 .3572 5.2299 66.8821 .14974 67.2845

The mean market to book value ratio of (1.4695) indicates that 31.95 percent of the firm’s market value is not reflected on the financial statements: Hidden value = [(1.4695 – 1.0000]/1.4695) * 100] = 31.95 percent.

Donley Carrington This finding supports previous research that indicated an existence of a gap between the market and book value of a firm (Lev 2001, Maditinos et al 2011). Lev (2001) study in the United States reported that 80 percent of the market value was omitted from financial statement; a more recent study by Maditinos et al (2011) reported a 40.96 percent hidden value in the Greek banking industry. Further analysis of the market to book value ratio revealed that 41.4 percent of the firms had a MB ratio less than 1, which suggests that companies in Caribbean either have negative intellectual capital or their market value is not a true reflection of the value of the firm. According to the efficient market hypothesis a high degree of correlation exists between share price and company information, which would include attributes of IC. Robinson (2005) argues that institutional features of emerging markets as the case of Caribbean Stock Markets are such that the markets tend to be informational and operationally inefficient, whereas an efficient financial market consists of numerous well‐ informed individuals, with ready and cheap access to information. His research concluded that the weak‐form of market efficiency cannot be rejected for the Caribbean as in this form of market efficiency prices tend to reflect historical information. Correlation analysis The results presented in Table 2, indicate that the market to book value ratio is only significantly related to the capital employed efficiency indicator. All other correlation indexes (MB correlated with HCE, SCE and VAIC) were not found to be statistically significant (Table 2). The financial performance measure ROA was found to be statistically significant to all the components as well as to the composite VAIC. These results differ from the findings of Pulic (2000) but are consistent with those of Stahle et al (2001). The HCE was found to be statistically significant with all the financial performance indicators. Table 2: Correlation analysis for study variables ROA MB ROE EPS ECIN HCE SCE CEE VAIC

ROA 1 .232** .570** .272** .459** .220** .145** .690** .163**

MB ROE EPS ECIN HCE 1 .561** 1 ‐.063 .181** 1 .131** .408** .280** 1 .084 .183** .230** .609** 1 .040 .070 .066 .074 .037 .165** .379** .013 .091 ‐.006 .047 .085 .083 .121* .114* **. Correlation is significant at the 0.01 level (2‐tailed). *. Correlation is significant at the 0.05 level (2‐tailed)

SCE 1 .052 .997**

CEE 1 .054

Hypotheses verification This study adapts a multiple‐regression method to examine and identify whether the financial performance indicators and market value is impacted by the IC efficiency indicators. In order to answer the first research question and the related hypotheses the following five regression models were used. The results of which are presented in table 3 (1) ROA = β0 + β1VAIC + ε (2) ROE = β0 + β1VAIC + ε (3) EPS = β0 + β1VAIC + ε (4) ECIN = β0 + β1VAIC + ε (5) MB = β0 + β1VAIC + ε Table 3: Summary results of five multiple regression models. Dependent variable ROA ROE ECIN EPS MB

VAIC Coefficient 0.163 0.085 0.121 0.083 0.047

t‐ statistic 3.325 1.711 2.454 1.679 0.944

p‐value .001 .088 .015 .094 .346

R square 2.7% 0.7% 1.5% 0.7% 0.2%

Donley Carrington These results indicate that the composite VAIC does not impact the financial performance indicators of ROE, ECIN or EPS. In addition, there is no significant relationship between MB and VAIC. On the other hand, the results show that there is a weak but significant relationship between VAIC and ROA. This finding supports prior studies (Chan 2009, Ting and Lean 2009). These findings indicate H1b, H1c, H1d, H1e cannot be supported. In relation to the research question as to whether the individual efficiency indicators have a positive relationship with the performance ratios, regression models 6 to 10 were used and the results presented in tables 4. ROA = β0 + β1HCE + β2SCE +β3CEE + ε (6) ROE = β0 + β1HCE + β2SCE +β3CEE + ε (7) EPS = β0 + β1HCE + β2SCE +β3CEE + ε (8) ECIN = β0 + β1HCE + β2SCE +β3CEE + ε (9) MB = β0 + β1HCE + β2SCE +β3CEE + ε (10) Table 4: Regression results Models 6 ‐ 10 Independe nt variable HCE

Model 6 ROA Coefficie t‐ nt statisti c .220 6.501*

SCE

.101

2.976*

CEE

.686

Adjusted 2 R F. value

.533

20.228 *

155.869*

Model 7 ROE Coefficie t‐ nt statisti c .184 4.075 * .043 .902 .378 .174

Model 8 EPS Coefficie t‐ nt statisti c .228 4.723 * .057 1.167

8.384 *

Model 9 ECIN Coefficie t‐ nt statisti c .608 15.539 * .047 1.191

.012

.243

.093

2.372*

0.49

.378

83.354*

29.584* 8.063* Note. * Significant at the 0.05 level

Model 10 MB Coefficie t‐ nt stat istic .076 1.5 40 .030 .60 9 .155 3.1 64* .024 4.339*

The results of the regression model 6 prove that a statistically significant and positive relationship among the efficiency indicators of HCE, SCE and CEE and ROA. In the case of regression models 7 and 9 statistically significant relationships existed between HCE and the financial performance indicators ROE and ECIN. These models revealed that CEE had a statistically significant relationship with ROE and ECIN. Model 8 revealed that only HCE had a significant relationship with EPS hence partially supporting the hypothesis 2b. In the case of Model 10, only CEE had a significant relationship with market to book value ratio, coupled with an extremely low R 2 (0.024) where only 2.4 percent of the variance in MB is explained these efficiency indicators. These findings support H2a, H2b, H2c, H2d, H3a, H4a, H4ba, H4d and H4e. Finally, the adjusted R2 increases substantially when VAIC is split into its components particularly strong for ROA (Table 4 model 6), and this finding is consistent with Clarke et al (2011). Overall the results revealed that the VAIC components have statistically greater explanatory power than the composite and this is consistent with prior research.

6. Conclusion This study sought to examine whether there were any significant relationship between IC and performance using the VAIC methodology in emerging capital markets such as the Caribbean. The results indicated that the composite VAIC was only significantly associated with the performance measure ROA which was consistent with prior research. The non‐significant association with MB was expected due to the large percentage of companies with MB ratio of less than 1. The results indicated a significant positive relationship between HCE and ROA, ROE, ECIN and EPS; on the other hand SCE has a significant positive relationship with ROA and ECIN. CEE has greater explanatory power than HCE in three of the models shown by its larger standardized coefficient, and therefore more dominant in VAIC when predicting performance. This result is consistent with prior studies Firer and Williams (2003), Chen et al (2005), Clarke et al (2011). This finding suggests in these emerging capital markets there is still a higher emphasis being place on physical capital assets than intellectual

Donley Carrington ones. SCE only had a significant relationship with one of the performance measures identified in this study and this finding is consistent with prior VAIC studies, (Chen et al, 2005; Shiu 2006; Clarke et al 2011). Policy makers should intensify their initiatives in order to encourage greater acceptance and understanding of the concept of IC.

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Donley Carrington Shiu H. J. (2006) “The application of the value added intellectual coefficient to measure corporate performance: evidence from technology firms”, International journal of Management, Vol. 23 No. 1 Stahle P, Stahle S. and Aho S. (2011) “Value added intellectual coefficient (VAIC): a critical analysis” Journal of Intellectual Capital Vol. 12 No. 4 pp. 531‐551. Stewart, T. (1997) Intellectual capital: The wealth of organizations, London, Nicholas Brealey Publishing. Sveiby, K. (1997) The New Organizaational Wealth: Managing and Measuring Knowledge based assets, San Francisco, Berrett‐Koehler. Sveiby K (2010) ‘Method of measuring intangible assets” available at: www.sveiby.com/articles/intangiblemethods.htm (accessed January 15, 2013) Ting I.W. K and Lean H.H (2009) “Intellectual capital performance of financial institutions in Malaysia” Journal of Intellectual Capital Vol. 10 No. 4 pp. 588‐99. Tsai, W. & Ghoshal, S. (1998) “Social capital and value creation: The role of intrafirm networks”. Academy of Management Journal, 41, 464‐477. Wang, W‐Y. & Chang, C. (2005), “Intellectual capital and performance in causal models: Evidence from the information technology industry in Taiwan”, Journal of Intellectual Capital, Vol. 6 No. 2, pp. 222‐236. Zeghal D and Maaloul A (2010) “Analysing value added as an indicator of intellectual capital and its consequences on company performance” Journal of Intellectual Capital Vol. 11 No. 1 pp. 39‐60

Human Resource Practices and Knowledge Sharing: The Mediator Role of Culture Delio Castaneda and Paul Toulson Pontificia Universidad Javeriana, Colombia & Massey University, New Zealand delio.castaneda@javeriana.edu.co p.toulson@massey.ac.nz Abstract: A big question for both academics and practitioners in ICKM is: How can human resource (HR) practices effect maximum leverage of the human capital pool in contemporary organizations through knowledge sharing? We know that HR practices are culture bound. The research evidence does not reinforce universal best HR practice because of the uniqueness of organizational cultures, comprised of both past and present social networks of employees providing both a historical and a current uniqueness in any organization. The resource‐based theoretical view suggests that an organization’s human capital pool is valuable. The empirical evidence reinforces this. The literature stresses the importance of knowledge sharing in the development of new knowledge for innovation and sustainable competitive advantage. So the nature and extent of knowledge sharing in an organization is also culture bound, and we suggest that organization culture becomes a mediating influence in the nature and extent of knowledge sharing, rather than simply HR practices. The role of culture as a mediator is often ignored or minimized in the practice of knowledge sharing. It is like the hidden part of an iceberg, and becomes visible when there is a clash between knowledge sharing activities and organization culture. So this is an important consideration to be included in knowledge management activities in organizations, and its role needs to be made clear. In this paper we review briefly the concepts of knowledge sharing, HR practices, and organizational culture, and the empirical literature of their relationships, in particular the role that culture plays in these relationships. The paper concludes with the development of a conceptual model of the role of culture as a mediator, which will provide the framework for future research in this area to test its importance. Keywords: human resource practices; human capital value; knowledge sharing; organizational culture

1. Introduction A big question for both academics and practitioners in ICKM is: How can human resource (HR) practices effect maximum leverage of the human capital pool in contemporary organizations through knowledge sharing? We know that HR practices are culture bound. The research evidence does not reinforce universal best HR practice because of the uniqueness of organizational cultures, comprised of past and present social networks of employees that provide both a historical and a current uniqueness in any organization (Boxall and Purcell, 2011). The resource‐based view (RBV) theory of strategic management (Penrose, 1959) suggests that an organization’s human capital pool is valuable, in the sense that the people, particularly the management teams, have knowledge and experience (together with their understanding of the organization’s environment) that are unique to the organization and therefore become very difficult to imitate or replace by the organization’s competitors. The theoretical contribution to the RBV of strategic scholars like Barney (1991), Wright, McMahon, and Williams (1994), and Priem and Butler (2001) suggest that the uniqueness of the organization’s human resources in terms of being valuable, rare, and non‐substitutable is due to three barriers to imitation. These are:

a. The unique conditions developed in the organization over time;

b. The social complexity of the organization’s human capital due to unique internal and external HR connections producing networks of interrelationships that are organization specific; and,

The causal ambiguity that arises from the uncertainty (inside and outside successful organizations) as to what actually contributes to their overall success.

The empirical evidence reinforces the RBV (Boxall and Purcell, 2011). We can see that these barriers are concerned very much with the culture of the organization. The efficacy of these networks of connections and interrelationships are to a large extent dependent on the sharing of knowledge within the human capital pool. The literature stresses the importance of knowledge sharing in the development of new knowledge for innovation and sustainable competitive advantage. So the nature and extent of knowledge sharing in an organization is also culture bound, and we suggest that organization culture becomes a mediating influence in the nature and extent of knowledge sharing, rather than simply HR practices that are designed to foster and support knowledge sharing among the employees.

Delio Castaneda and Paul Toulson The role of culture as a mediator is often ignored or minimized in the practice of knowledge sharing. It is like the hidden part of an iceberg, below the surface of the water, and becomes tangible when there is a clash between knowledge sharing activities and organization culture. So this is an important consideration to be included in knowledge management activities in organizations, and culture’s role needs to be made clear. In this paper we review briefly the concepts of organizational culture, knowledge sharing, and HR practices, and the empirical literature of their relationships, in particular the role that culture plays in these relationships. The paper concludes with the development of a basic conceptual model of the role of culture as a mediator, which will provide the framework for future research in this area to determine its role and importance as a significant mediator in knowledge sharing.

2. What is organizational culture? Organizational culture is defined as a shared values set that guides employees to communicate and act explicitly in the day‐to‐day workplace context (Alavi, Kayworth & Leidner, 2006). Organizational culture can be also defined as the shared, basic assumptions that an organization learned while coping with the environment and solving problems of external adaptation and internal integration that are taught to new members as the correct way to solve those problems (Park, Ribiere & Schulte, 2004). It is a difficult concept to grasp because there are many approaches to its definition and construction. Cheyne & Loan‐Clarke (2009) suggest that there are two independent meanings of culture, one is as a metaphor that gives meaning to organizations, and the other as an objective entity that describes what an organization is (in terms of its structure) or what it has (in terms of its behavior). Cheyne and Loan‐Clarke (2009) review the development by researchers of different categorizations or classifications as descriptive types of cultures that organizations fit. Examples they give are Deal & Kennedy’s (1982) four cultures, and Harrison’s (1972) power, role, task, and person culture typology.

3. Issues with culture measurement Given the complexities of the conceptualization of culture, there are a variety of approaches to its assessment and evaluation utilizing both qualitative and quantitative methodologies (Cheyne & Loan‐Clarke, 2009). Corporate anthropologists like Henderson (2011) suggest that culture cannot be measured directly without actually living in it and being a participant observer. It cannot be measured by quantitative surveys and standardize questionnaires that have “plagued” the organizational culture literature for years (Scheel & Crous, 2007). Since organizational culture is socially constructed, such standardized measures are inappropriate to diagnose its substance and effects. Scheel and Crous (2007) describe the use of appreciative enquiry (AI) as a form of participative action research in a Johannesburg‐based e‐learning company. The results of this research provided information for a support team of employees to leverage this company’s cultural capital through the implementation of a range of new HR initiatives that were approved by management. We would suggest that standardized measures and questionnaires designed to measure organizational culture are more suited to the measurement of organizational climate. So measures of organizational climate are confused as measures of an organization’s culture. Organization climate is a concept different from organizational culture. Cheyne and Loan‐Clarke (2009) suggest that organizational climate measures reflect employee perceptions of their work place environments. However organizational climate and culture are often used as meaning the same thing (Dennison, 1996). We like to think of them as being connected in the sense that using our metaphor of culture, climate is the visible part of the iceberg, while culture lies below the surface of the water. They are part of the same thing but have to be used differently in the effects on sought outcomes like employee knowledge sharing. In this sense we agree with Ashworth (1985) that both climate and culture are complementary in that climate is informed by culture by impacting on individual employees’ perceptions of their workplaces. Referring to our iceberg metaphor again the invisible part needs to be taken into consideration, as an organization navigates its way through turbulent times, not what is just visible.

4. Knowledge sharing and organizational capability Knowledge sharing (sometimes referred to as knowledge transfer) is a behavior highly associated to the achievement of organizational objectives. Knowledge sharing, in the context of this paper, can be thought of as an intermediate goal to the achievement of successful knowledge outcomes for an organization. We can think of the creation and application of knowledge for successful organization performance outcomes as being dependent on the sharing of it. Helmstadter (2003) defined knowledge sharing as voluntary interactions between human beings where the raw material is knowledge. People exchange: information from different sources, experience, insights, and tools which help to do the job better or to reach organizational objectives.

Delio Castaneda and Paul Toulson A body of literature is now developing with respect to the impact of HR practices on perceived knowledge sharing behaviors as antecedents for organizational capability in Pakistan (Iqbal, Toulson, & Tweed, 2011) and Malaysia (Abdul Jalal, 2012). Abdul Jalal (2012) also reports, in her study of employees from four Malaysian IT organizations, that their perceived organizational culture values, based on specific Malaysian cultural values, have a positive relationship with their perceived knowledge sharing success.

5. The role of culture in knowledge sharing Despite the conceptual complexity as to what it is, culture is both a key driver and inhibitor of organizational knowledge sharing (O’Dell, & Grayson, 1998). It provides the context within which organizational members create, acquire, share, and manage knowledge (Holsapple, K. & Joshi, 2001). According to Delong & Fahey (2000), there are four reasons why culture is the base of knowledge sharing: culture shapes assumptions about what knowledge is important, culture determines what knowledge belongs to the organization or to the individual, it creates a context for social interaction about knowledge and culture shapes the creation and adoption of new knowledge. In the same direction, Cabrera & Cabrera (2005) suggest that organizational culture influences knowledge sharing in two ways: first, by creating an environment in which there are strong norms regarding the importance of doing this behavior, and second, creating an environment of caring and trust. Trustworthiness has also been a cultural value identified in Abdul Jalal’s (2012) research. There is also an extensive literature suggesting that organizational culture drives organizational performance, and assumes that there is direct causative link between culture and performance. This is particularly a popular connection made by practicing managers based on that originally made by Peters and Waterman (1982) and its derivatives (e.g. Inkson, Henshall, Marsh, & Ellis (1986) reviewed by Cheyne and Loan‐Clarke (2009). The modern emphasis on culture change in organizations to accommodate the changes due to technological advances and innovations, and changing customer needs has had the result of the idea of “culture change” through restructuring and changing workplace environments. This has driven much strategic thinking so far as the achievement of organizational goals is concerned.

6. Human resource (HR) practices The fundamental assumptions of managerial thinking in this respect have also become central to the assumptions and beliefs in human resource management and its practices (Cheyne & Loan‐Clarke, 2009). A number of popular learned books have been written about the centrality of human resource management in terms of both initiating and managing change in organizations (Ulrich, 1997; Fitz‐enz, 1990). We would argue that the key to improving organizational performance is not so much through the actual culture of the organizations but in the ability and motivation of its workforces’ to share knowledge (Sveiby, 1997; Stewart, 1997). Hence we would argue that it is not the presence or absence of particular cultures that enhance organizational performance but the type and amount of knowledge sharing that occurs. In this sense certain cultures may enhance knowledge sharing behaviors which result in enhanced organizational performance. The reality is that the way organizations operate and their HR practices may actually militate against knowledge sharing activities rather than encouraging them. Organizations that value knowledge to their long term performance should have a deep understanding of the impact of culture towards knowledge sharing. Individuals bring their personal values and beliefs to the workplace and this impact the levels of knowledge sharing (Jalal, Toulson and Tweed, 2011). Tong (2013) also found that organizational culture influences knowledge sharing. It is a challenge for organizations to establish a culture that may develop and enhance their employees’ capability to share knowledge (Kim and Lee 2006).

7. Knowledge sharing cultures It has been suggested that there are three attributes in organizations associated with the development of knowledge sharing cultures. These are the cultural attributes of high solidarity and sociability, the promotion of fair processes and fair outcomes, and recognition of employees` work (Smith & Mckeen, 2002). High sociability strengthens the possibility to express and accept ideas. Solidarity builds a sense of trust. Additionally, according to the authors, in organizations where fair processes are perceived people go beyond their duties. Finally, when work is recognized by superiors, people tend to demonstrate stronger organizational citizenship behaviors like knowledge sharing. Hislop (2003) found that human resource (HR) concepts could be utilized to improve the understanding of the reluctance of employees to share their knowledge. Knowledge sharing behavior among employees in business environments is not automatic and not necessarily a natural

Delio Castaneda and Paul Toulson activity. It is highly dependent on organizational actions. Wang and Noe (2010) suggest that HR practices aim to facilitate employees’ knowledge sharing by encouraging fairness in decision making and open communication. Such practices are supportive of a knowledge sharing culture in organizations. HR practices also play a role in facilitating knowledge sharing by identifying those who have the required knowledge (through employee selection activities), those who need it (through performance review and development activities) and encouraging a knowledge sharing environment (through rewards and recognition). This has an impact on other outcome influences like increased commitment, lower turnover, higher productivity, better customer service, occupational health and safety, and ultimately better financial performance (Jiang, Lepak, Hu & Baer, 2012). This is a feature of knowledge sharing organizations where people are motivated to share and want to remain in organizations with knowledge sharing cultures. The above is the ideal but it is a fact that experts are less willing to codify or share their expertise with their coworkers when they think their expertise is a source of personal advantage (Bowman, 2002). People have a lower intention to participate if they feel their knowledge is not valuable (Wasko & Faraj, 2000). Part of the problem here with respect to an employee’s intention to share their knowledge relates to the macro issue of the transactional and competitive nature of organizational activity. According to Syed‐Ikhsan and Rowland (2004), employees need strong motivators in order to share their knowledge, particularly when the retention of knowledge has a transactional value attached to it. In this respect, an HR practice is the construction of trustworthiness, defined as faith in the trustworthy intentions of others and confidence in the ability of others (Cook and Wall, 1980). Trust and communication are positively related to knowledge sharing in organizations (Al‐Alawi, Al‐Marzooqi, & Mohammed, 2007; Smith and Rupp, 2002). Trust enhances people’s attitudes towards the sharing of their knowledge (Gruenfeld, Mannix, Williams & Neale, 1996). Interpersonal trust is known as an individual’s or a group’s expectancy in the reliability of the promise or actions of other individuals or groups (Politis, 2003). The factor of trust is a key enabler for knowledge sharing through informal interactions among employees (Bartol & Srivastava, 2002). In this respect the sharing of knowledge can also be seen as a transactional activity, in the sense that if employees share their knowledge with each other both benefit because a relationship is developed where the flow of knowledge is two way. It is less likely for a relationship to continue in informal knowledge sharing if the knowledge flow is only one way. Therefore some form of reciprocity is fundamental to any knowledge sharing arrangement. HR practices contribute to knowledge creation and innovation through the generation of the affective commitment necessary for employees to be willing to share their knowledge (Camelo, García, Sousa & Valle, 2011). HR practices like incentive compensation plans, performance appraisal systems, and face‐to‐face communication foster knowledge sharing, however, a mediator is the willingness to share knowledge (Liu & Liu, 2011). The evidence from meta‐analytic studies (Jiang et. al., 2012) suggests that bundles of HR practices ( HR systems) that enhance the skills, knowledge, and skills of employees and that motivate and give them the opportunity to contribute have direct benefits to the achievement of organizational outcomes. Polyhart, Van Iddekinge, and Mackkenzie (2011) found that the acquisition of generic human capital through selection practices in the study of restaurant chains leads to the development of unit specific capital. The criteria used in selection were age and those employees who scored at the top of the selection score range. The unit specific capital measures were the percentages of employees completing advanced optional training programs. They conclude that changes in in the flows of generic human capital contribute directly to changes in flows in developing unit‐specific human capital that in turn contribute to changes in unit performance flows.

8. Culture as a mediator of HR practices and knowledge sharing If culture is characterized by shared values that people have, and values guide behaviors, then culture mediates the relationship between HR practices and knowledge sharing. Organizational behavior is highly determined by organizational culture (Jarnagin & Slocum, 2007). HR practices have consequences on employees’ attitudes and behaviors only to the extent that they are consistently experienced and perceived by employees in intended ways (Bowen & Ostroff, 2004). Peck and Dickinson (2008) suggest that culture is a complex concept with a variety of ways of describing it, but in such activities as mergers, acquisitions, and alliances it becomes visible as a critical aspect of success. Many of these activities fail to produce anticipated benefits because they focus mainly on the structural changes (that is what are tangible) over the human (cultural) factors. They conclude that structural change is not sufficient to create cultural change.

Delio Castaneda and Paul Toulson In an earlier review of research relating to the value of HR practices as adding to the value of human capital in the development of intellectual capital in organizations (Castaneda & Toulson, 2012), we concluded that the centrality of HR practices in the enhancement of knowledge sharing behaviors is a fundamental to the achievement of organizations’ knowledge objectives. We also suggested that the research results on that relationship between HR practices, psychosocial variables and knowledge sharing remains unclear. The way we think about it then is that psychosocial variables are enveloped in organizational culture (i.e. the hidden part of the iceberg). These variables are definitely there but are difficult to document comprehensively and also are very difficult to measure in direct tangible ways. While this may be is seen as a weakness, so far as the measurement of value is concerned, particularly in business metric, it is also strength. It is strength in the sense that this is what provides the uniqueness of organizational value in terms of the RBV of an organization’s competitive advantage in terms of the three RBV criteria discussed in the introduction to this paper. A central issue is the reality that business behavior is basically transactional in nature with an emphasis on competition. It is this that often will act as an inhibitor to knowledge sharing, given the competitive nature of business activity that is often driven by short term objectives that are largely transactional in nature. While knowledge shared grows, there are a number of transactional aspects that can inhibit such behaviors. For instance individual reward practices that encourage competition between employees, and promotion practices based on individual performance, at the expense of other employees. Such individually based HR practices can have the opposite effect to negate the encouragement of knowledge sharing. These are all aspects that are part of the culture of organizations. Ajmal and Koskinen (2008) identify obstacles to knowledge sharing in project based‐ organizations, which are characterized by uncertainty and complexity (features of the RBV). They suggest, from the results of a small number of cross‐cultural studies that attention on cultural characteristics in the development of HR initiatives to encourage knowledge sharing needs to be given. So, a major cultural shift may be required to change employees’ attitudes and behavior so that they willingly and consistently share their knowledge (Alavi & Leidner, 2001). There is a strong and positive relationship between a collaborative culture and the effectiveness of knowledge sharing (Yang, 2007). While there is a present little empirical evidence that links culture to organizational performance (at least for the health and social care sectors), it is implicitly assumed that there is an association (Peck & Dickinson, 2008).

9. Conclusion So we would argue that certain HR practices can be used to encourage knowledge sharing by employees, while others that foster competition among employees may inhibit knowledge sharing behaviors. It is the organizational culture (the hidden part of the iceberg) that mediates the success or otherwise of knowledge sharing behavior, not HR practices themselves. So we suggest a very basic conceptual model shown in Figure 1 below: Organizational Culture

Human Resource Practices

Knowledge Sharing

Figure 1: The mediating role of organizational culture In Figure 1 the role of organizational culture is shown as mediator between HR practices on the left hand side and desired knowledge sharing behaviors on the right. The challenge is to undertake further research to understand the significant aspects of this mediating relationship. From our brief review of this fascinating area of enquiry we make some general conclusions:

Given the complexity of organizational culture and also its uniqueness in RBV terms, broad general models are only of limited use in diagnosing its effects.

Delio Castaneda and Paul Toulson

In the same way, its diagnosis is more effective by using qualitative (social constructionist) methodologies rather than quantitative standardized surveys that are limited to measuring organizational climate (which is perceptual in nature).

There is general empirical support that certain HR practices do foster cultures that foster knowledge sharing among employees. There is also evidence to suggest that the opposite may occur as a result of the predominate work culture.

A common ingredient in organizational culture and human resources practices to facilitate knowledge sharing is trust.

HR Practices, organizational culture, and knowledge sharing behaviors do impact on the achievement of organizational objectives in terms of successful outcomes.

The generality of models to describe these relationships may be suspect. While certain models may apply to particular types of organizations or industries, much of the diagnosis is at this stage organization specific given the RBV of strategic management.

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Organizational Antecedents Shaping Knowledge Sharing Behaviors: Empirical Evidence From Innovative Manufacturing Sectors Vincenzo Cavaliere1and Sara Lombardi2 1 Department of Business Administration, School of Business, University of Florence, Florence, Italy 2 Department of Business and Management, LUISS Guido Carli University, Rome, Italy vincenzo.cavaliere@unifi.it slombardi@luiss.it Abstract: How can firms act upon organizational tools when trying to foster employees’ knowledge sharing (KS) orientations? Given the lack of a clear understanding about how organizational factors affect KS processes, this paper aims at addressing this issue by starting from the criticality of knowledge assets especially for firms operating in dynamic markets. It has been shown that in order to be more valuable and be a source of competitive advantage, knowledge needs to be shared, as sharing ideas leads to new ideas creation and interpretation. Despite knowledge is hard to transfer and employees may be reluctant in sharing what they know, firms should know that managers can affect workers’ behaviors by dealing with organizational factors. In order to address this issue, we analyze web‐survey data of 758 knowledge workers from 24 international innovative manufacturing firms. By using Ordinary Least Square regression, we empirically test the relationship between hard and soft organizational KS antecedents and employees’ voluntary behavior in sharing knowledge (i.e. knowledge donating). On one hand, the results show significant and positive relation between innovative culture, top management support, autonomy in the job and knowledge donating, supporting our hypotheses; on the other hand, we found counter‐intuitive evidence about the positive and significant impact of both bureaucratic culture and operating procedures on our dependent variable. We hope this study may be a starting point for building a new understanding about the organizational interventions likely to stimulate intra‐firm KS activities. From a managerial perspective, this research suggests that being aware of the role played by job design as well as by organizational culture in shaping employees’ KS behavior may be fundamental to managers that are planning their strategies to better manage and exploit individual and collective knowledge. We conclude the paper by providing directions for future research that may help improving this contribution. Keywords: knowledge donating, job design, formalization, job autonomy, organizational culture

1. Introduction Knowledge management scholars have long been concerned about the way managers could lead organizational members to voluntarily share what they know within the company, thus underlining that the success of knowledge management activities is strongly linked to how intra‐organizational knowledge sharing (KS) is implemented. The relevance of knowledge sharing (KS) process is justified by the fact that sharing knowledge and information allows employees to talk and listen to each other, to stimulate new knowledge creation and to fasten mutual learning. KS is thus the first step organizations should take to value and exploit their knowledge. Studying KS processes within firms becomes particularly relevant when the focus is on knowledge‐intensive organizations where strategic knowledge is usually dispersed across highly skilled workers operating in different units, departments, or divisions and whose values needs to be exploited as much as possible in order to support the ability of the firm to remain competitive in its market. More specifically, KS becomes relevant for innovative companies operating in more than one country, as they usually have to account for the different cultural attitudes both when dealing with foreign partners and when managing their staff. By attempting to expand prior contributions’ findings on KS organizational enablers, this paper aims at answering the following research question: “what is the role played by organizational factors in shaping key workers’ KS behaviors?” In order to address this issue, we analyze a sample of 758 employees from 24 international and highly innovative manufacturing companies.

2. The strategic importance of sharing knowledge The knowledge‐based literature suggests that the development, exploitation and management of knowledge assets are crucial to the survival and prosperity of modern organizations (Huber 2001; Barney 1991). Hence, in

Vincenzo Cavaliere and Sara Lombardi order to struggle with fast increasing pace of competition, organizations have to develop strategic competences and knowledge (Aulawi et al. 2009; Teece et al. 1997; Grant 1996). More importantly, they firstly should understand how to value and exploit their internal knowledge assets. In this regard, a relevant issue is the one regarding knowledge transferability, especially when it is considered within the firm, rather than between firms (Grant 1996). Intra‐organizational knowledge sharing (KS) processes stimulates individuals to think critically, to express their creativity, to generate new knowledge, to enhance the overall innovation capability as well as to reduce learning efforts (Lin 2007). Following Quinn et al. (1996), “as one shares knowledge with other units, not only do those units gain information [...]; they share it with others and feedback questions, amplifications, and modifications that add further value for the original sender, creating exponential total growth” (p. 8). KS can thus be seen as a social interaction culture that helps employees exchange work‐related experiences, skills, and know‐how with colleagues (Lin 2007). From an individual perspective, KS involves listening and talking to others, providing them with information and know‐how potentially useful to better accomplish their tasks and solve problems more quickly (Cummings 2004; Reid 2003). However, studying KS calls for carefully looking at their fundamental constituents (i.e. individuals) (Foss et al. 2010; Felin and Foss 2005), as knowledge resources are first embedded in individual minds. Firms should thus understand how employees can be stimulated to enhance their participation in KS activities. Given that several factors (e.g. individual, organizational, technological, etc.) are likely to shape individuals’ KS behaviors and that trying to modify personal characteristics may be particularly difficult, we think that managers should consider the role played by organizational factors in fostering intra‐organizational KS processes.

3. Hypotheses development 3.1 Soft organizational enablers and KS Firms operating at international level should be very careful in managing cultural issues. This is due to the fact that they usually deal with partners located in different countries and hire workers coming from all over the world. Organizational culture has been described as “a system of shared values and norms that define appropriate attitudes and behaviors for organizational members” (O’Reilly and Chatman 1996). Consistently, it governs both the process of external adaptation and that of internal integration, by identifying the way things are done in the firm (Schein 1985). In the same vein, Hofstede (1991) argued that, in taking part in the organizational processes, employees will behave according to how they perceive the firm’s culture. Once they internalize the corporate cultural values, they convert their conducts consistently, letting the culture guide their tendencies and attitudes. Building on previous studies investigating the role played by organizational culture in companies’ knowledge management processes (De Long and Fahey 2000), we focus on two types of firm’s culture, which can be considered opposite to each other: innovative and bureaucratic culture. According to Deshpandé et al. (1993), they can be analyzed according to two dimensions: the focus either on organic processes (e.g., flexibility, spontaneity) or on mechanistic processes (e.g., planning, scheduling, order, stability) and the emphasis either on the internal work environment (e.g., integration, smoothing activities) or on the external one (e.g., competition, differentiation). An innovative culture is characterized by the need to boost entrepreneurship and creativity, as well as by the need for the firm to find new markets and growth opportunities outside (i.e. external work environment). Employees’ risk orientation and rapid adaptability to evolution are central to this organizational culture. Being innovative means being able to rapidly find new solutions and offer new products by reacting to the dynamism of the market through a high degree of flexibility (i.e. organic processes). Providing an organization with an innovative culture thus means valuing and exploiting employees’ creativity, that is, their capacity to generate new solutions and knowledge. Therefore, culturally innovative firms will more likely support social interaction and stimulate employees to mutually exchange opinions and ideas.

Vincenzo Cavaliere and Sara Lombardi Conversely, a bureaucratic culture tends to emphasize the implementation of procedures, rules, and the need for stability, efficiency, and formalization (i.e. mechanistic processes); at the same time, such a culture highlights the importance of the internal work environment, while leaving interactions with outside counterparts behind. Moreover, firms showing such a bureaucratic culture accentuate managers’ authority over lower organizational levels. In so doing, it leaves little room for employees’ creativity (Bates et al. 1995) as well as for opportunities for them to interact and exchange ideas. Prior researches (Silverthorne 2004) demonstrate that bureaucratic organizational cultures pose great challenges in maintaining employees’ job satisfaction, which is an important antecedent for KS behaviors (Cabrera et al. 2006). Given these arguments, we posit that: Hp1: An innovative organizational culture is positively related to employees’ orientation toward knowledge donating within the organization. Hp2: A bureaucratic organizational culture is negatively related to employees’ orientation toward knowledge donating within the organization. Top management support is a critical means through which values and norms are communicated across all organizational levels. Consistently, perceived top management support is likely to significantly influence employees’ willingness to share knowledge with colleagues, because it affects both quality and quantity of knowledge exchange (Lee et al. 2006). Of course, this support should be encouraging rather than coercive (Lin 2006; Connelly and Kelloway 2003; Storey and Barnett 2000; Davenport et al. 1998); this means that top management is expected to send employees suggestions and feedback about their KS participation in order to encourage it and improve it. As well as the role of organizational culture is to guide employees toward the adoption of certain values and behaviors, management team support is essential in motivating workers in making this adoption successful. We thus offer the following hypothesis: Hp3: Top management support is positively related to employees’ orientation toward knowledge donating within the organization.

3.2 Hard organizational enablers and KS Job design is defined as the set of opportunities and constraints related to assigned tasks and responsibilities affecting how individuals accomplish and experience their work (Hackman and Oldham 1974). Prior studies have demonstrated its relevance in shaping employees’ KS behaviors (Foss et al. 2009) given that, more than other organizational elements, such as firm’s culture or structure, job design is easier to change or manage. This paper explores the role played by the degree of autonomy and operating procedures existing within the firm on employees’ tendencies toward knowledge sharing. Autonomy describes the extent to which the job gives the opportunity to decide how and when a task is accomplished, providing individuals with freedom and independence in planning and conducting the job (Hackman and Oldham 1974). Scholars have demonstrated that autonomy positively influences employees’ KS orientations, as it increases individual motivation to participate in ideas exchange (Foss et al. 2009). Similarly, Cabrera et al. (2006) suggest that the positive relationship between job autonomy and KS could be due to the fact that more autonomous individuals feel a higher level of responsibility for their job; consequently, they are more likely to try to do the job efficiently and more motivated to share ideas and experiences with colleagues. The organizational operating procedures refer to the rules, prerequisites, and criteria that should be observed in accomplishing a work task. Thompson (1967) defined them as the way in which organizations coordinate pooled interdependences, which require to standardize work activities, minimize communication flows, and define clear rules to be followed in the job. As a consequence, the existence of operating procedures does not call for a high interaction among individuals or departments, because almost all activities have been already determined. Therefore, we hypothesize that: Hp4: The degree of autonomy in the job is positively related to employees’ orientation toward knowledge donating within the organization.

Vincenzo Cavaliere and Sara Lombardi Hp5: The degree of operating procedures is negatively related to employees’ orientation toward knowledge donating within the organization.

Soft organizational factors

The above discussion is summarised in Figure 1 below, representing the model we empirically test. Innovative culture

Bureaucratic culture

Hp1

Hp2

Top management support

Hp3

Knowledge Sharing

Hard organizational factors

+ Hp4

Autonomy in the job Hp 5 Operating procedures

Figure 1: Research model

4. Sample selection and research method We collected data using a web‐based questionnaire developed on the basis of a focused literature review. We firstly pretested the questionnaire with 53 middle managers of three companies to ensure that the items and the overall format can be easily understood. For the purpose of this paper, we analyze data from 24 international and highly‐innovative manufacturing firms located in a critical economic area in central Italy (Tuscany). The need to specifically look at this sample emerged as part of a broader institutional research project, aiming to understand the distinctive features of manufacturing companies, which play an important role in the region’s competitiveness. Prior to the questionnaire administration, we personally met each Human Resource Directors in order to explain the research purpose, the research instrument as well as the relevance of the phenomenon of interest. Together with them, we selected as many employees (i.e., individual respondents) potentially involved in knowledge sharing as possible. We thus aimed to involve gatekeeper roles, that are employees which, according to the literature (Cohen and Levinthal 1990), stand at the interface of either the firm or the external environment or between organizational subunits, and are thus likely to significantly contribute to the firm’s learning ability (i.e. absorptive capacity). In order to facilitate the exchange of different kinds of knowledge, these workers translate information into a form that can be more easily understood by anyone in the firm, thus playing a crucial role in supporting KS activities. In order to examine the risk of nonresponse bias we compared demographic variables and questionnaire responses between early and late respondents. Late respondents were defined corresponding to those who responded after receiving the first or the second reminder. The assumption here is that late respondents with missing values are closer to the nonresponding group than the group of early respondents (Rogelberg and Stanton 2007). As we found no difference between the two groups, we are confident that our data do not suffer from problems of nonresponse bias. Of the 1503 invitations sent out for participation in the survey, 758 questionnaires were filled in (50.4% response rate).

4.1 Measures Both the independent and dependent variables were operationalized through self‐reported measures. Despite the well‐known weaknesses of such measures, they are particularly valuable in human behavior studies (Howard 1994) and have been extensively used in researches about intra‐organizational knowledge processes (e.g., Cabrera et al. 2006; Bock et al. 2005; Levin and Cross 2004; Szulanski 1996). All scale were adopted from existing literature and all variables were measured using a 7‐point Likert type scale (from 1 = strongly disagree to 7 = strongly agree).

Vincenzo Cavaliere and Sara Lombardi Dependent variable. In this paper, we conceptualize KS participation as employees’ knowledge donating behavior. Van den Hooff and Van Weenen (2004) provided the three‐item scale to measure our dependent variable. Independent variables. The organizational culture scale (eight‐item scale) was adapted from Deshpandé et al. (1993), who built on Campbell and Freeman (1991). Top management support was measured through a four‐ item scale adapted from Tan and Zhao (2003). In order to measure hard organizational enablers we used Hackman and Oldham’s (1974) autonomy scale (two‐item scale) and Spector’s (1985) operating procedures’ scale. Authors added operating procedures’ scale a new item, in order to measure the degree of task bureaucratization. Control variables. We included a number of control variables to control for further factors that may affect employees’ knowledge donating: firm age, firm size, employees’ gender (i.e. dummy variable, 0=male, 1=female), age, tenure in the firm and their level of education (years of education). Scale measurement and items description for all variables are provided in the Appendix 1.

4.2 Findings Table 1 provides descriptive statistics and correlations among all variables. Given that some correlations are above the threshold of .30, which usually indicates potential multicollinearity in the data, we calculated the variance inflaction factor (VIF) for all variables we used in the regression. The VIF values are shown in Table 2 and indicate that multicollinearity is not a concern in our dataset. In order to test our hypotheses, we used OLS regression in Stata, whose results are shown in Table 2. All three models we built for studying knowledge donating include control variables related to individuals’ opportunities to engage in sending their knowledge to others. In Model 1, we included only the control variables; in Model 2, we added the soft organizational enablers as explanatory variables (i.e. innovative culture, bureaucratic culture, and top management support); Model 3 adds the hard organizational enablers we investigated (i.e. autonomy in the job and operating procedures). As for the control variables (Model 1), only firm size has a significant impact on knowledge donating, that is, the larger the firm, the more likely the employees will engage in knowledge sharing activities. Regarding the soft organizational factors we examined (Model 2), we found evidence about the positive influence of both innovative culture and top management support on employees’ knowledge donating. Hypothesis 1 and Hypothesis 3 are thus supported. Surprisingly, bureaucratic culture is found to be strongly and positively related to our dependent variable, contrasting with what we expected. Thus, Hypothesis 2 is not supported. Model 3 provides evidence about the positive impact of autonomy on knowledge donating orientation, thus supporting Hypothesis 4. It also shows that operating procedures are strongly and positively related to our dependent variable, contrasting our prediction. Hypothesis 5 is therefore not supported. Table 1: Correlations and descriptive statistics (n = 758) 1. Knowledge donating 2. Innovative culture 3. Bureaucratic culture 4. Top management support 5. Autonomy in the job 6. Operating procedures 7. Firm age

Mean

S.D.

Min

Ma x

4.82

1.25

4.66

1.44

4.83

1.28

4.88

1.61

5.31

1.34

4.25

1.28

25.31

14.10

10 11

1.0 0 .41 * .42 *

1.0 0 .59 *

1.0 0

.43 *

.52 *

.45 *

1.0 0

.30 * .39 * ‐ .13 *

.33 * .46 * ‐ .19 *

.22 * .56 * ‐ .07 *

.25 * .41 * ‐ .09 *

1.0 0 .25 * ‐ .13 *

1.0 0

1.0 0 ‐ .09 *

Vincenzo Cavaliere and Sara Lombardi Mean

S.D.

Min

Ma x

8. Firm size

955

909.5 2

261 6

.18 *

.34 *

.30 *

.22 *

.07 *

.24 *

‐ .27 *

1.00 0

9. Gender

.28

.45

‐.02

.09 *

.01 ‐.05 ‐.03

.00

.05

1.0 0

10. Tenure

10.26

8.39

‐.00

.00

.04

11. Education

2.88

‐.00

.05 ‐.03 ‐.03 ‐.03

.02 * ‐ .12 *

12. Age

8.51

‐.01 ‐.01

1.0 0 ‐ .12 1. .35* .34 * 00 * ‐ ‐ .61 .02 .1 1.00 .05 * 7*

.04

.05

.07 *

.11 *

.13 *

.08 * .07 * ‐ .07 * .07

.14 *

‐.06 .02

*Correlation is significant at 5% level Table 2: Results of multiple regression analysis for knowledge donating Variables Firm age Firm size Gender Tenure Education Employee’s age Innovative culture Bureaucratic culture Top management support Autonomy in the job Operating procedures _cons R2

Knowledge donating Model 1

Model 2

Model 3

‐0.007 (‐1.92) 1.14 *** 0.000 (4.54) 1.26 ‐0.000 (‐0.01) 1.02 0.006 (0.90) 1.77 ‐0.017 (‐0.91) 1.32 ‐0.005 (‐0.81) 1.60 *** 5.238 (13.77) 0.039

‐0.004 (‐1.33) 1.16 0.000 (0.27) 1.40 ‐0.016 (‐0.17) 1.03 0.004 (0.67) 1.78 0.008 (0.48) 1.34 ‐0.007 (‐1.30) 1.61 * 0.100 (2.43) 1.81 *** 0.211 (4.75) 1.60 *** 0.220 (7.62) 1.43 2.519*** (6.44) 0.268

‐0.002 (‐0.76) 1.17 0.000 (0.44) 1.42 ‐0.033 (‐0.35) 1.04 0.002 (0.34) 1.78 0.012 (0.77) 1.36 ‐0.010 (‐1.84) 1.62 0.051 (1.26) 1.90 0.151** (3.13) 1.85 0.194*** (6.65) 1.46 *** 0.145 (4.12) 1.17 *** 0.140 (3.34) 1.55 1.811*** (4.61) 0.304

t statistics in parentheses. Values in italics are VIFs. N=758 *

p < 0.05; ** p < 0.01; *** p < 0.001

100

10 11

Vincenzo Cavaliere and Sara Lombardi

4.3 Discussion Building on the literature on intra organizational KS processes, we attempt to contribute to the understanding of how companies can establish a successful KS strategy by acting upon organizational‐level factors. We provide evidence about the importance of building an innovative culture that fosters entrepreneurship and personal initiative, in order to stimulate knowledge exchange among employees. Innovative culture’s positive impact on KS shows that the more the creativity is stimulated within the firm, the more employees will be motivated to share their ideas with each other, by contributing to enhance KS at both individual and organizational level. Moreover, we confirm findings of prior studies about the critical role played by top management support toward the enhancement of KS behaviors. Surprisingly, we found both bureaucratic culture and operating procedures to positively and strongly influence employees’ knowledge donating orientations. We argue that this finding may be consistent with Sine et al.’s (2006) arguments, which point out that organizations need to provide themselves with a certain degree of formalization and bureaucratization to let information flow across departments. Finally, the positive influence of job autonomy on our dependent variable shows that the higher the employees’ freedom and independence in planning and conducting their job, the higher their participation in KS activities.

5. Conclusions The paper offers empirical evidence on the relationship between KS organizational enablers (i.e. hard and soft organizational factors) and knowledge donating process within a sample of 24 international and highly innovative manufacturing firms operating in Italy. Our findings show that managers should be careful in considering the impact of their firms’ culture on KS outcomes. Providing the company with a certain degree of bureaucratization, combined with a strong top management support may be a successful way to enhance the intra‐organizational flows of information. However, generalizing the findings of cross‐sectional studies has always many limitations, given that the statistical analysis is based on data collected at specific times, in specific firms and from specific employees. It may be then useful to design a similar study involving companies from different countries, for instance. Further longitudinal research may also help verifying the causality of our model, as a certain kind of engagement in KS in the past is likely to influence an employee’s willingness to do so in the future. Moreover, using perceptual instruments to measure our variables may represent a shortcoming. Hence, as suggested by Wang and Noe’s (2010) review of KS literature, a more objective measure of KS colleting third‐party and archival data may be useful to enrich the more common self‐reported assessment of KS activities. Certainly, our paper does not examine all possible enablers that may be critical for KS; therefore, future research could take into account other factors, both at individual level (e.g., demographic determinants) as well as at organizational one (e.g., leadership style, organizational structure) to improve the understanding of intra‐ organizational knowledge exchange. Similarly, the exploration of interaction effect may offer new empirical evidence on the phenomenon of interest. We also believe that a multilevel analysis (Kostova 1999) may be an interesting reason for digging into this topic in the future, in order to look at how KS occurs at different levels and, more importantly, how such different levels may influence each other.

Appendix 1 Table 3: Scale measurement and items description for all variables Construct

Knowledge donating

Innovative culture

Items description* To what extent do you agree with the following statements? a) When I have learned something new, I tell my colleagues about it b) When they have learned something new, my colleagues tell me about it c) Knowledge sharing among colleagues is considered normal in my company To what extent do you agree with the following statements? My company emphasizes: a) Growth and acquisition of new resources b) Dynamism, entrepreneurship and risk taking c) Commitment to innovation

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Source

Van den Hooff and Van Weenen (2004)

Deshpandé et al. (2003)

Vincenzo Cavaliere and Sara Lombardi Construct

Bureaucratic culture

Top management support

Autonomy in the job

Operating procedures

Items description* d) To what extent does your company consider innovators and risk takers as the best employees? To what extent do you agree with the following statements? My company emphasizes: a) Formal rules and policies b) Being very formalized and structured c) Permanence and stability d) To what extent does your company consider coordinators, organizers and administrators as the best employees? To what extent do you agree with the following statements? In my company, top managers: a) Think that encouraging knowledge sharing with colleagues is beneficial b) Always support and encourage employees to share their knowledge with colleagues c) Provide most of the necessary help and resources to enable employees to share knowledge d) Are keen to see that the employees are happy to share their knowledge with colleagues To what extent do you agree with the following statements? My job gives me: a) …considerable opportunity for independence and freedom in how I do my job b) … the opportunity to use my personal initiative or judgment in carrying out my job To what extent do you agree with the following statements? a) Many of the rules and procedures make it easier for me to do a good job b) My efforts to do a good job are seldom blocked by red tape c) Within this organization, work is frequently guided by rules and proceduresa

Source

Deshpandé et al. (2003)

Tan and Zhao (2003)

Hackman and Oldham (1974)

Spector (1985)

*All scale were measured using a 7‐point Likert scale from 1=Strongly agree to 7=Strongly disagree a

This item was developed by the authors and added to Spector’s (1985) scale

References Aulawi, H., Sudirman, I., Suryadi, K. and Govindaraju, R., (2009) “Knowledge Sharing Behavior, Antecedents and Their Impact on the Individual Innovation Capability”, Journal of Applied Sciences Research, Vol 5, pp. 2238‐2246. Barney, J. (1991) “Firm Resources and Sustained Competitive Advantage”, Journal of Management, Vol 17, No. 1, pp. 99‐ 120. Bates, K. A., Amundson, S. D., Schroeder, R. G. and Morris, W. T. (1995) “The Crucial Interrelationship Between Manufacturing Strategy and Organizational Culture”, Management Science, Vol 41, No. 10, pp. 1565‐1580. Bock, G. ‐ W., Zmud, R. W., Kim, Y. ‐ G. and Lee, J. ‐ N. (2005) “Behavioral Intention Formation in Knowledge Sharing: Examining the Roles of Extrinsic Motivators, Social‐Psychological Forces, and Organizational Climate”, MIS Quarterly, Vol 29, pp. 87‐111. Cabrera, Á., Collins, W. C. and Salgado, J. F. (2006) “Determinants of Individual Engagement in Knowledge Sharing”, International Journal of Human Resource Management, Vol 17, No. 2, pp. 245‐264. Campbell, J. P. and Freeman, S. J. (1991) “Cultural Congruence, Strength and Type: Relationships to Effectiveness”, in Woodman, R. W. and Passmore, W. A. (eds) Research in Organizational Change and Development, Vol 5, JAI Press, Greenwich, CT. Cohen, W. M. and Levinthal, D. A. (1990) “Absorptive Capacity: A New Perspective on Learning and Innovation”, Administrative Science Quarterly, Vol 35, No. 1, pp. 128‐152. Connelly, C. E. and Kelloway, E. K. (2003) “Predictors of Employees’ Perceptions of Knowledge Sharing Culture”, Leadership & Organization Development Journal, Vol 24, No. 5, pp. 294‐301.

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Vincenzo Cavaliere and Sara Lombardi Cummings, J. N. (2004) “Work Groups, Structural Diversity, and Knowledge Sharing in a Global Organization”, Management Science, Vol 50, pp. 352‐364. Davenport, T. H., De Long, D. W., and Beers, M.C. (1998) “Successful Knowledge Management Projects”, Sloan Management Review, Vol 39, No. 2, pp. 43‐52. De Long, D. W. and Fahey, L. (2000), “Diagnosing Cultural Barriers to Knowledge Management”, Academy of Management Executive, Vol 14, No. 4, pp. 113‐127. Deshpandé, R., Farley, J. U. and Webster, F. E. (1993) “Corporate Culture, Customer Orientation and Innovativeness in Japanese Firms: A Quadrad Analysis”, Journal of Marketing, Vol 57, pp. 23‐27. Felin, T. and Foss, N. J. (2005) “Strategic Organization: A Field in Search of Micro‐Foundations”, Strategic Organization, Vol 3, pp. 441–455. Foss, N. J., Husted, K. and Michailova, S. 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(2007) “Knowledge Sharing and Firm Innovation Capability: an Empirical Study”, International Journal of Manpower, Vol 28, No. 3‐4, pp. 315‐332. O’Reilly, C., and Chatman, J. A. (1996), “Culture as Social Control, Corporations, Cults, and Commitment,” in Research in Organizational Behavior (Vol. 14), eds. B. M. Staw and L. L. Cummings, pp. 157–200, Elsevier, North Holland, MI. Quinn, J. B., Anderson, P. and Finkelstein, S. (1996) “Leveraging Intellect”, Academy of Management Executive, Vol 10, pp. 7‐26. Reid, F. (2003) “Creating a Knowledge Sharing Culture Among Diverse Business Units”, Employment Relations Today, Vol 30, pp. 43‐49. Rogelberg, S. G., and Stanton, J. M. (2007) “Introduction: Understanding and dealing with organizational survey nonresponse”, Organizational Research Methods, Vol 10, pp. 195–209. Schein, E. H. (1985), Organizational Culture and Leadership, Jossey‐Bass, San Francisco, CA. Silverthorne, C. (2004) “The Impact of Organizational Culture and Person‐Organization Fit on Organizational Commitment and Job Satisfaction in Taiwan”, The Leadership & Organization Development Journal, Vol 25, No. 7, 592‐599. Sine, W. D., Mitsuhashi, H. and Kirsch, D. A. (2006) “Revisiting Burns and Stalker: Formal Structure and New Venture Performance in Emerging Economic Sectors”, Academy of Management Journal, Vol 49, No. 1, pp. 121‐132. Spector, P. (1985) Job Satisfaction, Sage Publication, Thousand Oaks, CA. Storey, J. and Barnett, E. (2000) “Knowledge Management Initiatives: Learning From Failure”, Journal of Knowledge Management, Vol 4 No. 2, pp. 145‐156. Szulanski, G. (1996) “Exploring Internal Stickiness: Impediments to the Transfer of Best Practice Within the Firm”, Strategic Management Journal, Vol 17, pp. 27‐43. Tan. H. H. and Zhao, B. (2003) “Individual‐ and Perceived Contextual‐Level Antecedents of Individual Technical Information Inquiry in Organizations”, The Journal of Psychology, Vol 137, No. 6, pp. 597‐621. Teece, D., Pisano, G. and Shuen, A., (1997) “Dynamic Capabilities and Strategic Management”, Strategic Management Journal, Vol 18, pp. 509‐533. Thompson, J. D. (1967), Organizations in Action, McGraw‐Hill, New York. Van den Hooff, B. and Van Weenen, F. D. L., (2004) “Committed to Share: Commitment and CMC Use as Antecedents of Knowledge Sharing”, Knowledge and Process Management, Vol 11, pp. 13‐24. Wang, S. and Noe, R. A. (2010) “Knowledge Sharing: A Review and Directions for Future Research”, Human Resource Management Review, Vol 20, pp. 115‐131.

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Teaching Cases for Capturing, Capitalizing and Re‐Using Knowledge: A Case Study in Senology Souad Demigha1 and Corinne Balleyguier2 1 Research Department of Computer Science (CRI), Sorbonne University, Paris, France 2 Gustave Roussy Institute, VilleJuif, France souad.demigha@u‐psud.fr corinne.balleyguier@igr.fr Abstract: This paper deals with the use of multiple ‘teaching cases’ based on multiple models and method‐based reasoning in the field of senology (breast cancer domain). ‘Teaching cases’ can be a good support in helping both expert and junior senologists in teaching and learning. Thanks to these ‘teaching cases’, the experts provide quality training to trainees (junior radiologists). In medicine, the method used in teaching is based on experiments, called ‘clinical cases’. We use the Case‐Based Reasoning (CBR) approach to represent these ‘teaching cases’. CBR starts with a set of cases or training examples; it forms generalizations of these examples, by identifying commonalities between a retrieved case and the target problem. This set of cases (experience) may then be re‐used when solving new problems, for example, when making new diagnoses decisions. The radiologist‐senologist can benefit from prior experience and cases. According to Kolodner (the founder of the CBR approach), CBR can mean adapting old solutions to meet new demands, using old cases to explain new situations; using old cases to critique new solutions, or reasoning from precedents to interpret a new situation or create an equitable solution to a new problem. We rely on these concepts to develop our reasoning for developing teaching strategies to accompany trainees in their learning advance and decision making. The paper will show that the CBR approach is very much suited to representing the knowledge in medical field, both in learning and training. With the CBR approach we can reason in many ways and represent knowledge efficiently, using the multiple models and methods we have developed. We combine object modelling with UML2 (Unified Modelling Language) with Case‐Based Reasoning to represent the knowledge present in the ‘teaching cases’. The outcome of the research is both conceptual and practical. It also has a methodological dimension. Keywords: teaching cases, models, methods, CBR, knowledge, senology

1. Introduction “The Case‐Based Reasoning (CBR) is the process of solving new problems based on the solutions of similar past problems” (Kolodner, 1993). It starts with a set of cases or training examples, it forms generalizations of these examples, by identifying commonalities between a retrieved case and the target problem. “CBR becomes a new technology of building intelligent CBR systems for teaching and learning” (Salem, 2011). These systems aim to improve education, and “may be used in real teaching, learning and training situations” (Katoua, 2012). In medicine knowledge is based on experience formalized as cases called clinical cases. These cases learned individually or in groups are examples resulting from real situations. “Case‐based learning becomes Case‐Based Reasoning when more than one case is provided” (Jarz et al, 1997). Case‐Based Reasoning involves reasoning about multiple cases and how prior solutions can be adapted to new problems or how prior cases are related to new cases. “The Case‐Based Reasoning represents the case knowledge as a set of cases” (Kolodner, 1993). This set of cases (experience) may then be re‐used when solving new problems, for example when making new diagnoses. The radiologist‐senologist can benefit from prior experience and cases (Demigha et al., 2013). In this paper, we present multiple ‘teaching cases’ based on multiple models and method‐based reasoning in the field of senology (breast cancer domain). ‘Teaching cases’ can be a good support in helping expert senologists in teaching and juniors in learning. Thanks to these ‘teaching cases’, the expert senologists can enrich their pedagogical contents and update them. These cases enable junior‐ senologists to learn from the experience of the expert senologists. We use the CBR approach to represent these ‘teaching cases’. In order to explore these cases, they must be stored into a ‘knowledge base’. To build this ‘knowledge base’, we will start by collecting the needs and wishes from user requirements. This collecting process of knowledge is named knowledge engineering. The knowledge engineering process plays a major and crucial role and represents the most time‐ consuming stage for any e‐learning software process. In medicine, current methods are not able to capitalize and re‐use knowledge acquired from experience. Re‐use is employed in an ad hoc manner. This ad hoc manner does not allow sharing accumulated experience, contrary to what medical experts wish to have. In a previous work, we have built an ontology to gather the experience of the numerous experts for a shared utilization (Demigha, 2007). It would allow homogenizing the knowledge on the same topics by standardizing

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Souad Demigha and Corinne Balleyguier the vocabulary and definitions. Identical notions should be labelled using the same terminology so as to compare them. This ontology represents the senology domain. It is based on the BI‐RADS (Breast Imaging Reporting and Data System) standard, on the scientific reports of the EBM (Evidence‐Based on Medicine) and on the reports and experience of senologists in the department of radiology of Gustave Roussy Institute (Paris) in view of representation of senologic knowledge and associated clinical reports. This ontology is organised as cases with the CBR approach and stored into a ‘knowledge base’. We combine object modelling with UML2 (Unified Modelling Language) with Case‐Based Reasoning to represent the knowledge included in the ‘teaching cases’. The paper is organised as follows: after an introduction to the design ‘teaching cases’ and an analysis of them in general, the issue of using multiple models and methods for design is discussed. We present in detail the adopted reasoning of selecting the best cases. We illustrate the overall of our approach with a real case‐study in senology. At the end of the paper, we provide a discussion of perspectives and future work and a conclusion of different concepts and techniques used to develop the ‘teaching cases’ in senology.

2. Objectives The objectives of our work are to build ‘teaching cases’ for learning and training for senology. These cases will be stored in a ‘knowledge base’ and structured as cases. These ‘teaching cases’ are based on multiple models and methods based reasoning with the Case‐Based Reasoning. The development of these ‘teaching cases’ forms part of a research project on the development of a learning management system in radiology‐senology named (eLMS‐RS) (Demigha et al, 2012). This system will be implemented in the department of radiology of Gustave Roussy Institute in Paris (France). These ‘teaching cases’ allow us to define the terminology of the domain by developing an ontology of the domain and gather the representative examples of problem solving by the experts. “A case is a contextualized piece of knowledge representing an experience that teaches a lesson fundamental to achieving the goals of the reasoned” (Kolodner, 1993). According to this same author, there are two parts of a case: (1) the lesson(s) it teaches and (2) the context in which it can teach its lesson(s). A case is also defined as a combination of a problem and a solution. To implement a CBR system, we have followed the knowledge engineering cycle as defined by Kolodner, to build this system; Kolodner guides us to the answer to the following questions:

What is the case?

What component parts does a case have?

What kinds of knowledge does a case need to encode?

What formalisms and methodologies are appropriate for representing cases?

How can we recognise the boundaries of cases, and how can they be divided into elements of the right size?

What is the retrieval strategy? What are the components of a relevant case?

How can old solutions be adapted? What are the modification rules?

How does memory change over time?

According to these questions and multiple answers found in the literature, we have structured the ‘teaching cases’, so that they can be represented in three ways and used for many purposes:

Cases including a problem description and solution can be used to derive solutions to new problems

Cases including a problem description and outcome can be used to evaluate new situations

Cases possessing a specific solution can be used to evaluate proposed solutions and anticipate potential problems before they occur

Both for trainees and trainers these ‘teaching cases’ can be of benefit to:

Define the ontology of the domain

Gather the representative examples of problem solving by the expert

Help trainers to improve patient follow‐up

Make case knowledge available and accessible to trainees and trainers for various lessons

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Give trainees and trainers a supplementary tool for rationalizing, improving and automating data access

Capitalize on the experience of experts

Help the trainee how to define his/her individual objectives

Define the trainee’s needs for education

Individualize the relationship with the trainees and adapt education to each one's skill

Increase the share of individual work (updating knowledge, practical exercises, self‐checking)

Think over the process of education

Validate knowledge

3. Related work Section 3 describes the CBR approach and its application in medicine, medical imaging, in learning and training.

3.1 Case‐based reasoning Case‐Based Reasoning (CBR) is an intelligent‐system based on reasoning. A case‐based reasoning works by matching new problems to ‘cases’ from a historical database and then adapt successful solutions from the past to current situations (Watson, 1997), (see Figure1).

Figure 1: The case‐based reasoning cycle CBR has been applied by many famous organizations such as IBM, Visa International, Volkswagen, British Airways, and NASA. Many case studies in the real world environment have shown that the techniques of design and implementation of Case‐Based Reasoning allow for the development of robust and reliable systems. CBR was earlier applied in medicine but only a little in medical imaging due to the complexity of the radiology domain. Among the oldest CBR systems developed in medical diagnosis, we can quote the following: (1) the system GUIDON (Clancey, 1987) is an expert system for teaching diagnosis and therapeutic rules of meningitis. It is a tutoring system based on the expert system MYCIN (Shortliffe, 1976), and its rules strategy; (2) the system PROTOS (Porter et al, 1986), was developed in the domain of clinical audiology; (3) the system CASEY (Koton, 1989) is aimed at diagnosing heart failure; (4) CASCADE (Simoudis, 1992) is a system for diagnosing the causes of crashes to the VMS computer operating system in order to suggest a solution; (5) the system PAKAR (Watson et al., 1994) identifies possible causes for building defects and suggests remedial actions. In the medical imaging field, we can quote: (1) the system MacRad (Macura et al., 1995), it is a Case‐ Based Retrieval system for radiology image resources. Recently, many other CBR systems have been developed; we can quote the medical CBR system developed by (Salem, 2007) which is a CBR system for Medical Diagnosis; (2) the Case‐Based Reasoning system for the diagnosis of individual sensitivity to stress in psychophysiology (Begum, 2009); (3) a Framework for Medical Diagnosis using Hybrid Reasoning (Deepli et al., 2010); (4) Case Based Medical Diagnosis of Occupational Chronic Lung Diseases From Their Symptoms and Signs (Tomar et al., 2011); and (5) Case‐Based Reasoning applied to Medical Diagnosis and Treatment (Blanco et al., 2013).

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3.2 Case‐based reasoning in learning and training In education, CBR has also found its place easily and is applied in many fields. We can quote the earliest systems: (1) the system DECIDER (Farrel, 1987), aims to help students understand or resolve a pedagogical problem by selecting and presenting appropriate cases from a database that responds to the student’s goal; (2) the system HYPO (Aleven et al., 1992), is used to generate fresh cases for analysis in response to a particular issue of interest as identified by a tutor. Nowadays (from 2005 until now), CBR continues to be applied fluently in many domains in both learning and training. We can quote: (1) the research work of the research team of Huang which consists of constructing a personalized e‐learning system based on genetic algorithm and CBR approach (Huang et al., 2006); (2) the research team of Praveen have introduced Case‐ Based Learning for teaching anatomy in a conventional medical school, (Praveen et al., 2011); (3) Case‐Based Medical Learning in radiological decisions using content‐based image retrieval has been designed and developed (Welter et al., 2011). We have not found a CBR training system in senology that covers all the processes and images. This has motivated us to improve on previous research work (Demigha, 2005) and provide new concepts and techniques. In this paper, we focus on the presentation of the ‘teaching cases’ developed for the ‘knowledge base’ of the eLMS‐RS system (Demigha et al., 2012).

4. Knowledge acquisition Section 4 describes the knowledge acquisition of senology.

4.1 Collecting data The process of acquiring knowledge consists of collecting data from users. We have collected data from the expert senologists of the Gustave Roussy Institute (Paris). These data were selected by the senologist, Dr C. Balleyguier. The selection was based on the best cases (old and current) useful for training and learning. These cases represent ‘clinical cases’ on real patients. They are associated with their images (mammography, echography, scanner, IRM and ultrasounds). The images are scanned and indexed by DICOM (Digital Imaging and Communication in Medicine). Diagnosis in senology is grounded on the BI‐RADS dictionary. This dictionary is updated according to the ACR (American College of Radiology) and NEMA (the Association of Electrical Equipment and Medical Imaging Manufactures) organisations. After collecting data and their related images, we have analysed them with a methodological analysis based requirement engineering (Rolland et al., 1999), (Demigha et al., 2001).We structured these data with the Case Based Reasoning. This patient‐oriented approach of CBR is complemented by evidence‐based medicine (EBM).

4.2 Case representation Case representation is the most important step in the CBR approach. In medicine, the domain modelling is extremely difficult to map into a logical formalization due to the lack of availability of complete causal models. A strategy for medical problem solving was adopted to use past cases as prototypal models. We have represented the cases using the object modelling and UML (Unified Modelling Language), (Demigha et al., 2004). The case representation model is structured according to the four phases of the senologic process:

Clinical examination includes data about health patient history (screening history, current health status, and previous clinical examination)

Image reading consists in searching and extracting relevant information (imaging data and

textual data)

Radiological interpretation is based both on clinical data and radiological data (information such as defined by the BI‐RADS standard)

Anatomic‐pathological examination depends on the result of radiological interpretation. It gathers information about anatomic‐pathological examination such as type of procedure, reporting source, laterality, histopathology, staging and therapy

We divide the case representation model into 3 hierarchic levels:

The first level (the case) is a patient at different intervals of treatment (time). A case may comprise several successive senologic episodes

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The second level (the sub‐case) is one senologic episode (clinical examination, image reading, radiological interpretation and anatomic‐pathology) for a patient The third level (the sub‐sub‐case) represents one phase of a senologic episode for a patient (clinical examination OR image reading)

The problem part of a sub‐sub‐case generally refers to solutions or more generally data produced in the previous phases of the same episode. For example, the problem part of the anatomic‐pathological phase contains data of the [radiological/ interpretation] solution. Experts’ experience is represented as knowledge; both product knowledge (mammographies and associated diagnoses) and process knowledge (heuristics) are considered. The case representation model complies with the standards defined for digital mammography and CAD mammography. In particular, we use DICOM and BI‐RADS dictionaries to describe and index data. Figure 2 presents an outline of the case representation model, by highlighting the links between the phases of the senologic process.

Figure 2: The case representation model with UML

4.3 Case indexing “A case’s indexes are combinations of its important descriptors, the ones that distinguish it from other cases”, (Kolodner et al., 1996). When a new case is added to the ‘knowledge base’, we assign indexes to it. The indexing problem is the problem of making sure that a case is assessed whenever appropriate. Good indexes for CBR satisfy two properties:

They should be abstract enough to retrieve a relevant case in a variety of future situations

They should be concrete enough to be easily recognisable in future situations

The indexing process is the process of retrieving relevant similar case(s) from the ‘knowledge base’. This process is called the ‘retrieval process’. The case indexing process relies on three approaches: nearest neighbour, inductive and knowledge guided or a combination of the three (Kolodner, 1993). 4.3.1 The MAP model The process model is represented by a MAP which guides trainees to progress in their learning. It is used to represent parts of processes included in the case representation model (the product model). A MAP is an intentional representation system based on two conceptions: intention and strategy. Intention captures the goal to be achieved and strategy is the manner by which to achieve the intention. A MAP is represented by a graph oriented and labelled. Intentions are represented by nodes and strategies are represented by the arcs.

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Figure 3: The MAP model 4.3.2 Similarities We have developed algorithms which exploit the layered structure defined in the case representation model to find similar ‘teaching cases’ by aggregating similar ‘sub‐teaching cases’ and ‘sub‐sub teaching cases’. The retrieval algorithm is embedded in a broader process perspective including the capture of the actual ‘teaching case’, the ‘knowledge base’ reasoning related to this ‘teaching case’ and the support to decision making by adapting the retrieved ‘teaching case’. An increasing aspect of the process model is its intentional dimension which makes possible the representation of different ways to achieve the result. In the radiology‐senology case representation model, ‘teaching cases’ are collections of objects, each of which are described by a set of attribute‐value pairs. The structure of an object is described by an object class that defines the set of attributes together with a type (set of possible values or sub‐objects) for each attribute. Object classes are arranged in a class hierarchy, that is, a tree in which subclasses inherit attributes as well as their definition from the current class. We define a hierarchy of attribute types. New types are defined by building subtypes of the existing elementary types. They differ in their usability: a type may be used as an immediate or derived type. While immediate types cover the whole range of possible values of a type, derived types get restricted in their range by defining an enumeration of elements of their elementary types or, in the case of numeric types, by specifying an interval. The approach we have chosen in order to determine similarities is to establish a comparison between attributes (attribute by attribute), then for each attribute find a corresponding comparison measure that is a local similarity measure. This determines a similarity between two attribute values, and for each object we determine a global similarity measure which determines the similarity between two objects (or between the case and the query) based on the local similarity of the belonging attributes. The local similarity measure makes it possible to compare any two type values. It returns a numeric value from the interval [0..1]. This value is further used in the computation of a global similarity.

5. Case study Section 5 illustrates a case‐study in radiology‐senology. This case‐study will show a real extract of the ‘knowledge base’ structured as cases with the Case‐Based Reasoning approach. The first copy of the ‘knowledge base’ includes 50 real clinical cases of real patients from the department of radiology of Gustave Roussy (Paris). We present here an illustration of a case representation and indexing process for the radiological interpretation process. We illustrate two ‘teaching cases’ documented by the expert radiologist Dr C. Balleyguier included in the ‘knowledge base’. We solve a new diagnostic problem by comparison with the most similar case between two ‘teaching cases’ present in the ‘knowledge base’. Table 1 illustrates the new problem representing the diagnosis of a new patient.

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Souad Demigha and Corinne Balleyguier Solving a new diagnostic problem represents a new problem to be solved by using the ‘knowledge base’. To solve this new problem:

We make several observations in the current situation

Observations define a new problem

Not all feature values need be known

The new problem is a case without solution. Table 1: A new diagnostic problem Problem (Symptoms) A 68‐year‐old female had a screening mammogram showing an irregular high density lesion with spiculated margins seen in the retro‐areolar region, with associated architectural distortion and nipple retraction. Clustered microcalcifications are noted within the lesion. Few lymph nodes are seen in axilla.

The ‘knowledge base’ illustrated describes four ‘teaching cases’: CASE 1, CASE 2, CASE 3 and CASE 4. We have selected four ‘teaching cases” for four different patients. To solve this new problem, we must consult the ‘knowledge base’ for retrieving similar cases. We compare the new problem with each case and select the most similar case. We must answer some questions:

When are two cases similar?

How to rank the cases according to their similarity?

We can assess similarity based on the similarity of each feature. Table 2 illustrates four ‘teaching cases’ in the ‘knowledge base’ of the ‘radiological interpretation’ of the senologic process. Table 2: ‘The knowledge base’ with four ‘teaching cases’

CASE 3

CASE 2

CASE 1

Problem (Symptoms) A 48‐year‐old female had a screening mammogram showing rounded densities with possible irregular borders amid dense breast tissue bilaterally. Solution Radiologic Interpretation: BI‐RADS 0 (additional imaging needed) One week later, she had spot compression views that showed the nodules to be regular and sharply defined. Ultrasound examination revealed cysts. Final classification as BI‐RADS 2 (benign finding). Patient should continue with routine breast cancer screening. Problem (Symptoms) A 57‐year‐old female completed a screening mammogram showing calcifications in the right breast. These lesions were confined to the upper outer quadrant but were scattered and round on magnification views. The only prior mammogram from 4 years previously, was of poor quality, and only showed a few scattered calcifications. Solution Radiologic Interpretation: BI‐RADS 3 (probably benign) Despite the lack of a recent prior comparison mammogram, the current calcifications were felt to be of low suspicion. During a discussion the patient was informed that the calcifications were felt to be of low suspicion. A repeat mammography was recommended in 6 months. Follow‐up mammogram at 6 months and subsequently at 1 year showed no change in these calcifications. Problem (Symptoms) A 55‐year‐old female had a screening mammogram that showed linear calcifications clustered tightly in the upper outer quadrant of the right breast. Magnification views confirmed these were clustered and that there was no associated mass. The calcifications were not present on a mammogram obtained 12 months earlier.

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CASE 4

Souad Demigha and Corinne Balleyguier Solution Radiologic Interpretation: BI‐RADS 4 (suspicious abnormality) Results were reviewed with the patient and biopsy was recommended. Vacuum‐assisted needle biopsy (Mammotome) was performed using mammographic stereotactic localization. Pathology showed atypical ductal hyperplasia. Subsequent excisional biopsy confirmed the absence of malignancy. Problem (Symptoms) A 63‐year‐old female completed a screening mammogram showing a 1‐cm spiculated mass with associated calcifications lateral to the left nipple area. This lesion was not present on prior mammograms. Solution Radiologic Interpretation: BI‐RADS 5 (highly suspicious of malignancy) Results were reviewed with the patient, and needle biopsy was recommended. Vacuum‐assisted needle biopsy (Mammotome) was performed using mammographic stereotactic localization. Pathology showed infiltrating ductal carcinoma, grade II.

Solving a new diagnostic problem: by comparing the new problem and the four ‘teaching cases’ stored into the ‘knowledge base

Re‐use the solution of CASE 4: we re‐use the solution of CASE 4

If we consider these four ‘teaching cases’ we notice that features of CASE 4 are more similar to this new problem. In this paper we do not compute similarities between cases but will describe them in detail in another paper. Table 3 illustrates the solving of the new problem by re‐using the solution of CASE 4. Table 3: Re‐use the solution of CASE 4 Problem (Symptoms) A 68‐year‐old female had a screening mammogram showing an irregular high density lesion with spiculated margins is seen in the retro‐areolar region, with associated architectural distortion and nipple retraction. Clustered microcalcifications are noted within the lesion. Few lymph nodes are seen in axilla. Solution Radiologic Interpretation: BI‐RADS 5 (highly suspicious of malignancy) Results were reviewed with the patient, and needle biopsy was recommended. Vacuum‐assisted needle biopsy (Mammotome) was performed using mammographic stereotactic localization. Pathology showed infiltrating ductal carcinoma, grade II.

Store the new experience: if diagnosis is correct, we store the new case in the memory

The ‘knowledge base’ is incremented with the new case and gives it the number 5. Table 4 illustrates the storage of the new ‘teaching case’. Table 4: Store the new experience

CASE 5

Problem (Symptoms) A 68‐year‐old female had a screening mammogram showing an irregular high density lesion with spiculated margins is seen in the retro‐areolar region, with associated architectural distortion and nipple retraction. Clustered microcalcifications are noted within the lesion. Few lymph nodes are seen in axilla. Solution Radiologic Interpretation: BI‐RADS 5 (highly suspicious of malignancy) Results were reviewed with the patient, and needle biopsy was recommended. Vacuum‐assisted needle biopsy (Mammotome) was performed using mammographic stereotactic localization. Pathology showed infiltrating ductal carcinoma, grade II.

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6. Discussion and conclusion Section 6 provides a discussion of perspectives and future work and a conclusion of the work presented in this paper. In this paper we have presented multiple models and methods for developing ‘teaching cases’ for a learning management system in breast radiology under development. We have used the Case‐Based Reasoning (CBR) approach to represent these ‘teaching cases’. We combined the CBR with the object modelling with the Unified Modelling Language (UML). This combination was very suitable for presenting complex data and images. The outcome of the research is conceptual and practical. This research contributes to the breast cancer diagnosis domain by defining a conceptual model for representing ‘teaching cases’ which are generic solutions and reusable in many different settings. It contributes to Case‐Based Reasoning by defining a case representation model and a retrieval algorithm which exploits this layered structure to find similar ‘teaching cases’ by aggregating similar ‘sub‐cases’ and ‘sub‐sub cases’. The next steps will concern the instantiation of the ‘knowledge base’ by all the ‘teaching cases’ with their related images available for learning and training and the complete process of the retrieval process and similarities.

References Aleven, V. and Ashley, K. D. (1992) “Automated Generation of Examples for a Tutorial in Case‐Based Argumentation”, in C. Frasson, G. Gauthier, & G. I. McCalla (Eds.), Proceedings of the Second International Conference on Intelligent Tutoring Systems, pp. 575‐584. Berlin (Germany): Springer Verlag. Begum, S. (2009) “A Case‐Based Reasoning System for the Diagnosis of Individual Sensitivity to stress in Psychophysiology”, printed by Mälardalen University Press Licentiate Theses, No.102 Västerås (Sweden). Blanco, X., Rodriguez, S. and Corchado, J.M.(2013) “Case‐Based Reasoning Applied to Medical Diagnosis and treatment”, Omatu.S et al (Eds), Distributed Computing and Artificial Intelligence, 10th International Conference, AISC 217, pp. 13‐146, Springer. CEBM, Centre for Evidence Based Medicine, http://www.cebm.net/. Clancey, W.J. (1987) “Guidon”, in journal Computer‐Based Instruction – Vol 10(1) and n°2. 6. Debrauwer, L and Karam, N. (2006) ” UML2: Entraînez‐vous à la modélisation”, Editions ENI. Deepli, A.J. and Rose, R.J. (2010) “a Framework for Medical Diagnosis using Hybrid Reasoning”, Proceedings of the International MultiConference of Engineers and Computer Scientists, pp. 228‐232. Demigha, S. and Balleyguier, C. (2013) “Learning and Training Modules in the ELMS‐RS System with the Case‐Based th Reasoning”, Edulearn, the 5 International Conference on Education and New Learning Technologies, pp.3309‐3315, Barcelona (Spain). th Demigha, S. and Balleyguier, C. (2012) “The System ELM‐RS: Methodologies, Tools and Techniques”, ICERI, the 5 International Conference of Education, Research and Innovation, pp. 4240‐4249, Madrid (Spain). Demigha, S. (2007) “An Ontology Supporting the Daily Practice Requirements of Radiologists Senologists with the Standard BI‐RADS”, ICEIS, the Ninth International On Enterprise Information Systems, Proceedings of Information Systems Analysis and Specification, Vol ISAS, pp. 243 249, Funchal, Madeira (Portugal). Demigha, S. (2005) “Un système à base de connaissances en radiologie‐sénologie dédié à la formation des radiologues‐ sénologues juniors”, PhD thesis in Computer Science, Université of Paris 1 Sorbonne. Demigha, S. and Prat, N., (2004) “A case‐based training system in radiology‐senology”, in Information and Communication Technologies: from Theory to Applications (ICTTA), Damascus (Syria). Demigha, S, Rolland, C. and Baum, T.P. (2001) “Crews‐l’Ecritoire Analysis for the Implementation of a Medical Image Database for Mammography”, Conference Proceedings ‐ SPIE Medical Imaging, PACS and Integrated Medical Information: Design and Evaluation , vol 4323, pp. 386‐396, San Diego, California (USA). DICOM, “Digital Imaging and COmmunication in Medicine”, http://medical.nema.org/. Farrell, R. (1987) “Intelligent Case Selection and Presentation”, Proceedings of the Tenth International Joint Conference on Artificial Intelligence, (IJCAI). Huang, M.J, Huang, H.S. and Chen. M.Y. (2006) “Constructing a personalized e‐learning system based on genetic algorithm and case‐based reasoning approach”, in Expert Systems with Applications, Elsevier. Katoua, H. (2012) “Reasoning Methodologies for Intelligent eLearning Systems”, International Journal of Computing Academic Research (IJCAR), 1 (1), pp. 36‐44. Jarz, E.M, Kainz. G.A. and Walpoth, G. (1997) “Multimedia‐based case studies in education: Design, development, and evaluation of multimedia‐based case studies”, Journal of Educational Multimedia and Hypermedia, 6(1), pp. 23‐46. Kolodner, J. (1993) “The Case‐Based Reasoning”, Morgan Kaufmann Publishers. Kolodner, J. and Leake D. (1996) “Case‐based Reasoning‐Experiences, Lessons and Future Directions,” AAAI Press/ The MIT Press.

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Souad Demigha and Corinne Balleyguier Koton, P. (1989) “Using experience in learning and problem solving”, Massachusetts Institute of Technology, Laboratory of Computer Science (PhD thesis). MIT/LCS/TR‐441. Macura, R.T. and Macura, K.J. (1995) “MacRad Radiology image resource with a case‐based retrieval system, in First International Conference on Case‐based reasoning approach: Research and Development, M.Veloso, A.Aamodt, (Eds), Berlin: Springer Verlag, pp.43‐54. Praveen. R.S. and Raksha.B. (2011) “Introduction of Case‐Based Learning for teaching Anatomy in a conventional Medical School”, JAnat. Soc. India 60(2), pp. 232‐235. Porter, B.W. and Bareiss, E.R. (1986) “PROTOS: An experiment in knowledge acquisition for heuristic classification tasks”, in Proceedings of the First International Meeting on Advances in Learning (IMAL), Les Arcs (France), pp. 159‐74. Radiology ACo. (2008) “ACR practice guideline for the performance of screening and diagnostic mammography”, Practice guidelines and technical standards, Reston: American College of radiology. Radiology ACo. (1998) “Bi‐RADS: ultrasound”, 1st ed. in: Radiology ACo, editor, Breast Imaging reporting and data system: Bi‐RADS atlas. 4th ed. Reston, Va. Rolland. C, Grosz. G. and Kla. R. (1998) ‘‘Experience with goal–Scenario coupling in Requirements Engineering’’, in the proceedings of the Fourth International Symposium on Requirements Engineering, Limerick (Ireland). Salem, A.B.M and Baloglu, U. (2007) “Web Enabled Patient Monitoring System: A Semantic Network Modeling”, Egyptian Computer Science Journal, 29(2). Salem, A.B.M, Ella Hassanien, B. and Ramadan, R. (2012) “Advanced Machine Learning Technologies and Applications” , First International Conference, AMLTA, Cairo (Egypt), Proceedings. Communications in Computer and Information Science, 322, Springer. Shortliffe, E.H. (1976 ) "Computer Based Medical Consultations: MYCIN”, American Elsevier. Simoudis, E. (1992) "Using Case‐Based Retrieval for Customer Technical Support", IEEE Intelligent Systems, 7(5), pp 10‐12. Tomar, P.P.S., Singh,R. and Saxena, P.K. (2011) “Case Based Medical Diagnosis of Occupational Chronic Lung Diseases From Their Symptoms and Signs”, International Journal of Biometrics and Bioinformatics (IJBB), 5(4). Watson, I.D. and Abdullah, S. (1994) “Developing Case‐Based Reasoning Systems: A Case Study in Diagnosing Building Defects”, in Proc. IEE Colloquium on Case‐Based Reasoning: Prospects for Applications, Digest No: 057, pp. 1‐3. Watson, I. (1997) “Applying Case‐Based Reasoning: Techniques for Enterprise Systems”, Morgan Kaufmann. Welter, P., Deserno,T.M. and Fischer, B. (2011) “Towards Case‐Based Medical Learning in radiological decision using content‐based image retrieval”, BMC Medical Informatics and Decision Making, 11:68.

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The Influence of ICT on the Communication of Knowledge in Academia Natalia Dneprovskaya and Irina Koretskaya Moscow State University of Economics, Statistics and Informatics (MESI), Moscow, Russia NDneprovskaya@mesi.ru ikoretskay@mail.ru Abstract: MESI has conducted research into knowledge communication within the academic environment. Participants from different Russian universities were involved in the research. The results show new ways of delivering knowledge to students, and of improving knowledge management tools and methodology. Keywords: information competence, knowledge flow, academic knowledge, online resource

1. Introduction The development of information and communications technology (ICT) has a great deal of influence on the information environment. Information tools and methodology are changing rapidly. There are many knowledge communications which can be used during vocational, scientific or ordinary activities. Usually knowledge sources are divided according to users and subjects. Scholarly journals publish scientific papers, which are considered as a source of new knowledge. There are a variety of resources for entertainment, inquiry, business and other purposes. The blending and even the replacement of one knowledge source by another are everywhere. In general, this shows increasing access to knowledge. But an assessment of the results of using such approach cannot be so optimistic because this depends on the quality of the knowledge resource. References to Wikipedia are increasingly common in student papers, replacing references to books and scholarly journals. Nowadays different sources of knowledge compete with a variety of online resources for the attention of Internet users. Every day, a student using the Internet has the difficult task of determining which online resource should be used for academic work. The study on knowledge communication in academia was initiated by the Moscow State University for Economics, Statistics and Informatics (MESI). The results of this research are not intended to confirm the facts of change but to find new ways of delivering knowledge to our students in order to improve education services, particularly e‐learning.

2. The educational aspect of changes in knowledge communication A core trend of modern society is the rising influence of knowledge in almost every industry. Knowledge communication has been shifting to the Internet, which represents a holistic information environment. The efficiency of knowledge communication influences the democratic and economic development of society (Tikhomirova, N.V, Tikhomirov, V.P., 2012). Modern society has been called the information society, which is distinguished by the rising role of information and information technologies. The influence of information technology on social and economic development is hard to overestimate. Different types of information resources – which used to be separate – now are combined into the holistic information environment of the Internet. (Dneprovskaya, N 2012) Professional databases of scientific and business information, electronic libraries, online resources, and electronic publications have become integrated international information resources which are available around the world. The provision of equal facilities for access to the holistic information environment will support economic growth potential. Access to the holistic information environment is fast becoming a decisive factor for economic and social development as well as civil rights and liberties. The G8 countries have signed the “Okinawa Charter on the Global Information Society” (Okinawa, July 22, 2000), which highlights the necessity of a free flow of

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Natalia Dneprovskaya and Irina Koretskaya information and knowledge as a basis for social development. The charter points to the development of the information society as a development of human resources capable of meeting the demands of the information age through education and lifelong learning. Access to information resources is particularly useful in cases where people possess information competencies, as this knowledge and skills cannot be provided immediately. Information competence training is caused by trends in the information environment. The main trend in the information environment is the explosion in the volumes of information. The volume of knowledge accumulated by humanity doubles every two to three years according to estimates. At the same time data storage doubles every three days. The increasing flow of information demands from people special knowledge and skills, which are known as information competence. The most significant achievement of the information society are the increasing opportunities to access and use knowledge. New media and information technologies are becoming an essential part of the student environment. Observations show that students do not distinguish between different sources of knowledge. Thus the search engine has the same value for students as does the scientific database. In general, this indicates a low level of information competency among modern students. Students do not pay enough attention in selecting knowledge sources, evaluating data, and analysis, and do not care about of the ethics of borrowing content (Urintsov, A.I., 2003). Students’ work with knowledge sources is characterized by a search for ready‐made solutions, rather than independent research activity. The lack of a systematic approach by students with regards to information competencies has to be recognized. There are countries developing information competencies in higher education: the USA and the UK. Increasingly, student papers include references to information sources such as Wikipedia, blogs, forums, etc. This has forced us to pay attention to the preferences of students whose side in solving various types of information tasks – indecipherable. Knowledge communication is undergoing significant changes in academia. For the university, this is important as it influences learning outcomes. On the one hand there are a number of advantages, including more intensive knowledge sharing between students. Lecturers also have more opportunities to access, follow and support their students’ ideas via social networking. However, the disadvantage is that valuable knowledge sources are overshadowed by easy‐to‐use web‐based services (Pavlekovskaya, I.V. 2007). Information about new knowledge communication tools allows us to design a new way to use it in the learning process. The development of educational materials should be aimed at organizing student research activities.

3. The psychological aspect of changes in knowledge communication By the 1960s‐70s, it was clear that information technology would play the main role in social development. The onrush of information technology has brought about a revolution in the economic and social spheres, in science, education, culture and in all daily activities. In the information society the main value is information and information technology (primarily digital). And we can suppose that this society will engender people with a new outlook. As society develops, the objectives, methods, results of student activities change, which in turn changes the education system and the system of knowledge, skills and abilities. We see an increasing role for self‐ education and increasing awareness of the importance of practical work at universities. Modern students are not interested only in gaining knowledge; they want to develop their abilities and competencies. Unfortunately, modern education has chosen to further formalize knowledge, assessing the formal competencies of schoolchildren and university students. In modern society the volume of knowledge looks more attractive than does its value.

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Natalia Dneprovskaya and Irina Koretskaya And that is the reason behind the formal approach to knowledge assessment (tests). But formal knowledge does not ensure that students will become successful professionals after graduation. A real professional must know where to find the information that he or she needs, and how to use it, in order to successfully navigate the issues rather than simply memorizing 20 definitions verbatim. Human memory, unlike animal memory, is mediated. And all possible references, dictionaries, and computer resources should be seen as a kind of blank media, thereby freeing the brain and the human mind to find creative ways to solve problems. In the information age, the volume of information will be important if it is systematized and conceptualized. A modern student consumes quite a lot of information through a variety of media (TV, radio, Internet, media) and the problem now is not that he does not know something, but that his knowledge is fragmented and sketchy. Students cannot summarize and analyze the material they already know to draw conclusions, they are unable to fully apply their knowledge, and sometimes they are not creatively productive. Training should be based not on "cramming" information, but rather on the ability to think logically. Today, however, "crossword thinking" is being promoted, the main feature of which is following a rigid structure, with no flexibility of thought or space to fantasize. “Crossword knowledge” is not real knowledge, but only the shell of knowledge, scraps of information from different, often random areas of knowledge, that do not add up in any system. The falsehood and livability of the ways mind training as external information and knowledge space rather than internal understanding well described in classical literature, such as in Hermann Hesse's “Glass bead game”. The essence of many pseudo‐innovative education programs aim, literally, to give students with new information in a short time. Students are beginning to demonstrate only fragmented, pseudo‐scientific formal knowledge, not understanding how and where to apply this knowledge. This is the result of "crossword thinking", which has become the most popular testing method. The main feature of the test is in the variety of prepared answers, with always only one correct answer. Thus, pupils and students are seen as an encyclopedic dictionary or reference. Creativity, and the ability to analyze, are not relevant ‐ mechanical knowledge without reflection is the only important element. The main requirement for a professional in the modern world is not only to possess specific knowledge, but to be able to understand the problem in a systematic approach. Thanks to information technology, students can explore different aspects of the problem, but in different areas. This gives rise to systematic thought. "The world view" does not comprise the total of images of individual phenomena and objects; it is a holistic picture of the system. And all cognitive hypotheses are based on the world view. So, modern education should be directed to the formation of the ability to reason, analyze material, and think creatively but critically, adopt unconventional solutions and not simply at using trash terminological research and popular literature. Another difficulty is the inability to separate reliable and meaningful knowledge from the data stream. There are many ways for modern students to obtain information, but acquiring reliable information is extremely complex and tortuous. A search engine may spit out a great mass of links, most of which absolutely do not reflect the sense of the search terms. For example, if we put the word the "whale" into a search engine, first we will get links to various websites of companies, films, and proper names ‐ and only in the middle will we find a reference to the fact that a “whale is a mammal”. And this is connecting with what we generally know. The student has to search for information about objects of which he has only a vague awareness. Consequently, modern education should provide skills and competencies, to be able to work with huge amounts of information, and to organize and systematize the information; education should give the opportunity to work with a large amount of information. (Koretskaya, I 2012) Informal professional communication between lecturer and students is an important part of the study process. This kind of communication on the one hand gives students the opportunity to discuss various academic problems and to present their ideas, and on the other hand gives lecturers gives a good creative recharge, allowing them to look at many issues from different perspectives. This kind of communication allows lecturer

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Natalia Dneprovskaya and Irina Koretskaya and students to feel closer to one another and creates an atmosphere of peer‐to‐peer collaboration, leading to the abandonment of the traditional model of the relationship between lecturers and students. A lecturer’s main goal does not consist only in delivering knowledge and information. He or she has to engage students in the values of culture, education and human values in general and professional values such as ethical orientations. The lecturer’s influence on students is very great, and all lectures should keep this in mind. Technological progress affects human availability, by making them more "close", more attainable. From a theoretical point of view, this leads to expansion and "erodes" human psychological borders. We can reach anyone at anytime by mobile or skype or social networks. And it makes people believe in the illusion of control. But it is only illusion, because telling a lie at a distance is much easier than in face‐to‐face dialogue. The ability to communicate with other people at any time crosses the bounds of privacy. People begin to believe that everybody has to be available by any means of communication at any time. And the inability to contact someone, to send a message, raises a whole range of feelings, from anxiety and bewilderment to outrage. Substitution personal interactions with virtual ones has a negative impact on the relationship between people. Everybody is expected to be available via ICT all the time. But there is no guarantee that the message will be delivered in time, that it will be read, or that we will receive a response. People can regulate their availability to others. Another problem is the increasing number of potential contacts. In itself this is not critical, but this "dispersion" substitutes for the quality of relationships. For young people, it becomes very important to have many “friends” in social networks, and they are interested in “likes” of their activity on social networks. The quantity of calls and text messages, and the number of “likes”, are seen as social recognition. Professional education is essential for the adaptation of the young man to solve a wide range of modern tasks. Progressive modern education enhances personal development in dealing with life's challenges in a changing modern world. It allows everybody to develop his or her creative potential, which is sorely needed in today’s technical world. New technologies offer new opportunities, changing our world and making it more comfortable. But this convenience and comfort is accompanied by changes in the structure of human motivation and needs, changing thought patterns and life and all this should be taken into account in education development.

4. Students’ views on knowledge communication This research was conducted to identify changes in knowledge communications amongst students. The student survey was conducted in the spring semester of 2013 among Russian students. The study involved 1,352 students at five universities. There were two universities from Moscow and three from other Russian regions. The study did not reveal any significant difference based on place of study. In the questionnaire, the students were asked to select and assess the value of the resources to deal with three types of objectives including everyday, educational and academic contexts. The results showed that students prefer search engines and Wikipedia. Also we see a dependence on the preferred knowledge source for each type of task. Students are more likely to turn to friends to solve everyday problems, and to the most highly regarded lecturers to address educational and academic matters. We see the same dependence in libraries as a knowledge source. The second group of questions concerned the evaluation of students’ satisfaction with the quality of the information received from relevant sources. Students using certain sources of knowledge are evaluated on their degree of satisfaction. The research allowed us to determine the share of students who do not use certain types of knowledge sources.

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Figure 1: Students appropriate knowledge source for solving everyday, educational and scientific tasks %

Figure 2: Distribution of students' satisfaction on knowledge sources for everyday tasks

Figure 3: Distribution of students' satisfaction on knowledge sources for educational and academic tasks

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Figure 4: The percentage of students who do not use the knowledge sources for educational and academic tasks The third group of questions was aimed at identifying ways to support students.

Figure 5: Students give preference to technological means of receiving tutorial support

5. Faculty views on knowledge communication During the research, 327 lecturers were asked to respond to questions about knowledge communication. Most of them (215 lecturers) represented universities from Moscow, with 112 lecturers from other regions of Russia. The main goal of the research was to investigate knowledge communication within the academic environment. Our respondents were from different departments, with most coming from IT departments (55%). There were 42% from psychology departments. We have chosen these departments as the main areas for our research because they represented two different directions – technology and humanities branches. We asked our respondents about means of getting references of information. There were no differences between technical and humanities lecturers. And there were no differences between Moscow and other regions of Russia. We also asked about how they consulted with students. There were differences between Moscow lecturers and regional lecturers.

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Figure 6: Faculty appropriate knowledge source for solving everyday, educational and academic tasks %

Figure 7: Lecturers give preference to technological means of providing tutorial support to students.

6. Conclusion At the present time, it is hard to overestimate the role of information in social and economic development. Information technologies have a significant influence on the modern student. The student has sufficient ICT competencies to allow him to easily handle new devices and online services. A person who possesses ICT competencies can use information resources more effectively for his education and profession. But the results define a new problem in socialization during the educational process. Face‐to‐face knowledge communication is being replaced by online resources. However, these resources do not completely satisfy students. There are two ways to overcome this problem. One is improve students’ ICT competencies. But they are skilled enough in ICT. The second is to update IT that students prefer (search engines). Meanwhile these technologies are not aimed at educational and academic tasks at all. We see this problem as being neither technological aspect nor educational. This problem is in knowledge communication. Knowledge communication includes the IT opportunities that are popular in our time, but also psychological issues such as face‐to‐face communication and dialogue between faculty and students.

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References Dneprovskaya, N. (2012) ‘Information competency development in higher education’ The use of multidisciplinary research for the introduction of new training programs/modules/and/or/ new teaching methods in the field of E‐Commerce: proceedings of the international conference of the “ECOMMIS” TEMPUS Project, 2‐4 April, 2012, Berlin, ECM‐Office. Koretskaya I. (2012) ‘Humanitarian aspect in Smart education’ Russia on the way to smart society, Moscow, IDO press SCONU (2004) ‘Learning Outcomes and Information Literacy’ UK: SCONUL. UNESCO (2007) ‘Understanding Information Literacy’ Primer Edited by the Information Society Division, Communication and Information. Sector Paris: UNESCO. http://unesdoc.unesco.org/images/0015/001570/157020E.pdf Tikhomirova, N.; Tikhomirov V. ed. (2012) ‘Russia on the Way to Smart Society’ Moscow, IDO press. Urintsov A.I., (2003) ‘Three‐level logic architecture in a distributed economic information system as an element in prompt economic adaptation’ Automatic documentation and mathematical linguistics Vol.37, No.3, New York, Allerton Press, Inc. Pavlekovskaya, I.V. (2007) ‘The use of social network analysis in modeling the organizational processes of information and knowledge circulation’ Automatic documentation and mathematical linguistics Vol.41, No.2, New York, Allerton Press, Inc.

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The Learning Journey of IC Missionaries: A Staged Approach John Dumay1 and Mary Adams2 1 University of Sydney, Sydney, Australia 2 Smarter‐Companies Inc, Boston, USA john.dumay@sydney.edu.au adams@smarter‐companies.com Abstract: The utilization of intellectual capital has often not been taken up as much as the proponents of IC may have wished. As Dumay (2012) outlines, there are barriers to implementing IC in organisations, and as academics and practitioners we need to overcome these barriers. We propose one way to do this is to provide reflections of the journey the authors have taken as a successful IC practitioner and a successful IC academic. Based on a constructivist learning theory (Chiucchi, 2013) we offer a staged model of IC development (Guthrie et al., 2012) outlining how we went through similar stages in personally understanding and deploying IC. To do this, Mary Adams and John Dumay trace their IC learning journey in three stages of understanding, knowledge‐izing and socialization. This paper contributes to the IC literature by providing an understanding of the growth a person may need to take in order to become an IC missionary, rather than merely an IC preacher (Dumay, 2013, p. 8). If this can be achieved, we can provide a forum for open conversations about the concept of IC and the tools available so we can empower people and organizations to experience their own IC. Keywords: intellectual capital, intangible capital, learning journey

1. Learning about and mobilising IC The utilization of intellectual capital (IC) has often not been the taken up as much as the proponents of IC may have wished. As Dumay (2012, pp. 4‐5) outlines, there are barriers to implementing IC in organisations based on how IC is theorised, and as academics and practitioners we need to overcome these barriers and progress IC as a useful management technology in organisations. However, in order for organisations to utilise IC, people are required to learn about and understand IC theory to in order to mobilise IC in practice. Thus, a critical issue is how people learn from IC theories and how individual learning transfers into practice (see Kim, 1993). So how is IC theorised? This question has intrigued both academics and practitioners since the term “intellectual capital” gained prominence in the early 1990’s (Stewart, 1994; Edvinsson, 1997; Edvinsson and Malone, 1997; Roos et al., 1997). As a result, IC has been theorised in different ways, and there is a “general acceptance of the tri‐partite representation of IC categories as human, structural and relational capital” (Dumay, 2009a, p. 192) which, when leveraged alongside knowledge, is used as a means of creating value (Stewart, 1997, p. x). However, regardless of how people theorise IC, the theories help individuals make sense of and internalise IC as a construct before they can mobilise IC. Therefore, how people learn about and mobilise IC is an area of interest for IC researchers because each person has a different learning journey (Chiucchi, 2013, p. 48). From the perspective of a learning journey, Chiucchi (2013, p. 48) argues “that actors must complete an experiential learning cycle to mobilize IC”. In her paper Chiucchi (2013) utilises Kolb’s experiential learning theory (Kolb, 1976, p. 22) to outline how she helped managers in an Italian company learn about and mobilise IC, concluding that a deep and continual learning process was essential for IC mobilisation. Thus, individuals do not just learn about IC once, rather their learning develops and changes over time as they interact with IC in different contexts. Therefore, how a person learns about and mobilises IC during their learning journey impacts the way they mobilise IC in the future. We also argue that individual academics and practitioners have gone through similar learning experiences in understanding, learning about and mobilising IC. This has been outlined by Guthrie et al. (2012, p. 69) as having occurred in three distinct stages. The first stage raised awareness of the potential for IC to create value, and why it was important while the second stage saw the development of knowledge about IC through research and the many frameworks for measuring and managing IC, which are typically based on a top‐down perspective (Petty and Guthrie, 2000, pp. 155‐6; Guthrie et al., 2012, p. 70). Since then a third stage of IC research has emerged “based on a critical and performative analysis of IC practices in action” Guthrie et al. (2012, p. 69). Hence we argue that individuals can trace their IC learning journey through three learning stages

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John Dumay and Mary Adams being understanding, knowledge‐izing and socialization. These three stages are aligned with Guthrie et al.’s (2012) three stages of IC development and with Kolb’s concept of experiential learning as outlined in Figure 1. Understanding

Knowledge-izing Transition

Concrete Experience

Reflective Observation

Active Experimentation

Concrete Experience

Socializing Transistion

Reflective Observation

Active Experimentation

Concrete Experience

Reflective Observation

Active Experimentation

Abstract Conceptualisation

First Stage IC

Second Stage IC

Third Stage IC

Figure 1: Stages of IC learning According to Kolb (1976, pp. 21‐2), learning is conceived as a four stage process, whereby concrete experiences are the basis for reflective observation. These observations then help us develop abstract conceptualisations to form new theories (or hypotheses), which are subsequently used as guides for active experimentation and to create new experiences. To be effective learners, Kolb argues that we need to combine these four abilities. Thus, a learner must be able to involve themselves fully, openly, and without bias in new experiences; observe and reflect on the experiences from different perspectives; create concepts by integrating observations into logical theories; and then apply the theories to make decisions and solve problems. We argue that as far as IC is concerned, individuals need to transition through the three stages of IC in order to effectively mobilise IC in particular contexts. We base our argument on the following logic. First, before an individual can become an effective IC practitioner they must first be convinced that IC is important by developing an understanding about IC and recognising IC’s potential. Second, the individual needs to develop personal knowledge about IC by applying one or more of the available frameworks for measuring, managing and/or reporting IC. Only after completing this step can the individual critically evaluate the effectiveness of IC in practice and truly begin to mobilise IC as a management technology to create value. To explore our staged IC learning model we now present the reflections of our personal IC learning and mobilising journeys.

2. Mary Adams’ IC learning journey I understand IC as an emerging set of theories which endeavours to explain changes occurring in the global economy as the industrial era ends and the knowledge era begins. The focus of much of my work involves understanding how these changes affect managing work and results at an organizational level. In some ways, everyone in the workforce has a personal story of learning and evolving to adapt to these changes. I am no exception and, as an active IC student and practitioner, I see a pattern in my own learning journey which I hope will provide lessons on how to facilitate such a journey for other practitioners

2.1 Understanding IC I am a child of the industrial era graduating from college three months before the first IBM personal computer (PC) hit the market, receiving a Masters degree in international management soon after that. Thus, my formal learning ended just before the PC revolutionized the workplace and helped speed the end of the industrial era. After a short stint as a graduate researcher, I joined Citibank where I began a 14‐year career as a lender. At Citibank, we all had early PC’s on our desks and used them for typing credit reports, and as Lotus 123 became more prevalent, for creating financial forecasts and corporate valuations. We spent hours using these new spreadsheets to analyse the many market transactions as part of the leveraged buyout boom during the 1980’s. At that point, the balance sheet and income statement could still go a long way in explaining how a company worked and how strong an outlook it had. We relied heavily on the numbers to analyse and justify a deal. This didn’t mean that we didn’t also analyse managers, operations, reputations and strategies. We used standard credit approval forms including sections for a written analysis of all these important aspects of a company. In

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John Dumay and Mary Adams retrospect, the nonfinancial analysis we did was primarily focused on IC. However, unlike our financial analysis, our business analysis did not follow any framework nor use standards for measuring the non‐financial drivers of success. The written analysis was essentially our personal conclusions made from talking to company managers through which lenders like me endeavoured to understand how the company operated at a deeper level. During my career as a banker, I grew adept at analysing the financials. However, one of my favourite parts of the analysis was the narrative that described the people and operations, which I later understood to be IC. At that point, I was an unconscious IC practitioner—focusing on IC without having any formal frameworks or understanding. This was a hallmark of industrial IC: the assumption that intangibles are “soft” and not meriting a disciplined approach. Eventually, my interest in corporate strategy and frustration with the strictures of a lender’s role lead me to leave banking and found a boutique management consulting firm in 1999. For the first few years of consulting practice, I continued to experiment build on my experience, using my banking skills to interview clients in what I came to brand as “strategic conversations”. Then, in the mid 2000’s, I discovered the language and the frameworks of the IC movement. The movement advocated explicitly recognising and measuring IC along with the huge growth in automation based on information technology in contrast to automation based on industrial technology. IT‐enabled production is largely an invisible process that uses and creates knowledge assets such as data, information, processes, intellectual property—collectively called IC. It was at this point where I had recognised the importance of IC and was now ready to transition to discover more about IC.

2.2 Knowledge‐ized IC The IC movement in the mid 2000’s studied the growing importance of knowledge as a corporate asset. The focus was on creating systems by which knowledge could be controlled and managed, largely from the top down. By some counts, there have been as many as 100 different IC frameworks published around the world in the last decade, attempting to create the perfect system for managing, measuring and reporting IC, of which none have become dominant, or broadly accepted. I followed the industrial approach to IC for several years, and I began by trying to sell my clients on the idea using one of the most successful commercial IC measurement systems developed in Sweden. It was a hard sell. Most businesspeople in the U.S. had a limited or no exposure to IC concepts. Many thought the ideas were interesting, but very few saw the connection with the jobs they had to do every day. So while I felt that many companies exhibited symptoms related to lack of understanding or measuring IC, few saw IC management as a cure. Despite the lack of uptake in my local market, I continued preaching the IC message, creating a static website called the IC Knowledge Center. I set the personal goal of adding three new references (articles, books, websites) to the website each month and then announcing the additions through a monthly newsletter. Over time, the IC Knowledge Center evolved from a static website to a group blog and by 2010, an on‐line community of IC practitioners. The practitioners (most of them also consultants) were interested in the intricacies of the theories. But even they were trying to find ways to sell the ideas to their clients, trying to try to explain it in a way that might spark people’s interest in the message. After a few years, I had developed a good bibliography and began working with my partner on a book. We slowly experimented with ways to insert IC into our consulting projects. As a result, we began to develop a tool set that helped us and our clients to solve everyday business problems: how to grow, how to increase performance, how to build company value how to build a reputation supporting continued success. In this way, IC for me was no longer an end itself, but rather a means to an end, leading us to become much less interested in preaching about IC and much more about using IC to help our clients solve problems. This was the beginning of the transition to a new stage in my personal development as an IC practitioner.

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2.3 Socialized IC I now see IC as a vehicle for empowering others for self‐discovery and growth. In fact, rather than being a system, I now see IC more as a holistic way of thinking and acting. The idea of holistic and systemic thinking comes out of the nature of the IC construct. If you think about the most commonly‐used categories of IC— human, relationship, structural and strategic capital—you quickly realize that they cover a broad range of roles and functions within an organization. IC provides a way for all the people in those disparate roles and functions to connect the dots between the organization’s stakeholders, the problems the organization solves for those stakeholders, and what they are doing individually, or as a function. This view contrasts with the cursory external review of IC analysis I did as a banker and emerging consultant— which was driven by an industrial view of the primacy of tangibles and the ability to rely on financial statements. It also contrasts with the one‐size‐fits‐all approach to IC systems that drew me into the field in the early 2000’s ‐ which was driven by something of a knowledge‐era version of industrialization, looking for new rules like those provided in the past by financial reporting. My current view sees IC as a skill set that can and should be learned by everyone who is ready for it, driven by a new view of organizations as social systems. And it is based in a very different assumption from the standard frameworks that seemed so attractive in the knowledge‐ized phase. This socialized view of IC focuses primarily on the unique IC of individual organizations. My current thinking and understanding of IC has been influenced by the emergence of social technologies and the growing role of collaboration. A lot of the headlines for social technologies have come from social media, with Facebook and Twitter showing what happens when citizens, employees and communities can share information in real time. But there is much more going on as these technologies empower people to work collaboratively in real time. What is happening now is no longer about knowledge alone, it is about knowledge put to work in collaborative environments fuelled by social technologies. In this socially‐enabled environment, an organization chart no longer explains how the organization works. Much more appropriate to the task is a network chart. But what’s going on inside the network? What resources are needed for the network to be successful? How do managers and/or the organization facilitate the success of the network? How will everyone measure the success of the network? For me, these questions point to IC thinking. Not a standard framework imposed from above, but rather a point of view, a skill set and, yes, a few new tools. The purpose of these new tools, however, is not to fit everything inside a model but, rather, to facilitate the discovery by the members of the team. To help people develop their own answers to these questions, I launched a new company to support and build the community at the IC Knowledge Center, now called Smarter‐Companies. From this new platform, we have introduced a set of simple open source tools that enable people to identify the relative importance of IC in their organizations, to identify the organization’s unique intangibles and then to create a one‐page visual model of how the intangibles work together. We then measure the intangibles in these inventories using stakeholder feedback. Using stakeholder assessment is consistent with the move toward social collaboration although it is an approach to measurement that has roots going back to the strategic conversations I endeavoured to have with my clients as a banker and an early IC consultant. Many people still gravitate and aspire to finding and creating financial metrics and/or indicators to provide the hard data that most businesspeople value. While I still see value in these measures, I have come to believe that the nature of intangibles and the power of the stakeholder’s view both point to the potential of assessments as a critical methodology for intangibles measurement. In the future, corporate measurement systems can and will include significant crowd sourcing. And I am convinced that they will be focused primarily on intangibles. My collaborative way of thinking about IC has helped me transition from the industrial model that was at the roots of the basic ideas I first learned about business and organizations and has taken me to a new level of understanding and achievement as an IC practitioner. My goal going forward is to help as many people as possible make a similar journey.

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3. John Dumay’s IC learning journey A few years ago I was involved in an exchange of emails with other academics as part of making arrangements to visit Rome for a small workshop on IC. In the exchange, one colleague wrote something that resonates with me to this day and surmises the way I currently see IC: “... I find [IC] not being practiced by managers far as much as it’s being preached by us academics…” Thus, the quote highlights to me that there is a lot of preaching about IC and its benefits, yet when I mention the term “Intellectual Capital” many people confuse it with intellectual property or claim never to have heard of the term before. Thus, it is not surprising that IC gets little recognition in many organisations and that the IC preachers are dismayed that their message is not being heard.

3.1 Understanding IC Similar to Mary Adams, I was a child of industrial age thinking. My first tertiary qualification, an “Instrumentation Engineering Technician – Industrial” diploma, essentially qualified me to work in a factory monitoring and repairing manufacturing processes. This began a decade of working in manufacturing and further studying management, developing my career as a factory manager. It was here that I was introduced to accounting and was taught how to manage the costs of the resources under my control. Here, human beings were a cost to be managed and controlled, and my performance was measured on the number of units produced and the efficient utilisation of the productive machinery under my control. However, knowledge was important and was related to training workers how to interact with a piece of production machinery. In the mid 1990’s, after working ten years in manufacturing, I took the opportunity to start up my own business, selling computers at a time when most homes and small businesses were yet to have ever purchased their first computer. Looking back, this was a risky venture because, at the time, I had no formal training in the working of computers, and neither did my friend who decided to join me in developing the business as a commissioned salesman. However, on the day I opened the business we decided that we would make sure we learned something new every day, and before long, we theorised that we would end up knowing enough. I was not afraid of learning about computers because of my engineering training which taught me how to learn about processes and machines that I didn’t know anything about. The first golden rule was to read the instruction manual before proceeding to trying to fix anything, and since computers came with instruction manuals and software came with installation instructions, I was confident I could use my engineering training to understand how to get computers and software operating for my customers. It wasn’t long before the business took off and I found myself not only selling computers and software, but also training customers how to use their computers, especially in small business applications where I began to specialise in setting up bookkeeping and small business accounting applications. The training I was providing was also the most lucrative part of the business as selling one PC would only contribute several hundred dollars of profit where as the training work had the potential of contributing several thousand dollars of profit. Hence, as in Mary’s story, I became an unconscious IC practitioner, leveraging my newly discovered knowledge of PCs and small business accounting software and creating value for myself and my customers by transferring that knowledge to my customers. Over the following five years, my business expanded into more of a consultancy business rather than the original computer store as I gained more value from selling my knowledge than I could have ever hoped to have gained from selling computer hardware. As a result of my increasing thirst for knowledge, I enrolled in an MBA program, and one elective class appealed to me because of my desire to leverage knowledge; it was called the Knowledge Management Study Tour of Northern Italy. It was this class that introduced me to the concept of IC and allowed me to see IC in action and suddenly all that I had believed in was now explained and demonstrated to me in a concept that made sense and advocated how knowledge could be used, alongside human structural and relational resources, to create value. I had been doing it all along and now knew why it worked. This was the event that transitioned me toward gaining further knowledge of IC.

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3.2 Knowledge‐ized IC As a result of my increased thirst for IC knowledge, I began my thesis studying IC in action under Professor James Guthrie at the University of Sydney. Fortunately I had available two research sites which allowed me to experiment with IC. However, like Mary, I was also initially stuck in a top‐down approach to IC, putting faith in the plethora of available frameworks for measuring, managing and reporting IC. Here, I initially observed that while no one framework had gained prominence the Danish IC statement guidelines (see Mouritsen et al., 2003) grabbed my attention. At the time, I naively thought that the Danish guidelines were the answer to developing IC knowledge. I theorised that by applying the guideline at the two research sites that all would be revealed, and my IC knowledge would be increased. How wrong I was! At the first research site, they were amenable to experimenting with the Danish guidelines, provided that the Balanced Scorecard (Kaplan and Norton, 1992) could be used in conjunction. However, when we rolled out the process for developing the organisation’s first IC statement, not everyone was thrilled about helping as they were reluctant to accept the IC concept. Some managers even went as far to claim that IC was nothing new and that managing, measuring and reporting IC was something they did under different labels such as human resource or customer relationship management. The second research site was even less enthusiastic, and told me that while they were happy to conduct research, they did not want just “another report”. In hindsight, these developments were beneficial to developing my IC knowledge, because had both sites enthusiastically agreed to produce IC statements, I would not have learned so much about the limitations of IC guidelines. As a result, I was able to experiment with different methods of measuring and reporting IC allowing me to develop a critical view of measuring, managing and reporting IC which I published in leading academic journals (see Cuganesan and Dumay, 2009; Dumay, 2009b, 2009a). Since then, I continued my research, developing my IC knowledge further. However, there was always something bothering me. I kept asking myself if IC is so great, why was the IC concept still so underutilised and what were the barriers to promoting IC as a concept (see Dumay, 2012)? To help me transition to socializing IC, I was invited by Leif Edvinsson to a small gathering of IC practitioners and academics in Heidelberg and to give an update on advancements on IC reporting in Australia.

3.3 Socialized IC On my way to Heidelberg, I began to think of the message I wanted to send to the attendees, and I recalled the message about us academics preaching about IC. Then it occurred to me that the title of the gathering was The next generation for IC / Intangibles Reporting. How odd I thought, after more than two decades we still have academics gathering to preach the benefits of IC reporting, yet after all this time, very few organisations issued IC reports. Thus, I concluded, if the message wasn’t being received and implemented, maybe the message and the way it was delivered needed to change. To highlight my point I identified all of those present in the room as being inside the “IC Cathedral” where they were the Cardinals and Bishops. The gathering was about preaching to the converted, rather than changing the hearts and minds of people in practice. Thus, I argued, we need to stop being IC preachers and become IC missionaries. Missionary, derived from the Latin word missionem, means the “act of sending” and a key activity of missionary work is education, not just preaching. Therefore, IC preachers need to change how the message is delivered, leaving the comfort of their pulpits and reaching into business schools and organisations, teaching the benefits of mobilising IC, rather than trying to produce IC reports. What the IC movement should be trying to do is embed IC as a fundamental part of business strategy, which in many schools, we are failing to do because many business schools still seem to be teaching industrial‐age strategy, based on creating economic value through competitive positioning (Porter, 1980), rather than knowledge‐age strategic thinking which advocates knowledge sharing and collaboration as keys to value creation. What we are attempting to do is develop is change a business culture that has its foundations in the industrial revolution and embed the culture of the knowledge revolution, and as we all know, cultural change is a slow process. Therefore, we can become IC missionaries, and change the way we educate new business leaders, can we ever hope that the IC concept will become socialized in the organisations of the future.

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4. Conclusion The main lesson learned from our reflections is that in the learning process, experience trumps telling, or more poignantly, the preaching of IC academics. Our conclusion is that academics and practitioners need to become IC missionaries, spreading the IC message through teaching and doing. In our IC missionary work we need to target students, educators, managers and policy makers who are the current believers of industrial age thinking and convert them into believers of the new knowledge age. Additionally, it is incumbent on us to create ways for individuals and teams to learn IC by experiencing it. Only then can IC ever become embedded in organisations and society. To progress missionary work the first commitment needs to come from policy makers who have the power to allocate resources to IC missionaries. This is required because we need to generate understanding at an early age with students, not just at the university level, but at any stage in primary and tertiary education where the curriculum addresses how value or values are created in society. At the organisational level, policy makers need to be converted and then convinced to provide the resources required to help educate and enable managers on how to mobilise IC in their organisations so they can knowledge‐ize IC in particular contexts. If this can be achieved, we will have created a society that encourages open conversations about IC and knowledge whereby the concept and the tools are available, empowering people and organizations to experience and benefit from IC. Only then, can we socialize IC so we are no longer reliant on industrial age thinking and can mobilise knowledge to create value for all.

References Chiucchi, M. S. (2013), “Intellectual capital accounting in action: Enhancing learning through interventionist research”, Journal of Intellectual Capital, Vol 14 No 1, pp. 48‐68. Cuganesan, S. and Dumay, J. (2009), “Reflecting on the production of intellectual capital visualisations”, Accounting, Auditing & Accountability Journal, Vol 22 No 8, pp. 1161‐86. Dumay, J. (2009a), “Intellectual capital measurement: A critical approach”, Journal of Intellectual Capital, Vol 10 No 2, pp. 190‐210. Dumay, J. (2009b), “Reflective discourse about intellectual capital: Research and practice”, Journal of Intellectual Capital, Vol 10 No 4, pp. 489‐503. Dumay, J. (2012), “Grand theories as barriers to using IC concepts”, Journal of Intellectual Capital, Vol 13 No 1, pp. 4‐15. Dumay, J. (2013), “The third stage of IC: Towards a new IC future and beyond”, Journal of Intellectual Capital, Vol 14 No 1, pp. 5‐9. Edvinsson, L. (1997), “Developing the intellectual capital at Skandia”, Long Range Planning, Vol 30 No 3, pp. 366‐373. Edvinsson, L. and Malone, M. S. (1997), Intellectual Capital: Realising your company's true value by finding its hidden brainpower, Harper Business, New York, NY. Guthrie, J., Ricceri, F. and Dumay, J. (2012), “Reflections and projections: A decade of intellectual capital accounting research”, British Accounting Review, Vol 44 No 2, pp. 68‐92. Kaplan, R. S. and Norton, D. P. (1992), “The Balanced Scorecard ‐ Measures that drive performance”, Harvard Business Review, Vol 70 No 1, pp. 71‐9. Kim, D. H. (1993), “The link between individual and organizational learning”, Sloan Management, Vol 35 No Fall, pp. 37‐50. Kolb, D. A. (1976), “Management and the learning process”, California Management Review, Vol 18 No 3, pp. 21‐31. Mouritsen, J., Bukh, P. N., Flagstad, K., Thorbjørnsen, S., Johansen, M. R., Kotnis, S., Larsen, H. T., Nielsen, C., Kjærgaard, I., Krag, L., Jeppesen, G., Haisler, J. and Stakemann, B. (2003), Intellectual Capital Statements – The New Guideline, Danish Ministry of Science, Technology and Innovation (DMSTI), Copenhagen. Petty, R. and Guthrie, J. (2000), “Intellectual capital literature review: Measurement, reporting and management”, Journal of Intellectual Capital, Vol 1 No 2, pp. 155‐76. Porter, M. (1980), Competitive Strategy: Techniques for Analyzing Industry and Competitors., Free Press, New York. Roos, J., Roos, G., Dragonetti, N. C. and Edvinsson, L. (1997), Intellectual Capital: Navigating in the New Business Landscape, Macmillan, Basingstoke. Stewart, T. A. (1994), “Your Company's Most Valuable Asset: Intellectual Capital”. Stewart, T. A. (1997), Intellectual Capital: The New Wealth of Organisations, Doubleday ‐ Currency, London.

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Knowledge Sharing and Innovation: An Empirical Study in Iraqi Private Higher Education Institutions Sawasn Al‐husseini and Ibrahim Elbeltagi Foundation of Technical Education, Institute of Administration Rusafa, Baghdad, Iraq University of Plymouth, School of Management, Plymouth, UK sawasn.al‐husseini@plymouth.ac.uk i.elbeltagi@plymouth.ac.uk Abstract: Knowledge and knowledge sharing are recognised as being important resources for competitive advantage, and key to enhancing the innovation of organisations. It is argued that the encouragement of knowledge‐sharing cultures within learning environments such as universities, can increase the quality of education and can create opportunities for innovation. This research aims to examine the impact of sharing (donating and collecting) knowledge on product and process innovation. A total of 230 usable questionnaires were collected from private colleges in Iraq. Structural equation modelling with AMOS 20 confirmed the importance of knowledge sharing in developing innovation in higher education. The results revealed that collecting knowledge has a greater effect on product and process innovation than donating knowledge. Guidelines are developed for academics as well as leaders, and evidence is provided in support of the use of sharing knowledge in enhancing product and process innovation within higher education in developing countries generally and in Iraq in particular. The implications of the findings and suggestions for future research are discussed. Keywords: knowledge sharing, product innovation, process innovation, higher education, developing countries

1. Introduction With globalisation and a rapidly changing environment characterised by frequent technological changes, today’s business organisations are facing new challenges. Higher education institutions like other organisations are affected by these rapid changes and increased demands (Mathew, 2010). These external pressures are forcing the education sector to be not only efficient and effective but also innovative (Herbst and Conradie, 2011). Knowledge and knowledge sharing (KS) are recognised as important weapons in producing a competitive advantage, and the key to enhancing innovation. It is argued that the existence of knowledge management and the promotion of a KS culture among organisational members play important roles in creating opportunities to be creative and form part of the learning process by converting the tacit knowledge embedded in individuals into explicit knowledge through interaction (Nonaka, 2006). Xiong and Deng (2008) argued that effective KS increased the accumulation of organisational knowledge and enhanced the capacity of their employees to do their jobs. Similarly, Bartol and Srivastava (2002) pointed out that KS among organisational members is an important instrument that increases the value of knowledge utilisation. In developing countries like Iraq, higher education is facing rapidly changing challenges, which require innovation (Herbst and Conradie, 2011). Educational markets are becoming increasingly global and the ability of Iraq’s education system to reach a global market will depend on how it makes changes in the following areas: systems, methods, curricula, and approaches. Within higher education environments, KS is a vital pillar of knowledge management (KM), and is critical to academic innovation (Daud and Abdul Hamid, 2006). Previous studies have looked at the relationship between KS and innovation (Yang, 2011; Zaqout and Abbas, 2012), however few touched on knowledge processes (donating and collecting) and their impact on teaching staff’s product and process innovation (Subramaniam and Youndt, 2005) within developing countries or more specifically in Iraq. Since innovation is important in private organisations including learning institutions such as universities (Ahmed and Shepherd, 2010), this research aims to examine the impact of knowledge sharing processes namely donating and collecting on product and process innovation using the context of Iraqi private colleges. The next section contains an overview of the literature as the basis for the main and sub‐hypotheses. Then, the method used to empirically analyse the hypotheses will be described, after which the findings will be presented. This will be followed by a discussion of the results and then, the implications of the research.

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Sawasn Al‐husseini and Ibrahim Elbeltagi Finally, the main conclusions will be outlined and some limitations and recommendations for future research will be pointed out.

2. Knowledge sharing and innovation Organisations are focusing increasingly on innovation as a key factor in success and competitive advantage. Innovative organisations have the capacity to improve individual and organisational performance, solve problems, and create competitive advantage (Liao and Wu, 2010). Nusair et al. (2012) defined innovation as the creation of new ideas, products, and processes, which have concomitant effects on performance. Previous literature reported that product and process innovation is essential for organisations, often determining an organisation’s success or failure (Liao and Wu, 2010). Within the higher education environment, Jaskyte (2004) noted that universities should adopt product and process innovation in order to raise educational performance. Educational quality is reliant on both product and process being adaptive to the changing environment (Obendhain and Johnson, 2004). Accordingly, in this research, innovation is taken to mean accepting, implementing, and developing educational products such as research projects, courses, teaching materials, and curricula, and process innovation focused on service delivery is assumed to include new technology and performance‐related pay. Knowledge consists of data that is raw numbers, facts and images that are derived from observation and not analysis, and information that summarises the data (Uriarte, 2008). In studying KS researchers acknowledge different types of knowledge, however, the most commonly used types of knowledge in the literature are tacit and explicit. Tacit knowledge is personal and embedded in the minds of people accumulated through study and experiences developed through social interaction and, thus, is difficult to share. In contrast to this, explicit knowledge denotes knowledge that is articulated, externalised and captured, and has a more tangible format such as is found in books, databases, models, procedures, rules and regulations, making it easily shared between individuals (Nonaka et al., 2006). Zheng et al. (2009) stated that KM includes acquisition, sharing and application. They asserted that innovation and effectiveness is achieved in KM when KS is taken into consideration. The knowledge‐based view recognises that for organisations, knowledge is a valuable resource. The role of knowledge and KS has emerged as an important area in the investigation of innovation in organisations (von Krogh et al., 2012). Hooff and Weenen (2004) described KS as a two‐dimensional process with organisational members sharing and exchanging their tacit and explicit knowledge. Through the processes of knowledge exchange, donation and collection, daily interaction creates new knowledge. Through KM processes and particularly KS, organisations can create opportunities for generating new ideas and developing innovation (Willem and Buelens, 2007). Innovation depends on employees’ knowledge, skills, and experiences in value creation (Wang and Wang, 2012).The knowledge‐based view suggests that organisations need practice in not only knowledge creation but more importantly in KS (Alavi and Leidner, 2001). Since knowledge is embedded in individuals, it is necessary to share it among organisational members so as to establish new routines and mentalities that will help them to solve problems (Cheng, 2012). When organisational members share their tacit knowledge and convert it into explicit knowledge through the processes of collecting and donating knowledge collective learning is generated which, in turn improves the stock of knowledge available to the organisation (Nonaka and Toyama, 2005). It is argued that KS among organisational members is likely to generate new ideas for developing product and process innovation (Mehrabani, 2012). Prior literature reported that KM and KS were antecedents of innovation. For instance, Smith et al. (2005) found them to be pivotal. Meanwhile, Jantunen (2005) found that knowledge dissemination to have an insignificant relationship with innovation, but knowledge application played an important role in supporting innovation. Similarly, Hung et al. (2010) indicated that knowledge creation, sharing, transfer, and application positively influence the speed, amount, and level of innovation through total quality management. Zaqout and Abbas (2012) pointed out that explicit and tacit knowledge acted as a bridge between trust, social networks, information and communication technologies and performance within Malaysian public universities. Cheng’s

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Sawasn Al‐husseini and Ibrahim Elbeltagi (2009) findings suggested that KS via interpersonal interaction and communities of practice is essential for improving teaching practice and the implementation of curricula. Although previous studies considered the relationship between KS and innovation, few touched on knowledge processes and their impact on teaching staff’s product and process innovation (Subramaniam and Youndt, 2005). Consequently, there is a need for research addressing the practical difficulties of KS for innovation (Xu et al., 2010) within developing countries and Iraq in particular, thus, this research proposes following: H1: Knowledge sharing through (H1a) donating and (H1b) collecting will positively influence product innovation in Iraq’s private colleges. H2: Knowledge sharing through (H2a) donating and (H2b) collecting will positively influence process innovation in Iraq’s private colleges.

3. Method This research used a quantitative approach to examine the effects of KS (donating and collecting) on product and process innovation. The quantitative approach sought to test theory so as to understand the measured phenomena (Saunders et al., 2009). A self‐administered questionnaire was developed, all of whose items were measured using five‐point Likert scales ranging from 1‐strongly disagree to 5‐strongly agree. The questionnaire was translated into Arabic using the translation back‐translation procedure.

3.1 Measures Twelve items were developed from Hooff and Weenen (2004) to measure KS, these reflected the exchange of teaching‐related knowledge, experience, and skills among teaching staff through the donating and collecting of knowledge. Thirteen items measured innovation, in terms of accepting or developing new ideas concerned with products and processes. Again, these items were developed from previous studies. Product innovation items were drawn from (Perri, 1993) and (Daft, 1978).These referred to the degree to which members of staff accepted, developed, and implemented new products such as courses, research projects, teaching materials, and curricula. The items for process innovation (Tsai et al., 2001), reflected the use of new methods of service delivery, through the development and use of new technology, and the implementation of incentives and rewards systems for members of staff.

3.2 Sample and procedure The research was conducted in the Iraq’s higher education sector. Stratified random sampling was used in this research. The main advantages of this type are accurate, easy, accessible, divisible into relevant strata, and low cost sampling (Saunders et al., 2009). Six private colleges were chosen in which to distribute the questionnaires using the delivery and collection method. There are many criteria took into consideration to select these colleges as the sampling frame: the concentration of the colleges and that they contained many and varied departments. This means that there were sufficient numbers of academic staff to distribute the questionnaire within them, and sufficient time and costs besides to the difficulties in delivering and collecting questionnaires that faced the researchers. In 1970, Iraq’s Ministry of Higher Education and Scientific Research (MOHESR) was founded. Its law confirms the autonomy of universities in scientific, administrative, and financial matters. By 2011, MOHESR had 24 public universities and 45 technical institutes and colleges, as well as 28 private colleges dealing with specific subjects. Private higher education emerged firstly in 1988. It uses talents, material and scientific capabilities in society to the benefit of the process of scientific structuring, and therefore contributes to development through the creation of specialties complementing those existing ones in public universities. This enables the establishment of a diverse scientific foundation in order to meet the needs of society, and, in addition, it contributes to reducing the unemployment rate by hiring staff, employees, and workers to work within the institutions. This sector is supervised directly by MOHESR but its budget is independent. The aim of these colleges is to graduate students in their chosen subjects, in the same way as public universities do, however, currently they offer only undergraduate‐level education. The latest statistics show the number of students enrolled to be 75,511 students (MOHESR, 2012). 300 questionnaires were sent to six private colleges, of which 230 were returned and usable for analysis. Table 1 gives the respondents’ characteristics (age, gender, marital status, tenure, etc.). There are more male

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Sawasn Al‐husseini and Ibrahim Elbeltagi (60.8%) than female respondents. In terms of age, tenure, and academic position, the respondents are distributed across the different categories. In terms of academic qualifications, the majority of the respondents (95.6%) held a Master’s degree or doctorate. Table 1: Profile of the sample Characteristic Gender Marital status

Age

Work experience

Academic qualifications Academic position

Male Female Single Married Divorced Widowed <29 30‐39 40‐49 50‐59 >60 <10 years 11‐15 16‐20 21‐25 >26 High diploma Master PhD Assistant lecturer Lecturer Assistant professor Professor

Frequency 140 90 89 120 2 19 35 25 30 79 61 56 30 36 69 39 10 90 130 60 90 55 25

% 60.8 39.2 38.6 52.2 1.0 8.2 15.2 10.8 13.2 34.3 26.6 24.3 13.2 15.6 30.0 16.9 4.3 39.2 56.5 26.2 39.1 23.9 10.8

4. Findings of the research Structural Equation Modelling (SEM) with AMOS was used to examine the effects of the KS processes (donating and collecting) on product and process innovation. The chosen to use AMOS in this research because of the availability of the programme and training in its use. The researchers found the programme beneficial to the statistical analysis in the research. SEM consists of two steps: a measurement model to evaluate the convergent validity of the constructs and a structural model to test and evaluate the causal relationships between the factors (Hair et al., 2010).

4.1 Validity and reliability of the model The model, which consists of four factors: knowledge donating, knowledge collecting, product innovation and process innovation was evaluated through Confirmatory Factor Analysis (CFA). The convergent validity was tested by investigating significant factor loadings of 0.5 or higher (Hair et al., 2010). Additionally, the average variance extracted (AVE) measure was used, this should be 0.5 or higher (Fornell and Larcker, 1981), items with factor loadings of less than 0.5 were deleted from the scale. Nine items did not make good contributions to their predicted constructs, and, therefore, were deleted from the scale to improve the model. Reliability was assessed separately for each dimension included in the model based on the Cronbach’s alphas and Composite Reliability (CR), each of which should exceed 0.7 (Hair et al., 2010). The results shown in Table 2 indicate that the convergent validity and internal reliability were satisfactory. All factor loadings and the CR and AVE were acceptable and significant. Discriminant validity was assessed using the criteria established by Fornell and Larcker (1981). According to them, the AVE should be greater than the squared correlations between the two constructs. The constructs for all of the data were found to be empirically distinct and the discriminant validity was confirmed statistically. Table 3 displays the means and standard deviations. Additionally, it shows that the variances extracted from the constructs were greater than all of the squared correlations between the items

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Sawasn Al‐husseini and Ibrahim Elbeltagi Table 2: Validity and reliability of the model (CFA) Factor

Item

AVE

KD within department

0.83

0.72

0.89

0.85

0.75

0.88

0.86

0.73

0.86

0.89

0.67

0.90

KD1

0.806

Knowledge donating (KD)

KD2

0.840

(F1)

KD outside department

KD3

0.900

KD4

0.850

KC within department

KC5

0.820

Knowledge collecting (KC)

KC6

0.860

(F2)

KC outside department

KC7

0.839

KC8

0.840

PD9

0.900

PD10

0.850

PD11

0.600

PD12

0.950

PC13

0.730

PC14

0.790

PC15

0.800

PC16

0.850

Product innovation (PD) (F3)

Process innovation (PC) (F4)

Note: AVE = average variance extracted; CR = composite reliability; α= Cronbach’s alpha, N= 230 Table 3: Means, standard deviations and discriminant validity analysis Factor 1‐knowledge donating 2‐knowledge collecting 3‐product innovation 4‐process innovation

Mean 3.220 3.400 3.147 3.240

SD 0.980 0.880 0.870 0.873

1 0.72 0.116 0.289 0.311

2 0.75 0.290 0.322

3 0.73 0.204

4 0.67

Note: SD = Standard Deviation, N=230 The measurement model was evaluated by using the fitness of fit indices as shown in Table 4. There were three basic indices: (1) the fit indices, including X², X²/df, the goodness‐of‐fit index (GFI), and the root mean square error of approximation (RMSEA). (2) Incremental fit measurement which includes a Normed Fit Index (NFI), and a Comparative Fit Index (CFI). (3) The Parsimonious Normed Fit Index (PNFI)

4.2 Structural model and hypotheses tests This research aimed to examine the effect of KS processes namely donating and collecting knowledge on product and process innovation in Iraqi private higher education institutions. Table 5 and Figure 1 show that the goodness‐of‐fit indices indicate adequate levels of fit for the model.

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Sawasn Al‐husseini and Ibrahim Elbeltagi H1 is concerned with the effect of knowledge donating on product and process innovation. The path coefficients were confirmatory at levels 0.523 and 0.608 respectively and their significance as shown by p<0.05 indicated that H1 is fully supported. H2 is concerned with the effect of knowledge collecting on product and process innovation. Table 5 shows effective sizes of 0.592, and 0.616 respectively, therefore, H2 is confirmed. Table 4: The fit indices of the model Fit indices χ²/ df CFI NFI TLI RMSEA PNFI

KS 1.599 0.990 0.975 0.986 0.052 0.862

Innovation 1.938 0.989 0.977 0.983 0.031 0.828

Target ≤ 2‐ 5 ≥ 0.90 ≥ 0.90 ≥ 0.90 < 0.05 – 0.08 The higher the better

Source

(Hair et al., 2010)

Table 5: Results of SEM Hypothesis H1a H1 H1b H2a H2 H2b H1, H2 Fit indices

Hypothesis path Estimate Results Knowledge donating → product 0.523** Supported Knowledge donating → process 0.608* Supported Knowledge collecting →product 0.592* Supported Knowledge collecting →process 0.616* Supported KS→ innovation 0.584** Supported χ²=642.500 with 462, χ²/df =1.390, RMSEA = 0.040, NFI= 0.900, CFI=0.969, TLI=0.967, PNFI= 0.872

Note: p*<0.05, p**< 0.01 In a further piece of analysis, the effects of donating and collecting knowledge within and outside departments are examined. The results show that collecting knowledge has a greater effect both inside (0.510) and outside (0.430) departments. While donating knowledge has a slightly smaller effect (0.424) within departments and appears to be unnecessary outside departments (0.233).

Figure 1: Results of SEM of the model

5. Discussion of the findings The results of the SEM supported the proposed relationships. Knowledge donating and collecting were found to be positively related to product and process innovation in private colleges (H1a‐b, H2a‐b). According to the knowledge‐based view, knowledge is a valuable resource for organisations (Nonaka et al., 2006). Knowledge and KS emerged as an important area in the investigation of performance and innovation (von Krogh et al., 2012). KS is two‐dimensional with organisational members sharing and exchanging both tacit and explicit knowledge. Daily interaction creates new knowledge through the processes of knowledge exchange donation and collection (Hooff and Weenen, 2004). The purpose of donating knowledge is to see tacit knowledge become explicit and owned by the entire group. Meanwhile, collecting knowledge refers to consulting and seeking it, this in turn improves the entire stock of knowledge available to the organisation (Nonaka et al., 2006).

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Sawasn Al‐husseini and Ibrahim Elbeltagi Product and process innovation is enhanced when organisational members exchange information, insights, skills, lessons learned, and experiences (Wang and Wang, 2012). The knowledge‐based view suggests that organisations need not only generate knowledge but also, more importantly share it. When knowledge is used, learning takes place, in turn, this develops the stock of knowledge available to the organisation (von Krogh et al., 2012). The results of the current research demonstrate that the members of staff surveyed have the willingness to donate and collect their skills, insights, experiences, expertise, information and notes both inside and outside their own departments. Consequently, this enables their colleges to improve their products (i.e. research and projects with other sectors, new courses, and curricula) and process (taking and developing training programmes and adopting new technology) innovation. These findings contradict Jantunen’s (2005) study, which concluded that knowledge acquisition and innovative performance do not have a significant relationship. However, it does support the assertions of previous research such as Cheng (2012) who indicated that promoting KS practice within an educational environment helps members of staff to discuss different ideas about teaching methods, experiences and skills that could increase the effectiveness of teaching and learning performance and thus supporting product and process innovation. This research has also revealed that collecting knowledge is more effective than donating knowledge both within and outside departments, whilst donating knowledge appeared to be unnecessary outside departments. This might be due to the Arabic culture and particularly that within the Iraqi academia, in which people tend to work in small groups with the same values, beliefs and problems. Therefore, donating knowledge within departments may be easier to achieve, whilst collecting knowledge requires actively consulting with colleagues so as to learn from them. Consequently, it requires the understanding of new ideas and culture. These results support the earlier, qualitative stage of this research project (Al‐husseini and Elbeltagi, 2012) and such results are congruent with proposals from Kamasak and Bulutlar (2010) who also found collecting knowledge to have more effect on innovation than donating knowledge.

6. Implications of the research This research aimed to examine the effects of donating and collecting knowledge on product and process innovation in Iraqi private higher education institutions. These relationships had not been studied previously in a higher education environment. This research makes a theoretical contribution to the literature on KS and innovation. KS is known to be a key factor in building competitive advantage and is the core component of innovation. This research’s results confirm the importance of KS and innovation theory and help to provide a better understanding of the linkages between KS and innovation. The research provides support for the knowledge‐based view of KS and the strength of the role KS plays in enhancing product and process innovation. The results imply that innovation emerges if knowledge‐intensive universities can encourage and create a KS culture among their teaching staff. Therefore, the results contribute significantly to the literature on KS and innovation, In addition, they provide a better understanding of these relationships in the educational environments of developing countries particularly Iraq a context that has been neglected in previous studies. From the methodological perspective, the research supports and demonstrates the validity and reliability of the KS and innovation scales, these provided a great fit with the results from the Iraqi higher education sector. The paper offers also a valuable example of a methodology, which might be used to track the extent of KS’s effect on innovation. This research has implications that could help academic leaders in higher education to establish KS strategies in order to achieve innovation in their universities. The results illustrate the importance of KS in Iraq’s private colleges and show that innovation requires members of staff to share and generate new knowledge. Therefore, leaders should design strategies aimed at encouraging teaching staff to engage in KS activities such as conferences, sessions, courses, etc.

7. Conclusions, limitations, and future research Organisations need knowledge and KS in order to enhance innovation. This research aimed to examine the impact of donating and collecting knowledge on product and process innovation in private higher education institutions in Iraq. The research findings demonstrate the importance of KS and innovation. Based on 230 responses, this research used SEM to test the hypothesised model and hypotheses. The results revealed that knowledge donating and collecting are antecedents of product and process innovation.

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Sawasn Al‐husseini and Ibrahim Elbeltagi The sample was drawn from the higher education sector. Hence, the results cannot be generalised to other sectors. Future research could explore such relationships in other sectors such as manufacturing. The model was applied in a developing country, namely Iraq. Future research could examine the model in other countries that share with Iraq similar structures, cultures, and contexts. Finally, the research tested the effects of donating and collecting knowledge on product and process innovation. Although the KS processes explained an acceptable level of variance in product and process innovation, future research could analyse intermediate constructs such as leadership style, which may produce better explanations of the development of innovation.

Acknowledgements This project has been funded by Ministry of Higher Education and Scientific Research in Iraq

Appendix Construct Knowledge donating Within department

Knowledge donating outside department

Knowledge collecting within department

Knowledge collecting outside department

Product innovation

Process innovation

Item Knowledge sharing with colleagues is considered normal thing in my department When I have learned something new regarding teaching profession, I tell my colleagues in my department about it. * When they have learned something new, my colleagues within my department tell me about it When they have learned something new, colleagues outside of my department tell me about it. When I have learned something new regarding teaching profession, I tell colleagues outside of my department about it. Knowledge sharing with colleagues is considered normal thing outside of my department. * I share any information I have with colleagues within my department when they ask for it. Colleagues within my department share knowledge with me , when I ask them about it. I share my skills with colleagues within my department when they ask me to.* I share information I have with colleagues outside of my department, when they ask me to. Colleagues outside of my department tell me what they know when I ask them about it. * I share my skills with colleagues outside of my department, when they ask me to. Our university is always delivering new courses for members of staff Our university constantly emphasises development and doing research projects Our university often develops teaching materials and methodologies Our university is extending its programmes/ services to new groups of employees not previously served by the university/institute. * Our university often develops new programmes/ services for members of staff and students Our university is developing new training programmes for staff members Our university implements an incentive system (i.e. higher salaries, bonuses, ‐‐) to encourage members of staff to come up with innovative ideas. * Our university often develops new technology (internet, databases, ‐‐‐) to improve the educational process. Our university encourages teamwork and relationships between staff members. * This university implements a reward system (i.e. promotions, thank‐‐‐) for members of staff to encourage them to come up with innovative ideas. New multimedia software, for educational purposes and administrative operations, is implemented by this university. * Our university is trying to bring in new equipment (i.e. computers) to facilitate educational operations and work procedures. Our university implements an incentive system (i.e. higher salaries, bonuses, ‐‐) to encourage members of staff to come up with innovative ideas. *

Note: (* ) Refers to the item deleted after running convergent validity test

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Big Data and Intellectual Capital: Conceptual Foundations Scott Erickson1 and Helen Rothberg2 1 Ithaca College, Ithaca, USA 2 Marist College, Poughkeepsie, USA gerickson@ithaca.edu hnrothberg@aol.com Abstract: The fields of knowledge management and intellectual capital have always distinguished between data, information, and knowledge. One of the basic concepts of the field is that knowledge goes beyond a mere collection of data or information, including know‐how based on some degree of reflection. Another basic concept is that intellectual capital, as a field, deals with valuable organizational assets which, while not formal enough to rate a designation as intellectual property, still deserve the attention of managers. Intellectual capital is valuable enough to be identified, managed, and protected. So what do we make of current trends related to big data, business intelligence, business analytics, cloud computing, and related topics? Organizations are finding value in basic data as well. How does this trend square with the way we conceptualize intellectual capital and value it? This paper will work through the accepted literature concerning knowledge management and intellectual capital to develop a view of big data that fits with existing theory. As noted, knowledge management and intellectual capital have both recognized data and information though generally as non‐value precursors of valuable knowledge assets. In establishing the conceptual foundation of big data as an additional valuable knowledge asset (or at least a valuable asset closely related to knowledge), we can begin to make a case for applying intellectual capital metrics and knowledge management tools to data assets. We can, so to speak, bring big data into the KM/IC fold. In developing this theoretical foundation, familiar concepts such as tacit and explicit knowledge, learning, and others can be deployed to increase our understanding. As a result, we believe we can help the field better understand the idea of big data and how it relates to knowledge assets as well as provide a justification for bringing proven knowledge management strategies and tools to bear on big data and business analytics. Keywords: knowledge management, intellectual capital, data, information, big data, business analytics

1. Knowledge The value of intangible assets to the organization has long been recognized, going back to classic economists such as Schumpeter (1934) and management theorists such as Drucker (1991). The idea that such intangibles might be a key source of competitive advantage also has a deep history, including Nelson & Winter (1982). Based on the resource‐based theory of the firm (Wernerfelt 1984), a more contemporary view has centered squarely on the key role of knowledge in obtaining and sustaining competitive advantage. Indeed, the knowledge‐based view of the firm (Teece 1998; Grant 1996) suggests that knowledge may be not only a source, but the source of unique, sustainable marketplace advantage. The fields of knowledge management (KM) and intellectual capital (IC) have to do with identifying and managing knowledge assets effectively in order to gain this competitive advantage. IC grew out of accounting and centers on identifying and measuring the knowledge assets of organization (Bontis 1999; Edvinsson & Malone 1997; Stewart 1997). KM is more about effectively managing these assets, through combination, sharing, and other methods leading to their growth (Zack 1999a; Grant 1996). Both fields have always focused on the nature of the knowledge assets. In intellectual capital, there is a standard distinction between human capital, structural capital, and relational capital (Bontis 1999). Human capital generally has to do with job‐related know‐how, structural capital with enduring knowledge existing within the organization (e.g. corporate culture), and relational capital with knowledge concerning external relationships (e.g. customers, suppliers, regulators). KM has focused on aspects of knowledge that can make it easier or harder to capture and/or share it such as tacitness vs. explicitness (Nonaka & Takeuchi 1996; Polanyi 1967), complexity, and stickiness (McEvily & Chakravarthy 2002; Zander & Kogut 1995; Kogut & Zander 1992). In addition, the field has also focused on tools and techniques that can be employed for KM purposes, especially those tools that may be more or less appropriate given the nature of the knowledge to be managed (Choi & Lee 2003; Schulz & Jobe 2001; Boisot 1995). Given full awareness of the circumstances and likely approach, techniques such as communities of practice, IT‐based knowledge markets, or other options can be applied (Brown & Duguid 1991; Matson, Patiath & Shavers 2003; Thomas, Kellogg & Erickson 2001).

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Scott Erickson and Helen Rothberg Organizational variables also matter, including absorptive capacity of the firm (Cohen & Levinthal 1990) and its degree of social capital (Nahapiet & Ghoshal 1998). The full range of knowledge characteristics and organizational capabilities can poses their own issues with workability, including how matters such as motivation and trust can influence participation. It’s really a matter of choosing the right approach for the circumstances of the firm and can be a complex decision.

2. Beyond knowledge One characteristic present in all of this research on knowledge, knowledge assets, and knowledge development is a clear emphasis on the nature of knowledge. A distinction between knowledge, information, and data is quite apparent and very deliberate (Zack 1999b). Data are simply observations, information is data in context, and knowledge is information subjected to experience, reflection, or some other practice providing a deeper understanding. The KM field, in particular, has always been quite clear about its subject matter being more difficult to manage knowledge, or know‐how, particularly since data and information are by definition explicit and easily exchanged through information systems. There is a recognized relationship between data, information, and knowledge, principally the potential for data and information to turn into knowledge upon reflection, experience or learning, providing good reason to term these undeveloped observations “preknowledge” (Rothberg & Erickson 2005). But there is still reluctance in the field to study any phenomenon not rising to the level of knowledge. One early exception to this view came from competitive intelligence (CI). CI evolved during the 1990’s, at much the same time as KM scholarship and practice was growing. At heart, the field is also about collecting knowledge, though it also includes data and information about competitors that is then organized, processed, and analyzed for key strategic and tactical insights (Prescott & Miller 2001; Gilad & Herring 1996; Fuld 1994). If one reviews CI scholarship, it often focuses on sources of information and related applications (Fleisher & Bensoussan 2002; McGonagle & Vella 2002). As just implied, this is often information or even raw data in addition to knowledge (e.g. financial reports or regulatory filings). The true value of CI, seen as CI operations mature within a firm, is in the range of intelligence‐gathering sources and networks, combined with the growing analytical skills of team members (Wright, Picton & Callow 2002; Raouch & Santi 2001). So KM and CI have readily apparent similarities (Rothberg & Erickson 2005; 2002). Collection and distribution of key knowledge or information are critical to both, and methods differ by the nature of the application. The broad strategy of seeking competitive advantage from knowing something the competition doesn’t (about your company or theirs) is identical. Where we begin to see differences, however, is in the nature of the inputs and what is done with them. As noted earlier, CI operations will often draw bits and pieces concerning a competitor from a variety of sources, and those inputs could include data, information, or knowledge. But it is in directed analysis and purposefully drawing insights from the information and data that CI begins to separate itself further. The objective of CI is actionable intelligence, so CI teams typically review all available resources to discern patterns or ideas about competitor behavior. Such operations are responsible for understanding competitor actions, uncovering the strategies behind the actions, and, at the highest levels, anticipating strategic and tactical moves (Gilad 2003; Bernhardt 1993). This attitude is rare in KM circles, but, as we’ll see, is a major driver behind the big data and business intelligence trend. Indeed, we believe it would be very surprising if KM scholarship and practice doesn’t also move in this direction. In some ways, the field has already started to shift. The rapid growth of various analytical and intelligence efforts in specific disciplines has shown that potentially valuable intangible assets are found in a variety of places inside and outside the firm. These assets may or may not fit within the traditional KM or intellectual capital frameworks, though most treatments of big data or business intelligence at least nod to KM or related systems (Bose 2009; Jourdan, Rainer & Marshall 2008). Andreou, Green & Stankosky (2007) developed a List of Operational Knowledge Assets (LOKA) to identify the wide variety of areas now contributing data, information, knowledge and/or intelligence to the organization. These include:

Market capital

Competitive intelligence

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Scott Erickson and Helen Rothberg

Enterprise intelligence

Human capital

Decision effectiveness

Organizational capital, and

Innovation & customer capital

While one could squeeze these items into the traditional intellectual capital categories of human, structural, and relational capital, the richer description adds greater context to the discussion (and brings in the competitor element). The LOKA approach also makes clear that information and data might be part of the intangible asset mix, albeit at a lower level than most knowledge assets. But it is clear the discussion is starting to expand beyond our traditional definitions of knowledge. Big data, business analytics, or whatever else one wants to call it will likely be a part of this discussion. By any definition, the advent of big data has been driven by the dramatic decrease in the cost of data processing. More power and decreased costs have led to an ability in many firms to store ever greater amounts of data and conduct more in‐depth analysis on a regular basis, either through their own IT systems or in the cloud (Bussey 2011; Vance 2011b). Cloud services are available at reasonable costs by any number of big providers, including such well‐known names as amazon.com, Google, and Microsoft. While surrendering the data to a second party gives away some level of control, security may actually be increased as the larger providers are usually more experienced at keeping data away from prying eyes. Many of the big data applications have to do with operational and/or transactional data, shedding light on operations, supply chain, or distribution channel performance or on customer/consumer behavior (Vance 2011a). Big data, in particular, has the potential to add value by providing transparency with immediate performance feedback, experimentation with quick results, more precise segmentation, more objective decision‐making (algorithms rather than humans), and new products (Manyika, et. al. 2011). Big data and business analytics bring new capabilities to the party, and we need to discuss how they fit within the knowledge management/intellectual capital universe.

3. Big data and business analytics In order to provide a framework for discussion, we created Table 1 from two sources. Initially, there is information concerning big data, taken from a McKinsey Global Institute (MGI) report (Manyika, et. al. 2011). This is combined with industry categorizations based on levels of intangible assets and competitive intelligence activity (Erickson & Rothberg 2013; 2012). From this table, we can begin to suggest some ideas concerning the relationship between big data and knowledge as well as what underlying concepts may explain differences present in the information. Table 1: Big data, knowledge, and competitive intelligence, by industry Industry

Stored Data per Firm (terabytes)

Security & investment Services Banking Communications & Media Utilities Government Discrete Manufacturing

3,866 1,931 1,792

Stored Data, US Industry (petabytes) 429 619 715

1,507 1,312 967

194 848 966

Insurance Process Manufacturing Resource Industries

870 831 825

243 694 116

Transportation Retail Wholesale

801 697 536

227 364 202

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Ease of Capture Factors (top factors, quintile)

SPF Category

Talent (1), data availability (2) Talent (1), data availability (2) Not included

SPF 30 SPF 30 SPF 45

Data‐driven mindset (1), data availability (1) Data availability (4) Talent (1), data availability (1) Talent (1), data availability (2) Talent (1), data availability (1) Data‐driven mindset (1), data availability (1) IT intensity (1), data availability (2) Data availability (4) IT intensity (2)

SPF 5 N/A SPF 45 SPF 30 SPF 30, 15 SPF 45, 30 SPF 5 SPF 15 SPF 15

Scott Erickson and Helen Rothberg Industry

Stored Data per Firm (terabytes)

Health Care Providers Education Professional Service

370 319 278

Stored Data, US Industry (petabytes) 434 269 411

Construction Consumer & Recreational Services

231 150

51 105

Ease of Capture Factors (top factors, quintile)

SPF Category

Data‐driven mindset (1), data availability (1) Talent (2) Talent (1), IT intensity (2) All (3) or below IT intensity (2)

SPF 45 SPF 30 SPF 15 SPF 30

The first three columns are taken straight from the MGI report, including the industry definitions, though sorted according to Stored Data per Firm for our purposes. Stored Data by US Industry was sourced from research firm IDC and is an estimate of the total data held by firms with more than 1,000 employees in each broadly defined industry. This number is then divided by number of firms to get the per firm figure in the second column. Per firm obviously provides a much different number as number of firms varies dramatically between concentrated industries like those in financial services and dispersed industries such as manufacturing. Figures are from 2008. The MGI report also provides an estimate of “Ease of Capture” of the value potential of big data for each industry. The estimate is based on four indicators, most of which have some relation to common knowledge concepts.

Talent would be closely related to our common understanding of human capital. In particular, human capital with a tacit emphasis as individual talent or know‐how may be difficult to share.

IT intensity has a connection to structural capital. Although the latter term has other facets (corporate culture and other enduring common knowledge of the organization), the IT structure of the firm for managing data, information, and knowledge is also a substantial part of structural capital. Another aspect of this indicator would be that the firm has a good amount of explicit knowledge (capable of management with IT systems) and/or data and information.

Data‐driven mindset goes back to human capital, specifically the knowledge of the firm’s managers and leaders.

Data availability is the one indicator that is not really knowledge‐related but has to do with the knowledge precursors, data and information.

The SPF column relates to a Strategic Protection Factor framework for analyzing the level of knowledge development in an industry (need for and use of KM) contrasted with the level of competitive intelligence activity (need for protection of knowledge assets) (Rothberg & Erickson 2005). Developed as an explanation for why aggressive investment in KM may or may not be an appropriate strategy, it also considers whether CI offense and defense are worth investment and effort. The categorizations in this table are based on concrete numbers from a large database constructed to validate the SPF framework (Erickson & Rothberg 2012) and additional analysis linking the SPF’s to big data (Erickson & Rothberg 2013). Conditions for each SPF can be summarized as:

SPF 45: High KM, High CI. A high level of knowledge development in industry and a high degree of competitive intelligence activity both exist. Investment in KM and in protection from CI are recommended for competitive success.

SPF 30: Low KM, High CI. Knowledge development is at a lower level but competitive intelligence activity remains high. Aggressive investment in KM may be unwise but protection measures are very important.

SPF 15: High KM, Low CI. Knowledge development is again high but now competitive intelligence activity is low. KM can be actively pursued without great worries about protection.

SPF 5: Low KM, Low CI. Neither knowledge development nor competitive intelligence is present to any significant degree. Investment in either knowledge development or knowledge protection is probably unnecessary.

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4. Discussion The nature of the SPF categories leads naturally into a discussion of how big data and business analytics fit into the standard KM and IC conceptual framework. Some of the SPF distinctions are counterintuitive, as we see in the middle categories (SPF 30, SPF 15) that knowledge has value to one party but not another. The extremes, where knowledge has value to everyone (SPF 45) or to no one (SPF 5) are more immediately understandable. But there are good reasons behind these results. When examining the characteristics behind SPF results, potential explanations emerge. Variables from the literature include knowledge characteristics (tacit/explicit, complexity, specificity to application or firm), knowledge types (human, structural, or relational capital), stage of industry life cycle, value chain location of critical knowledge, visibility, competitive intensity and others (Erickson & Rothberg 2012). In SPF 45, for example, a mix of valuable tacit and explicit knowledge exists but the emphasis is more on explicit than what one sees in other sectors. Valuable knowledge is also often found at multiple places along the value chain, not just in operations, just in marketing, or just in logistics, and can be hidden from view. Industries are mature but still in early stages with a high degree of competition, bordering on hypercompetition. In these industries it makes sense that knowledge management is active (taking tacit insights, making them explicit, and sharing throughout the firm and its network) but that competitive intelligence is needed and active, too. This is seen in the MGI results as well, and would suggest big data would fit right into these industries as a valuable contributor. With the exception of education (which is for‐profit in the SPF data, both for‐profit and non‐profit in the MGI data), big data is present in these industries, particularly discrete manufacturing (which would include industries like pharmaceuticals and semiconductors) and communications and media (with telecommunications and computers/software). We typically view these types of industries creating tacit learning from explicit knowledge assets, then turning the tacit learning into more explicit knowledge to be further distributed. Big data would add to this pattern, with tacit insights coming from big data and being turned into useful explicit knowledge. The pattern is already there to analyze intangible assets and turn them into something useful. SPF 30 is a different animal. This category is one with different perspectives on the value of knowledge as KM does not have a high value but CI does. So knowledge development is of little interest to the originating firm but is highly desired by competitors. What we believe we see in this group is explicit knowledge that is well‐ known throughout the industry with very little new, proprietary knowledge being created. When a tacit insight does come along, however, it is highly individualistic. There is an originating individual more than an originating firm, though the originating firm obviously benefits from the creativity. But the extremely tacit and personal nature of insight makes it hard to manage and duplicate through KM systems. The knowledge is rapidly incorporated into products and made explicit but, again, the creative process is hard to share with others in the company. CI in these industries is aggressive and significant precisely because creative insights are so rare, they are hidden, and competitor discovery is possible because of the explicit outputs. So competitors need to have a substantive operation in order to uncover the new insights, and payoffs are both possible and rewarding. The financial services industries are good examples of this group (investments, banking, insurance) as all have well‐ understood basic products, but new strategies or products (portfolio strategy, loan targets) can be a competitive advantage until uncovered and rapidly copied by competitors. Similarly, some natural resource industries, process manufacturers, and professional services (accounting, advertising) have industry‐wide knowledge that is only slowly adjusted with new insights, incorporated into processes, and then vulnerable to competitor copying. The big data implications are that these industries are significant users of data. And the MGI results reflect the previous conclusions as we see a combination of talent (tacit insights) and data applications (established explicit knowledge) present in many of these industries. Just like knowledge, much of the content of these databases is explicit and non‐proprietary, except in the details. But these industries are exactly the type to benefit from a combination of big data, carefully managed KM, and aggressive CI offense and defense. The vast amount of big data to be analyzed is the type of thing to lead into proprietary tacit insights. While there is

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Scott Erickson and Helen Rothberg little benefit to aggressive investment in KM systems, big data may provide a lower cost, lower risk (lost data to CI is much different than lost knowledge) approach to seeking those rare creative insights. SPF 15 has a similarly bifurcated situation with knowledge now of great value to the originator but little apparent CI activity. Industries in this category tend to be more mature, with established processes, established brands, and a great deal of explicit knowledge. This lends itself to KM, with different parts of companies learning from one another, encouraging the regular exchange of explicit knowledge about sourcing, operations, logistics, or other aspects of running the firm. Knowledge is valuable and explicit but CI is muted. A number of reasons likely exist, but a couple of readily apparent ones are in the openness of many of these industries. Wholesale, retail, construction, and some of these process manufacturers don’t have a lot of secrets—competitors can find out a lot about them by simply walking through or observing a facility and/or its outputs. So a full‐bore CI operation might not be necessary to uncover competitor insights. Another aspect of these industries is the concentration. In quite a number, as one looks into the specifics, there are dominant firms and/or strong brands. Such a situation may block any copying by a competitor, even if it fully understands the knowledge a firm employs. Everyone knows what Wal‐Mart does, for example, but copying its state‐of‐the‐art supply chain is another matter altogether given its size and installed base. The company can freely employ its accumulated knowledge without worrying overly much about competitors being able to duplicate its massive IT and logistics capabilities. Once again, the fit of big data into this structure is fairly clear. These industries run on data and explicit learnings based on the data. They are heavily dependent on well‐understood transactional, operational and logistical principles, and data and deeper analysis feed right into that. With established KM systems, they can readily take advantage of any new learnings. And, of course, with limited CI activity, they have little to fear from wide dispersion of new knowledge. The MGI data are a bit of a mixed bag but do show some emphasis on data availability and IT‐intensity, which makes some sense in these types of industries running on established principles and efficiency. Finally, SPF 5 shows little interest in knowledge on the part of the originator or its competitors. These are often highly mature industries with little new under the sun and possibly regulated. While any business can have a new, bright idea, they are few and far between in these industries and probably not worth aggressive investment to pursue. Here, they are illustrated by utilities and transportation. While there are some logistical complications to both, not much is proprietary or new, so KM is not actively pursued. Nor is there any reason to bother with CI if little can be learned from competitors. These industries do show something a little different in the big data results. The MGI data show that substantial data are present and some potential for capture and to be put to good use. Analysis of the wealth of data available in some of these industries may provide some opportunities we haven’t seen from a pure knowledge perspective. At the same time, managers should be aware of the seemingly limited payoff to come from tacit insights. New potential exists but should be seized with care.

5. Conclusions A natural connection exists between KM, IC, and the burgeoning trend toward the application of big data and business analytics. All deal with some sort of intangible asset, be it data, information, knowledge, or intelligence. By focusing on the strategic aspects of developing and protecting knowledge, we can get a better sense of when and how big data might fit into our conception of how knowledge assets can benefit an organization. By reviewing variables such as the nature of knowledge (tacit and explicit, in particular), we can get a handle of what types of knowledge is suitable to develop in various industries. From this perspective we can start to get an idea of when and where further contributions from big data may be helpful. Similarly, such variables can lend insight into the protection of intangible assets, and can give us guidance into whether data is at risk or not, and whether steps should be taken to protect it from competitive incursions. The natural connection between KM, IC, and big data is clear. Both fields will benefit from initial steps such as this to find ways to arrange a meeting of the minds.

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A Risk and Benefits Behavioral Model to Assess Intentions to Adopt Big Data José Esteves1 and José Curto2 1 E Business School, Spain 2 UOC, Spain jose.esteves@ie.edu jcurtod@uoc.edu Abstract: Everyday a constant stream of data is generated as a result of social interactions, Internet of things, e‐commerce and other business processes. This vast amount of data should be collected, stored, transformed, monitored and analyzed in a relatively brief period of time. Reason behind is data may contain the answer to business insights and new ideas fostering competitiveness and innovation. Big Data technologies/methodologies have emerged as the solution to this need. However, being a relatively new trend there is still much that remains unknown. This study, based on a risk and benefits perspective, uses the theory of planned behavior to develop a model that predicts the intention to adopt Big Data technologies. Keywords: big data, perceived benefits, risks, decomposed theory planned behavior, adoption

1. Introduction Understanding the adoption of information technology (IT) innovations continues to be a challenge for information systems (IS) researchers (Venkatesh, 2006). Every aspect of society, including business and culture, is currently in the midst of a technology‐based phenomenon. Advances in digital sensors, communications, mobile networks, storage, processing and cloud computing have given rise to huge collections of data, capturing valuable information to business, science, governments, and society (Bryant et al. 2008, Firestone 2010). By 2020, more than 2.7 zettabytes of data will be created annually reaching 35 zettabytes (IDC 2011) this will call into question the ability of firms to analyze information. Traditional decision‐making systems are incapable of adequately resolving this problem. Therefore, companies are starting to roll out their own Big Data initiatives and building massive database systems to drive significant new growth in their business operations (Manyika et al., 2011). Although the concept of Big Data exists since 2001 when the META Group analyst Doug Laney (Laney 2001) defined data growth challenges and opportunities as being three‐dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources), only in the last two years Big Data has become one of the IT industry’s hottest topics. In the press literature, Big Data is characterized as the new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high velocity capture, discovery and/or analysis (Woo et al. 2011). The Big Data market is expanding rapidly since many firms are expending significant resources on related projects, or are planning to. According to IDC (2012), this market is expected to grow from $3.2 billion in 2010 to $16.9 billion in 2015 based on the premise that these technologies will improve operational efficiency and drive innovation. Software Vendors such as IBM, Oracle, Microsoft, EMC or SAP, are already providing Big Data services as a source of competitive advantage for their customers. Big Data systems are being implemented in multiple industries, including commerce, science, and society (Bryant et al. 2008), but many companies still are not interested in this new trend. A Big Data survey conducted in June 2012 by IDC found that 47% of 502 companies across different industries think that they do not need Big Data technologies and 25.8% of them do not see the value it can generate for their companies. Simon (2010) provides a sobering statistic: three out of five Big Data projects do not meet expectations in terms of cost and performance. The major implementation costs are incurred during the integration of Big Data into the existing IT framework. Also, given the high level of sophistication required for Big Data projects (Mckinsey 2011), there are some fears related to the implementation playing against adoption.

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José Esteves and José Curto All together, these facts lead to the conclusion that the market is at an early stage of adoption, hence only early adopters are betting on these new technologies. Overall, Big Data represents a disruption in decision‐making by enabling business processes to be effectively based on information. Nonetheless, the main challenge at this point is not the deployment of the technology, but rather the transformation of the culture, processes, and people within organizations. The overall purpose of this study is to explore the impact of Big Data technologies perceived risks and benefits in the intention to adopt them. Since behavioral intention may not be reflected in actual use, this paper also examined the relationship between intended and actual use.

2. Theoretical background The academic literature on Big Data is still scarce. Recent articles published focus more on the software, algorithms and hardware needed for Big Data, especially in techniques such as Hadoop, while the adoption decision issues remain unattended. The initial definition of Big Data was composed of three‐dimensional characteristics (known as the 3vs model): volume, variety and velocity. Volume refers to the need for intensive and complex processing of data subsets that actually contain information of value for an organization. Variety refers to the combination of different types of data from different sources. The attribute of variety therefore alludes to the fact that data can come from inside or outside the organization, and may also be structured, semi‐structured, or unstructured. Finally velocity, not all of the data in an organization has the same urgency of analysis. There is a full range of velocities: from data that can be batch processed (as in the case of data warehousing) to data that must be processed in real time (when continuous data streams need to be analyzed). The key to understanding speed in Big Data is to clearly identify the informational requirements of the processes and business users. In 2012, Gartner updated its definition as follows: "Big Data are high‐volume, high‐velocity, and/or high‐variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization." (Laney 2012).

2.1 Big data perceived risks and benefits There is a fourth characteristic for Big Data: Value. In the context of Big Data, value refers to: (1) the cost of the technology, which has dropped to allow more companies to undertake this type of projects, and (2) the benefits generated by the use of Big Data (cost reduction, operational efficiency, and business improvements and new revenue streams) Like any other new technologies, Big Data comes with benefits and drawbacks. Table 1 presents a list of several key benefits and risks developed by Mckinsey Global Institute (2011). Table 1: Big Data perceived benefits and risks Benefits Creating transparency by making data accessible to relevant stakeholders in a timely manner Improve operational efficiency (cost, revenue and risk) Use data and experiments to expose variability and raise performance Segment populations to customize the way your systems treat people Use automated algorithms to replace and support human decision making Innovate with new business models, products, and services Sector‐specific business value creation

Risks Data quality Talent scarcity (lack of data scientists) Privacy and security concerns Big Data integration capabilities Decision‐making Organizational maturity level

2.2 Decomposed theory of planned behavior Decomposed Theory of Planned Behavior (DTPB) was raised by Taylor and Todd in 1995. DTPB is an extension of the Theory of Planned Behavior (TPB) developed by Ajzen (1988, 1991). TPB encompasses three constructs, the attitude toward the behavior, subjective norm, and perception of behavioral control – that when combined form behavioral intention. Intention is then assumed to be the immediate antecedent of behavior (Ajzen 2002). Table 2 presents brief descriptions of the constructs used in TPB.

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José Esteves and José Curto Table 2: Definitions of predictors of behavior in the theory of planned behavior (TPB) Construct Behavioral Intention Attitude Subjective Norm Perceived Behavioral control

Definition Refers to individual’s intention to perform a behavior and is a function of Attitude, Subjective Norm and Perceived Behavioral Control Refers to individual’s positive or negative evaluation of the behavior (Ajzen, 1988) Refers to individual’s “perception of social pressure to perform or not to perform the behavior” (Ajzen, 1988, p.132) Refers to the “perceived ease or difficulty of performing the behavior and reflects past experience as well as anticipated impediments and obstacles” (Ajzen, 1988, p.132)

Taylor and Todd (1995) also specified that, based on the diffusion of innovation theory, the attitudinal belief has three salient characteristics that influence adoption; relative advantage, complexity and compatibility (Rogers, 1983). Relative advantage refers to the degree to which an innovation provides benefits superseding those of its precursor. This may incorporate factors such as economic benefits, image, enhancement, convenience and satisfaction (Rogers 1983). Complexity represents the degree to which an innovation is perceived to be difficult to understand, learn or operate (Rogers, 1983). The complexity construct is extremely similar, although it is conceived in the opposite direction as ‘‘perceived ease of use’’ (Technology acceptance model, Davis 1989). Innovative technologies that are perceived to be easier to use and less complex have a higher possibility of acceptance and use by potential users. Thus, complexity would be expected to have negative relationship to attitude. Complexity (and its corollary, ease of use) has been found to be an important factor in the technology adoption decision (Davis et al. 1989).

3. Theoretical model and research hypotheses Synthesizing the theoretical background, we propose the following model (see figure 1) based on DTPB for understanding factors influencing Big Data adoption.

3.1 Antecedents of Big Data Adoption Based on DTPB, the adoption adopt Big Data will be determined by intention to adopt Big Data and perceived behavioral control. As a consequence, we hypothesize: H14. Perceived behavioral control has a positive effect on actual adoption of Big Data. H15. Intention to adopt Big Data has a positive effect on actual adoption of Big Data.

Figure 1: The proposed research model and research hypotheses

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3.2 Antecedents of big data adoption intention Based on DTPB, in our research model Big Data adoption intention is jointly determined by the individual’s Big Data Attitude, subjective norms, and Perceived Behavioural Control. Thus we hypothesize: H11. Attitude towards Big Data has a positive effect on intention to adopt Big Data. H12. Subjective norm has a positive effect on intention to adopt Big Data. H12.1. Media has a positive effect on intention to adopt Big Data. H12.2. Social influence has a positive effect on intention to adopt Big Data. H13. Perceived behavioral control has a positive effect on intention to adopt Big Data.

3.3 Antecedents of attitude Big Data requires of technologies that process and analyze large amounts of heterogeneous data within the right scope of time. These technologies includes A/B testing, association rule learning, classification, cluster analysis, crowdsourcing, data fusion and integration, ensemble learning, genetic algorithms, machine learning, natural language processing, neural networks, pattern recognition, predictive modeling, regression, sentiment analysis, signal processing, supervised and unsupervised learning, simulation, time series analysis and visualization, Massively Parallel‐Processing (MPP) databases, search‐based applications, data‐mining grids, distributed file systems, distributed databases, cloud computing platforms, the Internet, and scalable storage systems. Depending on the degree of knowledge of these technologies, an organization may consider that Big Data is more or less easy to use. It is reasonable to infer that the perceived ease of use positively influence the company’s perceived usefulness and intention to adopt Big Data. Therefore, we hypothesize that: H7. Perceived ease of use has a positive effect on attitude towards Big Data. Perceived Usefulness is defined as the degree to which a person believes that adopting Big Data would enhance his or her job performance (Davis 1989). Therefore, we hypothesize that: H6. Perceived usefulness has a positive effect on attitude towards Big Data Also, as previously discussed, there are three main reasons to Big Data adoption, namely: volume, variety and velocity. Thus we hypothesize: H1. Volume has a positive effect on perceived usefulness towards Big Data H2. Variety has a positive effect on perceived usefulness towards Big Data H3. Velocity has a positive effect on perceived usefulness towards Big Data As discussed in section 2.1, Big Data generates many potential benefits for companies such as cost control, revenue generation, risk control, decision‐making improving, etc. Therefore, it is reasonable to infer that Big Data Technologies perceived benefits positively influence the company’s attitude and intention to adopt Big Data. H5. Perceived benefits have a positive effect on attitude towards Big Data. Similarly, it is reasonable to infer that the perceived risks of Big Data negatively influence the company’s attitude and intention to adopt Big Data. Among them: Talent scarcity, organization maturity, Big Data internal capabilities and data quality. H4. Perceived risk has a negative effect on attitude towards Big Data. Compatibility is the degree to which the innovation fits with the potential adopter’s existing values, previous experience and current needs (Rogers, 1983). Tornatzky and Klein (1982) found that an innovation is more likely to be adopted when it is compatible with the job responsibilities and value system of the individual. Therefore, it may be expected that compatibility has a positive influence on Big Data adoption. The existence of information systems such as e‐commerce platforms, Enterprise Resource Planning (ERP), Business Intelligence (BI), Customer Relationship Management (CRM) or product lifecycle management (PLM), external sources of information and the need to make decision near real‐time are factors that generate Big Data situations. It is reasonable to infer that compatibility has a positive influence on attitude towards Big Data. Hence, we hypothesize:

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José Esteves and José Curto H8. Compatibility has a positive effect on attitude towards Big Data.

3.4 Antecedents of perceived behavioral control According to Ajzen (1988), Perceived Behavioral Control reflects beliefs regarding access to the resources and opportunities needed to perform behavior, or alternatively, to the internal and external factors that may impede performance of the behavior. This notion encompasses the component of “facilitating conditions” (Triandis 1980) and self‐efficacy (Bandura 1982). In this research, we define Perceived Behavioral Control as the degree to which external and internal factors influence, knowledge‐seeking behavior in an EKR. Thus, we hypothesize: H9. Self‐efficacy has a positive effect on Perceived behavioral control to adopt Big Data. H10. Facilitating conditions have a positive effect on Perceived behavioral control to adopt Big Data.

4. Research methodology Data for this study was collected using an online survey questionnaire. The participants in the survey were managers involved in Big Data adoption decision and usage such as CIOs, marketing directors, and business analytics managers. Based on the list of the top 100 Spanish companies firms, we contacted the users through email and/or Linkedin. The questionnaire has two parts. The first considers demographic information with control variables such as the job role of the participant, size of the company, and existence of a data mining data center. The second part considers the theoretical model. The measurement items in the questionnaire were developed for the decision variables of attitude, perceived behavioral control, intention to adopt, and actual adoption by adapting the measures proposed and validated by Azjen (2002) to fit the Big Data context. The total number of answers was 53. Table 3 reports the demographic breakdown of the research sample. Table 3: Research sample demographics Variable Business sector

Functional Area

Annual Revenue

Sub‐category Services Public sector Manufacturing Education Health/Pharmaceutical Banking/Finance Other Technology Marketing/sales Operations Finance Top management Other >10 million euros 10 to 50 million euros >50 million euros

Number (n=53) 15 11 2 2 3 7 13 27 7 4 3 3 8 13 5 35

% 28.3 20.75 3.77 3.77 5.66 13.21 24.53 50.94 13.21 7.55 5.66 5.66 15.09 24.53 9.43 66.04

A SEM technique was used to examine the relationships among the constructs. The Partial Least Squares (PLS) approach was chosen for its capability to accommodate small‐sized samples (Chin 1998). Further, PLS recognizes two components of a causal model: the measurement and the structural model. Additionally, PLS is especially suitable for exploratory research focusing on explaining variance. Given the aforementioned PLS seemed particularly relevant for this exploratory study – one that is limited by sample size.

4.1 Construct reliability and validity Table 4 shows the factor loadings, Cronbach’s alphas (A), Average variance extracted (AVE), and R2 values. All Cronbach’s alphas exceeded the recommended minimum value of 0.7 with the exception of perceived risks variable and, all of the observed construct reliabilities (C.R.) were higher than 0.8 (Fornell and Lacker 1981) with the exception of perceived risks variable. All construct loadings were found to be significant at greater than the recommended p‐value of 0.05 (Gefen and Straub 2005) and typically exceeded the recommended

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José Esteves and José Curto threshold value of 0.707 (Barclay et al. 1995) with the exception of perceived risk, perceived benefits and behavioral intention that were inferior in some constructs. Average variance extracted (AVE) was found to account for a minimum of 50 percent of the variance in each construct and the square root of AVE for each construct was much larger than the construct’s correlation with every other construct (Barclay et al. 1995; Gefen and Straub 2005). Measurement items loaded on their respective constructs at a value of at least 0.1 greater than their loading on other constructs (Barclay et al. 1995; Gefen and Straub 2005) and all items loaded higher on their intended construct than on any other construct. Hence, it was concluded that the construct measurement items were consistent and exhibited a substantial degree of convergent and discriminant validity. Table 4: Convergent, discriminant validity and reliability of measurements Factor ATT AA

BI COM MI PBC PEOU

SI FC VLCTY VLM VRT

Item ATT1 ATT2 ATT3 ‐

Loadings 0.972 0.976 0.970 ‐

PB1 PB2 PB3 PB4 PB5 PB6 PB7 BI1 BI2 BI3 C1 C2 MI1 MI2 MI3 PBC1 PBC2 PEOU1 PEOU2 PEOU3 PU1 PU2 PU3 PU4 PU5 PR1 PR2 PR3 PR4 PR5 SE1 SE2 SE3 SI1 SI2 SI3 FC1 FC2 VLC1 VLC2 VLM1 VLM2 VRT1

0.838 0.524 0.653 0.459 0.609 0.848 0.790 0.975 0.976 0.660 0.833 0.918 0.889 0.896 0.868 0.859 0.898 0.897 0.936 0.972 0.861 0.926 0.897 0.931 0.823 0.632 0.010 0.790 0.122 ‐0.627 0.827 0.965 0.950 0.954 0.935 0.840 0.912 0.911 0.756 0.885 0.895 0.751 1.000

AVE

Cronbach

Composite Reliability

0.946

0.712

0.981

0.407

1.000

0.568

0.475

0.812

0.859

‐

0.952

0.95

0.975

0.476

0.769

0.707

0.869

‐

0.782

0.862

0.915

0.772

0.706

0.871

0.706

0.875

0.933

0.954

‐

0.789

0.933

0.949

0.283

0.286

0.30

0.194

‐

0.84

0.904

0.94

‐

0.83

0.896

0.936

‐

0.83

0.797

0.908

‐

0.68

0.535

0.807

‐

0.68

0.548

0.8101

‐

1.000

‐

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José Esteves and José Curto

4.2 Path analysis SmartPLS (Version 2.0.M3) (Ringle et al. 2005) was used to evaluate the statistical significance and relative salience of the research hypotheses. Results of model testing indicated that the constructs included in the research model accounted for approximately 47.6 percent of the variance in the intention to adopt Big Data and 56.8 percent of the variance in actual use of Big Data (Figure 2). Chin (1998) notes that path coefficient values between 0.20 and 0.30 are adequate for meaningful interpretations. Thus, in particular, the results provided support for the significance of eleven research hypotheses. R2 values, which indicate the predictive power of the model, ranged from 0.28 to 0.7, indicating that the fit of the research model was acceptable.

Figure 2: Main study path model results

5. Discussion Adding to previous literature on Big Data, the first contribution of this study is the recognition that volume and velocity are the key aspects in Big Data adoption and they have a significant impact in the intention to adopt these technologies. Although, Variety seems not having still such effect, it is expected to become an important factor in determining adoption. The logic behind is that the more heterogeneous and unstructured the data is, the higher the barriers to capture and analyze data. What is clear is as corporate systems are built into Database Management Systems (DMBS), companies perceive volume and velocity as more urgent matters than variety. Also, companies have traditionally focused more on numerical and structured data rather than working with different types of data. However, with the increasingly diversity of data, being able to manage that aspect will play a key part in companies´ data strategy. Even though the traditional definition of perceived usefulness does not have an impact on the attitude toward Big Data, our model shows that perceived benefits have a significant impact on behavior. Thus, in the subsequent/confirmatory study we plan to use perceived benefits as the construct that replaces perceived usefulness. Regarding perceived risks, the exploratory results suggest that perceived risks variable and measurements need to be re‐defined. Construct loadings are not statistically relevant, so we need to adjust the constructs definition. Hence, the definition of the potential Big Data risks needs to be reviewed and perhaps extended with more risks. However, the results lead to the belief that perceived risks might have a moderate effect on the attitude towards Big Data adoption.

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José Esteves and José Curto Finally, our results suggest that Media and press news about Big Data have a stronger impact on the decision to adopt Big Data than social influences (friends and/or colleagues suggestion to adopt Big Data). Therefore the results indicate that specific opportunities as well as challenges exist in Big Data technologies adoption.

6. Considerations and future work This research‐in‐progress contributes to the existing body of knowledge on Big Data by developing a theoretical model to explore and predict the intention to adopt Big Data technology. By extending the theory of planned behavior with the concepts of perceived benefits, risks and perceived usefulness of Big Data, we seek to understand the adoption of Big Data. Overall, our exploratory results suggest that the proposed model is a first fruitful step to design a theoretical model to predict Big Data adoption. Also, our exploratory model provides insightful evidence to further research and analysis, especially in terms of perceived risks and the variables that impact on the attitude to adopt Big Data such as velocity and volume. As a future work, we will review the literature on Big Data risks and redesign the perceived risks construct and then conduct a confirmatory study with a bigger sample size.

References Ajzen, I. (2002). Perceived behavioral control, self‐efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32, pp 665–683. Ajzen, I. (1991). The theory of planned behavior, Organizational Behavior and Human Decision Processes, vol. 50, pp 179‐ 211. Ajzen, I. (1988). Attitudes, personality and behavior. Milton Keynes: Open University Press. Bandura A. (1982). Self‐Efficacy Mechanism in Human Agency. American Psychologist, No. 37, pp 122‐147. Bantleman, J. (2012, April 16). The big cost of Big Data. In E. Savitz, CIO network: Insights and ideas for technology leaders [Web log post]. Forbes Magazine. Retrieved October 4, 2012 from http://www.forbes.com/sites/ciocentral/2012/04/16/the‐big‐cost‐of‐big‐data/ . Barclay, D. W., Higgins, C. A., & Thompson, R. (1995). The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adaptation and Use as an Illustration, Technology Studies, 2(2), 285‐309. Benjamin Woo, Dan Vesset, Carl W. Olofson, Steve Conway, Susan Feldman, Jean S. Bozman. (2011). Worldwide Big Data Taxonomy, IDC report. Bryant, R. E., Katz, R. H., & Lazowska, E. D. (2008). Big‐data computing: Creating revolutionary breakthroughs in commerce, science and society. Computing Research Association. Chin, W. (1998). Issues and opinion on structural equation modeling, MIS Quarterly, 22(1), 7‐16. Dan Vesset, Benjamin Woo, Henry D. Morris, Richard L. Villars, Gard Little, Jean S. Bozman, Lucinda Borovick, Carl W. Olofson, Susan Feldman, Steve Conway, Matthew Eastwood, Natalya Yezhkova. (2012). Worldwide Big Data Technology and Services 2012–2015 Forecast, IDC. Davis, F.D. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, No. 35, pp 982‐1003. Deloitte (2012). Billions and billions: Big Data becomes a big deal, Deloitte, http://www.deloitte.com/view/en_GX/global/industries/technology‐media‐telecommunications/tmt‐predictions‐ 2012/technology/index.htm Firestone, C. (2010). Foreword. In D. Bollier, The promise and peril of Big Data (pp. vii ‐ ix), Washington, DC: The Aspen Institute, https://www.c3e.info/uploaded_docs/aspenbig_data.pdf . Gefen, D., & Straub, D. (2005). A Practical Guide to Factorial Validity Using PLS‐Graph: Tutorial and Annotated Example, Communications of the Association for Information Systems, 16(1), 91‐109. Hurwitz, J. (2012, Apr 30). The big deal about Big Data. Business Week, , 1. http://www.businessweek.com/articles/2012‐ 04‐23/the‐big‐deal‐about‐big‐data . Kiron, D. (2012). All fired up in massachusetts: The states new wave of Big Data companies. MIT Sloan Management Review, Vol. 53, No. 3, pp 1‐3. Lamont, J. (2012). Big Data has big implications for knowledge management. KM World, 21(4), 8‐11. Laney D. (2001). 3D Data Management: Controlling Data Volume, Velocity and Variety, http://blogs.gartner.com/doug‐ laney/files/2012/01/ad949‐3D‐Data‐Management‐Controlling‐Data‐Volume‐Velocity‐and‐Variety.pdf Laney D. (2012). Douglas, Laney. The Importance of 'Big Data': A Definition. Gartner. http://www.gartner.com/resId=2057415 Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. (2011). Big Data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute.

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José Esteves and José Curto Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior, Information Systems Research, vol. 2, No. 3, pp 173‐191. Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 (M3) Beta, Hamburg, Germany: University of Hamburg (http://www.smartpls.de). Rogers E. M. (1983). Diffusion of Innovations (3rd edition). London: The Free Press. Simon, P. (2010). Why new systems fail. Boston, MA: Course Technology, a part of Cengage Learning. Strenger, L. (2008). Coping with Big Data Growing Pains. Business Intelligence Journal, vol. 13, No. 4, pp 45‐52. Taylor, S. & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12, 137‐156. Tornatzky, L.G., & Klein N. (1982). Innovation characteristics and innovation adoption implementation: A meta‐analysis, IEEE Transactions on Engineering Management, 29, pp 28‐45. Triandis, H.C. (1980). Beliefs, Attitudes and Values. University of Nebraska Press, Lincoln, NE. Venkatesh, V. (2006). Where to go from here? Thoughts on future directions for research on individual‐level technology adoption with a focus on decision‐making. Decision Sciences, vol. 37, No. 4, pp 497–518. White, M. (2011). Big Data‐Big Challenges. Econtent, vol. 34, No. 9, pp 21.

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Bridging Knowledge Management Life Cycle Theory and Practice Max Evans1 and Natasha Ali2 1 McGill University, Montreal, Canada 2 University of Toronto, Toronto, Canada max.evans@utoronto.ca1 tasha.ali@mail.utoronto.ca2

Abstract: Knowledge Management (KM) research has focused separately on identifying valuable knowledge assets of the firm; understanding the KM life cycle; and suggesting initiatives, practices, and technologies that could enable effective KM. These three elements are vitally important, but very few studies have presented a holistic view of all three perspectives. This paper proposes three main objectives that together form a general framework for understanding and implementing knowledge management. The first objective is to explain and describe valuable, distinctive knowledge assets (Boisot, 1998) available to the organization. Knowledge assets may be understood as both the tangible assets that can be codified or encapsulated (referred to as knowledge containers (McElroy, 1999; 2003) and the intangible assets in the minds, bodies and relationships of the employees. Focusing on the knowledge asset is what distinguishes knowledge management from document management (McElroy, 1999). The second objective is to understand the life cycle of managing organizational knowledge, including its general stages and major activities. Many models of the KM life cycle have been proposed, and it may be possible and useful to integrate important elements from past research into a single model. To accomplish this, the paper will identify similarities and gaps from influential life cycle models, existing integrated models, and additional practice-based KM models and frameworks. The third objective is to build on the core principles and activities of previous frameworks and life cycle models to create a simple, practical incorporated second-generation KM life cycle model. The IOSAEC model is introduced and the main stages are summarized. Finally, to highlight the practical application of the IOSAEC KM Life Cycle model, sample initiatives and technologies are presented. Keywords: knowledge management; knowledge assets; life cycle; framework; initiatives; technologies

1. Introduction Effective KM promises many benefits for the organization and the individuals involved (Cyr & Choo, 2010). Nevertheless, most organizations face daunting challenges in leveraging their own knowledge. Knowledge needed in the organization exists somewhere but cannot be found; valuable knowledge is hoarded and not shared; knowledge gained from projects is not passed along; employees do not have access to the knowledge; or knowledge is lost when people leave (De Long & Fahey, 2000; Liebowitz, 2009). Much research in KM has focused separately on identifying valuable knowledge assets of the firm; understanding the KM life cycle; and suggesting initiatives, practices, and technologies that could enable effective KM. The three elements â&#x20AC;&#x201D; knowledge assets, life cycle stages, and enabling initiatives and technologies â&#x20AC;&#x201D; are vitally important, but very few studies have presented a holistic view of all three perspectives. This paper proposes four main objectives that together form a general framework for understanding and implementing knowledge management. The first objective is to explain knowledge assets and contextualize them as tangible and intangible assets available to the firm. The second objective is to understand the life cycle of managing organizational knowledge, including its general stages and major activities. Many models of the KM life cycle have been proposed, and it may be possible and useful to integrate important elements from past research into a single model. To accomplish this, the paper identifies similarities and gaps from influential life cycle models (Meyer & Zack, 1996; Wiig, 1993; McElroy, 1999, 2003; Bukowitz & Williams, 1999), existing integrated models (Dalkir, 2011; Kayani & Zia, 2012), and additional practice-based KM models and frameworks (Terra, 2005; Heisig, 2009). Next, based on this review, and rooted in second-generation KM principles (McElroy, 2003) an 1 integrated KM life cycle model (IOSAEC KM Life Cycle) is introduced. Finally, to highlight the practical application of the IOSAEC KM Life Cycle model, sample initiatives and technologies are presented.

2. Knowledge assets As previously mentioned, like physical assets, knowledge assets have economic value to the firm (Boisot, 1998). However, unlike physical assets, knowledge assets are difficult to properly conceptualize. According to 1

IOSAEC is an acronym for the stages represented in the model

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Max Evans and Natasha Ali Boisot (1998), “failure to properly conceptualize the nature of knowledge assets condemns firms” to use outdated and ineffective strategies and tactics (p. 2). KM strategies should begin with an understanding and identification of the knowledge assets available to the firm. Boisot (1998) defines knowledge assets as “stocks of knowledge through which different value added services flow” (p. 3). Dalkir (2011), building on Stewart (1994), explains a similar concept of intellectual assets as "an organization’s recorded information, and human talent [i.e. sum of what employees of the organization know and know how to do]" (p. 20). Building on these definitions, this paper will view a knowledge asset as a valueadded intellectual resource, which combines stocks of knowledge available to the firm. Stocks of knowledge have varying degrees of how formalized or codified they can be, with some never becoming fully formalized (i.e. remaining tacit). For example, intuitions or patterns employees use in problem solving may only be held in memory and never be fully articulated (Tsoukas, 2005ab; Boisot, 1998). This may be "because they are inarticulable or because they are too idiosyncratic to justify the effort involved in articulating them" (Boisot, 1998 p. 13). A full discussion of explicit and tacit knowledge is outside the scope of this paper. However, there are two important points that must be made. First, tacit and explicit knowledge are “two sides of the same coin” (Tsoukas 2005b, p. 158). Neither can exist without the other. Explicated artifacts act as guiding lights in providing meaning and interpretation to a tacit activity. “Uncodified knowledge provides background context and warrants for assessing the codified” (Duguid, 2005, p. 112). Second, different forms of knowledge require different strategies, initiatives, and technologies. Due to its complexity and difficulty to codify, the capture and transfer of tacit knowledge is slower and more costly (Grant, 2002; van den Berg, 2013; Kogut & Zander, 1992; Choo 2006; Boisot, 1998; Heiman & Nickerson, 2004). Further, not all tacit knowledge can be codified or encapsulated since some will ultimately remain in the human mind (Choo, 2002; Spender, 1996; van den Berg, 2013; Tsoukas, 2005ab) Based on the traditional explicit/tacit distinction, knowledge assets may consist of: 

Tangible assets (i.e. knowledge containers, representing the codified and encapsulated/embedded stocks of knowledge) and



Intangible assets, representing the tacit and embodied stocks of knowledge

2.1 Tangible assets: Knowledge containers The term knowledge container originates with McElroy (2003) who explains it as various types of codified shared knowledge (e.g. “automated, hard-copy, and other knowledge artifacts” p. 73). The author’s use of the term appears to be focused on declarative and procedural knowledge that is made explicit by the firm (e.g. business strategies, products, processes, information systems, documentation, etc.). Choo (2006) explains explicit knowledge as rule-based or object-based. Rule-based explicit knowledge is “codified into rules, instructions, specifications, standards, methodologies, classification systems, formulas” (p. 141). Object-based is further divided into knowledge that is represented as “strings or symbols (words, numbers, formulas) or is embodied [i.e. made tangible] in physical entities (equipment, models, substances)” (p. 141). Van den Berg (2013) refers to the former type of object-oriented knowledge as codified and to the latter type as encapsulated. Van den Berg (2013) argues that encapsulated knowledge has a different shape (i.e. form) than codified, since it cannot be directly observed. Directly observing encapsulated knowledge requires further unpacking (e.g. “reverse engineering a product, inspecting software code, or analyzing the composition of the substance” (Choo, 2006 p. 141). Encapsulated knowledge is also unique because it allows the user to gain utility from the functional use of an artifact without “the need to have substantive knowledge" about it (van den Berg, 2013 p. 168). Based on the above definitions, a knowledge container may be viewed as the tangible (i.e. in codified (rule or object-based) and encapsulated form) artifacts associated with a knowledge asset in the firm.

2.2 Intangible assets Intangible assets are an essential part of the efficient and effective use of knowledge assets. However, they cannot be codified or encapsulated in an object or rule, so they should be considered and managed differently than knowledge containers. Intangible assets are embodied in the employees of the firm and the relationships

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Max Evans and Natasha Ali between them. They reside in the minds of employees and their actions. As previously mentioned, many of these assets are abstract, making them hard to identify and communicate (Spender, 1996; van den Berg, 2013). The assets are instrumentalized in the way employees utilize relationships, information, equipment, artifacts, etc., to solve problems and make decisions (Polanyi, 1962; 1966; Polanyi & Prosch, 1975; Tsoukas, 2005b). One possible direction for understanding and identifying these tacit, intangible assets stems from the intellectual capital (IC) literature, which has made attempts to conceptualize and manage them. A complete discussion of the IC literature is outside the scope of this paper, but a summary of Bontis’ (2002) conceptualization of IC is provided in Table 1. Table 1: Summary of Bontis’ (2002, p. 629) IC definitions and conceptualization Human Capital Individual tacit knowledge (i.e. inarticulable skills necessary to perform function (p. 630)

Structural Capital Organizational embodied [and individually understood] structural tacit knowledge [i.e. relationships enabling the organization to function effectively] (p. 631).

Nature

Employee intellect

Codification Difficulty

High

Internal organizational routines and relationships Medium

Definition

Relational Capital Knowledge of market channels, customer and supplier relationships, as well as a sound understanding of governmental or industry association impacts (p. 632) External organizational relationships Highest

Now that knowledge assets are contextualized, the next objective to is to understand the general stages and major activities of a life cycle that manages them. The next section identifies and summarizes influential life cycle models (Meyer & Zack, 1996; Wiig, 1993; McElroy, 1999, 2003; Bukowitz & Williams, 1999), existing integrated models (Dalkir, 2011; Kayani & Zia, 2012), and additional practice-based KM models and frameworks (Terra, 2005; Heisig, 2009).

3. KM life cycles Six life cycle models are summarized in Table 2: McElroy (2003), Wiig (1993), Meyer and Zack (1999), Bukowitz and Williams (1999), Kayani and Zia (2012), Dalkir (2011). In addition, a new integrated model (IOSAEC) is introduced in the table and explained below. Two other life cycles by Heisig (2009) and Terra (2005) were considered in the creation of the IOSAEC model, but were not included in Table 2, as adequate details of the stages were not available. The Heisig (2009) life cycle stages appear to include: “create, store, share, and apply”; although he suggests, based in part on the analysis of 160 KM frameworks, that a KM framework should consist of “at least five core KM activities such as ‘identify’, ‘create’ ‘store’ ‘share’ and ‘apply’” (p.15). A summary of the stages in Heisig’s GPO-WM framework (2009) is not included because it is currently not available in English. An interpretation and contextual understanding of Terra’s (2005) life cycle stages was done using his classification of strategic, organizational development, and technological initiatives by stage. Terra’s (2005) life cycle stages include: “new knowledge and innovation, codification, organization, sharing, dissemination, and protection” (p. 19). Table 2: Summary of life cycle stages McElroy (2003)

Wiig (1993)

Meyer and Zack (1999, p.47-48)

Knowledge production: generate new knowledge “knowledge exists only after it has been produced” (p.74). Information Acquisition; Individual and

Build – acquire knowledge from a variety of sources including academic and employment experience

Acquisition – using a manufacturing process as a metaphor, the authors relate identification and input of source information to sources of raw materials, with the adage

Bukowitz and Williams (1999) Get – access and filter information to identify relevant and valuable content

158

Kayani and Zia (2012)

Dalkir (2011)

Discover/Dete rmine – seek the information from primary or secondary sources

Knowledge capture and/or creation – identify and code “internal and external knowledge” in and out of the firm

IOSAEC KM Life Cycle (2013) Identify

Max Evans and Natasha Ali McElroy (2003) group learning; - Knowledge claim formation Knowledge Claims - Knowledge Claim Evaluation Organizational Knowledge stage when ‘knowledge claims’ (which were formed and evaluated in the previous stage) are formalized at the organizational level Knowledge Integration -Sharing; Broadcasting; – Searching; Teaching

Solutions which feed into Distributed Organizational Knowledge

Business Processing Environment

Wiig (1993)

Meyer and Zack (1999, p.47-48)

Bukowitz and Williams (1999)

Kayani and Zia (2012)

Dalkir (2011)

IOSAEC KM Life Cycle (2013)

“garbage in, garbage out” (p.48)

Hold – contain knowledge in physical forms such as documents or in employee minds and memory

Refinement – can be physical or logical; add value to the repository in part by cleaning and standardizing data

Use – combine information in unique ways to enhance and support innovation

Obtain/Get – after the information is discovered, obtain the “target” information from the previously identified information sources

Assess Assess the value of knowledge and decide if it should be stored as ‘intellectual capital’

‘Organize’ and Store’

Pool – combine individual knowledge through the use of forums, technological tools, systems or group meetings

Storage/retriev al – the link between obtaining the data “the upstream Acquisition” and the refinement stages that supply the “product platform” (p.48) (i.e. information repository) as the information is created Distribution – “the form of the product delivered to the user” (p.48).

Learn – discovery of information in order to apply content based on experience and organization al memory

Filter/Refine – assess and reclassify the new information from the obtain stage

Knowledge sharing and dissemination – the sharing of information with others.

Contribute – participants share their knowledge and offer their comments into a shared space or ‘repository’ to assist and develop the overall community or organization Assess – evaluation of the dynamics

Share/Supply – share the refined information among organizational members

Contextualize - once the value is confirmed, then link the knowledge to practitioners most knowledgeabl e about how this information may be implemented in its context

Apply

Utilize/Apply – apply the shared

Knowledge acquisition and

Evaluate and Learn

Use – knowledge application in the context of work and decisionmaking scenarios

Presentation emphasizes context for the

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Max Evans and Natasha Ali McElroy (2003)

Wiig (1993)

–Business Process Behaviors of Interacting Agents (Knowledge Use); Organizational Knowledge ‘Containers’ -Individuals and groups - Artifacts and codifications Internal/Exter nal events which influence the Business Process Environment

Meyer and Zack (1999, p.47-48) user to effectively use the information

Feedback including the detection of problems influenced by internal/exter nal events – returns to the individual and group learning in the Knowledge production process

Bukowitz and Williams (1999) of knowledge acquisition and use of the organization in the form of intellectual capital

Kayani and Zia (2012)

Dalkir (2011)

knowledge in work contexts to increase the quality of the business operations

application – the information is ‘embedded’ (p.54) in the organizational procedures

Build/Sustain – to plan and allocate resources to support the attainment of future knowledge for the organization. Divest – evaluate “assets” which do not create value for the organization and allocate the associated resources elsewhere

Storage/Stock – store the information in various physical and virtual forms

Update - the information is updated by members of the organization to maintain currency and accuracy of knowledge

IOSAEC KM Life Cycle (2013)

Create

Delete/Remov e – purge information no longer pertinent to the organization from the system

3.1 The IOSAEC KM life cycle Several important stages from previously discussed popular life cycles and frameworks were adapted and incorporated to form the IOSAEC KM Life Cycle (Figure 1). In fact, five out six IOSAEC stages are ranked in the top six KM activities in Heisig’s (2009) study (see Table 3). Further, the IOSAEC life cycle incorporates secondgeneration KM principles and a potential for “double loop learning” as per McElroy (2003) and Argyris and Schon (1996). Second generation KM principles focus on how knowledge ‘emerges’ in an organization, rather than “its mechanical application of practice” (McElroy, 2003, p.70). Table 3: Comparison of IOSAEC KM life cycle stages to Heisig’s (2009 p. 9) most frequently discussed groups of KM activities IOSAEC KM Life Cycle Stages

Ranking among most frequently discussed groups of KM activities

1. Identify 2. Organize and Store 3. Share 4. Apply 5. Evaluate and Learn 6. Create

5 4 (As “Store”) st 1 rd 3 (As “Use”) N/A nd 2

160

Number of frameworks the activity was discussed in (160 Total) 65 66 97 79 N/A 87

Max Evans and Natasha Ali

Figure 1: IOSAEC KM life cycle Below is a brief stage-by-stage summary of the IOSAEC KM Life Cycle: ‘Identify’ ‘Identify’ is similar to several other life cycle stages including: “Create” (Heisig, 2009); “Get” (Bukowitz & Williams, 1999); “New Knowledge and Innovation” (Terra, 2005); “Knowledge production: Information Acquisition” (McElroy, 2003); and “Discover/Determine” (Kayani & Zia, 2012). From a strategic perspective, it helps to formally identify the knowledge assets before attempting to manage them and especially before implementing initiatives and technologies around their management. Identifying knowledge assets also provides a systematic way to put boundaries around the tangible and intangible stocks of knowledge. Some suggestions for classes of knowledge assets include: practice areas, key activities, products or services, core competencies, or any value-added activities. Young (2009) suggests focusing on “key knowledge required to drive performance” instead of “attempting to harvest all the knowledge in each practice area [since this] would require enormous resources and would be of questionable value” (p. 24). The admitted problem with using these examples as classification schema is that they assume knowledge assets to contain concrete knowledge with specific application (i.e. they fit in a box). In many cases (especially with intangible assets) the knowledge can be generalized along several dimensions, giving it higher value (Boisot, 1998). A good example of generalized knowledge may be just-in-time or lean principles. In such cases, it may make sense to identify a new knowledge asset. Once knowledge asset classes are identified, the next two tasks are 1) to identify what tangible (i.e. codified and encapsulated) knowledge to include in the knowledge containers; and 2) to identify intangible assets that are essential in creating and appropriating value from those knowledge containers. Van den Berg (2013) suggests giving priority to those that are distinctive and inimitable. Milton (2012) and Garfield (2012) echo similar advice with their suggestion to focus on non-routine and critical knowledge. Young (2009) suggests paying special attention to knowledge with a high risk of loss, low obsolescence, and low chance of replacement. The paradox of identifying knowledge assets is that they constantly evolve and may never be complete because there will always be as-yet-unidentified knowledge in the form of knowledge the firm knows that it does not know, does not know that it knows, or does not know that it does not know (Dalkir, 2011, p.73). ‘Organize and Store’ ‘Organize and Store’ refers to the activity of physically storing and linking encapsulated and codified knowledge. The ‘organize’ component of this stage is similar to the ‘refinement’ stage in Meyer and Zack’s (1999) life cycle, as it involves categorizing the data as an essential activity. As part of the organizational approach, the organization determines the criteria for knowledge. Accordingly, this information is sorted and classified based on its “administrative and descriptive characteristics” (Gilliand, 2008, p.9). In this sense, knowledge is encapsulated or codified with the purpose to add to the organizational memory.

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Max Evans and Natasha Ali The ‘store’ stage encompasses several examples to ‘retain’ content for organizational use described in other life cycles: “Store” (Heisig, 2009); “Storage/retrieval” (Meyer & Zack, 1999); “Storage/Stock” (Kayani & Zia, 2012). Similarities among “store” stages are the inclusion of technology to discuss the form and method of storage (e.g. the company intranet and databases as means to build corporate memory through the documentation of organizational practices, information systems, and archives of knowledge). ‘Share’ ‘Share’ emphasizes the availability of knowledge contributed by organizational members as part of a collaborative effort. ‘Share’ encompasses accessible knowledge offered by employees to improve individual and collective decision making and problem solving on projects and initiatives. In addition, ‘share’ refers to internal and external collaboration and communications. A highly popular KM activity, ‘share’ is adapted from several previous KM life cycles including: “Share” (Heisig, 2009); “Sharing” (Terra, 2005); “Knowledge Integration – Sharing” (McElroy, 2003); “Contribute” (Bukowitz & Williams,1999); “Share/Supply” (Kayani & Zia, 2012); and “Knowledge sharing and dissemination” (Dalkir, 2011). A pertinent example of ‘share’ is based on Wiig’s (1993) “Pool” – which combines organizational membership knowledge through the use of forums, technological tools, systems or group meetings. An explicit description for ‘share’, external to the organization, is included in McElroy’s (2003) ‘broadcasting’ component of the ‘Knowledge Integration’ stage. The similarities of the ‘share’ stages are in terms of communicative and collaborative processes. Often there is direct reference to examples of collaboration and communication technologies, social networking, and shared content management tools. ‘Apply’ ‘Apply’ is one of the most important stages in the life cycle, since it is the significant prompt or catalyst for further knowledge evaluation and creation. Organizational goals and plans require employees to act based on their knowledge base. Several life cycle stages were referenced for this stage including: “Apply” (Heisig, 2009); “Utilize/Apply” (Kayani & Zia, 2012); and “Knowledge acquisition and application” (Dalkir, 2011). In this stage, the ‘application’ and use of knowledge requires employees to act on information and artifacts. As employees ‘apply’ knowledge, they develop and build new approaches to situations. In this way, the ‘apply’ stage is core to supporting potential innovation among organizational members. The application of knowledge requires contextual understanding similar to the kind that takes place in McElroy (2003) “Business Processing Environment – Business Process Behaviors of Interacting Agents (Knowledge Use)” (p. 75). According to McElroy (2003) employees gain experience as they interpret influences in their environment. Their responses, in turn, flow back to the knowledge production entity (McElroy, 2003). The results of knowledge application, such as the lessons learned and the assessment of failures, provide the foundation for evaluation, learning, and knowledge creation. ‘Evaluate and Learn’ ‘Evaluate and Learn’ relates to Bukowitz and Williams (1999) stages of “Assess” and “Learn”. As employees complete projects and tasks, they develop and refine their intellectual capital (i.e. knowledge assets) through experiences. Feedback from employees about their activities may be evaluated based on “tangible” formal reports/prototypes or communicated through informal, verbal exchanges with other organizational members to learn and improve approaches to tasks. As members learn, they may evaluate their experiences and offer their lessons as part of a collective initiative. Through lessons, the employees contribute directly to the formal evaluation of their knowledge in the form of feedback after projects. As part of the evaluation process, employees may validate knowledge based on experience. This may prompt the employee to identify new knowledge to improve their experience, once compared with others. In a sense, the organization establishes a benchmark for experience as a form of organizational practice, which may be recorded as part of organizational memory. An employee may build on personal experience to improve individual performance. The employee also develops ‘intangible knowledge assets’ which may improve approaches to task management, problem solving, and decision-making. The tasks and projects also become part of corporate memory. The lessons learned from employee self assessment contributes to organizational memory. In these scenarios, both the ‘tangible’ and

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Max Evans and Natasha Ali ‘intangible’ parts of knowledge assets are evaluated as internal processes to build knowledge. McElroy (2003) identifies influences from internal and external events in terms of a flow of information which loops back or returns as feedback to the ‘individual and group learning’ aspect of “knowledge production” (McElroy, 2003, p.75). In this manner, ‘apply’ feeds into ‘evaluate’ which, in turn, flows into the next stage ‘create’ new knowledge (or it may loop and return to the ‘identify’ stage). According to McElroy (2003), organizational members use their knowledge (experience) and this leads to successful results or failure. As noted by Dalkir (2011), organizational members recognize “matches” (Dalkir, 2011, p.43) to constructs (Kuhlthau, 2004) which are developed based on experience. To describe this process, Dalkir (2011) refers to Argyris and Schon’s (1996) concepts of “single loop learning” and “double loop learning”. Any “mismatches” lead to either “single loop learning” (Argyris & Schon, 1996; Dalkir, 2011, p.43) or several failures result in “mismatches leading to doubt” (Dalkir, 2011, p.43). Such uncertainty prompts the user to seek new knowledge and “integrate new knowledge, this time via ‘double loop learning’” (Argyris & Schon, 1996; Dalkir, 2011, p.43). Similarly, the ‘evaluate’ stage in the IOSAEC has the potential to integrate ‘double loop learning’ (McElroy, 2003, p.72) by looping back to the ‘identify and organize’ stage (Argyris & Schon, 1996). ‘Create ’ As organizational members learn from their experience in the previous stage, they may ‘create’ new knowledge as responses to the “failures or successes” (Dalkir, 2011, p.43) of previous experiences. McElroy (2003) describes how information flows as feedback: “[to] detect […] problems influenced by internal/external events” (p. 75). The feedback may be based on personal (intangible) experience or on collaborative feedback (formal, tangible) and collective experience across the organization. This way, feedback is used to ‘create’ new knowledge that has been “contextualize[d]” (Dalkir, 2011, p.53) and validated by employee experiences.

3.2 Sample initiatives and technologies According to Birkinshaw and Sheehan (2002), KM initiatives and technologies are not universally applicable across a KM life cycle (i.e. each stage may require unique tools). In order to provide a practical perspective of the IOSAEC KM Life Cycle, Table 4 highlights and organizes significant initiatives, activities and technologies that may be used. Table 4 is not inclusive of all KM initiatives and technologies. Further, these initiatives and technologies are not necessarily exclusive to the life cycle stages they are indicated in Table 4 and may be subject to reclassification based on organizational context.

4. Conclusion This paper contributes to the KM literature in three ways. First, it helps to contextualize knowledge assets in the firm by explaining them as tangible and intangible assets. Next, it summarizes widely accepted KM life cycles and incorporates their most important stages and concepts into a new second-generation KM life cycle model. The IOSAEC Life Cycle model uses stages comprised of simple, practical, and prevalent terms. In fact, five of six stages included in the IOSAEC model were noted by Heisig (2009) as the most popular activities of all those appearing in 160 frameworks examined. Finally, the paper provides practical examples to the IOSAEC model by presenting and organizing select KM initiatives and technologies in the context of the life cycle.

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Max Evans and Natasha Ali Table 4: Sample IOSAEC KM life cycle initiatives and technologies (Sources: Dalkir, 2011; Terra, 2005; Barnes, 2011; Garfield, 2012)

References Argyris, C. & Schon, D.A. (1996). Organizational learning II: Theory method and practice. Reading, MA: Addison-Wesley Publishing Company Inc. Barnes, S. (2011). Aligning people, process and technology in knowledge management. London, UK: Ark Group. Boisot, M. H. (1998). Knowledge assets: Securing competitive advantage in the information economy. New York: Oxford University Press. Bontis, N. (2002). Managing organizational knowledge by diagnosing intellectual capital. In Nick Bontis and Chun Wei Choo (Ed.), The strategic management of intellectual capital and organizational knowledge (pp. 621-642). New York: Oxford University Press. Bukowitz,W.R., & Williams, R.L. (1999). The knowledge management fieldbook. Great Britain: Financial Times Prentice Hall. Choo, C.W. (2002). Information Management for the Intelligent Organization: The Art of Scanning the Environment (3rd ed.). Medford, NJ: Information Today, Inc. Choo, C. W. (2006). The knowing organization: How organizations use information to construct meaning, create knowledge, and make decisions. New York: Oxford University Press. Cyr, S., & Choo, C. W. (2010). The individual and social dynamics of knowledge sharing: An exploratory study. Journal of Documentation, 66(6), 824 - 846. Dalkir, K. (2011), Knowledge management in theory and practice. Cambridge, MA: Massachusetts Institute of Technology De Long, D. W., & Fahey, L. (2000). Diagnosing cultural barriers to knowledge management. Academy of Management Executive, 14(4), 113-127. Duguid, P. (2005). "The art of knowing": Social and tacit dimensions of knowledge and the limits of the community of practice. The Information Society, 21(2), 109-118. Garfield, S. (2012). KM Without the Name! Knowledge Management Specialties [PowerPoint slides]. KMWorld, Washington DC. Retrieved from http://www.kmworld.com/Conference/2012/Presentations.aspx Gilliland, A. J. (2008). Setting the Stage. In M. Baca (Eds.), Introduction to Metadata (1-19). Los Angeles, CA: Getty Research Institute. Grant, R. M. (2002). The knowledge-based view of the firm. In Nick Bontis and Chun Wei Choo (Ed.), The strategic management of intellectual capital and organizational knowledge (pp. 133-148). New York: Oxford University Press. Heiman, B.A. and Nickerson, J.A. (2004). Empirical evidence regarding the tension between knowledge sharing and knowledge expropriation in collaborations. Managerial and Decision Economics, 25(6/7), 401-20.

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Max Evans and Natasha Ali Heisig, P. (2009). Harmonisation of Knowledge Management â&#x20AC;&#x201C; Comparing 160 KM frameworks Around the Globe. Journal of Knowledge Management. 13(4), 4-31. Kayani, J. & Zia, Q.M. (2012). The Analysis of Knowledge, Knowledge Management and Knowledge Management Cycles: A Broad Review. International Journal of Academic Research in Economics and Management Sciences, 1(6), 152-162. Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3, 383-397. Kuhlthau, C.C. (2004). Seeking meaning: A process approach to library and information science. Westport, Connecticut: Libraries Unlimited. McElroy, M. (1999). The knowledge life cycle. ICM Conference ON KM. Miami, FL. Retrieved from http://www.macroinnovation.com/images/KnlgLifeCycle.pdf. McElroy, M.W. (2003). The new knowledge management: Complexity, learning, and sustainable innovation. Burlington, MA: KMCI Press/Butterworth-Heinemann. Meyer, M.H. & Zack, M.H. (1999). The Design and Development of Information Products. Sloan Management Review, 37(3), 43-59. Milton, N. (2012). Where to focus your knowledge retention capture. Knoco stories. September 6. Retrieved from http://www.nickmilton.com/2012/09/where-to-focus-your-knowledge-retention.html. Liebowitz, J. (2009). Knowledge retention: Strategies and solutions, CRC Press. Boca Raton. Polanyi, M. (1962). Personal knowledge. London, UK: Routledge and Kegan Paul. Polanyi, M. (1966). The tacit dimension (1st ed.). Garden City, New York: Doubleday and Company. Polanyi, M., & Prosch, H. (1975). Meaning. Chicago, IL: University of Chicago Press. Spender, J. C. (1996). Organizational knowledge, learning and memory: Three concepts in search of a theory. Journal of Organizational Change Management, 9(1), 63-78. Stewart, T.A. (1994). Intellectual capital: Your company's most valuable asset. Fortune, October 3, 68-74 Terra, J.C.C. (2005, April 25) Bridging the gap between KM theory and practice. Presented at the Faculty of Information, University of Toronto, Toronto, ON. Presentation retrieved from http://www.slideshare.net/jcterra/knowledgemanagement-1896839. Tsoukas, H. (2005). What is organizational knowledge? In H. Tsoukas (Ed.), Complex knowledge: Studies in organizational epistemology (pp. 117-140). New York: Oxford University Press. Tsoukas, H. (2005). Do we really understand tacit knowledge? In H. Tsoukas (Ed.), Complex knowledge: Studies in organizational epistemology (pp. 141-161). New York: Oxford University Press. van den Berg, H.A. (2013). Three shapes of organisational knowledge. Journal of Knowledge Management, 17(2), 159-174. Wiig, K.M. (1993). Knowledge management foundations : thinking about thinking : how people and organizations create, represent, and use knowledge. Arlington, TX: Schema Press. Young, T. (2009). Knowledge harvesting. IK Magazine, May, 24-27.

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Intellectual Capital Disclosure in IPO Prospectuses: Evidence From Technology Companies Listed on NASDAQ Tatiana Garanina1 and Alexandra Manuilova2 1 Department of Finance and Accounting, Graduate School of Management, St.Petersburg University, Russia 2 Graduate of Master in International Business Program, Graduate School of Management, St.Petersburg University, Russia garanina@gsom.pu.ru alexandra.manuilova@gmail.com Abstract: There is quite a number of papers, concerning Intellectual Capital itself, research on why the company should disclose the information on Intellectual Capital (IC), in which way, who is the target audience and why this information is of interest to participants of stock market. Mainly research covers official annual reporting and separate IC Statements. Much less time and attention was dedicated to IPO prospectuses IC information disclosure and even less to post‐issue stock performance in connection to the disclosure. This paper extends this line of investigation. It follows the existing research conducted by Singh and Van der Zahn (2009) in the index chosen and time of observation. Although, while the academics were concentrating on Singapore exchange, the focus of this paper are the IPOs of Technology companies on NASDAQ before and after the crisis 2008. Findings from this study provide a broader, long‐term image of the potential consequences of Intellectual Capital disclosure in IPO prospectuses and share price returns of Technology companies, which is of particular interest to investors due to sadly famous events of 2001. To measure the level of disclosure of Intellectual Capital, Disclosure Index is used. Post‐issue stock‐performance is calculated as buy‐and‐hold return for 500 days after listing. The sample consists of the technology companies (according to NASDAQ) that were listed on NASDAQ from 2002 to 2010. The goal of the paper is to define the relationship between the disclosure of information on Intellectual Capital and the 500‐day post‐issue stock performance on example of NASDAQ companies; The main objectives are to create a regression model with Intellectual Capital elements that will best possibly reflect the connection between Intellectual Capital disclosure and post‐issue stock performance, to compare the results with other similar studies and to interpret the differences. The main results are: Intellectual Capital disclosure has a positive effect on post‐issue stock performance; The influence of Intellectual Capital disclosure is higher for non‐manufacturing firms, small companies and firms that issue American Depositary Shares. Keywords: intellectual capital, initial public offering (IPO), intellectual capital information disclosure, NASDAQ, technology companies, IPO prospectus

1. Introduction: Reasons for intellectual capital information disclosure For decades the researches are trying to understand what are the main factors influencing the stock of the price and its movement. Academics have debated the relevance of accounting information for valuing firms. Most of the studies show a low earnings‐return association (Amir, Lev, 1996) and a lowering association between stock prices, earnings and book values (Brown, et al., 1999; Core, et al., 2003; Ely, Waymire, 1999a; Lev, et al., 1999). The higher is the awareness among investors about the importance of Intellectual Capital, the more they request the disclosure of such information. As a result, managers started to provide information related to Intellectual Capital voluntarily, seeking for lower average cost of capital, more accurate analyst forecasts, higher liquidity in capital markets and credibility among investors (Garcı´a‐Meca, et al., 2005). While many researchers dedicate their time to the problem of Intellectual Capital transparency and the factors that influence the decision of the company to disclose such information, very limited attention is paid to the consequences of Intellectual Capital disclosure. This work addresses this issue by examining the connection between the Intellectual Capital disclosure in the initial public offering (IPO) prospectuses and post‐issue stock performance. The goal of the research is to define the relationship between the disclosure of information on Intellectual Capital and the 500‐day post‐issue stock performance on example of NASDAQ companies.

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Tatiana Garanina and Alexandra Manuilova To be able to compare the results with similar research conducted on another market (Singapore Stock Exchange) alike research methodology is chosen. Research on Intellectual Capital disclosure has witnessed a significant growth in the past two decades (Garcı´a‐ Meca, et al., 2005). Stages of Intellectual Capital development, IC application to business and management issues and the application of IC research in different cultural contexts are traced in Tan (Tan, et al., 2008). Most of the studies are dedicated to a specific country, for example Australia (Guthrie, et al., 2000), Canada (Bontis, 2003), India ((Kamath, 2007); (Kamath, 2008)), Japan (Mavridis, 2004), Malaysia (Goh, et al.), Singapore (Tan, et al., 2007), Spain (Oliveras, et al., 2008), Taiwan (Chen, et al., 2005), UK (Williams, 2001). These studies give an overview of Intellectual Capital reporting in different countries across sectors. When disclosing the information on Intellectual Capital to the market the company achieves two goals: reducing information asymmetry amongst market actors and attaining market valuations that better reflect the risk profile of the firm (Dumay, et al., 2007). The empirical evidence also supports the advantages of the reporting of Intellectual Capital to external stakeholders. For example, the number of companies who are now reporting their Intellectual Capital increases and the frameworks for doing so are developing further (Edvinsson, 1997); (Meritum, 2002); (Mouritsen, 2002); (Mouritsen, et al., 2003). Studies show that financial analysts pay attention to Intellectual Capital information to some extent compensate themselves for the Intellectual Capital‐related information deficit of financial reports (Amir, et al., 2003) especially in non‐manufacturing industries. Though, on average, financial market specialists base their judgments on limited information ‐ based mainly on financial performance and tangible assets – with level of disclosure on intangible assets quite low (Lev, 1999). However this creates a potential problem in forecasting – the easily available financial information does not provide a feeling of what intellectual is all about: about future prospects, future growth and potential. When the economy moves to an increased reliance on intangible assets, the use and recognition of these assets becomes very important for the study. The research has identified the effects of Intellectual Capital on company/ stock performance (mainly R&D expenses or R&D intensity) in the context of capital markets. For instance, Lev and Sougiannis (1996) write about a significant inter‐temporal association between company’s research and development capital and subsequent stock returns. Aboody and Lev (1998) examined the performance of listed chemical companies from 1980‐1999 and show that a dollar invested in chemical R&D increases, on average, current and future operating income by $2. Translated to annual rate of return on investment, the after‐tax rate of return on investments in R&D in chemical industry is about 17%, indicating a very significant contribution of chemical R&D to corporate value (typical weighted‐average cost of chemical R&D is 8‐10 percent). Thus, in stock performance, chemical companies collectively outpaced the S&P 500 companies during 1985‐1998 (Ghosh, et al., 2007).

2. Literature gap: Hypothesis formulation There is quite a number of literature, concerning Intellectual Capital itself, research on why the company should disclose the information on IC, in which way, who is the target audience and why this information is of interest to participants of stock market. Mainly research covers official annual reporting and separate IC Statements. Much less time and attention was dedicated to IPO prospectuses IC information disclosure and even less to post‐issue stock performance in connection to the disclosure. This paper extends this line of investigation. It follows the existing research conducted by Singh and Van der Zahn in the index chosen and time of observation. Although, while the academics were concentrating on Singapore exchange, the focus of this paper are the IPOs of Technology companies on NASDAQ before and after the crisis 2008. Findings from this study provide a broader, long‐term image of the potential consequences of Intellectual Capital disclosure in IPO prospectuses and share price returns of Technology companies, which is of particular interest to investors due to sadly famous events of 2001. There was no research of this kind conducted before.

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Tatiana Garanina and Alexandra Manuilova In arguing that IC disclosure is diminishing the uncertainty, which always accompanies IPO, the result should be a smaller effect of post‐issue price drop. Nevertheless the research conducted by Singh and Van der Zahn shows exactly the opposite. The goal of this paper is to duplicate the research using different sample to check the results as this can become a new trigger for the development of the topic. The specific research hypothesis, therefore, is formalized as: H1: The level of IC disclosure in the prospectus of an IPO is inversely associated with the post‐issue stock performance. Additional attention would be paid to pre‐ and post‐crisis results and the fact that the sample consists of Technology companies.

3. Research methodology In order to conduct the research content analysis (and more specifically Disclosure Index) was employed for evaluating the level of IC disclosure. The regression model of Singh and van der Zahn (Singh, et al., 2009) was modified on the one hand, to include all the major variables in order to be able to compare the results, on the other hand, to take into account special features of the companies that get listed on NASDAQ and SEC regulation. To assess post‐issue stock performance market‐adjusted buy‐and‐hold returns applying a 500 trading‐day observation window is used. The following procedures are employed to measure various buy‐and‐hold returns. First, the basic raw buy‐and‐hold return for IPOj (Rj) is computed as: (1) where T = daily trading day holding period (for main observations 500 days); and Rjt = return on IPO share j during the trading period t inclusive of cash dividends paid during the trading period t. (Singh, et al., 2009) The average buy‐and‐hold return for the trading period t (Rt) is computed as: (2) where N ‐ number of IPOs included in the portfolio. Consequently, the mean market‐adjusted buy‐and‐hold return (MBHAR) for the daily trading period t (denoted as MBHART) is calculated based on the formula: (3) where rmt ‐ return on the market portfolio during the trading period t. MBHAR measures the compounded buy‐and‐hold returns an investor could earn from a portfolio of IPO stocks held till a given trading day T in excess of the buy‐and‐hold return the investor could have earned if holding the market portfolio for the same trading day period. 500 For the multivariate regression analysis the dependent variable proxy denoted BHARj is the 500 trading day compounded market‐adjusted buy‐and‐hold return for IPOj. Multivariate regression analysis is used as the main statistical test of the study’s prime conjecture of a negative association between the extent of Intellectual Capital disclosure in the prospectus and the post‐issue stock performance of NASDAQ IPOs. We posit the following regression model to test the primary conjecture:

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Tatiana Garanina and Alexandra Manuilova

(4)

where

Number of Intellectual Capital items disclosed voluntarily in the prospectus of IPOj divided by the total number of items from the 81‐item index relevant to IPOj αj is calculated as α=(N–Np–Ns)/N where N = number of common outstanding of IPOj after listing, Np = number of primary common outstanding shares offered by IPOj and Ns = number of secondary common outstanding shares offered by IPOj. Lnαj is then calculated as Lnαj =αj+ln(1 – αj) Indicator variable where IPOj is scored one (1) if it engages either of the top two underwriter firms (based on frequency) in the year of listing; otherwise scored zero (0) The ratio of proceeds reported in the prospectus of IPOj that is to be allocated to meet immediate working capital needs following listing to total proceeds to be raised by IPOj on listing as reported in the prospectus Square root of the total number of shares traded for IPOj during the 500 trading day observation window (less the first day of trading) divided by number of days shares in IPOj actively traded during the 500 trading day observation window Mom60j = percentage change in the NASDAQ Composite Index 60 days prior to the day of listing for IPOj and the day of listing for IPOj Natural logarithm of the difference between the closing price on the first day of trading and the initial offering price for firm j, expressed as a percentage of the initial offering price Natural logarithm of the projected market capitalization of IPOj on the day of listing derived from the total number of outstanding common shares of the firm on listing multiplied by offer price Natural logarithm of the net proceeds (based on prospectus projections) to be received by IPOj (expressed in USD) Square root of the number of days from the date of incorporation of IPOj to the date of listing for IPOj Indicator variable where the IPOj is scored one (1) if from the manufacturing industry as determined by NASDAQ specifications; other scored zero (0) Indicator variable where the IPOj is scored one (1) if the company is registered in an offshore; other scored zero (0) Indicator variable where the IPOj is scored one (1) if the company is doing business via Internet (meaning business model implies that business – sales, providing services, etc. – is done via Internet); other scored zero (0) Indicator variable where the IPOj is scored one (1) if the company has operations abroad; other scored zero (0)

Control variables are included in equation (4) to control for cross‐sectional variations for such characteristics as:

firm quality (i.e. ownership retention, underwriter reputation);

intended use of proceeds (i.e. ratio working capital to total proceeds);

investor interest post‐listing (i.e. level of underpricing, trading volume);

capital market sentiment (i.e. market momentum 60 days before listing);

information asymmetry (i.e. firm size); and

ex‐ante uncertainty (i.e. offering size and firm age).

Controls for industry sector influences are also included.

4. Sample To check the results of (Singh, et al., 2009), sample from another stock exchange was taken – from NASDAQ Stock Exchange. NASDAQ is famous for being an exchange for technology companies. As there is a lot of discussion going on about technology companies’ Intellectual Capital and disclosure of IC by technology

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Tatiana Garanina and Alexandra Manuilova companies, not only NASDAQ was chosen, but also the companies that NASDAQ classifies as “Technology companies”. To determine the timeframe for the sample the following logic was used:

As the observation window equals to 500 days the company has to be listed before October 2010 (+500 days=April 2012);

Due to time and resources limitations IPOs from the beginning of 2000s were taken

To avoid the effects of dot.com bubble, listings after 2002 were taken

The recent crisis of 2008 should be taken into account (officially the crisis continued from December 2007 to June 2009). All the IPOs are divided into 3 groups: those that were listed before, after and during the crisis (with consideration of 500‐day observation window):

Companies listed before the crisis – listing before August 2006

Companies listed during the crisis – listing from Dec 2007 to February 2008

Companies listed after crisis – listing from July 2009 to October 2010

Taking all those constraints into account we got the list of companies consisting out of 65 technology firms. Table 1 presents descriptive statistics for the sample. Table 1: Description of the sample

2002

2003

2004

2005

2006

2007

2009

Number of companies

% of manufacturing companies

25,0%

0,0%

41,7%

25,0%

33,3%

50,0%

% of ADS

25,0%

0,0%

33,3%

41,7%

25,0%

0,0%

% of Internet companies

25,0%

33,3%

41,7%

25,0%

33,3%

50,0%

Average % of IC items disclosed

22%

19%

23%

22%

19%

26%

22%

% of offshore companies

0,0%

33,3%

33,0%

25,0%

0,0%

25,0%

201 0 23 21, 7% 39, 1% 56, 5% 28 % 30, 4%

5. Research description As mentioned above the Disclosure Index used in the (Singh, et al., 2009) was applied for the research to be able to compare the results on 2 different markets. Some of the elements of the index were quite disputable. For example, some of the positions were too broad and obscure like “Description of the network of distributors and suppliers” could potentially score 1 just for the information about distributors or could score 1 for the information about both distributors and suppliers. Likewise, the description of the network could mean more or less detailed information – this is the limitation of this approach, this makes it more subjective. Due to this subjectivity and possibility of different interpretations, there was a set of rules established for conducting the Disclosure Index (Table 2): The only element that was mentioned by each company out of the sample is Executive stock‐incentive programme (falls under “Remuneration and incentive systems”). Technology companies are different from all the other companies, because their most valuable assets are intangible, including the research they make, software they develop etc. Therefore Human Capital is of extreme importance for such companies – stock‐incentive programme is a way for them to make sure the key personnel does not move to competitors because of the higher salary.

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Tatiana Garanina and Alexandra Manuilova Table 2: Rules designed for filling in the disclosure Index with data Disclosure Index position Employee breakdown by nationality Employee breakdown by level of education All the elements for IT capital External sharing of knowledge and information Details of future prospects regarding R&D

Rules for filling in the element of Disclosure Index More topical for Asian context, especially Singapore; for NASDAQ breakdown by number of employees in different countries (geographical) taken In all companies was used only for Researchers – if mentioned for researchers, then the company scores 1 When the company is the software developer, apriori it has IT systems developed inhouse. Because for some companies this is just one of the businesses and the case is disputable, to avoid subjectivity, every company that invests in IT gets scores for IT capital measures Software developers and IT systems users who work with open platforms, get 1

As the level of detail is not specified, the data on future spending (in USD), direction of future research, expected future results of R&D get scored 1

6. Research results In the section below main results of the research are presented – concerning Disclosure Index and regression analysis. In Table 3 the results of analysis of Disclosure Index are presented: Table 3: Number of disclosure index items disclosed

Year

Average for Disclosure Index overall Average for Human Resource Capital

2002

2003

2004

2005

2006

2007

2009

2010

Overall Average

17,8

15,0

18,0

17,7

15,0

20,3

17,5

21,8

19,0

4,5

3,3

4,8

5,0

3,3

4,7

3,5

5,3

4,7

Average for Customer capital

4,5

4,3

3,9

4,3

3,0

2,7

3,5

4,0

3,9

Average for IT Capital Average for Processes Capital

1,8

1,3

2,0

1,3

1,0

2,8

2,7

2,0

0,8

0,0

0,5

0,3

0,5

1,3

0,3

0,7

0,5

Average for R&D capital

3,5

4,0

2,9

3,1

3,3

5,7

3,0

4,3

3,7

Average for Strategic Capital

2,8

2,0

3,8

5,0

4,5

5,0

4,2

Number of Companies

The results also showed that there is no linear dependence of the level of disclosure and the year. The level of overall disclosure rises in 2004, 2007 and 2010 and fall in all other years during the observation window. We also found out that in 2010 on average there was more information disclosed on Intellectual Capital than during all other years (2002‐2009). On average, the companies are more ready to present information on Human Recourse capital (6% out of overall average 24%) than on any other type of capital. These results are consistent with what (Singh, et al., 2009) found during their study on Singapore Stock Exchange. Then follow Customer, Strategic and R&D capital with 5%. The last place is occupied by the Processes Capital. It is interesting to note that since 2002 to 2010 the level of disclosure on Strategic Capital disclosure doubled. It reached this level in 2007 and remains 6% since that time. There was a drop in disclosure level in the pre‐ crisis year 2007 for Customer ad IT Capital, but the growth for 3 other types of capital. Interestingly enough the least disclosed type of Capital – Processes Capital was best presented in 2007 – could be related to the growing risks for investors in anticipation of the crisis and an attempt to reassure them in company’s reliability. In Table 4 the results of the regression analysis are presented. The results of the regression analysis show that the significance of the model equals to 54% ‐ R Squared – 54% of the 500‐day returns fluctuations are explained by the factors in the model.

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Tatiana Garanina and Alexandra Manuilova Table 4: Statistical results of regression analysis in Excel Regression Statistics Multiple R

0,732184

R Square

0,535687

Adjusted R Square

0,282

Standard Error

1,01521

Observations

ANOVA

Significance F

Regression

47,67068

2,073049

2,036575

0,02633611

Residual

41,22647

1,031689

Total

88,96515

Coefficients

Standard Error

t Stat

P‐value

Lower 95%

Upper 95%

Lower 95,0%

Upper 95,0%

Intercept

‐0,734

1,416

‐0,518

0,607

‐3,579

2,111

‐3,579

2,111

Disclosure Working capital

0,052

0,030

1,722

0,033

‐0,009

0,112

‐0,009

0,112

0,139

0,584

0,237

0,813

‐1,034

1,311

‐1,034

1,311

Ln alpha

0,232

0,466

0,498

0,621

‐0,703

1,167

‐0,703

1,167

SqrtAge

0,000

0,008

‐0,012

0,990

‐0,015

0,015

‐0,015

0,015

LnUP

0,123

0,108

1,141

0,259

‐0,093

0,339

‐0,093

0,339

FSize*LnGP

‐0,001

0,004

‐0,229

0,820

‐0,009

0,007

‐0,009

0,007

MOM60

4,013

2,232

1,797

0,078

‐0,472

8,497

‐0,472

8,497

ManInd

‐0,121

0,409

‐0,297

0,768

‐0,943

0,700

‐0,943

0,700

Underwriter

0,565

0,342

1,654

0,104

‐0,121

1,252

‐0,121

1,252

SqrtATV

0,000

‐2,007

0,050

‐0,001

0,000

‐0,001

0,000

International

0,562

0,355

1,581

0,120

‐0,152

1,276

‐0,152

1,276

Offshore

‐0,081

0,446

‐0,183

0,856

‐0,977

0,814

‐0,977

0,814

Internet

0,124

0,368

0,338

0,737

‐0,615

0,864

‐0,615

0,864

ADS

0,615

0,452

1,360

0,180

‐0,293

1,523

‐0,293

1,523

The most significant result is the fact that, in contrast to the research conducted by (Singh, et al., 2009), the Disclosure Index has a positive influence on the 500‐day returns, which is an opinion supported by majority of literature. From all the factors included in the model, the significant ones (checked by p‐value – should be <0.05 for 5% significance level) are the following:

Disclosure Index

Trading Volumes

Momentum of the market (with 10% significance level)

The importance of trading volumes and its direct relation to the post‐issue stock performance could be explained by its influence on the liquidity of the stock. Normally liquid stocks are less vulnerable to market sentiments than the ones which investors are not always sure how to sell fast and without significant losses. The findings suggest a consistent positive association between the level of Intellectual Capital disclosure in IPO th prospectuses and market‐adjusted buy‐and‐hold returns. The highest return show the 4 quartile companies – companies that disclosed most information on Intellectual Capital and lowest return after 500 days showed

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Tatiana Garanina and Alexandra Manuilova the companies with lowest level of IC information disclosure. This result confirms the findings of the model that Intellectual Capital disclosure has a positive effect on the post‐issue stock performance. It is interesting though that this effect becomes visible (obvious) after 175 days – before this period the 4th quartile shows worse results than other companies and overall sample. The results of regression analysis as such and comparison of the buy‐and‐hold returns and level of disclosure are opposite from the ones in (Singh, et al., 2009), which was based on the Singapore market data.

7. Conclusions In recent years, companies’ disclosure of information has gained increased attention due to globalisation and integration of capital markets, tougher competition, new dominating industries, greater mobility of monetary and actual goods, and developments in IT and the Internet. Reports ( (Eustace, 2001); (FASB (2001) Norwalk, 2001); (Upton, 2001)) and academic papers ((Lev, 2000); (Beattie, et al., 2002)) argue that demand for external communication or providing information on knowledge‐based resources externally is growing as enterprises increasingly base their competitive strength and thus the value of their company on know‐how, skilled employees, patents, and other intangibles. This demand for external communication applies to both traditional annual reporting and newer types of reporting such as Intellectual Capital statements, supplementary business reporting and prospectuses. Theory suggests that additional relevant non‐financial information is expected to lower the cost of capital (Verrecchia, 2001) because increased disclosure lowers investor uncertainty about the future prospects of the company and facilitates a more precise valuation of the company (Botosan, 1997). Related to this argument, the disclosure of Intellectual Capital information is expected to reduce information asymmetry, to enhance stock market liquidity and raise demand for companies’ securities (for example (Diamond, et al., 1991)). Both Botosan (Botosan, 1997) and Richardson and Welker (Richardson, et al., 2001) confirm this by making a conclusion that the quality and quantity of financial disclosure is inversely related to the cost of equity capital for companies (Bukh, et al., 2005). The company publishes its IPO prospectus in order to promote the share among investors. Therefore an admission to listing on the stock exchange offers a unique opportunity to study the amount and type of voluntary information chosen for disclosure to the capital market. Mather with colleagues (Mather, et al., 2000) argue that management has an incentive to present the company in the best possible light in order to maximize the proceeds of the issue (Aharony, et al., 1993). That is why the initial public offering prospectus provides insight into which types of information are selected by a company and its advisors for presenting the company in relation to analysts and investors. Ang and Brau (Ang, et al., 2002) claim that higher company transparency before the IPO decreases the flotation costs of the IPO, and Schrand and Verrecchia (2004) (Schrand, et al., 2004) further find that greater disclosure frequency in the period prior to the IPO leads to lower underpricing effect. Factors explaining abnormal post‐issue stock performance are considered a major fertile area of empirical investigation. Prospectus disclosure is thought to be one of the factors that may have an effect on post‐issue stock performance. Nonetheless, empirical investigations exploring this issue are few and far between. Motivated by prior mixed findings and general lack of analysis, a key contribution of the research is to provide additional evidence toward understanding any disclosure/post‐issue stock performance association. So the conducted literature overview provides the confidence in topicality of the issue of Intellectual Capital disclosure. However not many studies are dedicated to practical influence of such disclosure, therefore this study is a valuable work for future researches. In addition to that, not much attention is paid to IC disclosure in IPO prospectuses. That is why this literature and research gap is covered by the conducted research. The initial hypothesis of the work stated that there is an inverse relation between the level of Intellectual Capital disclosure in IPO prospectus and 500‐day post‐issue stock performance. This hypothesis proved to be wrong – the relation is positive that goes in align with the previous research of this kind. That means that the more the company discloses information on Intellectual Capital, the better is the 500‐day post‐issue stock performance – and this is main managerial application of the work – the opportunity for the company to manage the post‐issue performance of its stock.

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Tatiana Garanina and Alexandra Manuilova In addition to that, the following results were obtained by dividing the sample in groups, according to different characteristics:

For small companies it is important to have a most popular (for the year of listing) underwriter;

For small size firm there is a positive relation between the fact that the company is manufacturing and its post‐issue stock;

The higher the level of net proceeds for small companies, the better the post‐issue stock performance of the stock after listing;

For the recently (after crisis) listed companies significant factors are: Disclosure Index (positive relation), the company is an offshore company or not (positive effect for the offshore companies) and the firm is an Internet firm or a conventional one (negative relation, meaning better results for conventional, not Internet companies);

For new established companies the level of IC disclosure is more important than for long existing companies;

For non‐manufacturing companies there are several significant factors: disclosure of Intellectual Capital information (positive effect on post‐issue stock performance), the difference between the closing price of the first day of trading and the initial offering price (positive relation), the average trading volume for the newly listed stock (negative relation);

For foreign companies that issue American Depositary Shares on NASDAQ disclosure of IC information plays a significant positive role for the stock post‐issue performance;

For Internet companies its post‐issue stock performance is positively correlated with the difference of the first day closing price, is very much (positive reaction) affected by the sentiment of the market 60 days prior to listing and by the trading volumes during first 500 days of listing (negative relation). It is a positive sign for investors when Internet company has international operations;

For offshore companies disclosure of information on IC is important, as well as a momentum of the market.

The analysis of literature on the topic of Intellectual Capital in general and its disclosure in particular showed the following gaps in current research: Intellectual Capital disclosure in developing countries: there were some studies conducted in Malaysia (Goh, et al.), Sri Lanka (Abeysekera, et al., 2005) and India (Kamath, 2008), but, for example, nearly no research on the topic in China. Duplication of the same research on other markets or also on NASDAQ but with bigger sample (also other industries included) could bring more interesting insights and make the reliability of the results higher. There can be other suggestions for future research in this sphere. The main result of the research is different from the one obtained by Singh and van der Zahn (2009) on Singapore Stock Exchange. Most probably this results from specifics of the markets and regulation. Further research in this field and tests on other markets are needed to be able to give a certain answer. The model also showed that Intellectual Capital doesn’t explain fully (can’t predict to the full extent) the behavior of the stock after listing (tested for the period of 500 days) – for better analysis financial indicators should be also used. However, the results confirm once again the importance of the disclosure of Intellectual Capital information and its disclosure.

References Abeysekera, I., Guthrie, J. (2005). “An empirical investigation of annual reporting trends of intellectual capital in Sri Lanka”, Critical Perspectives on Accounting, Vol 16, No. 3, pp 151‐63. Aboody, D., Lev, B. (1998). “The value relevance of intangibles: the case of software capitalization”, Journal of Accounting Research, Vol 36, No. 3, pp 161‐91. Aharony, J., Lin, C.J., Loeb, M.P. (1993). Initial public offerings, accounting choices, and earnings management, Contemporary Accounting Research, No 10, pp 61‐81.

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Tatiana Garanina and Alexandra Manuilova Amir, E., Lev, B. (1996). “Value‐relevance of nonfinancial information: the wireless communication industry”, Journal of Accounting and Economics, No. 22, pp 3‐30. Ang, J.S., Brau, J.C. (2002). “Firm transparency and the costs of going public”, Journal of Financial Research, Vol 25, No 1, pp 1‐17. Beattie, V., Pratt, K. (2002). Disclosure items in a comprehensive model of business reporting: an empirical evaluation. University of Stirling. ‐ Stirling. Bontis, N. (2003). “Intellectual capital disclosure in Canadian corporations”, Journal of Human Resource Costing and Accounting, No. 7, pp 9‐20. Botosan, C.A. (1997). “Disclosure level and the cost of equity capital”, The Accounting Review, Vol 72, No. 3, pp 323‐49. Brown, S., Lo, K., Lys, T. (1999). “Use of R2 in accounting research: measuring changes in value relevance over the last four decades”, Journal of Accounting and Economics, Vol. 28, No. 2, pp 83‐115. Bukh, N., Christensen, K., Mouritsen, J. (2005). Knowledge Management and Intellectual Capital. Establishing a Field of Practice: Palgrave MacMillan. Chen, M.C., Cheng, S.J., Hwang, Y.C. (2005). “An empirical investigation of the relationship between intellectual capital and firms’ market value and financial performance”, Journal of Intellectual Capital, Vol 6, No. 2, pp 159‐76. Core, J.E., Guay, W.R., Van Buskirk, A. (2003). “Market valuation in the new economy: an investigation of what has changed”, Journal of Accounting and Economics, Vol 34, No. 1‐3, pp 43‐67. Diamond, D., Verrecchia, R. (1991). “Disclosure, liquidity and the cost of equity capital”, Journal of Finance September, Vol 1, pp 1325‐60. Dumay, J.C., Tull, J.A. (2007). “Intellectual capital disclosure and price‐sensitive Australian Stock Exchange announcements”, Journal of Intellectual Capital, Vol 8, No. 2, pp 236‐255. Edvinsson, L. (1997). “Developing intellectual capital at Skandia”, Long Range Planning, Vol 30, No. 3, pp 366‐73. Ely, K., Waymire, G. (1999). “Intangible assets and stock prices in the pre‐SEC era”, Journal of Accounting Research, Vol 37, No. 3, pp 17‐44. Eustace, C. (2001). The intangible economy: impact and policy issues. Report of the High Level Expert Group on the Intangible Economy, EU Commission: Brussels. FASB, Norwalk CT (2001). Improving business reporting: insights into enhancing voluntary disclosures. Steering Committee Business, Reporting Research Project: Financial Accounting Standard Board. Garcı´a‐Meca, E. (2005). “Bridging the gap between disclosure and use of intellectual capital information”, Journal of Intellectual Capital, Vol 6, No. 3, pp 427‐40. Ghosh, D., Wu, A. (2007). “Intellectual capital and capital markets: additional evidence”, Journal of Intellectual Capital, Vol 8, No. 2, pp 216‐235. Goh, P., Lim, K. (2004). “Disclosing intellectual capital in company annual reports: evidence from Malaysia”, Journal of Intellectual Capital, Vol 5, No. 3, pp 500‐10. Guthrie, J., Petty, R. (2000). “Intellectual capital: Australian annual reporting practices”, Journal of Intellectual Capital, Vol 1, No. 3, pp 241‐54. Kamath, B. (2008). “Intellectual capital disclosure in India: content analysis of “TecK” firms”, Journal of Human Resource Costing and Accounting, Vol 12, No. 3, pp 213‐24. Kamath, G.B. (2007). “The intellectual capital performance of the Indian banking sector”, Journal of Intellectual Capital, Vol 8, No. 1, pp 96‐123. Lev, B. (2000). Communicating knowledge capabilities. working paper. Leonard N. Stern School of Business, New York University, NY. Lev, B., Sougiannis, T. (1996). “The capitalization, amortization, and value‐relevance of R&D”, Journal of Accounting and Economics, No. 21, pp 107‐38. Lev, B., Zarowin, P. (1999). “The boundaries of financial reporting and how to extend them”, Journal of Accounting Research, Vol 37, No. 2, pp 353‐85. Mather, P., Ramsay, A., Steen, A. (2000). “The use and representational faithfulness of graphs in Australian IPO prospectuses”, Accounting, Auditing & Accountability Journal, Vol 13, No. 1, pp 65‐83. Mavridis, D.G. (2004). “The intellectual capital performance of the Japanese banking sector”, Journal of Intellectual Capital, Vol 5, No. 1, pp 92‐115. Meritum Project. (2002). Guidelines for Managing and Reporting on Intangibles (Intellectual Capital Report). European Commission: Madrid. Mouritsen, J. (2002). “Developing and managing knowledge through intellectual capital statements”, Journal of Intellectual Capital, Vol 3, No. 1, pp 10‐29. Mouritsen, J. (2003). “Intellectual capital and the capital market: the circulability of intellectual capital”, Accounting, Auditing & Accountability Journal, Vol 16, No. 1, pp 18‐30. Oliveras, E. (2008). “Reporting intellectual capital in Spain”, Corporate Communications: An International Journal, Vol 13, No. 2, pp 168‐81. Richardson, A.J., Welker, M. (2001). “Social disclosure, financial disclosure and the cost of equity capital”, Accounting, Organizations and Society, No. 26, pp 597‐616. Schrand, C., Verrecchia, R.E. (2004). Disclosure choice and cost of capital: evidence from underpricing in initial public offerings: The Wharton School, University of Pennsylvania.

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Organisational Learning and Problem Solving Through Cross‐firm Networking of Professionals Mahmood Ghaznavi, Paul Toulson, Martin Perry and Keri Logan Massey University, New Zealand M.Ghaznavi@massey.ac.nz P.Toulson@massey.ac.nz M.Perry@massey.ac.nz K.A.Logan@massey.ac.nz Abstract: This paper provides empirical evidence to explain the underlying mechanics involved in cross‐firm informal knowledge collaboration among socially connected individuals. Data is collected through a questionnaire survey to investigate how knowledge workers develop and sustain informal knowledge collaboration outside of their work organisations; and how such knowledge collaboration affect problem solving and organisational task performance. Results indicate that norms of reciprocity, interpersonal trust, and informal information exchanges develop network transactive memory systems (TMS) among socially connected individuals. TMS is defined as: awareness about the locus of expertise, belief in others’ competence; and ability to coordinate diverse expertise. Network TMS provide larger pool of expertise to help knowledge workers resolve complex work problems and improve task performance. Keywords: informal knowledge collaboration, norms of reciprocity, interpersonal trust, transactive memory systems, problem solving, task performance

1. Introduction and theoretical background Research on organisational learning and innovation has primarily focused on the development of new processes and products to gain competitive success. Although research in this area has contributed much insight, it has tended to overlook the role of individuals, who learn and apply knowledge to develop innovative products and processes. While discussing the sources of knowledge and ideas for problem solving and innovation, researchers argue that internal (i.e. organisational) channels may not necessarily provide all relevant and up‐to‐date knowledge (Teigland and Wasko, 2003, Levin and Cross, 2004). The networking research indicates that external connections of knowledge workers as important sources of new ideas and problem solutions (Benner, 2003, Allen et al., 2007, Cross and Jonathon, 2004, Dahl and Pedersen, 2004). Nonetheless, the actual process of learning and innovation through personal (informal) networking is largely a ‘black box’, not allowing researchers to convincingly argue what enables informal knowledge collaboration among individuals in the absence of formal structures and organisational control mechanisms. Organisational learning refers to the processes through which an organisation gains new knowledge about its environment, goals, and processes (Argyris, 1999). Some researchers describe an organisation as an entity that can learn at its own and adapt to the changing environment (Easterby‐Smith et al., 2000, Senge, 1990). According to Simon Herbert (1991), organisations learn in three ways: (1) an organisation incorporates new knowledge by implementing new systems and projects, (2) an organisation hires people with knowledge that does not already exist in the organisation, and (3) individuals within the organisation learn new facts or new ways of doing things. Nevertheless, an organisation’s ability to learn and survive in the changing environment is largely dependent on its people, who may actually create and apply knowledge to bring innovation and improve productivity (Davenport, 2005). Given the views of the many researchers, it can be safely argued that organisations survive by learning new ideas and transforming these ideas into innovative products and processes; and individuals play a key role in achieving this. Organisations provide various opportunities and access to a variety of knowledge sources so that individuals can learn and apply knowledge to develop innovative products and problem solutions (Hannah, 2004, Lawson et al., 2009). The question arises: whether organisational knowledge sources are sufficient to resolve all sorts of complex problems and develop innovative solutions? In today’s ever‐changing and competitive world, it is not possible for an individual (even for an organisation) to retain and/or provide all relevant and up‐to‐date knowledge to resolve continuously emerging customer problems and demands for innovative solutions. Previous patterns of solutions and organisational knowledge base my not provide sufficient help to resolve many non‐routine problems. In view of this, knowledge workers’ routine work practices are becoming less limited to their own particular workplaces, rather, they frequently cross organisational boundaries to discuss

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Mahmood Ghaznavi et al. work problems with professional colleagues in other organisations (Teigland and Wasko, 2003, Benner, 2003); who might have faced similar challenges in their own work settings. Some researchers argue that most new ideas come from outside organisations; as internal (organisational) knowledge sources (e.g. databases, routines, immediate colleagues) often contain redundant knowledge (Teigland and Wasko, 2003, Levin and Cross, 2004). Organisational members benefit from external connections as they gain access to knowledge and ideas for problem solving and innovation. The ties between individuals and their social capital facilitate the transfer of knowledge from external sources; irrespective of the rules, hierarchies, and organisational constraints (Inkpen and Tsang, 2005, Smedlund, 2008). Researchers have explored the links between informal information sharing and organisational learning by examining the process of collective invention (Allen, 1983, Cowan and Jonard, 2003, von Hippel and von Krogh, 2003). The term ‘collective invention’ was coined by Allen (1983) through his cutting edge research on the design of blast furnaces in the UK iron industry. Allen observed that the free revealing of technical information by the owners of blast furnaces resulted in rapid design improvements of the blast furnaces. The idea of collective invention was further elaborated by Von Hippel (1987) and Schrader (1991); who documented the optimisation of production processes in US mini‐mill steel firms as a result of informal information trading among engineers of the competing mini‐mill firms. These researchers identified informal information trading as the process of information seeking and transfer through informal personal contacts of employees. Studies focusing on informal information trading (Schrader, 1991, von Hippel, 1987, Teigland and Wasko, 2003) highlight norms of reciprocity (i.e. receiving and returning favours) as a governing principle in developing sustainable knowledge exchange relationships among socially connected individuals. Social networking studies identify the role of trust in fostering knowledge exchange relationships among individuals (Nahapiet and Ghoshal, 1998, Levin and Cross, 2004, Inkpen and Tsang, 2005). Researchers also discuss the role and application of transactive memory systems (TMS) in knowledge seek and transfer among individuals (Jarvenpaa and Majchrzak, 2008, Brandon and Hollingshead, 2004). Researcher define TMS as the knowledge of self and others’ areas of expertise, belief in the competence of others; and ability to coordinate expertise for problem solving (Wegner, 1987, Brandon and Hollingshead, 2004, Lewis et al., 2005). Based on the theories of reciprocity, interpersonal trust, and TMS, the next section presents a research model and hypotheses to investigate the process of cross‐firm informal knowledge collaboration among socially connected individuals.

2. Hypotheses and research model Researchers argue that knowledge is a valuable resource and its transfer may incur significant cost due to opportunistic behaviour of the receiving party (Schrader, 1995, Inkpen and Tsang, 2005). In view of this, the social capital theory (SCT) highlights the role of norms of reciprocity and trust in knowledge transfer among individuals. Evidence indicates that norms of reciprocity help to develop sustainable knowledge exchange relationship among professionals (Ghaznavi et al., 2012, Teigland and Wasko, 2003). Researchers also agree on the mediating role of trust in reducing fears of opportunism and fostering knowledge sharing relatinships among individuals (Abrams et al., 2003, Chowdhury, 2005). We argue that norms of reciprocity establish a certain degree of trust among interacting parties by eliminating fears of misconducts and opportunism; and affect an individual’s ability to coordinate diverse expertise from outside his or her work organisation. This develops our first set of hypotheses, as under: H1a: Higher the norms of reciprocity, higher the level of trust among the inetracting parties. H1b: Norms of reciprocity help in the development of TMS among socially connected individuals Researchers argue that trust is an alternative way of managing risk and develops goodwill about the behaviour of the other party, (Tsai and Ghoshal, 1998, Mayer et al., 1995). In the absence of the ability to directly monitor or control the activities of others, trust is necessary to overcome fears of misconduct and opportunism (Levin and Cross, 2004, Abrams et al., 2003). Interpersonal trust in this study is defined as the beliefs of an individual about the behaviour of others people in his social (personal) network that others will behave according to his expectations and self‐interest (Tsai and Ghoshal, 1998). We suggest that interpersonal trust increase instances of knowledge exchange among socially connected individuals and would help to develop TMS by affecting one’s ability to coordinate specialised knowledge for problem solving.

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Mahmood Ghaznavi et al. H2a: The higher the interpersonal trust, the more cross‐firm informal knowledge collaboration occur between socially connected individuals H2b: Interpersonal trust help in the development of TMS among socially connected individuals TMS researchers claim that task interdependence is essential for the proper functioning of TMS; as each member of the group contributes his or her expertise to achieve common organisational goals (Brandon and Hollingshead, 2004, Lewis et al., 2005). Jarvenpaa and Majchrzak (2008) studied the application of TMS in mix‐ motive inter‐organisational collaboration and found that TMS could also develop and work in ego‐centered networks of professionals; where task interdependence cannot be assumed as members belong to different organisations and work contexts. Jarvenpaa and Majchrzak (2008) suggested that members of ego‐centered networks assume future collaboration needs in the absence of current task dependencies. It has been further argued that all relevant know‐how and expertise to resolve complex problems may not available within organisation and among collocated workers (von Hippel, 1994, Dutton, 2008, Teigland and Wasko, 2003). Organisational members, therefore, need to rely on their external connection to get useful knowledge for problem solving and ideas for innovation. We, therefore, propose a link between cross‐firm informal knowledge collaboration among socially connected individuals and the development of their network TMS, as under: H3: Cross‐firm informal knowledge collaboration help to develop TMS among socially connected individuals Previous studies have found a positive link between cross‐firm informal information trading and collective innovation in firms (Schrader, 1991, von Hippel, 1987). Teigland and Wasko (2003) study, however, did not find any direct relationship between external information trading and individual performance; rather they highlighted that a firm’s performance can be improved through its ability to integrate and process external information and by making it a part of its internal memory. Due to this contradictory evidence, we wish to examine the relationship between cross‐firm knowledge collaboration and organisational task performance. H4: Cross‐firm informal knowledge collaboration between individuals improve organisational task performance Previous studies indicate strong positive links between TMS and performance of groups within organisations (Lewis, 2003, Anand et al., 1998). We have proposed that network TMS developed through the activities of socially connected individuals, who belong to different organisations and workgroups, can also help to improve organisational task performance. H5: TMS developed through cross‐firm knowledge collaboration positively affect organisational task performance Figure 1 provides an overview of the research model and hypotheses for testing.

Norms of Reciprocity H1b H1aa

Interpersonal Trust

Transactive Memory

H2b

H3 H2a H4

Knowledge Collaboration

Task Performance

Figure 1: Research model

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Mahmood Ghaznavi et al.

3. Research design and data analysis 3.1 Data collection We conducted our research with professionals (knowledge workers) who are associated with the New Zealand Knowledge Management (NZKM) network. A survey questionnaire was sent to all 500 members of the group. A total of 194 completed responses received (representing a response rate of 38.8%). Respondents of the survey (as shown in Table 1) belong diverse industry types and professions. The survey questionnaire asked professionals about their informal knowledge sharing practices with informal contacts; that exist outside of their work organizations. The survey also asked them about the impact of such knowledge sharing practices on problem solving and organisational task performance. Table 1: Demographics (the number of subjects N = 194) Demographic characteristics Male Female Age Under 30 years 30‐39 years 40‐49 years Over 50 years Industry Sector Power, transport & Construction Information Media & Telecommunications Financial Services Professional, Scientific, and Technical Services Education and Training Government Services Others Type of Profession Chief Executives and General Managers Business Administration Managers ICT Managers Information and Knowledge Managers Information and Organisational Professionals Tertiary Education Teachers Scientist and researchers Others

Number 107 87 7 48 63 76 14 25 10 56 30 40 15 12 12 15 40 50 15 14 20

Frequency 51% 49% 4% 25% 32% 39% 7% 14% 6% 30% 16% 18% 8% 6% 6% 8% 22% 30% 8% 7% 11%

3.2 Measurement development Measurement items for the dependent and independent variables were mainly derived from previous studies and customised according to the need of this study. Items of TMS were based on Lewis, Lange and Gillis (2005). Items of the reciprocity norms were developed and adapted from Wasko and Fraj (2000)http://www.sciencedirect.com.ezproxy.massey.ac.nz/science/article/pii/S0747563209000417 ‐ bib96, and Kankanhalli et al. (2005). Items of trust were adapted from Zaheer, McEvily and Perrone (1998), and items of informal knowledge sharing were taken from the study of Teigland and Wasko (2003). All independent and dependent variables (except informal knowledge sharing) were measured using multiple items on a five‐point Likert scale (ranging from strongly disagree ‘1’ to strongly agree ‘5’). Instances of cross‐firm informal knowledge sharing were assessed by asking respondents to indicate how often they engage in specific knowledge sharing activities (1 = “Never”, 2= “Once a day”, 3= “Once a week”, 4= “Once a month”, 5= “A few times a year”). To establish content validity, a two‐stage pretesting of the questionnaire were performed. First, a group of fellow researchers assessed questions wording, ease of understanding, logical consistency and sequencing of items. Second, a pilot survey was completed with the executive committee members of the NZKM Network in order to further refine the structure and content of the instrument.

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3.3 Analysis and results The research model shown in figure 1 was tested through confirmatory factor analysis (CFA) using AMOS Graphics and structural equation modeling (SEM) using WarpPLS 3.0. CFA confirms fitness of the measurement model, while SEM tests the structural relationships among the latent constructs. The two‐step approach was used and recommended by many researchers for: 1) testing the structure of the measurement model; and 2) pathways of the latent constructs (Anderson and Gerbing, 1988, Byrne, 2010, Hair et al., 2010). 3.3.1 Measurement model validation CFA results exhibited a good level of fit of the measurement model with the collected data. All the fit indices for the initial and modified CFA model (as shown in Table 2) indicated an acceptable fit based on the criteria specified by Hu and Bentler (1999) and Kline (2005). The measurement model was further evaluated for its reliability, convergent and discriminant validity following the guidelines specified in the prior literature (Kline, 2005, Hair et al., 2010). For testing convergent validity we compared average variance explained (AVE) of all factor with the standard value (i.e. 0.50). All values were either very close or above the benchmark. For discriminant validity, we compared the square root of the AVE (on the diagonal in the matrix a shown in Table 3) with their respective inter‐factor correlations. All the diagonal values are greater than the correlations which demonstrated sufficient discriminant validity. The composite reliability for all factors was above the minimum threshold of 0.70. In conclusion, the measurement model demonstrated sufficient reliability and validity as well as model fit indices. Table 2: Model fit indices for the measurement models Model fit indices

Observed value

Recommended value

Chi‐square/degree of freedom (CMIN/DF) Comparative fit index (CFI) Goodness‐of‐fit (GFI) Adjusted Goodness‐of‐fit (AGFI) Root mean square error of approximation (RMSEA)

1.40

< 3.00

.96 .91 .88 .04

>.90 >.90 >.80 < .08

Table 3: Validity and reliability CR AVE Inf_KS TMS Inf_KS 0.74 0.49 0.70 TMS

0.82 0.49

0.13

0.70

Trst

RCPR PERF

Trst

0.80 0.58

0.26

0.44 0.75

RCPR

0.81 0.58

0.09

0.44

0.75

PERF

0.87 0.69

0.11

0.54

0.53

0.67

0.82

3.3.2 Test of the structural model Results of hypotheses tests with path coefficients and significance values are shown in Figure 2. Figure 2 provides a summary of the results. The relationship between norms of reciprocity (RECPR) and interpersonal trust is highly significant (β = 0.64, p < .01), supporting our first hypothesis H1a. H1b is also well supported (β = 0.27, p < .05), indicating the role of interpersonal trust in the development of network TMS. H2a is supported as trust increases instance of informal knowledge sharing (INF_KS) across organisations (β = 0.25, p < .01). Interpersonal trust also play a significant role in the development of TMS (β = 0.19, p <.05), supporting H2b. The hypothesized path from INF_KS to TMS is also highly significant (β = 0.22, p < .01), thus supporting H3. The direct relationship between cross‐firm informal knowledge sharing and organisational task performance is not significant (β = 0.14, p ns), thus rejecting our H4. Our last hypothesis H5 is well supported (β = 0.54, p < .01), confirming that TMS developed through cross‐firm informal knowledge collaboration positively impact organisational task performance (PERF).

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Figure 2: Analysis results

4. Discussion and implications The problems and solutions continuously emerge in this world of knowledge and makes it harder for an individual (or even a firm) to retain all relevant and up‐to‐date knowledge. Existing solutions and organisational sources of knowledge are unlikely to provide employees with the knowledge required to resolve complex work challenges. Modern knowledge workers turn to informal sources and often rely on their personal contacts to get advice on problem solutions and ideas for innovation. This can include making use of the linkages that employees develop with professional colleagues outside of their current work organisation. Little is known how knowledge workers develop and maintain informal knowledge collaboration with professional colleagues; who are not immediate colleagues and supposed to do joint problem solving. Prior research indicates that individuals are more likely to share knowledge when they perceive that others will also do the same (Yuan et al., 2005). Researchers highlight norms of reciprocity as an important antecedent for informal information trading among individuals (Schrader, 1991, Teigland and Wasko, 2003). Our analysis indicates that norms of reciprocity develop interpersonal trust among the interacting parties and enhance their ability to coordinate knowledge from external sources. Knowledge workers, who believe in norms of reciprocity, develop level of interpersonal trust with colleagues outside of their current work organisation. They believe that their personal contacts will come to help them in the time of their need and provide relevant knowledge or expert advice to resolve their problem situation. Researchers agree on the positive role of trust in dealing with risk and uncertainty, and accepting vulnerability (McAllister, 1995, Mayer et al., 1995). Since, sharing work‐specific knowledge with people outside organisations entails risk and makes oneself vulnerable to the actions of the other party, a certain degree of trust is crucial among the collaborating parties in order to avoid potential damages. Our analysis confirm the mediating role of trust in fostering informal knowledge collaboration among individuals who belong to different organisations. Our results also indicates that interpersonal trust contributes to the development of TMS among socially connected individuals. This may be due to increase in one’s ability to coordinate specialised knowledge from diverse sources for problem solving. The link between trust and transactive memory is indicated by few other researchers who highlight the role of trust and trustworthiness in TMS development (Ashleigh and Prichard, 2012). The direct link between cross‐firm informal knowledge sharing and TMS is not significant which is in consistent with the Teigland and Wasko (2003) study that external knowledge is not able to improve performance unless it becomes part of the organisational memory or embedded in organisational routines. Our analysis further

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Mahmood Ghaznavi et al. indicates that cross‐firm knowledge collaboration develop TMS among socially connected individuals by enhancing their competence‐based trust and knowledge coordination abilities. TMS improve task performance through coordination and combination of specialised knowledge of the group members (Lewis et al., 2005, Alavi and Tiwana, 2002). Literature indicates that TMS develops and works in groups that comprise of collocated workers who have tight interdependencies to achieve common goals (Lewis et al., 2005). We have investigated whether TMS develop and work among individuals who work in different organisations and where joint problem solving cannot be assumed. Our findings indicate that repeated exchanges of information help to develop network TMS among socially connected individuals. Such TMS improve organisational task performance through coordination and combination of diverse expertise. The above findings could have implications for further research. Researchers may identify useful ways to coordinate and consolidate distributed cognition and diverse expertise for problem solving and innovation. Small and medium enterprises (SMEs), that do not spend much on R&D, can encourage employees to develop and maintain personal knowledge networks. SMEs can enhance their knowledge base by seeking ways to develop network TMS of employees through providing them opportunities for networking and cross‐firm knowledge sharing.

5. Contribution to theory and practice An organisation learns through its individual members who directly or indirectly affect its performance (Argyris, 1999). Since individuals act as learning agents for an organisation, the processes of individuals’ learning can significantly impact organisational performance. This study seeks to highlight the value of cross‐ firm informal knowledge sharing among individuals in improving productivity and performance of organisations. It can contribute to the existing literature on cross‐firm informal knowledge sharing and network TMS, as follows:

Theoretically, it can provide some insight into the process of cross‐firm informal knowledge collaboration among individuals and the development of network TMS; as a result.

Empirically, it will add to the limited studies done with regard to cross‐firm informal knowledge sharing and network TMS, thereby, allowing future research to build upon the results of this study.

Practically, it attempts to improve managerial understanding about the role and significance of cross‐firm informal knowledge collaboration and network TMS of individuals. This increase in understanding may bring some managerial support to employees who are engaged in informal knowledge sharing outside of their work organisations.

6. Conclusion In this paper informal knowledge collaboration refers to a process that enables provision of task‐related information and know‐how through social (personal) connections. Taking various dimensions of the social capital theory (norms of reciprocity and interpersonal trust), we have demonstrated the process of informal knowledge collaboration among socially connected individuals; and highlight the mediating role of TMS in achieving organisational task performance. Previously cross‐firm knowledge sharing has been mainly studied in the context of informal information trading in specific sectors and industry clusters. Result of this study indicate that cross‐firm knowledge collaboration is a common phenomenon among knowledge workers; as they frequently seek and arrange work‐specific knowledge from professional colleagues outside of their work organisations. Norms of reciprocity and interpersonal trust help them manage such knowledge collaboration in the absence of organisational control mechanisms. Informal knowledge collaboration enhance their local TMS and transform into a network TMS. Such network TMS help organisational members to locate and access specialized knowledge and improve task performance through coordination and combination of the diverse expertise.

7. Limitations This study has many limitations. Although, we have tried to collect data from a variety of industry sectors and professions, the results cannot be generalized for all industry sectors and work environments due to smaller sample size. Organisational task performance in this study is measured through self‐reporting measures (although it is an acceptable practice in social science research), we cannot make claim on the actual value achieved (in terms of personal capability development and organisational performance) from cross‐firm

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Mahmood Ghaznavi et al. informal knowledge collaboration. Our responses may also include a socially desirable response bias. Based on the results of this study, we can only broadly argue that cross‐firm informal knowledge collaboration and the development of network TMS will bring positive outcomes for an organisation. Some evidence from prior research support this, but further empirical research is necessary to develop any convincing argument. Future research may include some definite indicators to measure actual improvement in the performance as a result of network TMS develop through cross‐firm informal knowledge collaboration among knowledge workers.

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Knowledge Orientation in Information Intensive Organisations: Is There a Change in Paradigm? Paul Griffiths1 and Teresita Arenas2 1 The Birchman Group; Visiting Faculty, Henley Business School, Chile 2 Universidad Tecnica Federico Santa Maria, Chile paul.griffiths@vtr.net teresita.arena@usm.cl Abstract: In a prior paper published in 2008 a framework to align intellectual capital (IC) management with business strategy was proposed. The framework presents two alternative orientations to managing IC: People‐based networks, and Technology‐based networks. Doing a multiple case study the research led to a useful finding: Organisations that operate in an Operational Excellence value discipline favour technology‐driven knowledge networks, while organisations that operate in Customer Intimacy or Product Leadership tend to favour people‐driven networks.. The key paradigm detected in that study is (a) that technology‐based networks are top‐down initiatives and require significant investments, while people‐ driven networks are bottom‐up and require small cash investments but significant man‐hours; and (b) that organisations did not push for a high development on both types of networks, but opted for one or the other (aligned with its value‐ proposition). Under the suspicion that the advent of Web 2.0 and other recent developments could have undermined the prior paradigm, this exploratory research surveys 16 companies in Chile and finds indications that there could be significant changes; the paper attempts to find explanation for this and proposes the need for further research in this field. Keywords: IC alignment, strategy, knowledge orientation, people‐based networks, technology‐based networks

1. Introduction In the knowledge economy business success depends ever more on the effective use of intangible assets such as knowledge, skills and the ability to innovate. As a result of this the last twenty years have witnessed increasing research in the field of knowledge management and intellectual capital (Viedma & Cabrita, 2012; Arenas et al., 2013) and one of the main thrusts is to arrive at models that will align knowledge management with business strategies. Based on that knowledge management is a business process through which firms:

Create, synthesise and share their collective information, insights and experience;

Combine the latter with information from external sources; and

Put all this compound information to use in solving business problems

(Ezingeard et al., 2002; Griffiths & Remenyi, 2008; Ruggles, 1998; Sarvary, 1999) which denotes a highly social connotation to knowledge management. One such model is that presented by Griffiths & Remenyi (2008) that breaks down approaches to knowledge management into People‐driven networks and Technology‐driven networks, and establishes a link between the emphasis on these kinds of network and the value discipline of the organisation (Treacy & Wiersema, 1995). The Griffiths & Remenyi (2008) model suggests that organisations that operate in an Operational Excellence value discipline favour technology‐driven knowledge networks, while organisations that operate in Customer Intimacy or Product Leadership tend to favour people‐driven networks. This finding was backed by logic: The value proposition of Operational Excellence companies is based on efficiency through “re‐usability”, while the value proposition of Customer Intimacy is based on best customised solution and Product Leadership on creativity, both of which are better leveraged on original work supported by people‐based networks. The key paradigm detected in that study is (a) that technology‐based networks are top‐down initiatives and require significant investments, while people‐driven networks are bottom‐up and require small cash investments but significant man‐hours; and (b) that organisations did not push for a high development on both types of networks (characterised as utopia) but opted for one or the other (aligned with its value‐proposition). Under the suspicion that the advent of Web 2.0 and other recent developments could have undermined the prior paradigm, this exploratory research tackles the question “Are people‐based networks and technology‐ based networks really proscriptive of each other?”

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Paul Griffiths and Teresita Arenas As in Griffiths & Remenyi (2008), knowledge in this paper is characterised as information plus the causal links that help to make sense of that information (Savary, 1999). So if data is information devoid of context, and thus information is data in a context, knowledge is information with causal links. Knowledge implies at least an explanation, if not a prediction. In section 2 this paper will synthesise the old paradigm; in section 3 it will reflect on the changes that have taken place since the old paradigm was articulated. Section 4 will describe the methodology, and section 5 will analyse the evidence that emerged from the empirical work. Sections 6 and 7 will present a discussion around new concepts and present a possible response to the research question.

2. Old paradigm

III

Technology--Based Network Technology

H -> Extramural

Researching with organisations in knowledge intensive industries two clear trends emerged: While some organizations rely on a knowledge codification strategy that seeks to make knowledge independent of individuals and store it in repositories for users to access through information and communications technology (ICT) tools (Davenport & Hansen, 2002), others rely on a personalization strategy that emphasizes the channeling of individual expertise to the right place at the required time through person‐to‐person interaction (Bartlett, 2000; Griffiths & Remenyi, 2007). The issue of how outward looking the organization has to be in its knowledge management also emerged as a core concept. Organisations need to find the right balance, the most effective blend, between internal and external content, and avoid the trappings of being too introverted, too satisfied with their own view of the world. Their internal networks need to link up with other networks in their areas of expertise (Bartlett, 2000; Collins, 2002,1998; Ezingeard et al., 2002) This can be represented in a two‐dimensional space as shown in Figure 1. One axis represents the degree of development of knowledge management founded on Technology‐based networks, from Low to High. The other axis represents the degree at which the organization has developed its person‐to‐person knowledge sharing capabilities (People‐based networks.) In both cases the “High” development indicates a robust integration with other knowledge networks.

Personal Network

Figure 1: Knowledge management founded on technology‐based networks vs. personal networks (Griffiths & Remenyi, 2007) By placing the organisations on this plane according to their approach to knowledge management, and then anlysing their business strategy expressed by the value proposition they make to their clients, a clear pattern emerged linking knowledge‐management‐approach to business‐strategy. Organisations that build their competitive positioning on delivering tailor‐made, one‐off services to their Clients, place themselves in quadrant II. That is they approach knowledge management by developing strong people‐based networks. They create knowledge by motivating their staff to write thought leadership pieces that crystalise the products of their interaction with external research centres and reflections on the outcomes of their projects. Knowledge is shared in person‐to‐person relationships within and across communities of practice. As a result, knowledge creation and sharing is a bottom‐up process that promotes creativity and originality, and requires a relatively low investment. Producing original solutions to Client problems are a core process of these organisations and technology investments are focused on supporting this process through groupware and person‐to‐person

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Paul Griffiths and Teresita Arenas communications. The strategy consulting firms are typical of this model (Bartlett, 2000; Griffiths & Remenyi, 2007) Organisations that compete by building scale and efficiency and found their value proposition on replicating proven solutions, place themselves in quadrant IV. Their knowledge management initiative is top‐down, with standards and rigid guidelines created at the centre. They make significant investments in ICT networks aimed at producing large document repositories and powerful search engines. They populate these repositories by motivating their staff to upload the deliverables of their projects, which their colleagues can later use as “accelerators” for subsequent engagements. The more extreme of these organisations attempt to completely automate the search processes and eliminate interpersonal knowledge sharing. Others tend to prioritise technology networks but complement them with people‐based networks. The core process is managing projects to replicate solutions as fast and efficiently as possible. The enterprise resource planning (ERP) implementation firms are typical of this business model (Davenport & Hansen, 2000; Griffiths & Remenyi, 2007; Haas & Hansen, 2005). The prior study arrived at that start‐up and boutique consulting firms are forced into quadrant I. Their value proposition to clients is based on trust from the clients’ management, or on specific expertise of their leaders. Their staff tends to have strong interpersonal relationships, but they are placed at the low end of the “People‐ based network” axis because they do not have formal knowledge sharing processes, policies for promoting knowledge creation, or structured communities of practice. They are also at the low end of the “technology‐ based networks” because they do not have the substantial resources required for investments to move up this axis. The study also discovered that the hybrid approach to knowledge management of quadrant III was probably a utopia, and utopias are dangerous: they mobilize in the short but paralyze in the long term (Romano de Sant’ Ana, 2006). A hybrid approach to knowledge management appears to be a reflection of ambiguity and avoiding decisions on business strategy (e.g., going after every opportunity, without being realistic on the organisation’s ability to deliver value), or falling to the siren call of offering a “one‐stop‐shop” to its client base. The study depicted that there were a few highly visible failures in the 1990s: For example EDS trying to move up the value chain by acquiring AT Kearny to offer business strategy services as well as ICT outsourcing; or Booz‐Allen & Hamilton trying to take advantage of the ERP wave by moving down the value chain from business strategy to implementation services. But it is by now clear that the factors that make a firm good at creating personalized products or leading edge services makes it inefficient at replicating solutions and, vice‐ versa, those factors that make it effective at replicating standard solutions introduces rigidities that make its bespoke services or break‐through products blunt and colourless (Treacy & Wiersema, 1995). The study articulated that, precisely, one of these factors is how the organization manages knowledge (Griffiths & Remenyi, 2007; Griffiths & Remenyi, 2008.) In summary, the old paradigm states that on the one hand an approach to knowledge management driven by people‐based networks is bottom‐up, requires low cash investment but significant personal time; it relies heavily on tacit knowledge and creates an environment for moving knowledge from tacit to explicit. On the other, an approach to knowledge management driven by technology‐based networks is top‐down; requires high investment and relies mainly on explicit knowledge. The study did not find any leading organisation that was strong on both people‐based and technology‐based networks, so a significant premise was that attempting that was a utopia and revealed a lack of commitment in terms of the value proposition of the organisation. This is represented in table 1.

3. Beyond Utopia ‐ what has changed? Many changes have happened in the business world since 2008 when the old paradigm was articulated. Some of them are the result of policies that have been in place for decades but are only now taking effect; others are the result of changing demographics that were predictable and are now a reality; others are effects of the advent of new technologies; and yet others are second order effects of all the prior factors that have combined in different and extraordinary ways to change the context in which businesses operate. In the following paragraphs a few of the ones considered to be most relevant to this study will be reviewed.

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Paul Griffiths and Teresita Arenas Table 1: Linking knowledge‐management approach to strategy in the old paradigm Business Strategy/Value Proposition

Approach to Knowledge Management

Trust or Narrow Expertise Based

Quadrant I ‐Low on Technology‐based Networks ‐Low on People‐based Networks

Based on personal knowledge of leader Low investments in ICT repositories No formal knowledge sharing processes

Thought Leadership/Personalisation

Quadrant II ‐Low on Technology‐based networks ‐High on People‐based networks

Requires a relatively low investment Motivate staff to write thought leadership pieces Knowledge is shared in person‐to‐person relationships Within and across communities of practice Knowledge creation and sharing is a bottom‐up process Promotes creativity and originality

Hybrid/Utopia

Quadrant III ‐High on Technology‐based networks ‐High on People‐based networks

Void ‐ No examples found A reflection of ambiguity Avoiding decisions on business strategy

Productivity/Re‐usability

Quadrant IV ‐High on Technology‐based networks ‐Low on People‐based Networks

Knowledge management initiative is top‐down Standards and rigid guidelines created at the centre Promotes efficiencies Large document repositories and powerful search engines Populate repositories by motivating staff to upload eliverables Use as “accelerators” for subsequent engagements Completely automate the search processes and eliminate interpersonal knowledge sharing

The policies of the last 15 years on diversity of the workforce are taking effect. By diversity of the workforce we mean the policies and practices to include in the organisation's workforce people who are considered to be, in some way, different from those in the prevailing constituency (e.g., ethnicity, race, religion, sexual orientation, physical disadvantages, gender, class, public assistance status). Diversity is: "...otherness or those human qualities that are different from our own...The variety of experiences and perspective which arise from differences in race, culture, religion, mental or physical abilities, heritage, age, gender, sexual orientation, gender identity and other characteristics" (UCSF, 2012.) A recent McKinsey & Co. study found that in the last thirty years women went from holding 37 percent to 47 percent of the jobs in the US, and it estimates that this shift alone accounts for approximately one quarter of its GDP. There are many reasons for which organisations have gone down this path (ACAS, 2012; Green et al., 2012; Kerby & Burns, 2012; McInnes, 2013):

As an act of social responsibility, incorporating people from a "disadvantaged" group and thus giving those individuals the opportunity they need for making a living and at the same time relieve the tax‐supported social services from the cost of maintaining them;

As a resource imperative due to changing demographics in the workforce as today's labour pool in many contexts is dramatically different form that in the past and management has realised that many of the brightest brains are in minority groups so they cannot allow discriminatory preferences to impede their organisations from attracting the best qualified talent;

As a legal requirement as in many nations non‐discriminatory legislation has come into force and breeching it can lead to fines or loss of government contracts;

As a marketing strategy as buying power in today's global economy is represented by people from multiple backgrounds so having representatives of all those walks in life within the workforce helps in developing products and services that will appeal globally;

As a communications strategy as having a diverse workforce will help the organisation interact with all its business partners, suppliers and channels that will most likely also have diverse members;

As a means of reducing turnover costs as businesses that fail to foster inclusive policies find that many of their bright employees are forced to leave due to being uncomfortable in a hostile work environment;

As a means of fostering creativity as a Forbes (2011) study of 321 global enterprises found that 85 percent of them agreed that diversity is crucial for innovation;

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Paul Griffiths and Teresita Arenas Experience shows that retaining a diverse workforce can be more demanding than hiring one ‐ the more successful companies are those that recruit diversely because they are diverse rather than despite being minorities and aim at making their workforce profile resemble the communities where they operate (WSJ, 2013) Another factor is the change in demographics. One aspect of this is ageing of the workforce, and in its traditional form as stated in the 1967 Age Discrimination in Employment Act (ADEA) an ageing workforce was defined as "working individuals who are 40 years or older." More recent trends tend to promote that "ageing" is not merely about chronology but also about knowledge currency (Bockman & Sirotnik, 2008) and this seems to be a response to the reality that in the US, according to the Bureau of Labor Statistics, more than 25 percent of the labour force has reached retirement age (resulting in a potential worker shortage of 10 million) and that according to APQC an estimated 40 percent of the US workforce will be eligible for retirement in the next five years. It is also a fact that older workers continue to work, or retirees come back to work even, due to several reasons but most compellingly for financial ones (APQC, 2013; Lockwood, 2003). At present, baby boomers (born between 1946 and 1964) comprise 46 percent of the US workforce, with the first wave reaching retirement age as we write. Around 10,000 baby boomers will turn 65 each day for the next 16 years. They should naturally be replaced by Generation X (born between 1965 and 1983) but these only comprise 29 percent of the workforce, so it is almost impossible for this ‘changing hands’ to happen if the baby‐boomers leave en‐masse. We now have Generation Y (those born after 1983) or the internet generation, entering the workforce (Work & Family, 2013) which means that as a result of ageing there are four distinct generations in the workforce: The Veterans (born before 1945) that are returning from retirement, the Baby‐ boomers that are postponing retirement, Generation X and Generation Y. This introduces many complexities into HR Management, not least more health related problems and disabilities of the ageing, intergenerational conflicts, different skills and capabilities to absorb new technologies, unbalanced inter‐generation cost‐benefit of knowledge sharing and, in general, more needs for flexibility (APQC, 2013; Barnes et al., 2009; in 't Hout et al.,2010; Tishman et al., 2012). The third significant change since 2008 has been the technological one. During this period we have had the playing out of the effects of Web 2.0 and the incorporation of social media tools into the enterprise, and the advent of the phenomenon that has been called "Big Data." AIIM (2013) defines Enterprise 2.0 as "a system of web‐based technologies that provide rapid and agile collaboration, information sharing, emergence and integration capabilities in the extended enterprise." But it is far more than just technology ‐ it means a shift in culture. Technology needs to be easy to use and straightforward to enable staff to search, it needs to enable them to link with other people with similar interests, to make authoring of content simple, it needs to be social in the sense of transparency and cutting across diversity (Hinchcliffe, 2007; McAfee, 2009; Raskino, 2007) but all these attributes must be embraced by staff to "drive specific processes and outcomes" (Deloitte, 2013). Unfortunately, there is evidence that that has not happened; that organisations have invested in social networking technologies, but this does not translate into corporate intellectual capital and thus performance (Arenas, 2012). A heartbeat captured by a remote monitoring medical device; a photograph of a car accident captured by a mobile phone; a recording of a baby’s first word; a footing of a theft on a CCTV security system; each twitter message (CNN, 2012)...and not satisfied with the surface of the Earth the last great invention is ALMA (ALMA, 2013) that can detect and record stellar data in the profundities of the Universe as never before. “Big Data” is defined as the real time collection in multiple formats, analyses and visualisation of vast amounts of data produced by the everyday life of the Universe. It enables us to become aware of, measure and understand aspects of our existence in ways never before possible. And it is accelerating at a numbing rate. Just to put things into perspective, since the advent of writing 5,000 years ago until 2003 humanity produced 5 billion gigabytes of data. In 2011 it took humanity only two days to produce the same amount again; and in 2013 that amount of data is produced every 10 minutes – “Big” is a gross understatement but there is really no other word in the English language to describe this. Whatever we call it, this raw information is “fuelling a revolution which many people believe may have as big an impact on humanity going forward as the Internet has over the past two decades” (Smolan & Erwitt, 2012.)

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Paul Griffiths and Teresita Arenas All these phenomena have combined in different ways to produce a business environment where intangibles are highly valuable, far more so than tangibles, in what we now know as the “knowledge economy” (Viedma & Cabrita, 2012)

4. Methodology This is an exploratory piece of research that uses quantitative methods in a multiple case study analysis. The empirical part consists of doing a survey on the knowledge orientation of 16 companies in Chile whose logistics/supply chain managers or supervisors attended a postgraduate diploma course on innovation at the Universidad de Chile. The course contains a module on knowledge management of which one of the authors is the lecturer. The survey was completed by the participants in the classroom, on March 16th 2013. The instrument is an adaptation to Chile of a questionnaire developed by Prof. Dan Remenyi and applied extensively in the UK. It is broken down into three sections. The first section requests information on the company and demographic information on the informant, and the third one gives the informant the opportunity to give additional unstructured information. The body of the questionnaire is the second section that has 40 questions to be responded on a 9‐point Lickert scale where 1 means "Strongly Disagree" and 9 means "Strongly Agree" with the statement. The informants were given the option to give their names or remain anonymous; nine out of the sixteen identified themselves. A copy of the instrument and the list of companies that took part in the survey can be obtained from the authors.

5. Analysis of evidence The questions were grouped into three categories. The first category includes all those questions that depict the depth of people‐based knowledge networks; the second category includes all those questions that represent the depth of technology‐based networks; and the questions in the third category represent the general maturity of knowledge management in the organisation. The questions in each category are described in table 2. Table 2: Relevance of questions Category People‐based networks Technology‐based networks General KM Maturity

Questions 4, 6, 9, 10, 11, 24, 32, 39 8, 12, 15, 16, 33, 36, 38, 40 1, 2, 3, 5, 7, 13, 14, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 34, 35, 37

Each responding company was given three scores, one for each of these categories that responded to the average of the responses to the questions in the categories. The scores obtained for each company are given in table 3. Table 3: Scores per company (from 1= low to 9 = high) Company

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

People‐based Networks

Technology‐based Net.

3,1 3,3 4,0 5,0 6,9 3,6 6,8 3,9 2,9 7,3 6,0 3,6 7,0 4,5 2,9 4,8

2,5 4,4 1,5 3,6 5,4 1,0 4,4 1,5 4,0 6,5 6,6 3,8 7,5 4,4 2,6 6,3

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General Maturity

2,4 3,6 2,7 4,5 4,9 1,8 5,5 3,2 2,8 7,0 6,5 3,4 6,7 3,5 3,3 6,2

Paul Griffiths and Teresita Arenas These scores were represented in a bubble graph where the General KM Maturity is represented by the area of the bubble, as shown in figure 2. er o cs kr o w te n d es a b ‐y g lo o n h ce T

9,0 8,0 7,0

Quadrant IV

Quadrant III

6,0 5,0 Series1

4,0 3,0 2,0 1,0

Quadrant II Quadrant I

0,0 ‐1,0 0,0

2,0

4,0

6,0

8,0

10,0

People‐based network score

Figure 2: Mapping of the sample companies on the four quadrants Just by observing figure 2 it can be seen that the responses are quite different from what would be expected according to the old paradigm. While it would have been expected that responses would concentrate in quadrants II and IV, reality is that they lie on the diagonal between quadrants I and III. It is interesting that those that report the greater maturity in knowledge management (represented by the larger area of the bubbles) lie in quadrant III. This means that what used to be a utopia in the prior paradigm, is mainstream in the new setting. It is now possible to score high on both people‐based networks and technology‐based networks, and the more mature companies in knowledge management pursue this.

6. Discussion So the question that needs to be discussed at this point is what has enabled or driven this change in approach to knowledge management, and can it be explained by the trends that have emerged over the last four or five years and are given in section 3 above. One interpretation is that technology‐driven networks have become obsolete. This is because being selective and doing quality checks on all material fed into knowledge repositories has become unmanageable and too costly. The comments on Big Data in section 3 are indicative that volumes of information have grown exponentially and it is no longer possible to do thorough gate‐keeping. Without screening of material the Andersen Consulting ideal of a young consultant being on equal terms with an experienced one by being able to find reliable knowledge in a repository (Davenport & Hansen, 2002) is no longer feasible. Knowledge repositories exist and are no doubt needed, but they are not enough. Access to these repositories needs to be complemented by people‐networks where expert advice may be obtained before blindly re‐using intellectual capital pieces. Another interpretation is that the change in demographics and the possible retirement “in droves” of the baby‐boomer generation has put urgency into transferring knowledge from the older generation to the younger one. This does not allow for the patient construction of detailed and thorough repositories, but tends to favour personal contacts as a quicker form of knowledge transfer. As in 't Hout et al. (2010) points out, the older generation has no incentive to feeding knowledge bases – it requires a significant effort and they get little in exchange. This would favour people‐based networks at the expense of technology‐based ones. The advent of simple and cheap technologies in the social networking space as opposed to the sophisticated and highly expensive intelligent search tools related to knowledge repositories, have made adoption of the former easy and at the expense of the latter. These social networking solutions have created a platform for people‐based networks that naturally displace technology‐based ones. Finally the diversity of the workforce that has taken force in the last few years may be another factor that is influencing this trend. A diverse workforce needs to be integrated, and people‐based networks seem a more

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Paul Griffiths and Teresita Arenas appropriate way than having people operate as solitary navigators in the unfathomed depths of knowledge repositories.

7. Conclusions This research appears to strongly support that the old framework that linked business strategy (i.e., value proposition to clients) to knowledge management approach in terms of technology‐based vs. people‐based networks is no longer valid. This loss of validity is the result of a change in paradigm due to a series of technological, demographic and cultural developments that were described in section 3. We are now left with the challenge of finding a new framework or models that will help managers deliver value from their knowledge management efforts and investments. An alarming finding is that despite the widespread adoption of social networking tools and the development of far stronger personal relationships, individuals have not learned to convert this relationship intellectual capital into performance (Arenas, 2012; Arenas et al., 2013). Organisations need to focus on educating and training their staff to improve on this front. Clearly this research has limitations. The set of companies on which the empirical world is based is small in number, and restricted to a small market as is Chile. Moreover, the informants in those companies were essentially restricted to the logistics and supply chain functions, which normally depend heavily on personal relationships to help their business flow. On top of that, the knowledge orientation and maturity is self‐ reported and thus may be biased. Further studies will have to be made in other markets, in other functions, and in other sectors.

References: ACAS (2012) The benefits of having a diverse workforce, Advisory, Conciliation and Arbitration Service, www.acas.org.uk/index.aspx?articleid=3725 AIIM (2013) What is Enterprise 2.0 http://www.aiim.org/What‐is‐Enterprise‐20‐E20 ALMA (2013) Atacama Large Millimetre/submillimetre Array http://www.almaobservatory.org/ APQC (2013) Transferring and Applying Critical Knowledge, An APQC Best Practice Study, http://www.apqc.org/download‐ transferring‐and‐applying‐critical‐knowledge?sid=40335, downloaded on 22JUL13. Arenas, T. (2012), “Diseño de un método para diagnosticar el capital intelectual de una región” Aplicación a la Región de Valparaíso‐Chile. Tesis Doctoral, Universidad de Barcelona Arenas, T., Griffiths, P.D.R. & Freraut, A. (2013) An individual‐centred model of Intellectual Capital Forethcoming Barnes, H., Smeaton, D., Taylor, R. (2009) An Ageing Workforce: The Employer's Perspective, Report 468, Institute of Employment Studies, Brighton:UK Bartlett, C.A. (2000) McKinsey & Company: Managing Knowledge and Learning, Harvard Business School Case Study, No. 9‐ 396‐357 Bockman, S. & Sirotnik, B. (2008) The Aging Workforce: An Expanded Definition, Business Renaissance Quarterly, Vol. 3, No.3 Collins, R. (2002 [1998]) The Sociology of Philosophies: A Global Theory of Intellectual Change, Belknap‐Harvard University Press, ISBN 0‐674‐00187‐7 CNN (2012) What data says about us, downloaded on 13JAN13 from http://money.cnn.com/gallery/technology/2012/09/10/big‐data.fortune/index.html Davenport, T.H. & Hansen, M.T. (2002) Knowledge Management at Andersen Consulting, Harvard Business School Case Study 9‐499‐032 Deloitte (2013) Enterprise 2.0 ‐ What it means to be a Social Enterprise, http://deloitteblog.co.za/2013/04/23/enterprise‐2‐ 0‐what‐it‐means‐to‐be‐a‐social‐enterprise/ Ezingeard, J‐N., Leigh, S. & Chandler‐Wilde, R. (2002) Knowledge management at Ernst & Young: Getting value through knowledge flows, ICIS Conference Proceedings, 2002 Green, K.A., Lopez, M., Wysocki, A. & Kepner, K. (2012) Diversity in the Workplace: Benefits, Challenges, and the Required Managerial Tools, Document HR022, Food and Resource Economics Department, University of Florida Griffiths, P.D.R. & Remenyi, D. (2007) Using Knowledge for Competitive Advantage in Professional Services: A Case Study, Proceedings of the 4th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning, University of Stellenbosch Business School, South Africa, 15‐16 October, Edited by Dan Remenyi, pp.169‐ 178. Griffiths, P.D.R. & Remenyi, D. (2008) Aligning Knowledge Management with Competitive Strategy: A Framework, Electronic Journal of Knowledge Management, Special Edition, Edited by Prof Rembrandt Klopper, Vol. 6, Issue 2, pp.125‐134 http://www.ejkm.com/volume‐6/v6‐2/v6‐i2‐art5.htm Haas, M.R. & Hansen, M.T. (2005) When Using Knowledge can Hurt Performance: The Value of Organisation Capabilities in a Management Consulting Company, Strategic Management Journal, Vol. 26, pp. 1‐24

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Paul Griffiths and Teresita Arenas Hinchcliffe, D. (2007) The state of Enterprise 2.0, ZDNet, www.zdnet.com in 't Hout, R., Vrancken, J. & Schrijnen, P. (2010) Wiki‐based Knowledge Management in a Transport Consultancy, a Case Study, The Electronic Journal Kerby, S. & Burns, C. (2012) The Top 10 Economic Facts of Diversity in the Workplace: A Diverse Workforce is integral to a Strong Economy, Center for American Progress, www.americanprogress.org/issues/labor/news/2012/07/12/11900/the‐top‐10‐economic‐facts‐of‐diversity‐in‐the‐ workplace/ downloaded on 13JUN13 McAfee, A. (2009) Enterprise 2.0: How to manage social technologies to transform Your Organisation, Harvard Business School Publishing: Boston Raskino, M. (2007) In 2008, Enterprise Web 2.0 Goes Mainstream, Gartner Research, December 17 Sarvary, M. (1999) Knowledge Management and Competition in the Consulting Industry, California Management Review, Vol.41, No.2, Winter, pp.95‐107 Smolan, R. & Erwitt, J. (2012) The Human Face of Big Data, Against All Odds Productions: Sausalito, California Tishman, F.M., Van Looy, S. & Bruyere, S.M. (2012) Employer Strategies for Responding to an Aging Workforce, The NTAR Leadership Center, Rutgers, The State University of New Jersey Treacy, M. & Wiersema, F. (1995) The Discipline of Market Leaders, Reading, Mass.: Addison‐Wesley UCSF University of California, San Francisco (2012) Managing Diversity in the Workplace, Human Resources/Guide to Managing Human Resources, Chapter 12, ucsfhr.ucsf.edu/index.php/pubs/hrguidearticle/chapter‐12‐managing‐ diversity‐in‐the‐workplace/ downloaded on 14JUN13 Work and Family Researchers Network (2013) Definitions of Aging Workforce, https//workfamily.sas.upenn.edu/glossary/a/aging‐workforce‐definitions downloaded on 13JUN13. WSJ (2013) How to Increase Workforce Diversity, The Wall Street Journal US Edition, http://guides.wsj.com/management/building‐a‐workplace‐culture/how‐to‐increase‐workplace‐diversity/

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The Impact of HRM Practices on Knowledge Sharing Behaviour: Unexpected Results From Knowledge Intensive Firms Salman Iqbal, Paul Toulson and David Tweed School of Management, Massey University, Manawatu Campus,Palmerston North, New Zealand S.Iqbal@massey.ac.nz P.Toulson@massey.ac.nz D.M.Tweed@massey.ac.nz Abstract: This paper will investigate the effect of specific human resource management (HRM) practices on knowledge sharing behaviour among employees of knowledge intensive firms (KIFs). Based on previous research, a conceptual model is developed for the study and hypotheses are formulated.The cross‐sectional dataset comes from a sample of 390 employees from19 KIFs in Pakistan. Structural equation modeling (SEM) techniques are applied to test the proposed hypotheses. The results suggest that collaborative HRM practices have a direct positive effect on employees’ knowledge sharing behaviour. Surprisingly, we find that employees’ knowledge sharing behaviour is independent of reward systems and employees’ recognition. We suggest that organisational learning environments based on collaborative HRM practices can help employees’ knowledge sharing behaviour and improve the capability of both individual and organisational capability. This empirical study is based entirely on employees’ perceptions; therefore, the results of this study are from an employee’s perspective, rather than from a management perspective. Therefore the paper makes a valuable contribution, given the lack of empirical studies focusing on the South East Asian region. The two main contributions of this study are: first, the examination of the impact of specific HRM practices on employees’ knowledge sharing behaviour; second, the examination of knowledge‐sharing outcomes in terms of improved individual and organisational capability. This study is beneficial for researchers, practitioners, and those interested in organisational structure and relationships across organisations in the knowledge context. Keywords: collaborative HRM practices, knowledge sharing, reward systems, employees’ recognition, individual capability, organisational capability

1. Introduction In knowledge‐intensive organisations, human resource management (HRM) practices are one of the major antecedents of knowledge creativity through knowledge sharing. This is achieved by leveraging human capital and the provision of benefit to both individuals and organisations through improved capability (Ipe, 2003; Liao, Fei, & Chen, 2007; Lin, 2007). Despite the potential here, the knowledge management (KM) literature has made only limited use of HRM concepts and frameworks (Chiang, Han, & Chuang, 2011; Connelly, Zweig, Webster, & Trougakos, 2012; Hislop, 2003). Recent studies suggest that knowledge sharing can be advanced through bridging both KM and HRM fields (Oltra, 2005; Svetlik & Stavrou‐Costea, 2007). The knowledge management literature has explained the background of employees’ knowledge sharing. However, there is a lack of research on the strength of the relationship between HRM practices and knowledge sharing behaviour (Fong, Ooi, Tan, Lee, & Chong, 2011; Oltra, 2005; Riege, 2007). Several studies in various business sectors suggest that organisations need to pay attention to HRM practices to facilitate knowledge sharing behaviour (Boselie, Dietz, & Boon, 2005; González, Giachetti, & Ramirez, 2005). However, a number of these specific relationships have not been supported in Asian countries and merit further investigation. This study aims to focus on a developing country, Pakistan, and investigates the strength of the relationships between HRM practices and employees’ knowledge sharing behaviour, based on their perceptions.The paper is structured so that following this introduction, we review the relevant literature, and consequential hypotheses are proposed to explain how HRM practices relate to knowledge sharing and capability. We then briefly present the data collection process, followed by results of hypotheses testing, discussion and conclusions.

2. Literature review and hypotheses There are certain HRM practices that can be effective in supporting knowledge sharing behaviour in knowledge intensive organisations, like, recruitment and selection, employees’ collaborative practices, reward systems, employees’ recognition, and performance appraisal (Cabrera & Cabrera, 2005; Huselid, 1995; Lepak & Snell,

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2002). In this study, specific HRM practices including: reward systems, employees’ collaborative practices and recognition are examined for possible effects on employees’ knowledge sharing behaviour.

2.1 Reward systems The reward systems and employees’ recognition are key components of HRM practices that can enhance employee motivation to share knowledge. To achieve this, rewards should be given to those employees who spend their time facilitating and working with other staff, especially in collaboration with other members in work places (Song, 2009; Sweeney & McFarlin, 2005). Employees perceive that open and transparent rewards influence their knowledge sharing behaviour and add value to the organisational capability (Riege, Bartol & . Srivastava, 2002; Cabrera, Collins, & Salgado, 2006; 2005; Wah, 1999; Wang & Noe, 2009). Along with individual monetary incentives, organisations can facilitate knowledge sharing through group‐based reward systems to promote teamwork in work places (Bartol & Srivastava, 2002; Zhuge, 2008). Reward systems can create a sense of legal obligation among employees to share their personal knowledge with other members to achieve set targets (Song, 2009). Thus, organisational support, in terms of rewards, can reduce individual knowledge sharing barriers and support individual learning. Knowledge learning is a behavioural construct; therefore, rewards should be given to effect a change in the behaviour of an individual to participate in knowledge sharing activities.

2.2 Employees’ collaborative practices Collaboration is a mechanism to act systematically and think broadly (Noorderhaven & Harzing, 2009). However, even the minimum level of collaborative practices demands knowledge and information sharing (Kuldeep, 2004; Youndt & Snell, 2004). Employees’ knowledge sharing can be facilitated indifferent ways. Employees learn when they meet with experts in their own fields. Sahin (2007) and Noorderhavenand and Harzing (2009) put emphasis on individual knowledge sharing through team work, and the establishment of communities of practice to obtain competitive advantage by building core competence. Employees’ collaborative practices through knowledge sharing at the organisational level can be enhanced by setting different achievable targets through the use of multi‐disciplinary teams within the organisation. Hence, employees’ collaborative practices can improve organisational performance by engaging employees in a learning environment. Such environment facilitates employees to use their personal knowledge to achieve established targets.

2.3 Employees’ recognition Employees’ knowledge sharing behaviour can be influenced through intangible rewards like recognition and promotion in workplaces (Ipe, 2003). Employees who are willing to share and create knowledge should be visible in the workplaces to encourage knowledge sharing activities (De Long & Fahey, 2000; Michailova & Husted, 2003; Riege, 2007). Individual employees can be promoted on the basis of their innovative and quality inputs in workplaces (Scarbrough, 2003). Employees’ recognition can improve employees’ involvement and positively change their knowledge sharing behavior in work places (Iqbal, Abdul Jalal, Toulson, Tweed, 2012).

2.4 The role of trust There are internal and external factors attached to employees knowledge sharing. The internal factor is perceived power (within the organisation) that results from knowledge sharing, while the external factor includes building trust with the recipient through Interpersonal similarities (Ipe, 2003). HR managers can facilitate interpersonal trust between employees through providing a team‐based environment. Also, employees can mingle easily in networks on and off the job, which can boost the knowledge sharing process.

2.5 Knowledge sharing linked to organisational and individual capability In today’s knowledge economy, most organisations are attempting to be innovative to maintain competitive advantage. Harrison and Samaon (2002) suggest that most organisations attempt to enhance learning capability to improve innovation in order to achieve competitive advantage. Organisations’ learning capabilities depend on employees’ knowledge sharing that can lead to improved creativity and innovation (Aulawi, Sudirman, Suryadi, & Govindaraju, 2008; Birchall & Tovstiga, 2006; Ellonen, Blomqvist, & Puumalainen, 2008). Focusing on employees’ knowledge sharing is one of the main reasons for continuous successful innovation in several multinational companies. Several multinational companies’ managers have

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realised that employees’ knowledge plays a significant role in product development to improve organisational capability (Collins & Smith, 2006; Howells, 1996). Hence, employees’ knowledge sharing can improve organisational capability The present study examines the linkages between employees’ knowledge‐sharing and individual capability in terms of learning and innovation. Learning at the organisational level is linked to the individual’s learning ability (Lesser & Everest, 2001). Reychav and Weisberg (2008) observe that individuals who share contextual knowledge, innovative ideas, successes and failures with others colleagues have higher‐level learning through better job involvement. Individuals share their experience for their personal and professional development through peers’ feedback (Davenport & Völpel, 2001). Therefore, sharing tacit knowledge can improve the value of an individual’s capability in terms of his/her knowledge validity through feedback. Based on our literature review we propose the following hypotheses that are based on employees’ perceptions within their organisations: H1: Reward systems have a positive effect on employees’ knowledge sharing behaviour. H2: Employees’ collaborative practices have a positive effect on employees’ knowledge sharing behaviour. H3: Employees’ recognition has a positive effect on employees’ knowledge sharing behaviour. H4: Interpersonal trust among employees has a positive effect on employees’ knowledge sharing behaviour. H5: Employees’ knowledge sharing has a positive effect on organisational capability. H6: Employees’ knowledge sharing has a positive effect on individual capability.

Figure 1: Proposed model

3. Survey instrument A survey instrument developed specifically for this study was based on our review of the literature, as shown in Table 1. The survey instrument items were adapted from previous studies. The items validated from previous studies were further tested for reliability. The survey instrument is in two sections. The first section seeks demographic information about the respondents and includes questions to establish age, gender, educational qualifications, length of employment, and the number of employees in the organisation. The second section consists of statements to measure the dimensions proposed in the model in Figure 1. Five‐ point Likert scale items anchored to1 (strongly disagree) and 5 (strongly agree) are used. Table: 1: Instrument measurement Construct HRM practices

Dimension Employees’ Collaborative practices

Items description (Employees’ participation) (Teamwork)

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References (Kuldeep, 2004) (Youndt, 2004)

Salman Iqbal , Paul Toulson and David Tweed Construct

Dimension Rewards Recognition

Items description (Fairness) (Process) (Recognition)

References (Sweeney & McFarlin, 2005) (Balkin & Gomez‐Mejia, 1990) (Davenport & Prusak, 1998)

Knowledge sharing Trust

Sharing Donating and collecting Interpersonal Competence‐ based Learning

(Experience sharing) (Information sharing) Knowledge exchange (Among Peers), Between employees and management)

(Bock, Zmud, Kim, & Lee, 2005), (Reychav & Weisberg, 2009) (Van den Hooff & Van Weenen, 2004)

Innovation Human capital pool

(Services) (Knowledge storage)

(Shu‐hsien, Wu‐Chen, & Chih‐Chiang, 2007),(Tsai, Huang, & Kao, 2001) (Youndt, 2004)

Individuals’ Capability Organisational capability

(Cook & Wall, 1980) (Mooradian, Renzl, & Matzler, 2006) (Pearce, 1993)

3.1 Question items Here are some question items regarding different latent constructs shown in Table 2. Table 2: Question items snapshot Latent construct Reward systems Employees’ collaboration Employees’ recognition Trust Employees’ knowledge sharing Organisational capability dividual capability

Question item(s) I feel that the monetary rewards given by the organisation to employees for sharing knowledge are fair. Our employees interact and exchange ideas with people from different areas of the company. I am satisfied with the non‐monetary rewards that I receive in exchange for the knowledge I give to the organisation. If I got into difficulties at work I know my colleagues would try and help me out. People in my organisation frequently share knowledge based on their experience. Our organization embeds much of its knowledge and information in structure, and systems. The knowledge I receive from my colleagues helps me at work.

4. Data collection We obtained samples from populations in the educational and telecommunication sectors. For this study, the population of interest was employees identified as knowledge workers in Pakistani organisations. Data collection took place between mid‐November 2011 and early February 2012. The potential sample frame was consisting of full time employees working in Telecommunication and higher education sectors of the province Punjab, Pakistan. A simple random sampling (probability) technique was applied to in these sectors, to select sample organisations. Initially thirty companies were contacted, however, due to severe weather events and the consequential flooding which occurred in Pakistan at the time the data was collected, only 19 companies made up the final sample. A total of 600 questionnaires were distributed, out of which, a total of 390 useable questionnaires were used in the data analysis, representing a response rate of 65%. The response rate was encouraging, given that the questionnaire was relatively long. Baruch (1999) suggests that the average response rate is 55.6% in academic studies based on 175 studies reported in journal publications.

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5. Results Our descriptive results show that 73.6% of the respondents were male and 26.4% were female. The distribution of respondents’ indicated that almost half of the respondents (49.2 percent) fall in age band of 21‐ 30 years. Educationally, 53.1% have Master’s degrees, 31.3% have Bachelor’s degrees and 9.5% have PhDs. Respondents’ work experiences show that those who have one to three years of total experience accounted for 73.6%, followed by almost 10% of respondents have work experience between 5 ‐10 years. Rest of the respondents has work experience from 11 and more years. For the statistical treatment of the hypothesised proposed model (as shown in Figure 1), we used the two‐step method recommended by several researchers (Anderson & Gerbing, 1988; Hair, Black, Babin, Anderson, & Tatham, 2005; Lin & Lee, 2004; Sit, Ooi, Lin, & Chong, 2009). Thus, we first developed the measurement model based on confirmatory factor analysis (CFA). CFA can help to evaluate the reliability and validity of the measurement model (Ooi,Cheah, Lin, & Teh, 2012). On the results of CFA, we built the structural model.

5.1 Goodness of fit indices Six common measures were used, to measure the goodness of fit of the measurement model. Segars and Grover (1998), and Lin and Lee (2005), suggest that the common measures are, the ratio of χ2 (statistics to the degree of freedom (df), comparative fit index (CFI), goodness‐of‐fit index (GFI), adjusted goodness‐of‐fit index (AGFI), normed fit index (NFI) and root mean square error of approximation (RMSEA). As shown in Table 3, normed χ2 (the ratio between χ2 and the degree of freedom to assess the model fit) was 2.96. This result is less than 3.00 that indicates a good model fit (Bagozzi & Yi, 1988), Other fit indices also show good fit for this structural model. The goodness‐of‐fit index (GFI) is 0.94 and exceeds the recommended cutoff level of 0.90. The comparative fit index (CFI) and normed fit index (NFI) are 0.92 and 0.89 and exceeds the recommended cutoff values (Browne & Cudeck, 1993; Ryu, Ho, & Han, 2003). The root mean square error of approximation (RMSEA) is 0.07, which is below to the maximum recommended value of 0.08 (Browne & Cudeck, 1993). Hence, our model shows a good fit according to the data set. Table 3: Measurement model fit Goodness‐of‐fit measures

Recommended values

χ2 GFI

Test statistics/df

≤ 3.00* ≥0.90*

AGFI

CFI NFI RMSEA

≥0.90* ≥0.90* ≥0.80** ≤ 0.08*

CFA model 2.96 0.94 0.90 0.92 0.89

0.07

*(Bagozzi & Yi, 1988; Browne & Cudeck, 1993; Hu & Bentler, 1999) and **(Ryu, et al., 2003)

5.2 Hypothesis testing To determine the validity of the paths in our hypothesised proposed model, the statistical significance of all the structural parameter values were examined. The results from the analysis suggest that hypotheses, H2, H4, H5 and H6 were strongly supported, whereas, hypotheses H1 and H3 were not supported as shown in Table 4. Table 4: Hypothesis testing Hypothesis

Path

Reward Æ KS Collaborative practicesÆ KS Recognition ÆKS TrustÆ KS KS Æ Organisational Capability KSÆ Individual Capability

H3 H4 H5

Path coefficient 0.025

Std. error 0.074

Critical ratio 0.341

P ‐value

Remarks

0.733

0.345

0.129

2.671

0.008**

Not supported Supported

‐0.077

0.103

‐0.744

0.457

0.570

0.151

3.780

***

Not supported Supported

0.354

0.065

5.403

***

Supported

0.260

0.058

4.516

***

Supported

*** Significant at p < 0.001, and ** Significant at p < 0.01

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6. Discussion Our results suggest that employees’ collaborative practices show a positive effect on their knowledge sharing behaviour at (β=0.345, p< 0.001). This result is consistent with previous research on knowledge creativity and organisational learning (Hsu, 2008; Ipe, 2003). The finding is also consistent with previous studies of employees’ knowledge sharing behaviour in knowledge intensive organisations, reported by Sohail and Daud (2009) in Malaysia, Chen and Burstein (2006) in China, and Seonghee and Boryung (2008) in South Korea. However, our results show that the causative relationship between reward systems and knowledge sharing was not statistically significant at (β=0.025, p>0.05). Similarly the causative relationship between employees’ recognition and knowledge sharing was not statistically significant at (β=‐0.077 p>0.05). Generally, the rewards systems and employees’ recognition are one of the main components of HRM practices that can enhance employee motivation to share knowledge. Previous literature suggests that material rewards have a positive effect on employees’ knowledge sharing behaviour (Ipe, 2003), especially in collaboration with other employees (Song, 2009; Sweeney & McFarlin, 2005). Contrary to the above expectations, this study finding suggests that employees’ knowledge sharing behaviour is independent of reward systems and employees’ recognition. These surprising findings, related to reward systems and employees’ recognition are consistent in studies conducted in Asian countries, from previous research on the causative relationship between reward systems and employees’ knowledge sharing behaviour. For instance Bock and Kim (2002) suggest that rewards (routine annual monetary rewards) negatively impact employees’ knowledge sharing behaviour in the Korean public sector. Similarly such routine reward systems can only provide temporary compliance to employees’ knowledge sharing behaviour (Dong, Liem, & Grossman, 2010). Kohn (1993) suggests that, reward systems can affect negatively and terminate relationships among employees’ and managers. This occurs because employees who are rewarded feel they are achievers, while other employees may feel losers by not being rewarded for their efforts. This situation may create unnecessary competition among employees. Our result show that inter‐personal trust has a strong positive effect on employees’ knowledge sharing behaviour at (β=0.570, p< 0.001). This finding is consistent with previous work on inter‐personal trust and employees’ knowledge sharing behaviour (Levin & Cross, 2004; Mooradian, Renzl, & Matzler, 2006). As discussed earlier, the importance of the learning environment, and trust among employees play a significant role to build a learning environment. The learning environment depends on trust and respect between the knowledge seekers and experienced skilled employees (knowledge donors) (Goh, 2002; Grey & Garsten, 2001). This study found that employees’ knowledge sharing has a strong positive effect on organisational capability at (β=0.354, p< 0.001). This result is consistent with previous work on knowledge sharing and organisational capability. Lin (2007) suggests that employees’ knowledge sharing has positive effect on organisational capability, particularly in terms of learning and innovation capability through implementation of innovative ideas. Similarly, a research conducted in Spanish companies, suggest that positive employees’ knowledge sharing behaviour can effect organisational capability through improved learning capability (López‐Cabrales, Real, & Valle, 2011). Further, our results show that employees’ knowledge sharing has an effect on individual capability at (β=0.260, p< 0.001). This result is consistent with that reported by Reychav& Weisberg (2009) and Oldham (2003). This paper contributes in literature and practices, first, from analysing collaborative practices and knowledge sharing behaviour. An important feature that is common in knowledge intensive firms (KIF), where, knowledge is considered to be a source of power; it is an important asset that may decide the employability of individuals (Rowley, 2000). The results of this study show that employees’ collaborative practice directly has an effect on their knowledge sharing behaviour in the work places. This result supports that the view that employees’ collaborative practices provides opportunities for employees to discuss their past successes and failures, and consequently improves professional relationships in their organisations (Cabrera & Cabrera, 2005; van den Hooff, Schouten, & Simonovski, 2012). Employees’ knowledge sharing helps them in their every day decision making processes in workplaces (Harvey & Fischer, 1997). Second contribution comes from analysing the rewards and recognition as part of HRM practices in encouraging knowledge‐sharing behaviours. Our findings suggest that rewards are not significantly associated with employee knowledge sharing behaviour. Further, as discussed earlier, Kohn’s (1993) results were based on managers and CEO’s perceptions and not on the perceptions of employees. However, this study finding are based on employees perceptions and suggests that rewards are less effective to improve knowledge sharing behaviour compared to other HRM practices like

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employees’ collaboration and participation. This study suggests that employees’ perceptions about incentives in Telecommunication and higher education sectors are similar to the perceptions of the managers. This study shows emphasis on employees’ personal development in two knowledge based business sectors of Pakistan. Some reasons for personal development could be due to the new emerging technologies, current dynamic business environment and more opportunities for experienced individuals around the globe.

6.1 Limitations and future research Although our results are persuasive, there are several limitations to this study, and the results should be interpreted with some caution. Further examination and additional research should be conducted before applying these findings to HRM practice. First, the sample was drawn from two Pakistani sectors; hence, research samples from other Pakistani business sectors including banking, health and services sectors can be included. Further, the research model can be tested further using samples from other countries, since cultural differences among organisations affect employees’ perceptions regarding knowledge sharing, and further testing would provide greater insight into the research questions. Second, several significant results have been obtained; however a larger sample, that brings more statistical power, would allow more sophisticated statistical analysis and greater precision.

7. Conclusion We conclude that our results show that Pakistani employees perceive that their personal and professional developments through collaborative practices are more important to improve organisational capability than are rewards and recognition. In contrast, reward systems and employees’ recognition have no impact on employees’ knowledge sharing behaviour. The reason we can make such a claim is because employee knowledge sharing behaviour is independent of rewards and recognition. We suggest that our results may be indicative that the first and highest priority for organisations, at least the ones studied in Pakistan, is to provide support for employees’ collaborative practices. In addition, knowledge management in Pakistan is in its infancy stage. So to boost the knowledge sharing processes within organisations, managers could focus on their policies related to managing employees’ knowledge in organisations. Previous literature seems to emphasise the importance of rewards and recognition in driving knowledge sharing behaviour. However, there is no effect when compared to collaborative practices as a driver to improve individual and organisation capability in this study. Our results need to be thought of in the context that this is an employee perception study in knowledge intensive organisations. As a final note we must emphasise the importance of further research to investigate this interesting result. We therefore suggest a much larger sample, not confined to Pakistani knowledge workers, but utilising the employees of other countries. We are cautious about making strong recommendations for HRM practices that encourage knowledge sharing and organisational capability, based on this one study. However, we are encouraged by this finding and believe it is worthy of further investigation by others in the field.

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Looking Further Into Externalization Phase of Organizational Learning: Questions and Some Answers Palmira Juceviciene1 and Ramune Mazaliauskiene2 1 Kaunas University of Technology, Kaunas, Lithuania 2 Mariu Hospital, Kaunas, Lithuania palmira.juceviciene@ktu.lt mazaliauskiene@gmail.com Abstract: Organizational learning during which organizational knowledge is created is a necessary condition if the organizations seek for competitive advantage. SECI model proposed by Nonaka and Takeuchi (1995) is probably the most successful explanation of organizational learning. It describes the process as based on four phases: socialization, externalization, combination and internalization. Later the model was improved by including Ba, which means place where organizational learning occurs (Nonaka, Toyama and Konno 2000). Many authors agree that the externalization phase is the most complicated one from the four phases described in the SECI model. It is because that during this phase an explicit collective knowledge has to be created from the tacit knowledge of the individuals belonging to that group. In order to enable this phase it has to be provided with an especially ingeniously organized Ba. What kind of implications could be helpful? The aim of this paper is to discuss the opportunities for the development of the Ba for the externalization phase of organizational learning. The aim is reached by exploring the idea of the reflective team suggested by T. Andersen (1991). Until now it has been used mostly in family therapy. The interdisciplinary analysis provided in this paper resulted with the conclusion that the reflecting team can enrich the dialoguing Ba as it keeps the boundaries open, tolerates the variety of the opinions, additionally creating a safe and respectful space, supporting the dialogue and reflecting the processes that occur in the knowledge creating group. This theoretical investigation allowed to raise the questions that requires an empirical investigation in order to be answered; that investigation would allow to determine the peculiarities of the use of reflecting team in the process of knowledge creation during the externalization phase. Keywords: organizational learning, externalization, tacit knowledge, explicit knowledge, dialoguing Ba, reflecting team

1. Introduction Organizations can gain competitive advantage through their ability to create and use new organizational knowledge. The SECI model created by Nonaka and Takeuchi (1995) is one of the most successful theories which can explain the creation of organizational knowledge. Later the model was improved by including Ba, which means place where organizational learning occurs (Nonaka, Toyama and Konno 2000). Nonaka, Toyama and Byosiere (2001) pointed out that externalization is the key to knowledge creation because it creates new explicit concepts from tacit knowledge. Different authors propose (Nonaka and Konno 1998; Nonaka and Toyama 2007; Nonaka, Toyama and Byosiere 2001; Juceviciene and Mozuriuniene 2011) several alternatives of the course of externalization process. Theoretically and empirically based explanation of the externalization process (Juceviciene and Burksiene 2009; Burksiene 2012) will be used in this work: at the beginning of externalization phase, shared tacit knowledge created through socialization first of all turns into explicit personal knowledge, and only then, after discussion between individuals, it turns into explicit collective knowledge of the group. Both processes are complicated, especially the second one. In order to develop Ba, the researchers (Nonaka, Toyama and Konno 2000; Toyama and Nonaka 2000; Reinmoeller and Chong 2002; Hemmecke and Stary 2004; Ambrosini and Billsberry 2007; Choo and de Alvarenga Neto 2010) propose to use dialogues enriched with metaphors, analogues. Juceviciene and Burksiene (2009) proposed to use the method of constructing the collective concept maps (CCM) for the second stage as a method that not only helps to reach consensus but which supports better structuring of the collective thoughts. Yet, even if the mentioned empirical research has showed that positive results were reached, not all questions about the Ba development are answered. Particularly: how to develop the dialogue itself and discussion in the group if the discussion is held not among the management specialists but the professionals from other fields (physicians, social workers, etc.)? The purpose of this paper is to discuss the opportunities for the development of dialoguing Ba by looking further into externalization phase of organizational learning. The goal is reached by exploring the reflective team suggested by T. Andersen (1991). Until now it has been used mostly in family therapy.

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Palmira Juceviciene and Ramune Mazaliauskiene The analysis of the research literature was used in order to reach the aim that is pursued. Firstly, the paper explores the problems and their reasons during externalization phase. Secondly, it focuses on the essence of the reflecting team and its opportunities. Thirdly, it explains how the reflecting team can be used in developing of dialoguing Ba, and solving the difficulties of externalization process.

2. Externalization phase and its difficulties In SECI model externalization is a way to convert individual knowledge into group knowledge (Nonaka 1997; Nonaka and Konno 1998; Nonaka, Toyama and Konno 2000; Nonaka and Toyama 2007). On the other hand ‐ it is a phase when tacit knowledge is converted into explicit (Nonaka, Toyama, Byosiere 2001). Externalization is a key to knowledge creation as it creates new explicit concepts from tacit (ibid). Externalization is a process that decreases the total entropy of knowledge by structuring and integrating newly created knowledge into already existing knowledge systems (Hemmecke and Stary 2004). Nonaka and Toyama (2007) emphasize the process during which new knowledge becomes accessible for a more wide organization. So, new ideas and insights can be discussed and developed further. The process of externalization enables people from different contexts to share tacit knowledge (Hemmecke and Stary 2004). In order to carry out this process fluently Nonaka and Toyama (1998) propose to evaluate individual, social, cultural and historical contexts during externalization phase. The transfer of the tacit knowledge Nonaka (1995) considered being the most important; and Bennet and Bennet (2008) say that the more complex is the situation the more important becomes tacit knowledge. Thus the transfer of tacit knowledge can be critically important for the organization at the critical moments of its existence. Different authors give rather different explanations of this stage. Juceviciene and Burksiene (2009), Burksiene (2012) theoretically first and empirically later have justified the following way: in order to share knowledge first of all the tacit knowledge of individual is transformed into explicit personal knowledge of the individual; after that‐ into explicit knowledge of the group. Both processes are complicated, especially the second one. Accepting the significance and the complexity of the externalization phase, creation of the conditions enabling the knowledge transfer is a real challenge. Conversion of tacit knowledge into explicit can be influenced by dialogue and mutual reflection especially if the dialogue involves storytelling and metaphors (Ambrosini and Billsberry 2007). Nonaka and Takeuchi (1995) emphasize the conversion of tacit knowledge into explicit using dialogue and reflection during externalization phase; this articulation occurs using symbolic language, and new concepts and prototypes are created during this process. During externalization phase individuals try to use the language in its any form; if these methods are insufficient then the participants have to create reflection and further interaction (Toyama and Nonaka 2000; Nonaka, Toyama and Konno 2000). Metaphors play an important role in creation of new concepts and theories, and using the already known ones (Andriessen 2005). Using the contradictions found in the metaphor and the harmony of the analogy later, team members work with each other in order to express tacit knowledge (Nonaka, Toyama and Konno 2000). When one speaks about the cognitive/epistemic enabling conditions it is important to mention that it includes at the first moment seemingly contradicting requirements: the necessity of different kind of knowledge and different contexts and the necessity of common knowledge based on common understanding and mental models (Choo and de Alvarenga Neto 2010). Both of these requirements can reinforce each other. Externalization is the process that is characterized by a high motivation. The success of the knowledge conversion depends on the ability to use metaphors, analogies and cognitive models (Nonaka, Toyama and Konno 2000). The effectiveness of externalization phase can be reinforced by learning and motivation. The motivation is the fuel that feeds the actions to be done (ibid).

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Palmira Juceviciene and Ramune Mazaliauskiene An important concept in the SECI model is the concept of Ba (Nonaka, Toyama and Konno 2000). Knowledge creation is the main difference between Ba and other human interaction (Nonaka, Toyama and Konno 2000). Ba creates the basis for individual and collective knowledge (ibid). The dialoguing Ba of the externalization phase is a mutual interaction of the individuals when face to face individual mental models and possibilities are shared and transformed into common concepts (Toyama and Nonaka 2000). According to the authors (ibid), this Ba is associated with the dialogues between participants who articulate their tacit knowledge through dialogues (Nonaka, Toyama and Konno 2000); articulated knowledge is returned back to every individual, and later articulation occurs through self‐ reflection. Dialoguing Ba contains both collective, and face to face interactions (Toyama and Nonaka 2000). So far as it is mentioned in the introduction of this work Juceviciene and Burksiene (2009), Burksiene (2012) propose another possibility for a dialoguing Ba of externalization stage: first of all using reflection and the constructing of the individual concept maps (ICM) (Novak and Gowin and Johansen 1983) the members of the employees group are given the possibility to express their tacit knowledge, after that they are proposed to construct group knowledge creating the collective concept map (CCM). Ba can occur spontaneously, but (and this is more important) it can be created purposefully (Nonaka, Toyama and Konno 2000). The critical factor in such a case is to select people with a certain set of specific knowledge and competencies in order to carry to life a particular task (ibid). When one thinks about the organization as about an organic configuration of Ba, one can predict what kind of knowledge might be created, what people must be involved and what interaction among these people must occur (Nonaka and Toyama 2002). Toyama and Nonaka (2000) write that during the externalization stage participants have to share time and space having the direct experience. According to them close physical contact is important in sharing context and formulating common language between the participants. In other words Ba has the quality of “being here and now”, it can appear and disappear quickly (Nonaka and Toyama 2002). But Ba can exist in virtual or mental space; it doesn’t have to be related with certain time and space. If tacit knowledge has the quality of “here and now”, during the externalization it can occur beyond a certain time and a certain space; the conclusion that Ba synthesize past, present and future can be made (Toyama and Nonaka 2000). The problems during the process of organizational learning occur because of fail to manage context and conditions. It means there is need for people, time and space that are necessary for creation of “good” Ba that enables the participants of knowledge creation process to share the knowledge they possess and to create new knowledge which is of crucial importance for the successful existence of the company in the competitive surrounding. If time and space are the tasks that can be solved rather easily, problems with people especially with their management competence often arise. In knowledge organizations (such as not only high technologies creating and business organizations, but universities, medicine institutions, etc. as well) that need high level professionals in their field (scientists, physicians, social workers, etc.) problems arise when these professionals are involved in solving management issues of the organization (Juceviciene and Burksiene 2009; Burksiene 2012). These problems are severe and difficult to solve when these professionals have little experience of the group work and are weak in knowledge on management. These problems can arise when there is a big power distance between the employees as well (Hofstede 2001). In that case Ba has to be enabled for reflection, dialogue, discussion additionally.

3. The reflecting team The reflecting team is a concept that originates from the theories used in family therapy‐ a trend in psychotherapy that was rapidly developing at the second half of the 20th century as a different way to work with families (Goldenberg and Goldenberg 1996). In 1985 Andersen and his colleagues introduced what he called „the reflecting team“ at the beginning, or „the reflecting process“ later. The main principles to work using the reflecting team have been described by him and other authors in the book „The Reflecting Team: Dialogues and Dialogues about the Dialogues“(1991) and in some articles. The description of the session with the participation of the reflecting team looks simple enough: the therapist talks with family members. The reflecting team members (usually two) sit a bit aside and listen to what is

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Palmira Juceviciene and Ramune Mazaliauskiene discussed in the room. The questions that are usually asked in order to analyse the situation are: „What is a problem? “, „Who agrees with that? “, „Who disagrees? “, „What are the explanations? “, and „Who is involved?” (Andersen 1991; Lax 1991). After listening approximately for 10 ‐15 minutes, or even 45 minutes, the therapist invites to listen to team reflections. Team reflections usually last for 5 ‐15 minutes; at that time the team members talk only with each other about what they have seen or heard, disclosing differences, showing different perspectives and ideas. (Andersen 1987; Andersen 1991). It is extremely important that everything is said hypothetically, etc. using the expressions „I am not sure“, „it seems to me“, „probably“, “I feel that“, and so on. Reflection must have a quality of subtle preliminary suggestions, not of the decisions or interpretations (Lax 1991). After reflections every member of the family is asked: was it useful for those who listened and in what way; this is an invitation for further discussions. This process is repeated few times during the session that usually lasts one hour. The evaluation of the possible changes is very important: “opened arms” and “opened eyes” at the moment when the family leaves the room show that changes are on‐going (Andersen 1991). One of the main principles is not to use a hypothesis at the beginning of the work. The family decides the direction of the interview. Andersen (1991) considered that the hypotheses made beforehand can influence the direction of the interview towards the themes that are more important to the reflecting team, not the family. Such kind of the attitude helps to create the atmosphere of trust in the room where the session takes place. Being in a non‐ expert position means to change opinions and ideas with the family while reflecting processes that take place in the therapy room. It also means to put aside the expert position and to put both systems‐ the helping system and the problematic system on the same level. The course of the session is not decided beforehand. Non‐ expert position during the psychotherapeutic sessions allows thinking that using this way to work everyone is equal: psychotherapist, members of the reflecting team and the problematic family or any other human system. The reflecting team uses attitude “both, and” instead of “either, or” (Andersen 1987; Andersen 1991; Lax 1991). Such attitude is opposed both to the previous family therapy schools and to the way the family itself sees the difficulties experienced. Introducing this kind of attitude (Andersen 1991) considered it to allow being respectful and natural during the session instead of being professional and alienated. Andersen (1991) says that additional aspect of the meaning always has to be positive and never negative; the team has to be discreet, positive, respectful and creatively free. Positivity is extremely important in situation when families experiencing very painful situations are met; these situations can be psychosis, suicides, violence, etc. Reflective thinking is very important in the work of the reflecting team as it helps to think about oneself, own ideas, beliefs, and it can support better understanding the systems of other persons (Andersen 1991). The proposal of Andersen (1991) is to avoid interventions as people who do not like it can strengthen their own state of stagnation. Interventions can cause the feeling of unsafely that will disturb persons participating in therapy process, too. Probably the most important idea used in the work of the reflecting team is dialogue. “The reflecting team is one way to have a particular kind of conversation on purpose, a dialogue, that invites comparison of different viewpoints…It is designed to give everybody concerned the opportunity to shift position on purpose, e.g., from listening to participating, from talking to listening, and back again…The comparison of these dual perspectives can promote a genuine double description” (Katz 1991, p. 99‐100). The language that is used is very important: Katz (1991) writes about the extensive use of the metaphors in the work of reflecting team: she cites Koestler (1975) stressing that the metaphor is another pathway to provide slightly different viewpoint that may connect two previously unknown frames of reference and create perspective.

4. The use of the reflecting team to solve the difficulties of the externalization phase Peculiarities and possibilities of the reflecting team allow thinking, that it can enable dialoguing Ba of the externalization process.

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Palmira Juceviciene and Ramune Mazaliauskiene In the first stage of the externalization phase tacit knowledge of the group members who will construct the group knowledge is externalized. In this stage there is no need for the reflecting team as the reflection is made by the participants themselves. The reflecting team can be useful in externalization phase when explicit personal knowledge is transferred into explicit group knowledge. Dialogical way to behave, discussion that has to be combined with watching, noticing, listening and reflection are of extreme importance to the group that constructs knowledge. We will analyse the possibilities to use the reflecting team in the externalization process having in mind that:

The work of the group members is based on the method of constructing of concept maps;

Group members meet for the generating of the common knowledge already having constructed individual concept map (ICM) each;

In group work based on dialogues and discussions the collective concept map (CCM) is constructed. It happens in such way: group members sit around the round table (in order not to create physical conditions for hierarchical relationships), they select the “writer” who will draw elements of the concept map one by one, and the links between them, but only after group members reach a consensus decision. The “Writer’s” role is just additional for the selected group member as he/she is equal with all participates in the construction of CMM. When the “writer” is selected acknowledgement with individual concept maps of each member starts: they move around from one group member to another till the acknowledgement continues. Usually it lasts some quarter of an hour. Then it is time for the dialogues when group members discuss the ambiguities that arouse in the process of acknowledgement with ICM. Usually it lasts up to half an hour, but can be longer as well. Then the discussions start when group members discuss every element of CCM and the relationships between elements. The control of consensus is committed to the “writer”: every time when he/she is addressed asking to draw a certain part of the map he/she has to check if all the group members agree with the decision taken. Leadership during the work is not regulated but it is desirable to have it “moving” between group members and being conditioned by the need of expert competence (on subject or in team‐ work). Usurped leadership when a particular group member starts to impose personal opinion to the others is not desirable. Distributed leadership when the leader encourages every group member to speak out voluntary revealing to public doubts and contradictions is highly desirable.

The multi‐fold participation of the authors of this article in the process of externalization, that is performed using concept maps, and the observation of it as well, allows proposing that the main problems in the process of externalization are the following:

Group members are not familiar with the rules of the construction of CMM, especially the consistency of the steps of this process;

The problem of working in a group as in a team: unequal relationships, the usurpation of leadership (leader as a boss), or, difficulties to agree on distributed leadership;

Lack of trust (especially due to the doubts about the competence);

Problems in listening and hearing;

Difficulties to reflect‐in‐action;

Difficulties to agree, to reach consensus;

Problems to make decision (paralysis of problem analysis).

How could at least some of these difficulties/problems be solved by using the reflecting team in the process of externalization? What doubts are caused by the use of the reflecting team? As reflecting team promotes non ‐ hierarchical relationships it has a direct influence on the formation of equality of the relationships between the group members. Some parallels can be made with a “good” Ba (Nonaka and Toyama 2002) that can be compared with a space in which every participant is at the same distance from the centre. In such a way authors (ibid) point out that in a good Ba everyone is equal. The other aspect is that the members of the reflecting team have a possibility to notice the problems/difficulties with keeping the rules for the generating of the CCM, as well as leadership realisation in the group during the CCM construction. The reflecting team members’ time‐to‐time talking with each other

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Palmira Juceviciene and Ramune Mazaliauskiene and revealing in non‐direct way the problems they notice and possible ways of their solution creates support system to the CCM group. Trust is another important issue, and its presence among the knowledge creating group members is one of the Ba activating processes (Nonaka, Toyama and Konno 2000). So everything that benefits to the development of trust, benefits to the enabling conditions of knowledge creation. Respect to the system together with the absence of prejudices declared by the reflecting team helps to create an atmosphere of trust in the room, even if there are doubts about certain things, competencies, e. g. To create the secure and trustful atmosphere helps the notion of the reflecting team to avoid interventions. If to have a thorough look at the process of knowledge creation it is evident that it is not interventional in its essence; any intervention can have an unpredictable influence on people who take part in the process. Interventions can decrease the feeling of safety of people who participate in the knowledge creation process; and the dialoguing Ba will not be enabled. Listening and then responding, shifting from talking to listening are important stages of the dialogue. Reflecting team can help the CCM group, if notices that some of the members of this group experience problems and these problems are repeated in a way that the dialogues themselves become problematic or the participation of the group members in it. One of the main qualities of the dialoguing Ba of externalization period is reflectivity. In this case it’s difficult for the reflecting team to notice what is going in the minds of every member of CCM creating group, do they reflect, yet, it’s possible to watch if the CCM group itself performs reflection from time to time. This process is stimulated by the “writer’s” periodically asked questions about everybody’s satisfaction with the decisions taken (consensus). If the reflecting team notices that reflection is problematic talking with each other they mention a subtle way in what way the reflection has to be performed Another very important notion of the reflecting team is “both, and” perspective. It resembles one of conditions enabling and supporting knowledge creation‐ the “requisite variety” (Nonaka, Toyama and Konno 2000). The process with the reflecting team present offers a variety of versions about the “reality”, because every member of the CCM group has his/her own version, and additionally there are the versions of the therapist and the members of the reflecting team. According to Nonaka and Toyama (2002) Ba includes various contradictions; it needs different contexts to exist. The perspective “both, and” helps to include various perspectives, various contradictions. It helps to reach the consensus, too. Some questions still have to be answered, e.g. can the reflecting team help to make the decisions as it declares being non‐ interventional. Some doubts can be expressed while discussing the importance of positivity‐ one of the principles of the reflecting teams work. Positivity certainly contributes the feeling of security of the CCM group but still can be questionable as necessary factor in the process of knowledge creation: usually creation of organizational knowledge is not so dramatic or painful process as dealing with abuse, suicides or psychoses that are met in family therapy.

5. Conclusions The externalization stage raises a lot of questions how to make it as effective as possible. Dialogue, discussion and reflection are of critical importance for the quality of shared dynamic context, so called a dialoguing Ba. That is why method such as reflecting team that support dialogue and reflection can be useful to enable the dialoguing Ba. The principles of the reflecting team such as putting aside the expert position and hierarchy, creating the atmosphere of trust and respect influence the dialogical atmosphere. Reflecting team can enrich the dialoguing Ba as it keeps the boundaries open, tolerates the variety of the opinions, additionally creating a safe and respectful space, supporting the dialogue and reflecting the processes that occur in the knowledge creating group. Yet, not all questions about the possible use of the reflecting team as a tool enabling dialogical Ba are answered. Main questions still waiting for the answer are these: is the reflecting team obliged to follow the principle of positivity while during the talk between the members disclosing the problems observed to the

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Palmira Juceviciene and Ramune Mazaliauskiene CCM group or maybe a constructive critics could be more effective? What additional competence in comparison with the traditional reflecting team could enable this team being especially effective? What is a practical efficiency of appliance of theoretically based reflecting team use in the externalization stage? So, further investigation, particularly – an empirical research, has to be provided in order to answer these questions. But the purposefulness of the use of the reflecting team proved in this article at least allows to pay the attention of the leaders of the knowledge organizations and the employees to the possibilities of the use of this team.

Acknowledgements This research is funded by the European Social Fund under the Global Grant measure.

References Ambrosini, V. and Billsberry, J. (2007) „Person‐ Organization Fit as an Amplifier of Tacit Knowledge”, [online], Paper presented at the 1st Global e‐Conference on Fit, www.fitconference.com. Andersen, T. et al. (1991) The Reflecting Team: Dialogues and Dialogues about the Dialogues, W. W. Norton and company, New York. Andersen, T. (1987) „The Reflecting Team: Dialogue and Meta‐ Dialogue in Clinical Work”, Family Process, No. 26, pp 415‐ 428. Andriessen, D. (2005) “On the metaphorical nature of intellectual capital: A textual analysis”, Paper for the 4th International Critical Management Studies Conference. Cambridge, United Kingdom, pp 1‐ 19. Bennet, D. and Bennet, A. (2008) “Engaging Tacit Knowledge in Support of Organizational Knowledge”, The Journal of Information and Knowledge Management Systems, Vol 38, No. 1, pp 72‐ 94. Burksienė, V. (2012) „The Organizational Learning of Sustained Development“, Doctoral dissertation, Kaunas Technology University, Kaunas. Choo, Ch. W. and de Alvarenga Neto, R. C. D. (2010) “Beyond the Ba: managing enabling contexts in knowledge organizations”, Journal of Knowledge Management, Vol 14, No. 4, pp 592‐ 610. Goldenberg, I. and Goldenberg, H. (1996) Family Therapy. An Overview, Brooks/ Cole Publishing Company. Hemmecke, J. and Stary, C. (2004) “A Framework from the Externalization for Tacit Knowledge Embedding Repertory Grids”, Proceedings of the 5th European Conference on 'Organizational Knowledge, Learning and Capabilities', Innsbruck, Austria. Hofstede, G. (2001) Culture’s consequences: Comparing Values, Behaviors, Institutions and Organizations across Nations, Sage, London. Juceviciene, P. and Burksiene, V. (2009) „A Model of Organizational Learning for Solution of Problems of Sustainable Development“, Changes in Social and Business Environment: proceedings of the 3rd international conference, November 4‐5, 2009, Technologija, Kaunas, pp 167‐174. Juceviciene, P. and Mozuriuniene, V. (2011) „Organization‘s Knowing or Organizational Knowledge?”, Proceedings of the th 8 International Conference on Intellectual Capital, Knowledge Management and Organizational Learning, Bangkok, Thailand. Nonaka, I. and Takeuchi, H. (1995) The Knowledge‐ Creating Company: how Japanese Companies Create the Dynamics of Innovation, Oxford University Press, New York. Nonaka, I. and Toyama, R. (2002) „A firm as a dialectical being: towards a dynamic theory of a firm”, Industrial and Corporate Change, Vol 11, No.5, pp 995‐ 1009. Nonaka, I., Toyama, R. and Byosiere, P. (2001) „A theory of organizational knowledge creation: understanding the dynamic process of creating knowledge”, In Dierkes, M.; Antal‐ Berthoin, A.; Child, J.; Nonaka, I. (Eds), Handbook of Organizational and Learning Knowledge Creation, Oxford University Press, New York, pp 491 ‐517. Nonaka, I., Toyama, R. and Konno, N. (2000) „SECI, Ba and Leadership: a Unified Model of Dynamic Knowledge Creation”, Long Range Planning, No. 33, pp 5‐ 34. Nonaka, I. (1997) „Organizational Knowledge Creation”, Knowledge Advantage Conference. Nonaka, I. and Konno, N. (1998) „The Concept of „Ba“: Building a Foundation for Knowledge Creation”, California Management Review, Vol 40, No.3, pp 40‐54. Nonaka, I. and Toyama, R. (2007) „Why Do Firms Differ? The theory of the Knowledge‐ Creating Firm”, In Ichijo K, Nonaka I. „Knowledge Creation and Management. New Challenges for the Managers“, Oxford University Press, Oxford, pp 13‐ 32. Novak, J.D., Gowin, D.B. and Johansen, G.T. (1983) „The Use of Concept Mapping and Knowledge Vee Mapping with Junior High School Science Students”, Science Education, Vol 67, No. 5, pp 625‐645. Reinmoeller, P. and Chong, L. C. (2002) „Managing the Knowledge‐ Creation Context: A Strategic Time Approach”, Creativity and Innovation Management, Vol 11, No. 3, pp 165‐ 174. Toyama, R. and Nonaka, I. (2000) “What is a Good Ba? ‐ The Role of Leadership in Organizational Knowledge Creation”, Hitotsubashi Business Review, Vol 48, No.1, pp 4‐ 17.

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Smart Development: A Conceptual Framework Robertas Jucevicius and Laura Liugailaite – Radzvickiene Kaunas University of Technology, Kaunas, Lithuania robertas.jucevicius@ktu.lt laura.liugailaite‐radzvickiene@stud.ktu.lt Abstract: The article addresses a relatively new and complex problem in social sciences. The term smart development has been transferred from technological to social sciences several years ago. Researchers have been analysing the fragmented aspects of the problem, however, systemic research in the field is hard to find. A theoretically sound concept of smart development is yet to be created. In social sciences, the substance of smart is quite different and more complex, compared to technological sciences. This is due to the nature of social systems because it is widely acknowledged that biological and social systems are among the most complex ones. This is why the scientific analysis of smartness in social systems and the substance of smart development of social systems is an important and challenging scientific endeavour per se. Another aspect of the theoretical problem is the positioning of the smartness category in social sciences among other related categories, such as knowledge, knowing, innovativeness, and intelligence. Some researchers regard smart development as the integration of ICT into everyday life and state functions (Komninos, 2011; Bailey and Ngwenyama, 2011), while others highlight the importance of knowledge management (Garcia, 2007; Yigitcanlar, 2010). Still others emphasize the coherence of infrastructure with objectives, the importance of learning, innovation, and networks (Allwinkle and Cruickshank, 2011; Kuk and Janssen, 2011), or stress the importance of business systems being a critical element of any competitive economy (Whitley, 1992). The idea of smart specialization which has been proposed by a group of researchers back in 2008 has been spreading fast in practice and has successfully become a platform for economic and social development. However, we still see a lack of a more detailed scientific interpretation and conceptualization of this phenomenon. Not clearly understanding what those categories really mean, what criteria should be used in assessing the smartness in strategic documents, many costly mistakes are likely to be made. Keywords: smart, smartness, knowledge, intelligence, digital, learning, social system

1. Introduction The aim of this article is to discuss the concept of smart development from different theoretical perspectives: knowledge, intelligence, learning, innovation and network based development of social systems, such as the state, city, region, society, and economy. The research problem is both multidisciplinary and interdisciplinary by its nature, and calls for the integration of knowledge from various fields. The research is important not only from academic, but also from a practical point of view for its great policy implications. Strategies, such as ‘Europe – 2020’ and those of many European countries place key emphasis on smart development. For example, four dimensions of smart development have been distinguished in the ‘Lithuania 2030’ strategy: smart state, smart society, smart economy, and smart governance. The EU support for the member states in the period 2014‐2020 will primarily be used to achieve the developmental objectives by employing smart specialisation strategies. A lack of clear understanding of what those categories really mean, what criteria should be used in assessing the smartness in strategic documents may lead to many costly mistakes to be made. The research group ‘National competitiveness and innovation’ at Kaunas University of Technology, Lithuania, has initiated a research project ‘Smart development of social systems’. The aim of the research is to ground the concept of smart development integrating the ideas from different competing concepts. Thus the theoretical outcome of this research has a potential of becoming a theoretical and methodological platform for designing and implementing smart development strategies on different levels of the state. The article consists of two parts. The aim of the first one is to discuss different interpretations of the concept of smartness. A number of different theoretical concepts that could contribute to a better understanding of the smartness concept are discussed in the second part of the article. The possibility of integrating those concepts into the concept of smartness, as an umbrella concept, is also suggested in the paper.

2. Understanding smartness Challenges of climate change, a deteriorating environment and, finally, the current financial crisis call for the need of reconsidering traditional economic models of the national and regional development. This is especially true for the Europe with quite a diversified social and economic variety and experiencing multiple challenges

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Robertas Jucevicius and Laura Liugailaite – Radzvickiene to sustaining prosperity and economic growth. The ‘Europe – 2020’ strategy confronts those challenges and structural weaknesses by proposing progress on three priorities of growth: smart, sustainable, and inclusive growth. The concept of sustainable growth in the strategy has a clearly defined focus on the promotion of the competitiveness of the EU economy. Leadership in green technologies, promoting smart grids, improving the business environment and influencing the consumer choice are the key points in achieving targeted results. Smart growth, which is the second priority, is mainly about improving performance in education, research, and innovation. The third dimension and priority of the European strategy is inclusive growth which is mainly about increasing the employability of the population. This is not only about creating new jobs but more about developing adequate skills and competences. However, a certain level of consistency between those priorities is required. The concept of smart development and, particularly, smart specialization is called to be some kind of an integral platform. Over the last couple of years this concept has become central to finding the tools for the economic development of different regions, nations, and the EU. Scientific literature proposes a number of different definitions what smartness, smart development mean. However, there is not one commonly agreed definition that could serve as a common ground of understanding of what is being discussed. The term smart is largely used in publications of engineering and technology and it recently has made its way into social sciences. In social sciences, the substance of smart is quite different and more complex, compared to technological sciences. It is due to the nature of social systems because it is widely acknowledged that biological and social systems are among the most complex ones. This is why the scientific analysis of smartness in social systems and the substance of smart development of social systems is an important and challenging scientific endeavour per se. Probably the greatest attention in scientific literature and in developmental practice has been given to the concept of the smart city. The concept of the smart city has been discussed from quite different perspectives: a Triple‐Helix model, which emphasizes smart cities as a process of cultural reconstruction underpinned by policy, academic leadership, and corporate strategy in their guidance, human capital as the most important component (Hollands, 2008), modern information technology being as a core of any smart city (Chourabi et al, 2012), and others. There is little doubt about the importance of managerial and organisational factors affecting the policy context. The City’s governance structure influences the nature and the likely outcomes of a smart city initiative. The policy context may or may not be nurturing to the smart city initiative. It also deeply influences the model of governance used for and within smart initiatives, which might range from hierarchical to network approaches (AlAwadhi and Scholl, 2013). Citizens and communities are the key beneficiary of all smart initiatives. At the same time, they should serve as a lead stakeholder and a decisive social agent in such initiatives. Quite a number of attempts to present the dimensions of the smart city can be found in scientific literature. For example, Chourabi et al (2012) suggest eight dimensions of such a city: technology (used as infrastructure, a backbone, enabler, and facilitator of initiatives and for meeting/overcoming challenges); management and organization (such factors as project size, managers’ attitudes and behaviour, organizational diversity, alignment of organizational goals, and compliance to change influence projects); policy (the policy environment of the initiative); governance (the governance model, the authority, and the involvement of stakeholders in the initiative); people and communities; economy (economic inputs to and outcomes of smart city initiatives, such as business creation, job creation, talent attraction, workforce development and retention, and improvement in productivity); built infrastructure (the impact of the initiative on improving and leveraging the built infrastructure); natural environment (emphasis on preserving and protecting the natural environment). Some authors try to understand the essence of the smart city, comparing it with the features of the intelligent city. Probably the most classical definition of the intelligent city has been presented by Komninos (2008). He stresses the following key dimensions:

the application of a wide range of electronic and digital technologies to communities and cities;

the use of information technologies to transform life and work within a region; embedding such ICTs in the city;

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the territorialisation of such practices in the way that brings ICTs and people together so as to enhance innovation, learning, knowledge, and problem‐solving that the technologies offer.

Hollands (2008: 306) suggests that some cities are smarter from others because they are ‘territories with a high capacity for learning and innovation, which is built in to the creativity of their population, their institutions of knowledge production, and their digital infrastructure for communication’. Learning and innovation as well as technologies are also considered among the most important by the majority of other authors.

The Report ‘Cities of Tomorrow: Challenges, visions, ways forward’ (2011) developed by the European Union expert group focusses on very broad aspects of the successful city, the precondition of which is smartness. Another broad field in the discussions about the smartness of social systems are regions. However, there is a little difference between theoretical approaches and those used to explain the origin of the smart city. This is quite understandable because cities usually are a part and an important element of any region. The most essential difference is in applying the concept of smart specialisation as a platform for the development of a regional innovation strategy. It is hard to expect being smart without being innovative. At the same time, innovation is just one, even though a very important precondition of becoming smart. Other qualities, such as sustainability, intelligence, a wide use of IT, or learning processes in the community should be in place. However, the concept of the smart specialisation of the region has a potential of integrating most of those qualities. This concept needs a broader discussion in order to better understand the possibilities of employing different theoretical approaches in the development of smart social systems such as city, region, the state, or community. ‘The underlying rational behind the smart specialisation concept is that by concentrating knowledge resources and linking them to a limited number of priority economic activities, countries and regions can become — and remain — competitive in the global economy’ (Guide to Research and Innovation Strategies for Smart Specialisation, 2012: 11). This type of specialisation allows regions to take advantage of scale, scope, and spill overs in knowledge production and use, which are important drivers of productivity. Smart specialisation is not about creating technology or business monoculture and uniformity. It may sound strange, but smart specialization is able to contribute to the diversity of activities. Indeed, regions can and should sustain multiple lines of priorities. However, the emerged structural changes, generated by the smart specialisation strategies aimed at fostering cross‐sectorial or cross‐border cooperation, favour generating ideas for new innovative applications and integrated solutions. It creates a certain kind of originality and specialisation to differentiate itself from competing regions. The smart specialisation of the regions does not mean creating something very new. First of all, it is about the modernisation of existing industries, technology, business models integrating the development of specific applications of a Key Enabling Technology to improve efficiency and quality in the existing (perhaps traditional) sector. The other way is a transition from the existing sector to a new one, based on cooperative institutions and processes, i.e. the collective R&D, engineering, and manufacturing capabilities that form the knowledge base for development of a new activity. This can suddenly reverse previously low growth activities into attractive. Such a radical foundation involves the co‐emergence of R&D / innovation and the related entrepreneurial activity. However, imitating good practices and experiences of other successful regions, or trying to imitate their strategies can hardly lead to success, because most of the regions have their own and, to some extent, unique environment and context. Smart specialisation is about generating unique assets and capabilities based on the region's distinctive industry structures and knowledge bases. The creation of new knowledge or absorption of external knowledge is of big importance for the development of smart specialisation of a particular region. However, one should look broader, focussing on the creation of so‐called entrepreneurial knowledge and spirit. The entrepreneurial knowledge is not just about science and technology; it is a much broader issue. The knowledge of the market growth potential, present and possible competitors and competitive environment, the perception of trends in social and business environment is not of less importance. The integration of such knowledge opens a window for the creation of a new vision, re‐ configuration of available resources and the development of new strategies and business models. The concept of smartness may be also applied to the state, nation, community, governance, business system, etc. All of those are subjects for separate and extensive studies. The research group ‘National competitiveness and Innovation’ at Kaunas University of Technology, Lithuania, has started extensive investigation in all of those fields. The key findings are likely to be presented in further publications.

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3. Diversity of theoretical concepts There is a big variety of competing theoretical concepts and approaches, used in explaining smartness of a particular social system: the state, region, city, economy, community, etc. Probably the most important are the theories and concepts of knowledge management, intelligence, the learning organisation, sustainability, innovation systems, business systems, agility, networking, and digital social systems. All of them have their unique as well as overlapping qualities that could be considered as dimensions of smartness. Some of those concepts will be discussed below. Intelligence is the art of monitoring weak signals which tell us whether the social system is on the right track or not. The concept of intelligence sees the world as a shifting variety of social systems and views each system as a communication network with its own ‘personality’ and culture, interacting in a variety of ways, and exercising its intelligence function in the service of its goals (Dedijer, 1993). This concept is becoming one of the priorities for researchers in many fields of social sciences. So far, business has paid most attention to this concept, trying to understand how to develop an intelligent institution (Quinn, 2005; Underwood, 2002; etc.). The authors focus mainly on the issues of business intelligence or competitor intelligence, trying to develop tools and approaches which would allow a company to preserve its competitiveness. Dedijer (1993), Toffler (1991), and others stress technological and social aspects of intelligence. Choo (1998), Friedman et al (1997) focus on organizational learning as the key characteristic of intelligence in an organizational setting. However, both qualities are interrelated and require not just individual knowledge, but also organizational knowledge and well developed internal and external networks, and also a supporting infrastructure. Organizational intelligence refers therefore to many more than just intellectually competent individuals. Intelligence is an attitude which not only provides knowledge, but spurs wisdom because of its comprehensive view regarding the interrelationship of all the aspects of economics, politics, business, and, consequently, the behaviour of all social actors. The research regarding intelligent state, region, community, or the nation is quite limited. Business intelligence and, to some extent, the intelligent city make an exception. The key aspects of the city, considered intelligent, may be further discussed. According to Komninos (2011), the first academic paper on intelligent cities was by Laterasse and it appeared in 1992; however in 1986 a book by Lipman, Sugarman, and Cushman (eds.) was published, where the intelligent city was viewed as a digital city, including teleports as flexible communication nodes linked to technological innovations. Komninos (2006: 1) states that ‘intelligent cities and regions are territories with a high capacity for learning and innovation, which is built‐in the creativity of their population, their institutions of knowledge creation, and their digital infrastructure for communication and knowledge management’. Later (Komninos, 2011: 174) he discusses city’s spatial intelligence which ‘refers to the ability of a community to use its intellectual capital, institutions and material infrastructure to deal with a range of problems and challenges’. The Intelligent Community Forum (2013) characterizes intelligent communities by five indicators: broadband connectivity (vital to economic growth); knowledge workforce (creating economic value); digital inclusion (providing skills training and promoting the benefits of being included into the broadband economy); innovation (which produces job growth in modern economies and invests in e‐government programs); marketing and advocacy (sharing the story with the world and building a new vision of the community from within). According to Rodrigues and Tomé (2011), intelligent cities are knowledge cities and digital cities put together, whereas smart cities are medium sized intelligent cities. Santinha and de Castro (2010) describe intelligent cities by internal (for instance, high quality service supply, attractive environmental urban design, qualified human resources, technologically innovative organizational environment, public participation, etc.) and external (such as information collection, integration in thematic networks, information dissemination in strategic and global perspective) characteristics. Concluding the literature review, it is important to state that the intelligent city consists of two main domains, digital and knowledge based. They overlie each other to become a basis for innovation, higher life quality and sustainable development. Another theoretical approach, able to contribute to a better understanding of the concept of smart development, is the theory of knowledge management. The theory itself does not need deeper introduction. However, some aspects of its application to the development of smart social systems could be discussed.

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Robertas Jucevicius and Laura Liugailaite – Radzvickiene There are numerous publications regarding knowledge based development of the city, region, or the state. Some examples of how the knowledge city is perceived by different authors are introduced next. Del Rosario González Ovalle, Alvarado Márquez, and Martínez Salomón (2004: 108) propose to treat the knowledge city ‘as the one that searches for the creation of value in all its areas and develops high standards of life’; it significantly invests in education and research. Ergazakis et al (2006) provide characteristics of the knowledge city, which are: provision of instruments to make knowledge accessible to all knowledge agents; ability to generate, attract and retain highly skilled citizens and knowledge workers; embedded KM processes and procedures in everyday city’s life; climate conducive to the production and dissemination of innovative ideas. Quite a different understanding of the knowledge city is provided by Garcia (2007). According to her, the knowledge city could be understood in the perspective of generic system of capitals (identity, intelligence, financial, relational, human individual, human collective, instrumental – material, and instrumental – intangible), therefore the citizenship ‘undertakes a deliberate, systematic attempt to identify and develop its capital system, with a balanced and sustainable approach’ (Carrillo, in Garcia, 2007: 21). Later, Ergazakis et al (2009) introduce several aspects similar to Garcia’s proposed understanding of the knowledge city: multi‐ ethnic character (or identity of the city) and support of innovation and value creation for citizens (which potentially could be created through development of human capital). It is worth noticing that different aspects focus on digital (Wi‐Fi networks, metropolitan web‐site) and learning (digital libraries network) facets. Rodrigues and Tomé (2011: 351) suggest that knowledge cities can be understood ‘as urban areas where knowledge is created and applied, based on the attraction of knowledge workers, and leading to the formation of clusters of activities that produce goods and / or innovative and competitive services’. To conclude, the concept of the knowledge city is one of the most frequently analysed in scientific literature; therefore, there exist some unified characteristics, namely: the development based on knowledge creation and use, high added value products and services, importance of intellectual capital, and investment in research and innovation. Clearly, there is certain commonality with the dimensions of the smart city. The concept of digital social system is also able to contribute to the development of the concept of smart development and may be presented in brief. Van den Besselaar (in Nunes, 2005) describes three typologies of the digital city meanings: digital cities are expected to be cities as nodes in global networks; then, they represent themselves as community networks, based on the World Wide Web; finally, they are referred to as virtual communities and virtual environments, typically including 3‐D technologies and in some cases designed to allow citizens to participate in ongoing urban planning processes. The concept of the digital city itself is quite obvious: it emphasises information communication technologies as the basis for socioeconomic development. Therefore, it is not possible to describe the digital city without having an ICT infrastructure. When properly developed it encourages successful realization of city processes in the virtual environment and promotes economic growth (Ngwenyama ir Morawczynski, 2009; Jehangir et al, 2011). Furthermore, many researchers acknowledge the global phenomenon of pervasive ICT (Landry, Mahesh and Hartman, 2005; Mhlanga, 2006; Breznitz et al, 2011; Moser, Bruppacher and Mosler, 2011; and many other). This pervasion comes in digital air space, digital information networks, digital transactions, virtual architecture, urban geographical positioning systems, and many other domains. A number of cities are positioning themselves as digital even if they apply different strategies. In general, being digital does not mean being smart. However, that dimension is very important. A geographically defined learning concept emerged in the 1970s with the project ‘Educating Cities’, funded by OECD and aimed at exploring how seven cities encourage a culture of lifelong learning (Longworth and Osborne, 2010). According to Plumb, Leverman, and McGray (2007), neoliberal ideologies created an environment to strive for the learning city as an end product. They criticize the advocates of the learning city not embracing it as a living and social context. The concept of the learning city itself has a clear connection with the concept of learning which remains to be the main aspect in all scientific publications of this subject. McFarlane (2011) argues that the conception of learning has been based on three processes: translation (through which learning is produced as a sociomaterial epistemology of displacement and change), coordination (that enables learning as a means of linking different forms of knowledge, coping with complexity and facilitating adaptation), and dwelling (through which learning operates as a way of seeing and inhabiting the world). Lifelong learning at an individual and

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Robertas Jucevicius and Laura Liugailaite – Radzvickiene collective/organisational level, learning partnerships, learning communities, innovation, continuing development, and the quality of sustainable life describe the learning city (Jucevičienė, 2010). Longworth and Osborne (2010: 373) treat the learning city as the city ‘with plans and strategies to encourage personal growth, social cohesion, and sustainable wealth‐creation through the development of the human potential of all its citizens and working partnerships between all its organisations’. It should be noticed that the dimension of sustainability is accounted in the concept of the learning city, region, or communities (Morgan, 2009; Sun, 2007; Oprean et al, 2011). The learning city is also linked to innovation (Shapira and Youtie, 2008; Jucevičius, 2004). From the perspective of smart development, the dimension of organisational learning is expected to play an important role and should be taken seriously. The concept of smartness of a particular social system – the city, region, state, and community, may be understood as an integral construct composed of different theoretical concepts and approaches. Each of the earlier discussed approaches as well as some others that have not been presented in this article has a number of qualities that could be considered as the dimensions of smartness. The same applies to the concept of smart development. The integrity of smart development is presented in Fig.1.

Figure 1: Theoretical framework of the concept of smart development

4. Conclusions Despite numerous publications on the topic, the concept of smartness, smart development, and the phenomenon of smart social systems such as the state, region, city, etc. remain a more practical rather than theoretical construct. The concept itself and its application to different fields of human activities call for systemic investigation. The substance of smartness of social systems is quite different and more complex, compared to technological sciences due to the nature of such systems ‐ it is widely acknowledged that social systems are the most complex ones. The concept of smart development could be better understood if a number of related theories and conceptual approaches were employed. The idea of smartness of the social system calls for integrating different qualities such as intelligence, networking, agility, innovation, learning, richness of knowledge and IT. None of different theoretical concepts sometimes used as synonyms of smartness do not cover all most important qualities of the phenomenon.

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Acknowledgements This research is funded by the European Social Fund under the Global Grant measure.

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Big Data, Tacit Knowledge and Organizational Competitiveness Nowshade Kabir1 and Elias Carayannis2 1 Grenoble Graduate School of Business, Email: nowshade@gmail.com 2 George Washington University School of Business, Email: caraye@gwu.edu eunika@innovation3d.fr Abstract: In the process of conducting everyday business, organizations generate and gather a large number of information about their customers, suppliers, competitors, processes, operations, routines and procedures. They also capture communication data from mobile devices, instruments, tools, machines and transmissions. Much of this data possesses an enormous amount of valuable knowledge, exploitation of which could yield economic benefit. Not too long ago, to obtain value from data, it was necessary to collect data on purpose based on specific objectives. Today, to keep up with the information explosion, and possible use of the data in future, combined with decreasing cost of storage capabilities and ubiquitous connectivity, intentionally or being compelled due to regulatory or other reasons, organizations are amassing big amount of data. Many organizations are taking advantage of business analytics and intelligence solutions to help them find new insights in their business processes and performance. For companies, however, it is still a nascent area, and many of them understand that there are more knowledge and insights that can be extracted from available big data using creativity, recombination and innovative methods, apply it to new knowledge creation and produce substantial value. This has created a need for finding a suitable approach in the firm’s big data related strategy. In this paper, the authors concur that big data is indeed a source of firm’s competitive advantage and consider that it is essential to have the right combination of people, tool and data along with management support and data‐oriented culture to gain competitiveness from big data. However, the authors also argue that organizations should consider the knowledge hidden in the big data as tacit knowledge and they should take advantage of the cumulative experience garnered by the companies and studies done so far by the scholars in this sphere from knowledge management perspective. Based on this idea, a big data oriented framework of organizational knowledge‐based strategy is proposed here. Keywords: big data, tacit knowledge, big data strategy, knowledge management, knowledge strategies and organizational knowledge

1. Introduction One of the key driving forces of knowledge economy is knowledge intensity of economic activities (Smith, 2002). In recent decades increasing dependence of economy on knowledge has bolstered by rapid pace of technological innovation and information technology revolution. This, in turn, propelled the emergence of new knowledge‐based industries and augmented share of knowledge as a resource in economic input in most traditional industries. Knowledge, now, is recognized as a pillar of innovation, a source of economic growth and a central element in organization's competitive advantage (Stehr, 1994). This heightened importance of knowledge, in part thanks to globalization and speedy technological advancement, obliges organizations to pay serious attention to their existing, potential and growing knowledge resources. Present phenomenal growth of knowledge resource can be attributed to several factors such as continuous advances in information technology related hardware, development of new algorithms and programs, ubiquitous access to information thanks to the Internet and steady decline of cost related to data creation, transmission and storage. In recent years, the combination of these factors has also prompted the appearance of a new knowledge resource, which is capable of further revolutionizing organizational knowledge landscape. This new knowledge resource is big data! Big data is a unique knowledge resource that is immensely valuable to any organization. It helps transforming many of the traditional methods of conducting business activities. Insights and knowledge from big data boost management’s ability to take well‐informed decisions (Provost and Fawcett, 2013). Efficient use of data created and located within a firm and collection and analysis of critical data from external sources impact a firm's product, process and strategic innovation as well as marketing and operational capabilities. Current development shows that big data has already become a major catalyst in bringing sweeping changes to a range of business processes in many industries. As a result of this, organizations’ interest in big data initiatives has intensified significantly. A study done by Tata consulting (2013) shows that almost half of the companies surveyed have introduced some types of big data projects, and they are expecting a very high return from these initiatives.

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Nowshade Kabir and Elias Carayannis No doubt that big data is considered as a valuable knowledge resource. If that is the case, what type of knowledge is found in big data? Can this knowledge be considered as tacit knowledge? What should be the right strategy for organizations to handle a knowledge resource as complex as big data? In the article we try to answer to these questions and offer a big data related strategy framework. The rest of the article is divided into several parts: a short discussion on the present interest in big data followed by a review of big data concept, analysis of knowledge and tacit knowledge in the context of big data, a holistic big data strategy model with explanation and finally, the closing remarks.

2. Why now? The emergence of big data phenomenon is the result of a blending of several rising trends: the proliferation of social and business networks, the growth of mobile telecommunication, dramatic cost reduction in data collection, storage, processing and transportation and the increased deployment of sensors and machine to machine communication along with technological advancement in cloud computing, smart ICTs, data mining and analytics (OECD, 2011). LaValle et al. (2010) assert that companies that use business information and analytical tools in their differentiation strategy have twice as many chances to be in the group of top performers than lower performers of their industry. Big data can produce minimum two types of values to an organization. Firstly, it can be a source of innovation. Specially, it can enable development of new products, processes and services. Secondly, use of various analytics on big data can generate knowledge and insights that can support and improve organizational decision making significantly (Provost and Fawcett, 2013). The present interest in big data grew mostly thanks to these new value creation possibilities that were unavailable to most companies even recently due to the high cost of data storage, processing and analyzing.

3. Big Data – the concept Big data is a concept that means, firstly, the volume of the data is too large. Secondly, it is impossible to analyze it using conventional technologies, and thirdly, special tools and treatment are necessary to extract knowledge from it (Manyika et al., 2001). Another way of viewing big data is to regard it as a massive pool of data that allows creating insights and values that are not possible to generate from smaller scale of same data (Jacobs, 2009). Douglas Laney (2001) of Gartner, while explaining the challenges related to data growth noted that there are three dimensions to this problem: increasing amount of data – the growth of its volume, inflow and outflow speed of data – its velocity and heterogeneity of the data types and sources – data variety, the three Vs. This has become the industry standard in characterizing big data. However, many argues that along with this model, value, veracity and variability also should be included as they are more important than the attributes of 3Vs (Swoyer, 2012).

4. The concept of knowledge Definition of knowledge in organizational science differs from the classical epistemological view of knowledge as "justified true belief" (Nonaka and Von Krogh, 2009). Despite its long history, the concept of knowledge is still subjective, complex and opaque. As a result, we see numerous variations of definitions of knowledge depending on discipline, context, approach and task at hand. In a broader sense, and for the purpose of this article, knowledge can be defined as information that is validated, contextual, relevant and actionable (Soliman and Youssef, 2003). Another similar definition is, knowledge is tested, validated and codified information (Earl, 1994). Scarbrough & Barrel (1996) propose the content theory of knowledge, where knowledge is deemed as an object that can be codified and stored. This approach of objectification of knowledge brings flexibility to the perception of knowledge. Knowledge as an object can be acquired, integrated, stored and disseminated much like a commodity and becomes a tradable product (Carlsson et al., 1996). In knowledge science knowledge is also considered as information with meaning, information is data with context and data is a basic element of analysis (Boisot, 1995). This concatenation of data, information and knowledge is the most popular model of their correlation in knowledge and information literature (Rowley, 2007).

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5. Tacit knowledge Much of the theoretical understanding of tacit knowledge in knowledge science derives from Polanyi's concept of tacit and explicit knowing (Polanyi, 1962). Tacit and explicit knowledge are two sides of knowledge continuum (Nonaka and Takeuchi, 1995). Explicit knowledge is the type of knowledge, which can be expressed using common language and codes. It is fully transferable and easy to share (Nonaka, 1991). Tacit knowledge, on the other hand, is subjective and informal (Polanyi, 1958; Nonaka, 1995). Taking after Polanyi's view, the concept of tacit knowledge and its place in organizational knowledge creation was largely popularized by Nonaka (1995) and defined as knowledge that indwells human mind and body (Nonaka and Von Krogh, 2009). Many subjectivist scholars believe that tacit knowledge cannot be articulated, captured or interpreted in any form as this type of knowledge gets developed and remains embodied only in the human mind (See Tsoukas 2006). However, others conclude while some tacit knowledge is impossible to explicate, most tacit knowledge can be codified (Nonaka, 1995; Collins, 2010). We believe that reality exists independently from the human mind and knowledge, including tacit knowledge, can reside in various other silos apart from the human cognizance (Searle, 1993). Many other scholars also support this notion. Walsh and Ungson (1991) posit that knowledge resides in five venues of an organization: people, roles and organizational structures, operating procedures and practices, culture, and the physical structure of the workplace. Hershbach (1995) believes Technological activities embody a larger portion of tacit knowledge than we normally recognize. Some researchers describe tacit knowledge as uncertain, unstructured, indeterminate, and indirect (See Kikoski and Kikoski, 2004) and others conclude tacit knowledge is the kind of knowledge which is implied but not yet documented (Junnarkar and Brown, 1988). These views support the idea that knowledge, insights, patterns, indicators and pointers embedded in big data and waiting to be extracted are a form of tacit knowledge.

6. Knowledge management strategy Organizational knowledge management strategy refers to planning and deployment of methods, processes, procedures and guidelines of knowledge acquisition, organization, utilization and distribution in order to achieve business goals. Knowledge being a valued resource, knowledge management strategy must be always aligned with the organization's business strategy (Eisenhardt and Santos, 2002). For example, focus of knowledge management strategy can be the development of intellectual capital using both knowledge exploration and knowledge exploitation and as a result gain competitive advantage (Zack, 1999) Knowledge exploitation strategy builds upon existing knowledge and knowledge exploration on acquisition of new knowledge. Both of them are vital in organization's overall knowledge strategy (Ichijo, 2002). These knowledge strategies encompass knowledge processes that include knowledge creation, acquisition, integration, sharing, replication, storage, organization, measurement and identification (Grant, 2008) and require performing balancing act between external and internal factors relevant to organization’s goals.

7. Big data strategy The sudden emergence of big data as a source for new knowledge, valuable insights, and innovation and, as a result, competitive advantage has caught many companies off‐guard. The fact that management can have a more holistic picture of their business and convert that knowledge to make more informed decision and improve overall company performance is forcing firms to adopt comprehensive big data related knowledge strategies. Mere adoption of a strategy based on industry experience is not good enough. Knowledge strategy, in this case, must be aligned with the expected insights and knowledge received from big data and correlated to the business strategy, so that this new knowledge can be implemented across the board. This means focusing on not just understanding how the insights and knowledge can be infused in the business processes but also take necessary actions to embed the new knowledge in the business processes of most critical areas starting from new product development to customer satisfaction and from manufacturing to logistics.

8. Big data strategy framework Rubenstein‐Montano et al. (2001) asserts that a holistic framework of knowledge management that covers general requirements and can be followed by any knowledge management initiative independent of methodologies and tools is essential. Following this suggestion in this paper we propose a universal strategy framework suitable for any organization in relation to big data initiatives from knowledge management strategy perspective.

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Figure 1: Big data strategy framework

9. Prerequisites An organization must possess or develop several critical preconditions in order to implement an initiative successfully, to execute the processes smoothly and to ensure having expected outcome.

10. Management support For any transformation oriented knowledge project to become successful, it requires strong support from management (Davenport et al., 1997). Management support should include: giving clear motivational message to the organization about the importance of the big data project and its benefits in company's success, participating in identifying objectives and domain of the big data projects, allocating finance and other required resources and monitoring success. Success of a big data project depends among others on having a clear understanding of what types of knowledge and insights are necessary in a decision making process. Often, this requires knowledge way beyond data engineering skills of a data specialist. On the other hand, the business decision makers also need to have knowledge about what type of big data can provide needed insights. This means people involved in the big data project either have to have the necessary knowledge and education or they have to develop needed skills and core competencies. Senior management's commitment and involvement in facilitating learning are crucial in building an adequately knowledgeable team capable of accomplishing big data project related assignments.

11. Infrastructure Organizational Infrastructure includes people, process, technology, structure and their correlation. Big data related infrastructure needs to be focused on innovation and knowledge creation and, as a result, should have a high degree of flexibility and freedom. To achieve set strategic objectives organizational structure and roles should ensure a seamless flow of best practices throughout the firm. Strategic goals setting and decision making in relation to the big data project should come from top management. If the big data initiative envisioned to be a large project, it makes sense to appoint a chief data officer who can oversee all data related projects.

12. People Big data projects need to have different skills set than organizations are normally accustomed to. This is one of the added reasons why it is necessary to pay special attention to the key success factor of a big data project ‐ people. Depending on the kind of technologies the company is planning to implement, it would require at the IT level specialists in cloud architecture, Hadoop, MapReduce, Semantic Webs and number of other key areas. Vital to big data project are the holders of a new job title called data scientist. Data scientists are necessary for making sense from big data. Business intelligence professional understands the business decisions needs and capable of analyzing the big data in order to divulge correlations, knowledge and insights.

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13. Data‐driven culture Organizational culture is the collective programming that includes vision, norms, values, symbols, rituals, beliefs, habits and attitudes of the members that work as a normative glue in unifying the organization and influence the behavior of an individual member (Hofstede, 1996).Having a data‐driven culture that fosters implementation of big data projects is imperative for a firm that is striving to have competitive edge using data‐based decision‐making and business analytics. Data‐driven culture means having a clear understanding among the employees that data is everybody's business not just IT department's and data has to be taken in consideration in almost all decision making. A study by Economist Intelligence Unit shows that there is a strong positive link between data‐driven decision‐making and organizational performance. Moreover, data driven companies with superior performance regard data sharing as a valuable process. They also consider that shared data needed to be used across the board and all units should collect data proactively (The Economist, 2013).

14. Absorptive capacity The concept of "Absorptive Capacity" is defined as "ability of a firm to identify, assimilate and exploit knowledge from the environment" (Cohen and Levinthal, 1989: p. 569). Absorptive capacity is considered as part of dynamic capabilities of the firm and are divided as potential absorptive capacity, which derives from knowledge acquisition and integration abilities and realized absorptive capacity, which encompasses transformation and exploitation of knowledge (Zahra and George, 2002). Absorptive capacity is the firm's capability of developing skills related to tacit knowledge (Mowery and Oxley, 1995). Kim (1997) deems that it is the firm's learning and problem solving ability and Kedia and Bhagat (1988) view that absorptive capacity is firm’s ability to transform in accordance with technological shift.

15. Processes A key reason for paying attention to processes in strategy is the need for the organization to grasp how technologies, people, and processes in combination influence its business performance.

16. Goal setting The first and foremost goal for a company that is seriously investing in big data should be to depict a clear vision that emphasizes on the expected strategic outcome from the realization of the big data projects. In setting goals and developing roadmaps, all relevant departments and units need to participate. Setting achievable and measurable goals is vital for the success of a big data project as half of the big data projects initiated never get completed (LaValle et al., 2011).

17. Team building Because of the complex knowledge and skill set that are required for receiving effective results from a big data project, it is necessary to organize the team according to organization's business objectives. The two most needed members of such a team are a data scientist and a business analytics professional. Other members may include IT specialists and workers from the business department most relevant to the data project. For example, if the big data team is working on finding a solution related to marketing, for best result it has to incorporate people from the marketing department as well (Ohlhorst, 2013). Mistake will be to assign the team to IT department. Analyzing information from a number of large corporations, researchers found that while IT departments are highly efficient in data storage and protection, they are unable to offer solutions that can convert data into business value (Beath et al., 2012). More over, organizations that are endowed with a large amount of big data, they have 70 percent more chances of having business intelligence projects initiated by the business community rather than IT people (Rowe and White, 2012).

18. Technology selection Big data projects are complex systems requiring various types of information technologies that encompasses from storage to applications and include data warehouse solutions, information and data management, virtualization and visualization, different analytical tools to name a few. These elements can be divided into three categories: Warehouse infrastructure, big data analytics platforms and big data applications. Big data analytics is not a recent phenomenon. Business intelligence tools are getting used in business decision making for more than several decades. What is new now is the explosive growth of data and capacity to store that data. The sudden popularity of big data can be attributed to the new technological platforms that have

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Nowshade Kabir and Elias Carayannis emerged recently. They are capable of processing and analyzing data in various structures outperforming traditional database technologies in massive scale. Selection of needed technologies will depend on the followings: data amount, speed of data flow, structure of data expected to be used, integration requirement of the data, expected outcomes from the data analysis, users' need, costs, etc.

19. Metrics selection The criticism of financial performance based management style that does not accommodate knowledge as one of the most valuable assets has been well documented (Meyer and Gupta, 1994). Efforts have been made to develop performance measurement frameworks that are more encompassing and comprehensive in relation to intangible assets (Epstein and Manzoni, 1997) and which in various degrees encapsulate knowledge assets measurements (See for example: Edvinsson and Malone, 1997). Since, big data analytics don't impact on the revenue generation directly, the ROI analysis metrics should include indirect benefits that emanate from the big data initiative.

20. Plan implementation In line with the strategic goals and expected outcome, a firm needs to create and deploy a roadmap of big data initiative. Along with setting objectives and milestones, selecting teams members and developing proof of concept one more important issue is to identify and obviate stifles related to the specificity of big data initiative environment. Big data fundamentally differ from any other technology related projects. At one side, the team members work with the high velocity, high volume, high intensity and complex data in a real‐time environment of discovery and innovation, but the insights and knowledge garnered in this environment ultimately need to be aligned with traditional technology based environment of data compliance, governance, security and perfunctory decision making. Organizations should be aware that this coupling of the two different environments might not go smoothly and may have a negative impact on the implementation of a well developed plan.

21. Outcome The big data generated by the organization's business processes and operational activities, which include innovation and knowledge related activities, as well as employee's skill development, have all the potential to become instrumental to developing competitive advantage. The big data base innovations are still in its infancy! Early signs from various large corporations, however, demonstrate the immense possibilities that are hold in the tacit knowledge hidden in big data.

22. Improved human capital One of the fundamental elements of organizational intellectual capital is human capital (Edvinsson and Sullivan, 1996). Stewart (1999) defines intellectual capital as a combination of intellectual elements that include knowledge, information, intellectual properties and experience that are applied to generate wealth. The execution of big data projects requires hiring new talents and developing new professional skills among existing workers. The experiences of the professionals developed in the process of big data project are indubitably valuable assets. Their contribution to the creation of new knowledge and innovative products, services and processes has a positive influence on the top and bottom lines of an organization.

23. Innovation Most organizations understand that key to sustainable competitive advantage in today's globalized and wired world is innovation. In fact, Innovation capabilities, arguably, are the most important determinant of firm's performance (Mone et. al., 1998). Big data is an enabler, a driver and a source of new products, processes, services, strategies and business models (Manyika et al., 2011). Through big data capturing, aggregating, storing and analyzing companies from every industry and sector have the potential to reap benefits of innovation. Innovations originated and spawned from big data can be divided into three categories: Big data‐driven innovation: Innovation where big data is the primary material in the development of a product, service, process or model. One example is high speed trading. Big data enabled innovation: In an innovation where big data works as a catalyst. Examples are: Determining marketing campaign effectiveness, using sensors data to predict machinery failure, monitoring customer's experience of a product and finding design and manufacturing problems.

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Nowshade Kabir and Elias Carayannis Big data related innovation: Technology, process and service innovation that opens new possibilities in handling big data. Example could be a new in‐house business analytics technique.

24. New knowledge base Knowledge acquired from diverse sources is crucial for creating new knowledge. Organizations pursue externally sourced knowledge actively as the more knowledge absorbed from external sources the better the chances of new types of knowledge recombination and generation (Cohen and Levinthal, 1989). Developing dynamic capabilities that help recognizing new possibilities and capturing new business opportunities thanks to aggressive acquisition of external knowledge, which in turn leads to better innovation, is a key to firm's competitiveness (Zhou and Uhlaner, 2009). Big data initiative develops a kind of dynamic capability that contributes significantly to organizations knowledge base in respect to knowledge repositories, employees' knowledge foundation and absorptive capacity.

25. Conclusion In this paper, we have explored the idea that knowledge residing in the big data is indeed tacit and in most of the cases open to explicability. Once extracted this new knowledge can be transferred, used and shared much like any other explicit knowledge. This new and unique knowledge has all the potential of creating economic value for an organization and bolster innovation, productivity and growth. Thus, It is also a possible major source of competitive advantage. We then proposed a big data centric knowledge strategy framework that outlines requirements, processes and outcomes of a big data initiative that aims at creating competitive advantage. We recommend consulting and adopting the strategy framework prior to engaging in a big data project. The main limitation of this paper is, although, in this paper we have delineated a strategy model that can be implemented in any organization, the concept is not validated by any empirical research. We suppose that study covering multiple industries on the impact of this strategic framework is necessary to identify its strength and weakness.

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The ADIIEA Cycle: Creating an Integrated Framework for Business Processes and Organizational Learning John Lewis Kent State University, Kent, USA John@ExplanationAge.com Abstract: This paper introduces the Innate Lesson Cycle (ADIIEA) as a uniting and integrated framework for business process operations and organizational learning. Thus far, the Knowledge Management (KM) and Organizational Learning (OL) movements have tried to “teach KM” to organizations as an “add‐on” while assuming that current business models are sound. Instead, we find that current business models are based on industrial age factory process work, and fail to keep up with the learning and innovation demands of the knowledge economy. This paper suggests that these current business models be replaced, not complimented, with a learning‐based model. In the epistemological formulation of this learning model, ADIIEA is compared with the SECI model, and its underlying assumptions about tacit and explicit knowledge as appropriate foundational underpinnings are challenged. Instead of a “noun” approach to knowledge foundations (tacit and explicit knowledge), a “verb” approach (questioning, reflective, and reactive modes) to knowledge foundations is illustrated to be appropriately compared to required business process operations. Additionally, this approach is shown to be epistemologically aligned with the fundamental symbols of language, where we universally find the question mark, period, and exclamation point, respectfully. From this verb‐based foundation, several applications of ADIIEA are then illustrated to address current issues found in business processes, policy‐making, talent management, and knowledge system’s user experience and information architecture. Keywords: organizational learning, epistemology, theory of knowledge, process management, innovation, human capital

1. Introduction This paper seeks to contribute to the foundational epistemological discourses in knowledge management (KM) and Organizational Learning (OL) by proposing the ADIIEA (pronounced uh‐dee‐uh) model, also known as the Innate Lesson Cycle. This model is intended to provide not just a new learning model, or a new business process model, but a new integrated framework for all business processes—which is fundamentally based on learning. The Organizational Learning movement has thus far assumed that business entities have a sound business process model, and that we just need to introduce an additional element that addresses knowledge and learning. But pursuing this approach has led ultimately to this quote from Peter Senge: “Making learning an “add‐on” to people’s regular work, has probably limited more organizational learning initiatives than any other factor” (Senge 2006). Instead of this continued “additive” approach, the question now becomes: how should we “replace” the current business process models with one that is based fundamentally on learning itself? And before we can answer this question, we should first ask: what are we currently trying to solve for? One of the early goals of the Knowledge Management movement was to support the challenges of information overload (Levy 2009). The large consulting companies were the early adopters of KM, but when their approaches to “managing and sharing what they know” were given to other companies, we just ended up with “information junkyards” (Dalkir 2005). Perhaps, instead of trying to solve for information overload, KM should be solving for information anxiety. In his 1989 book Information Anxiety, Richard Saul Wurman got it right in his description of the problem. Some think that information anxiety is simply due to the sheer amount of information that is available, but Wurman points to the real source of anxiety: the gap between “what we understand and what we think we should understand” (Wurman 1989). And this gap has only widened since 1989. The organizational problem is fundamentally not just about access, but about understanding. The fundamental topic should not be just about knowledge sharing, but about organizational learning.

2. Literary context The knowledge cycle within an organization has been popularly described as a process of socialization‐ externalization‐combination‐internalization ‐ known as the SECI model (Nonaka & Takeuchi 1995). But as pointed out in a critical analysis of this model (Bratianu 2010), the underlying dimensions to this model are

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John Lewis based on a big assumption: “The assumption that knowledge is created through conversion between tacit and explicit knowledge allows us to postulate four different “modes” of knowledge conversion” (Nonaka 1994). This assumption has placed limitations on the usefulness of this model (Bratianu 2010). And this paper seeks to examine these fundamental assumptions to address knowledge sharing and also knowledge creation and innovation. The current assumption, that explicit and tacit knowledge are the exclusive fundamentals needed, for knowledge sharing and organizational learning, has not been found to be sufficient (Puusa & Eerikäinen 2010). Puusa & Eerikäinen point to the possible root of this problem ‐ that the origins of explicit and tacit knowledge have been misinterpreted. Polanyi (Polanyi 1958; 1966) stated that all knowledge has a tacit component, but did not intend that we treat tacit knowledge as a separate category of knowledge outside the holistic view of knowing (Puusa & Eerikäinen 2010). And yet, discourses in KM are “still characterized by a dichotomy rather than complementary views” of knowledge (Heisig 2009). This is a profound issue when solving for both knowledge sharing and also knowledge creation. “This epistemological question about the relationship between tacit and explicit knowledge is important because it lies in the very heart of the KM theory” (Virtanen 2013). As with any mental model, this mental model of knowledge fundamentals will affect our ability to perform in this field. A mental model is a “concentrated, personally constructed, internal conception, of external phenomena (historical, existing or projected), or experience, that affects how a person acts” (Rook 2013). In our case, we could add, how a “profession” acts, as we consider our mental model of the fundamentals that drive KM and OL.

3. The ADIIEA construct The epistemological approach for the ADIIEA construct is based on the study of language and explanations. In reviewing the world’s languages, the first fundamental commonality we find is for the types of thoughts we wish to convey—and the clues are found at the end of each sentence. The mind is either asking a question, and we communicate this using a question mark (?); or it is making a reflective statement, and we communicate this using a period (.); or it is making an emphatic reactive statement with conviction, and we communicate this using an exclamation point (!). In the Spanish language, acknowledging and communicating these three modes of the mind is so important that we also find clues at the beginning of each sentence: the inverted question mark (¿) initiates each question, and the inverted exclamation point (¡) initiates each reactive statement (Lewis 2013). From the study of language, these are just three punctuation marks, among many others. But from the study of epistemology, we accept these as the three response modes of the mind, being represented with symbols, to facilitate the communication between minds. Within the field of Knowledge Management, we interview experts and decision makers to find out how they do what they do and why they do it. Frequently, they can’t answer the question. They can do the work—but not say how or why. We call this “tacit” knowledge. It is like you being able to leave a store within a large mall, and go through a large parking lot with several levels—and you still find your car. But you could never have written down all of those steps such that I could have recognized and found your car. When we can say or write down what we know, we call this “explicit” knowledge. Sometimes while pushing someone to answer why they made a decision, usually their first translation from tacit to explicit is an emphatic reactive statement with conviction. For example: “Because I am the boss so I get to make these kinds of decisions!” The exclamation point is the tell‐tale sign that we are getting explicit knowledge directly from tacit knowledge—still a reactive, not reflective answer. The ADIIEA construct differs from the “tacit versus explicit knowledge” approach in several ways. Instead of a dichotomy, there are three core fundamentals. And instead of the focus on the noun of knowledge, it is on the verb of how we are thinking and communicating, in that we are operating in a mode of reactive, reflective, or questioning. And instead of assuming that knowledge creation happens somewhere in the conversion between tacit and explicit knowledge, this new construct places creation directly within the mode of questioning.

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John Lewis We learn that Albert Einstein was “creative” but are not taught that it all came from his ability to formulate a question: “What would I see if I could ride on a beam of light?” Only with this “question” does a student “know” the theory of relativity. A modern model of knowledge types must include “questions” as an innately symbiotic component of that model. So now we ask: what fundamental drivers operate on our response modes, for when we engage each one? Our response modes, as reactive, reflective, or questioning, are innately linked with our workability beliefs. Some people say that “it either works or it does not work.” But they are skipping over the workability belief that all projects must operate within: “it could work.” Figure 1 illustrates this innate link between our three response modes and our three workability beliefs.

Figure 1: The three workability beliefs and three response modes Within the innate relationships between the three response modes and the three workability beliefs, we find the following:

Questioning does not innately occur when operating under “does work”

Questioning operates innately during “won’t work” and “could work”

Reflective does not innately occur when operating under “won’t work”

Reflective operates innately during “could work” and “does work”

Reactive does not innately occur when operating under “could work”

Reactive operates innately during “does work” and “won’t work”

These innate relationships produce the six phases of the ADIIEA construct (figure 2). ADIIEA stands for: Automation, Disruption, Investigation, Ideation, Expectation, and Affirmation. Given it is founded on workability beliefs, ADIIEA does not assume that learning is based on a cyclic conversion between tacit and explicit knowledge (SECI model), but that learning is based on knowing if something works. So the ADIIEA model is also known as the Innate Lesson Cycle. The location of each phase is like working against gravity; we tend to rest at the bottom and operate in Automation, on autopilot, without much thought, reactively assuming that it works. The Automation phase is a reactive mode of the mind which can be found operating in both humans and machines. It is not a mode reserved solely for machines. Automation is where we begin and where we end. It allows us to be proficient at doing what works. And we are content at staying with our routine until we find we are in disruption. Rather than simply listing six phases of a new learning or business model, the value of understanding that these six phases are created by underlying relationships is that it allows us to examine these fundamental elements in more detail. We can examine the nature of each phase and the nature of the lines that we need to cross to be about to enter and exit each phase.

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Figure 2: The innate lesson cycle (ADIIEA) Moving through the Innate Lesson Cycle is unlike moving through current business models, which are simply linear directions, expecting learning to occur like a factory operation. Rather than dictating a change process which is linear in nature, ADIIEA allows for moving between phases by simply asking “how” (forwards) or “why” (backwards). Allowing for a change project to “rock” between phases is more aligned with our “thought” process, instead of a “factory” process. And sometimes navigating within ADIIEA means staying in the bottom half: the half‐pipe. With ADIIEA, we can visually see the two major types of learning processes: full‐cycle and half‐pipe. Some people will want to learn by asking questions and coming up with ideas to try and test for themselves (Investigation, Ideation, Expectation, Affirmation) and some people will want to learn by asking someone else (Affirmation to affirm rather than confirm). Figure 3 illustrates learning in the half‐pipe.

Figure 3: Half‐pipe of the innate lesson cycle Traditional education is based on “half‐pipe” instruction, which only supports half of our natural learning cycle. Have you ever wondered why the term “Drill & Practice” has two words? They are relative: the first time you have a fire drill, it’s a drill—but the tenth time, it’s practice. The term “Education & Training” works the same way; “here’s what works, now you try it.” Half‐pipe learning was designed to move students into Automation as quickly as possible. When the task was to prepare WWII soldiers and Industrial Age factory workers, this approach worked just fine.

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John Lewis In the industrial economy, we only needed about 20% of people to have a college degree and to think full‐ cycle, with about 80% of people needing to come up to speed quickly to operate in the half‐pipe. Now, the needs have reversed. In the knowledge economy, we need about 80% of people to be able to think full‐cycle (the nature of their job) with just 20% trained in the half‐pipe. From the perspective of organizational learning, we can think of full‐cycle thinking as change management, and think of half‐pipe thinking as sustain management. This is how we can picture learning as THE business model, rather than learning as an “add‐on” to business operations. Figure 4 illustrates “change and sustain” management, with the primary management and process functions listed for each phase. Note that all sustaining operations, including risk management, are based on half‐pipe thinking. Automation is to be codified and controlled. We prepare and react to disruptions. And we cede to some authority and cite them as a reference for any reflective answers related to what we should be doing in routine. Note that all change/process management operations are based on full‐cycle thinking. Disruptions are to be defined and measured for scope and prioritization. Investigation is designed to analyze and discover. Ideation includes more than creativity, it requires a design and plan, which can be measured for ingenuity and the risk‐ reward. Expectation requires something to be developed and implemented, which can be measured for progress and accomplishment. Affirmation requires the evaluation of any ideas, to ensure that they “work,” and also that the knowledge of what works and does not work is shared in the organization for those who want to cite this knowledge as they operate in the half‐pipe.

Figure 4: Change and sustain management within the innate lesson cycle The purpose in creating an integrated framework for business processes and organizational learning is to stop the current practice of assuming that business models are sound, and therefore any idea of learning in the organization needs to be an “add‐on” model or discussion. With the Innate Lesson Cycle (ADIIEA) we innately describe the operations within an organization as related to learning. Learning is no longer something that is said to be the core function of an organization; learning is now something that can be shown as the core function of an organization. The purpose of ADIIEA is to replace the current “box‐checking” models with a “sense‐making” model, to help prepare organizations for the knowledge economy.

4. Application scenarios In applying ADIIEA within an organization that already operates from a contemporary process model, it helps to directly compare ADIIEA with their existing model. Again, the purpose of ADIIEA is to stop trying to

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John Lewis approach organizations with the idea of organizational learning as an “add‐on” to their existing operations, and to replace their existing operational models with one that is based on learning rather than factory operations. Table 1 provides a direct comparison between the steps that are performed within ADIIEA and the steps performed in other contemporary process models. The DMAIC model is used as part of a Six Sigma management strategy that has helped many companies bring costs and quality under control through a focus on operational efficiencies. The DMAIC model somewhat aligns with our Innate Lesson Cycle (ADIIEA), but the single step to “Improve” is not broken out into the ADIIEA phases of Ideation, Expectation, and Affirmation. According to the DMAIC mindset, “Six Sigma” literally means that we are only allowed to have three defects per million units. And while this mindset works well for manufacturing processes, do we really think that ideas will pop out of our heads like cans of soup from a factory, without any so‐called defects? The only real “magic” behind DMAIC is that it recognizes the need for “control” in the new status quo, to be able to accomplish the factory‐like goals of the model. This, plus some statistical tools to help identify the root causes of problems during Investigation, is all this model offers. Table 1: ADIIEA vs contemporary process models ADIIEA Phases

ADIIEA Steps

DMAIC Steps

PDCA Steps

Making a Law Steps

Automation old status quo

Codify Control

Old Status Quo

Disruption

Define Measure

Interested Party dissatisfied

Investigation

Analyze Discover

Analyze

4‐Act

Interested Party defines the issue

Ideation

Design Plan

Improve

1‐Plan

Interested Party proposes solution Congress member submits the bill

2‐Do

Bill needs approval from congress Bill sponsor actively seeks support

Expectation

Develop Improve Implement

Affirmation

Evaluate Share

Improve 3‐Check

Automation new status quo

Codify Control

Control

Bill is signed into law Judicial Branch enforces the law Citizens expected to obey new law New Status Quo

The classic business model PDCA provides another list of steps: Plan, Do, Check, and Act. As the last step, it defines “Act” as the investigation into solving why your plan did not work. Other business models suggest an investigation before the first plan is developed, but this model says to plan first and investigate last, if the idea doesn’t work. And finally, as we consider a country as a learning organization, we can compare ADIIEA with the process of policy‐making. Making a new law is a process that is projected from the mind. From the status quo (Automation), an interested party becomes dissatisfied with the definition of what works (Disruption). Then they define the issue for what won’t work (Investigation) and propose a solution (Ideation), called a “bill.” The bill’s sponsor seeks the votes needed (Expectation), hoping it will be signed into law (Affirmation), thereby becoming the new status quo (back to Automation). We can memorize the steps for how a bill becomes a law, or we can recognize that we are simply projecting our Innate Lesson Cycle onto the social arena. Our textbooks say that to create a new law, the first step is for a member of Congress to submit a bill. But these instructions start at Ideation (submit bill) and skip over the prior ADIIEA phases, which would help the topic make sense, and help empower citizens to see their part early in the process. Transparency should include knowing how one disruption was prioritized over the others; what investigation was done into finding a root cause; and why the given bill (idea) was chosen over other improvement ideas.

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John Lewis In addition to the applications of process management, ADIIEA also has implications for talent management. Talent Management is a broad field, but at its core is about aligning talent with the needs of the organization. The needs of a learning organization include learning—not learning as something separate from business outcomes—but learning as the primary sustaining function of the organization. This requires planning from the Innate Lesson Cycle, not an organization chart. Each industry has specific job titles related to an alignment with the Innate Lesson Cycle of the organization. The titles may change, but the mental work is similar. For example, the questioning skills needed to perform as an investigator in one industry are very similar to the skills needed to perform as a researcher in another industry. Similarly, designers are needed in ideation, developers are needed in expectation, and testers are needed in affirmation—regardless of the field or domain. ADIIEA also has implications for the design of knowledge management systems. As we saw earlier, there are two fundamental ways that we find an answer: either we stay in the half‐pipe and cite an authority for an answer, or we track the full‐cycle answer to understand the options considered for all the phases of the Innate Lesson Cycle. We see both of these fundamental ways to answer questions within the knowledge systems used in our organizations. For example, a company will have a Web site with “authoritative” answers, usually in the form of FAQ (Frequently Asked Questions), where customers can find the definitive answer to their question. Companies do not want to provide customers access to their internal “collaborative” system, where several people and departments are involved in providing their opinion and rationale for the best authoritative answer for their customers. Systems that support authoritative answers have progressed to allow many customers to find the answer to their question themselves, in a “self‐service” system, rather than needing to call and talk to a person in a “call center” at a company. And systems that support the collaboration of ideas have progressed to allow for many more ideas from diverse groups to be considered. Sometimes we find a hybrid system that does not try to find the authoritative answer or the collaborative online environment, but will try to find “an authority” on the subject, as an “Expert Finder” type of system. But these are systems designed from the premise that we need to ask the question “who” because answers to “what” and “why” will not or cannot be found directly. As the demands increase for transparency into the decision making process, the “Expert Finder” systems will be used less as an authoritative output and more as an input into who should be participating in the collaborative environment. In addition to finding technological progress with authoritative and collaborative systems, as separate systems, we will see progress in how they integrate into a single merged system. Imagine reading an FAQ on a company Web site and calling to complain that the answer does not consider your situation. Imagine a worker in the call center clicking on that authoritative answer to indicate that it should be reviewed. Imagine the executives involved in making that decision getting notified that there may be a condition they did not consider when making that decision, and their entire decision logic appears for them to re‐review. Imagine the transparency in the decision making process when you, as the customer, are notified automatically that the authoritative answer has been updated. Now imagine a similar system helping not just corporate organizations but also state, national, and international organizations. Instead of just reciting the “law,” we can click to find the bill or court decision, what situations were considered—and even read the “dissenting” votes in a court opinion. “Learning organizations are possible because, deep down, we are all learners” (Senge 2006). By replacing an organization’s current primary operational model with one that is based on the foundations of learning, then learning is not an “add‐on” to business, but the way of business.

5. Conclusion The current approach to introducing the topic of organizational learning into organizations has been to assume that an organization has sound business processes, so learning is an “add‐on” topic, or is dismissed as simply

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John Lewis individual training. Trying to “teach KM” to organizations, outside the fundamental topic of business operations, will continue to have diminishing returns. Instead, this paper recommends a new approach, whereby ADIIEA, as a model of learning, replaces the current business processes, to create an integrated framework for business processes and organizational learning.

References Bratianu, C. A (2010) Critical Analysis of Nonaka’s Model of Knowledge Dynamics, Electronic Journal of Knowledge Management Volume 8 Issue 2 (pp193 ‐‐200) Dalkir, K. (2005) Knowledge Management in Theory and Practice, Burlington MA: Elsevier Butterworth‐Heinemann. Heisig, P. (2009), Harmonisation of Knowledge Management – Comparing 160 KM Frameworks Around the Globe, Journal of Knowledge Management, Vol. 13 No. 4, pp. 4‐31. Levy, M. (2009) Leveraging Knowledge Understanding in Documents Electronic Journal of Knowledge Management Volume 7 Issue 3, (pp341 ‐ 352). Lewis, J. (2013) The Explanation Age, 3rd ed., Charleston: Amazon Create Space. Lytras, M.D. and Sicilia, M.A. (2005) The Knowledge Society: A Manifesto For Knowledge and Learning, Int. J. Knowledge and Learning, Vol. 1, Nos. 1/2, pp.1–11. Nonaka, I. (1994) A Dynamic Theory of Organizational Knowledge Creation, Organization Science, Vol.5, No.1, February, p. 14. Nonaka, I., and Takeuchi, H. (1995) The Knowledge‐Creating Company: How Japanese Companies Create the Dynamics of Innovation. New York: Oxford University Press. Polanyi, M. (1958) Personal Knowledge. Towards a Post Critical Philosophy. London: Routledge. Polanyi, M. (1966) The Tacit Dimension, London: Routledge & Kegan Paul. Puusa, A. and Eerikäinen, M. (2010) Is Tacit Knowledge Really Tacit? Electronic Journal of Knowledge Management Volume 8 Issue 3 (pp307 ‐ 318) Rook, L. (2013) Mental Models: A Robust Definition, The Learning Organization Vol. 20 No. 1, 2013 pp. 38‐47 Senge, P. (2006), The Fifth Discipline: The Art and Practice of the Learning Organization, 2nd ed.,Doubleday Currency, New York, NY. Virtanen, I. (2013) In Search for a Theoretically Firmer Epistemological Foundation for the Relationship Between Tacit and Explicit Knowledge, The Electronic Journal of Knowledge Management Volume 11 Issue 2 (pp118‐126) Wurman, R. S. (1989) Information Anxiety, New York: Doubleday.

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Working Meetings as a Tool for Knowledge Management and Trust Building Palmira Lopez‐Fresno1 and Taina Savolainen2 1 Spanish Association for Quality, Spain 2 University of Eastern Finland, Business School, Finland correo@palmiralopezfresno.com taina.savolainen@uef.fi Abstract: This paper discusses and examines the role of working meetings as a tool for knowledge management and trust building. Given the prevalence and practical significance of meetings for organizations, it is important to study their role and impact. Working meetings are one of the basic tools in organizations for collaboration and group cohesion, and a significant vehicle for communication. They play an important role in information and knowledge sharing, knowledge creation, coordination, decision making, problem solving and strengthening of group relationships, inside and outside the organization, and contribute to build or destroy trust. As necessary as meetings are, they are also very costly ‐ costs can exceed 100 million dollars per year in big organizations– and frequently unproductive. Unless properly managed, they can be a waste of valuable financial and emotional resources, with negative impact on trust, vitality, innovativeness and competitiveness. So, there is a need to assure meeting effectiveness. Good meeting planning, preparation, realization, assessment and follow‐up are needed to achieve it. But also meeting facilitators, as “leaders”, play a critical role, as they are responsible for creating a trustful climate, and for successfully conducting the meetings to achieve the objectives each meeting should have, covering the topics on the agenda and doing it in a positive environment and within the planned time frame. The main point of discussion is crystallized in the suggestion that meetings have an impact in integrative group behaviour, cooperation, knowledge creation and sharing. Building and maintaining trust are of utmost importance to develop human capital for sustaining innovativeness and vitality in organizations. Originality of the paper is based on exploring the role of working meetings in knowledge management, and trust building as an essential element of meetings. Implications are made of how to increase effectiveness of working meetings. Keywords: collaboration; knowledge management; meetings; teams; trust; vitality

1. Introduction Meetings are an important part in organizational life. Much of the time, particularly at management level, is consumed in meetings and the trend is increasing (Romano and Nunamaker, 2001; MCI Inc, 1998; López‐ Fresno and Grandes, 2010). According to an MCI white paper (1998), approximately 11 million meetings take place in the United States every day; Van Vree (1999) found that in organizations with 500 or more people, managers spent around 75% of their time preparing and executing meetings, and at least 10% in companies with fewer than 10 people. People meet because “holding different jobs they have to cooperate to get a specific task done. The knowledge and experience needed in a specific situation are not available in one head, but have to be pieced together out of the knowledge and experience of several people” (Drucker, 1967). Belief in the adage "two heads are better than one" may be found in the widespread employ of meetings in many societies (Hill, 1982; Cortina, 2013; Schwartzman, 1989). Research shows that group performance exceeds individual performance (Hall, 1994; Hill, 1982) and employees express the desire to work together in groups. Hall (1994) found in a three‐year survey of more than 10,000 U.S. employees that 97% reported they need conditions that encourage collaboration to do their work. Meetings are a common tool, used for a variety of purposes. As a vehicle for communication and collaboration, they are a mechanism to disseminate vision, design and deploy strategic plans, make decisions, develop products, share information and for training, among others (McComas, 2003; Rogelberg et al, 2007; Tracy and Dimock, 2003). They are also helpful for gathering ideas and brainstorming, and play a large role in employee involvement, socialization, relationship building and shaping the culture (Rogelberg et al, 2007). In meetings plans are made, problems are solved, important organizational processes take place (Cohen et al, 2011; López‐ Fresno and Portocarrero, 2009) and trust is built (López‐Fresno and Grandes, 2010). Meetings reinforce formal and informal reporting structures and provide clues about organizational values and how power is distributed. Meeting effectiveness is affected by organizational culture as well as they contribute to reinforce or modify it.

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Palmira Lopez‐Fresno and Taina Savolainen Overall, meetings provide and drain resources to and from employees and managers (Allen et al, 2012). Meetings are necessary and important in any organization as mechanisms to induce action and reinforce group cohesion and cooperation. But although necessary, they often are unproductive and wasteful (Mosvick and Nelson, 1987; Monge et al, 1989; Romano and Nunamaker, 2001). Unproductive meetings generate important financial and emotional costs (Romano and Nunamaker, 2001 estimated meeting expenses ranged from costs of $30 million to over $100 million per year to losses between $54 million and $3.7 billion annually) and have a negative impact on productivity and competitiveness (3M and Drew, 1994; Rogelberg et al, 2010; Cohen et al, 2011; López‐Fresno and Portocarrero, 2009; López‐Fresno 2010, 2011; Romano and Nunamaker, 2001). According to the affective events theory, unproductive meetings are enduring sources of frustration and dissatisfaction, and in turn influence overall job attitudes (Basch and Fisher, 2000; Fisher, 2002), affecting trust and organizational culture. Research on job‐satisfaction determinants further substantiate the connection between meeting satisfaction and job satisfaction (Basch and Fisher, 2002; Rogelberg, 2006; Rogelberg et al, 2010). Tracy and Dimock (2004) observed that through meetings groups solve and create problems, give information and misinformation, develop and rework policies, make retooled decisions, and while doing these focal activities they build or fracture sense of community and solidify or cause tension among participants. Meetings are where groups celebrate and challenge institutionally important values; they are also sites where people display their own power and resist the demands of others. Meeting facilitators or coordinators play a critical role, as “leaders” conducting the meeting. Unless properly managed, meetings can be a waste of valuable financial and emotional resources, with negative impact on trust, vitality, innovativeness and competitiveness. So, there is a need to assure meeting effectiveness (Rogelberg et al, 2007; López‐Fresno, 2009, 2010, 2011). Good meeting planning, preparation, realization, assessment and follow‐up are needed to achieve meeting effectiveness, but also meeting facilitators, as “leaders”, play a critical role, as they are responsible for creating an adequate climate for collaboration, information sharing, knowledge creation and trust building, and for successfully conducting the meetings to achieve the objectives each meeting should have.

2. Understanding working meetings 2.1 Definition Literature review reveals different definitions of meetings, but some common characteristics underlie. Auger (1964) focused meetings as “bring together a group of people that share a definite purpose and value stems from that definite purpose”. Schwartzman (1986) defined meetings as “pre‐arranged gatherings of two or more individuals for the purpose of work‐related interaction”. For Rogelberg (2006) they are “purposeful work‐related interactions occurring between at least two individuals, that have more structure than a simple chat, but less than a lecture, and can be conducted face to face, in distributed settings (eg., conference calls) or a combination”. Webster dictionary (1998) defines meeting as an “act or process of coming together that may be a chance or a planned encounter.” To Goffman (1961), a meeting is that which “occurs when people effectively agree to sustain for a time a single focus of cognitive and visual attention.” Hildreth (1990) added the concept of a shared goal, and defined meeting as a “communication encounter between persons for a common purpose.” Nunamaker et al. (1996) incorporated the concepts of physical and temporal dispersion, considering meeting as “any activity where people come together, whether at the same place at the same time, or in different places at different times”. Romano and Nunamaker (2001) combined elements found in the literature and defined meeting as “a focused interaction of cognitive attention, planned or chance, where people agree to come together for a common purpose, whether at the same time and the same place, or at different times in different places.” To López‐ Fresno and Grandes (2010), two perspectives should be considered when defining a meeting: functional and structural. From a functional perspective, “a meeting is the activity or process of joining two or more people, at a time and given environment, to achieve a common purpose”. From a structural perspective, “a meeting is a group of people interacting in order to achieve a common purpose”. Combined both perspectives, meeting was defined as "a group of people whose activity is intended to build trust and responsibility on the objectives and consequences for which it was convened" (López‐Fresno and Grandes, 2010).

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Palmira Lopez‐Fresno and Taina Savolainen These definitions include several important dimensions of meetings, such as focused interactions; joint process; group of people; common purpose; level of formality; temporal and physical dispersion. Each of these dimensions may affect the working meeting itself and the support required to improve group productivity and morale and overall organizational culture, where trust is an important dimension.

2.2 Meeting design and process focus Meetings are processes that develop in “focused” interactions between people, subjected to social dynamics before, during and after the meeting itself (Cooren, 2007; Mirivel & Tracy, 2005). They play an important role in employee socialization, cooperation and culture shaping. As processes, no two meetings are alike; they differ in many ways, based on the purpose and objectives to be covered, people involved, size of the group, tools used, management styles and overall design of the meeting (Cohen et al, 2011; Tracy and Dimock, 2003; Schwartzman, 1986). A good structure, preparation and conduction of the process along its several phases (meeting planning, preparation, realization, assessment and follow up) will contribute to meeting effectiveness (Barker, 2011; Cohen et al, 2011, HBR, 2011; López‐Fresno and Grandes, 2010; Rogelberg, 2007). Also meeting design characteristics (eg. a clear and realistic agenda) reduce general meeting dread and relate to meeting effectiveness (Nixon and Littlepage, 1992). The agenda gives structure to the meeting and if properly and ethically prepared (no hidden agenda, topics or objectives) and distributed in advance, clarify which are the real objectives of the meeting, what are expected from each participant and which information is important to the specific person or group of people (Barker, 2011; López‐Fresno and Grandes, 2010; López‐Fresno, 2011). Lack of agenda suggests inadequate planning (3M Meeting Management Team and Drew, 1994; Monge et al, 1989; Mosvick & Nelson, 1987). But although agendas are considered important and even essential to the success of meetings, research shows that they are often not used or not communicated prior to meetings (3M Meeting Management Team and Drew, 1994; Romano and Nunamaker, 2001). No goals or agenda was the second most commonly reported meeting problem in Mosvick and Nelson’s (1987) survey. Monge et al (1989) found that 32% of respondents reported that their meetings had no stated agenda; 29% had written agendas distributed prior to meetings, and 17% had verbal agendas stated in advance; 9% of the meetings had written agendas distributed at the start of the meeting. These authors also found that even when a written agenda is distributed before a meeting, there may still be underlying issues not stated on the agenda (hidden agenda). 63% of respondents indicated they felt underlying issues were present in meetings. Out of them, 30% reported the presence of underlying issues to a small extent; 21% to some extent; and 12% to a great extent. Hidden agenda, topics or objectives undermine trust, cooperation and group cohesion, with negative impact on organizational culture (López‐Fresno and Grandes, 2010; Nixon and Littlepage, 1992) Additional to a clear structure of the process and consideration of design characteristics (eg. agenda time, etc.), organizational culture and the behaviour and competence of meeting facilitator will play a crucial role in meeting effectiveness. They may facilitate or hindrance information and knowledge sharing, knowledge creation, collaboration, group cohesion, and may contribute to build or destroy trust. In positive environments, explicit and tacit knowledge is shared and collaboration and group cohesion enhanced, in a way that may not be done otherwise. A meeting is an exercise of leadership, a relational process with technical and social implications. The person who leads or coordinates the meeting have the opportunity to get decisions made and to contribute to enhance organizational culture. He or she conducts the group along the different phases of the process, to make possible to achieve the set objectives, going through all topics considered in the agenda, within the planned time frame and in a positive environment that reinforce group identity, cooperation and cohesion (López‐Fresno and Grandes, 2010). In summary, meeting facilitator or coordinator, as “leader” of the meeting, should provide vision (perspective of what is intended to achieve), direction (pointing out where to go) and security (developing and enhancing confidence on what is being achieved to meet the objectives).Through the coordination of the meeting, his or her knowledge, values, results orientation, abilities and competencies to lead and conduct the group, to manage time, to synthesize, etc., will be continuously observed, assessed and taken as a good or bad reference. They act as role models. So, to lead or coordinate meetings is one of the leadership abilities that all managers should have, but also an exercise of social individual responsibility (López Fresno, 2012, 2013; Cortina, 2013). As professionals they should act ethically and do their best to make meetings productive and contribute to create positive environment and culture, where trust is one critical

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Palmira Lopez‐Fresno and Taina Savolainen pillar (trustworthy environment). If they promote or allow inappropriate behaviour, they will lose authority and will negatively influence the culture. If this is very relevant in presence meetings, it gains even more importance in virtual meetings, more susceptible to manipulation (López‐Fresno and Grandes, 2010).

3. Trust as intangible, intellectual asset in meetings Trust is a multi‐faceted and multi‐disciplinary issue that has been widely studied in different fields of science over the last years (Burke et al., 2007; Ebert, 2009). It is combined of several rational, cognitive and affective components (McAllister, 1995) and has been defined in many ways, yet the concept remains without a generally accepted definition (McEvily et al, 2003). Rotter (1967), one of the earliest trust theorists, defined interpersonal trust as expectancy by an individual or a group that the word, promise, verbal or written statement, of another individual or group can be relied upon”. Mayer et al (1995: 712) defined trust as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party”. Mayer’s model focuses the formation of trust between actors in a relational context. This means that trust is created, built and sustained by and between people; it evolves over time based on repeated interactions and information available and shared between parties (Mayer et al, 1995). Trust results from collaborative interaction between organizational actors in processes such as communication, cooperation and information sharing (Burke et al, 2007). Processes that normally take part in working meetings. Trust is an intangible, relational asset for co‐operation between people, and a managerial resource and skill for knowledge sharing and creation and for developing human intellectual capital. The elements that underlie trusting relationships involve the individual’s feeling of being competent, safety and caring, a sense of autonomy, consistency and fairness in social relations (Gillespie and Mann, 2004; Savolainen, 2011). In the workplace context, intellectual resources such as trust are used and “owned” by the organization, in principle. Trust is multi‐level and reciprocal in nature. This means that the effects, means and consequences of trust concern both the individual and organizational level (Savolainen, 2011). The individual builds and breaks trust, but the benefits and unfavourable effects spread to groups and organizations. More broadly, the effects are seen in different structures and processes in the organizational and societal level. This especially applies to ethical activities. An individual’s unethical actions propagate detrimental consequences to the organizational level and even the entire society. Subsequently, in co‐operation one party can trust the other that he or she will not act deceptively trying to gain short term advantage. As to meetings, they involve both opportunities and risks as well as interdependence. Interpersonal interaction is dependent on others’ behavior (Ferrin et al, 2011). Trust involves accepting vulnerability, which is based on positive expectations of the intentions other people and is therefore an integral element in meetings. It is thus important to understand trust building in meetings. In leadership individual leaders pursue improving the outcomes of meetings. Meeting facilitators or coordinators act as “leaders”. Through their doing and performance at the meetings they may facilitate or hindrance information and knowledge creation and sharing, collaboration, group cohesion and wisdom to garner collective intelligence and bring it to solve a problem or achieve a goal together. They conduct the group along the different phases of the process to make possible to achieve the set objectives in a positive environment (López‐Fresno and Grandes, 2010). How meetings are conducted and their result are indicators of organizational culture, a reflection of organizational “health”, as they express "ways of doing and managing" when it comes to leadership and people management, time management and results orientation. Meeting effectiveness has impact on each individual, in the group to which he or she belongs, and in the organization as a whole. So they contributes to create or destroy a trustworthy environment, as well as they are affected by it. Considering these implications, leading a meeting is an exercise of individual social responsibility (López‐Fresno, 2012, 2013; Cortina, 2013), as also is the doing and performance of the participants. Trust “belongs” to each member of the work community; everyone is responsible to build it (Ikonen and Savolainen, 2011). Of leaders trustworthiness and commitment is expected. This means competence, integrity and predictability. They are manifested in open communication, ethical behaviour and doing the best in any activity and are also seen as the main responsibilities of professionals. Leaders may

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Palmira Lopez‐Fresno and Taina Savolainen promote them and, hence, build trust. Trustful leadership enables interaction, communication and cooperation. Leadership by trust may be a powerful intangible asset which reinforces good workplace climate, makes knowledge sharing more effective (Savolainen, 2011) and creates vitality and energy that enables growth of human capital and profitable performance. In short, intrinsically trust is a fragile intangible asset. It can be built or broken by an individual, but he or she alone cannot utilize it or carry its unfavourable consequences. Trust influences relationships and structures, socially and collectively. At the individual level, trust can be invisible, even “tacit” and easily breakable (Savolainen, 2011), while at the organizational level it is usually more tangible. Trust can be sensed for example in the culture and atmosphere as well as in various relationships. In meeting behaviours, the benefits and consequences of trust are multifaceted and become visible both within and between organizations and actors. Meetings play an important role in trust building.

4. Knowledge sharing and creation Pertaining to meetings and trust, knowledge creation and sharing play a very significant role. Conceptualized by Nonaka and Takeuchi (1995), knowledge basically adopts the forms of explicit and tacit. Explicit knowledge is in written forms such as books, reports, etc, and it is easy to identify, articulate, detain and share. Tacit knowledge is defined as consisting of intuition, feelings, perceptions and beliefs deeply embedded in the ways of thinking, talking and working, and in relationships. It is difficult to understand, observe, acquire and share, and is diffused in the organization. Thus it is complicated to articulate, which makes it difficult to convert, transfer and share between people. People need tools, motives and supporting atmosphere for sharing. Group dynamics and workplace climate are the most critical factors in sharing of tacit knowledge (Savolainen and López‐Fresno, 2012). Sharing appears as a chain of events starting from the identification of key knowledge needed and the individuals in need of knowledge. The process proceeds to choosing methods and tools of sharing. Sharing and receiving knowledge occur in personal interaction. Meetings are an important tool, whether they are formal or informal, specific (eg. training meetings) or general (eg. working meeting). Unless shared, tacit knowledge is not converted to explicit and cannot be utilized for human intellectual capital development. Trust plays an important role in tacit knowledge sharing. Sharing and co‐operation require trust and also a willingness to co‐operate. Trustful workplace climate is characterized by open communication at the individual and group level, functioning relationships between group members and commitment to common goals. Trust creates openness and freedom (Savolainen, 2011). Thus, sharing knowledge calls for trust, which requires positive attitude to co‐operation. Internalizing of knowledge takes place if willingness and motive for knowledge storing on the individual’s personal knowledge base exists. Motivation requires trustful leadership which enables willingness to grow and learn. Essential to leadership by trust is enabling environments that nurture knowledge sharing and, hence, innovativeness. At meetings, meeting facilitators or coordinators, as leaders, play an important role to conduct and manage the meetings with effectiveness, with the aim to achieve the objectives set, covering all issues of the agenda, within the time frame planned, and in a positive‐ trustworthy atmosphere that contributes to reinforce group cohesion and disseminate and enhance organizational values. In summary, sharing of knowledge occurs as an interactive process between actors and trust facilitates it. Meetings are a vehicle for knowledge sharing and creation, where meeting facilitators should act as leaders.

5. Conclusions, implications and further research Working meetings are one of the basic tools in organizations for information and knowledge sharing, knowledge creation, coordination, decision making, problem solving and strengthening of group relationships, and play an important role in building or destroying trustworthy environments, as well as they are affected by them. Meetings are necessary, but also important economical and emotional resource consuming, yet many meetings are unproductive. So there is a need and responsibility to increase meeting effectiveness. The main implication for managers is that trust between group members is needed for stimulating knowledge sharing and knowledge creation, and for organizational vitality and competitiveness. Meetings should be productive for the benefit of all attendees, the group they belong to and the organization as a whole. Trust may make meetings more effective and meetings may contribute to reinforce trust. All participants, whether they coordinate or participate with other functions, can help to achieve this, but mainly leaders, as role models.

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Palmira Lopez‐Fresno and Taina Savolainen Responsibility “belongs” to each member, and each and all of them should contribute to meeting effectiveness, facilitate cooperation, and enhance vitality, innovativeness and competitiveness. Furthermore, the paper suggests the main areas and actions to increase working meetings effectiveness as follows i) to promote trustworthy environments; ii) to implement programs to develop competencies in meeting management; iii) to plan meetings in advance with a realistic and clear agenda; iv) to establish clear ground rules to ensure ethical behaviours and effectiveness; v) to conduct meetings with responsibility, as a leadership exercise; vi) to ensure decisions are made accordingly to the set objectives, and responsibilities and deadlines identified and agreed; vii) to assess the meetings and implement the necessary improvement actions; viii) to follow up decisions and actions agreed. Successful organizations do not regard meetings as a necessary evil, rather they view them as a strategic resource and seek ways to get the most of them. Meetings could be seen like games of “negative sum”, “zero sum” and “positive sum” or cooperative games. In the latter, everybody win, because whatever the outcome, social interaction generates trust, harmony, empathy and mutual credit. In summary, it is “social capital”. Extrapolating to meetings, collaborative meetings are efficient and create tight bonds for cooperation. Trustful leadership forms powerful ties for cooperation and effectiveness in meetings. This is a discussion paper on interrelation of working meetings, knowledge management and trust. Building and maintaining trust are of utmost importance to develop human capital for sustaining innovativeness and vitality in organizations. Meetings play an important role in this. As an exploration to the topic with theoretical discussion, the paper suggests ideas and issues for further research. The main research question is how meeting management is developed and what the impact training has on meeting effectiveness. Research in progress, based on the preliminary case study findings in this paper, will be focused on studying this question and sub‐questions inferred from it. For example, impact of meeting management training at top and middle management levels on meeting effectiveness, considering variables such as open communication, role of trust, knowledge sharing and improvement actions‐innovation. Double blind analysis will be applied to identify results from groups with and without previous training. At this stage, the research has employed a qualitative research approach and case study method which will be still used in the future. However, based on the qualitative findings, a quantitative methodology will also be used.

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Knowledge Management: A Business Plan Approach Elizandra Machado, Ana Maria Bencciveni Franzoni, Helio Aisenberg Ferenhof and Paulo Mauricio Selig PPGEP, Universidade Federal de Santa Catarina, Florianópolis, Brazil elizandra_machado@hotmail.com afranzoni@gmail.com dm@gotroot.com.br selig@deps.ufsc.br Abstract: When viewing a business opportunity in the market and wanting to exploit it, is necessary to make a business plan. This plan will draw a new company to attend to the needs of a particular market niche. However, if we take into consideration the current marketing context, in the new era of knowledge‐base firms, there is great need to deal with Knowledge Management, it is necessary that new companies born differently. This article aims to highlight the importance of Knowledge Management (KM) in Business Plans. This article is an exploratory literature review, establishing the relationship between intellectual capital and its importance for new businesses through a business plan. The method used in the paper is structured in three parts: Literature Review, Research Development and Empirical Evidence. Through the questionnaire applied to 21 professionals (managers, consultants and lecturers) from Brazil, Spain and Germany. It was identified that there was an agreement of most experts highlighting the importance of the inclusion of KM in the preparation of Business Plans. Keywords: knowledge management, business opportunity, business plans

1. Introduction To succeed in the venture, it is necessary to act strategically and with enthusiasm, so that the result is a company planned with Knowledge Management vision and aligned from the beginning with the knowledge of people, technologies and processes. Entrepreneurship is the process of identifying, developing and realizing a vision of life. The vision may be an innovative idea, an opportunity, or simply the best way to do something. The consequence of this process is the creation of a new business, or a new venture, the challenges of take calculated risks (Johnson, 2004). On the other hand, Pee and Kankanhalli (2009, p. 79) state "organizations are increasingly investing in initiatives for Knowledge Management (KM) to promote the sharing, application and creation of knowledge for competitive advantage." In this sense, Knowledge Management plays an important role in organizations. Increasingly, knowledge is being seen as a key driver of value, in other words, wealth for organizations. Large companies are turning their efforts to manage processes, people and technologies, which seen as the basis of Knowledge Management (KM). Business Plan is considered a document that contains the characterization of the business, how it operates, its strategies, its plan to capture a slice of the market and the projected expenditures, revenues and financial results. Support the existence of Business Plans is a benefit to the company's success (SALIM et al, 2005; KRAUS; SCHWARZ, 2007). Business plans are considered an effective tool in managing a business project, with many advantages, for example, the knowledge gained in the process of elaboration. It is necessary to focus on, from the beginning, in some criteria that in the future can be a competitive differentiator, as knowledge can create wealth. Another important factor is the appreciation of the people, in other words, intellectual capital. According to Díaz and Guild (2003) evaluation of intangibles in business plans for investors is an important factor of recent interest, especially in the evaluation of early‐stage technology‐based companies. The entrepreneur, opening his business, should take into account a new vision including the Knowledge Management. The Business Plan is a fundamental tool to support the entrepreneur this vision of KM. It is important to consider the Business Plan aligned with KM since the beginning of the business. A systematic

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Elizandra Machado et al. literature review reveals a lack of this type of study. The existence of a detailed business plan to guide the future entrepreneur to analyze the Knowledge Management. It is in this context that this article aims to highlight the importance of use of Knowledge Management into the Business Plan.

2. Theoretical reference 2.1 Knowledge management The economy is undergoing significant changes in the advent of high speed Internet, which has contributed to the rise of the knowledge economy. Especially in countries where manpower is cheap and the small businesses are contracted to perform manual labor. With the emphasis on the knowledge worker, people with higher education, who are succeeding to excel in this new economy, also called knowledge society (SWITZER, 2008). In Brazil, some organizations are investing in the Knowledge Management, such as Aracruz, Caixa Economica Federal, Camargo Correa, Banco do Brazil, Editora Abril, Eletrobras, Embrapa, Natura, Petrobras, Promon SABESP SERPRO, and Votorantim Group, where there are positions as manager of Knowledge Management. In companies Worldwide, Knowledge Management has been present for longer, and companies have positions as Directors of World Knowledge Management or Chief Knowledge Officer (CKO) (TERRA, 2005). According Pee and Kankanhalli (2009), organizations are increasingly investing in Knowledge Management initiatives to promote sharing, creation and application of this knowledge as a competitive advantage. Competitive advantage is a consequence of organizational Knowledge Management, which has drawn the attention of academics, consultants and practitioners as an increase of improve performance, productivity and creativity, as well as facilitating innovation in organizational sets (APOSTOLOU; MENTZAS, 2003). Terra (2002) points out that in Brazil the knowledge resource is increasing rapidly, as well as its importance to business performance, and the challenges imposed by the recent economic liberalization makes Knowledge Management essential for Brazilian companies. Zhenzhong Ma and Kuo‐Hsun Yu (2010) state that the area of Knowledge Management in a global context is considered a new area and in evolution, firming academic legitimacy. According to Servin (2005) Knowledge Management involves three key elements known as Tripod: people, technology and processes. The most important component of the tripod is people, undoubtedly. Because it is they who create, share and use knowledge. Processes and technologies allow the realization of knowledge management in organizations, but it is the people who decide to use it (or not). The tripod of Organizational Knowledge Management, as Servin (2005) is based on three structural fundamentals:

People: incorporate skills through knowledge, the main factor of value creation;

Processes: organizing tasks and activities of the organization;

Technology: by means of support processes and people.

The Asian Productivity Organization (APO) suggests a fourth element, leadership (APO, 2009). This fourth element can be understood as a need to highlight an aspect that could go as an unnoticed element in people, and these four elements are seen as accelerators in initiative for Knowledge Management in organizations.

2.2 Entrepreneurship Technological innovations are providing a significant increase in the field of entrepreneurship. This was leveraged by knowledge, as a factor of production, which is providing enterprises to add value to the business. To Hatzikian (2007), the growth and importance of knowledge as a production factor, is what determines innovation in enterprises, which can be explained by the continuous accumulation of technical knowledge over time and the use of communication technologies that make knowledge to be available rapidly worldwide. The importance of entrepreneurship through innovation happens to be focused on results, and deals with broader innovation concerns. This is increasingly promoting wealth for the economy of organizations to

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Elizandra Machado et al. innovate, create and market new products, services, business practices, thus involving the effective use of innovation to create new ventures and initiatives that create value for the organization (JABEEN, 2010). According to Nicolsky (2008. P. 1), "innovation is intended to give more competitiveness to a technology or technological discovery of a product or process, expanding the company's participation in the market and thus adding economic value and profitability”. The desire to endeavor is also the desire to create rules, setting goals and objectives based on market perceptions. In general, people seek through entrepreneurship to develop a project about a perceived market opportunity. This project is called a business plan that serves to conduct the study on the viability of future development (QUADROS, 2004). Brazilian population has excelled in the field of entrepreneurship, to be motivated, creative and dynamic to undertake endeavor through innovation. Entrepreneurs are increasingly gaining government incentives for new businesses and expansion of existing ones. The country has a program called the National Initiative for Innovation ‐ PRO‐INOVA, that aims to increase the innovation capacity of companies in Brazil, seeking to sensitize, educate and mobilize businessmen and society to the importance of innovation as a tool for sustainable growth and competitiveness. In this context, science is providing a significant growth in innovation, uniting institutions and governments to focus on science policy. Research, technological development, policies, innovative performance, along with new ways of considering the innovation has caused changes in thinking regarding innovation‐related policy (HATZIKIAN, 2007). Entrepreneurship is being driven by business incubators in the new economy, where it is necessary to create forms of cooperation to sustain in the market. In Brazil there are also National Incubator Technology Parks (NIP). It is an incubation process for forming mechanisms companies, and this program seeks to strengthen the institutional and financial efforts to support enterprises resident in incubators and technologic parks. For Leite (2000), an incubator is an enterprise that collaborates in the execution of an economic development strategy from a microenvironment where a company can develop. The incubated company can use space offered and an appropriate set of supporting service area. Thus, incubated company uses the given structure of the incubator to develop new business. Incubation, for technology‐based enterprises, is defined as an incubator that houses companies whose products, processes or services are generated from results of applied research and technology which is high value (DORNELAS, 2012). According Baêta (1999) technology incubators are organizations that harbor nascent ventures, usually derived from scientific research, whose project involves innovations. Such organizations offer space and subsidized services that promote entrepreneurship and development of products or processes of high scientific and technological content. According Andreassi (2007) technological innovation can also be considered a key part in achieving the competitiveness of a country. Therefore Brazil has been increasingly encouraging the creation of incubators.

2.3 Business plans as a performance indicator Formerly the decisive factor of production was the land, and later capital (tangible), today the decisive factor is the people and their knowledge. When potential investors evaluate a business plan, they usually focus on hard data. However, these financial indicators do not reflect all the possibilities for the future success of the business mostly technology‐based companies. The results of these traditional assessments often lead to rejection of potential technology‐based enterprises (DIAZ and GUILD, 2003). To Deponti; Eckert and Bortoli (2002) the term in Portuguese “indicador” (meaning indicator) originates from the Latin "indicare" verb meaning “to point”. In Portuguese, “indicador” means that makes known, reveals, proposes, suggests, exposes, mentions, advises, remembers. In the process of setting up a business it is necessary to have a performance indicator, which indicates and advises on the future of new business. The role of a business plan is to provide guidance for the development of a new company, product launches, etc. Thus, it should contain everything that is crucial to the success of the initiative, which leads to the need to

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Elizandra Machado et al. consider Knowledge Management. Therefore it needs to be planned and analyzed already in business planning. Creation of a new business comes through the identification of an opportunity, with reflection and planning about the idea. With that, it is necessary to acquire knowledge about the business that will be created, an important factor for the success of the venture. It is during this phase that one will acquire all the information and knowledge needed to leverage one’s future endeavor. Thus, the preparation of the business plan is a kind of knowledge acquisition and explanation (BERNARDI, 2009; DORNELAS, 2012; LACRUZ, 2008; Nonaka and TAKEUSHI 1997; SWITZER, 2008; WILLIAMS, 2002). According Cecconello and Ajzental (2008), it is essential to expand the knowledge about the new business to decrease and reduce the doubts that may arise for the decision maker. The development of the Business Plan leads the entrepreneur to focus on the analysis of the business environment, objectives, strategies, skills, structures, organization, investments and resources and the study of viability of the business model. To Bernardi (2009. p.3) planning propitiates:

Better understanding of the business;

Determination and understanding of the vital variables and criticism;

Clarity about what to do and what not to do;

Vision of opportunity;

Creative and innovative approaches;

Setting goals and observation congruence of model elements.

To Bernardi (2009) development of a business plan involves five steps: the idea and design business; collection, data preparation, data analysis, assembly plan, and evaluation plan.

3. Method This article is a qualitative research that establishes relations between Knowledge Management and its importance for new businesses through a business plan. To Godoy (1995), the qualitative approach involves data described in processes, beliefs, values and attitudes and therefore it cannot be standardized or quantified. The research literature is classified according to Gil (2002) is performed using materials already prepared mainly scientific articles and books. With regard to objectives, this research is classified as exploratory. According to Mattar (2005), "the exploratory method raises awareness and understanding of a research problem". Corroborating this line of thought, Hair Jr. et al. (2005, p.84) states that the exploratory research is especially interesting for companies that have a strategy of innovation, which can use this method to discover new ideas and technologies that meet real business needs. The method of this paper is structured in three steps: 1) Literature Review, 2) Research 3) Development and Empirical Evidence.

Literature Review search consisted of the topic addressed in the database: Scopus® that searches in parallel in the databases and ScienceDirect® and Emerald® and was chosen because it is recognized in academia and is the most relevant to the topic. The keywords used to search the database were: Business Plan and Knowledge Management. In the first scan using just the Business Plan keyword returned 3269 documents and refining the research by using the second keyword Knowledge Management returned 14 documents.

The first article on Business Plans and Knowledge Management date of 1999, and in 2001 there were two publications, and in 2003 only one publication. In 2005 and 2006 we obtained two publications per year. In 2007, four publications. In 2008 and 2009, two publications each year. Until July 2011, we found two other publications. The 2003 publication is on the subject intangible asset business plans. In publications shown in Figure 1, the authors who approach the subject of research are Switzer (2008), Hatzikian (2007), Kraus and Schwarz (2007) and Diaz and Guild (2003). Switzer (2008) presents a study aimed to develop a more effective way for companies to compete successfully in the rapidly evolving global market and help them to change their management style traditional approach to Knowledge Management. One of the arguments is that due to

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Elizandra Machado et al. the rapidly changing global economy, companies need to consider a number of changes in strategy, and must begin to focus more on Knowledge Management.

Figure 1: Publications by year Business Plans related to the Knowledge Management database Scopus Hatzikian (2007) states that many public and private organizations launched initiatives to develop tools and methodologies to support innovation management and entrepreneurship. The strategies and organizational changes applied to the business should take into account the challenges of the new knowledge economy. Companies should and tend to focus primarily in the areas of project management and development of business plans. Kraus and Schwarz (2007) analyze the correlation of success of small businesses with planning a Start‐up and business plans before the foundation of the company. Diaz and Guild (2003) approach the active theme intangible in Business Plans. They emphasize that the valuation of intangibles in business plans for investors is an important factor of recent interest, especially in the evaluation of early‐stage technology‐ based enterprises, where investors and entrepreneurs are challenged to properly assess new opportunities when facing of business idea.

The development of the research consisted of examining the hypothesis about the themes in the form of questionnaire with questions for empirical proof.

In empirical testing we used a sample of the non‐probabilistic intentional, which were selected 21 experts (subject matter experts) from belonging to micro, small, medium and large companies (Table 1).

Out of these 21 experts, 7 are classified as business managers, 7 as lectures in Knowledge Management, business plans and / or related subjects and 7 are organizational consultants. This is a sample defined by convenience, so companies and subjects are a purposive sample, selected according to ease of access (BOYD; WESTFALL, 1971). Table 1: Organizations of the experts ORGANIZATION SEBRAE NACIONAL

SIZE Large

SEBRAE ESTADUAL

Large

FUNDAÇÃO CERTI ALTO QI SOFTPLAN

Mid Small to mid Large

IGMEP INSTITUTO STELA

Large Small

SERPRO ACATE

Large Not informed

CRETATEC OBIZ

Micro Micro

PRATICAL ONE DIGITRO

Micro Large

SAPIENS PARQUE

Mid

UFSC

Large

UDESC

Large

UNIVERSIDADE DE WIESBADEN (ALEMANHA)

Large

UNIVERSIDADE AUTÔNOMA DE MADRI (ESPANHA)

Large

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Elizandra Machado et al. For primary data collection we carried out face‐to‐face interviews with experts, through open and closed questions. The use of interviews allows us to identify the different ways of perceiving and describing the phenomena. It became a classic technique of obtaining information in the social sciences, with widespread adoption in areas such as sociology, communication, anthropology, business, education and psychology (Duarte, 2005). According to Gil (1999. P. 117), the face‐to‐face interview is defined as a technique in which the researcher meet in person with the investigated and asks questions in order to obtain data of interest for the problem associated with the research.

4. Analysis and interpretation of results 4.1 Approaches to knowledge management in business plans

It is essential when planning that a new business theme Knowledge Management is approached: the entrepreneur should analyze the tools and strategies of knowledge, together with the people, processes and technology, because this way the organization will be built since its inception with evidence of a culture of Knowledge Management.

People, process and technology management need to be defined in the preparation of business plans: people, processes and technology are the components of Knowledge Management. This includes those in planning a new business mean that these components will be examined from the beginning, skills of people to be recruited and selected. They must have a profile collaboration, creation, dissemination and sharing of knowledge to contribute to Knowledge Management. And organizational processes and technologies must be planed considering the needs of Knowledge Management.

4.2 Results and analysis of research The results and analysis of the research were collected through a questionnaire with closed questions applied to business managers, lectures and organizational consultants, which aimed to:

Check the importance of: people, process and technology management definition in the preparation of business plans;

Check on the importance: it is essential that when planning a new business theme Knowledge Management is approached.

For better clarity and understanding, we decided to present the tabulation of responses in two ways (tables and figures). Tables are the results of the questions without taking into consideration the type of specialist, while the figures present the results by type of specialist (manager, consultant and lecture). 4.2.1

It is essential when planning a new business theme that Knowledge Management is approached, so that from the beginning the company shows signs of a culture of Knowledge Management

It is noticeable (Table 2) that most agree (67% strongly agree and 33% partially agree) that when planning a new business theme Knowledge Management to be discussed from the beginning that the company shows signs of a culture of Knowledge Management. The data in Figure 2 show that 100% of the experts agree with the questioning. Table 2: Plan a new company with the theme of knowledge management Options

Frequency

Strongly agree

67%

Partially agree

33%

No opinion

Partially Desagree

Strongly Desagree

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Elizandra Machado et al.

Source: Research data Figure 2: About planning a new company with the theme knowledge management 4.2.2 People, process and technology management need to be defined in drafting of Business Plans Results in Table 3 reveal that 57% of respondents strongly agree that people, process and technology of management need to be defined in the preparation of business plans, 38% partially agree and 5% partially disagree. As seen in Figure 3, all lectures agreed and consultants and most managers (85.7%). Table 3: People, process and technology management need to be defined in the preparation of business plans Option

Frequency

Strongly Agree

57%

Partially Agree

38%

No Opinion

Partially Desagree

Strongly Desagree

Figure 3: People, process and technology management need to be defined in the elaboration of business plans Given the above it can be concluded that experts have accepted the approaches of business plans in Knowledge Management. It can be also emphasizes that the vast majority of experts have already incorporated in day to day the Knowledge Management proceedings, some of them in a larger scale than others. On the other hand experts consider the business plan a tool of great use for all kind of companies from start‐ups, micro enterprises, small, medium to large. And also the universities and other support entities.

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5. Final thoughts It can be argued that entrepreneurship in the knowledge society requires new tools to analyze the viability and success of the business. When planning a new venture, usage of a business plan should be considered, which can assist to trace a more successful path to be followed by the entrepreneur. In this study we observed that the business plan is a tool used in business of those surveyed in the initial phase or the launch of new product lines and expanding the business. It is also evident that there are incentive programs and support to entrepreneurs in creating new business and one of the requirements is the business plan to raise funds and investors. For a new company, it is important to develop a business plan. This makes the entrepreneur seek information and learn about the possibilities of its future business, whether technical, operational and ways to manage the business. It is in this context that Knowledge Management, when the entrepreneur starts with the acquisition of prior knowledge of the future business and market. To highlight the importance of incorporating Knowledge Management in the elaboration of the business plan, we conducted a review of the literature on Knowledge Management, business plan and entrepreneurship. In order to identify more clearly the research problem, we also performed a systematic review of the business plan and the aspects related to Knowledge Management theme, which have not been mentioned by the literature. A systematic review through searching into the database Scopus was made, and it is possible to find classical authors and recent international research on the topic. From the literature review we developed the questionnaire, which was subsequently subjected to three groups of experts. In literature and empirical evidence we identified the need for a new way to administer/manage new business in the context of the knowledge economy. Like most business plans are based on tangible assets, arising from a historical context in which it was valid without the look of Knowledge Management, this new economy is important to the development of business plan based on intangible also considering knowledge management. Therefore, it is necessary for organizations to boost these three elements: people, processes and technologies. They are fundamental to the success of Knowledge Management in the organization, so it must be analyzed in the preparation of the business plan. Regarding future research, we identified the need to conduct case studies in knowledge intensive companies business plan with an approach to Knowledge Management.

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Elizandra Machado et al. Hair J.; MONEY A.; BABIN B; PHILLIP S. (2005) Fundamentos de métodos de pesquisa em administração. Porto Alegre: Bookman. Hayton, J.C. (2005) Promoting corporate entrepreneurship through human resource management practices: A review of empirical research. Human Resource Management Review, v.15, n. 1, p. 21–41, 2005. Hatzikian, J. (2007) Research and Technological Development Policy and Innovative Performance: The Greek Case Within the EU. Contributions to Economics. Technological Institute of Athens, Department of Business Administration, Athens, Greece, p. 229‐248. Jabeen, M. (2010) Innovative entrepreneurship: Alumni Organization of Internacional Relations of international relations department, university of Balchistan. Internaticional Conference on Information Management and Enginneering (ICIME)p.530‐532. Johnson, H. P (2004) Educational experiences of habitual entrepreneurs. Dissertation School of Graduate Studies, University of the Incarnate Word. Kraus, S.;Schwarz,E. (2007) The role of pre‐start‐up planning in new small business. International Jornal of Managament and Enterprise Development. v.4 n.1,p.1‐17. Lacruz, J.A. (2008) Plano de negócio: passo a passo. Rio de Janeiro: Qualitymark. Leite, E. (2000) O Fenômeno do Empreendedorismo: Criando Riquezas. Recife, XXII Encontro Nacional de Engenharia de Produção, Recife. Mattar, F. N.(2005) Pesquisa de Marketing. 3ª ed. São Paulo: Atlas, 2005. Nicolsky, R. (2008) Os desafios para transformar conhecimento em valor econômico. Disponível em: <http://www.comciencia.br/reportagens/cientec/cientec12.htm>. Acesso em:22 set. Nonaka, I.;Takeuchi, H. (1997) Criação de conhecimento na empresa: como as empresas japonesas geram a dinâmica da inovação. 13a ed. Rio de Janeiro: Campus. Patton, M. Q. (1987) How to use qualitative methods in evaluation. Newbury Park: Sage, 176 p. 1987. Pee, L.G.; KankanhallI A. (2009) A Model of Organisational Knowledge Management Maturity Based on People, Process, and Technology. Journal of Information & Knowledge Management, v. 8, n. 2, 2009. Quadros, F.Z. (2004) Plano de Negócios e a pequena empresa de base tecnológica: um estudo de caso na incubadora de empresas MIDI Florianópolis. 2004. 133 f. Dissertação (mestrado em Engenharia de Produção) – Programa de Pós‐ Graduação em Engenharia de Produção, Universidade Federal de Santa Catarina, Florianópolis, 2004. Servin, G. (2005) ABC of Knowledge Management. NHS National Library for Health. Switzer, C. (2008) Time for change: empowering organizations to succeed in the knowledge economy. Journal of Knowledge Management. v. 12, n. , p. 18‐28, Emerald Group Publishing Limited. Terra, J. C.C. (2002) Portais Corporativos: A Revolução na Gestão do Conhecimento. São Paulo: Negócio Editora. Terra, J. C.C. (2005) Reflexões sobre a evolução da Gestão do Conhecimento no Brasil. Revista Produto & Produção. v. 8, n.2, p.05‐09. Williams, E (2002) Plano de Negócio: 25 princípios para um planejamento consistente. Tradução de Eliana Rocha‐ São Paulo: Publifolha. Zhenzhong M.; KUO‐HSUN YU (2010. Research paradigms of contemporary knowledge management studies. Journal of Knowledge Management, v. 14, n. 2.

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Relationship Between Knowledge Management and SME´s Performance in México Gonzalo Maldonado Guzmán, Gabriela Citlalli López Torres and María del Carmen Martínez Serna 1 Universidad Autónoma de Aguascalientes, Aguascalientes, México gmaldona@correo.uaa.mx gclopto@correo.uaa.mx mcmartin@correo.uaa.mx Abstract: At the moment, the area of Knowledge Management has been considered in the literature on business management as a new discipline with an important contribution to the development and the implementation of business strategies in organizations. At the same time, companies, independently of their size, have considered and adopted knowledge management as an additional strategy to improve operations and revamp its profitability. These companies have obtained important benefits, among them, major levels of general performance. However, even though there are many studies regarding those Knowledge Management studies, describing its impacts and factors to improve companies’ performance, nowadays these approaches have demonstrated an evolution of such elements and performance benefits in organizations. Therefore there is a need of empirical evidence about the benefits from Knowledge Management initiatives and consolidated implementations onto the organizations’ performance. In other words, such benefits should not be only investigated based on financial terms but on a broader sense that allows focusing on the accomplishment of today pursued ‘total customer satisfaction’ in businesses. Moreover, this kind of research needs to be carried out in small and medium‐ sized enterprises (SMEs) and not only in global enterprises located in well‐developed countries, like the case of Mexico country. This research presents then a study with the objective to measure the impacts of Knowledge Management through four main dimensions to SMEs performance, workers training, knowledge management policies and strategies, acquisition of external knowledge and organizational culture. In this research a structural equation modeling (SEM) has been applied using EQS® in order to validate the established hypotheses. This is by using a sample of 124 companies. Thus, this research presents the relationship between Knowledge Management and SME’s performance, in Aguascalientes, one of the most industrial states in the country of México, which is describing a significant positive relationship between these elements. Keywords: knowledge, performance, knowledge management, SMEs

1. Introduction Since some decades knowledge management (KM) has gained interest among researchers, academics and professionals from the business management field, which has been translated into a great deal of studies. However, most of the studies presented in the literature have been focused on presenting a theoretical analysis of this construct but given less importance to KM applications in companies (Palacios and Garrigós, 2006). Few of these studies, where implementing KM in organizations, commonly have done it through intellectual capital, patents development, data bases creation, innovation or performance of big companies in well developed countries (Palacios and Garrigós, 2006), with no attention to small and medium‐sized enterprises (SMEs). Dibella and Nevis (1998) considered that the organization concept has to be an essential factor in the KM adoption inside companies; therefore KM has to be understood from a business global point of view. In consequence, KM definition has to be a formal profess which defines the type of internal information to be used in order to improve a business (Roy, 2002). In that sense, KM should be defined as the effective use of systems to gather, use and reuse generated knowledge inside an organization (Davis, 2002), because the use of KM systems has provided business growth during the last ten years (Teece, 2001; Castillo, 2002). It is needed to provide to literature more empirical evidence about the consequences which can have an effective and efficient KM in organizations, overall in SMEs, which have to include competitive advantajes(Hall, 1993; Connor and Prahalad, 1996); innovation (Nonaka and Takeuchi, 1995; Dove, 1999; Antonelli, 1999; Carneiro, 2000); problems anticipation (Carneiro, 2000); increment in organization understanding (Buckley and Carter, 2000); efficient use of information (Carneiro, 2000); enterprise performance (Wiig, 1997; Teece, 1998). Although current literature, there is great deal of studies that have analyzed KM in SMEs (Beijerse, 2000; Lim and Klobas, 2000; Frey, 2001; Sparrow, 2001; Heng, 2001; Kautz and Thaysen, 2001; Wickert and Herschel, 2001; Salojärvi et al., 2005; Gray, 2006; Moffett and McAdam, 2006; Chan and Chao, 2008; Kruger and Johnson, 2009), there was no study relating KM

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Gonzalo Maldonado Guzmán, Gabriela Citlalli López Torres and María del Carmen Martínez Serna and SMEs’ performance in both developed and under development countries. Therefore, Beijerse (2000); Claycom et al. (2001), Salojärvi et al. (2005) and Kruger and Johnson (2009) considered the need to work more on empirical studies which analyze the relationship between KM and SMEs, specially in countries under development.

2. Knowledge management and performance Recently, in current literature, Knowledge Management (KM) has been considered as important resources, which apart from generate major competitive advantages to companies (Nahapiet and Ghoshal, 1998; Ginsburg and Kambil, 1999), as an essential function to provide and coordinate mechanisms, which increment organizations’ resources into capabilities (Darroch, 2005). Arthur Andersen Business Consulting (1999) concluded that KM could provide quality and quantity on innovative knowledge that the organization requires to improve its performance. Besides, the skills evaluation of workers can be focused on value adding (Niven, 2003; Boomer, 2004), and on the workers’ willing to share knowledge (Foster, 1999; Skyrme, 1999; Moore et al., 2001; Niven, 2003; Boomer, 2004), when the company evaluates how to distribute in a more efficient and effective human resources for KM. In this context it is possible to define the following hypothesis related to KM measurement: H1: Better workers training, better KM; H2: Better application of KM policies and strategies, better KM; H3: Better creation and acquisition of external knowledge, better KM; H4: Better organizational culture, better KM. This way, Decarolis and Deeds (1999) concluded that all used variables for operational and organizational knowledge flow only the geographical area was significant, because it influences external knowledge acquisition to organization. On the other hand, KM has been considered in the current literature as an important topic in business management and evaluation of organizations performance has been modified with KM development (Chin‐ Tsang, 2009). However, in the current era of total customer satisfaction, relationships management with clients and KM’s clients, are the two most important topics in organizations (Chin‐Tsang, 2009). Therefore, if in the current literature KM is considered significantly improving operation processes in organizations, then it would be fundamental to consider the performance indexes of organizations, in the frequency of resolutions to operational problems (Arora, 2002), and in meeting customers’ needs (Wu, 1998), which allow improving a system to support decision making in the organization (Foster, 1999; Skyrme, 1999; Boomer, 2004).

3. Methodology A survey was designed for SMEs managers and it was applied through a personal interview to the 130 selected companies in a period between September and December 2010. Finally, 125 surveys were completed, obtained a 96% response rate and ±1% error interval. Table 1 summarizes the most important aspects of this research. Table 1: Research design Characteristics

Research

Population

130 SMEs

Geographical area

Aguascalientes state (México)

Sample

SMEs of 20 ‐ 250 workers

Data collection

Personal Interviews to managers

Sampling

Simple sampling

Sample size

125 SMEs

Sample error

+/‐ 1% error, confidence interval 99% (p=q=0.5)

Field work

September to December 2010

3.1 Measures development For this research, knowledge management was measured through four dimensions: a) workers training, measured with a scale of 5 items, adapted from Bontis (2000) and OECD (2003); b) knowledge management policies and strategies, measured with a scale of 13 items and adapted from (2004; 2007); c) creation and acquisition of external knowledge, measured with a scale of 5 items adapted from OECD (2003) and Bozbura

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Gonzalo Maldonado Guzmán, Gabriela Citlalli López Torres and María del Carmen Martínez Serna (2007); and d) organizational culture effects, measure through a scale of 4 items and adapted from OECD (2003) and Bozbura (2007). All items from four dimensions were measured with a Likert scale of 5 items, 1= totally disagree and 5= totally agree. In order to measure SME performance, traditionally various authors have constructed indexes form SMEs managers perspectives, about its competitive position about market quota, profitability and productivity obtained by companies in a period of time (AECA, 2005), therefore performance was measured by a scale of 12 items proposed by Quinn and Rohrbaugh (1983).

3.2 Reliability and validity In order to evaluate reliability and validity of the employed instrument, a Confirmatory Factor Analysis (CFA) was employed through the method of maximum plausibility, using EQS (Bentler, 2005; Byrne, 2006). At the same time, reliability of the measure scales was evaluated with Cronbach’s Alpha and Composite Reliability Index (CRI) (Bagozzi and Yi, 1988), and other estimation methods were used when normality is assumed, for which recommendations from Chou, Bentler and Satorra (1991) and Hu, Bentler and Kano (1992) were used in order to correct statistics from the used estimation model. Therefore, robust statistics were used that provide a better evidence of adjustment indexes (Satorra and Bentler, 1988). The results obtained from the application of CFA are presented in Table 2, which suggest that the final measurement model provides a good adjustment 2 of statistical data (S‐BX = 287.487; df = 224; p = 0.000; NFI = 0.888; NNFI = 0.935; CFI = 0.943; and RMSEA = 0.048). Also, as evidence of convergent validity from the theoretical model the results from the CFA demonstrate that all items from the related factors are significant (p < 0.001), the size of all standardized factor loads are superior to 0.60 value as recommended by Bagozzi and Yi (1988), and the Extracted Variance Index (EVI) from the relationship among factors is higher than 0.50 as suggested by Fornell and Larker (1981). Table 2: Internal consistency and convergent validity of the theoretical model Variable

Index

BFT1 BFT3 BFT4 BPE1 BPE2 BPE6 Policies and Strategies (F2) BPE7 BPE8 BPE9 BKO1 Acquisition of External Knowledge BKO2 (F3) BKO3 BKO4 BOC1 Effects from Organizational Culture BOC2 (F4) BOC4 F1 F2 Knowledge Management F3 F4 Workers training (F1)

Variable

Performance

Index PE1 PE2 PE3 PE4 PE5 PE6 PE10

Factor Load Robust T value 0.779*** 0.850*** 0.769*** 0.710*** 0.742*** 0.846*** 0.781*** 0.713*** 0.769*** 0.799*** 0.780*** 0.732*** 0.668*** 0.815*** 0.817*** 0.727*** 0.882*** 0.724*** 0.702*** 0.911***

1.000 9.944 8.084 1.000a 9.415 7.353 6.282 5.441 7.261 1.000a 13.104 9.902 7.281 1.000a 12.177 8.084 11.601 5.464 7.492 7.554

Factor Load Robust T value 0.685*** 0.828*** 0.815*** 0.764*** 0.768*** 0.747*** 0.627***

Cronbach’s Alpha

IFC

0.842

0.842 0.640

0.889

0.892 0.580

0.832

0.834 0.557

0.827

0.830 0.620

8.993

0.900 0.563

Cronbach’s Alpha

IFC

1.000 7.762 7.997 7.352 7.253 8.094 5.655

0.883

= Parameters constrained to that value in the identification process ; *** = p < 0.01

254

EVI

0.883 0.656

S‐BX2 (df = 224) = 287.487; p < 0.000; NFI = 0.888; NNFI = 0.935; CFI = 0.943; RMSEA = 0.048 a

EVI

Gonzalo Maldonado Guzmán, Gabriela Citlalli López Torres and María del Carmen Martínez Serna In relation to discriminate validity, its measurement with two different test is presented in Table 3. Hence, below diagonal the Confidence Interval Test (Anderson and Gerbing, 1988) which demonstrate that with a 95% confidence interval, none of the two individual elements of the latent factors of the correlation matrix have value 1.0. Above diagonal the Extracted Variance Test is presented (Fornell and Larcker, 1981), which demonstrate that EVI between constructs is higher than the square of extracted variance. Therefor, considering these obtained results it is possible to conclude that the different measurements made through the theoretical model show sufficient evidence of reliability and discriminant and convergent validity. Table 3: Discriminate viability of the theoretical model Variables

Knowledge Management

Performance

Knowledge Management

0.656

0.092

Performance

0.146 ‐ 0.462

0.563

The diagonal represents the Extracted Variance Index (EVI), whereas above diagonal part of the variance (squared correlation). Below diagonal the estimate of factors correlation is presented with a 95% confidence interval.

4. Results In order to validate this research hypotheses it was used Structural Equation Modelling (SEC) with EQS (Bentler, 2005; Byrne, 2006; Brown, 2006), which allow a comparison of the theoretical model and to obtained statistical results to contrast hypotheses. In this way, nomological validity of the theoretical model was analyzed through the Squared Chi Test, which allows comparing the theoretical model with the measurement model. It was possible to identify that no significant differences from the theoretical model are good explaining the observed relationships among latent constructs (Anderson and Gerbing, 1988; Hatcher, 1994). The obtained results from SEC application are presented on Table 4. Table 4: Results from hypotheses test Hypotheses

Structural Relation

Standardized Coefficient

Robust t value

H1: Better the workers training, better knowledge management

Workers training ‐> KM

0.230***

9.404

H2: Better the policies and strategies, better knowledge management

Policies and Strategies ‐> KM

0.565***

16.621

H3: Better the effects from organizational culture, better knowledge management

External Knowledge Acquisition ‐> KM

0.233***

9.874

H4: Better the acquisition of external knowledge, better knowledge management

Effects from Organizational Culture ‐> KM

0.494***

13.874

H5: Better knowledge management, better organizational performance

KM ‐> Performance

0.664***

22.69

S‐BX2 (df = 217) = 286.030; p < 0.000; NFI = 0.883; NNFI = 0.946; CFI = 0.956; RMSEA = 0.044

Results obtained from SEM application can be seen in Table 4. Hence, in relation to Hypothesis 1 (H1), the results obtained β = 0.230, p < 0.01 demonstrate that workers training have positive impact on knowledge management in SMEs. About the Hypothesis 2 (H2), the results obtained β = 0.565, p < 0.01 indicate that

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Gonzalo Maldonado Guzmán, Gabriela Citlalli López Torres and María del Carmen Martínez Serna execution of KM policies and strategies has a positive and significant impact on knowledge management in SMEs. In relation to hypothesis 3 (H3) the obtained results β = 0.233, p < 0.01 presents that an acquisition of external knowledge has positive and significant effect on KM in SMEs. In regards to hypothesis 4 (H4) the obtained results β = 0.494, p < 0.01 indicate that organizational culture have positive and significant effects on KM in SMEs. Finally, hypothesis 5 (H5), results obtained β = 0.664, p < 0.01 demonstrate that KM has positive and significant effects on SMEs performance. In summary, it is possible to conclude that the four used factors to measure knowledge management are good indicators of knowledge management in SMEs. Also, knowledge management is a good indicator of SMEs performance in those located in Mexico.

5. Discussion and conclusions The obtained results in this empirical research provide sufficient empirical evidence that show existence of a close relationship between knowledge management and manufacturing SMEs performance in Aguascalientes State, in Mexico. Therefore, it is possible to conclude that, first of all, in order to achieve a significant increment of performance levels organizations have to adopt and implement an efficient knowledge management, because it is precisely knowledge management one of the few organizational strategies that can noticeably improve the business performance. Secondly, it is possible to conclude that knowledge management also is an efficient and effective organizational strategy that provides good results to performance level in organizations. Therefore, manufacturing SMEs managers have to search for a good training in this topic, because it depends not only on its proper adoption and application both internally and externally but also in the expected results in terms of major levels of performance. Thirdly, it is feasible to define that an increment on knowledge management will strongly depend on the skills and knowledge owned by the organization’s workers and staff, because they are the ones who can transform knowledge into new products and services. In consequence, managers of manufacturing SMEs have to design and implement a training program for both current and prospect workers. It is important, at this moment, to have a thought about this topic, go beyond obtained results and to discuss future research, for example, what effects can SMEs performance have if other scales was used including quantitative variables to measure knowledge management?, what results can be obtained if quantitative variables were used to measure both knowledge management and organizations performance?, what type of knowledge management has better effects in SMEs performance?, these and other questions that can arise from this research can be answered in future research.

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Knowledge Sharing and Intellectual Liabilities in a Global Perspective Maurizio Massaro1, Roland Bardy2 and Michael Pitts3 1 University of Udine, Italy 2 Florida Gulf Coast University, USA 3 Virginia Commonwealth University, USA maurizio.massaro@uniud.it rbardy@t‐online.de mwpitts@vcu.edu Abstract: Intellectual capital (IC) and knowledge sharing (KS) are key elements for fostering firm value. Recently, this assumption has been called into question as there may exist negative and destructive effects in both IC and KS. Through a case study of 'Engineering Ltd.", this paper examines the 'dark side' issues associated by improperly implementing knowledge sharing . The subject of our study, “Engineering Ltd.” , is an engineering consultancy company with 10,000 employees and $1.5 billion in revenue. The case study is used to scrutinize the major risks of knowledge sharing and to introduce possible solutions. Keywords knowledge sharing, intellectual liabilities, global

1. Introduction There should be no doubt that Knowledge Management (KM) and Intellectual Capital (IC) are key elements in enhancing organizational performance (Marr & Chatzkel, 2004; Ikujiro Nonaka & Takeuchi, 1995; Von Krogh, Ichijo, & Nonaka, 2000). In spite of recognizing the importance of KM and IC, the literature on KM and IC has not reached a unique definition and several approaches are considered to be valid depending on the particular needs of the respective analyses (C. C. Huang, Luther, & Tayles, 2007; Marr & Chatzkel, 2004). From a general perspective, KM is concerned with knowledge acquisition, generation and sharing processes, while IC focuses on the value opportunities deriving from harnessing companies’ intellectual capacity (Y.‐C. Huang & Wu, 2010; Shih, Chang, & Lin, 2010). Thus, while KM supports organizational learning flows, intellectual capital allows its storage within people, procedures or relationships (Bontis, 1999, 2005). According to previous studies, KM and IC influence each other,and several authors have recognized the need for deeper analyses of this interdependency (Hsu & Sabherwal, 2012; Seleim & Khalil, 2011; Shih et al., 2010; Zhou & Fink, 2003). Most studies in this field seem to support the idea that a higher level of IC can increase economic value, and thus, it is argued, knowledge management processes should be oriented towards supporting processes that accumulate knowledge (Seleim & Khalil, 2011; Shih et al., 2010; Zhou & Fink, 2003). Interestingly, a different perspective has emerged, which views IC as having both positive and negative effects. The destructive side of IC, known as “Intellectual Liabilities” (ILs) has been established as a new research field (Caddy, 2000; Harvey & Lusch, 1999). ILs have been defined as “potential non‐physical causes of organizational deterioration” (Giuliani, 2013, p. 129) and several barriers have been detected by which organizational performance deteriorates (De Long & Fahey, 2000; Lilleoere & Hansen, 2011; Riege, 2007). While most studies analyze barriers that affect the effectiveness of knowledge management, less attention has been paid to the fact that a poorly managed KM process could create new obstacles (Newell, Scarbrough, & Swan, 2001), increase valueless costs and ,therefore, support the development of ILs. Thus, while most of the studies focuses on the 'bright side' of knowledge management ‐ where a well‐organized process can contribute on company’s value through IC accumulation ‐ we can see a “dark side” when ineffective knowledge management and the buildup of knowledge barriers lead to ILs. This paper analyzes the dark side of knowledge management focusing on knowledge sharing (KS) processes and describes how knowledge barriers can lead to intellectual liabilities accumulation. The paper is developed through study of “Engineering Ltd.” (a disguised name), a global engineering consultancy company with over 10.000 employees. Our methodological approach differentiates our study from previous research for several reasons. First, we conducted an in‐depth analysis of knowledge management orientation in the company through using content analysis of its strategic plan (as this has a specific section on Knowledge Sharing). We analyzed internal documents in order to understand how the commitment to managing knowledge‐ sharing

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Maurizio Massaro, Roland Bardy and Michael Pitts processes is distributed within the organization. Secondly, we examined the company procedures for knowledge sharing, and we analyzed their respective ICT tools and procedures. Third, we sent out a semi‐ structured questionnaire to 12 top managers all over the world to capture their point of view on knowledge sharing issues. We believe this approach allows us to seek complementarities versus dissimilarities in theory building. More precisely, we focus on how knowledge barriers (KB) contribute to the development of ILs. Our approach searches for similar and different paths of evolution comparing: i) the relationship between KB and ILs; ii) the theoretical relationship reported in previous studies between KS and IC. By comparing similarities and differences between the cycle IC‐KS and the cycle ILs‐KB we aim to contribute to the literature on intellectual liabilities formation. The paper is organized as follows: We present the theoretical background from previous studies on IC‐ILs and KS‐KB. A methodology section illustrates the methodological approach, and afterwards we present our case study. A discussion section illustrates a multilevel process model where KB and ILs connections are exhibited, and we compare this to existing literature on KS‐IC relationships. Several prepositions that sum up the results are displayed and issues of further analysis are enumerated. A conclusion paragraph ends the paper.

2. Theoretical background and research questions 2.1 Intellectual capital and intellectual liabilities According to Huang et. al. (2007), IC is a wide concept whose understanding depends on business related disciplines. Most well‐known models of IC typically consist of three main elements: human capital (HC), structural capital (SC) and relational capital (RC). HC is usually described as a bundle of competencies, experience and skills (Choo & Bontin, 2002; Guerrero, 2003; Kong, 2008). It represents tacit knowledge stored inside peoples’ minds. SC refers to the knowledge embedded within the organization in databases, written procedures and so on, and it supports human capital in daily activities (Aramburu & Sáenz, 2011; Roos, Roos, & Edvinsson, 1998; Stewart, 1997). It has been also described “as the value of what is left when the human capital or employees go home at night” (J. Roos et al., 1998, p.42). RC is shaped by a bundle of formal and informal relationships that connect the organization with external stakeholders (Bontis, 1999; Roos et al., 1998; Stewart, 1997), and it acts as a multiplying element (Kong, 2008), enabling external acquisition of know‐ how and facilitating dialogue (Marques, José, & Caranana, 2006). Interestingly, while HC, SC and RC represent a fundamental classification within the whole IC discipline, most authors seem to be silent on the possibility of negative effects (Garcia‐Parra, Simo, Sallan, & Mundet, 2009). Still, there are sources which consider the dark side of intellectual capital. Three main conceptualizations are: i) a depreciation of the value of IC (Abeysekera & Guthrie, 2004; Caddy, 2000); ii) a nonmonetary obligation (Garcia‐Parra et al., 2009; Harvey & Lusch, 1999), iii) a potential non‐physical cause of organizational deterioration (Giuliani, 2013).

2.2 Knowledge sharing and knowledge barriers There is no unique definition and no uniform approach to knowledge sharing in the literature. On the one side, several authors view knowledge sharing to be both the exploitation of existing knowledge and the exploration of new knowledge (Lilleoere & Hansen, 2011). On the other side, Grant (1996) sees knowledge sharing as the means to ensure the distribution of existing knowledge within or across organizational boundaries. The SECI model (I. Nonaka & Takeuchi, 1995) is recognized as one of the most well‐known models for explaining the knowledge cycle. This model also deals with tacit knowledge and explicit knowledge (I. Nonaka & Takeuchi, 1995). While the first is easier to share across organizational units such as departments, functions and groups due to its ‘‘stickiness’’, the latter is harder to communicate because it is socially embedded and based on personal experiences (I. Nonaka & Takeuchi, 1995; Von Krogh et al., 2000). According to Lilleore and Hansen (2011), “Knowledge sharing can positively influence organizational performance through sharing both tacit and explicit knowledge” (Lilleoere & Hansen, 2011, p. 54). Several barriers can reduce the ability of organizations to share knowledge (Lilleoere & Hansen, 2011; Riege, 2005, 2007). Szulanski (1996) found that knowledge sharing could be inhibited by: i) lack of absorptive capacity within organizations; ii) casual ambiguity of the shared knowledge; iii) difficult relationship between senders and the receivers. Riedge (2005, 2007) categorized knowledge sharing barriers into i) individual barriers (like: apprehension or fear, …); ii)organizational barriers (like: lack of leadership, …); iii) technological barriers (like: lack of compatibility between diverse IT, …). Most studies focus on the bright side of knowledge management: in principle, effective and purposeful sharing of knowledge translates into accelerated organizational performance. Only rarely do

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Maurizio Massaro, Roland Bardy and Michael Pitts we see reports on the dark side. E.g., Newell et al. (2001) emphasize that there is a potential to disable such processes.

2.3 Connecting knowledge barriers and intellectual liabilities IC and KM have a mutual interaction (Dunkelberg, Moore, Scott, & Stull, 2013; Seleim & Khalil, 2011; Shih et al., 2010; Zhou & Fink, 2003). While KM is used to develop and maintain IC (Y.‐C. Huang & Wu, 2010; Seleim & Khalil, 2011), IC increases the absorptive capacity of the organization (Cortini & Benevene, 2010; Seleim & Khalil, 2011). More precisely, socialization can facilitate the conversion of new tacit knowledge and involves accumulation of HC and RC sharing and transferring experience. The conversion of tacit knowledge into explicit knowledge can create an accumulation of OC and enhances systemic institutionalized knowledge (Von Krogh et al., 2000). Companies with a strong OC can easily transfer knowledge that is created and embedded within papers and formal tools like software, databases, and so forth. The mutual influence of IC on knowledge sharing and ,vice versa, has been empirically studied by Seleim and Khalil (2011) at 38 Egyptian software firms. When it comes to knowledge barriers, i.e. problems that reduce knowledge sharing opportunities, create true liabilities and diminish company value, we must examine i) fear of being misjudged, which can encourage people to fractionally share their knowledge(usually the less valued), ii)increased workload and expenses, iii) decreased value gaining opportunities. Knowledge sharing software can induce people to post redundant questions instead of looking for available knowledge resources, not to spread knowledge that is already available in other tools ,or spread knowledge that is valueless, which consumes employees’ daily routines, thereby increasing cost and reducing efficacy.

2.4 Research questions When it comes to the dark side of knowledge sharing, it is about the roles of IC in creating value and of ILs in destroying value. Value creation and value destruction develop along different paths (Giuliani, 2013). In this study we aim to reach a better understanding of the connections between knowledge barriers (KBs) and ILs. We explore the following research questions: Research question 1: How do Knowledge Barriers influence the development of Intellectual Liabilities? Research question 2: What are major similarities and differences between the cycle knowledge sharing => intellectual capital and the cycle knowledge sharing barriers=> intellectual liabilities?

3. Methodology We utilized a case study approach to develop the purpose of this study. This allows us to i) develop tracking paths as to “how” specific decisions were taken and implemented (Yin, 1981); ii) analyze multiple observations on complex relational processes (Eisenhardt & Graebner, 2007); iii) draw the significance of various interconnected levels of analysis (Hall, 2006); iv) find similarities and differences by comparing our findings with previous theory (Ridder, Hoon, & McCandless Baluch, 2012). We aim to develop an “antagonistic positioning” (Ridder et al., 2012), which means that what we develop through our analysis is “framed towards theory that – although sharing a proximal phenomenon of interest – remains distinct from the focal theory domain” (Ridder et al., 2012, p. 7). The object of our case study is a leading global company in the field of engineering consultancy labeled “Engineering Ltd.” (EL) who has more than 10,000 employees. EL has followed a path of mergers and acquisitions in order to establish its presence in the marketplace. Originally, EL focused only on developing local business.t As pressures grew to increase revenue, and as new markets emerged globally, increased attention began to be paid towards knowledge sharing. Our methodology consisted of interviewing the with12 top corporate managers and then performing content analysis of internal documents (strategic plans, internal procedures, etc…).

4. Findings Personal knowledge barriers and their impact on intellectual liabilities Having the right knowledge in the right people is one of the most common issues in knowledge management. Barriers originating from individual behavior or ’ perceptions can relate to either individuals or groups within

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Maurizio Massaro, Roland Bardy and Michael Pitts or between business functions. According to the interviewees, Engineering Ltd suffers from several personal barriers which impede effective knowledge sharing. One major problem is lack of time. The employees are more focused on “billable hours” since it is perceived these are the most recognized hours. Several individuals stated that when business is good, people are very focused on their daily routines, and the whole organization is focused on productivity. Managers recognized that: "We are under pressure, they tend people want to do chargeable work rather than to share knowledge. We have Technical Communities, but they need a dynamic leader that drives them. And it is hard to find such leaders since leading a TC requires a lot of commitment that goes beyond your daily routine and chargeable hours." Moreover, the company suffers from a general fear of sharing, which contradicts Engineering Ltd being a 'knowledge‐based' company. In order tom to overcome this hurdle Engineering Ltd developed several tools that allow employees to share knowledge, particularly knowledge embedded in reports. But the managers stated : "People have a general fear of being wrong due to the existence of a review system that validates contents brought by users to the platform." The growing importance of the problem is particularly critical as few offices, especially those in the in emerging markets, are growing revenues, (other offices are at a loss) The more work is reduced the more the attention is placed on billable hours,. The value in time efficiency the company could achieve through a knowledge sharing process is considered to be worth more than any isolated increase in revenues produced in a single branch office. The immediate impact of this situation is a growing reduction in human capital. 'Hidden' knowledge reduces the opportunity for globally growing employee competencies. Thus, a depreciation of human capital was recognized combined with a significant risk that hidden knowledge could leave the company or, worse, the company would be forced to reproduce knowledge that already exists One manager stated: "A very important personal barrier is the fear of being wrong when people share...Just few people bring original stuff in there, but not because they don’t know just because sometimes they don’t want so we have to spend a lot to educate our new employees with knowledge that we already own." Organizational knowledge barriers and their impact on intellectual liabilities Engineering Ltd acknowledged that it has several organizational knowledge barriers. For example, time spent for sharing knowledge is not traceable nor is it captured by any time/activity sheet tool. Also, sharing knowledge is considered irrelevant for career improvements and incentives with rewards for promoting collaboration and knowledge sharing being weak. The managers stated: "Knowledge sharing tends to be more just a day‐to‐day thing that happens, people don’t report it. Everybody can participate in Technical Communities, but none calls that part of the job...Individuals spend time in sharing knowledge, for example senior people share knowledge with junior people, room training, etc., because they feel it is a personal mentoring role. But if you make this part of the individual assessment, you get a better chance of people doing things more effectively." Even though the company has a strategic plan with a dedicated section on knowledge sharing, this is poorly communicated as no strategic statements are shared among business functions. Despite introducing specific tools for fostering knowledge sharing, there are no performance‐measurement tools that can be utilizes in order to verify improvements. Several internal documents reported: "Major improvements required are an effective integration of the organizational impact assessments. Our communication action plans will need to include touch points for aligning our communication among all management levels in order to fill this gap and make appropriate linkages in our messaging to inter‐related initiatives." The confusion about the importance of knowledge exchange has turned corporate intellectual capital into intellectual liabilities. As a consequence, Engineering Ltd suffered from increased employee turnover as employees were unaware as to expectations. Therefore, firm human capital has depreciated which may become the intangible that leads to organizational deterioration.

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Maurizio Massaro, Roland Bardy and Michael Pitts One manager said: "One of the major causes of our employee resign [sic.]is because they don’t know what is expected from them. Thus, we are losing abilities of people that we have trained within the company and we reduce our ability to stay efficient." Poor communication on knowledge sharing decreases the propensity to use knowledge repository tools. The company commented that the amount and value of the knowledge stored has plummeted. For example: "Although several tools have been developed, people still fall back to things like e‐mails in terms of sharing knowledge and communicating. Technical Communities are one of the main facilities of Knowledge Sharing and they’re pretty much voluntary. Only 10% of our organization use knowledge repository tools, and often the quality of what is published is pretty low... Technological knowledge barriers and their impact on intellectual liabilities Engineering Ltd is aware of technological knowledge barriers. In fact, the company has developed a strong social orientation by pushing social networking, blogging, setting up social‐network platforms (with added capabilities such as project‐shared workspaces),and group events. However, this created unrealistic expectations about what could be done. There was a growing use of social network platforms, but this only served to create an increased flow of repetitive information. Instead of searching within the existing knowledge base employees began raising questions through the social‐network platforms. Time was spent communicating knowledge already available in other tools, or which was valueless and consumed employees’ daily routines, thereby increasing cost and reducing efficacy. The unrealistic expectations exhibited drove non‐ monetary inefficiencies with the employees causing (unwittingly) value deterioration. Quoting some of the managers: "Looking at our social network tool you can see the same questions that are asked again and again, causing a general abuse of time for people that ask questions and people who answer them." Developing a causal map Findings were analyzed through specific causal maps developed for each interviewed managers. A unique and shared causal map where developed as a result of the discussion. Figures 1 summarizes the dynamics of knowledge sharing barriers and intellectual liabilities recognized. Legenda A) depreciation of the value of IC (Abeysekera and Guthrie, 2004; Caddy, 2000); B) nonmonetary obligation (Garcia‐Parra et al., 2009; Harvey and Lusch, 1999) C) non‐physical causes of organizational deterioration (Giuliani, 2013). Human liabilities and knowledge sharing barriers Our findings show that human liabilities are generated by personal barriers, organizational barriers and technological barriers. The failure to share, the absence of clear organizational aims, and the introduction of poorly explained ICT tools will have the following consequences: i) increase in people turnover, thus boosting human capital deterioration; ii) increase of hidden knowledge, causing IC depreciation and organizational deterioration; iii) generate a non‐monetary obligation for employees who are led to use some tools, but do not use it properly. These results show a partial difference with previous studies on the connection between KM and IC. Studies by Seleim and Kahlil (2011) and Hsu and Sabherwal (2012) did not find a significant connection between knowledge sharing and human capital. Both studies speak of an inverse relationship where human capital enhances knowledge sharing. Different results where reached by Shish et. al. (2010) for the banking sector. They found a significant positive correlation between knowledge creation and human capital. By logic, repeated application of knowledge in a given task fosters learning by people and organizations (Eisenhardt & Martin, 2000). So, while the theoretical approaches point to a positive connection between knowledge sharing and human capital, empirical results seem to be contradictory. Moving from the 'bright' side to the 'dark' side,

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Maurizio Massaro, Roland Bardy and Michael Pitts our results seem to indicate that knowledge barriers can produce human liabilities and empirical evidence supports this approach. Personal Barriers to KS

Organizational Barriers to KS

Hiding of knowledge because of the fear of sharing [A+C]

Increasing of people turnover because of unclear objectives [A+C]

Reduced problem finding because of people lack of time and will [C]

Reduced amount and value of knowledge stored [A+C]

Technology Barriers to KS Increasing expectation paid by employee on ICT tools [B]

Human Liabilities

Organizational Liabilities

Increasing of valueless information flows [C]

Reduced efficacy and effectiveness of internal processes [A+C]

Relational Liabilities

Reduced opportunity of strengthening relationships with stakeholders [A]

Reduced sharing and usage of relationships among branches[A+C]

Figure 1: Overview of the dynamics of interrelation among knowledge barriers and intellectual liabilities

5. Discussion and conclusion To sum up, we identified particular connections between KB to knowledge sharing and human capital. In particular we propose: Proposition 1. Personal barriers, Organizational barriers and technological barriers increase human liabilities by causing human capital deterioration, non‐monetary obligation formation and organizational deterioration. Organizational liabilities and knowledge sharing barriers We found that organizational liabilities can be generated by personal barriers, organizational barriers and technological barriers. On one hand, lack of time can reduce stored knowledge and harm problem finding attitudes within the organization, which then deteriorates organizational capital. On the other hand, the increase of valueless information flows reduces the effectiveness of organizational processes and constitutes non‐physical causes of organizational deterioration. This result seems to be partially in line with previous studies which found that knowledge conversion from tacit to explicit knowledge leads to an increase in the overall organization’s IC (Ikujiro Nonaka & Takeuchi, 1995). Hult et. al. (2004) showed that information distribution can affect shared meaning within the organization, influencing both human capital and organizational capital. Interestingly, prior empirical studies did not fully show a statistically significant connection between knowledge sharing and organizational capital (Hussi, 2004; Seleim & Khalil, 2011). Our empirical findings seem to suggest an exceptional effect between the bright and the dark side of knowledge management. While knowledge barriers produce organizational liabilities, knowledge sharing produces organizational capital. Earlier empirical evidence did not fully verify this hypothesis, but our results seem confirm it. Thus, we propose: Proposition 2. Personal barriers, organizational barriers and technological barriers increase organizational liabilities causing organizational capital deterioration and organizational deterioration.

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Maurizio Massaro, Roland Bardy and Michael Pitts Relational liabilities and knowledge sharing barriers Our findings corroborate that relational liabilities are generated by personal and organizational barriers. Hidden knowledge can cause relational capital deterioration and reduce the sharing and usage of relationships among branch offices. Relational capital can be damaged, and the company’s overall organization can be deteriorated. This seems to be completely in line with previous studies on the connection between KM and IC. A company’s value is dependent on the ability to create an effective knowledge sharing process which enables the use of knowledge that is embedded with its partners (Kianto, Hurmelinna‐Laukkanen, & Ritala, 2010). Moreover, the rate at which organizations access and reuse knowledge influences the effectiveness of knowledge transfer (Watson & Hewett, 2006) and thus contributes to create trust between network members, which increases relational capital. Seleim and Kahlil (2011) found a statistically significant connection between knowledge sharing and relational capital that was not recognized for other forms of connections between KM and IC. This could be translated into the connection between KB and ILs. In fact, failures to satisfy external actors’ non‐monetary obligations might cause a depreciation of company’s relational capital (Garcia‐Parra et al., 2009). This causes us to propose: Proposition 3. Personal barriers and organizational barriers increase relational liabilities fostering relational capital deterioration, non‐monetary obligation formation and organizational deterioration.

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Maurizio Massaro, Roland Bardy and Michael Pitts Huang, C. C., Luther, R., & Tayles, M. (2007). An evidence‐based taxonomy of intellectual capital. Journal of Intellectual Capital, 8(3), 386–408. doi:10.1108/14691930710774830 Huang, Y.‐C., & Wu, Y.‐C. J. (2010). Intellectual capital and knowledge productivity: the Taiwan biotech industry. Management Decision, 48(4), 580–599. doi:10.1108/00251741011041364 Hult, G. T. M., Ketchen, D. J., & Slater, S. F. (2004). Information Processing, Knowledge Development, and Strategic Supply Chain Performance. Academy of Management Journal, 47(2), 241–253. doi:10.2307/20159575 Hussi, T. (2004). Reconfiguring knowledge management – combining intellectual capital, intangible assets and knowledge creation. Journal of Knowledge Management, 8(2), 36–52. doi:10.1108/13673270410529091 Kianto, A., Hurmelinna‐Laukkanen, P., & Ritala, P. (2010). Intellectual capital in service‐ and product‐oriented companies. 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Innovating Corporate Management: Introducing Environmental Aspects to Design Activities Eunika Mercier‐Laurent IAE Research Center, Lyon3 University, Lyon, France eunika.mercier‐laurent@univ‐lyon3.fr Abstract: The recent trends of innovation, social innovation and environmental concern as well as the current economic context require a different management. The transition is not trivial as it involves another way of thinking and acting. Companies pushed by new ISO norms and Corporate Social Responsibility movement focus on sustainability but forget often the economic aspects, the vital component of firms ecosystems. This paper describes a way of innovating in management via strategic knowledge management related to eco‐design process. This applied research project connects several areas, such as eco‐design, sustainability and information system. The principal objective is to elaborate a smart toolbox to assist all management levels in integrating the environmental aspects into their design process and other relative activities, including real time innovation. The applied method is that of holistic and system knowledge management. Experimentations are made with an international company and results will be tested in other companies, mainly SME. This article briefly mentions all system components of a design process and points out main difficulties. Then we discuss principal results and give some perspectives for the future. Keywords: innovation, management, eco‐design, eco‐innovation, knowledge management, innovation ecosystems

1. Introduction The current economic context in the developed countries is evolving from industrial economy to the knowledge economy. This change is not trivial, because it involves different ways of thinking and doing things. In the same time multiple‐interrelated eco‐crises (finance, health, CO2, climate change, population, food, water, waste, forests, etc.) according to Savage (2), motivate us to rethink the industrial model in fundamental ways: education, organization and even our economy. Companies are doing efforts integrating the environmental aspects into their activity. Several norms such as ISO 26000, ISO 14006:2011 for eco‐design, and others were established to help and control them. Large companies deal with introducing environmental aspects via Corporate Social Responsibility approach. Another trend is to focus on Product Service System (PSS) where the ownership of the tangible product is retained by the service provider. He sells the “functions” of the product, via modified distribution and payment systems, such as sharing, pooling, and leasing (Baines et al 2007). Knowledge Economy is everywhere in communication, but few really know how to turn knowledge to economic values. Many large and middle size enterprises hired Sustainability Chief Officer. Most of them focus on the norms as model but they are so complex that it is impossible to respect all items, required for certification, without a help of one or several experts. The integration of the environmental aspects into SMEs activities is a strong constraint, because it is seen as an extra duty to accomplish, involving often change in enterprises processes and in the way they work with stakeholders. In the same time the economic crisis and related short‐term strategy as well as the lack of adequate and simple tools discourages them. Traditional design tools such as Product Life Management (PLM) and Life Cycle Assessment (LCA) help evaluating the potential environmental impacts associated with all the stages of a product's life, but remain too complex for SMEs. The idea of Convergence project is to fulfil the needs for simple guide by creating an intelligent assistant toolbox to help them introducing the environmental aspects into design of products and in the same time make them e‐co‐innovate – Mercier‐Laurent (2011).

2. Current research and experiences Usually design is relative to product, but it could be also associated with service or activity. Amid designer, the process of design involves directly or indirectly various professionals such as marketing, management, human resources, information system, sales, sustainability specialist, financial and others. Companies practicing Knowledge Management include stakeholders into the process. As consequence, several domains should be considered here: sustainability, design, management, knowledge management, innovation and information system.

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2.1 Sustainability With the intensive industrialisation in the 1960s some people tried to capture attention on the fact that such a way of doing business without taking into account the ecosystems has a heavy effect on planet and living. Companies’ duty is to take into account the impact of human activity as well on company levels as on individual level – Eckholm (1976), Falk (1971), Lenkowa (1969). But the quick business was stronger that reason. The second wave begun probably with Rio (1992) summit; it holds on regular basis. Many voices as those of Captain Cousteau, The Earth Charter (Earthcharter), Al Gore, Nicolas Hulot, Yann Arthus‐Bertrand (Home) and others made the care of the planet a priority. European project REACH (Reach) was devoted to chemical pollution due to massive use of fertilizers and insecticides and set some sustainability rules.

2.2 Design Design can be considered as activity and process involving aesthetic, functional, economic, social, cultural and environmental dimensions. It involves insight, imagination, thought, knowledge, research, modelling, simulating and risk taking. Some consider companies have to be design centred (Yazdani 1999), others talk about user‐ or human‐centred design (Cooley and Cooley 1980, Seely‐Brown et al 2010). Co‐design with partners (Detienne et al 2005) is practicing for some complex and knowledge‐intensive products, such as airplanes. Co‐design with client could be also practiced, but it is unusual. Few companies run the User’s Clubs aiming at involving clients into improving products and inventing new ones (Mercier‐Laurent 2011). Nature– inspired design or biomimicry (Benyus 1998, Biomimicry) makes its come back, however the impact in term of integration with ecosystems is often missing. Design support systems such as Catia, TurboCAD, SolidWorks, AutoCAD, TRIZ and others begin introducing the estimation of the environmental impact (Zwolinski at al 2010), but did not integrate the powerful simulators as a decision tool “before implementing”, allowing to test the use of the appropriate materials and reducing water, energy and recycling. The mentioned tools include knowledge management principles and facilitate a partial knowledge flow.

2.3 Management From management point of view, companies tried various methods such as taylorism, Strategic Business Units, Business Process Reengineering and Total Quality Management. Corporate Social Responsibility (CSR) can be considered as one of them. It is mainly focus on reducing the CO2 emission, energy optimization, transportation and eco‐responsible products (CSR). The contribution of economists to this movement is an alternative way of considering products ‐ as a source of services (Baines et al 2007, Bourg and Buclet 2005, Van Halen et al 2005). In this context the knowledge economy can create more opportunities at the condition to master the transformation of knowledge into economic and others, including intangible, values. The fifth generation of management described by Charles Savage (Savage 1990) considers Knowledge as an asset. In fact only few companies try to evolve to this generation through a global Knowledge Management. Some nd th make a leap frog directly from 2 to 6 generation, innovation‐based, without taking into account all th fundamentals for success from 5 generation (Amidon et al 2005, Mercier‐Laurent 2011).

2.4 Knowledge management Knowledge Management which began simultaneously in management and artificial intelligence areas has now many facets. In our context the KM way of thinking and the attitudes of “knowledge cultivator” include already the eco‐principles and integrate them naturally to all activities. Technology for knowledge processing helps to organize, optimize and manage the whole flow (Amidon et al 2005, Mercier‐Laurent 2011). Design requires multidisciplinary knowledge; better it is organized more efficient the whole process will be. The vocabulary created years ago by multidisciplinary Knowledge Management movement, such as stakeholders, collaboration, learning enterprise, community of practice, innovation and sustainability is now used in many domains including design (Mercier‐Laurent and Reyes 2008). This fact facilitates the co‐construction of a common language in the extended business network (EBN) context.

2.5 Innovation Organized and optimised flow of knowledge amplifies the innovative capacity of individuals and of companies. The design process begins by an idea, which is a starting point also for the innovation process. Two are indisociable and influence each other. In the crisis time the eco‐activities, including eco‐innovation are seen as

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Eunika Mercier‐Laurent generating jobs and activity and are the priority for the United States, Europe (Europa 2020) and other developed countries.

2.6 Information system The role of information system is to support the eco‐design activity (Rio et al 2011) via appropriate applications. The theory of information system brings the system approach and its representation of serving strategic, middle and operational levels.

3. Project The trigger of the Convergence project was a specific need – help for integration of the environmental aspects into design process and education of the relative new attitudes. As mentioned before, the initial goal of this project is to address the lack of simple and easy tools able to assist designers in their work. It is also helping companies and in particular SME understanding and taking into account the environmental aspects in their design and other related activities. From the research point of view this project was born from the encounter between eco‐design and knowledge innovation (Mercier‐Laurent and Reyes 2008, Reyes 2007). It is a multidisciplinary project that aims in making work together three PhD students from eco‐design management, sustainability and information systems areas. The main difficulty is not to define and conceive the above tool, but to create a real collaboration between these students, having their specific background, “talking” specific languages and using their own domain related way of thinking and working. They should to converge and focus on strategic, middle and operational levels of eco‐design and try to e‐co‐innovate as well. To achieve the above goal the appropriate method is necessary.

3.1 Method The applied method is a combination of holistic knowledge management (Amidon 1997) and users’ oriented or bottom‐up KM (from specific need), supervised by top‐down (vision‐strategy‐tactics) (Mercier‐Laurent 2011). It includes innovating with customers from management point of view (Amidon 1997) and takes the inspiration from extreme programming principle (Extreme programming) from software design point of view. It is also influenced by machine learning approaches (Quinlan and Michalski 1983) – generalisation from specific cases. According to KADS principles (Schreiber et al 1993), inspired by Newell 1982, Chandrasekeran 1983 and Clancey 1985, the knowledge related to design and about various elements of context is also gathered. It contains as well politics as laws, environmental norms, knowledge about clients, trends, risks and opportunities. The current initiatives and actions have to be analysed and their motivations understood. The expressed needs are considered with a global, holistic and system KM approaches in aim to take into account the various components, people and company strategy. The current situation is “measured” interviewing selected persons on all company levels. This forms an image of current situation and an ideal one seen by company professionals. Then we define a distance between current situation (point A) and the goal (strategic vision) using radar chart and set specific indicators (Amidon 1997, Mercier‐Laurent 2011). The ideal situation reflects the collective vision of participants. To achieve the goal the series of actions should be decided (strategy) and the progress should be measured using a set of indicators (tactics). This approach is top‐down. Once this diagnosis accomplished we need to understand the flow of information and knowledge on and between three levels: strategic, middle and operational. The first draft of the toolbox specifications can be set to build a demo to be tested by selected professionals in a company. The feedback will bring elements for improving the toolbox and influence the strategy (bottom up). The resulting toolbox can than be tested in others companies in aim to make it more generic.

3.2 Current work The project began by an initial study of the current situation in partners companies including context, activities in design and other related areas. After initial study of all useful internal companies’ documents and state of the art in the involved areas, a meeting was hosted by main partner company. It was very useful to gather all

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Eunika Mercier‐Laurent contextual knowledge about their spirit, work mood and culture, environment, interests and priorities, actions, motivations and ambitions. All these elements served to elaborate a relevant questionnaire in aim to get an understanding and image of the current efforts in integrating the environmental aspects to their activities and the way they measure a progress. Initially, each student focused on his/her own research objective wanted to ask his/her own questions, which is not optimal for reviewed persons and poor for students. After an intensive “innovation café” they succeed in elaborating core common questions. Then they spent few days discussing with company professionals involved into the whole cycle of life and discovering their activities and connections between them, as well as the internal and external exchanges of information and knowledge. As an element of the whole context, they also taken into account the current study made by an expert in sustainability and they discover that the company decided to adopt SAP. People working on a transition to SAP are defining the company processes. The “sustainable” SAP is organized around three pillars which are: playgrounds, products and communities. We also discovered other current initiatives in a company relative to sustainable development. All these elements are important because they produce and use knowledge and are involved in the innovation process. A mapping of exchanges was drawn and analysed on both operational and strategic levels. It brought a significant help to suggest the actions aiming in achieving the goals (ideal situation). The “back bone” connecting three levels (strategic, tactics and operational) design activities was defined and a scenario was elaborated and partially tested.

3.3 Results The results of the first stage of project are the convergence of students in elaboration of a common and specific in the same time questionnaire. These questions were of considerable help to understand how the company works, the role of each professional, the flow of information and knowledge they produces, internal and internal exchanges. The students discovered the importance of contextual knowledge and current initiatives, as well as the communication actions. The gathered knowledge served in the first time to elaborate a mapping of exchanges and to understand the system dynamics of them. Then they made a synthesis of the interviews, filled the gap and defined the functions of the future intelligent assistant able to bring the company from the point A to the ideal situation. They try to represent and measures some selected company values in term of tangible and intangible capitals (Edvinsson and Malone 1997) and evaluate a potential influence of the environmental aspects and innovation on them. The first scenarios of introducing environmental aspects by top down, bottom up and middle‐up‐down were simulated (Zheng et al 2013), specification was written and the first version of demo is under development. Next step is to test it in real situation and gather feedback from partner’s company and others we wish to involve.

4. Discussion and perspectives The results after two years of the project are promising. The initial difficulty of understanding each other resulting from component areas specific language and apparently different motivations are now clarified. Another difficulty to face by PhD students is to acquire a capacity to switch between two objectives: those of PhD and those of partner’s company. The appropriate method help focus on a common objective considered from various point of view. The understanding is now that the eco‐design can be regulated by norms, but can be also an attitude and daily practice. It is the same importance at all company levels. The applied method helps also in associating the interviewed persons to the co‐design of a tool and makes them feel as co‐creator. The main scenarios of introducing environmental aspects on three levels – strategic, middle management and operational are elaborated and validating. They served as a base for specifications of demonstration under development. The convergence of SAP processes and eco‐activity including innovation has to be studied. We also plan to test serious games as a tool for introducing the eco‐attitudes as a part of knowledge cultivator culture. Described innovating in management applies as well in companies as for conducting multidisciplinary research projects.

References Amidon D.M. (1997) The Innovation Strategy for the Knowledge Economy, Butterworth Heinemann, Boston Amidon D.M., Formica P. and Mercier‐Laurent E. (2005) Knowledge Economics – Emerging Principles, Practices and Policies, Tartu University Press, ISBN: 9985‐56‐939‐3, vol II, p. 227‐266

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Eunika Mercier‐Laurent Baines T. S., Lightfoot H., Steve E., Neely A., Greenough R., Peppard J., Roy R., Shehab E., Braganza A., Tiwari A., Alcock J., Angus J., Bastl M., Cousens A., Irving P., Johnson M., Kingston J., Lockett H., Martinez V., Michele P., Tranfield D., Walton I. and Wilson H (2007) State of the art in Product Service System, Proc. IMechE Vol. 221 Benyus J.M. (1998) Biomimicry – Innovation Inspired by Nature, Quill Biomimicry http://www.cbid.gatech.edu/ Bourg D. and Buclet N. (2005) Economie de fonctionnalité, Futuribles, Paris Chandrasekeran B.(1983) Towards a Taxonomy of Problem Solving Types, AI Magazine IV‐1 Clancey W.J.(1985) Heuristic Classification, Artificial Intelligence 27 Cooley M. and Cooley S.(1980) Architect or bee? The Human‐Technology Relationship, Langley Technical Services Detienne F., Martin G and Lavigne E. (2005) Viewpoints in co‐design: a field study in concurrent engineering, Design Studies 26, 3, p. 215‐241 Earthcharter www.earthcharterinaction.org Eckholm E.P.(1976) Losing Ground. Environmental Stress and World Food Prospects, W.W. Norton and Company, New York Edvinsson L. and Malone M.S. (1997) Intellectual Capital: Realizing Your Company’s True Value by Finding its Hidden Brainpower, Harper Business, New York Extreme programming www.extremeprogramming.org/ Home http://www.youtube.com/watch?v=jqxENMKaeCU&wide=1 Falk R.A. (1971) This endangered planet: prospects and proposals for human survival, Random House New York Hernandez Pardo R., Brissaud D., Mathieux F. and Zwolinski P. (2011) Contribution to the characterisation of eco‐design projects, International Journal of Sustainable Engineering 24, 4, p. 301‐312 Europa 2020 http://ec.europa.eu/eu2020 Lenkowa A. (1969) Oskalpowana ziemia, Omega, Wiedza Powszechna, Warszawa Mercier‐Laurent E. (2011) Innovation Ecosystems, Wiley Mercier‐Laurent E. and Reyes T (2008) Ecodesign as a prospective innovation driver for companies, IDMME‐ Virtual Concept, Beijing Newell A. (1982) The Knowledge Level, Artificial Intelligence 18 Quinlan J.R. and Michalski R.S. (1983) Machine Learning – An Artificial Intelligence Approach, p.463‐482, Tioga, Palo Alto, 1983 Reach http://ec.europa.eu/environment/chemicals/reach/reach_intro.htm Reyes, T. (2007), Ecodesign in SME, PhD,Université du sud Toulon‐Var Rio (1992) The Rio Declaration on Environment and Development http://www.unesco.org/education/information/nfsunesco/pdf/RIO_E.PDF. Rio M., Reyes T. and Roucoules L. (2011) Toward Proactive Eco‐design Based on Engineer and Eco‐designer’s Software Interface Modelling, The Design Society 2011 Savage C. (1990) 5th Generation Management: Integrating Enterprises through Human Networking, The Digital Press, Bedford Savage C. (2) www.kee‐inc.com/slides.htm Schreiber G., Wielinga B. and Breuker J. (1993) KADS, A Principled Approach to Knowledge‐Based System Development, Academic Press 1993 Seely‐Brown J., Hagel III J. and Davison L. (2010) The Power of Pull: How Small Moves, Smartly Made, Can Set Big Things in Motion, Basic Books, Philadelphia Van Halen C., Vezzoli C. and Wimmer R. (2005). Methodology for Product Service System Innovation, Assen: Uitgeverij Van Gorcum, p. 21, ISBN 9023241436 Yazdani B. (1999) Four Models of Design Definition: Sequential, Design Centred, Concurrent and Dynamic, Journal of Engineering Design, Taylor & Francis Zwolinski P., Kara S. And Manmek S. (2010) Comparison of Eco‐Design Tools for the Conceptual Design Phase, 17th CIRP International Conference on Life Cycle Engineering, Hefei, ANHUI, China CSR http://ec.europa.eu/enterprise/policies/sustainable‐business/corporate‐socialresponsibility/index_en.htm Zhang F., Maud R., Allais R., Zwolinski P., Reyes‐Carrillo T., Roucoules L., Mercier‐Laurent E. and Buclet N. (2013) Toward an systemic navigation framework to integrate sustainable development into the company, Journal of Cleaner Production Volume 54, 1 September 2013, p. 199–214

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Examining the Transfer of Academic Knowledge to Business Practitioners: Doctoral Program Graduates as Intermediaries Madora Moshonsky1, Alexander Serenko1 and Nick Bontis2 1 Faculty of Business Administration, Lakehead University, Thunder Bay, Canada 2 DeGroote School of Business, McMaster University, Hamilton, Canada mjday@lakeheadu.ca aserenko@lakeheadu.ca nbontis@mcmaster.ca Abstract: The relevance of academic research and its transformation into practice has become one of the most frequently debated topics within and outside of academia. Many academics, practitioners and government officials state that academic research has made little impact on business practice. Little empirical research, however, has been done to support or refute this claim. The purpose of this study is to explore whether practitioners, who hold a business or management Ph.D. degree, act as intermediaries in the transfer of academic knowledge from academia to industry. A model of knowledge transfer was used as a lens of analysis. Twenty Ph.D. graduates were interviewed and the data were subjected to deductive content analysis to test current knowledge transfer theory. First, it was found that doctoral program graduates employed in the non‐academic sector acquire new knowledge through a variety of channels. The most popular knowledge acquisition source is academic journals, followed by practitioner outlets and discussions with colleagues. Second, the knowledge that practitioners received during their Ph.D. training is applied outside of academia. Third, the lack of demand for evidence‐based knowledge in industry deters practitioners from using academic research. Fourth, when practitioners remain involved in the academic domain, they are more likely to access and apply academic knowledge. Fifth, the attitude of an employer or a client impacts the probability of the practitioner using academic literature in his or her decision‐making processes. Sixth, the findings emphasize the influence of organizational culture in determining the sources of knowledge that practitioners access and apply to perform their tasks. It is recommended that doctoral program curricula include more applied knowledge, and non‐academic organizations provide their knowledge workers with access to academic literature. The results reinforce the importance of understanding the relationship between a source and a receiver as studied in this case between academia and practice where doctoral program graduates act as intermediaries. Keywords: knowledge transfer, knowledge worker, research relevance, research impact

1. Introduction and literature review 1.1 The great divide The argument on the perceived irrelevance of academic business research dates back to the 1980s when academic institutions were criticized for placing priority on scientific rigor over relevance to industry (Bennis and O'Toole, 2005). The disconnect between academics and practitioners has been deemed “the Great Divide” in that the theoretical contributions of researchers go unimplemented in practice (Rynes, Bartunek and Daft, 2001). The very value and relevance of academic research has been called into question as a result of the perceived lack of applicability and generalizability of academic knowledge. For example, the utilization of academic research on a regular basis by human resource managers is below 1% (Rynes et al., 2001). A flurry of papers has since been published which reflects the divide between academia and practice (Starkey and Madan, 2001). Knowledge has been defined as an individual’s ability to act (Berger and Luckmann, 1966). It provides a justification and motivation to alter decisions. Accordingly, industry practitioners require knowledge in developing and implementing an action strategy. Therefore, academic knowledge is only relevant to industry if it motivates practitioners to take action inspired by its content. Booker, Bontis, and Serenko (2008) studied how business professionals access and utilize academic research in their daily work. They found that while practitioners value academic research; it is the accessibility of this research that produces the detachment. This accessibility refers to the receiver’s ability to effectively consume knowledge. Simmons et al. (2001) established that the process of knowledge transfer mostly fails on the side of the receiver (i.e., the practitioner). Additionally, Serenko, Bontis and Hull (2011) determined that books act as knowledge transfer agents, and future research should explore transfer mechanisms, including direct and indirect channels. Direct channels of knowledge transfer occur when an individual accesses, understands, and executes the knowledge

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Madora Moshonsky, Alexander Serenko and Nick Bontis directly from the source (i.e., from an academic publication). Knowledge is transferred through an indirect channel when the knowledge is modified and/or distributed to the end user by an intermediary. However, academic works are usually targeted to other academics, reviewers, and editors. They are written in a complicated language, contain jargon, present advanced statistical techniques, have abstract ideas and theories, and assume the reader’s familiarity with academic research in general. Thus, the accessibility of academic publications is a major barrier for the transfer of academic research to practice because practitioners often lack academic training, which is required to read and understand academic works. At the same time, graduates of doctoral business programs who are employed in the non‐academic sector (e.g., managers who hold a Ph.D. in Business) are fully qualified to read academic publications and use academic findings in their decision making. This study attempts to contribute to the knowledge base by exploring whether doctoral business program graduates who work in industry are knowledge ambassadors acting as an indirect channel of knowledge transfer between academics and practitioners. Particularly, the purpose of this study is to explore whether doctoral business program graduates who enter the non‐academic workforce acquire, utilize, and disseminate academic knowledge in their daily decision making.

1.2 What is knowledge transfer? Knowledge transfer has become one of the most important strategic organizational tools. It is the key concept that all successful managers are aware of and apply in their daily work (Cavusgil, Calantone and Zhao, 2003). When knowledge is allowed to flow within an organization, it enables organizational learning. When people face an opportunity or a problem, they require accessible knowledge to make the required modifications to their behavior. The value of knowledge lies in its ability to help managers undertake better actions and improve their decision making (Davenport and Prusak, 1998). There are various theories explaining knowledge transfer and how knowledge is communicated from one individual to another. In the past, knowledge was considered an object which could simply be passed from one person to another without regard for the surrounding context (Parent, Roy and St‐Jaques, 2007). It was also assumed that knowledge transfer was a hierarchical, top to bottom interaction where the receiver of the knowledge was a passive actor (Roling, 1992). However, this traditional model has been criticized for its linear perspective, which ignores context and exchanges between the two participants. Instead, the knowledge transfer process is bi‐directional, and it mostly fails on the receiver’s side (Szulanski, 1996). Therefore, the receivers cannot be passive entities that are bestowed knowledge from a source. Instead, they must be active problem‐solvers who generate their own knowledge base (Hutchison and Huberman, 1994). Knowledge transfer is a result of the interaction within a dyadic relationship (Knights and Scarbrough, 2010). The newer process‐based knowledge transfer models are of the social constructivist perspective, which assumes that knowledge has an individual meaning to different people based on their experiences (Parent et al., 2007). Process‐based models take into account the environment in which the knowledge is transferred and applied. In an organizational context, the legitimacy of new knowledge is validated against the organization’s culture (Roling, 1992), and received knowledge is adapted to fit the receiver’s individual situation (Foss and Pedersen, 2002).

1.3 The knowledge transfer model Knowledge transfer occurs over a variety of mediums through direct or indirect methods. A direct channel means that the receiver accesses the material written by the creators of the academic knowledge through mediums including journals, books, and conference proceedings. However, practitioners are rarely directly exposed to or utilize current academic material (Pearson, Pearson and Shim, 2005). Therefore, these practitioners should access knowledge through indirect channels where the knowledge is transformed by an intermediary into an accessible format that is applicable to the receiver’s environment (Nohria and Eccles, 1998). Understanding and identifying effective indirect channels is key to conveying academic research to practitioners (Serenko et al., 2011). For example, medical patients avoid information they believe themselves to be unqualified to consume and instead defer to the information provided by their health care providers as authorities (Baxter and Braithwaite, 2008). In comparison, practitioners who do not possess a Ph.D. can indirectly access academic material by communicating with practitioners holding a Ph.D., as a channel for knowledge transfer. This indirect channel

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Madora Moshonsky, Alexander Serenko and Nick Bontis occurs when the non‐Ph.D. holding individuals are exposed to academic theory through the Ph.D. graduates who possess the capacity for synthesizing and communicating the initially inaccessible knowledge. In order to explore the dissemination of knowledge, this study adapted the process‐based model of knowledge transfer proposed by Liyanage et al. (2009) (see Figure 1). This model assumes that knowledge is not an object which can be passed in static form from one person to another because it is through the process of interaction that an individual attaches new meaning to its environment (Parent et al., 2007). This model depicts a process which occurs at different organizational levels. Both the source and the receiver of the knowledge have to actively engage in the knowledge transfer process and possess the necessary capabilities for the receiver to be able to effectively gain new knowledge and be able to act upon it. Each step in the knowledge transfer process must be completed before proceeding onto the next. If not all of these steps are completed, then the process of knowledge transfer cannot have occurred, and the recipient’s behavior will not be impacted by the knowledge. Knights and Scarbrough (2010) posit that the need for such a model is emphasized by the constant debate in this field. The process‐based model of knowledge transfer begins with the recipient identifying what kind of knowledge is required to solve a particular problem. Therefore, the receiver must be able to correctly assess the situation and the surrounding environment. The receiver must next acquire this knowledge, which is currently known as information ‐ knowledge that is unprocessed. It is in the third stage that information is transformed into new knowledge that builds on the recipient’s existing knowledge, skills, or capabilities. The integration of this knowledge in the fourth step involves adapting the knowledge to the situation and environment at hand and making it ‘useful’. In the next stage, this knowledge is applied to the current problem in an actionable strategy. The last step of knowledge retention was added to the original model because new knowledge should have a lasting impact on the constructive reality of the recipient.

Figure 1: The process‐based model of knowledge transfer. Adapted from Liyanage et al. (2009) Overall, the purpose of this study is to explore whether doctoral business program graduates, who are employed in the non‐academic workforce, acquire, utilize, and disseminate the academic knowledge in their daily decision making. The following research questions are proposed:

Through what channels do doctoral business program graduates acquire new knowledge?

How does academic knowledge impact the daily routine of doctoral business program graduates working in the non‐academic sector?

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To what extent do doctoral business program graduates transfer academic knowledge to practitioners in their organizations?

2. Methodology Twenty semi‐structured phone and Skype interviews with practitioners who possess a Ph.D. degree were conducted during 2012‐2013. These practitioners obtained their Doctorate in Canada in Business, Management, or a management‐related discipline. The rationale for using Doctoral degree holders relates to the previously identified problem of the accessibility of academic research. Past studies revealed that academic papers are inaccessible to most practitioners due to jargon, length, writing style, and complicated statistics. Scholarly papers also mostly contain theoretical recommendations that need to be converted to practical application (Booker et al., 2008; Serenko, et al., 2012). Additionally, most business practitioners are unaware of the existence of scholarly publications. In studying individuals who are equipped with the necessary skills and experience to utilize academic material, this acts to negate the inaccessibility issue. Therefore, this study focuses on alternative explanations for the gap in the transfer of academic knowledge to practice. Participants were recruited through two manners: Internet searches and referrals (i.e., snowballing). The interview questions were designed to follow the process of knowledge transfer and explore how the participants progressed through the various stages of the proposed model (see Online Appendix at http://www.aserenko.com/ICICKM2013.pdf). The interview protocol was subjected to peer face validation by consulting a group of business faculty members to address concerns of ambiguity and social desirability. The participants included ten consultants, six government employees, three investment managers, and one private company employee. The interviews were analyzed using content analysis, which is a systematic process of analyzing written, verbal, or visual content. The data was analyzed to determine the underlying relationships among an individual’s characteristics, values, experiences, and environment with their demand, valuation, and use of knowledge. The raw data from the interviews was transformed into manageable content categories based on systematic coding. The codes used to analyze the data for this study were developed based on the process‐based model of knowledge transfer (Figure 1), aspects of holistic knowledge transfer, and known antecedents and barriers of knowledge transfer. While traveling the dynamic path of deductive content analysis, the codes were continually reevaluated and transformed as the analysis progressed. The researchers then returned to existing theory which might explain observed phenomena and further direct the analysis. This check of reliability also involved an additional review of the data to ensure the material was analyzed properly. Theory triangulation was achieved by incorporating multiple theories which builds credibility of the findings. Additionally, the differing perspectives of each participant in the study also contributed to data triangulation through the use of multiple data sources. The data analysis process was facilitated through the use of a qualitative data analysis program NVivo. Nvivo was used to organize and analyze the content from the interviews through queries, visualization, and report generation.

3. Findings Eleven participants were female and nine were male. The participants graduated from their respective Ph.D. degrees between 1991 and 2010 with the average year 2005 (i.e., seven years ago). Figure 2 shows that the participants possessed a wide range of Ph.D. degrees.

3.1 Knowledge awareness Knowledge awareness was focused on what knowledge the practitioners believed they required to perform their job. Each participant described what he or she believed was necessary knowledge to search for to perform job‐related duties. Four categories of necessary knowledge emerged (Figure 3). Often, participants stated they require a variety of knowledge. Out of the 20 participants, nine subscribed to an alert system which notified them of new knowledge from a source (Figure 4).

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Figure 2: Ph.D. Degrees of participants

Figure 3: Perceived knowledge requirement

Figure 4: Use of alert tools Additionally, the participants described how they decide which source is required (Figure 5). The responses were coded in the following manner: 1) situational (i.e., the participant decides which source to access based on the nature of the problem he or she encounters); 2) timely (i.e., the source which provides the quickest answer); 3) internal experience (i.e., the past experience of the individuals themselves and of their colleagues); 4) audience (i.e., the group that the practitioner will present the new knowledge to); and 5) reliability (i.e., how consistent the source is with providing accurate, proven information). In terms of discrepancies between

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Madora Moshonsky, Alexander Serenko and Nick Bontis consultants and government employees, government employees were slightly more likely (67% versus 50%) to mention theory and methodology as required knowledge.

Figure 5: Decision criteria

3.2 Knowledge acquisition Figure 6 presents knowledge sources that practitioners access on a regular basis. Academic journals were mentioned most frequently with eleven practitioners stating they access this source. On average, the practitioners would consult with three different sources.

Figure 6: Knowledge sources accessed In focusing on academic research, the participants were asked how they access academic findings (Figure 7). Note that four individuals said they didn’t have access to academic literature, and if they wanted to they would have to pay for it personally. Nine practitioners said they only access academic literature when they encounter a problem that requires it. Five responded that they did access academic literature on a regular basis. The participants were asked if the source of knowledge they access changes when they are faced with a critical or previously inexperienced situation. Four practitioners said that they do not access different sources when there are special circumstances. The most popular response stating they would access their colleagues in this situation received six instances. While government employees all had access to academic literature at work, the only consultant with access to academic literature at work owned his or her own practice. Consultants had the largest barrier to accessing academic literature because they had to rely on their colleagues who had access or personally pay for it.

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Figure 7: Access to academic literature

3.3 Knowledge transformation The knowledge transformation stage in the knowledge transfer process involves converting the newly accessed knowledge into a useable form for its intended consumers. The participants were asked if when they accessed academic literature it contributed to the development of their knowledge base. Eleven participants stated that they gained new knowledge when they accessed academic literature. Four said that sometimes it was new and sometimes it was not. Three respondents mentioned that academic literature did not contribute to their knowledge base. Two respondents did not comment. Additionally, the practitioners were asked if accessing academic literature had improved their skills or capabilities. Ten responded that accessing academic literature had improved a skill or capability in the past. Three said that this occurred sometimes, and five said that it did not. One participant said that he or she gained new knowledge from academic literature but that it did not enhance his or her capabilities. There are again a few differences between the responses from consultants and government employees. Five of the six government employees answered that new knowledge is gained by accessing academic literature and one said sometimes. In comparison, of the nine consulting responses four said yes, three said sometimes, and two said no.

3.4 Knowledge integration The knowledge integration portion of the interview studied how the practitioners found academic research fit into their work environment by addressing organizational needs. The participants were asked to describe the general usefulness, applicability, and relevance of academic research in performing job related duties. Five participants stated that academic literature is unconditionally relevant and applicable to their work responsibilities. Six replied that academic literature is relevant in creating the foundation for their knowledge but not for implementable recommendations. Three do find relevance and usefulness in academic knowledge but noticed that it requires extensive transformation in order to be applied. Six participants replied that is was rare that they were able to apply academic knowledge because of its lack of relevance or usefulness. Many answered that the applicability and relevance of academic research is based on certain conditions, which included language, sample type, scope, and intended audience. In a comparison between the ten consultant participants with the six government participants, only one consultant said that academic knowledge is relevant and applicable to his or her working environment whereas three government employees said yes.

3.5 Knowledge application Knowledge application occurs when the transformed knowledge is utilized to address the current problem the practitioner has encountered. In this stage in the knowledge transfer process the practitioners acted upon the

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Madora Moshonsky, Alexander Serenko and Nick Bontis new knowledge they obtained. The participants were asked how frequently they applied academic literature, and the responses were categorized as: 1) regularly; 2) sometimes; 3) rarely; and 4) never (Figure 8). The participants were also asked if their company or clients recognized the value in applying academic research to solve managerial problems. The responses were coded as: 1) yes (6 responses); 2) sometimes (4 responses); and 3) no (10 responses). Yet again there is a stark contrast between the answers provided by the consultant and government participants. Only two consultants (20% of the population) versus three government employees (50%) affirmed that they apply academic knowledge on a regular basis. The majority (50%) of consultants revealed that they rarely applied academic knowledge. Additionally, there was a difference in whether or not the participants believed that their company or clients value the application of academic knowledge because most consultants stated that it was not valued.

Figure 8: Practical application of academic knowledge

3.6 Knowledge retention Knowledge retention ensures that the academic knowledge acquired and utilized by practitioners is embedded in the organization for future consultation and action. For knowledge to be retained, it must have a lasting impact on the individual’s or company’s behavior. The participants were asked if they see themselves as translators of academic research by making academic knowledge usable for those who would not be able to attain it themselves. The replies were: 1) yes (9 responses); 2) sometimes (5 responses); or 3) no (6 responses). Participants believed that educational differences did not create an unbridgeable gap. The majority of government employees replied that they transfer academic knowledge to others with a 67% response rate. In comparison, only 30% of consultants believed they perform this function, and 40% said that it can occur sometimes.

3.7 Other observations It was observed that most participants, who regularly use academic knowledge, published in peer‐reviewed journals and conference proceedings after obtaining their Ph.D. degree. Teaching university courses also increased the likelihood of an individual using academic literature. The audience to which the practitioner presented his or her recommendations greatly impacted the sources the practitioner employed. If the audience required evidence‐based knowledge then academic research is more likely to be used. One of the strongest indicators of the probability of an individual accessing and applying academic literature is whether or not his or her clients or company values academic knowledge. If the client or company does not value academic research, the likelihood of the practitioner referencing the material declines. One reason why this relationship could exist is that if the participant’s employer does not value academic content, than it will not pay for the practitioner to have access to this material.

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4. Discussion and conclusion The overall purpose of this study was to investigate what knowledge sources practitioners with a Ph.D. prefer to incorporate into their decision making process and take action. It was revealed that the doctoral graduates acquire new knowledge through a variety of channels. The most popular format was academic journals, followed by practitioner outlets. The next most common sources were the knowledge these practitioners received during their Ph.D. degree and through discussion with colleagues. As illustrated in the case of the consultant participants, if there is a barrier to the access of academic literature, the likelihood of the individual utilizing this source diminishes. Additionally, this study explored to what extent these practitioners utilize academic knowledge in their work as well as transferring its content to others. While academic knowledge was not habitually applied by every participant, it did have a lasting impact on those individuals who regularly utilized this material. All but two of the participants referred to academic material to some extent. In many instances, academic literature was accessed in response to a problem encountered, because staying current with academic thinking was simply not feasible. The extent to which these practitioners act as an intermediary between academia and practice was examined. It was discovered that while there are some deterrents to the Ph.D. graduates behaving in this fashion, e.g., the perceived lack of value of academic research from peers and clients, practitioners can still fulfill this function. Additionally, the participants outlined how academics can make academic research applicable to their work environment, such as rich executive summary and a more generalizable sample population. The exception to this was the meta‐analysis. These analyses were noted for an ability to lend value to practitioners due to a summative nature. In conclusion, there is a strong argument for the academic society to maintain contact with doctoral graduates because it increases the probability of these practitioners consuming, implementing, and transferring academic knowledge. One implication for practice is that there must be a greater demand and appreciation for evidence‐based knowledge. The current organizational cultures outside of the public sector are not conducive to a practitioner accessing and applying academic literature. These results enhanced understanding of the factors that affect a doctoral business program graduate’s likelihood of acquiring, utilizing, and disseminating academic knowledge. Second, implications from this study would be relevant to the education of doctoral business program graduates because these graduates can be valuable knowledge distribution channels that can enhance the productivity and quality of an organization. Therefore, they should be prepared with the skills and experiences during their education necessary to act as an intermediary that promotes the benefits of academic literature. Third, organizations employing doctoral degree holders should consider providing them with access to academic literature, which may improve their decision making. Most importantly, these individuals may act as knowledge ambassadors to deliver academic knowledge to their colleagues and present it in an appropriate format. This, in turn, may improve overall organizational performance. Particularly, this is an important issue for consulting companies, which, in contrast to public organizations, rarely provide their employees with access to academic material. Recently, the role of the academic institution was questioned regarding its responsibility with respect to the accessibility and distribution of academic research. This study argues that the characteristics of academic research identified as a barrier by these practitioners are important criteria for an academic to publish in a scholarly journal. This includes a narrow scope, limited generalizability, language, and the sample population. If academics were to present their research to accommodate practitioners, they would never be published in academic journals. As this is a significant aspect of an academic’s performance evaluation, it doesn’t make sense for them to do this – therefore, it is not aligned between the stakeholders. While some participants believed these institutions should be changing to address what industry values, others argued this is not, and should not be the function of universities, echoing the debate in academic circles. However, it is unclear if an academic institution or academic journal can be sustainable if it does not fill industry’s need for knowledge – can it be a self‐sufficient industry with academics publishing solely for themselves? With consultants increasingly be viewed as a viable alternative for academic knowledge dissemination, this is becoming an urgent matter for policy‐makers.

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Madora Moshonsky, Alexander Serenko and Nick Bontis Considering an increasing competitiveness of an academic job market around the world, more doctoral business program graduates will join the non‐academic sector in the future. Therefore, they need to receive not only theoretical but also applied knowledge during their training. Particularly, an ability to convert academic findings to actionable items should be strongly emphasized. For this, changes to the academic curricula are required at both institutional and national levels.

Acknowledgements This study is kindly supported by the grant 864‐07‐0181 from the Social Sciences and Humanities Research Council of Canada (SSHRC).

References Baxter, L. A. and Braithwaite, D. O., Eds. (2008) "Engaging theories in interpersonal communication: multiple perspectives". Sage, Los Angeles, CA. Bennis, W. G. and O'Toole, J. (2005) "How business schools lost their way", Harvard Business Review, Vol. 83 No. 5, pp 96‐ 104. Berger, P. L. and Luckmann, T. (1966) The social construction of reality: A treatise on the sociology of knowledge, Anchor Books, New York, NY. Booker, L., Bontis, N. and Serenko, A. (2008) "The relevance of knowledge management and intellectual capital research", Knowledge and Process Management, Vol. 15 No. 4, pp 235‐246. Cavusgil, S. T., Calantone, R. J. and Zhao, Y. (2003) "Tacit knowledge transfer and firm innovation capability", The Journal of Business and Industrial Marketing, Vol. 18 No. 1, pp 6‐22. Davenport, T. H. and Prusak, C. (1998) Working Knowledge, Harvard Business School Press, Boston. Foss, N. J. and Pedersen, T. (2002) "Transferring knowledge in MNCs: The role of sources of subsidiary knowledge and organizational context", Journal of International Management, Vol. 8 No. 1, pp 49‐67. Hutchison, J. and Huberman, M. (1994) "Knowledge dissemination and use in science and mathematics education: A literature review", Journal of Science Education and Technology, Vol. 3 No. 1, pp 27‐47. Knights, D. and Scarbrough, H. (2010) "In search of relevance: Perspectives on the contribution of academic‐practitioner networks", Organization Studies, Vol. 31 No. 9, pp 1287‐1309. Liyanage, C., Elhag, T., Ballal, T. and Li, Q. (2009) "Knowledge communication and translation ‐ a knowledge transfer model", Journal of Knowledge Management, Vol. 13 No. 3, pp 118‐131. Nohria, N. and Eccles, R. G. (1998) "Where does management knowledge come from?", in: J. L. Alvarez (Ed.), The diffusion and consumption of business knowledge, pp 278‐304. Parent, R., Roy, M. and St‐Jaques, D. (2007) "A systems‐based dynamic knowledge transfer capacity model", Journal of Knowledge Management, Vol. 11 No. 6, pp 81 ‐ 93. Pearson, J. M., Pearson, A. and Shim, J. P. (2005) "The relevancy of information systems research: The practitioner's view", Information Resources Management Journal, Vol. 18 No. 3, pp 50‐67. Roling, N. G. (1992) "The emergence of knowledge systems thinking: A changing perception of relationships among innovation, knowledge process and configuration", Knowledge and Policy, Vol. 5 No. 1, pp 42‐64. Rynes, S. L., Bartunek, J. M. and Daft, R. L. (2001) "Across the great divide: Knowledge creation and transfer between practitioners and academics", Academy of Management Journal, Vol. 44 No. 2, pp 350‐355. Serenko, A., Bontis, N. and Hull, E. (2011) "Practical relevance of knowledge management and intellectual capital scholarly research: Books as knowledge translation agents", Knowledge and Process Management, Vol. 18 No. 1, pp 1‐9. Serenko, A., Bontis, N. and Moshonsky, M. (2012) "Books as a knowledge translation mechanism: Citation analysis and author survey", Journal of Knowledge Management, Vol. 16 No. 3, pp 495‐511. Simmons, P. G., Dawley, D., Richie, W. and Anthony, W. (2001) "An exploratory examination of the knowledge transfer of strategic management concepts from the academic environment to practicing managers", Journal of Managerial Issues, Vol. 13 No. 3, pp 360‐376. Starkey, K. and Madan, P. (2001) "Bridging the relevance gap: Aligning stakeholders in the future of management research", British Journal of Management, Vol. 12 No. S1, pp S3‐S26. Szulanski, G. (1996) "Exploring internal stickiness: Impediments to the transfer of best practice within the firm", Strategic Management Journal, Vol. 17 No. 1, pp 27‐43.

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The Influence of Cultural Factors on Creation of Organization’s Knowing Vyda Mozuriuniene1, Palmira Juceviciene2 and Kestutis Mozuriunas1 1 Comfort Heat Ltd., Vilnius, Lithuania 2 Kaunas University of Technology, Kaunas, Lithuania vim@comfortheat.eu palmira.juceviciene@ktu.lt kestutis@comfortheat.eu Abstract: Organization‘s knowing (Stankeviciute, 2002) is the most common category which describes all the knowledge (explicit, implicit and tacit), existing in the organization (according to Juceviciene, Mozuriuniene (2011), characteristic for individual, group and organization’s levels). This knowledge also includes the type of knowledge which is significant for organization and created within the organization. It is called an organizational knowing. However, some knowledge in organization exists ’unofficially’, since organization considers it insignificant. It is created in non‐formal way, sometimes even accidentally. This is informal organization’s knowing. The model of organizational knowledge (SECI) by Nonaka, Takeuchi (1995) reveals that the interaction and transformation of tacit and explicit knowledge takes part during the interaction of individuals. The authors indicated four stages of organizational knowing creation: socialization, externalization, combination and internalization. Johnson (2007) criticized the theory of organizational knowing creation developed by I. Nonaka and claimed that knowledge could be treated only on individual level, but not on group‘s level, since all knowledge is related to an individual person as a primary carrier. Johnson (2007) submitted his own approach to the creation of explicit knowledge from tacit knowledge. Precisely, creation, and not the conversion, of tacit knowledge into explicit knowledge are emphasized in Johnson’s approach. Juceviciene, Mozuriuniene (2011) did not contrast the approaches developed by Nonaka, Takeuchi (1995) and Johnson (2007). They believe that each stage of SECI model by Nonaka and Takeuchi (1995) could be accompanied by Johnson‘s individual learning which emerges in the process of constructing his/her own personal knowledge. Juceviciene, Mozuriuniene (2009) argue that this knowledge is not necessarily recognized as important for organization. This is a significant assumption to discuss not only organizational knowledge and knowing, but also organization’s knowledge and knowing. As Czarniawska (2007) states, knowledge is essentially related to the human actions, and the different types of business activities are the deep reflection of cultural roots and knowledge structure. Hofstede (1990) claimed that national cultural dimensions influence the organization’s culture and its activity results. Can national cultural dimensions make influence on the creation of organization’s

knowing and to what extent? Which stages of creation of organization’s knowing are considerably influenced by cultural dimensions? The aim of the article is to reveal the cultural factors that influence the creation of organization’s knowing. Aiming to highlight the characteristics of organization’s knowing creation and the cultural factors that influence this process, the analysis of scientific literature is applied. The empirical study was carried out in the multinational company within its subsidiaries in three Baltic countries. Firstly, the article presents the characteristics of organization’s knowing creation and the cultural factors influencing this process revealed in the analysis of scientific literature. Secondly, the methodology of empirical study is introduced. Thirdly, the analysis and discussion of the empirical findings on the influence of cultural factors on the creation of organization’s knowing is presented. The conclusions make the final section of the article. Keywords: organization’s knowing, cultural dimensions, tacit knowledge, explicit knowledge

1. Introduction The process of globalization reveals the importance of organization’s international competitiveness, the cultural differences among countries. The enterprises enter the international business sphere and encounter the external and internal difficulties that are more complicated than those in local market. Under such circumstances the exceptional, unique organization’s knowing (further OK), that is difficult to duplicate, has a great value. The concern about OK and the cultural factors that influence its creation is related with the enterprises’ need to gain stronger competitive advantage. The creation of organization’s knowing has a particular significance for the companies that are involved in international business, since multiculturalism may create the added value of OK and, respectively, may become the barrier of knowledge creation, increase misunderstanding. Can cultural factors make influence on the creation of OK and to what extent? Which processes of OK creation are considerably influenced by cultural dimensions? These questions, still underaddressed by the researchers, are raised in this paper. Its aim is to reveal the cultural factors that influence the creation of OK.

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2. The characteristics of organization’s knowing creation and the influencing cultural factors Organization’s knowing reflects the holistic approach to knowledge that exists in the organization and is related to people, technologies, structures and organization’s culture. According to Stankeviciute (2002), OK implies the constantly changing knowing because of the interaction between organization’s members and their groups’ tacit knowing and explicit knowledge. Juceviciene, Mozuriuniene (2011) emphasized that the OK embraces the whole knowing of all individuals, who make an organization, the knowing of all groups in organization and the knowing of organizational level; i.e. explicit, implicit and tacit knowing. So, OK embraces the organization’s culture as the sense of unity prevailing in the organization that was formed on the basis of common, interrelated values. Nonaka and Takeuchi (1995) developed the dynamic model of organizational knowledge creation (SECI) and explained its four stages: a) socialization when the organization members act together and the shared tacit knowledge is being formed; b) externalization, when organization members in the act of collective dialogue transfer their tacit knowledge by transforming it into collective explicit knowledge; c) combination takes place in the higher organization level, when the collective explicit knowledge of several groups is combined with the organizational knowledge that belongs to the entire organization; d) internalization, when the collective explicit knowledge on organization level is transformed into the group and individual tacit knowing (in the practical activities and learning). Johnson (2007) criticized the theory of organizational knowing creation developed by Nonaka (1990) and claimed that knowledge could be treated only on individual level, since all knowledge calls for an individual person as a primary carrier. Juceviciene, Mozuriuniene (2011) claimed that the approaches developed by Nonaka, Takeuchi (1995) and Johnson (2007) could be treated as mutually complementary. Each stage of SECI model (socialization, externalization, combination and internalization) could be accompanied by Johnson‘s individual learning when employee constructs his/her own personal knowledge. More than that, Juceviciene, Mozuriuniene (2011) noticed that this knowledge is not necessarily recognized as important for organization. It is informal knowledge, officially “behind the doors” of organization. This informal knowledge could be formed in the collective levels of organization. In this way, two structural parts of OK were discerned: a) organizational knowing (different kinds of organization’s knowledge that is recognized as significant for organization’s performance), this is officially recognized OK, supported and fostered by organization; b) informal OK; i.e. knowledge that exists unofficially on the individual, group and organizational levels. There is empirical evidence to suggest that both structural parts of OK exist (Juceviciene, Mozuriuniene, 2009), and the organizational knowing and informal OK make influence on the success of employees’ performance in the individual, group and organizational levels. So, the creation of OK could be described by the symbiosis of Nonaka, Takeuchi (1995) and Johnson’s (2007) ideas:

Socialization;

Externalization;

Combination;

Internalization;

Individual’s independent learning.

Knowledge is essentially related to human actions, and all types of business activities have certain shape and deeply reflect the cultural roots and knowledge structure (Czarniawska, 2007). What influence do cultural dimensions make on the creation of OK? The theorists of knowledge management make parallel between culture and knowledge management. According to Jucevicius (2004), culture in this case is perceived to be wider as compared to ethnical/national categories. However, under the globalization influence the national differences remain and their role becomes more evident as never before. Hofstede (1980, 2001) claims that the organizational forms of economic activity in different countries are influenced by the cultural values of the society. Hofstede (2001) emphasizes the value systems in the society

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Vyda Mozuriuniene, Palmira Juceviciene and Kestutis Mozuriunas that are historically inherent. Santoro and Gapalakrishnan (2000), Walczak (2005) consider structure and culture as two interrelated variables that are essential for the success of knowledge management. So, as Hofstede (1980) states, the cultural dimensions of employees (power distance, individualism versus collectivism, masculinity versus femininity, uncertainty avoidance) influence organization‘s culture. Since OK embraces the sense of value integrity based on organization’s culture, it could be assumed that the creation of OK is also influenced by employees’ cultural dimensions. Is it really so? If yes, then how and to what extent can the specific cultural dimensions influence the creation of OK? This article explores the influence of cultural factors on the creation of OK by comparing the subsidiaries of the same multinational company (further MnC), located in different countries. Therefore, the traditional approach to culture was applied which considers culture as a factor for differentiating countries or organizations (Hofstede, 1980). However, it is quite complicated to define what factors in the culture determine the creation of OK. As Jucevicius (2004) states, culture is a complex and deep phenomenon with a few objective criteria to describe. According to Hofstede (1990), in the company a big variety of national cultures exists and manifests in a form of different labor values under the same work structures. The values of national level are mainly formed in family and at school, while the organizational practices, including knowledge, are formed in the socialization at workplace when the basic values of employees had been already formed (Jucevicius, 2004). So, the employees’ national cultural dimensions may influence the stages of OK creation in the same way as organizational level factors do. At the same time the national cultural characteristics influence organization’s culture as context. The empirical study by Laurent (1986) proved that the attitudes about management work were more varied among the managers from the subsidiaries of one MnC located in different countries than among the managers from different companies of the same country. Adler assumes (1997) that the strong culture of MnC may diminish the impact of national culture, but organization’s culture is not able to eliminate it. So, according to Hofstede (1980), national cultural dimensions influence the organization’s culture and its results, and the organization’s culture of MnC is mainly determined by the national cultural dimensions of the country it represents. In other words, the national culture of the country the headquarters of MnC are located in influences the whole culture of MnC organization and, similarly, all the stages of creating OK. Relying on Hofstede‘s (1980) approach to the culture as the factor that differentiates countries or organizations, it is expedient to define if the essential cultural dimensions described by this author could be treated as informative cultural factors that influence the creation of OK. The analysis of these cultural dimensions is focused on their power to help the employees of the implied subsidiary to create OK. Power distance is defined as the extent to which the society accepts that power is distributed unequally. This indicates the country‘s attitude towards hierarchy, authority and power relationships. Managers and employees in cultures exhibiting a large degree of power distance accept the inequalities. They agree that hierarchy is a natural inequality. Authority is more centralized, the employees are expected to implement the orders, and the orders are not questioned. Top management is not easily accessible. Employees avoid the open presentation of their opinions and are unwilling to demonstrate disagreement. There is no strong mutual trust. People usually act according to the formalized rules. Most probably, these cultures are more oriented to the recognized knowledge, i.e. explicit organizational knowledge, instead of OK, which emphasizes tacit individual and collective knowledge. It could be assumed that an organization that relies on power distance misses the opportunities for competence development that is enabled by individual and collective knowing based on tacit knowledge. The managers and subordinates in low power distance cultures treat each other as equals. The horizontal relationships prevail. The solidarity among employees is observed. The organizations of this culture emphasize the importance of human resources. The relationships of employees are not only formalized by regulations, they are not predetermined. It causes the creation of tacit knowledge. Therefore, the culture of low power

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Vyda Mozuriuniene, Palmira Juceviciene and Kestutis Mozuriunas distance enables to achieve higher organizational competence. The low power distance in the subsidiary company could be defined by these parameters of employees‘ perception:

The relationship among company‘s managers and subordinates is friendly and liberal;

The company values ‘what you say, and not who you are’.

The dimension of uncertainty avoidance defines the way people solve problems about future uncertainty and to what degree they feel the threat over the unclear situations, try to avoid them. This dimension reveals to what extent particular groups tolerate uncertainty. Countries with high uncertainty avoidance prefer the regulations, rules, the detailed agreements in advance. Managers are less willing to take risks in decisions and responsibilities; they are more detail – oriented. These cultures, as those with high power distance, emphasize the explicit knowledge. The organization in this way limits its competence and does not evaluate tacit knowledge in individual and collective levels. Countries with low uncertainty avoidance less rely on strict rules and procedures and focus on flexible competence. Managers are more oriented to strategic questions and ready to take risks in decisions, they are not afraid of responsibility. These cultures are characterized by creativity and both explicit and tacit knowledge. Low uncertainty avoidance as the factor of OK creation may be defined by these parameters:

Company makes flexible decisions, responding to the existing situation;

Company understands that a lot of things depend on the way people perceive and interpret them.

The continuum of individualism and collectivism refers to the degree to which individuals are integrated into groups. Individualism dimension describes to what extent the individual interests are preferred over group’s interests. This dimension describes how a member of the society values himself/herself as compared to the group he/she belongs to. Cultures with high individualism emphasize individual knowledge (explicit and tacit), the independence is valued. Countries with low individualism level focus on collectivism. Personal identity is based on the membership in a group. Collectivist society demands for stronger emotional dependence on organization and, respectively, the higher organization’s responsibility against its employees. It is likely that low individualism (or high collectivism) makes favourable conditions for collective knowledge (explicit and tacit). Collectivism is perceived by employees as the factor of OK creation and may be defined by these parameters:

Company values and supports teams and team working;

Collective atmosphere prevails among the company’s employees.

Masculinity and femininity dimension reveals what type of organizational behavior prevails. Masculinity discloses the extent to which personal and professional goals are important in society and the societal preferences on goal attainment or task implementation. It is related to the traditional understanding of male and female roles. From the organizational perspective, masculinity is work – oriented ego. The role is defined as ‘living for working’, i.e. orientation to goal attainment. These cultures, as those with individualism dimension, emphasize the explicit and tacit knowledge on individual level. Femininity implies social ego and is related to ‘working for living’, i.e. orientation to task implementation in shared activities. The group relations are important. So, there is a big probability that explicit and tacit knowledge on collective level would be created and applied. Femininity as a cultural factor that influences creation of OK may be defined by the following parameters:

The priority in company is given to group decision making and search for consensus;

Nurturing climate for employees prevails in company.

3. The methodology of empirical study The theoretical assumptions have to be proved by empirical findings, by defining if the discerned cultural factors make a considerable influence on the creation of organization‘s knowing. The organization for the empirical study was selected according to these criteria: a) it has the essential features of knowledge organization (specialized knowledge is the main source of created value; the knowledge management system is implemented); b) it is multinational, so that the employees of several subsidiaries could be involved in the

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Vyda Mozuriuniene, Palmira Juceviciene and Kestutis Mozuriunas study in the context of one company. The selection of respondents was targeted; the sample consisted of the knowledge workers. The empirical study was carried out in MnC with its headquarters located in Finland and the subsidiaries in 9 countries. The empirical study involved the subsidiaries in Estonia (EE), Latvia (LV) and Lithuania (LT). The empirical study was based on investigating the cultural factors described by Hofstede (1980). We, however, have not applied Hofstede methodology for our empirical study, but referred to the meanings of national cultural dimensions by Hofstede in the discussion of our empirical findings. The questionnaire was based on the parameters of cultural factors presented in the first part of the paper. While constructing the structured questionnaire for the survey, we combined these parameters with four stages of knowledge creation of SECI model and the idea of individual‘s knowledge formation defined by Johnson (2007).The main part of the questionnaire was made in a form of matrix (the horizontal axis describes the parameters of OK creation, the vertical axis presents the parameters of cultural factors). In the process of constructing the questions for survey we considered that the problem of defining tacit knowledge is firstly related to its transformation into explicit knowledge. So the questions encouraged employees’ reflection, and the reconstruction was stimulated with the aim to help employees make their tacit knowledge on past activities explicit. The employees of subsidiaries in Lithuania, Latvia and Estonia were surveyed, accordingly, 29, 32 and 32 persons. The data was processed by calculating the Means (M) of the scores. The significant data is considered to be equal to or higher than the average rating of the scale (M≥1,50, in the scale from 0 to 3). According to the calculated values, the influence degree M=1,50÷1,70 is considered to be moderate, while M=1,71÷2,20 is considered to be stronger than moderate.

4. The analysis and discussion on cultural factors influencing organization’s knowing creation The empirical study (see table 1) reveals that the majority of data indicates the moderate and stronger that moderate influence of cultural factors on the creation of OK. The study results revealed that the most significant impact on the development of organization‘s knowledge in all the stages of OK creation is determined by low power distance (LPDI), collectivism and femininity dimension. Let us go deeper into the analysis of cultural factors‘ influence on separate processes of OK creation. Socialization This process defines the creation of tacit knowledge when people are involved and communicate in shared activity. The high power distance, discussed in the theoretical part, is considered to be an unfavourable condition. And, on the contrary, low power distance could make a positive impact on tacit knowledge. The subsidiaries under research, which belonged to the type of small enterprises, maintained horizontal, not vertical, relationship that is an excellent context for low power distance. These cooperational and communicational possibilities as the assumption for creating collective tacit knowledge is emphasized in a number of works (Jarvis, Holford and Griffin, 2004). The study disclosed that low power distance (LPDI) had the similar influence on the employees of all the subsidiaries in the Baltic countries, and a little bit stronger influence on Lithuanian company (M= 1,86) as compared to Latvian (M=1,69) and Estonian (M=1,60) companies. So, the question is: are the differences, noticed among the employees of subsidiaries, determined by the differences in national cultural dimensions? The answer to the first question is possible only with a certain reservation, since our methodology is different to Hofstede‘s one, even if the content of factors is very similar. As it is obvious from the research based on Hofstede methodology (see table 2), the lowest power distance (33) is attributed to Finland (MnC headquarters). Then Estonia follows (40), Lithuania (42) and Latvia (44). The latter two were obtained by Heutinger (2008). Table 1: The influence of cultural factors that determine OK creation in the subsidiaries

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Vyda Mozuriuniene, Palmira Juceviciene and Kestutis Mozuriunas Characteristics of cultural factors Stages of OK creation

Socialization

Externalization

Combination

Internalization

Individual’s independent learning

Country

Low power distance (LPDI)

Low uncertainty avoidance (LUAI)

Collectivism (IND/COL)

Femininity (MAS/FEM)

1,86

1,55

1,66

1,52

1,69

1,29

1,38

1,46

1,60

1,70

1,80

1,59

1,74

1,51

1,84

1,72

1,70

1,34

1,49

1,51

1,84

1,76

1,91

1,86

1,85

1,67

1,79

1,81

1,44

1,37

1,47

1,84

1,73

1,78

1,86

1,72

1,37

1,65

1,54

1,50

1,32

1,33

1,46

1,60

1,49

1,74

1,88

1,72

1,53

1,52

1,60

1,61

1,46

1,42

1,44

1,78

1,63

1,71

1,72

Hence, in socialization low power distance makes the greatest influence on tacit knowledge in Lithuanian subsidiary, even though this country shows moderate power distance (see table 2). Thus, it could be assumed that the culture of MnC subsidiaries, including Lithuanian one, is more influenced by the headquarters located in low power distance country (Finland) than by the national culture of subsidiary’s country. Furthermore, the educational background of employees (dominates higher education) and the type of their activity (knowledge employees) has to be taken into account. The employee profile like this naturally calls for the need of low power distance. Low uncertainty avoidance (LUAI) and collectivism (IND/COL) both have a bit stronger than moderate effect on knowledge creation in socialization process only in Estonian company (M=1,70; M=1,80), less influence on Lithuanian employees (M=1,55; M=1,66), and little influence on Latvian employees (M=1,29; M=1,38). All the Baltic countries, respectively, are characterized by quite high uncertainty avoidance and individualism (UAI=60, 65, 63; PDI= 60, 60, 70; see table 2). The value of UAI (59) for Finland means the moderate influence and is the least among the countries in the study. In this way, however, it is quite difficult to define what has the main influence on the fact that Estonian company has low uncertainty avoidance. Nevertheless, it could be assumed that the specific profile of employees may exert the influence (e.g. higher education, type of activity, related to knowledge creation). The influence of collectivism is distributed as follows: stronger than moderate in Estonia, moderate in Lithuania and smaller than moderate in Latvia. IND/COL dimension (as collectivism), according to Hofstede methodology, is weak in Estonia and Lithuania and even weaker in Latvia. It could be stated that collectivism influence in socialization process in Estonian and Lithuanian companies is related to the profile of knowledge employees and their activities rather than the national cultural dimensions (referring to Hofstede). The enterprises of the Baltic countries make the similar sequence in terms of femininity influence on knowledge creation, but femininity has less powerful effect as compared to other cultural factors (see table 1). According to Hofstede, the femininity (FEM) dimension is strongly manifested in all four countries (see table 2), especially in Latvia. Thus, probably other reasons, but not the national cultural characteristics, determine low influence of femininity on knowledge creation in the enterprises under research. This could be related to the type of organizational activities based on technical scientific knowledge (this type prevails in the subsidiaries), which determine the natural science type reasoning of employees (Cohen, 1990). That could be a reason of masculinity. Externalization This process of knowledge creation is related to the manifestation of tacit knowledge and construction of collective knowledge. The study results revealed that all four cultural dimensions (LPDI, LUAI, IND/COL, MAS/FEM) are perceived by the employees of Estonian company (M= 1,84;1,76;1,91;1,86), then follows

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Vyda Mozuriuniene, Palmira Juceviciene and Kestutis Mozuriunas Lithuanian company and Latvian company (see table 1). As it was discussed earlier, the data both in Estonian and Lithuanian companies are not consistent with the national cultural dimensions set by Hofstede methodology (see table 2). This could be related to knowledge employees’ profile and the distinctive features of their activity. Table 2: Values of Hofstede cultural dimensions Country

Power distance (PDI)

Uncertainty avoidance (UAI)

Individualism and collectivism (IND)

Masculinity/ femininity (MAS)

Index Finland (FI)

Estonia (EE)

Latvia (LV)

Lithuania (LT)

Sources: Finland, Estonia ‐ Hofstede and Hofstede (2005), Latvia, Lithuania ‐ Huettinger (2008). Combination In this process the leading position, according to all cultural factors, is taken by Estonian company (LUAI, IND/CO, MAS/FE, accordingly, M=1,73;1,78;1,86). The second position is taken by Lithuania, except LPDI, the value of which is 1,85 for Lithuania. Estonia, although, has 1,84 for LPDI. The third position is taken again by Latvian employees, whose evaluation of the cultural factors equals or is less than 1,50. In combination, similarly to externalization, the cultural factors perceived by Estonian and Lithuanian employees are not consistent with the national cultural dimensions set by Hofstede methodology. Internalization In this stage the organizational level knowledge created in combination process is acquired by employees and their groups. The training and learning takes place, knowledge is applied in workplace till it becomes the self of the employees, it is embedded in activity and becomes tacit. The study revealed that in this stage employees identified the influence of cultural factors in the same manner as in combination stage, but the absolute values of these factors are smaller. In Latvian case, the value of M is less than 1.5 (see table 1) in three cases out of four. So, the similar conclusions on the impact of employees‘ profile could be made in internalization stage as it was done in the analysis of other processes of creating organizational knowledge. Individual‘s learning (especially reflection) During learning processes, the influence of cultural factors is similar to the cases of combination and internalization (see table 1). The special attention has to be paid to Latvia where the employees, unlike in Estonia and Lithuania, identified the influence of cultural factors in different manner: almost all cultural factors had little influence on all the processes of OK creation (see table 1). Latvia, according to Hofstede cultural dimensions (see table 2), is exceptional with its especially high femininity and high individualism (low collectivism) as compared to other Baltic countries and Finland. So, it is understandable, why the influence of collectivism is very weak. However, the influence of low uncertainty avoidance and femininity is also weak, while, according to Hofstede cultural characteristics, they are strongly represented (UAI=63 and MAS=9). The profile of Latvian employees was not distinct from the employees in other Baltic countries. This means that the reasons should be searched elsewhere, perhaps in the external environment of the company. It should be mentioned that at the period of empirical study one of the biggest banks of that country was declared bankrupt; therefore, it is natural that the company and its employees could feel the economic and psychological difficulties that impeded the processes of knowledge creation and diminished the influence of other factors. Of course, our empirical study could be repeated in other multicultural companies that have large subsidiaries. A bigger sample of informants would enable to carry out broader and more precise statistical analysis of the data.

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5. In conclusion The creation of OK is impacted by the cultural factors that may exert the positive influence:

low power distance;

low uncertainty avoidance;

collectivism;

femininity.

These cultural factors determine the creation of organization‘s knowledge in the processes of socialization, externalization, combination, internationalization and individual‘s independent learning. It is defined that:

The biggest influence on these processes is made by the factors of low power distance, collectivity and femininity.

The influence of these cultural factors on OK creation depends more on the profile of employees and their activity type than on the national cultural dimensions of the countries the headquarter and subsidiaries are located in.

Some extreme external factors may obscure the favourable context of knowledge employees profile and their activity type, and in this case the influence of cultural factors on OK creation becomes weak.

Aiming for the successful creation of OK, the managers in knowledge organizations have to focus on hiring competent knowledge employees, low uncertainty avoidance, collectivity and femininity. However, they have to bear in mind that the extremely unfavourable external factors (e.g. the consequences of economic crisis) may become the main obstacles that impede knowledge employees‘ activity in developing OK and decrease the influence of cultural factors perceived by employees.

6. Acknowledgements This research is funded by the European Social Fund under the Global Grant measure.

References Adler, N.J. (1997) International dimensions of organizational behavior, 3rd ed., Shout‐Western College Publishing, Cincinnati. Czarniawska, B. (2007) “Forbidden Knowledge. Organization Theory in Times of Transition”, Management Learning, Vol 34, No. 3, pp 353‐365. Heuttinger, M. (2008) “Cultural dimensions in business life: Hofstede’s indices for Latvia and Lithuania”, Baltic Journal of Management, Vol 3, No. 3, pp 359‐376. Hofstede, G. (1980) Culture’s consequences, Sage, London. Hofstede, G. (2001) Culture’s Consequences. Comparing Values, Behaviors, Institutions and Organizations across Nations, nd 2 revised ed., Sange Publications, London. Hofstede, G. and Hofstede, G.J. (2005) Cultures and Organizations – Software of Mind, McGraw‐Hill, New York. Jarvis, P., Holford, J. and Griffin, C. (2004) The theory and practice of learning, Kogan Page, London. Johnson, W.H.A. (2007) “Mechanisms of tacit knowing: pattern recognition and synthesis”, Journal of Knowledge Management, Vol 11, No. 4, pp 123‐139. Juceviciene, P. and Mozuriuniene, V. (2009) “The relationship between organization‘s knowing and organization‘s knowledge: the boundaries of recognition and formalization”, Economics and Management, No. 14, pp 1129‐1139. Juceviciene, P. and Mozuriuniene, V. (2011) “Organization’s Knowing or Organizational Knowing?”, Proceedings of the 8th International Conference on Intellectual Capital, Knowledge Management and Organizational Learning, Bangkok, Thailand, November. Jucevicius, G. (2004) Integrated Approach to Management Models in the Context of Economic Transformation: Cultural and Institutional Perspectives, Doctoral dissertation, Kaunas University of Technology, Kaunas. Laurent, A. (1986) “The cross‐cultural puzzle of international human resource management”, Human Resource Management, Vol 25,No. 1, pp 91–102 Nonaka, I. and Takeuchi, H. (1995) The Knowledge Creating Company, Oxford University Press, New York. Santoro, M. and Gopalakrishnan, S. (2000) “The institutionalization of knowledge transfer activities within industry‐ university collaborative ventures”, Journal of Engineering and Technology Management, No. 17, pp 299‐319. Stankeviciutė, J. (2002) Methodology of the Enhancing of Organization’s Knowing, Doctoral dissertation, Kaunas University of Technology, Kaunas. Walczak, S. (2005) “Organizational Knowledge Management Structure”, The Learning Organization, Vol 12, No. 4, pp 330‐ 339.

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A Structural Equation Model of Organizational Learning Based on Leadership Style in Universities Fattah Nazem, Mona Omidi and Omalbanine Sadeghi Department of Education, Roudehen Branch, Islamic Azad University, Roudehen, Iran nazem@riau.ac.ir mona_omidi62@yahoo.com osadeghi82@yahoo.com Abstract: The purpose of the present study was to provide a structural equation model of organizational learning based on leadership style in universities. The population of the research included all employees of Islamic Azad University (Roudehen, Damavand, Pardis, and Bomehen branches and educational centers) in Iran. 559 employees were selected using stratified and cluster random sampling methods. The research instruments were as follows: Bass and Avolio’s (1996) leadership style questionnaire which consisted of 41 items with two underlying constructs of transformational leadership and transactional leadership and Cronbach Alpha of 0.93; and Watkins and Marsick’s (1997) organizational learning questionnaire which consisted of 43 items with three underlying constructs of individual level, group level, and organizational level and Cronbach’s Alpha of 0.97. The results of path analysis using LISREL software indicated that dimensions of leadership style had a direct effect on organizational learning with the indices of 0.92. The model also showed that the factor of transactional leadership had the highest direct effect on the organizational level in the factor of organizational learning. It was also concluded that the proposed model showed full fit. Keywords: structural equation model, organizational learning, leadership style, universities

1. Introduction and purpose of the study Practitioners and managers know that competition and environmental turbulence due to rapid and unexpected changes are inescapable features of a global world. In such a scenario, developing new competences and capabilities has gained importance, and this places learning at the center of the organization. This has lead to the development of new organizational forms known as "organizational learning" (Franco & Almeida, 2011). Over the course of the last few years, the idea of learning organizations has been drawing more and more attention (Starkey, 1996). As employees and managers makeup an integral element of organizations, it is likely that they play an essential role in learning organization. Organizations can then except to gain knowledge from these individuals (Kanter, 1983; Kim, 1993). Researchers have studied the processes of models and theories of the way that people can learn as a collective group and adapt to environmental changes (Schwandt & Marquardt, 2000; Senge, 1990; Watkins & Marsick, 1996). Nowadays, organizations are under severe pressure to learn faster and more effectively in order to promote a learning environment (Kline & Saunders ,1998). Organizational learning has been associated with organizational theory since the 1930s (Argyris, 1999). Huber (1991) defines organizational learning as the processing of information with the aim to store knowledge in the organizational memory. According to Huber (1991), organizational learning consists of four constructs: (1) information acquisition; (2) information distribution; (3) information interpretation; and (4) organizational memory. Sanchez (2005) extend Hubers’ information‐processing perspective to include behavioral and cognitive changes which should, in turn, have an impact on organizational performance. Watkins and Marsick (1993, 1996) identifies seven core practices at the individual, group, and organizational levels as follows:

Individual level

Creating continuous learning opportunities

Promoting inquiry and dialogue

Team/group level

Encouraging collaboration and team learning

Organizational level

Creating systems to capture and share learning

Empowering people toward a collective vision

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Connecting the organization to its environment

Providing strategic leadership for learning

Given the turbulent environments that organizations work within, continuous learning is a key driver of their ability to remain adaptive and flexible – that is, to survive and effectively compete (Burke ,2006). Organizational learning is one of the most important sources of sustainable competitive advantage that companies have (de Geus, 1988), as well as an important driver of corporate performance (Stata, 1989). Effective leadership is the main cause of competitive advantage for any kind of organization (Avolio et al., 1999; Zhu et al., 2005). Leaders are conferred the opportunity to lead, not because they are appointed by senior managers; they lead because they are perceived and accepted by followers as leaders (Boseman, 2008). Since organizations face a lot of environmental pressures, there is an urgent need to change, so change is possible and must be done through the process of organizational learning, and the beginning of the movement of change is through leadership by transforming the culture of the old work to a new culture (Lakomski, 2001). This is in addition to the fact that the leadership is responsible for the education and rehabilitation of individuals in the organization . Burns (1978) suggests two types of leadership: transactional and transformational. Transactional leadership encompasses the initiation of structure, role clarification, meeting the needs of individuals, and the distribution of compensation and penalty in accordance with employee’s job performance. This style consists of management by exception and or contingent reward (Bass, 1985, p.581). Transactional leaders guide their followers toward establishing goals by clarifying role and task requirements (Robbins & Judge, 2011, p. 424), inducing apparent change in an individual’s behavior (Burns, 1978), trying to understand the needs of the individual and helping him/her build up the confidence needed to successfully accomplish assigned tasks (Rosenbach et al., 1996). Transformational leadership is more of an intellectually stimulating leadership style. While transactional leaders segregate the organization into parts, transformational leaders consider the organization as one large harmonized working system (Bass &Avolio, 1989). Transformational leaders “inspire followers to transcend their self‐interests for the good of the organization and can have an extraordinary effect on their followers” (Robbins & Judge, 2011, p. 424). Bass (1998) believes that transformational leaders are agents of change who boost and direct individuals towards a new set of corporate values and actions. Transformational leaders encourage individuals to have a unique vision of the future, share organizational values and beliefs, go beyond their expectations, maintain self‐interest, and pursue personal recognition and rewards (Rosenbach et al., 1996; Robbin & Judge, 2011). Transformational leadership is more of an intellectually stimulating leadership style. While transactional leaders segregate the organization into parts, transformational leaders consider the organization as one large harmonized working system (Bass & Avolio, 1989). Transactional leadership emphasizes standardization, efficiency, control, and formalization. This refines and refreshes current learning unlike transformational leadership that is used to promote learning in a situation of change. This role is helpful in encouraging individuals to utilize and benefit from current learning embedded in the organization’s culture, procedure, strategy, and structure (Waldman et al., 2001). The most widely used measure of leadership in organizations is the Multifactor Leadership Questionnaire (MLQ), originally constructed by Bass (1985) and expanded by Bass and Avolio (1996), to measure nine factors of what is known in the leadership literature as " the full range leadership model". Following is a brief description of the factors measured.

Idealized influence: A manager‐leader with idealized influence underlines the ideological and moral implications of his decisions, and by role‐modeling shows his willingness to sacrifice private interests for the good of the organization.

Inspirational motivation: Leaders who create motivation through inspiration formulate a clear and inspiring vision of the organization's future. In their behaviors toward people they praise acts done for the common good, express optimism about the future of the organization, show enthusiasm for shared topics, and radiate confidence that the aims will be achieved.

Intellectual stimulation: Leaders who are characterized by the ability to create intellectual stimulation cause their people to look at old problems in new ways, encourage them to " think differently," and legitimize creativity and innovation. In their conversations and discussions they often search for different angles to solve problems, and they regularly examine basic assumptions to see whether they are still viable.

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Fattah Nazem, Mona Omidi and Omalbanine Sadeghi

Individualized consideration: Leaders high in individualized consideration relate to each employee personally and not just as "one more"; they treat each employee as an individual with needs, abilities and aspirations different from those of others, they help their workers to develop their strong points, and they spend much time guiding and training their people. The approach of such leaders is basically non‐punitive. They are ready to learn equally from successes and failures.

Contingent reward: Contingent reward involves an interaction between leader and followers that stresses an exchange. For example, the leader provides appropriate rewards when associates attain agreed objectives. The emphasis is on facilitating this achievement.

Management by exception: There are two factors of " management by exception" : active and passive, When active, the leader monitors to safeguard against mistakes and allows the status quo to exist without being changed. When passive, the leader intervenes to make some correction only when things go wrong. Generally, the modes of reinforcement are correction, criticism, negative feedback, and negative contingent reinforcement, rather than the positive reinforcement used with the contingent reward leadership. Punishment is also used in conjunction with management by exception.

The first four factors (idealized influence, inspirational motivation, intellectual stimulation, and individualized considerations) are known as "transformational leadership factors", the others are termed " transactional leadership factors". The MLQ was designed to measure the full range of leadership behaviors, so it also includes a non‐ leadership factor termed " laissez‐faire". This is the avoidance or absence of leadership, and is the most inactive style. The nominal leader avoids intervention. Decisions are often delayed; feedback, rewards, and involvement are absent, and no attempt is made to motivate followers or to recognize and satisfy their needs. (Bass 1985; Bass & Avolio, 1996). Past research has indicated that transformational and transactional leadership styles are positively related to learning organization processes. For example, Lam (2002) and Sadler (2001) found that transformational leadership has a positive influence on emphasizing and encouraging teamwork and involvement within the workplace. Also, Bass (1997) and Bass and Avolio (1990) found that transactional leadership considerably helps develop learning organization efficiency. Thus, it may be presumed that both transformational and transactional leadership styles have positive impact on the performance of learning organization. Pinlu (2010) focused on the relationship between leaders’ behaviors and organizational learning actions and found that transformational leadership behaviors correlated significantly with organizational learning actions. Idealized influence and inspirational motivation of leadership were found to be related to dimensions of individual and organizational learning. Contingent reward, that is, transactional leadership behavior, was found to be related to creating continuous learning opportunities and encouraging collaboration and teamwork. Three full‐range leadership key results: leaders’ extra effort, leader effectiveness, and satisfaction with leadership were correlated with organizational and individual aspects of a learning organization. Mahseredjian (2011) found that leadership styles correlate with organizational learning in a Non‐Western Culture. He also found that transformational leaders have a more profound influence in cultivating a learning organizational environment than transactional leaders. Sahaya (2012) focused on a learning organization as a mediator of leadership style and firms’ financial performance. Castiglione (2006) focused on Organizational learning and transformational leadership in the library environment and found that Librarians are experimenting with organizational learning and new management styles in an attempt to cope with rapid change. Transformational management styles can be learned and applied by library administrators. The extent to which library administrators are using transformational management techniques to cope with change remains obscured by the fact that appropriate surveys have not been conducted. Nafei et al. (2012) focused on leadership styles and organizational learning in an empirical study in Saudi Banks in Al‐Taif Governorate Kingdom of Saudi Arabia, and this study revealed that the aspects of leadership styles have a significantly direct effect on organizational learning. Zagorsel et al. (2009) focused on transactional and transformational leadership impacts on organizational learning, and the results showed that transformational leadership has a strong impact on all four constructs of organizational learning. Taking the results of the above mentioned studies into account, the purpose of the present study is to construct a structural model of organizational learning based on leadership style in universities.

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2. Research questions

What is the structural model of the organizational learning based on leadership style in universities?

Which variables have the highest effectiveness on organizational learning?

How predictive is leadership style on promoting organizational learning?

How much is the goodness of fit in this study?

3. Method of the study The research methods which used in this study were: library research to access the theoretical framework and the related literature; and the survey method to collect, classify, describe, and analyze the data. The population of the research included all employees of Islamic Azad University (Roudehen, Damavand, Pardis, and Bomehen branches and educational centers) in Iran. In order to estimate the least volume of

z 2σ n = d 2 sample,

formula was used. Regarding the minimum sample required for the staff’s group which was estimated at 559 individuals, the same number of questionnaires of leadership style and organizational learning were administered to the staff members, who were selected, using stratified and cluster random sampling method. The research instruments were as follows: Bass and Avolio’s (1996) leadership style questionnaire which consisted of 41 items with two underlying constructs of transformational leadership (idealised influence: attributes, idealised influence: behaviours, inspirational motivation, intellectual stimulation, and individualised consideration), and transactional leadership (contingent reward, management by exception: active, management by exception: passive) and Cronbach’s Alpha of 0.93; and Watkins and Marsick’s (1997) organizational learning questionnaire which consisted of 43 items with three underlying constructs of individual level, group level, and organizational level and Cronbach’s Alpha of 0.97.

4. Findings of the Study The data collected from the administration of the instruments were analyzed. These data included the different indexes of central tendency, variability and the distribution of staff’s groups, the staff members’ scores obtained from leadership style and organizational learning questionnaires and their related components. The distribution of the staff members’ scores in the given variables had tendency toward normality. Transformational Leadership

Individual Level

0.87

0.83 0.68 Organizational Learning

Leadership Style

Transactional Leadership

0.86 0.89

0.89

Group Level

Organizational Level

Figure 1: Path analysis model for components of leadership style and organizational learning As shown in Figure 1, the Lambda rate of external latent variable of leadership style components was 0.83 for transformational leadership, and 0.89 for transactional leadership. It is worth mentioning that their accumulation forms the leadership style variable with the effectiveness rate of 0.68. It means that 68% of the variation in the dependant variable of organizational learning is explained by a collection of these indexes. The variable of transactional style indicates the highest amount of internal consistency in the external latent variable. The Lambda rate of internal latent variable of organizational learning components was 0.87 for Individual level, 0.86 for group level, and 0.89 for organizational level. Their accumulation forms the organizational

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Fattah Nazem, Mona Omidi and Omalbanine Sadeghi learning variable. The variable of organizational level indicates the highest amount of internal consistency in the internal latent variable. Since the model’s goodness of fit index is 0.92, it can be stated that it has an acceptable fit. The calculated index indicates the direct effect of leadership style components on organizational learning. Moreover, the model shows that the highest direct effect is related to transactional leadership, the component of leadership style, on organizational learning in organizational level. The following table presents the indexes related to the model’s fit: Table 1: Model’s fit indexes Interpretation Rate Index High fit (equal to, or more than 0.90) 0.90 Lewis‐Tucker (Non‐normed fit index) High fit (equal to, or more than 0.90) 0.90 Bentler‐Bonett’s (Normed fit index) High fit (more than 0.70) 0.71 Hoelter High fit ( less than 0.05) 0.048 Root Mean Square Error (RMSE) High fit (more than 0.90) 0.92 GFI

The five goodness of fit indexes presented model’s fit and empirical data. Therefore, desirability adaptation is provided for the designed model and empirical data, and can approve it as an appropriate model for the organizational learning.

5. Discussion and conclusions The results of path analysis using LISREL software indicated that dimensions of leadership style had a direct effect on organizational learning with the indexes of 0.92. The model also showed that the factor of transactional leadership had the highest direct effect on the organizational level in the factor of organizational learning. Stasny (1996) found out the effect of the transactional leadership on organizational learning. In his research, he discovered that transactional leadership form 96% organizational learning. In another research, Ash (1997) found the relationship between the transactional leadership and organizational learning. Furthermore, Rijal (2010) found a positive relationship between the style of transformational leadership and the development of organizational learning. Amitay et al. (2005) showed the role of organizational leaders in determining the efficiency of organizational learning. Nafei et al. (2012) in their study, found that transactional leadership is a must in participation of the staff in organizational learning, and also transformational leadership is a necessity for strengthening organizational learning which leads to the increase of performance and competition. In a study done by Singh (2010), a positive relationship between the collaborative and transitive leadership and organizational learning was emphasized. Regarding the important role of organizational leaders in strengthening organizational learning, in particular, the relationship between leadership styles and organizational learning, a lot of studies have been done, like Zagorsek et al. (2009) and Garcia‐Morales (2008). In fact, a tool that can facilitate an organization's process of adapting to changes in the current competitive climate is organizational learning, which can be considered as the precursor of change(Rijal,2010). The results of the studies conducted by Nordtvedt (2005) and Lin (2006) also clarified that using organizational learning and effective teaching in organizations would enhance the income, market share, profitability, and company’s performance and played a leading role in the increase of innovation rate. Studies have shown that organizational learning affects competitive advantage (Jashapara 2003), financial and nonfinancial performance (Bontis et al. ,2002; Škerlavaj & Dimovski 2004;Dimovski & Škerlavaj ,2005; Jimenez‐Jimenez & Cegarra‐Navarro, 2006), tangible and intangible collaborative benefits in strategic alliances (Simonin ,1997), the unit cost of production (Darr et al., 1995) and innovation (Llorens et al. ,2005). Given the significance of organizational learning for corporate performance, understanding ways in which managers can influence the learning process in organizations is becoming increasingly important. Lei et al. (1999), Llorens Montes (2005), Senge (1990), and Swieringa and Wierdsma (1992) emphasize the importance of leadership for organizational learning. Slater and Narver (1995), and Snell (2001) describe capability with regard to transformational leadership as one of the most important means of developing learning organizations, while recent theoretical developments emphasize the importance of a contingent approach toward leadership and organizational learning. Effective leadership is the main cause of competitive advantage for any kind of organization (Zhu et al., 2005). Regarding the findings of the present research, the managers are recommended to provide employees with an atmosphere in which: they feel proud of their status; they are encouraged to reflect on ideas which have not been challenged yet; staff's capabilities are developed; employees are given the chances to be trained.

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Fattah Nazem, Mona Omidi and Omalbanine Sadeghi In addition, university managers should take a leadership method which enables them to:

Make staff respect them through proper behavior and deeds.

Show their qualification and capacity.

Keep calm in chaotic situations.

Trust in their beliefs, ideas, and values.

Criticize the traditional methods of performance.

Furthermore, regarding the fact that transactional leadership has the greatest effect on strengthening organizational learning in universities, the followings are suggested:

Managers should discuss what staffs expect to receive.

Managers should reward the staffs who are cooperative.

Managers should monitor the staffs' mistakes.

In order to prevent the staffs' mistakes, managers should supervise their activities.

Managers should take time to solve the problems.

Managers should negotiate with staff and let them know the expectations of the organization.

The staff should know how to satisfy the expectations of the organization and receive the related rewards.

In conclusion, the newly‐proposed results in this research can be effectively employed to enhance the organizational learning in similar organizations.

Acknowledgements This paper is extracted from a research project sponsored by the research department of the Islamic Azad University, Roudehen Branch to whom I owe a debt of gratitude.

References Amitay, M., Popper, M., &Lipshitz, R. (2005) "Leadership style and organizational learning in community clinics", TheLearning Organization, 12(1), 57‐70. Argyris, C. (1999) On organizational learning (2nd ed.). Oxford: Blackwell. Ash, D. B. (1997) Transformational leadership and organizational learning; Leader actions that stimulate individual and group learning, [D.E. D. dissertation], Muncie, Indiana. Avolio, B. J., Bass, B. M., & Jung, D. I. (1999) "Re‐examining the components of transformational and transactional leadership using the Multifactor Leadership Questionnaire", Journal of Occupational and Organizational Psychology, Vol. 72, pp. 441‐62. Bass, B.M. (1985) Leadership and Performance beyond Expectations, Free Press, New York, NY. Bass , B.M., & Avolio,B.J. (1989)"Potential biases in leadership measures: How prototypes, leniency, and general satisfaction relate to ratings and ranking" ,Educational and Psychological Measurement,49(3),509‐522. Basse , B.M., & Avolio,B.J. (1990) The implications of transformational and transactional leadership for individual, team and organizational development in Richard, W.W. and William A.P.(Eds), Research in Organizational Change and Development,Vol.4, Jai Press , Greenwich ,C.T. Bass, B. M. & Avolio, B. J. (1996) Manual for the Multifactor Leadership Questionnaire, Mind Garden, Palo Alto, CA. Bass, B.M.(1997)"Does the transactional‐transformational leadership paradigm transcend organizational and national boundaries" ? American psychologist, 52(2),130‐9. Bontis, H.,Crossan, M., & Hulland, J. (2002)" Managing an organizational learning system by aligning stocks and flows", Journal of Management Studies, 39( 4), 437‐469. Boseman, G. (2008)" Effective leadership in a changing world",Journal of Financial Service Professionals, 62(3), 36‐38. Burke, S.C. (ed.) (2006)" What type of leadership behaviors are functional in teams"? A metaanalysis.Leadership Quarterly, 17, 288‐307. Burns, J.M. (1978) Leadership,Harper & Row, New York, NY. Castiglione, J , (2006)" Organizational learning and transformational leadership in the library environment" , Library Management , 27( 4/5), 289‐299. Crossan, M. M., Lane, H. M., & White, R. E. (1999)" An organizational learning framework: From intuition to institution", Academy of Management Review, 24(3), 522‐537. Darr, E.D.,Argote, L., & Epple, D. (1995) "The acquisition, transfer, and depreciation of knowledge in service organizations: Productivity in franchises", Management Science, 41(11), 1750‐1762. De Geus, A.P. (1988) Planning as learning, Harvard Business Review, 88( 2), 70‐74.

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Fattah Nazem, Mona Omidi and Omalbanine Sadeghi Dimovski, V. (1994) Organizational Learning and Competitive Advantage: A Theoretical and Empirical Analysis, Cleveland: Ohio State University. Fiol, C. M., & Lyles, M. (1985)" Organizational Learning", Academy of Management Review, 10(4), 803‐813. Franco, M., & Almeida, J. (2011)" Organizational learning and leadership styles in healthcare organizations: An exploratory case study", Leadership & Organization Development Journal,. 32(8), 782‐806. Garcia‐Morales, V. J., Matias‐Reche, F., & Hurtado‐Torres, N. (2008) "Influence of transformational leadership on organizational innovation and performance depending on the level of organizational learning in the pharmaceutical sector", Journal of Organizational Change Management , 21( 2), 188‐212. Huber, G. P. (1991)" Organizational learning: The contributing processes and the literatures", Organization Science, 2(1), 88‐115. Jimenez‐jimenez ,D., & Cegarra – Navarro ,J.G. (2006) The performance effects of organizational learning and market orientation, Industrial Marketing Management, In Press. Kanter, R.M. (1983) Change Masters, Simon & Schuster, New York, NY. Kline, P., & Saunders, B. (1998) Ten steps to a learning organization, 2nd edition, Arlington,VA: Great Oceans Publishers. Lakomski, G. (2001)" Organizational change, leadership and learning: Culture as cognitive process", The International Journal of Educational Management, 15(2), 68 -77. Lam, Y.L. (2002) "Defining the effects of transformation leadership on organization learning: a cross‐cultural comparison School", Leadership & Management, 22( 4), 439‐52. Lei, D., Slocum, J.W., & Pitts, R.A. (1999)" Designing organizations for competitive advantage: The power of unlearning and learning", Organizational Dynamics, 37( 3), 24‐38. Llorens Montes, F.J. (2005) "Influence of support leadership and teamwork cohesion on organizational learning, innovation and performance: An empirical examination", Technovation, 25, 1159‐1172. Mahseredjian , A , Karkoulian , S , & Messarra , L , (2011)" Leadership styles correlate of learning organization in a Non‐ Western culture" , The Business Review, Cambridge , 17 ( 2 ) ,269‐277 Nafei, W. A., Khanfar, N. M., & Kaifi, B. A. (2012)" Leadership styles and organizational learning: An empirical study on Saudi banks in Al‐Taif governorate kingdom of Saudi Arabia", Journal of Management and Strategy, 3( 1), 2‐17. Nordtvedt, L., P. (2005) Organizational learning from international business affiliations: Effects on the effective and efficient transfer of knowledge on firm performance,[Ph.D. dissertation] , United States, The University of Memphis. Pinlu , Y . (2010) The relationship between leaders’ behaviors & organizational learning actions[PH.D dissertation ] , Marian university . Rijal, S. (2010)" Leadership style and organizational culture in learning organization: A comparative study, International Journal of Management & Information Systems", Fourth Ouarter, 14(5), 119‐127. Robbins, P.S., & Judge.A.T. (2011) Essentials of Organizational Behavior, 14th edition. Prentice Hall, New Jersey. Rosenbach, W., Saskin, M., & Harburg, F. (1996) The leadership profile, National Fire Academy Executive Development. Sadler, P. (2001) Leadership and organizational learning. In M. Dierkes, A. B. Antal, J. Child & I. Nonaka (Eds.), Handbook of organizational learning and knowledge (pp. 415‐427). New York: Oxford University Press. Sahaya , N , (2012)" A learning organization as a mediator of leadership style and firms’ financial performance" , International Journal of Business and Management , 7 (14) , 13. Sanchez, R. (2005) Knowledge management and organizational learning: Fundamental Concepts for Theory and Practice. Schwandt, D. R., & Marquardt, M. J. (2000) Organizational learning: From world‐class theories to global best practices. London: CRC Press LIC. Senge, P. M. (1990) The fifth discipline: The art and practice of the learning organization. New York: Currency Doubleday. Senge, P.M. (1990)" The leader’s new work: building learning organizations",Sloan Management Review, 32( 2), 7‐24. Simonin, B.L. (1997)" The importance of collaborative know‐how: An empirical test of the learning organization", Academy of Management Journal, 40( 5), 1150‐1173. Singh, S. K. (2010) Benchmarking leadership styles for organizational learning in Indian context, Benchmarking, An International Journal, 17( 1). 95‐114. Škerlavaj, M., & Dimovski, V. (2004)" Study of the mutual connections among informationcommunication technologies, organisational learning and business performance", Journal of East European Management Studies, 11( 1), 9‐29. Slater,S.F., & Narver ,J.C.(1995)"Market orientation and the learning organization" : Journal of Marketing ,59(3) , 63‐74 . Snell, R.S.(2001)" Moral foundations of the learning organization", Human Relations,54,319‐342. Starkey, K. (1996) How Organisations Learn, International Thompson Business Press, London. Stasny, K. M. (1996) The effects of dimensions of transformational leadership on the conditions for organizational learning and sources of knowledge utilization in restructuring schools, [ph. D. dissertation], The University of New Orleans. Stata, R. (1989)" Organizational learning: The key to management innovation", Sloan Management Review, 30(1), 63‐74. Swieringa, J./Wierdsma, A. (1992): Becoming a Learning Organization. Reading, MA: Addison‐Wesley. Waldman, D. A., Ramirez, G. G., House, R. J., & Puranam, P. (2001) "Does leadership matter? CEO leadership at tributes and profitability under conditions of perceived environmental uncertainty",Academy of Management Journal, 44, 134‐ 143. Watkins, K. E., & Marsick, V. J. (1993) Sculpting the learning organization: Lessons in the art and science of systemic change. San Francisco: Jossey‐Bass. Watkins, K. E., & Marsick, V. J. (1996) In action: Creating the learning organization. Alexandria: American Society for Training and Development.

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The Compilation of a Structural Model for Organizational Learning Based on Social Capital in Universities Fattah Nazem, Omalbanine Sadeghi and Mona Omidi Department of Education, Roudehen Branch, Islamic Azad University, Roudehen, Iran nazem@riau.ac.ir osadeghi82@yahoo.com mona_omidi62@yahoo.com Abstract: The purpose of the present study was to provide a structural model of organizational learning based on social capital in universities. The population of the research included all employees of Islamic Azad University (Roudehen, Damavand, Pardis, and Bomehen branches and educational centers) in Iran. 559 employees were selected using stratified and cluster random sampling method. The research instruments were as follows: Watkins and Marsick’s (1997) organizational learning questionnaire which consisted of 43 items with three underlying constructs of individual level, group level, and organizational level and Cronbach Alpha of 0.97 and Abili and Abilis’ (2010) social capital questionnaire which consisted of 24 items with three underlying constructs of cognitive dimension, relational dimension, and structural dimension with Cronbach Alpha of 0.94. The results of path analysis using LISREL software indicated that dimensions of social capital had a direct effect on organizational learning with the indices of 0.95. The model also showed that relational dimension in social capital had the highest direct effect on the organizational level in organizational learning. It was also concluded that the proposed model showed full fit. Keywords: structural model, organizational learning, social capital, universities

1. Introduction and purpose of the study In a climate of accelerating change, organizations can not flourish without nurturing the seeds of learning. Established as a wellspring of value‐producing knowledge, organizational learning is bulding block to innovative, quality, and profitable products and services (Argyris & Schon, 1999; and Schwandt & Marquardt, 2000).Organizational learning means the process of improving actions through better knowledge and understanding. (Fiol & Lyles, 1985, p. 811). King (2002) asserted that organizational learning is important to the success of quality‐focused organizations since only through learning can organizations capture and retain the knowledge necessary to continually refine and improve business processes responsible for product and service quality. One of the obstacles in institutionalizing organizational learning is believed to be the lack of effective leadership (Joeong, 2004; Beard, 2006). Organizations ought to take into account the way leaders educate the staffs regarding the role of organizational learning. Leaders should create an atmosphere in which organizational learning is institutionalized in the organization. This can, finally, lead knowledge and information systems, which are of determining factors in any organization, into organizational learning under the leaders’ support. Senge (1990) identifies the five disciplines associated with the organizational learning to be personal mastery, mental models, systems thinking, team learning, and building a shared vision for the organization. Organizational learning culture refers to “an organization skilled at creating, acquiring, and transferring knowledge, and at modifying its behavior to reflect new knowledge and insights” (Garvin, 1993, p. 80). Supervisor developmental feedback provides valuable information to employees and enables the employees to learn, develop, and make improvements on the job (Zhou, 2003). Scholars holding the social interaction view argue that organizational learning emerge amid the social interactions of employees. Senge (1990) emphasized the interaction among employees on the base of personal mastery in his concept of organizational learning. Cook and Yanow (1993) suggested that organizational learning is a formal or informal collective process of exploration and practice, and it is a cultural phenomenon. They emphasized that organizational learning is a phenomenon of collective learning. Organizational learning is related to change, innovation, technology and product development, and organization profit, continual progress in these areas enables the organization to reinvent itself and avoid stagnation (Argyris & Schon, 1996; Handy, 1996; Senge, 1990; Nonaka, 1994). Crossan et al. (1999) proposed an organizational learning framework with four processes – intuiting, interpreting, integrating and institutionalizing; these processes link the individual, group and organizational levels. Watkins and Marsick’s (1993, 1996) identify seven core practices at the individual, group, and organizational levels as follows:

Individual level

Creating continuous learning opportunities

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Promoting inquiry and dialogue

Team/group level

Encouraging collaboration and team learning

Organizational level

Creating systems to capture and share learning

Empowering people toward a collective vision

Connecting the organization to its environment

Providing strategic leadership for learning

Social capital is necessary for sustained learning and knowledge creation within organizations( . Dovey and Singhota 2005). Social capital is a jointly owned set of resources that accrue to an individual or group by virtue of their social connections, and can be significant in knowledge acquisition and transfer between network members (Inkpen & Tsang, 2005). Macinko and Starfield(2001) reviewed social capital at four levels: socio‐ cultural, neighbourhood, behaviour and attitudes. The two last mentioned are composed of individual‐ level attributes, such as participation in social networks, commitment to cooperation and interpersonal trust. The role of trust in social capital can be considered crucial, because it plays the main role in establishing social networks, and provides teamwork activities with required background. People generally have the ability to secure benefits through memberships in networks and other social structures (Portes, 2000). Social capital allows individuals to gain and make use of various resources (information, services, money, and ideas) and to anticipate reciprocal and trustful relationships emerging in networks of association. Social capital can therefore be defined as an attribute of individuals, but only by virtue of their participation in a group. The extent to which people participate in social, civic and other voluntary activities constitutes an essential part of their individual‐level social capital (Fassin, 2003; Macinko & Starfield, 2001; Lindstrom, 2005; and Poortinga, 2006). Social capital has three interrelated dimensions: structural, cognitive, and relational (Nahapiet & Ghoshal, 1998).

Cognitive dimension: The cognitive dimension of social capital refers to attributes like a mutual belief or shared paradigm that promotes a common understanding of collective goals and the proper ways of acting in the social environment (Tsai & Ghoshal, 1998). The social capital's cognitive dimension may enable knowledge sharing in the sense that stories, shared language, customs and traditions can bridge the tacit‐explicit division as well as division in terms of; for example, old‐timers‐newcomers (Hinds & Pfeffer, 2003).The cognitive dimension refers to those resources that provide shared representations, interpretations, and systems of meaning among parties. This includes shared language and codes as well as shared narratives, which increase the mutual understanding among individuals and help members to communicate more effectively (Cabrera & Cabrera, 2005).

Structural dimension: The structural dimension of social capital focuses mainly on the density of networks and on bridging structural holes (Burt, 1992; Wasserman & Faust, 1994). Structural social capital facilitates information sharing, and collective action and decision making through established roles, social networks and other social structures supplemented by rules, procedures and precedents (Uphoff, 2001).

Relational dimension: The relational aspect of social capital consists of having a strong identification with the collective (Lewicki & Bunker, 1996), having a sense of reciprocity or obligation to contribute to the collective (Coleman, 1990), and abiding by the norms of the collective (Putnam, 1995) which are part of the collective’s climate. Social capital is an additional mechanism for enhancing knowledge transfer both within and between organizations (Yli‐Renko et al., 2002; Widen‐Wulff & Ginman, 2004; Nahapiet & Ghoshal, 1998; Inkpen & Tsang, 2005).

Washington (2008) found that organizational social capital would strengthen the positive relation ship between access to business knowledge and organizational learning. On the other hand, these data indicated that organizational social capital weakened the positive impact of access to organizing knowledge on organizational learning. Adams’ (2006) data obtained from 591 subjects in two separate organizations provided support for the overall model indicating a relationship between social capital and mindful use, as well as a relationship between mindful use and organizational learning. The research purpose is to construct a structural model to assess organizational learning Universities based on the social capital.

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2. Research questions

What is the structural model of the organizational learning based on social capital in universities?

Which variables have the highest effectiveness on organizational learning?

How predictive is social capital for promoting organizational learning?

How much is the goodness of fit in this study?

3. Method of the study The research methods which were used in this study are: library research to access the theoretical framework and the related literature; and the survey method to collect, classify, describe, and analyze the data. The population of the research included all employees of Islamic Azad University (Roudehen, Damavand, Pardis, and Bomehen branches and educational centers) in Iran. In order to estimate the least volume of

z 2σ n = d 2 sample,

formula was used. Regarding the minimum sample required for the staff’s group which was estimated at 559 individuals, the same number of questionnaires of social capital and organizational learning were administered to the staff members, who were selected using stratified and cluster random sampling method. The research instruments were as follows: Watkins and Marsick’s (1997) organizational learning questionnaire which consisted of 43 items with three underlying constructs of individual level, group level, and organizational level and Cronbach's Alpha of 0.97. The items embedded in the questionnaire were of three types. The first type of items consisted of those items which tested the subjects' organizational learning at the individual level. The second and the third types of items consisted of items which investigated the subjects' organizational learning at the team and organizational levels respectively. Abili and Abilis’ (2010) social capital questionnaire which consisted of 24 items with three underlying constructs of cognitive dimension, relational dimension, and structural dimension with Cronbach's Alpha of 0.94. The details of each dimension have been mentioned in the literature review. In sum, cognitive dimension refers to the subjects' belief and shred patterns among the employees. The structural dimension includes the items which refer to the existence and power of networks. And, relational dimension includes the items which examine the existence of networks which contribute to the strength of the organization. The results of the study were calculated through path analysis using LISREL software.

4. Findings of the study The data collected from the administration of the instruments were analyzed. These data included the different indexes of central tendency, variability and the distribution of staff’s groups, the staff members’ scores obtained from social capital and organizational learning questionnaires and their related components. The distribution of the staff members’ scores in the given variables had tendency toward normality.

Figure 1: Path analysis model for components of social capital and organizational learning

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Fattah Nazem, Omalbanine Sadeghi and Mona Omidi As shown in Figure 1, the Lambda rate of external latent variable of social capital components was 0.92 for relational dimension, 0.91 for cognitive dimension, and 0.90 for structural dimension, it’s worth mentioning that their accumulation form the social capital variable with the effectiveness rate of 0.81. It means that 81% of the variation in the dependant variable of organizational learning is explained by a collection of these indices. The variable of relation dimension indicates the highest amount of internal consistency in the external latent variable. The Lambda rate of internal latent variable of organizational learning components was 0.86 for Individual level, 0.85 for group level, 0.91 for organizational level. Their accumulation form the organizational learning variable. The variable of organizational level indicate the highest amount of internal consistency in the internal latent variable.Since the model’s goodness of fit index is 0.95, it can be stated that it has an acceptable fit. The calculated index indicates the direct effect of social capital components on organizational learning. Moreover, the model shows that the highest direct effect is related to relation dimension, the component of social capital, on organizational learning in organizational level. The following table presents the indices related to the model’s fit: Table 1: Model’s fit indices Index Lewis‐Tucker (Non‐normed fit index) Bentler‐Bonett’s (Normed fit index) Hoelter Root Mean Square Error (RMSEA) GFI

Rate 0.93 0.92 0.74 0.032 0.95

Interpretation High fit (more than 0.90) High fit (more than 0.90) High fit (more than 0.70) High fit (less than 0.05) High fit (more than 0.90)

The five goodness of fit indices presented model’s fit and empirical data. Therefore, desirability adaptation is provided for the designed model and empirical data and can approve it as an appropriate model for the organizational learning.

5. Discussion and conclusions The results of path analysis indicated that since model’s goodness of fit index is 0.95, it can be stated that it has an acceptable fit. The calculated index shows the direct effect of emotional intelligence components (0.17) on organizational learning. The results of this study are in line with the research by Washington (2008), and Adams (2006). In his model, Adams (2006) refers to the role of social capital which leads to organizational learning. Furthermore, in his study, Washington (2008) found the relationship between social capital and commercial knowledge as well as organizational learning. Drucker (1994) describes the twenty‐first century as the “age of social transformation” and Baker (1990) argues that success in business is achieved through developing and leveraging social capital. Social capital is “network ties of goodwill, mutual support, shared language, shared norms, social trust, and a sense of mutual obligation that people can derive value from”. It is about relationships and connections with various communities (Putnam, 1995). The central idea behind social capital theory is that social relationships among people are valuable assets that can foster social affairs and access to knowledge (Nahapiet &Ghoshal, 1998). Researchers examined the influence of social capital on human capital development (Coleman, 1988; Loury, 1987) and on the economic performance of firms (Baker, 1990). In the past few years, there has been an increased interest in organizational learning since it has been suggested to have great potential for influencing competitive advantage, organizational control and intelligence, exploitation of knowledge and technology, and other critical organizational outcomes (Templeton, Lewis, & Snyder, 2002). Since organizational learning can increase a firm’s capacity to take effective action (Kim, 1993) and can mobilize tacit knowledge, it can lead to greater firm effectiveness (Barney, 1991). Close attention to organizational learning is an absolute necessity within all organizations particularly higher education institutions. Many authors agreed that “… successful organizations that forge ahead in a rapidly changing business environment will do so through creating and sharing new knowledge” (Argyris & Schon, 1996; Senge, 1990; Petrides, 2002).The result of the study conducted by Beard (2006) showed that the indices of organizational learning included identities, thoughts, common ideas, group working and group learning, sharing information and systematic thought, having leader, staff’s skills, and competition. Miller (1996) also found out that the successful results of organizational learning are: successful financial and business performance, self‐learning at individual and team/group levels, and group learning. Moreover, Duffy (2002) indicated that the key to significance and high quality in fostering values, nurturing personal qualifications, and caring social values in strengthening team/group leadership lies in following organizational learning. The results of the studies conducted by Nordtvedt (2005) and Lin (2006) also clarified that using organizational learning and effective teaching in organizations would enhance the income, market share, profitability, and

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Experts should have common objectives and values.

Experts should have access to various resources in order to accomplish their vocational duties.

Experts should be motivated to accomplish the goals and missions of the organization.

Experts should look at each other’s work and criticize it.

When something goes wrong, experts should discuss in order to solve it.

Experts should agree on what the important issue is for the organization.

Experts should help each other, and share the desired information voluntarily.

Experts should fully grasp the goals and mission of the organization.

In addition, taking into account that the relational dimension has the highest effect on strengthening the organizational learning, the followings are suggested:

Experts should trust each other.

Experts should consider themselves as a member of a big family within the organization.

Team work should be encouraged.

Experts should respect each other’s feeling.

Experts should be honest with one another.

Experts should have commitment to the goals of the organization.

In conclusion, the newly‐proposed results in this research can be effectively employed to enhance the organizational learning in similar organizations.

Acknowledgements This paper is extracted from a research project sponsored by the research department of the Islamic Azad University, Roudehen Branch to whom I owe a debt of gratitude.

References Abili,K ., & Abili,M. (2011) Social Capital Management in Iranian Knowledge‐Based SMEs, ECIC2011, University of Nicosia, Cyprus,18‐19 April 2011. Adams, H. L. (2006) Mindful use as a link between social capital and organizational learning: an empirical test of the antecedents and consequences of two new constructs, [ph.D. dissertation], Maryland, University of Maryland. Argyris, C. & Schon, D.A. (1996) Organizational learning II: Theory, method, and practice. Reading, MA: Addison‐Wesley. Argyris, C. , & Schön, D. (1999) Die Lernende Organisation: Grundlagen, Methode, Praxis. Stuttgart. Baker, W. (1990)" Market networks and corporate behavior", American Journal of Sociology, 96, 589–625. Barney, J. (1991) Firm resources and sustained competitive advantage, Journal of Management, 17, 99‐120. Beard, R. (2006) Accreditation processes and organizational learning capabilities in institutions higher education. [Ph.D. Dissertation],Capella University. Burt, R. (1992) Structured Holes, Harvard University Press, Cambridge, MA. Cabrera, E. F., & Cabrera, A. (2005)" Fostering knowledge sharing through people management practices",International Journal of Human Resource Management,16(5), 720‐735. Coleman, J. (1988)" Social capital in the creation of human capital", American Journal of Sociology, 94(suppl): 95–121 Coleman, J. (1990) Foundations of social theory, Harvard University Press, Boston, MA. Coleman, James Samuel( 1990) Foundations of social theory. Cambridge, MA: Harvard University Press. Cook, S.D.N. & Yanow, D. (1993) ``Culture and organizational learning'', Journal of Management Inquiry, 2 ( 4), 373‐90. Crossan, M., Lane, H. & White, R. (1999) “An organizational learning framework: from intuition to institution”, Academy of Management Review,34 ( 3), 523‐37. Dovey, K., & Singhota, J. (2005) "Learning and knowing in teams", Development and Learning in Organizations, 19(3), 18‐ 21. Drucker, P. (1994)" The age of social transformation", Atlantic Monthly, 274(5), 53‐80. Duffy, A., (2002) Collective executive leadership: An exploration of this new leadership phenomenon and its relationship to organizational learning, performance and results. [Ed.D. Dissertation], Pepperdine University.

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(1993) Sculpting the learning organization: Lessons in the art and science of systemic change, San Francisco: Jossey‐Bass. Watkins, K. E., & Marsick, V. J. (1996) In action: Creating the learning organization. Alexandria: American Society for Training and Development. Widen‐Wulff, G. & Ginman, M. (2004) "Explaining knowledge sharing in organizations through the dimensions of social capital", Journal of Information Science, 30(5), 448‐458. Yli‐Renko, H., Autio, E. & Tontti, V. (2002) "Social capital, knowledge, and the international growth of technology‐based new firms", International Business Review, Vol. 11, pp. 279‐304. Zhou, M. (2003) “Contemporary Trends in Immigration to the United States: Gender, Labor Market Incorporation, and Implications for Family Formation.” Migraciones Internacionales 2 (2): 77‐95.

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Structural Equation Modeling of Intellectual Capital Based on Organizational Learning in Iran's General Inspections Organization Faezeh Norozi, Fattah Nazem and Mina Mozaiini Department of Education, Roudehen Branch, Islamic Azad University, Roudehen, Iran mozaiinim@yahoo.com nazem@riau.ac.ir mozaiinim@yahoo.com Abstract: This study is to present a model of intellectual capital based on the organizational learning in Iran's General Inspections Organization (GIO). The population of the study included all the employees of GIO in Tehran city totaling 675. Using simple random sampling, 392 were finally selected. The research instruments were two questionnaires: Watkins and Marsick's (2004) Organizational Learning Questionnaire with 43 items and the reliability index of 0.97 which has three levels of individual, group (team), and organizational, and Bontis's (2004) Intellectual Capital Questionnaire with 50 items and the Alpha Cronbach index of 0.97 which includes three dimensions of human capital, structural capital, and relational capital. The results of path analysis indicated that aspects of organizational learning have a direct impact on intellectual capital (0.66). The authors' suggested model showed that the variables of individual and group learning in organizational learning have the highest impact on the structural capital. The results also revealed that the model has an acceptable fit. Keywords: intellectual capital, organizational learning, iran's general inspections organization

1. Introduction With a historical review of how big companies and organizations are established in the past decades, it is found out that they are not sufficient enough to compete since, like dinosaurs, they fail to adapt themselves with the process of social and international changes. In each organization, there is a potential ability which can be exploited appropriately for making strategic changes in the organization, and lead it through organizational goals and perspectives. For evoking this organizational power along with strategic changes, it is necessary to reinforce the necessary motivation in staff which can be made through cooperation among organizational proper structure. Therefore, organizational hierarchy, people, and groups can make appropriate binds for increasing organizational function in learning process. Big organizations with traditional structures do not comprise enough ability and flexibility for convergence with environmental changes and have to either change their structure or be armed with the tools by which they can achieve the ability to deal with universal changes. One of the most prominent of these tools is establishment of learning organization and organizational learning. Peter Drucker, the famous scholar in management, believes that from now on the key to organizational success is knowledge. He adds that the value is comprised through innovation and production and both are due to knowledge exploitation. That is, human and knowledge are the distinguished effective keys in organizations. Nowadays in behavioral issues the focus is on competitive, not structural, benefits. The competitive benefit is an issue where the environment is competitive; in a competitive environment, competitive benefit is in the hand of technology‐based thought. This kind of technology seeks for transforming the state of inertia to the state of change and then lead change towards the establishment of learning organization. Since 1990, learning has been defined as the ability based on growth improvement and capability. How a competitive benefit is made depends on especial characteristics of each organization. However, it is obvious that human and knowledge have discriminating role in this way. He further stated that learning is a source of competitive benefit. Learning is related to the change i.e. learning should lead to change and change to behavior; otherwise, it is useless. Peter Senge believes that in the contemporary age the organizations are successful whose staff try to promote their level of abilities and the manager’s duty is to set a situation in which all staff move towards promoting their abilities. Senge (1990) defines organizational learning as a process and group of actions that leads to staff learning and includes distinct organizational behavior which is exercised in learning organization. In fact, the learning environment is prepared for all members and people continuously make efforts to change whatever learned. This is where self‐change is on the spotlight. An organization may undertake to deliver education and development but if does not possess the ability of self‐ change against sudden and unexpected change in the environment, the fragility is probable. The reason of annihilation of lots of companies in universal field is also the same. Because the more competitive the environment, the more responsive the organization should be (Khalili Araghi, 2003).

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Faezeh Norozi, Fattah Nazem and Mina Mozaiini A great deal of attention has been paid to organizational learning amongst organizations which are interested in competitive benefit, innovation, and effectiveness. Dajson (1993) believes in organizational learning as’ a way by which organizations create, complete and organize to adjust and develop knowledge, routine workflow related to activities within their culture, as well as organization efficiency through improvement in the application of human resources wide range of skills’(Aboee & Ardakani, 2000) Organizational learning and intellectual capital are the issues that have now found their ways in management science and in the field of organizational behavior and theory. Due to recent theories and research projects in management, these two variables are receiving increasing importance and have turned to be the pivotal issues in management. Organizational learning, one of the new arguments in organization theories, was proposed in 1930s (Argyris, 1999). Studying the organizational processes, models, and theories, researches defined organizational learning as a kind of group learning which is effective and quick to adapt with the environmental changes and complexities in organizations (Argote, 1999). Nowadays with the beginning of knowledge‐based economy, knowledge comparing with other productive factors such as land, capital, and machinery has more importance and traditional factors of production has lost its vital role as if in this economy, knowledge is considered the most prominent factor in production and is known as the most important competitive benefit of an organization (Seetharaman, 2002). Nowadays, intellectual capital management can lead the organization to more successes in future horizons of competitive markets (Bernnan, 2000). The centrality of ideas by Edvinsson & Malon's (1977) is based on the importance of intellectual capital in organizations, key features, measures and managerial approaches of this capital. Based on this hypothesis, establishment of intellectual capital management system in the organization is the most important step in development of acquiring value system and supporting this system from this capital. Another important aspect which attracted a lot of attention from theoreticians and researchers is the attention to aspects of social capital as one of the effective factors in forming intellectual capital in the organization. Intellectual capital can be considered as a scientific package consisting of a set of invisible and hidden sources, principles, culture, behavioral models, capacities, capabilities, structures, interactions, and processes which lead to science or come out of it. According to this theory, establishment of this intellectual capital management system in the organization is the most important step in development of acquiring value system and supporting this system from this capital (Edvinsson & Malon's, 1977). Organizations should make an environment for sharing, transferring, and exchanging science amongst members and train them towards conceptualizing their exchanges. In line with the increase of complexity and speed of environmental changes of organizations, creating common systematic thought and insight in organization and also making organizational learning and changing individual learning to organizational is a need; hence, if collections of factors such as structures, ways, strategies, processes, and human resources can be adjusted with proper model of organizational learning, one can better react against organizational competition (Nonaka & Takeuchi, 1995). th

On the other hand, most groups which conduct the surveys of 20 century believe that the world is in an unprecedented situation. Compared with the previous years when everything was unchanging, the situation is changing with an increasing rate in most parts of the world and in most aspects of life. Hence, the world of today and tomorrow is the world of change, the world in which the only fixed element is change itself (Zali, 1998). Therefore, to adjust ourselves to the changing world, each organization should succeed in dealing with this kind of change and transformation and constitute new skills and perspectives. One of the wise answers to the environment of change is the increase of awareness of the importance of wisdom and learning. Iko Jiro, a famous expert in Japanese study, expresses that ‘in the economy that the only fixed thing, is the state of change, one of the reliable and durable sources of competitive benefits is wisdom’. When demands change, technologies develop, and competitors increase over night, those companies are successful which create new wisdom, spread it widespreadly through the organization and demonstrate it in the form of new technologies and productions (Radding, 1998). The research by l Meng‐Yuh Cheng, Jer‐Yan Lin, Tzy‐Yih Hsiao, Thomas W. Lin (2010) entitled ‘investment of organizational sources of intellectual, competitive, and functional capital’ illustrated the significant relationship between intellectual capital and company function. The results also showed that innovative

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Faezeh Norozi, Fattah Nazem and Mina Mozaiini capacities and the process of adaptation should be attended and organizations are to focus on value‐added activities of human resources to enhance their performance. The research by Andres et al (2010) entitled ‘capacity of dynamic learning and creating practical knowledge; clinical research and development’ in a pharmacology company demonstrated that capacity of dynamic learning exists and is under the effect of company culture, available skills and suitability, organizational structure, stimuli for learning, capacity for permanent change and leadership. The justification behind it is that the probability of creating practical knowledge is a fragile process which is to be managed with care and is far more complex than the one literature shows. The results of a research by Emi et al (2006) entitled ‘the relationship between potential of organizational learning and quality culture for comprehensive quality management: a case study in research and education of Applied Science and Technology’ illustrated that there is a positive correlation between organizational learning potential and quality culture. Ming and Chang (2007) conducted a research on “the relationship between leadership, organizational culture, exploitation of learning organization, and staff job satisfaction". The main aim of this article was the review of relationship between leadership, organizational culture, exploitation of learning organization, and staff job satisfaction. The results of this study showed that boundaries of different functions of learning organization have significant difference in dimensions of leadership, organizational culture and exploitation of learning organization. Both leadership and positive organizational culture can significantly affect exploitation of learning organization. The research conducted by Yang (2007) entitled ‘the effect of sharing science in organizational learning and efficacy’ showed that sharing knowledge leads to a change of individual and group knowledge to organizational knowledge which in itself ends with the effectiveness of organizations. The purpose of the present study is to present a structural equation model for intellectual capital New economy imposes new requirements to the enterprises. The strategic role of intellectual capital in value creation is widely discussed both on theoretical (Bontis, 2001) and empirical (Chen et al., 2005) levels. However, measuring the intangible resources presents a problem today. There are several methods both financial and nonfinancial ones that allow us to manage them, to provide benchmarking, and to analyze its value added function (Sveiby, 2007). Several studies suggest proofs regarding the point that the intellectual capital has positive impact on market value, productivity, return on assets and sales growth (Diez et al., 2010). based on organizational learning in Iran's General Inspections Organization.

2. Methodology Descriptive research is a kind of research that describes the situation of one or more variables in a population and its and its variables are usually easy to diagnose. However, in such kind of research one can not name the variables as a dependent variable and an independent variable. In correlational research, we investigate the relationship between two or more variables in a special statistical group corretational studies can have two simple variables or many complicated variables.

3. Population, sample, and sampling All the managers and staff of Iran's General Inspections Organization included the population of the study who were totally 675. The sample was selected using simple random sampling and the sample size, according to Krejcie‐Morgan's (1970) table, was decided to be 248.

4. Research instruments There were two measurement tools administered in this study: Watkins'(2004) Organization Learning questionnaire with three dimensions of individual learning (items 1 – 13), group learning (items 14 – 19), and organizational learning (items 20 – 43) with the Cronbach Alpha of 0.97. Bontis (2004) Intellectual Capital questionnaire with three dimensions of human capital (items 2, 4, 6, 7, 10, 13, 17, 20, 23, 29, 30, 31, 33, 36, 37, 40, 41, 44, 47, 50), structural capital (items 3, 11, 12, 14, 18, 19, 25, 26, 32, 38, 39, 45, 46), and relational capital (items 1, 5, 8, 9, 15, 16, 21, 22, 24, 27, 28, 34, 35, 42, 43, 48, 49) and the Cronbach Alpha of 0.97.

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5. Results The following figure indicates the model of relationship between the dimensions of organizational learning ‐ including individual level (13 items), group level (6 items), organizational level (24 items) ‐ as an independent variable and the dimensions of intellectual capital ‐ including human capital (20 items), structural capital (13 items), relational capital (17 items) – as a dependent variable which is not different from the real model of data. The lambda rate of external latent variable for dimensions of organizational learning includes individual level (0.92), group level (0.92), organizational level (0.89) whose accumulation forms the organizational learning variable. It means that 0.66% of the variation in the dependant variable of intellectual capital is explained by a collection of these indices and the rest is predicted by the other variables. The variables of individual and group levels indicate the highest and the variable of organizational level indicates the lowest amount of internal consistency in the external latent variable. Individual Level 0.92

Group Level

Organizational Level

0.92

Human Capital

0.79

Organizational Learning

Intellectual Capital

0.66

0.89

Structural Capital

0.89 0.81

Relational Capital

Figure 1: Structural equation model of organizational learning and intellectual capital The lambda rate of internal latent variable for dimensions of intellectual capital includes human capital (0.79), structural capital (0.89), and relational capital 0.81). Since the model’s goodness of fit index is 0.94, it can be stated that it has an acceptable fit. The calculated index indicates the direct effect of organizational learning dimensions on intellectual capital. It means that 66% of the variation in the dependant variable of structural capital as a dimension of intellectual capital is explained by the variables of individual and group levels as two dimensions of organizational learning. The following table presents the indices related to the model’s fit: Table 1: Model’s fit indices Interpretation Rate High fit (more than 0.90) 0.93 High fit (more than 0.90) 0.92 High fit (more than 0.70) 0.72 High fit (equal to or less than 0.05) 0.044 High fit (more than 0.90) 0.94

Index Lewis‐Tucker (Non‐normed fit index) Bentler‐Bonett (Normed fit index) Hoelter Root Mean Square Error (RMSE) GFI

The five goodness‐of‐fit indices indicate presented model’s fit and empirical data. Therefore, desirability adaptation is provided for the designed model and empirical data and can approve it as an appropriate model for the intellectual capital based on the organizational learning. In conclusion, there is a complete fitting in the model suggested by the researchers because the non‐normed fit index of Tucker‐Lewis (0.93) and normed fit index of Bentler‐Bonett (0.92) are higher than 0.90. Furthermore, the Hoelter index (0.72) is higher than 0.70 and shows a desired fit. The RMSE (0.044) is also less than 0.05 and reveals fitting of the researchers' model.

6. Conclusion and discussion The results of the present study demonstrated that there is a significant relationship between organizational learning and intellectual capital and organizational learning has direct effect on intellectual capital in an organization. Certainly, according to the researcher's model, the most effect is related to the impact of learning in individual and group levels, as dimensions of organizational learning, on structural capital, as dimensions of intellectual capital.

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Faezeh Norozi, Fattah Nazem and Mina Mozaiini The study conducted by Chang et al (2010) entitled ‘investment of intellectual, competitive capital and company function showed that there is a significant relationship between intellectual capital and company function. The results also showed that innovative capacities and the process of adaptation should be attended and organizations are to focus on value‐added activities of human resources to enhance their performance. The research by Andres et al (2010) entitled ‘capacity of dynamic learning and creating practical knowledge; clinical research and development’ in a pharmacology company demonstrated that capacity of dynamic learning exists and is under the effect of company culture, available skills and suitability, organizational structure, stimuli for learning, capacity for permanent change and leadership. The justification behind it is that the probability of creating practical knowledge is a fragile process which is to be managed with care and is far more complex than the one literature shows. The research conducted by Yang (2007) entitled ‘the effect of sharing science in organizational learning and efficacy’ showed that sharing knowledge leads to a change of individual and group knowledge to organizational knowledge which in itself ends with the effectiveness of organizations. It has also been obvious in research questions that there is a significant relationship between organizational learning and intellectual capital of the staff in Iran's General Inspections Organization and based on which variable of organizational learning was illustrated. Therefore, organizations are permanently under the effect of surrounding environment which are known as effective factors; the factors and variables which are less under supervision and control of organizations. Hence, if such companies can recognize and control such effective surrounding environment and reduce their complexity, they can continue their life. Managing different modern organizations is possible only through internal and external environmental circumstances and based on change and transformation. Organizations should also learn sooner and adjust their speed with environmental changes, otherwise they will be eliminated. Management scholars also believe the best way for avoiding demise of organization is increasing knowledge and spreading knowledge amongst staff of different levels. Everybody knows that only those organizations can survive which manage their actions with proper decision. However, proper decision is impossible without knowledge and the future of competitive benefits is wisdom. In this situation, only those organizations can have a successful function which can use knowledge as a power factor and competitive and permanent benefit. Each person is an effective and valuable factor in organization which creates a network of relations which allows people to develop their capabilities, to search for innovation, and to create new knowledge in the organization. Accordingly, the present study, among different affecting factors, scrutinizes intellectual capital and organizational learning. The estimated mean shows that organizational learning and intellectual capital have a good position in Iran's General Inspections Organization. Iran's General Inspections Organization with available duties and missions has widespreadly a special and specialized place in the country because of increasing level of people and government expectations. So it should step forward with maximum potential towards interaction, hardware and software capacity making, exploitation of power and available properties. Hence, according to the results of present study, it seems that Iran's General Inspections Organization has mostly reached its goal which is changing the organization to the specialized organization in inspection, spreading organizational inspection and changing it to the thorough inspection, empowering national and… inspections, exploiting quality management and improving resources and using efficient staff (Mirzakhani Ahranjani, 1999). The findings of the study have left us a few suggestions to promoting intellectual capital in General Inspections Organization:

The staff should freely speak about mistakes to gain experience.

The staff should be given the skills needed for accomplishing the duties.

The teams should emphasize on group duties as well as the achievements on the parts of individuals.

The teams should get reward because of teamwork.

The organization should participate all the staff to design the general goals.

The staff should be encouraged for solving organizational problems.

The leaders should permanently seek for opportunities to learn.

The organization should upgrade its staff skills with the updated knowledge.

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Teams should be ensured that their suggestion is important for their organization.

The organization should help the staff to make a balance between work and family.

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The Construction of an Operational-Level Knowledge Management Framework Jamie O’Brien St. Norbert College, De Pere, Wisconsin, USA Jamie.obrien@snc.edu

Abstract: This paper focuses on responding to the area of frameworks for conceptualising Knowledge Management. The primary aim of this research is to operationalize a Knowledge Assessment Framework (KAF). The development of a KAF is important for organizations for three reasons. It moves away from macro knowledge indicators and suggests more succinct knowledge activities. Firstly, the use of knowledge assessment allows firms to pinpoint knowledge gaps. Secondly, it allows firms to manage knowledge more effectively. Thirdly, it gives organizations a diagnostic tool with which to gauge their knowledge base. The effective management of knowledge can be considered a competency that enables a greater level of service to be extracted from other resources within the organization. The methodological underpinning of this research is outlined below. This paper presents a new framework with which organisations can use to gauge their knowledge gaps. An interpretivist paradigm was followed while using two case studies to achieve the research strategy. Some of the methods of data collection included; interviews; observation; document and record investigation and computer assisted analysis using nVivo. This paper constructs a conceptual Knowledge Management framework for use by practitioners by employing a research strategy that builds a working framework. The proposed framework would offer a lens to organisations with which they could use to gauge their knowledge base. This would improve awareness in the areas of knowledge acquisition, sharing, learning and reuse. A growing area of KM is its application in the public sector. Insights from this study may also be important across both private and public sector. Keywords: knowledge management; knowledge assessment framework; case study

1. Introduction This paper is the second in a series related to the broad area of KM. Specifically, the present research attempts to develop a mechanism that will assist managers in evaluating their current KM capability and thereby facilitating effective measurement of the impact of KM initiatives (strategic and operational) upon business performance. This paper will be divided into two parts. In part one, the paper will discuss the background of the KAF, alternative KM frameworks, and the methodology used to develop the KAF. In part two, the application of the KAF at firm-level will be discussed along with the future plans of this research. The original data was collected between 2009 and 2011. For reasons of confidentiality, the medical device organizations used in the case studies referenced will be referred to “Medi-case A” and “Medi-case B”. The author posits that in order to possess effective measurement capability, the organization must first evaluate.

2. Background As already established in previous research (O’Brien, 2013a, 2013b; Moffett and Humphreys, 2012; OECD, 2007, 2006, 1996; Lev and Daum, 2003) there is a need for knowledge assessment at organizational level. This has been discussed in the field of KM for some time. KM, since the early 1990s, has tried to establish itself among practitioners and academics as an area of study for ensuring organizational competitiveness and, ultimately, longevity. U.S. spending alone on KM initiatives grew by 16%, to account for $73 billion in 2007, according to a report by AMR research (McGreevy, 2007). As a discipline, however, KM shows immaturity (Burton-Jones, 2008). A plethora of definitions for the term Knowledge Management exist with the only consensus seeming to be that it refers to organizational knowledge and ultimately leads to organizational competitiveness (Burton-Jones, 2008). The OECD recognizes that “knowledge management practices seem to have a far from negligible effect on innovation and other aspects of corporate performance. But there is little systematic evidence of just how great an effect knowledge management has. Among the various categories of knowledge-related investments…knowledge management is one of the areas about which little is known in terms of quality, quantity, costs and economic returns” (OECD 2004, p.1). Whether one considers KM simply as a competitive necessity (Romberg, 1998a), a strategic resource (Earl, 1994; De Long and Miller, 1997), or the source of competitive advantage (DeLong and Miller, 1997; Gantz, 1998; Nonaka, 1991; Davenport, 1996; Seemann, 1996; Parlby, 1999a; Havens and Knapp, 1999), it is evident that it represents an important business issue. Both Davenport (1996) and De Long and Miller (1997) report that the vast majority of knowledge managers have to rely on anecdotal data in order to justify knowledge investments

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Jamie O’Brien simply because they have not been able to develop sufficient quantitative metrics. Seemann (1996: 6) stresses that any knowledge-oriented initiative can and must be measurable – both qualitatively and quantitatively. Indeed, Scheraga (1998, p. 27) holds that the whole concept of measurement is not as difficult as it first appears and that it should employ the same practices that are used to determine the value of other parts of the business. However, he offers little in the way of practical application concerning how best to proceed. Bushko and Raynor (1998) note that measurement is an important, but as yet unresolved, issue within the wider KM sphere. Other writers (Parlby, 1998a, 1998b, 1999; KPMG, 1999) also emphasize the importance of evaluating current KM practices and processes. However, this research holds that there is a symbiotic relationship between the notions of measurement and evaluation and that the two cannot be examined effectively in isolation.

3. Concerns within the discipline Indeed KM, as a discipline, has been somewhat limited in its attempts to comprehend its underlying and fundamental concepts; in essence, it is striving to manage what it does not fully understand. Some authors in the area who have studied the concept of knowledge and management have realized that the terms are indeed mismatched (Alvesson and Karreman, 2001; Wilson, 2002) or have been too broadly used (Ruggles, 1998). What is needed is a classification of the types of knowledge that, firstly, can be managed and, secondly, impact on organizational performance and even classification of the types of KM. Researchers seem to have difficulties in defining what KM or knowledge is and “black box” the issue (Moffett and Hinds, 2010; Lloria, 2008; Alvesson and Karreman, 2001). Writing in the area of KM comes from both academic and practitioner sources with some seeing the field as one driven by consultancy companies rather than academic research in which there is a disconnect between the theory and practice (Wilson, 2002). The variety of terms is a problem that Alvesson and Karreman (2001) dub the relabeling effect. Each needs to inform the other sufficiently. The field of KM does, however, originate from a worthy base – an economically fuelled recognition of the growing importance of knowledge as an input to the organization when compared to the traditional material inputs “as free natural resources and cheap labor are exhausted, the last untapped source of competitive advantage is the knowledge of people in organizations” (Davenport 1997, p. 191). To date, KM has predominantly focused on the existence and importance of knowledge internal to the organization as evident from KM definitions. Bassi (1997) defines KM as the process of identifying/creating, capturing and applying organizational knowledge to exploit new opportunities and enhance organizational performance. Research in the area of KM has tended to focus on knowledge workers (Drucker, 1959), knowledge organizations (Sveiby and Risling, 1986), and knowledge creation and sharing (Nonaka, 1991, 1995) within these organizations. Research, such as Matusik (2002), attempted to create a typology of knowledge external to the organization, although increasingly, research has shown that knowledge is dispersed outside the firm’s boundaries and among other companies, customers, suppliers, universities, national labs, industry consortia, start-up firms and individual minds (Chesbrough, 2003). Given the importance of knowledge in all areas of daily and commercial life, it stands to reason that organizational knowledge, both internal and external, should become part of any organization’s KM strategy. As Ruggles (1998) states, KM; “is more than just a sales pitch. It is an approach to adding or creating value by more actively leveraging the know-how, experience and judgement resident within, and in many cases, outside of an organization” (p. 80)

4. Alternative frameworks/approaches Several other knowledge management frameworks/approaches were examined. It is important to note here that many models and frameworks deal with assessing knowledge management, rather than evaluating/understanding knowledge or culture as in some of the more recent KM studies Peng et al., (2012).

4.1 Category models Early KM “category models” categorized knowledge into discrete elements (SEIC model) (Nonaka and Takeuchi, 1995). This model took a high-level conceptual representation of knowledge and the knowledge creation process. Tacit and explicit elements defined by Polanyi’s intuitive and unarticulated knowledge and Hedlund’s (1994) explicit drawings highlighted further categories. Hedlund and Nonaka’s (1993) “knowledge carrier” model assumes individual, group, org and inter-organizational domain, but assumes carriers of knowledge can be segregated. Von Krogh and Nonaka (2001) offer a strategic framework for knowledge, conceptual in nature, which discusses leveraging your current activities, exploiting the knowledge, both internal and external, from outside the firm, and probing other domains to build upon. However, the framework has been critiqued for focusing on spending patterns and training budgets. Within the intellectual

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Jamie O’Brien capital frameworks, even Skandia’s approach to KM encompassing the human, customer, process, and growth elements embraces the scientific and commodity approach to KM, ignores somewhat the political and social aspects of KM.

4.2 Socially constructed models Some of the socially constructed models of KM show how knowledge is linked to the social and learning processes within the organization (the learning organization). For example, Demerest’s (1997) adaptation of Staunton’s (1989) model emphasizes the construction of knowledge in the firm. Constructed knowledge is embodied within the organization, not just through explicit programs. The OECD’s (1996) more macro approach developed indicators for knowledge inputs, stocks, networks, learning, and outputs. Within some of the methodological advances in the field, King and Zeithaml (2003) provide four steps, interviewed CEOs to identify the feasible set of knowledge resources, and presented to managers to see if they perceived this “value-add” for the organization.

4.3 Maturity/audit models Gallagher and Hazlett (2000) introduced “KM3” a KM maturity model as an evaluation tool that focused on knowledge infrastructure, knowledge culture, and knowledge technology. Moffet’s (2002) MeCTIP model is, by her own admission, more suggestive rather than descriptive. More recently, Choi et al (2008) Socio-Technical Enablers Hypothesis posited that elements of trust, intrinsic reward versus extrinsic rewards and knowledge management-system quality would lead to increased knowledge sharing and better sharing behavior. Handzic et al’s (2007) KM audit model discusses KM contingencies. It offers that organizations need to mentor; however, it does not explain if mentoring is an issue or why it might be an issue. A criticism of these models and frameworks is that many are simply too conceptual in nature. They offer definitions and categorizations of knowledge. This is one of the main criticisms of the KM field. It offers definitions and broad, soft terms. The outcomes of the KAF presented in this research will attempt to offer organizations valuable insight into their knowledge base. For example, rather than stating that organizations need to have a KM response in place such as “socio-technical enablers” as offered in Handzic’s model, the KAF will be deployed to help gain insight into knowledge sharing problems or learning issues, or, for example, within the organization, why a group of engineers is unable to locate certain knowledge. The framework uses the literature to inform and drive the analysis. Table 1 below highlights some of the KM frameworks within the literature. Table 1: Other KM frameworks Author Hedlund & Nonaka (‘94)

Framework Knowledge Carrier

Principle Organizational Domains

Von Krogh & Nonaka (‘01)

Strategic Framework

Knowledge as resource

Skandia

IC Models

Human/Process/Growth

Demerest (‘97), Staunton (‘89) OECD (‘96)

Socially Constructed

Social Learning

Macro Indicator Framework

Knowledge Metrics

King & Zeithaml (‘03)

Methodological Framework

Perceived value of knowledge

Choi et al (‘08)

Socio-Technical Enablers

KMS quality factors

Gallagher & Hazlett (‘00) Handzic (‘07)

Culture/Tech/Infrastructure KM Audit

Maturity Model: knowledge evaluation KM contingencies

Moffett (‘02)

MeCTIP Model

Suggestive Model

5. Methodology In this study, the conceptual framework was developed using in-depth interviews, document analysis, nonparticipant observation, and computer-aided analysis using QSR nVivo. The interviews devised in this study resembled a series of probes. This ensured that all topics of concern were covered. The recognition that other aspects may emerge also was a key concern and was allowed for in the interview. This allowed the interview guides to be modified over time to focus attention on areas of particular importance. The interview questions were mixed, in that some were open and phenomenological and used to ease the respondent into the interview, also allowing the respondent and author to begin a “conversation with purpose.” Other questions were more focused in that they asked about a specific phenomenon, but only if this had not been addressed

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Jamie O’Brien previously in the generic phase of the interview. A list of the respondents and their roles is presented in Table 2 below. Table 2: List of respondents Job Description Shift Lead Senior Manager Shift Lead IT lead Experienced Engineer HR lead Design Services Lead Shift Lead Experienced Engineer Frontline IT Novice Frontline Engineer HR/Knowledge Champion Experienced Engineer Middle Manager Experienced Engineer Experienced Engineer Middle Manager Experienced Engineer Senior Manager Experienced Engineer Experienced Engineer IT lead Experienced Engineer/Knowledge Champion Manufacturing Lead Frontline Engineer Supply Chain Supply Chain Novice Frontline Engineer Experienced Engineer Novice Frontline Engineer Experienced Engineer Experienced Engineer

Case A A A A A A A A A A A A B B B B B B B B B B B B B B B B B A A B

6. Methods in detail Observation data were used for the purpose of descriptions of settings, activities, people and the meanings of what is observed from the participants’ perspectives (Guba and Lincoln, 1985). This is done by enabling the author to see things participants themselves are not aware of or those they are unwilling to discuss (Patton, 1990). The in-depth interview and observation phases were aided by the collection and analysis of documents. Attewell and Rule (1991, p.319) openly advocate interviewing both managers and employees because this will provide a richer perspective on the phenomena under investigation. For this reason, the author interviewed employees from a variety of organizational levels. Prior to conducting the interviews respondents were provided with an outline detailing the purpose and nature of the study. In addition, since many respondents requested some indication of the types of questions that were going to be asked, the author, following Faison (1996), provided preliminary copies of the interview schedule in advance. This placed many interviewees at ease and the author is convinced that this procedure contributed greatly to the willingness of many to participate, and also did not generate scripted answers. As each interview progressed, responses to individual questions were carefully probed to elicit further details on specific issues (Fearon and Philip, 1998). In an effort to triangulate the data being gathered from the interviews, the author requested access to company documentation on KM. A qualitative content analysis technique (Carney, 1972; Agar, 1980; Miles Huberman, 1994) was employed in order to extract key themes as well as similarities and differences between responses. Having transcribed the interviews, respondents were given the opportunity to proof read the transcript of their interview to ensure that it was indeed an accurate representation of their views and opinions (Tuunainen and Saarinen, 1997). Following each interview, a “Contact Summary Sheet” was filled in by the interviewer – this permits the

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Jamie O’Brien interviewer to “develop an overall summary of the main points in the contact” (Miles and Huberman, 1994, p.51). Transcripts of the interviews were coded. The coding scheme used in this analysis is a mixture of an inductive approach and “start list” approach (Miles and Huberman, 1994, p.58). An initial coding scheme for the data was suggested by previous examination of literature, but as interviews took place the scheme was permitted to inductively evolve from the data. First-level coding was descriptive with a second-level focusing on the development of patterns from the data. These codes and patterns were then used to develop the KAF.

7. Generalizability A major question relating to any research project is the ability to generalize the findings. Walsham (1995) asserts that “...a critical issue for authors concerns the generalizability of the results from their work” (p. 79). In discussing the generalizability of qualitative studies, Walsham suggests that authors should not underestimate the generalizability of their findings. Firestone (1993) also proposes that when it comes to generalizability, qualitative methods are “...not at any great disadvantage” (p. 16). While statistical analysis seeks statistical generalizability, qualitative analysis seeks analytic generalizability (Firestone, 1993). The former involves sample-to-population extrapolation and necessitates probability sampling. The latter does not rely on probability sampling and involves generalizing to a theory. Furthermore, Bryman (cited in Saunders et al., 1997, p. 225) states that it is the very fact that qualitative studies can be in-depth that adds to their potential generalizability. Having conducted an in-depth qualitative study, an author can acquire a level of knowledge that may generate increased understanding when applied to a variety of other situations – thus increasing generalizability.

8. Access to documents Access to documents was provided early in the research; this continued to inform the research and acted as a buffer against interviewer bias and gaps in perception versus reality. Documents first were received regarding an “Inventory Management” project, but also included documentation about the history of the company, which was most useful. The documentation allowed for the factual and chronological elements of the case to be detailed, which overcame the challenge of respondents confusing the timeline by merging events. Coffey and Atkinson (1996) acknowledge this phenomenon and view it as the past being shaped by narrative. The use of the qualitative data analysis program QSR nVivo greatly facilitated analysis. NVivo is a key tool for contemporary qualitative data analysis. The program assists the author in the coding process by creating containers for categorized text. These containers, or nodes as referred to in the program, can be grouped hierarchically to form sub-categories of broader concepts (tree-nodes). In addition, QSR nVivo does not take any control over the analytical process from the author. NVivo was useful in this respect as it concisely displayed all developed nodes and allowed further arrangement into sets and parent-child categories.

9. Interpretation Finally, drawing conclusions and verification involved the interpretation of data and the drawing of meaning in the form of a report or case. Ideally, the research case will be a rich, tightly woven account that “closely approximates the reality it represents” (Strauss and Corbin, 1990). Here, these processes appear linear in nature and description, but in reality, they occurred almost simultaneously and repeatedly throughout the lifetime of the study. The sample is opportunistic in nature, selected on the basis of perceived relevance and access, with no attempt being made to ensure statistical representativeness. Attwell and Rule (1991), along with Babbie (1995), claim that statistical sampling is often abandoned in field work due to practical constraints. Therefore, following Eisenhardt (1989), the author decided to select the sample based on the principle that participants would likely be significantly and directly interested in and/or involved in the phenomenon under investigation. Hence, the sample was selected to provide breath of coverage rather than depth. The purpose at this stage is merely to identify common themes and to facilitate further interviews and in-depth case studies as well as the development and refinement of the KAF. In Figure 1 below, the pre-deployment conceptual KAF is presented. The aim of the subsequent sections is to highlight some of the findings from deploying a KAF at firm-level.

10. Construction of the framework The construction of the framework can be summarized into several key points. The framework offers the organization an initial lens through which it may examine its knowledge activities by focusing on some key indicators. From these key indicators (knowledge acquisition, sharing, learning and re-use) critical knowledge

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Jamie O’Brien activities can be identified (see summary of case findings). It is important to note, as stated already, the activities and findings generated are unique. The findings are unlikely to be generic for other organizations because of the nature of knowledge itself. The organization then can locate areas where gaps are present and can counter these gaps accordingly. The framework offers key knowledge activities, such as acquisition, sharing, learning and re-use, and then explores certain knowledge gaps for each activity. Some key knowledge gaps found in the cases are locating knowledge, documenting, internal informal network, training, mentoring, and lessons learned (O’Brien, 2013a). Ordering workers on certain paths does not foster the high degree of personal commitment on which effective KM initiatives are built. This framework enables management to provide workers with a conceptual tool that will help them make sense of their own experience. Once communicated properly from top management to the front-line employees, the results of this research will provide workers the freedom and autonomy to complete goals and will aid in the implementation of KM initiatives. This is important because, though the visions of top management are necessary, they will not be enough on their own. The best way forward for management is to clear obstacles and prepare the groundwork for KM initiatives to take hold.

Figure 1: The conceptual KAF (Adapted from; O’Brien, 2013b)

11. Summary of case findings After deployment of the KAF, the cases highlight several points for organizations interested in understanding their knowledge base. The analysis moves beyond simply looking at the framework itself and offers some interesting insights. Within the organizations it was observed that across groups, cross-functional sharing is siloed, which leads to a lack of knowledge sharing. Some of the reasons for this, as highlighted by the framework, are several instances of knowledge hoarding. Furthermore, the knowledge networks in both organizations are informal in nature. Coupled with the complexity of navigating the knowledge network and instances of knowledge hoarding, employees new to the organization find it difficult to locate knowledge. The external knowledge network also is shown to be secondary to the internal knowledge network. The results also show that the implementation of several KM initiatives is hindered because staff does not have sufficient time. Employees regularly spend their day navigating their informal network only to complete their actual work at home. Furthermore, lessons learned and knowledge re-use is not given attention when project targets have to be met. Training at both organizations is very formal in nature. Due to time issues and informal network dependence, there is a lack of formal systems use; however, a problematic learning cycle is being fostered because of a reliance on formal systems training. In many instances formal training is not entirely sufficient for complex knowledge work. These findings are adapted from and can be read in more detail in O’Brien (2013a).

12. Research probes The OECD (2007, 2004, 1996) alluded to the need for greater understanding of the indicators of knowledge at firm-level. This framework offers an initial understanding of these indicators and their associated knowledge activities at organizational level. When the organization decides to deploy the framework, it can do so by using the probes as presented in Figure 3. These were generated after the analysis of the case data. In conjunction with return visits to both case organizations, follow up discussions, and presentations, these were collaborated on by the author and the organizations while being informed by the original literature (O’Brien, 2013a; 2013b). In conjunction with the framework itself, these probes provide a useful set of questions for inquiry at organizational level. These probes are characterised by the very knowledge they seek to explore: They are less about passing on information and focus more on how people acquire/share/learn and re-use knowledge. They

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Jamie Oâ&#x20AC;&#x2122;Brien focus on the continuous dynamic ever-changing nature of knowledge. They focus on the potential knowledge capacity within the organization and the personal experiences of the employees. The success of using these probes in conjunction with the framework depends on the discovery, dissemination and understanding of the knowledge explored. For example, with regard to knowledge acquisition, the first four probes would be explored. It is recommended within the literature (Nonaka, 2007) that middle management undertake the deployment of any conceptual framework. This is done in conjunction with and by using the probes. Since middle management is the primary conduit between senior management and the frontline employees, it is their responsibility to help frontline employees make sense of their experiences (Nonaka, 2007). Earlier, Figure 2 showed the synthesis of this application. Once the knowledge activities are populated, further analysis reveals the primary knowledge gaps within the area of deployment, whether that is team-, department- or organization-wide. Once the organization or the persons conducting framework deployment highlight the knowledge gaps, actions and recommendations can be made.

Figure2: Application of the framework

13. Future research The author firmly acknowledges that the present research could be improved in several ways. The development of weighting indicators using a scale would be very useful in attributing values or metrics to a variety of indicators. This was one area at which the author had looked; however, due to what the paper could realistically achieve was a factor in not following this research path. The scale development work of Churchill (1979) and Diamantopoulos (2001) would be particularly useful here in future studies. Other facets of the organizationâ&#x20AC;&#x2122;s knowledge activities could be further explored, such as other external activities, focused multi-

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Jamie O’Brien level facets of the organization and other areas not explored by this paper. For instance, the findings arising from the study could be further analyzed by testing hypotheses in an in-depth explanatory study.

Framework Probes Describe what you do prior to a project, to create/acquire/share/re-use knowledge. Knowledge Acquisition

How are these actions carried out with regard to: system b. person c. process What do you do to keep your knowledge up to date? How is knowledge creation encouraged/fostered? Issues/problems? Describe what you do during a project to create/acquire/share/re-use knowledge.

Knowledge Sharing

How is knowledge sharing encouraged/fostered? Issues/Problems? Describe what it is like trying to locate the person with the relevant knowledge surrounding a problem. Describe what it is like when somebody comes to you with a knowledge problem. Describe the use of your formal network within the organization. Importance/frequency/issues?

Knowledge Learning

Describe the use of your informal network within the organization. Importance/frequency/issues? Do you need to go outside the organization (external network) to solve problems? After the completion of a project, how is knowledge creation/acquisition/sharing/re-use fostered? Lessons Learned/knowledge outputs/affect existing processes?

Knowledge ReUse

Describe how learning is accomplished within the organization. Describe formal learning: system/courses/workshops. Describe informal learning: mentoring/on-the-job training. What is the critical knowledge base of your organization?

Figure 3: Framework probes

14. Conclusion This study is part of an ongoing research program dedicated to addressing the need to develop mechanisms to evaluate and measure the effects of KM initiatives on strategic and operational business performance (both the hard and soft issues). Of particular concern is the ability to (a) evaluate where a company is going with KM, and (b) measure the so-called (strategic and operational) benefits that are being achieved. I believe that the KAF, although still in early form, can assist managers in getting the most from expenditure on knowledge management activities. Through exploratory techniques, organizations were able to conduct a “Knowledge Management Gap Analysis” and thereby assess the firm’s current capability in relation to KM. With further research and development, it is envisaged that the KAF will prove to be a useful diagnostic tool for those currently engaged in, or considering embarking upon, a KM initiative.

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Facilitators, Inhibitors, and Obstacles – a Refined Categorization Regarding Barriers for Knowledge Transfer, Sharing, and Flow Dan Paulin and Mats Winroth Department of Technology Management and Economics, Chalmers University of Technology, Gothenburg, Sweden dan.paulin@chalmers.se mats.winroth@chalmers.se Abstract: Within the KM area, numerous terms are frequently used. Among terms that have high significance for change in the knowledge structure, terms related to distribution or dispersion are common. Four of those terms are Knowledge Transfer (KT), Knowledge Sharing (KS), Knowledge Flow (KF), and Knowledge Barriers. Barriers come in many forms, ranging from strictly individual/personal barriers through group‐related barriers, intra‐ and inter‐organizational barriers, barriers related to national differences, as well as an array of technology‐related barriers. Several authors have developed categories with the purpose to create a structure. However, these categories only focus on type of barrier. There is a lack of categorization that divides barriers due to their relative effect on the KT and KS or KF. The purpose is to present a refined categorization regarding factors that influence knowledge transfer, sharing, and flow and to show how previously identified barriers fit into this new categorization. This research has been carried out as a literature review. Previously identified influencing factors have been re‐categorized. The research has resulted in a proposition of factors influencing knowledge dissemination and the following terms are suggested: (1) Facilitators (which denominate factors with positive influence), (2) Inhibitors (factors with negative influence) and (3) Obstacles (factors that obstruct knowledge dissemination, until certain conditions or levels are fulfilled). The proposed categorization is supported by descriptive examples taken from in‐depth case studies of four multinational manufacturing companies with product development in Sweden and manufacturing in China The refined categorization will enable practitioners and academics alike to develop suitable tools to further enhance the collective (or individual) understanding of the mechanisms behind knowledge dissemination. Keywords: knowledge sharing, knowledge transfer, knowledge barrier, facilitator, inhibitor, obstacle

1. Introduction, aim, and paper structure During the last fifteen years there has been a strong stream of research as well as corporate initiatives on the topic of Knowledge Management (KM). Knowledge is regarded by many as a strategic asset of firms, in line with the reasoning of Spender (1996) on the knowledge‐based theory of the firm, and with Teece (1998) on the essence of capturing value from knowledge assets. These assets should be managed in order to create a beneficial competitive position for the firm, and several authors have proposed solutions to manage knowledge within (e.g. Nonaka and Takeuchi, 1995) and between companies (e.g. Easterby‐Smith et al., 2008). Within the KM area, numerous terms are frequently used. Among terms that have high significance for change in the knowledge structure, terms related to distribution or dispersion are common. Three such enabling terms are Knowledge Transfer (KT), Knowledge Sharing (KS), and Knowledge Flow (KF) (Schwartz, 2006). Another highly significant, but obstructing, term is Knowledge Barrier (KB). Barriers come in many forms, ranging from strictly individual/personal barriers through group‐related barriers, intra‐ and inter‐organizational barriers, barriers related to national differences, as well as an array of technology‐related barriers. Some authors have proposed categories within these barriers in order to create a structure (e.g. Bartezzaghi et al., 1997; Lindkvist, 2001; Riege, 2005; Riege, 2007). One such proposition has been presented by Riege, who in several practitioner‐oriented papers (Riege, 2005; Riege, 2007; Schleimer and Riege, 2009) has summarized several of these barriers, proposed managerial solutions to them, and applied the categories individual/people, organizational, and technological. However, the proposed categories only focus on type of barrier. There is a lack of categorization that divides barriers due to their relative effect on the KT and KS or KF. Certain influencing factors have positive effects on KS, some have negative effects, and some have varying effects depending on context and management. One contribution here comes from Søndergaard et al. (2007) in which the terms facilitators and barriers are used. Highlighted factors are leadership, organizational, and individual factors, plus three so‐called sub‐factors: trust, individual motivation, and geographical location; all may act as both barriers and enablers. Main limitation of this case study is that it describes a single‐company setting. Søndergaard et al. (2007, p. 432) hope that “future

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Dan Paulin and Mats Winroth studies will look closer at particular knowledgeable activities in order to be able to make stronger claims about facilitators and barriers”. In this paper, these issues are addressed, a refined categorization is proposed, and examples from industrial cases are used to support the proposal. This paper presents a refined categorization regarding factors that influence KT, KS, and KF, and shows how previously identified “barriers” fit this categorization.

2. Background/previous research In this section, the theoretical base is presented. The main part of this section is focused on existing categorizations and previously identified barriers.

2.1 Terminology The origins of the terms KT, KS, and KF can be traced back to Plato and Aristotle, but the re‐emergence of the terms seems to come from two directions. Firstly: through Polanyi’s writings and discussions on tacit and explicit knowledge and the strategic management literature. Secondly: from literature dealing with product innovation and technology transfer (e.g. Allen (1977), and Clark and Fujimoto (1991) ). Initially, the terms KT and KS were used synonymously (e.g. Badaracco, 1991; Hansen, 1999), but lately the terms have been separated. After having reviewed literature on KM, Paulin and Suneson (2012) conclude that the terms are vague and that the vagueness is mainly related to the analytical level. KT is primarily used on higher ontological levels e.g. industry, company, organization, and group, while KS is used on the level of individuals. KF is a term related to KT and KS, and according to Ribière (in Schwartz, 2006) it is considered a broader concept than KT and KS. KF could also be exemplified with the streams in a KM value chain (Shin et al., 2001). In this paper the term Knowledge Dissemination (Paulin, 2013), or KD, will be used, encompassing all these three terms. Numerous factors influencing KD (KBs or barriers for KD) have been presented. In this article, a more original definition of KB, highlighted by Attewell (1992), will be used. Attewell regards a KB as lack of knowledge, which means that the individual with this lack cannot grasp the content of the subject that is being discussed before he/she has obtained a sufficient knowledge base. The lack of a frame of reference from memories and experiences makes the topic impossible to understand or to connect to previous knowledge. To cover all factors that affect KD, the term influencing factors will be used from now on.

2.2 Knowledge dissemination model In several highly cited papers dealing with KT or KS (e.g. Szulanski, 1996; Cummings and Teng, 2003; Chini, 2005; Liyanage et al., 2009; Duan et al., 2010), the view is influenced by the classical communications model originally presented by Shannon and Weaver (1949). Paulin (2013) combined this model with contributions from Lindkvist (2001), Cummings and Teng (2003), Minbaeva (2007), and Duan et al. (2010) to a research model named the Knowledge Dissemination Model (KDM). The model (see Figure 1) includes five main components: Actors, Content, Media, Context, and Activity. The KDM is used here as a way to structure the results of the literature review,

3. Literature review It is necessary to try to get a better understanding of what influence the outcome of knowledge dissemination activities in order to improve and manage the utilization of knowledge. The literature on KT and KS is extensive and there are numerous authors who have identified different factors that influence these processes, however there are few extensive compilations. Within the KT area there is a couple that stands out. Cummings and Teng (2003) and Duan et al. (2010) have been mentioned previously. Another example of an overarching compilation is provided by Riege (2007). He proposes actions to overcome knowledge transfer barriers in MNCs and he addresses 20 different individual, 14 organizational, and six technological barriers. However, he partly bases his advice on findings described in a previous paper (Riege, 2005) in which he presents 39 knowledge‐sharing barriers divided into 17 individual, 14 organizational, and 8 technological. This creates confusion, since it is not clear what the author means when it appears that he uses the terms as substitutes for one another. Two papers which focus on KS are Wang and Noe (2010) and Søndergaard et al. (2007). Thus, in Wang and Noe (2010), three other categories are used (environmental, individual, and motivational factors) in which multiple sub factors are included, while in Søndergaard et al. (2007), three categories (leadership, organizational, and individual factors) and three sub‐factors (trust, individual motivation and geographical

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Dan Paulin and Mats Winroth location) are examined. However, neither of the categorizations used include any qualitative dimension other than the occasional use of barriers, enablers, facilitators, or constraints. Nor are there any known authors that have discussed a compilation of influencing factors from that viewpoint. Instead, contributions from the mentioned compilations are used to form a base of factors influencing knowledge dissemination. In Table 1 below, these factors are summarized and structured in line with the KDM.

Context

Actor, source

Activity Media

Actor, recipient

Content

Figure 1: The knowledge dissemination model (Paulin, 2013) Table 1: A summary of factors influencing knowledge dissemination Component Influencing factors Authors in the KDM Actor, Articulability. Embeddedness. Protectionism. Ability Cummings and Teng, 2003; Riege, 2005; source to share. Ambiguity Minbaeva and Michailova, 2004; Simonin, 1999 Actor, Learning culture. Priority. Absorptive capacity. Cummings and Teng, 2003; Kayes et al., 2005 (in recipient Knowledge level. Duan et al., 2010); Szulanski, 1996; Mu et al., 2010; Attewell, 1992; Riege, 2005 Actors Knowledge distance. Openness. Trust. Motivation. Gupta and Govindarajan, 2000; Delios and Age distance. Gender distance. Leadership. Björkman, 2000; Minbaeva et al., 2003; Osterloh and Frey, 2000; Szulanski, 2000 (all in Duan et al., 2010); Cummings and Teng, 2003; Riege, 2005; Goh, 2002; Kalling, 2003 Content Causal ambiguity. Type of knowledge Goh, 2002; Szulanski, 2000 (both in Duan et al, 2010); Szulanski, 1996; Riege, 2005 Media Linguistic distance. IT‐systems. Communication Kayes et al, 2005; Syed‐Ikhsan and Rowland, channels. Transfer channels. 2004 (all in Duan et al, 2010); Schomaker, 2006; Ambos and Ambos, 2009; Davenport et al, 1998; Rhodes et al, 2008; Riege, 2005 Context Strength in ties between groups. Organizational Hansen and Løvås, 2004; Abou‐Zeid, 2005; De distance. Physical distance. Physical space. Distance Long and Fahey, 2000; Goh, 2002; Ipe, 2003; between norms. Cultural distance. Learning/sharing Schlegelmilch and Chini, 2004; Inkpen and Pien, culture. KM integration. Organization size. 2006; Seibert et al, 2001 (all in Duan et al, Organizational priority. Environmental uncertainty. 2010); Hansen, 1999; Nonaka, 1994; Cummings Relationship. Social capital. Available/suitable and Teng, 2003; Albino et al, 1998; Riege, 2005; space. Available time. Ambos and Ambos, 2009; Goh, 2002; Liao and Hu, 2007 Activity Frequence/intensity in transfer activities Cummings and Teng, 2003

Most of the authors of the original papers have presented scientific support for, or in some cases indications, the kind of influence these factors have on knowledge dissemination. The influence can be positive, negative, or of another character. This original support will be used as the base for the following proposition.

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4. Proposed categorization – FIO FIO stands for Facilitators, Inhibitors, and Obstacles. These three terms assist us in grouping the different influencing factors based on their effect on knowledge dissemination. In this paper facilitator is defined as a factor that has positive influence on knowledge dissemination. This definition connects to Søndergaard et al. (2007). Inhibitor is defined as a factor that has a negative, moderating influence and obstacle, is defined as a factor that obstructs KD, until certain conditions or levels are fulfilled. Examples of each term are presented in chapter 6.

5. Research context and methodology To support the aim of the paper through descriptive examples, a qualitative research strategy and comparative research design are deployed (Bryman and Bell, 2007). Four companies, companies Alpha, Beta, Gamma, and Delta, from different industries – industrial machinery, home furnishing, telecommunications, and manufacturing – were selected. Since the product realization process bridges organizational and cultural as well as linguistic and mental boundaries, this process was selected for this study. A broad definition of the product realization process is used (Bellgran and Säfsten, 2010). This study focuses on R&D–Manufacturing interface. Additionally, product development is performed in one country (in all cases, Sweden) and manufacturing is performed in another country (in all cases, China).

5.1 Information and data collection Main information sources were semi‐structured interviews and informal discussions being performed over five years (2005‐2010). Furthermore, written information such as official company documentation, in‐company material, and questionnaires, was used. Interviews were structured around an interview guide where questions had been developed through a narrative review (Bryman and Bell, 2007). Interviews were taped, transcribed by someone else than the interviewer(s), checked and corrected by interviewer(s), and fed back to interviewees for validation. Each interview (10 in total) lasted for 50 minutes to 2 hours. Numerous informal discussions and e‐mail conversations have been held with different personnel during the five‐year period. Case companies were included in the empirical base for another research activity including a web‐based questionnaire covering supplementary knowledge‐sharing issues (Paulin, 2010). Official documents have been collected primarily via Internet, and in‐company material has been supplied by company representatives. In this paper, the examples are very brief however richer case descriptions can be found in Paulin (2013).

6. Influencing factors in action – examples of facilitators, inhibitors, and obstacles In this chapter, examples of factors in each category are presented followed by brief examples of influencing factors in action taken from the four case companies.

6.1 Examples of facilitators The first example in this category is a complex factor, motivation. Numerous studies support this as facilitator and the number of antecedents is as many. Within this factor, the antecedent the senders’ ability to share is highlighted here. Minbaeva and Michailova (2004, p. 668) hypothesize that “[t]he greater the ability and willingness of knowledge senders to transfer knowledge, the higher the degree of knowledge transfer to the subsidiary”. They conclude that the ability to transfer knowledge has strong positive effect on knowledge transfer but willingness is not supported. To exemplify this factor, we can contrast Gamma’s actions for preparing source staff through a cultural preparation kit (which increases the source’s ability to adapt its sharing style to better fit the recipients) with the lack of sufficient language skills of the source at Alpha. The second example is the recipient’s absorptive capacity from Szulanski (1996). He does not test the relation between absorptive capacity and knowledge transfer per se, but between lack of absorptive capacity and stickiness (which is used as a proxy for inhibiting knowledge sharing in this article). The findings indicate a strong relationship. He concludes by stating that knowledge‐related influencing factors are even more important than motivational factors when it comes to knowledge transfer. This can be exemplified by Delta’s strategy to employ equally educated people at source, and the recipient contrasted with Beta’s communication problems due to recipients’ low ability to understand advanced technical specifications.

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Dan Paulin and Mats Winroth The third and final example here is available time. This factor has been studied by several researchers, but here the conclusions by Goh (2002) are seen as representative for the majority of studies. He states that availability of time as a resource can amplify knowledge‐transfer activities and enhance the effects. Here, we highlight Delta’s strategy to implement on‐site learning for extended periods of time and contrast it with Alpha’s problems of high staff turnover at recipient unit.

6.2 Examples of inhibitors The first example in this category is knowledge distance, from Cummings and Teng (2003). Their hypothesis is: “Transfer success decreases as knowledge distance between source and recipient increases” (ibid., p. 47). Their study could confirm the hypothesis and that there is a negative relationship between transfer success and knowledge distance. Here, we exemplify by pointing out Gamma’s solution of mutual knowledge creation within industrialization project, compared with Beta’s strategy of leaving production development to the manufacturer. The second and third examples are geographical distance and linguistic distance, from Ambos and Ambos (2009). Regarding geographical distance, “[the] relationship of PCM [personal coordination mechanisms] and knowledge transfer effectiveness is moderated by geographical distance between sender and recipient. The smaller the geographical distance, the higher the effectiveness of knowledge transfers via PCMs” (Ambos and Ambos, 2009, p. 4). Their hypothesis is supported. Regarding linguistic distance, the hypothesis is: “The relationship of PCM and knowledge transfer effectiveness is expected to be moderated by linguistic distance between the sender and recipient. The smaller the linguistic distance, the higher the effectiveness of knowledge transfers via PCM” (Ambos and Ambos, 2009, p. 5). This hypothesis was clearly supported by their study. For geographical distance, we point out Alpha’s and Gamma’s differences in relationships between the source units on one side, and their co‐located Swedish recipient units and their distant Chinese units on the other. For lingual distance, we highlight Beta’s strategy of using bilingual intermediaries in contrast to Alpha’s communication problems. The fourth example is arduous relationship between sender and recipient. This influencing factor was studied by Szulanski (1996). He concludes that an arduous relationship is one of the three most important barriers for KT that was included in his study (the others being lack of absorptive capacity of the recipient and causal ambiguity). In another study, Hansen (1999) used an inverted terminology with focus on strength in ties between groups. This study shows that weak ties between groups are negative for highly complex knowledge to be transferred (strong ties are thus positive), which partially supports Szulanski’s claims. Gamma’s solution to set up a joint project team can be contrasted to the source at Alpha and its careful, protective, approach towards the recipients. The fifth, sixth, and seventh examples are recipient’s causal ambiguity, barren organizational context, and unprovenness, from Szulanski (1996). He does not test the relation between causal ambiguity and knowledge transfer per se, but between causal ambiguity and stickiness. Findings show that there is a strong relationship. Regarding inhibitors of barren organizational context and unprovenness, Szulanski (1996) concludes that there are relationships between them and stickiness, that they have some influence, but they are not as strong as between causal ambiguity and stickiness. Examples of barren organizational context are from Gamma’s strategy to bridge the units via the industrialization project and from Beta’s strategy to outsource manufacturing. Alpha’s and Gamma’s strategy, to deploy only products that already have been produced close to R&D units, is a solution to deal with the unprovenness issue.

6.3 Examples of obstacles The first example is trust. There are many studies that have examined the effect of trust on cooperation, knowledge transfer, and other related topics. Goh (2002, pp. 25‐26) states that “[a] high level of trust is […] an essential condition for a willingness to cooperate”, in line with the definition of an obstacle. Trust is also regarded as an antecedent to the inhibitor of arduous relationship, but displays other characteristics in line with Goh’s reasoning. From our cases, we point to the team‐strengthening activities at Gamma in order to build trust and its strategy of re‐using industrialization teams from one project to another. On the other hand, we exemplify with Beta’s conscious distancing and its trust issues.

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Dan Paulin and Mats Winroth The second example is taken from Gold et al. (2001) who identify basic infrastructure and sharing capabilities as necessary prerequisites for sharing practices. This is an overarching and complex factor (in line with motivation, which was discussed in section 5.2). Since exact causal relationship between factor and knowledge sharing is unclear, but there still are strong indications that a basic level of infrastructure and sharing capabilities is necessary, it belongs to this category. Beta’s common product databases are an example of existing infrastructure, while Alpha’s priority of protecting its intellectual property has meant that this infrastructural dimension is missing. The third example is technological know‐how, addressed by Attewell (1992) in a study of technology diffusion regarding business computing. This factor indicates that recipient needs technical know‐how about new technology in order to be able to adopt it. Thus, there might be a fourth category in the refined view presented here, but since this factor can be overcome in time and with development of such know‐how, it is classified as an obstacle. We use Gamma’s solution of co‐developing production solutions, which creates knowledge buildup and knowledge creation within project teams, in contrast to lack of knowledge in understanding advanced technical specifications by recipients at Beta.

6.4 Classification of influencing factors in line with FIO During the literature review, additional influencing factors were found. These are presented in Table 2 The signs are put into brackets for factors where clear empirical support, from the original studies cannot be found. A plus sign (+) is used to depict a facilitator, a minus sign (‐) corresponds to an inhibitor and a vertical line (|) corresponds to an obstacle. Original studies are studies performed by authors (other than the authors of this paper) where factors influencing KD have been identified and their effects determined. Table 2: Classification of influencing factors according to the FIO structure Component in the research model

Type of influence

Factor

+ + + +

Frequency / intensity in transfer activities Ability to share Absorptive capacity Openness. Motivation. Leadership Strength in ties between groups. Organization size. Relationship. Social capital. Social proximity. Available time IT systems

Activity Actor, source Actor, recipient Actors

Learning culture. Priority Physical space. Learning/sharing culture. KM integration. Organizational priority. Available/suitable space

Actor, recipient

Actor, source Actors

‐

Embeddedness. Ambiguity Knowledge distance Causal ambiguity. Unprovenness. Arduous relationship. Organizational distance. Geographic / physical distance. Distance between norms. Cultural distance. Environmental uncertainty Linguistic distance

(‐) (‐)

Articulability. Protectionism Age distance. Gender distance

Actor, source Actors

Technical know‐how

Actor, recipient

Trust

Actors

Basic infrastructure and sharing capabilities

Media

(+), (‐) (+), (‐)

Communication channels. Transfer channels Type of knowledge

Media Content

+ + (+) (+) ‐ ‐ ‐

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Context Media

Context

Content Media

Dan Paulin and Mats Winroth This classification is a summary of previously published studies in order that external support can be regarded as satisfactory.

7. Discussion In the majority of literature on influencing factors, one of the terms ‘knowledge barrier’, ‘barrier for knowledge transfer’, ‘barrier for knowledge sharing’, and ‘barrier for knowledge flow’ is used. In order for any decision‐maker to develop appropriate policies, strategies, and operative solutions, it would however be advantageous to have a refined understanding of mechanics behind knowledge dissemination. Barrier is an important notion. This paper does not propose to abandon this notion. It is only regarded as a synthesis of the two terms ‘inhibitors’ and ‘obstacles’. It is also a conscious choice to talk about ‘influencing factors’, since there are both positive and negative factors affecting knowledge dissemination. A sole use of ‘barriers’ without the positive dimension ‘facilitators’ implies that knowledge dissemination is something negative, which it certainly is not. When it comes to interpretation and classification of influencing factors in the proposed FIO‐categories or in part of the knowledge dissemination system, there might be disagreements. In order to forestall such a debate, some explanations are given in the following subsections. In this paper, a view of ‘knowledge as something that exists in a socially constructed context’ (K‐SCC) is taken. For some notions used here (e.g. recipient), linguistic stringency is deviated from, because the original authors have another view and the prime purpose here is not to align their notions with a K‐SCC view. This can also explain some of the classifications made. In the proposed classification, factors have been credited with positive, negative, or obstructive traits. This might be a too simplistic understanding of the effects that influencing factors have on knowledge dissemination. There are studies, which imply that some factors have a stronger impact than others (e.g. Szulanski, 1996), and there are studies that imply that actors from different contextual settings perceive the same factor differently (e.g. Sveiby,2007).

8. Conclusions and indications of future research directions Effective knowledge dissemination is important for any company, organization, or individual who strives for development and improvement. This paper contributes to the ongoing development of Knowledge Management as well as any related area (such as Strategic, Technology, or Engineering Management) in that it refines the current view on how to classify factors that influence knowledge dissemination (transfer, sharing, and flow). The proposed categories are Facilitators, Inhibitors, and Obstacles (or FIO) and this refined categorization will enable practitioners and academics to develop suitable tools to further enhance the collective (or individual) understanding of the mechanisms behind one of the central notions in our increasingly knowledge‐centered world. There are limitations of the proposed classification, especially regarding the dynamic behavior of the strength of the influencing factors when combined and/or set in different contexts. There are other authors who have studied the dynamic behavior, but there is still a clear need to expand research in this area. Another research stream that might contribute to development of the proposed categorization is the research regarding process‐centered knowledge transfer models (e.g. Hansen, 1999; Kwan and Cheung, 2006;. This stream enables the proposed model to become even more refined since it allows a scrutiny of each influencing factor related to the different phases in knowledge transfer (e.g. motivation, matching, implementation, and retention as summarized by Kwan and Cheung (2006)).

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Sydney, Australia

11th International Conference on Intellectual Capital Capital, Knowledge Management and Organisational Learning University of Sydney Business School The University Of Sydney Sydney, Australia

7-8 November 2014 For further information contact info@academic-conferences.org or telephone +44-(0)-118-972-4148