Ijebea vol1 print 1 by iasir journals

ISSN (ONLINE): 2279-0039 ISSN (PRINT): 2279-0020

Issue 9, Volume 1 & 2 June-August, 2014

International Journal of Engineering, Business and Enterprises Applications

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States. Offices Overseas: India, Australia, Germany, Netherlands, Canada. Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, ijebea@gmail.com

PREFACE We are delighted to welcome you to the ninth issue of the International Journal of Engineering, Business and Enterprises Applications (IJEBEA). In recent years, advances in science, engineering, and business processes have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. IJEBEA is publishing high-quality, peer-reviewed papers covering a number of topics in the areas of business process models, engineering and enterprise applications, knowledge engineering science, modeling and designing, control and deployment techniques, e-Commerce applications, B2B and B2C applications, Protocol management and channel management, Mobility, process, engineering, security and technology management, Semantic Web and interfaces, Enterprise applications for software and web engineering,

open-source

platforms,

Human

resource

management,

Operations

management, Organizational and management issues, Supply chain management, Strategic decision support systems, Cloud computing, Risk management, Information technology, Information retrieval systems, Aspect-oriented programming, e-Libraries and e-Publishing, Data mining and warehousing, Distributed AI systems and architectures, Bioinformatics and scientific computing, Knowledge and information management techniques, and other relevant fields available in the vicinity of engineering, business and enterprise applications. The editorial board of IJEBEA is composed of members of the Teachers & Researchers community who have expertise in a variety of disciplines, including business process models, software and technology deployments, ICT solutions, and other related disciplines of engineering, business and enterprise applications. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest researchers in the field of emerging technologies especially related to engineering, business and enterprises applications. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory, GetCITED, DOAJ, SSRN, TGDScholar, WorldWideScience, CiteSeerX, CRCnetBASE, Google Scholar, Microsoft Academic

Search,

INSPEC,

ProQuest,

ArnetMiner,

Base,

ChemXSeer,

citebase,

OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library, arXiv, ERIC, EasyBib, Infotopia, WorldCat, .docstoc JURN, Mendeley,

ResearchGate,

cogprints,

OCLC,

iSEEK,

Scribd,

LOCKSS,

CASSI,

E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the IJEBEA for entrusting us with the important job. We are thankful to the members of the IJEBEA editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions. We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this ninth issue, we received 92 research papers and out of which only 23 research papers are published in two volumes as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the fields of engineering, business and enterprises applications.

This issue of the IJEBEA has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in engineering, business and enterprise applications and may open new area for research and development. We hope you will enjoy this ninth issue of the IJEBEA and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The International Journal of Engineering, Business and Enterprises Applications (IJEBEA), ISSN (Online): 2279-0039, ISSN (Print): 2279-0020 (June-August, 2014, Issue 9, Volume 1 & 2). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India. CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands. Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia. Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States. CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London. ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom. Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India. Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India. Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India. REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain. Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA. Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman. Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia. Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India. Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) ( Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati-517502, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India. Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune -411 041, India. Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106, India. Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India. Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India. Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India. Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia. Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg., R.K.University, Kast urbhadham, Tramba, Rajkot-360020, India. Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India. Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana. India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar,Punjab (India). Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur, 313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia-81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women’s College, Gardanibagh, Patna, Bihar. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia. Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009,India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University,Coimbatore-641003,TamilNadu,India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066. Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India. Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057, India. Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India. Prof. (Dr.) B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India.

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Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Eng.,Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty,Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT, Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India. Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale , Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka, India.

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Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman. Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India. Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India. Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia. Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India. Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal. Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi- 835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS- 38655, USA. Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India. Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal, India. Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu, India. Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India. Prof. (Dr.) K. Ramesh, Department of Chemistry, C.B.I.T, Gandipet, Hyderabad-500075. India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India.

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Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics, Bhilai Institute Of Technology, Durg (C.G.) 491001, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan. Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel. Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India. Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’s SCHOOL, ATHANI Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India. Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering, Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India. Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology,Jalandhar,Punjab, India. Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology , Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand, India. Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India. Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India. Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura, India. Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India. Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai-400103, India. Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode

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Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, Tamil Nadu, India. Prof. (Dr.) Har Mohan Rai , Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand. Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India. Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India. Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036,India. Prof. (Dr.) Aparna Sarkar,PH.D. Physiology, AIPT,Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP ,India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. . Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, INDIA. . Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University,Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV), Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar,PhD(CS), M.Phil(CS),MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana) Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College ,Govind Nagar,Kanpur208006, India Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura Prof. (Dr.) T Venkat Narayana Rao, C.S.E,Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India.

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Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India. Satya Rishi Takyar , Senior ISO Consultant, New Delhi, India. Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh, Punjab, India. Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India. Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India. Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India. Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India. Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India. Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India. Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India. Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India. Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India. Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India. Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India. Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana. Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany. Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany. Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India. Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra. Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India. Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India. Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India. Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India. Sumit Sharma, Team Lead, American Express, New Delhi, India. Vivek Gautam, Senior Software Engineer, Wipro, Noida, India. Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India. Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India. Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India. Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India. Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India. Naresh Setia, Senior Software Engineer, Infogain, Noida, India. Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India. Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India. Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India. Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India. Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India. Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India. Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:                                                    

e-Commerce applications using web services B2B and B2C applications Advanced web service technologies including security, process management and QoS Surveillance technologies and security policies Security for protocol management Resource and channel management Mobility management Network Security management Technology management Information security management Semantic web for e-Business and e-Learning e-Learning design and methodologies Instructional design methodologies Content management and development Knowledge and information management techniques Enterprise Applications for software and web engineering Open-source e-Learning platforms Internet payment systems Techniques for B2B e-Commerce e-Business models and architectures Service-oriented e-Commerce Human resource management Business-oriented and consumer-oriented e-Commerce Development of e-Business and applications Supply chain management Strategic decision support systems Enterprise resource planning and e-Business Intranet and extranet business applications Enterprise-wide client-server architectures Information systems analysis and specification Strategic issues in distributed development Semantic web technologies and cloud computing Legal aspects of e-Government Risk management Methods and tools for e-Government e-Democracy and e-Voting Operations management Information technology Information retrieval systems Aspect-oriented programming e-Libraries and e-Publishing Intelligent tutoring systems Digital libraries for e-learning Web-based learning, wikis and blogs Social networks and intelligence Social science simulation Information retrieval systems Wired and wireless data communication networks Data mining and warehousing Distributed AI systems and architectures Bioinformatics and scientific computing Knowledge and information management techniques

TABLE OF CONTENTS (June-August, 2014, Issue 9, Volume 1 & 2) Issue 9, Volume 1 Paper Code

Paper Title

Page No.

IJEBEA 14-304

Investigating the effects of process parameters on MRR in WEDM using Molybdenum wire Baljit Singh, Dr. B.S. Pabla, Manju Saroha

01-05

IJEBEA 14-308

The Influence of Intellectual Capital towards Firm Value with Independent Commisioner and Audit Committe As Moderating Variables Etty Murwaningsari

06-11

IJEBEA 14-315

The Impact of positive cash operating activities on the Cost of Debt: International Evidence Dr. Saadani Ghali, Harit Satt

12-20

IJEBEA 14-316

Optimisation of RCC Beam Bikramjit Singh, Hardeep Singh Rai

21-34

IJEBEA 14-320

An Investigative Study on Factors Causing Job Choice of Business School Students in Private Universities of Bangladesh Sadia Tangem, Mohammad Ishtiak Uddin

35-39

IJEBEA 14-326

Consumer Perception Regarding Eco-Friendly Fast Moving Consumer Goods in India Sudhir Sachdev, Vinod Mahna

40-43

IJEBEA 14-327

Analysis of Manufacturing Competency for an Automobile Manufacturing Unit Chandan Deep Singh, Jaimal Singh Khamba

44-51

IJEBEA 14-329

The human resources policy of the Health Ministry in Angola - from current practice to the desired praxis Pedro J. M. Gomes, Pedro F. Franque

52-56

IJEBEA 14-334

Investigating Location Differences in Influence of Parameters on Organizational Role Stress among IT Sector Employees Deepa Mohan, Sudarsan N

57-62

IJEBEA 14-337

Review on Concrete Subjected to Elevated Temperature Maya T M, Nivin Philip, Dr. Job Thomas

63-69

IJEBEA 14-338

Global Talent Management Strategies for High Performance Culture Dr. A.Narasima Venkatesh

70-72

IJEBEA 14-341

Role of CRM in Business Sector Dr.T.Vijayaragavan

73-76

Issue 9, Volume 2 Paper Code

Paper Title

Page No.

IJEBEA 14-342

Dual Mechanism to detect DDOS Attack Priyanka Dembla, Chander Diwaker

77-82

IJEBEA 14-357

The influence of the Authentic Leadership in the commitment of Health Professionals in Angola Pedro J. M. Gomes

83-88

IJEBEA 14-362

Experimental Study of Non-Destructive Test on Steel Fibre Reinforced Concrete Mandeep Singh, Inderpreet Kaur

89-95

IJEBEA 14-368

Conceptualisation of Business Rules Rakhi Dewan, Rekha Sachdeva

96-98

IJEBEA 14-371

Research on fluid in an open container with a complex force field applied Penev Mariel, Stoyanov Ivaylo

99-103

IJEBEA 14-373

Analysis of Strategic Success for an Automobile Manufacturing Unit Chandan Deep Singh, Jaimal Singh Khamba

104-111

IJEBEA 14-374

Design of Adder logic cell with XOR gate Chandrahash Patel, Dr. Veena C.S.

112-115

IJEBEA 14-379

Discrete Optimisation of One way Slab using Genetic Algorithm Hatindera Singh, Hardeep Singh Rai, Jagbir Singh

116-121

IJEBEA 14-385

Fuzzy Clustered Speaker Identification Prof. Angel Mathew,Preethy Prince Thachil

122-125

IJEBEA 14-386

Parameter Analysis of Hybrid Power System with UPFC V. K. Bhola, T. Sharma, P. Saini

126-129

IJEBEA 14-387

Impact of Communication in Predicting Adoption of Drugs Treating Hypertension Dipanjan Goswami, D. R. Aggarwal, Neera Jain,

130-139

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Investigating the effects of process parameters on MRR in WEDM using Molybdenum wire Baljit Singha, Dr. B.S. Pablab, Manju Sarohac Department of Mechanical Engineering, Govt. Polytechnic, Sonipat, Haryana, India b Professor, Department of Mechanical Engineering, NITTTR, Chandigarh, India c Assistant Professor, SES, B.P.S. University, Khanpur Kalan, Sonipat, Haryana, India __________________________________________________________________________________________ Abstract: The goal of this research work was to create guideline for Wire Electrical Discharge Machining (WEDM) of Titanium Alloy (Ti6 Al4V), using Molybdenum wire as a cutting tool. The Ti alloy possesses excellent bio compatibility to human beings, especially with tissues or bones. In general, the material of wire (electrode) is taken as brass/copper in WEDM. But in this work, Molybdenum wire is used.The main goal of this research work was to investigate the effect of various process parameters on material removal rate (MRR) while machining Ti6AL4V work piece on WEDM using Molybdenum wire. The important process parameters are pulse on time (Ton), pulse off time (Toff), peak current (IP), servo voltage (SV) and wire feed rate (WF). The optimum process parameters for maximum material removal rate (MRR) were to be determined experimentally using DOE, use of Taguchi method. The analysis of results (i,e. parametric contribution of each process parameter on MRR) was determined by ANOVA using SPSS software (Statistical Package for Social Sciences). The optimum process parameters were determined, from the graphs generated between S/N ratios to each design factors. At last, the specimen (work piece) was machined at optimum process parameters to find the improvement in MRR with acceptable surface finish (max. 3.5 µm). Keywords: Wire electrical discharge machining (WEDM), material removal rate (MRR), ANNOVA, Taguchi method. __________________________________________________________________________________________ a

I. Introduction Wire Electric Discharge Machining (WEDM) is extension of EDM. It was introduced in late 1960s', and has revolutionized the tool and die, mold, and metal-working industries. The WEDM uses a thin metallic wire to cut a programmed contour in a work-piece and capable of machining all electrically conductive materials. In Wire EDM the spark always takes place in the dielectric of de-ionized water. The electrolyte acts as a coolant and flushes away the eroded metal particles. With WEDM technology, complicated difficult-to-machine shapes can be easily cut. The high degree of accuracy and the fine surface finish make WEDM valuable. It is the key process for surface modification in extrusion die and blanking punches, manufacturing industries, where material is removed by controlled erosion through a series of electric sparks. Now days, WEDM is an important machine tool to produce complex and intricate shapes of components in areas such as tool and die making industries, automobile, aerospace, nuclear, computer, and electronics industries. II. Basic Principle of Wire EDM Process Electrical discharge wire cutting, more commonly known as wire-EDM (WEDM), is a spark erosion process used to produce complex two-and three-dimensional shapes through electrically conductive work-pieces by using a thin wire electrode. The material is removed by means of a series of recurring electrical sparks irrespective of mechanical properties such as hardness, strength, and toughness. There is never any mechanical contact between the electrode and work-piece. Table 1: Comparison between Brass and Molybdenum Wire Sr. No Molybdenum Wire 1.

High Tensile Strength (950 Mpa)

High Melting Point (2623°C )

Brass Wire Low Tensile Strength (310 Mpa) Low Melting Point (900°C) -6

Low Coefficient of Thermal Expansion (5×10 m/mk)

High Coefficient of Thermal Expansion (18×10-6 m/mk)

High Brinell Hardness (1500 Mpa)

Low Brinell Hardness No (60)

Minimum dia. of Wire is 0.1 mm

Minimum dia. Of wire is 0.25 mm

Suitable for High speed machining

Suitable for Low speed machining

Suitable for semi-finish work

Suitable for highly finish work

Acceptable Electrical Resistivity Coefficient (5.2×10-8 Ω m)

Electrical Resistivity Coefficient

Thermal Conductivity (138 Wm-1K-1)

Thermal Conductivity (115 Wm-1K-1)

10.

Used for multi-passes, very high life

Used only for single pass, less life

(5.9×10-8 Ω m)

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Baljit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August., 2014, pp. 01-05

III. Literature Review Several researchers have made many attempts to improve the performance characteristics of WEDM like surface roughness, cutting speed, dimensional accuracy and material removal rate. WEDM is an essential operation for generating very complicated shapes with high level of accuracy in several manufacturing processes. The right selection of wire is a very important task for optimum output. But the full potential utilization of this process is not completely solved because of its complex and stochastic nature and more number of variables involved in this operation. Spedding and Wang [1] Developed mathematical models to predict material removal rate and surface finish while machining D-2 tool steel at different machining conditions. It was found that there is no single combination of levels of the different factors that can be optimal under all circumstances. Y.F.Luo et. al. [3] investigated that wire rupture in Wire Electrical Discharge Machining (WEDM) process was a serious problem. A solution in the form of a new computer-added pulse discrimination system was developed on the basis of voltage waveform characteristic. Also, proposed an index to monitor wire rupture and its relation with machining parameters and metal removal rate (MRR). Kozak et al [7] presented a study of wire electric discharge machining (WEDM) of low conductive materials and found that total electrical resistance between work-piece and tool (wire electrode) vary during machining, depending upon the clamping position. This change in resistance caused a change in material removal rate (MRR) and average surface roughness that lead to the poor quality of products. Ramasawmy et.al [11] described the multi objective optimization of the WEDM process using parametric design of Taguchi methodology. It was identified that the pulse on time and ignition current intensity has influence more than the other parameters. Mahapatra and Patnaik [13] developed relationships between various process parameters and responses like MRR, SR and kerf by means of non-linear regression analysis and then employed genetic algorithm to optimize the WEDM process with multiple objectives. H.Singh and R.Garg [16] investigated the effects of various process parameters of WEDM to reveal their impact on MRR of hot die steel (H-11), using one variable at a time approach. Ravindranadh B. et al [19] presented the influence of machining parameters on surface roughness and material removal rate of high strength armour steel using wire cut electrical discharge machining (WEDM). Results show that pulse-on time, pulse-off time, spark voltage were found significant variables to material removal rate (MRR) and surface roughness (SR) of the material. After a comprehensive study of the existing literature, it was concluded that the effect of process parameters on Titanium alloy has not been fully explored on WEDM with Molybdenum wire as an electrode. IV. Design of Experiments Design of Experiments (DOE) is a powerful statistical technique, to study the effect of multiple variables simultaneously. It is used to find out the relationship between the different factors affecting a process and output of that process. After analysis, the results of these experiments are helpful to identify the optimal conditions, most dominating factors, least dominating factors and the possibility of interactions and synergies between factors. The experimental methods proposed by Taguchi involves orthogonal arrays to organize the process parameters and their levels to determine the factors most affecting the product quality with minimum numbers of experiments, thus saving of time and resources (A) Selection of Orthogonal Array In present experimental work, seven process parameters with two levels and three levels (21×37) have been decided. It was desirable to have three minimum levels of process parameters to reflect the true behavior of output parameters of study. Taguchi recommends orthogonal array (OA) for lying out of experiments. To design an experiment is to select the most suitable OA and to assign the parameters and interactions of interest to the appropriate columns. The use of linear graphs and triangular tables suggested by Taguchi makes the assignment of parameters simple. Table 2: Range and Level of Machining Parameters Levels

Machining parameters

Coolant Pressure (bar) ‘A’ Pulse On Time, (µs) ‘B’ Pulse Off Time, (µs) ‘C’ Peak Current, (IP) (amp) ‘D’ Servo Voltage, (SV) (v) ‘F’ Wire Feed, (WF) (m/min) ‘F’ Wire Tension (WT) (gm) ‘G’

Low 1 110 50 2 60 10 1200

Medium --115 55 4 80 15 1300

High 4 120 60 6 100 20 1400

(B) Material of Work Piece The material of the component (work-piece) was Titanium alloy (Ti6Al4V). It has excellent bio compatibility, especially when direct contact with human tissue or bone. The density of Ti6Al4V was taken 4.3 cm3/gm. The

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chemical composition of the work-piece material is given in Table 3. The six specimens were prepared for experimental purpose. The size of each specimen was 50 mm×50 mm×4 mm. Table 3: Chemical Composition of Ti Alloy Min. Max.

Al 5.5 6.76

V 3.5 4.4

C -0.08

N -0.5

O -0.2

H -0.0125

Fe -0.3

Y -0.005

Others -0.1

Ti Balance

(C) Material of Electrode (Wire) The Eco-Wire EDM machine was loaded with Molybdenum wire, 0.18 dia., supplied by M/s Wangpin Machine Tool Accessories Co., Ltd, situated at Hongkong, (Head Office), China. The chemical composition of Molybdenum wire is given in Table 4. Table 4: Chemical Composition of Molybdenum Wire Mo C N O Fe ≥99.35 <0.006 <0.002 <0.003 <0.003 Remarks: 0.2% – o.6% of doped element in Mo which is not treated as impurity content

Ni <0.002

Si <0.002

V. Experimentation From the selected L18 array, nine experiments were to be conducted at minimum coolant pressure and rest nine experiments for maximum coolant pressure. The value of each experiment is the average of three experiments conducted at each setting. All the experiments were conducted for each combination of design factors as per selected L18 orthogonal array accordingly. (A) Metal Removal Rate Calculation The recorded values of the loss of mass and machining time of work piece for all the eighteen experiments were used for calculation for metal removal rate (MRR), see Table 5. The relation for calculation of MRR is as below: Loss of Mass (g) = Mass [before experiment] – Mass [after experiment] (1) Volumetric Material Loss (cm³) = Loss of mass (g) / Density of Material (g/cm³) (2) Material Removal Rate (mm³/min) =Volumetric material loss (mm³)/Machining Time (min.) (3) (B) Calculation of S/N Ratio The experimental observations were further transformed into a signal-to-noise (S/N) ratio. Taguchi method was used to analyze the result of response of machining parameters for “Higher is Better” criteria. The Signal to Noise ratio is the ratio of signal to noise where signal represents the desirable value and noise represents the undesirable value. The S/N Ratio is statistic and denoted by η with a unit of db. The higher observed value shows the better machining performance in case of MRR, it is referred to Quality Engineering. It is termed as “Higher is the Better” (HB). The S/N ratio for all eighteen experiments was calculated as shown in Table 5. Table 5: Material Removal Rate and S/N Ratio Calculation Mass Before Machining

Mass After Machining

Loss of Mass

Machining Time (Min.)

Volumetric Material Loss (cm³)

MRR (mm³/Min)

S/N Ratio db

42.610

41.51

1.10

22.33

0.2483

12.21

21.73

41.10

40.450

1.06

18.70

0.2392

12.79

22.14

40.450

39.370

1.08

17.42

0.2437

13.99

22.92

42.100

41.050

1.05

12.57

0.2372

18.86

25.51

41.050

39.855

1.19

12.63

0.2697

21.36

26.59

39.855

38.775

1.08

13.73

0.2437

17.76

24.99

42.820

41.700

1.12

12.28

0.2528

20.59

26.27

41.700

40.640

1.06

12.47

0.2392

19.19

25.70

40.640

39.490

1.15

14.93

0.2595

17.39

24.80

10.

42.720

41.630

1.09

15.63

0.2460

15.74

23.94

11.

41.630

40.510

1.12

18.68

0.2528

13.53

22.57

12.

40.510

39.330

1.18

21.77

0.2663

12.24

21.99

13.

41.850

40.650

1.20

21.37

0.2708

12.67

21.89

14.

40.650

39.290

1.36

12.20

0.3069

25.16

28.08

15.

39.290

38.170

1.12

12.78

0.2528

19.78

26.24

16.

42.520

41.340

1.18

17.38

0.2663

15.32

23.71

17.

41.340

40.020

1.32

12.38

0.2979

24.07

27.34

18.

40.020

38.600

1.42

11.92

0.3205

26.89

28.68

Experiment No 1.

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Baljit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August., 2014, pp. 01-05

VI. Results and Discussion All experiments were conducted to study the effect of process parameters over the output response characteristic with the process (design) parameters and interactions assigned to columns. The experimental results for MRR are given in Table 5. All eighteen experiments were conducted using Taguchi Method to investigate the effect of process parameters on the output parameter MRR. In present study all the designs, plots and analysis have been carried out using Statistical Package for Social Sciences (SPSS) software. Finally, according to Taguchi Method, the significant parameters (factors) were found by using Analysis of Variance (ANOVA) Technique, which represents the relationship between machining parameters and the process performance. (A) Effect of Various Parameters on MRR The significance weightage of each design factors was seen from the ANOVA and F-Test. But to know how much each factor was affecting the S/N Ratio, the interactive graphs were plotted for each factors and the optimum value of each factor was found out as shown in window by choosing appropriate menu options in SPSS software.

b = Ton

Fig. 1: Effect of ‘Ton’ on MRR

c = Toff

Fig. 2: Effect of ‘Toff’ on MRR

d = Peak Current

Fig. 3: Effect of ‘Peak Current’ on MRR

f = Wire Feed

Fig. 5: Effect of ‘Wire Feed’ on MRR

e = Servo Voltage

Fig. 4: Effect of ‘Servo Voltage’ on MRR

g = Wire Tension

Fig. 6: Effect of ‘Wire Tension’ on MRR

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Baljit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August., 2014, pp. 01-05

(B)

Analysis of Results

The results found from S/N response graphs generated by SPSS software for all design factors, it was concluded that the selected seven design factors at Level A3, B3, C2, D3, E2, F2, and G2 were found the suitable set of parameters for the maximum MRR. It was observed that the optimal process parameters for highest MRR are quite different. VII. Conclusion On the basis of the experimental results, the calculated S/N ratio (db), the analysis of ANOVA using SPSS software, F test values and confirmation test, the following conclusions are drawn for the effective machining of Ti alloy work-piece on Eco Wire CNC EDM machine, using Molybdenum wire: Pulse on time (Ton) is the most significant influencing machining parameter having direct effect on metal removal rate (MRR) in the machining of Ti alloy and maximum MRR was obtained at Ton 120 µsec. Pulse off Time (Toff) is the second most significant influencing machining parameter and optimal value of Toff was found at 55 µsec for maximum MRR. It was also concluded that for rough and high-speed cutting (for higher MRR), the optimal wire feed rate of Molybdenum wire was 15 m/min for Ti alloy work-piece. And the optimal wire tension was found 1300 gms. It was concluded that for rough and high-speed machining, the optimal value of servo voltage is 80 volts. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.

Spedding, T. A., Wang, Z.Q. (1997), “Parametric optimization and surface characterization of wire electrical discharge machining process”, Precision Engineering, 20(1), 5-15. Luo, Y.F. (1997). “The dependence of inter space discharge transitivity upon the gap debris in precision electro-discharge machining.” Journal of Materials Processing Technology. 68: pg. 121-131. Y. F. Luo (1999). “Rupture failure and mechanical strength of the electrode wire used in wire EDM.” Journal of Materials Processing Technology, 94 (1999) 208-215. Lee, S.H. and Li, X.P. (2001). “Study of the Effect of Machining Parameters on the Machining Characteristics in Electrical Discharge Machining of Tungsten Carbide.” Journal of Materials Processing Technology. 115: pg. 344-358. Tosun, N., Cogun, C. and Pihtili, H.(2003), “The effect of cutting parameters on wire crater sizes in wire EDM”, International Journal of Advanced Manufacturing Technology,21,857-865. F. Klocke, D.Lung, D.Thomaidis, G. Antonoglou. “Using Ultra-Thin Electrodes to Produce micro-parts with wire-EDM”, Journal of materials Processing Technology, 149 (2004) 579-584. Kozak, J., Rajurkar, K.P., Chandarana, N. (2004), “Machining of low electrical conductive materials by wire electrical discharge machining (WEDM) process”, Journal of Materials Processing Technology, 149, 266-276. Tosun N. , Cogun C. and Tosun, G. (2004), “A study on kerf and material removal rate in wire electrical discharge machining based on Taguchi method”, Journal of Materials Processing Technology, 152, 316-322. Miller, S. F., Shih, A. J., Qu, J. (2004). “Investigation of the spark cycle on material removal rate in wire electrical discharge machining of advanced materials”, International Journal of Machine Tools & Manufacture, 44, 391–400. Liao, Y.S. and Yu, Y.P. (2004), “The energy aspect of material property in WEDM and its application”, Journal of Materials Processing Technology, 149, 77–82. Ramasawmy, H.,Blunt, L. and Rajurkar, K.P.(2005).“Investigation of the relationship between the white layer thickness and 3D surface texture parameters in the die sinking EDM process.”Precision Engineering.29: pg.479–490. Mahapatra, S. and Patnaik, A. (2006), “Parametric optimization of wire electrical discharge machining (wedm) process using Taguchi method”, Journal of the Braz. Soc. of Mech. Sci. & Eng., 28, 422-429. Mahapatra, S. S. and Patnaik, A. (2007), “Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method”,. International Journal of Advanced Manufacturing Technology, 34, 911-925. Kanlayasiri, K., Boonmung, S. (2007), “An investigation on effects of wire-EDM machining parameters on surface roughness of newly developed DC53 die steel”, Journal of Materials Processing Technology, 187–188, 26–29. Vishal parashar et.al (2009), “Investigation and Optimization of Surface Roughness for Wire Cut Electro Discharge Machining of SS 304L using Taguchi Dynamic Experiments”, International journal of engineering Studies, 257-267. H.Singh and R. Garg (2009), “Effects of process parameters on material removal rate in WEDM”Journal of Achievements in Materials and Manufacturing Engg., vol 32, issue 1. Kapil Kumar, Sanjay Aggarwal (2012), “Multi-objective Parametric Optimization on Machining with Wire EDM”, Int. J. Adv. Manufg. Tech, 62 (2012) 617-633. M. T. Antar, S.L. Soo, D. K. Aspinwall, D.Jones, R. Perez (2011), “Productivity and work piece Surface Integrity when WEDM aerospace alloys using coated wires”, Procedia Engineering, 19 (2011) 3-8. Ravindranadh Babbiti, V. Madhu, A.K.Gogia (2013), “Effect of wire EDM machining parameters on surface roughness and material removal rate of high strength Armor steel”, Material and manufacturing processes, DOI:10.1080/10426914.2012.736661. Nixon Kuruvila and Ravindra H. V., “Parametric influence and optimization of wire EDM of hot dies steel”, Machining Science and Technology, 15:1, 47-75. Wire EDM “The Fundamentals” by Donald B. Moulto, EDM Network, Sagar grove, IL, USA.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net The Influence of Intellectual Capital towards Firm Value with Independent Commisioner and Audit Committe as Moderating Variables Etty Murwaningsari Faculty of Economics, Trisakti University, Jakarta, Indonesia __________________________________________________________________________________________ Abstract: This research investigates whether intellectual capital have an influence on firm’s value, with independent commisioner and audit committe as the moderating variables. This research uses several control variables: capital structure, size, leverage, and audit quality. The sampling technique used was purposive sampling method, with 258 observations being drawn from 86 manufacturing companies listed on the Indonesia Stock Exchange between 2010 and 2012. This research uses multiple regression analysis, namely Moderating Analysis Regression. The result indicates that intellectual capital has a significant effect on firm’s value. As for the moderating variables, there are mixed findings. Audit committe strengthens the influence of intellectual capital on firm’s value, while independent commissioner does not have any significant influence. The control variables also suggest mixed findings. Capital structure, size, and audit quality have a positive significant effect on firm’s value, while leverage has an insignificant effect. Keywords: Intellectual Capital, Independent Commissioner, Audit Committee, Firm’s value. ________________________________________________________________________________________ I. Introduction Globalization leads to a rapid change, where firms must strive to enhance their values by expanding the use of resources and their most important assets from tangible into intangible assets. One component of intangible assets is intellectual capital which contained one crucial element, i.e. a person’s intellectual capacity or knowledge. Realizing the importance of intellectual capital for the firm’s growth, firms put a growing attention to the intellectual capital management. Intellectual capital is one of the main ingredients for the firms to create the added value needed to boost their competitiveness. Firms that have excellent competitiveness will be able to compete and survive in the business environment. Guthrie and Petty (2000) stated that intellectual capital streghtens firms’ competitiveness in achieving their goal (i.e. optimizing the firm’s value). The firm’s value is reflected in its stock price, where the difference between the stock price and the book value of the assets owned by the firm indicate the presence of a hidden value. The hidden value is believed to be the intellectual capital which is recognized and valued by the market. Therefore, there is a growing recognition of the intellectual capital in boosting the firm’s market value. As stated by Chen et al. (2005), intellectual capital gives the positive influence to the firm’s market value. Intellectual capital measurement has been introduced by Pulic (2000) by using the Value Added Intellectual Coefficient (VAIC™), a measure for assessing the efficiency of added value as a result of the firm’s intellectual abilities. The other factor that affect the firm’s value is the good corporate governance. Corporate governance posesses a controlling ability that can accomodate the different interests between principal and agent, so that a financial report with qualified information can be produced (Jansen and Meckling, 1976), which in turn will reflect a better firm’s value. Corporate governance sets the firm’s goal optimally, effectively, and efficiently, through the usage of the supervisory and the risk mitigating roles. Therefore, an effective supervision is needed by the concerned sections in the management of the firm. One of the most important aspects to implement the concept of good corporate governance is to ensure the availability of independent commissioner and audit committee. Based on the description above, the research purpose is to asses the following: (1) the influence of intellectual capital towards firm’s value; (2) the independent commissioners as a moderating variable can strengthen the influence of intellectual capital towards firm’s value; and (3) the audit committee as a moderating variable can boost the influence of intellectual capital towards firm’s value. II. Literature and Hypothesis Intellectual capital is the information and knowledge applied to the work so that it will produce value (Williams, 2001). Intellectual capital covers all the workers’ knowledge, organization, and their ability to produce the added value and lead to the sustainable competitive advantage. Bontis (1998) said that intellectual capital can be identified as an intangible asset (resources, capabilities, and competencies) which will drive the organizational performance as well as the value creation. Further, Bontis et al. (2000) explained the fundamental elements of the intellectual capital, i.e. human capital, structural capital, and physical capital.

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Independent commissioner is a commissioner who is not a member of the management, the main stakeholders, or any officials related both directly and indirectly to the main stakeholders of a firm. National Committee on Corporate Govenance Policies (2006) issued guidelines about the independent commissioner of a public company. The guideline stated that independent commissioner is responsible in overseeing the policy and actions of the board of directors, as well as giving them advice when necessary. Audit committee is formed to help the board of directors by undertaking the responsibility to check the financial information of a firm for the purposes of the stakeholders or other parties. Based on the regulation of the capital market supervisory board in Indonesia (BAPEPAM No.Kep-29/PM/2004 on 24th September 2004), audit committee members are required to be independent and should have at least one person competent in the fields of accounting and finance. High firm’s value can make the markets believe in the firm’s performance and its prospect in the future. Maximizing the firm’s value is very important, because the prosperity of the stakeholders will also be maximized. However, according to Jansen and Meckling (1976), conflict between management and principal (agency problem) may cause share value to be corrected and decrease the firm value. Capital structure is a permanent expenditure which reflects a balance between the long-term debt and capital owned. As stated by Modigiliani and Miller (1958), increasing the use of debt will provide benefits in the form of tax payments and the increase in the profits of shares to be received by a shareholder, so that the ultimate goal of a firm to maximize the prosperity of shareholders will be achieved. Leverage ratio is the comparison between total of debt and total of capital. A low leverage value indicates a better ability for the firm to pay the total debt using the total funds owned by the firm (Copeland T.E dan J.F. Weston, 1992). Therefore, the decrease of leverage will increase the firm value. Firms with larger assets will invest a greater amount of capital. Also, a larger amount of sales implies a more frequent turnover in the firms and also a bigger market capitalization, which in turn will make the firm better known to the public. The larger a company is, the more competitive that company is, compared to the other main competititors. Thus, the investors will give positive responses to the firm’s value. The audit quality can be seen from the size of the public accounting firms. The big four of public accounting firms perceived better audit quality compared to the non big four. In general, a better audit quality reflects a better firm’s value. Krishnan and Schauer (2000), Kim et al (2003), and Khrishnan (2003) use the size of the public accounting firms to measure audit quality, treating it as a dichotomous variable and a dummy assuming 1 and 0 each for the large and the small public accounting firms. III. Formulating Hypothesis In the theory of stakeholder, all firm activities will end up at the creation of value. The concerned parties (stakeholders) will respect the firm that creates value; because with the creation of a good value, the firm will become more capable to fulfill the interests of the concerned parties (Belkaoui 2003). In the context of intellectual capital, the creation of value can be done by maximizing the utilization of each elements of intellectual capital (human capital, physical capital, and structural capital) (Bontis et al, 2000). As one of the concerned parties, investors in the stock market will show appreciation for the excellence of intellectual capital owned by the firm by investing in the firm. The increase in investment will, in turn, raise the firm’s market value. According to Firer and Williams (2003) and Chen et al, (2005), intellectual capital gives a significantly positive influence to the firm’s value. Based on the description above, the proposed hypothesis is: H1: Intellectual capital will positively influence firm’s value. Audit committee provides a supervision mechanism which will improve the quality of current information between the firm’s owner and manager, especially in a financial reporting that allows a variation in information disclosure (Barako, 2007). Further, Barako (2007) stated that there is a positive relationship between the presence of audit committee and the firm’s disclosure. As emphasized by Li et al. (2008), a firm with a larger size of audit committee tends to provide a greater intellectual capital disclosure in its annual report. The market reaction would be different between a firm which forms an audit committee and a firm without audit committee. Audit committee is the independent party who is assigned to supervise the process of financial reporting. Hence, the market will react stronger to the annual report of firm which has an audit committee. According to Harjoto et al, (2007), a firm’s value could increase by the application of corporate governance. Further support can be found from several research, e.g by Siallagan and Machfoedz (2006) and Black (2001), where the audit committee gives a significantly positive influence to firm’s value. Based on the description above, the proposed hypothesis is: H2: Audit committee will strengthen the influence of intellectual capital towards firm’s value. Independent commissioner is the neutral party in a firm who is expected to bridge the information asymmetry between owner and manager by encouraging the other members of the board of commissioners to do a better supervisory duty. If the supervision is done effectively, it would lead to a better firm’s management, where the

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management will disclose all the information, including the information about intellectual capital (White et al., 2007). Cerbioni and Parbonetti (2007) found out that independent commissioner may positively influence the disclosure of intellectual capital. White et al. (2007) and Li et al. (2008) also mentioned that there is a positive significant relationship between independent commissioners and the disclosure of intellectual capital. Furthermore, Li et al. (2008) explained that the expertise and extensive experience of an independent commissioner will encourage the management to increase the value of intellectual capital disclosure to the stakeholders. This is in accordance with the stakeholder theory; investors will appreciate a firm which is able to create an added value, where one way to create it is by applying the intellectual capital. Thus, independent commissioner as the neutral party is needed to help in arranging the strategy to apply the intellectual capital optimally in order to increase the firm’s value. According to Harjoto et al., (2003), the firm’s value may be increased due to the presence of corporate governance that runs properly. The study is supported by the results from Barnhart and Rosenstein (1998) which proved that a higher representation of independent commissioner causes a higher effectiveness of the corporate board, so it can increase the firm’s value. Based on the elaboration above, the proposed hypothesis is: H3: An independent commissioner will strengthen the influence of intellectual capital towards firm’s value. IV. Research Methodology The data used in this study is a secondary data from the Indonesian Stock Exchange and the Indonesian Capital Market Directory. The research sample is a group of industrial/manufacturing company registered during the period 2010-2012. During the period, 86 companies are compatible with the criteria based on the purposive sampling. This method resulted in a pooled data consisting of 258 observations. The hypotheses test is conducted using the analysis of multiple regression, namely Moderating Analysis Regression. The model equations are as follows: 1st Regression Model to test hypothesis 1: FIRM VALUE = α0 + α1IC + α2CS + α3LEV + α4FS + α5AQ + e 2nd Regression Model to test hypothesis 2 and 3: FIRM VALUE = γ0 + γ1IC + γ2InC+ γ3AC+ γ4IC*InC + γ5IC*AC+ γ6CS+ γ7LEV + γ8FS+ γ8AQ + e note: IC is Intellectual Capital, InC is Independent Commisioner, AC is Audit Committee, CS is Capital Structure, LEV is Leverage, FS is Size of Firm, AQ is Audit Quality. V. Result and Discution The descriptive statistics in Table 2 suggest that the mean value of intellectual capital has a considerable distance from the maximum value, meaning that the mean value of intellectual capital of the manufacturing firms is good enough. The frequency tests in Table 3 suggest that, of the total 258 observations, a majority of the firms (65,1%) have an independent commissioner with an accounting or finance as the educational background, while 95% of the firms have an audit committee with an accounting or finance as the educational background. Also, 62,4% of the firms are using the non big four – public accounting firms. The data normality in this research is tested with the Kolmogorov-Smirnov. The test result showed that asymp.sig is smaller than α=0.05%, where the first regression model is 0,610 and the second regression model is 0,381. Thus, it can be concluded that the regression of residual is normally distributed. The classical assumptions tests is also conducted to test: (i) the homoskedasticity assumption using the Glejser and bivariate tests; (ii) the no-multicollinearity assumption using the VIF test; and (iii) the no-autocorrelation assumption using the Durbin-Watson test. The test results show that the regressions do not experience heteroskedasticity and autocorrelation. However, the VIF test on the second regression model suggests that a substantial degree of multicollinearity is present. According to Gujarati (2009), the occurrence of multicollinearity in Moderating Regression Analysis (MRA) is not a serious problem, as long as a high RSquared value is achieved. He suggested that it is due to the interaction between the independent variable χ1 and χ2 in the moderating variable (χ1*χ2). The regression results in Table 4 suggest several things. First, intellectual capital has a positive and significant effect towards firm’s value. This implies that any firms who can manage their intellectual resources to the full potential will be able to create a greater added value and a more competitive advantage, which then will converge to the increase of firm’s value. This research results supports the findings in Firer and Williams (2003) and Chen et.al. (2005).

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Second, based on interaction ICxAC, the audit committee is proven to strengthen the influence of intellectual capital towards firm’s value. This result is consistent with the previous research (e.g. Li et al., 2008), which stated that the audit committee has a positive influence on the intellectual capital disclosure. This result also showed that the presence of the audit committee will give a greater pressure to the management to disclose intellectual capital in the firm’s annual report. Similar to other research (e.g. Siallagan and Machfoedz, 2006), the audit committee may provide a significant positive influence to the firm’s value. Hence, the result of this study indicates that the audit committee posesses an important and strategic role in maintaining and supervising the credibility of the process of preparing the financial reports. Third, based on interaction ICxInC, the independent commisoner could not strengthen the influence of intellectual capital towards firm’s value. This may happen due to the existence of a practice among some independent commissioners in Indonesia, whereby the independent commisioner delegates the responsibility of overseeing the financial report to the audit committee. Thus, the role of the independent commisoner in the intellectual capital disclosure is very low. Hence, the presence of the independent commisoner could not strengthen the influence of intellectual capital towards firm’s value. Last, the results on the control variables are as follows. The effect of capital structure to firm’s value is positive and significant. This result is consistent with the trade off theory, where debt utilization at the specified amount will increase the firm’s value. Size of the firm and audit quality also show positive and significant effects, while the leverage shows insignificant results on both regression models. VI. Conclusion Based on the analysis in the previous section, it can be concluded that: 1. Intellectual capital shows a positive and significant influence towards firm’s value. It means that a higher institutional capital will cause a higher value of the firm. This result conforms to past research conducted by Firer and Williams (2003) and Chen et.al. (2005). 2. Audit committee strengthens the influence of intellectual capitals towards firm’s value. This result is consistent with the previous research conducted by Li et al., (2008), which stated that the audit committee has a positive influence to the intellectual capital disclosure. 3. Independent commissioner could not strengthen the influence of intellectual capitals towards firm’s value. This result differs from previous research conducted by Cerbioni and Parbonetti (2007) as well as White et al., (2007). It may be caused by the role of independent commisioner in the intellectual capital disclosure of Indonesian firm is very low, hence the independent commisioner is not able to strengthen the relationship between the intellectual capital with the firm’s value. 4. Among the control variables, capital structure, size of the firm and audit quality have positive and significant effects as predicted, while leverage shows an insignificant result on both regression models. The managerial implications that can be summed up from this research: 1. For the investors, this research results can give an insight in analyzing the factors which influenced the firm’s value, so that it can be used as the basis of making an investment decision. 2. For the firms, this research results can give an insight to the firm’s management; that the presence of effective independent commisoner and audit comiitee may push forward the disclosure of intellectual capital in the financial report, which eventually will increase the firm’s value. Limitations of this research: 1. This research only samples manufacturing firms, so the results can not be generalized. 2. This research only uses the independent commissioner and the audit committee in proxying the corporate governance, so the results are less comprehensive. 3. Limitations in the number of control variables used in this research, so it may influence the result of this research. Suggestion for future research: 1. Widens the sample to include not only the manufacturing industry and extends the research period. 2. Adds other variables which is derived from other components of corporate governance such as the managerial ownership, the public ownership, as well as the need to add the control variables, since omitted variables may cause a biased result.

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References Bapepam No. Kep 29/PM/2004. Tentang Pembentukan dan Pedoman Pelaksanaan Kerja Komite Audit. Barako, G., Phil H. dan H.Y Izan. (2007). Factors Influencing Voluntary Corporate Disclosure by Kenyan Companies. International Review, Vol. 14, No. 2, Hal. 107-125. Belkaoui, Ahmed Riahi, (2003). Intelectual Capital and firm Performance of US Multinational Firms: a Study of The Resource-Based and Stakeholder Views. Journal of Intelectual Capital. Vol. 4. No.2.pp.215-226. Barnhart, S.W. dan Rosenstein, S. (1998). Board Composition, Managerial Ownership, and Firm Performance: An Empirical Analysis. Financial Review 33: 1-16. Black, Bernard (2001). The Corporate Governance Behavior and Market Value of Russian Firms. Emerging Markets Review, Vol. 2, pp. 89108. Bontis, N. (1998). Intellectual Capital: An Exploratory Study That Develops Measures and Models. Management Decision,Vol. 36, No. 2, Hal : 63-76. Bontis, N., W.C.C. Keow, dan S. Richardson. (2000). Intellectual Capital and Business Performance in Malaysian Industries. Journal of Intellectual Capital,Vol. 1, No. 1, Hal. 85-100. Cerbioni, F. dan Parbonetti, A. (2007). Exploring The Effects of Corporate Governance on Intellectual Capital Disclosure: An Analysis of European Biotechnology Companies. European Accounting Review, Vol. 16, No. 4, Hal. 791–826. Chen, M.C., S.J. Cheng, Y. Hwang. (2005). An Empirical Investigation of the Relationship Between Intellectual Capital and Firms’ Market Value and Financial Performances. Journal of Intellectual Capital, Vol. 6 No. 2. pp. 159-176. Copeland T.E dan J.F. Weston. (1992). Financial Theory and Corporate Policy, 3rd Edition. Addison-Wesley Publishing Company. Firer, S. dan S. M. Williams. (2003). Intellectual Capital and Traditional Measures of Corporate Performance. Journal Of Intellectual Capital, Vol.4, No.3. Gujarati, Damodar N. (2009) Basic Econometrics. fifth ed, New York: McGraw-Hill Guthrie, J. dan Petty, P. (2000). Intellectual Capital Literature Review: Measurement, Reporting and Management. Journal of Intellectual Capital.Vol.1 No.2.pp. 155-75. Harjoto, Maretno A. dan Hoje Jo. (2007). Corporate Governance and Firm Value: The Impact of CSR. Social Science Research Network. Jensen, M.C., and Meckling, W.H. (1976). Theory of the Firm : Managerial Behaviour, Agency Costs and Ownership Structure. Journal of Financial Economics, 3:305-360. Kim., J., Chung, R. And firth, M. (2003). Auditor Conservatism, Asymmetric Monitoring and Earnings Management. Contemporary Accounting Research, 20 (2), 323-359. National Committe on Corporate Governance Policies. (2006). General Guidance on Indonesian Good Corporate Governance. Jakarta. Krishnan, G.V. (2003). Does Big 6 auditor industry expertise constrain earnings management? Accounting Horizons, 17 (Supplement), 115. Krishnan, J. and Schauer, P.C. (2000). The Differentiation of Quality Among Auditors: Evidence from the Not-for-Profit Sector. Auditing: A Journal of Practice and Theory. 19 (2), 9-26. Li, J., R. Pike dan R. Haniffa. (2008). Intellectual Capital Disclosure and Corporate Governance Structure in UK Firms. Accounting and Business Research, Hal. 137-159. Modigliani, Franco, and Miller, Merton H. (1958). The Cost of Capital, Corporate Finance, and The Theory of Investment. American Economic Review, 48(3): 261-280. Pulic, A. (2000). VAIC – An Accounting Tool for IC Management. International Journal of Technology Management, 20(5). Siallagan, Hamonangan dan Mas’ud Machfoedz. (2006). Menakinesme Corporate Governance, Kualitas Laba, dan Nilai Perusahaan. Simposium Nasional Akuntasi 9 Padang, Indonesia. White, Gregory, Alina Lee and Greg Tower. (2007). Drivers of Voluntary Intellectual Capital Disclosure in Listed Biotechnology Companies. Journal of Intellectual Capital, Vol. 8, No. 3, pp. 517-537. Williams, M. (2001). Is Intellectual Capital Performance and Disclosure Practices Related? Journal of Intellectual Capital, 2(3): 192-203.

Table 1 : Variable and Measurement Variable

Measurement

Dependent 1. Firm’s Value

Independent 1. ValueAdded Coefficient (VAIC)

Intellectual

Moderating 1. Audit Committee 2. Independent commisioner

Control Variable 1. 2. 3. 4.

Capital structure Leverage Size of firm Audit quality

Q = Firm’s Value ; MVE = Equity Market Value ; D = Book Value of Total Debt ; BVE = Equity Book Value VAIC = VACA + VAHU + STVA VACA = Value added divided by capital employed (available funds: equity) VAHU = Value added divided by human capital (employees’ expenses, i.e. total of salary and the employees recorded in financial report) STVA = Structure capital (value added - human capital) divided by value added Value added= total revenue-total expense Dummy variable (1 = firm has a member of audit committee with accounting background; 0 = no member of audit committee with accounting background) Dummy variable (1 = firm has a member of independent commisioner with accounting background; 0 = no member of independent commisioner with accounting background)

Debt to Equity Ratio= Leverage = Log Asset is used to measure size of firm Dummy variable (1 = for a firm which is audited by the big four KAP; 0 = a firm which is audited by the non big four KAP)

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Table 2. The results of descriptive statistics N

Minimum

Maximum

Mean

Std Deviation

Firm Value Intelectual Capital Capital Structure

258 258 258

.22 -11.26 .04

54.98 16.14 7.17

21.131 38.328 10.889

422.136 301.234 104.094

Leverage Size of firm Valid N (listwise)

258 258 258

.04 10.02

1.47 14.26

.4471 120.838

.21242 .68840

Table 3. Frequency test results

Table 4. Results of 1st and 2nd regression model test 1st Regression Dependent Var: Firmâ&#x20AC;&#x2122;s Value Independent Variable :

Coeff. (B)

Intellectual Capital (IC) 0.028 Audit Committee (AC) Independent Commisioner /InC Interaction IC x AC Interaction IC x InC Capital Structure (CS) 0.085 Firm Size (FS) 0.364 Audit Quality (AQ) 0.221 Leverage (Lev) -0.250 F-test Adjusted R-squared Note:** = significant 10%,

2nd Regression

Sig.

Coeff. (B)

Sig.

0.044*

-0.295 -2.188 -0.07 0.368 0.015 0.648 0.450 0.161 -0.202

0.059** 0.012* 0.963 0.085** 0.734 0.030* 0.000* 0.047* 0.145 0.00 0.543

0.035* 0.000* 0.004* 0.113 0.00 0.392 * = significant 5%

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net The Impact of positive cash operating activities on the Cost of Debt: International Evidence Dr. Saadani Ghali , Harit Satt Université Sidi Mohamed Ben Abdellah Faculté des Sciences Juridiques, Economiques et Sociales – FES, Marocco School of Business Administration, Al Akhawayn University, P.O. Box 104, Avenue Hassan II, Ifrane 53000, Morocco.

__________________________________________________________________________________ Abstract: This paper identifies the affiliation between the ending cash balance of the operating section in the cash flow statement and the bonds ratings. Our sample includes 600 companies from 26 countries. The study was conducted over a period of 18 years. An Ordered Probit regression analysis had been applied to identify how the positive cash balance of the operating section in the cash flow statement shapes the probability of escalating the bonds ratings. We find burly proof that the positive operating cash balance considerably affects the bonds ratings. In other words, when a company is able to generate enough cash from its main operating activities, the likelihood of having higher bonds ratings raises; this entails a low cost of debt since higher bond ratings have been proven to lessen the company’s cost for raising funds (in the form of bonds). The results add more confirmation to the creditors’ rights shields and how it affects the cost of debt. Keywords: credit ratings, operating cash position, default risk. _______________________________________________________________________________________ I. Introduction Information is the key to efficient functioning of the stock markets. Securities get priced correctly when the relevant information about companies get incorporated into the prices. Financial analysts play an important role in this process by bringing out new information about companies. Under normal circumstances, Stakeholders and more precisely creditors view analysts’ research reports, forecasts, and recommendations as relatively accurate sources of information and use them in their rating decisions. In Brunnermeier and Pedersen (2009), for instance, a large market shock triggers the switch to a low liquidity, high margin equilibrium, where markets are illiquid, resulting in larger margin requirements. Previous studies identified the importance of cash management mechanisms and how beneficial they are to companies if applied properly. Acceptable level of liquidity should allow companies to have access to debt financing straightforwardly and at the lowest costs (interest). Having access to the financing sources at relatively low costs allows the company to gain a competitive advantage over others. This competitive advantage enables the company to boost its income since the costs for acquiring debts becomes low. Cash management, which is perceived as one of the important mechanisms of good firm’s performance, may play an important role in enhancing the positive image about the financial situation of the company. Very positive cash balances imply that the company is solvent and can meet its short term obligation without any liquidation costs. However, consulting the ending cash balance for the year (from the cashflow statement or the comparative balance sheet) can be sometimes misleading. Companies generate (use) cash from (in) three main activities: investing, financing and operating. Investing activities includes every activity that is related to changes in tangible assets and more precisely long term assets (properties, plants and equipments). That is to say, a positive cash balance resulting from this section may cause questions to take place. If a company is generating cash from its operating activities, meaning that the company is selling its means of production (downsizing), a fact that is not appreciated by stakeholders and more precisely creditors. Financing activities include any changes related to Long term debts (loans, bonds and notes payable) and stockholders equity. Under this section, positive cash balances mean that the company is raising capital either by writing-off bonds, acquiring loans or issuing stocks. A positive balance doesn’t imply any information, unless one knows how this money was spent and how much it cost, keeping in mind the financial leverage and the ideal capital structure. On the other side, a negative cash balance in this section implies that the company is either repurchasing its own common shares outstanding or paying off its debt. Zeidan(2010) had claimed that in almost all cases, a negative cash ending balance in the financing section implies good signals; it means that the company has the cash requirement that enables it to meet its liabilities. Last but not least, the operating section, which is the section of concern in this research. The net cash balance from this section, if positive, implies that the company is able to generate enough cash from its operating activities, so we have the right to not worry about the company’s future, Amat (2013). On the other hand, if the

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ending cash balance of this section is negative, it implies that the company is not able to generate enough cash from its main operations; this will make all stakeholder worried about the company’s future even in the short run. From all the three sections discussed above, auditors and analysts base their companies’ valuation mostly on the net cash generated from the operating activities. It does not mean that the financing and investing sections of the cashflow statement are useless, but it signifies that the operating section is more informative mainly because of the nature of activities and transactions that it encompasses, Ojo and Marianne (2013) Positive cash balances results in positive signaling to all stakeholders, by implying that the company has the ability to meet all obligations and consequently reduces the external financing costs for companies. This is because, creditors, as well as shareholders, will know that the company can pay them back anytime and, hence, ask for lower returns since they have clearer ideas about the company’s perspectives and liquidity risk levels. Actually, positive operating cash balance may have other impacts on a company. For example, if we prove that the positive operating cash balance affects positively companies’ bonds ratings, we can conclude that low default risk leads to, relatively, lower costs of debt given that Kisgen and Strahan (2009) proved that higher ratings lead creditors to ask for lower returns. Actually, higher ratings of bonds were found to reduce the creditors’ risk which is assigned to the company inability to pay back its debts (the default risk). As a result, the creditors’ risk perception, for companies with high ratings, becomes lower and the company’s cost of debt decreases since the creditors end up asking for relatively lower required returns. All in all, very few work related to the impact of cash management or default risk levels on companies’ cost of debt has already taken place, but no study tried to explore the following hypothesis: do rating agencies value the operating cash balance of a company when rating firms’ bonds? If our empirical results approve this hypothesis, we can conclude that the positive operating cash balance is another variable that leads to lower costs of debts. Our goal is to empirically find out how operating cash balance of the cashflow statement affects the cost of debt for companies. More precisely, we intend to identify whether the rating agencies decisions to rate firms’ bonds are affected by the company’s operating cash position (whether negative or positive). Our study is similar in spirit to Hamdi et al. (2013) who study the value of the auditor choice and how it affects the corporate bond rating. II. Literature review Information and good corporate governance is the key to efficient functioning of the stock markets. Securities get priced correctly when the relevant information about firms get incorporated into the prices. Financial analysts play an important role in this process by bringing out new information about firms, mainly their profitability and liquidity. Under normal circumstances, stock market participants view analysts’ research reports, forecasts, and recommendations as relatively accurate sources of information and use them in their investment decisions. Jensen and Meckling (1976) suggest that, as information intermediaries, financial analysts are able to mitigate the agency problems present within firms. Merton (1987) argues that the market value of a firm is an increasing function of the breadth of investor awareness. Berger (1995) has discovered a positive relationship between the return on equity and the ratios of capital to assets. He explained that by having higher capital ratio, the cost of funds on account and the quantity of funds required would be lower. As a result, the firm’s net interest income will increase and thus the profitability too. On the other hand, Navapan and Tripe (2003) have concluded the opposite. They have found that a negative relationship between capital and profitability exists. Kontus (2012) explained that an increase of short-term debt leads to a decrease of profitability that is shown in terms of return assets. Odders-White and Ready (2006) argued that companies with more liquidity have better credit quality than companies with less liquidity. Companies with high liquidity, they are less likely to default; “they have assets that they can use in case of emergency”. The authors add, usually companies with more liquidity are always enjoying high quality credit terms and they always opt for more. From the side of creditors, mainly banks, good customers enjoy their privileges and they do their best not only to keep them, but they opt for more. In addition, Butler et al. (2005) discovered that liquidity affects the cost of issuing equity, and especially the direct cost of issuing debt. In other words, companies with higher liquidity have less risk, and thus lower interest rate. Oppositely, companies with lower liquidity have higher risk for return and therefore higher interest rate. Deloof found that working capital management is considered one of the major components of corporate finance as it has a direct impact on companies’ profitability and liquidity. Consequently, in order to create the highest shareholder value, having an efficient management of working capital would be primordial; in fact, most of companies try to maintain an ideal level of working capital that will boosts and raises their value (Deloof, 2003; Afza & Nazir, 2007). However, Matuva (2010) found that there are some decisions that incline to increase the profitability and thus reduce the chances of suitable liquidity. Oppositely, if we focus only on liquidity, it may minimize the potential of companies’ profitability. In addition, Lazaridis and Tryfonidis (2006) found that there is an arithmetical relationship between profitability that is measured through Gross Operating Profit, and the

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cash conversion cycle. They found that managers have the ability to create price for shareholders by handling suitably the cash conversion cycle and by maintaining each component to an optimum level. III. Liquidity and the Cost of Debt Different firm’s specific parameters have been found to influence the company cost of debt. Jenzazi (2010) found that the company’s cash management affects the cost of debt. In his paper, the cash management had been assigned a score from 0 to 4 on the basis of different criteria (refer to table 1 for more information about these criteria) and the results suggested that as the score increases the cost of debt decreases. The above arguments lead us to the following testable hypothesis: H1: Generating positive cash balance will reduce the company’s cost of debt financing. H2: Generating positive net cash provided form operating activities leads to higher bonds ratings. Our study will contribute to the scarce existing literature in several ways. First, we will try to assess the perception of the corporate bond market of the quality of the company’s liquidity. Second, contrary to Jenzazi (2010) and the other studies, our study will focus on this issue in an international context. This will allow us to better understand the functioning of the different debt markets around the world. More importantly, this will give us the valuable opportunity to see how external governance mechanisms (such as the legal and extra-legal institutions) interact with the internal mechanisms (in our case cash generated from operating activities) to enhance the overall governance quality in one country. IV. Methodology and Descriptive Statistics A. Specifications The purpose of this research is to study the relationship between the positive operating cash and the bonds ratings. In order to study the relationship between these two variables, the following general specification is going to be used: Bond Rating = f (operating cash position, Issuer Characteristics, Issue Characteristics) This model includes three major determinants (Operating cash position, Issuer Characteristics, and Issue Characteristics) of bond ratings. The issuer characteristics variables include the company profitability (measured by the company’s return on assets, the company size which measured by the company total assets, the company risk that is measured by the company variability of earnings, and the leverage that is measured by the debt to equity ratio). The issue characteristics variables include the issue size or the size of the bonds, the bonds maturity, and the convertible provision (an option that gives the right to a bondholder to exchange the bonds for shares). The ratings that are used for the bonds belong to seven different ordering categories (illustrated by the S&P ratings). This implies that the Ordered Probit Model can be used since the bond rating is an ordinal variable. B. Data Sources and Variables Our sample includes 600 companies operating in different 26countries. Table 2 gives a description of the sample and the distribution of the 600 observations. The observations are from 2002 to 2012. The S&P credit ratings were used in order to get the bonds ratings. The ratings range from AAA to D and include 22 possible ratings. These ratings illustrate the creditworthiness of companies. In other words, they give an idea about company’ abilities to repay back their loans obligations when they are due. As it is shown in Appendix A, the initial ratings that are suggested by S&P have been converted to ordering numbers ranging from 1 as being the lowest rating to 7 as being the highest rating. The ratings were converted on the basis of the research that was conducted by Ashbaugh, Collins, and LaFond (2006). The bonds ratings data was retrieved from F- Database. Table 1: Variables Description and Sources Variable Bonds Ratings

Description Appendix A gives detailed information about this ordinal variable. The bond ratings that are used by S&P are converted to a range from 1 to 7 where 1 is the lowest rating and 7 the highest rating. The rating of bonds depends on the company bonds portfolio.

Company’s Cash balance

A dummy variable that is assigned 1 if the company’s yearly operating cash balance is positive and 0 otherwise.

Company Profitability

A variable that measures the profitability of the company by dividing its net income to its total assets The company size is determined by its total assets in dollar amounts.

Company Size Company risk Bonds Maturity

Convertible Provisions

The company’s risk is measured by the standard deviation of the net income of every company in the sample. A variable that measures the log maturity in years. The weights are determined by the size of the issuance of the maturity class to the total size of the issuance for a given year. Then, the weights are multiplied to the respective maturity and added to get the bonds weighted average maturity. A dummy variable that gives 1 to companys with convertible provisions and 0 to

Source

F-Database

W-S Database W-S Database W-S Database W-S Database

W-S Database W-S Database

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companys with no convertible provisions. These provisions allow the bondholder to convert his or her bonds to shares. A variable that identifies the size of the issuance.

Issue Size Leverage Creditors Rights

Public Registry

Efficiency of Bankruptcy Process News Circulation Manufacturing Trades Finance Utility

A variable that identifies the leverage of the company; measured by dividing the company debts to its equity. This variable is an index that ranges from 0 to 4. When a country imposes restrictions in the favor of creditors, 1 is added to its score. When the secured creditors ensure that they will get their investment back, the score becomes 2. When the secured creditors are the first to receive their money in case of bankruptcy, the score becomes 3. At the end, when the secured creditors donâ&#x20AC;&#x2122;t wait till the problems are solved to get their money back, the score becomes 4. Public registry is a database that is developed by public authorities. This database includes all the debt positions of borrowers in the economy. The collected information is available to all financial institutions. The variable is assigned 1 if the country has a public registry and 0 otherwise. When a company incurs bankruptcy costs, theses costs are deducted from the company terminal value and this value is discounted to get the present value. The higher the value, the better the company. Daily newspapers sold divided by the number of citizens

W-S Database W-S Database

Djankov et al. (2005)

Djankov et al. (2007) Dyck and Zingales (2004)

Dummy variable that equals 1 if the company operates in the Manufacturing industry; 0 otherwise Dummy variable that equals 1 if the company operates in the Trades industry; 0 otherwise Trades Dummy variable that equals 1 if the company operates in the Finance industry; 0 otherwise Finance Dummy variable that equals 1 if the company operates in the Utility industry; 0 otherwise.

The operating cash balance is a dummy variable which takes the value 1 if the companyâ&#x20AC;&#x2122;s operating cash balance is positive and 0 otherwise. The issue and issuer variables are control variables that are added to the model in order to give more explanations related to the bonds ratings. Table 1 gives a detailed description of the variables that were used in our study. The data for the control variables was retrieved from W.S Database. Table 2: Sample Description The panels below give a description of the sample that was used to derive the outputs. Panel A specifies the countries that companys in the sample operate in. Panel B gives the distribution of the observation on a yearly basis (starting from 1996 to 2006). Panel C gives a description of the observations based on the industry. Panel A: Sample Distribution per Country

Panel B:Sample Distribution per Years

Country

Years

Number

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Total

2 23 22 55 100 120 122 55 45 43 13 600

Argentina Australia Austria Brazil Canada Chile Colombia Denmark Finland France Germany Hong Kong Indonesia Israel Italy Japan Korea (South) Malaysia Mexico Netherlands New Zealand Norway Philippines Poland

Number 8 11 8 23 136 7 1 7 7 23 35 12 3 4 27 12 22 2 14 13 1 6 6 2

Percent 1.33 1.83 1.33 3.83 22.67 1.17 0.17 1.17 1.17 3.83 5.83 2.00 0.50 0.67 4.50 2.00 3.67 0.33 2.33 2.17 0.17 1.00 1.00 0.33

Percent 0.33 3.83 3.67 9.17 16.67 20.00 20.33 9.17 7.50 7.17 2.17 100

Industry Number Percent Manufacturing 230 38.33 Transport 10 1.67 Trades 40 6.67 Panel C: Sample Distribution per Industries Financial Services 243 40.50 Utility 77 12.83 Total 600.00 100.00

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Portugal Singapore South Africa Spain Sweden Switzerland Taiwan Thailand Turkey United Kingdom United States Total

10 10 1 8 19 15 13 4 1 123 6 600

1.67 1.67 0.17 1.33 3.17 2.50 2.17 0.67 0.17 20.50 1.00 100.00

The bonds ratings, the convertible provision, and the issue size (the issue characteristics) were computed following a portfolio approach as Anderson, Mansi and Reeb (2003) and Boubakri and Ghouma (2008) applied in their papers. The total company issues for every year were gathered and the size of the issue to the total issues was the weight that we used to compute the average bonds ratings, the convertible provision, and the issue size for every company over every year of the period of the study. After defining the variables that are included in our model, the bond rating model can be expressed as the following: Prob. (Bonds Ratings=X) = F (b₁. operating cash position + b₂. Company Profitability + b₃. Company Size + b₄. Company Risk + b₅. Bonds Maturity + b₆. Convertible Provisions + b₇. Issue Size + b₈. Leverage + Institutional variables + Year Dummies+ Industry Dummies + ei); Where X belongs to {1, 2, 3, 4, 5, 6, 7} V. Empirical Results Panel (A) in table 3 gives the descriptive statistics for the variables that were used in our study. The panel starts by the credit rating variable; the mean for this variable is 4.432, which is equivalent to an S&P rating of BBB+. Table 3: Summary Statistics The table is split into three panels. Panel (A) illustrates the descriptive statistics, Panel (B) illustrates the correlation analyses, and panel (C) gives a mean test comparison using the T-test and the Wicoxon-MannWhitney tests. The variables that are used are the following: Bond Ratings which is an ordinal number that ranges from 1 to 7 as the later being the highest rating and the former the lowest rating. Auditor’s Choice: a dummy variable that assigns 1 to companys that have their auditor from the big five group and 0 otherwise. Company Profitability: the company profitability measured in term of its return on assets. Company Size: the total assets were used to get the size of the companys that are included in the sample. Company Risk: it is measured by the standard deviation of net income. Bonds Maturity: the average maturity for the bonds portfolio issued by a company; weights were assigned on the basis of the size of the issuance to the total issuances. Convertible Provisions: a dummy variable that gives 1 to companys with the convertible option and 0 otherwise. Issue Size: it represents the size of the issuance in term of dollars. Leverage: the company leverage is measured by the debt to equity ratio. The stars that appear in the tables mean the following: *** for a significance that is lower than 1%, ** and * are for a significance that is lower than 5% and 10% respectively. Panel A: Descriptive Statistics Variable Bonds Ratings Cash position Company Profitability Company Size (in million of U.S Dollars) Company risk Bonds Maturity (in years) Convertible Provisions Issue Size Leverage

Observations 600 600 600 600

Mean 4.432 0.423 4.134 89.89

Standard Deviation 1.321 0.342 23.543 1.54

600 600 600 600 600

435,534.7 6.43 0.034 746,923.4 432.367

654,087.3 0.543 0.457 4,687,234 1,432.674

The following descriptive statistics refer to the issuer characteristics variables that were used in our study. The operating cash position is the first variable and has a mean of 0.71; this means that around 71% of the companies that are included in the sample have a positive operating cash balance. Concerning the profitability of the companies, the mean average for the return on assets is 4.03. The mean of the company size was found to

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equal 65 million dollars; this was measured by averaging the total assets of the 600 companies that constitute the sample. Concerning the issuance variables, the mean average for the bonds maturity is 5.44 years. The second variable in this category is the convertible bonds option; the mean for this variable is 8.5% meaning that 8.5% of the companies have offered this option to their bondholders. Panel (B1) from table 3 illustrates the correlation between our dependent variable (Bond Rating) and the operating cash position, the issue characteristics variables, and the issuer characteristics variables. The results demonstrate that different independent variables are significantly correlated with the bonds ratings. The operating cash position, the company performance, the company size, and the convertible option were found to be positively correlated to the bonds rating at significance levels of less than 1%. The company leverage was found to be positively correlated at a significance level of 5%. One variable (bonds maturity) was found to be negatively correlated with the Bond Ratings at a significance level of less than 1%. The issue size and the company risk were found to be not significantly correlated to the bonds ratings. Panel B1: Correlation between the operating cash position and Bonds Ratings Variable

Bonds Ratings

Cash Position

Company Profit

Company Size

Company risk

Bonds Maturity

Convertible Provisions

Issue Size

Bonds Ratings

1.000

Cash position

0.1305 (0.0016)***

1.000

Company Profitability

0.1156 (0.0006)***

0.0568 (0.02340)**

1.000

Company Size

0.3688 (0.0005)***

0.0543 (0.0334)*

-0.1433 (0.887)

1.000

Company risk

0.0209 (0.4534)

-0.0432 (0.3645)

-0.0366 (0.5976)

0.6789 (0.0004)***

1.000

Bonds Maturity

-0.2345 (0.0003)***

0.321 (0.2342)

-0.0033 (0.8766)

-0.3456 (0.0000)***

-0.0854 (0.4434)

1.000

Convertible Provisions

0.2345 (0.0000)***

0.0322 (0.6300)

0.0543 (0.5324)

-0.0543 (0.0065)***

0.0654 0.3324

0.0432 (0.0322)**

1.000

Issue Size

0.0480 (0.1690)

-0.0212 (0.5431)

0.0057 (0.8700)

0.0268 (0.4432)

0.1655 (0.0000)***

-0.0751 (0.0312)**

-0.0174 (0.6175)

1.000

Leverage

0.0865 (0.0345)**

-0.0643 (0.0778)*

-0.0083 (0.6753)

0.1045 (0.0123)***

0.0001 (0.8654)

-0.1144 (0.0064)***

-0.0539 (0.1345)

0.0045 (0.9753)

Leverage

1.000

To test our first hypothesis, we propose to run the mean comparison tests. To do so, we split our sample into two sub groups: a first group of companies that have a positive operating cash balance and a second group that includes the remaining ones. The T-test output confirms our hypothesis since the mean for the first group (4.7) is greater than the mean of the second group (4.1). Moreover, the T-Test and the Wilcoxon-Mann-Whitney test confirm that difference between the two means is significantly different from zero (5% significance level). This implies that the companies belonging to the positive operating cash group enjoys higher credit ratings. Panel B2: Correlation between the Bonds Ratings and the Institutional Variables Variable

Bonds Ratings Creditorsâ&#x20AC;&#x2122; Rights Public Registry Efficiency of Bankruptcy Process

Bonds Ratings

Creditorsâ&#x20AC;&#x2122; Rights

Public Registry

Efficiency of Bankruptcy Process

News Circulation

1.000 0.1567 (0.0000)*** 0.1556 (0.0003)*** 0.0554 (0.4325)

1.000 -0.3453 (0.0000)*** 0.5643 (0.0000)***

1.000 -0.8765 (0.0000)***

1.000

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Saadani Ghali et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 12-20

News Circulation

0.1255 (0.0000)***

0.6543 (0.0000)***

-0.1245 (0.0000)***

0.6543 (0.0000)***

1.000

Panel A from Table 4 identifies the results for the Ordered Probit estimation for the bonds ratings. Most of the results were as we expected before running the regression. The results imply that the positive operating cash balance have a positive significant impact on the bonds ratings (+0.4 at a significance level of 5%). This support our first hypothesis since being able to generate cash from company’s main operations increases the probability of enabling the company to have higher bonds ratings. The company profitability and size have a positive significant impact on the bonds ratings. On the other hand, the convertible bonds option is the only issue variable which significantly impacts the bonds ratings of companies positively. The other issue and issuer variables have no significant impact on the bonds ratings. The results for the other control variables have met our expectations since they affect the bond ratings positively at significant levels. The total increase in cash (from all activities) affects positively (+0.3) the bonds ratings at significance level of 5%. This finding approves our second hypothesis since we have found that higher positive cash balances scores leads to higher bonds ratings. Table 4: The Effect of company’s operating cash on Bond ratings The table gives the output for the Ordered Probit Regression of the Bond Ratings as being the dependent variable. The variables that are listed below are: Bond Ratings which is an ordinal number that ranges from 1 to 7 as the later being the highest rating and the former the lowest rating. Company’s cash: a dummy variable that assigns 1 to companies that have a positive cash operating balance and 0 otherwise. Company Profitability: the company profitability measured in term of its return on assets. Company Size: the total assets were used to get the size of the companies that are included in the sample. Company Risk: it is measured by the standard deviation of net income. Bonds Maturity: the average maturity for the bonds portfolio issued by a company; weights were assigned on the basis of the size of the issuance to the total issuances. Convertible Provisions: a dummy variable that gives 1 to companies with the convertible option and 0 otherwise. Issue Size: it represents the size of the issuance in term of dollars. Leverage: the company leverage is measured by the debt to equity ratio. Concerning the other variables, more description is given in table 1. The stars that appear in the tables mean the following: *** for a significance that is lower than 1%, ** and * are for a significance that is lower than 5% and 10% respectively. Dependent Variable = Bonds ratings Company’s operating cash position

Expected Sign

Model

0.341 (0.044)** 0.0123 (0.005)*** 55.6 (0.000)*** -232 (0.765) -0.543 (0.345) 0.600 (0.000)*** 3.65×10⁹ (0.678) -0.000 (0.234) 0.244 (0.056)** 1.432 (0.000)*** 0.006 (0.003)*** 0.235 (0.075)* 0.344 (0.333) -0.008 (0.876) 0.788 (0.003)*** 0.624 (0.054)* 600 13.67% 234.77

Company Profitability

Company Size (in billions of U.S Dollars)

Company risk (in millions of U.S Dollars)

Bonds Maturity

Convertible Provisions

Issue Size

Leverage

Creditors Rights

Public Registry

Bankruptcy Efficiency

News Circulation

Manufacturing Trades Finance Utility N Pseudo R² LR – Chi²

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Saadani Ghali et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 12-20

Significance

(0.0000)***

Jenzazi (2010) found that the bond rating is positively affected by the company’s liquidity but his research was limited to the overall cash position and took into consideration companies operating in the U.S only. Our findings suggest that, on an international scale, the bond ratings are significantly impacted by operating liquidity. Having positive operating cash balance, allows the company to enjoy a relatively higher bond ratings compared to companies with negative operating cash balances. As a result, the costs for incurring debts (in the form of bonds) are lowered since creditors ask for relatively lower premiums for lending their money. VI. Limitations We face one major limitation at the level of the sample representativeness. Actually, we took the data on the bonds ratings from the F-Database and the data on the auditors from the W-Database. The matching of the two databases provided us with 600 observations that follow the distribution which is described in table 2. This fact could affect the representativeness of our sample. VII. Conclusions In our research we study the relationship between the companies’ liquidity and the bonds ratings on an international scale. Our sample includes 600 companies from 26 different countries and the data is taken over a period of 10 years (from 2002 to 2012). The results of the Ordered Probit regression approve our expectations. In other words, we prove that when a company has a positive operating cash balance, the probability of having higher ratings for its bonds increases. This evidence suggests that the company’s liquidity and more precisely the extent to which companies are able to generate cash from their mean operations affects their cost of debt; having a positive operating cash position allows the company to enjoy relatively higher ratings for its bonds and this leads to relatively lower costs of debt (in the form of bonds). The outcome of this research will add to the existing literature since no previous studies related to that field were done on a national or international scale. Having positive operating cash balance implies that the company is doing well in its main operations, enabling it to enjoy relatively lower cost of debt and this can increase its profitability and earnings. Previous studies had used the change in total cash balance as a proxy for liquidity; however, many companies are able to inflate their cash position using the investing and financing activities. Once limiting the proxy to only the cash generated from operating activities, we are already excluding different sources of cash that can manipulate the results. Moreover, even within the operating cash, there is still some room for manipulation and misleading. Sometimes expenses such as depreciation, can be considered as a source for operating cash; however in reality it is not; instead, it is only a non-cash expense and that is why it is considered as a source of cash; furthermore, increases in accounts payable are also considered as sources of cash under the indirect method, however in reality they are not a source of cash, instead, it is just postponing the payment of current expenses to an upcoming period. References Adam E., Max H. and Marlene P. “Disaggregating operating and financial activities: implications for forecasts of profitability” Review of Accounting Studies Volume 19, Issue 1 , pp 328-362 Defond, M. and J. Jiambalvo. “Debt covenant violation and manipulation of accruals.” Journal of Accounting and Economics 17 (1994): 145-176. Bhojraj, S. and P. Sengupta, (2003), “Effect of Corporate Governance on Bond Ratings and Yields: The Role of Institutional Investors and the Outside Directors.” The Journal of Business, 76, pp. 455-475.. Bhattacharya, N., F. Ecker, P. Olsson and K. Schipper (2009), “Direct and mediated associations among earnings quality, information asymmetry and the cost of equity,” Working Paper, Fuqua School of Business, Duke University. Boubakri, N., and Ghouma, H., (2007), “Creditor Rights Protection, Ultimate Ownership and the Debt Financing Costs and Ratings: International Evidence. Brennan, M., T. Chordia and A. Subrahmanyam (1998), “Alternative factor speciﬁ cations, security characteristics and the cross-section of expected stock returns,” Journal of Financial Economics, 49, 345-373. Callen, J.L., and D. Segal. 2005. Empirical tests of the Feltham-Ohlson (1995) model. Review of Accounting Studies 10: 409-429. Dechow, P. M., R. G. Sloan, and M. T. Soliman. Implied equity duration: A new measure of equity risk. Review of Accounting Studies 9, 197-228. Dechow, P.M., and W. Ge. 2006. The persistence of earnings and cash flows and the role of special items: Implications for the accrual anomaly. Review of Accounting Studies11(2/3): 253-296. Gilson, S. C. “Transactions costs and capital structure choice: Evidence from financially distressed firms.” Journal of Finance 52 (1997): 161-196. Frankel, R. 2009. Discussion of ‘Are special items informative about future profit margins?’’ Review of Accounting Studies 14: 237-245 Hamdi B. Hatem G. and El-Mehdi A. (2014) “Auditor Choice and Corporate Bond Ratings: International Evidence” International Journal of Economics and Finance; Vol. 6, No. 1; 2014 Kasznik, R. (1999), “On the association between voluntary disclosure and earnings management,” Journal of Accounting Research, 37 (1999), 57–81. Kim, O. and R. E. Verrecchia (1994), “Market liquidity and volume around earnings announcements,” Journal of Accounting and Economics, 17, 41-67. Klock, M., S. Mansi and W. Maxwell, (2005), “Does corporate governance matter to bondholders.” Journal of Financial and Quantitative Analysis, 40, 4, pp. 693-720.

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Kubota, K. and H. Takehara (2009), “Information based trade, the PIN variable, and portfolio style differences: Evidence from Tokyo stock exchange ﬁ rms,” Paciﬁ c-Basin Finance Journal, 17, 319-337. Nissim, D., and S.H. Penman. 2001. Ratio analysis and equity valuation: From research to practice. Review of Accounting Studies 6(1): 109-154. Sengupta, P., (1998), “Corporate Disclosure Quality and the Cost of Debt.” The Accounting Review, 73, pp. 459-474. Shleifer, A. and Vishny, R., (1997), “A Survey of Corporate Governance”. Journal of Finance, vol. 52, issue 2 Watts, R. L. and J. L. Zimmerman. Positive Accounting Theory. Englewood Cliffs, N. J.: Prentice Hall, 1986. Willenborg, M. “Empirical Ana lysis of the Economic Demand for Auditing in the Initial Public Offering Market.” Journal of Accounting Research 37 (1999): 225-238 Zhiyan c. , Ganapathi S. Narayanamoorthy. “Accounting and litigation risk: evidence from Directors’ and Officers’ insurance pricing” Review of accounting studies, Volume 19, Issue 1 (2014) :1-44

Appendix A: S&P Credit Ratings Conversion S&P Bonds Ratings New Ratings

From D to CCC+ 1

From B- to B+ 2

From BB- to BB+ 3

From BBBto BBB+ 4

From A- to A+ 5

From AA- to AA+ 6

AAA 7

Page 20

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Optimisation of RCC Beam Bikramjit Singh1, Hardeep Singh Rai2 Civil Engineering Department Guru Nanak Dev Engineering College Ludhiana, Punjab, INDIA Abstract: In the present research work the optimisation of reinforced cement concrete doubly reinforced beam subjected to imposed load has been done. In this research work the principle design objective is to minimise the total cost of beam after full filling all the requirements according to IS 456:2000 and in other case of ductile detailing, additional requirements according to IS 13920:1993 are used. To optimise the overall cost of beam, objective function is used and the codal requirements are used as design constraints. All the design variable are taken as discrete variables. The cost comparison between with ductile detailing results and without ductile detailing results have been done. In the present research work Genetic Algorithm is used with the help of MATLAB software. Keywords: Optimisation, Genetic Algorithm, Matlab, Doubly reinforced beam. I. INTRODUCTION Optimum design of Reinforced Cement Concrete (RCC) elements plays an important role in economic design of RCC structures. Structural design requires judgement, intuition and experience, besides the ability to design structures to be safe, serviceable and economical. The design codes do not directly give a design satisfying all of the above conditions. Thus, a designer has to execute a number of design analyse cycles before converging on the best solution. The optimisation involves choosing of the design variables in such a way that the overall cost of the beam is minimum, subject to the satisfaction of behavioural and geometrical constraints as per recommended method of design codes. A designer’s goal is to develop an “optimal solution” for the structural design under consideration. Material cost is an important issue in designing and constructing reinforced concrete structures. The main factors affecting cost are the amount of concrete and steel reinforcement required. It is therefore, desirable to make reinforced concrete structures lighter, while still fulfilling serviceability and strength requirements. The optimization of reinforced concrete structural elements is more challenging than the optimization of members made of isotropic material e.g. steel. The main difference comes from the fact that more combinatorial characteristics exist in determining the sectional dimensions and the number of reinforcing bars for reinforced concrete members than steel members, which are usually prefabricated with a finite number of sections. In addition to the discrete and combinatorial nature of the sectional dimensions and the number of reinforcing bars, topological reinforcement details specified in the design code make optimization of reinforced structures even more complicated. Even then Optimization algorithms are becoming increasingly popular in engineering design activities, primarily because of the availability and affordability of high speed computers. They are extensively used in these engineering design problems where the emphasis is on maximizing or minimizing a certain goal. Civil engineers are involved in designing buildings bridges, dams and other structures in order to achieve a minimum overall cost or maximum safety or both. Practical application of these solutions, however, requires additional modifications to fit the discrete nature of the structural design variables. Structural optimization is the selection of design variables to achieve its goal of optimality defined by the objective function for specified loading or environmental conditions, within the limits (Constraints) placed on the structural behavior, geometry or other factors. In this research work optimization technique based on Genetic algorithm method has been modeled in MATLAB. II. OPTIMISATION TECHNIQUE The genetic algorithm (GA) is a heuristic search technique based on the mechanics of natural selection developed by John Holland. Koza provides a good definition of a GA: The genetic algorithm is a highly parallel mathematical algorithm that transforms a set (population) of individual mathematical objects (typically fixed- length character strings patterned after chromosome strings), each with an associated fitness value, into a new population (i.e. the next generation) using operations patterned after the Darwinian principle of reproduction and survival of the fittest and after naturally occurring genetic operations.

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Bikramjit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 21-34



Genetic algorithms use a population of points at a time in contrast to the single-point approach by the traditional optimization methods. That means, at a given time, Genetic algorithms process a number of designs.  Genetic algorithms do not require problem-specific knowledge to carry out a search. For instance, calculus-based search algorithms use derivative information to carry out a search. In contrast to this, Genetic algorithm are in different to problem-specific information.  Genetic algorithms work on coded design variables, which are finite length strings. These strings represent artificial chromosomes. Every character in the string is an artificial gene. Genetic algorithms process successive populations of these artificial chromosomes in successive generations.  Genetic algorithms use randomized operators in place of the usual deterministic ones. a) DISCRETE OPTIMISATION In most practical problems in engineering design, the design variables are discrete. This is due to the availability of components in standard sizes and constraints due to construction and manufacturing practices. A few algorithms have been developed to handle the discrete nature of design variables. Optimisation procedures that use discrete variables are more rational ones, as every candidate design evaluated is a practically feasible one .This is not so where design variables are continuous, where all the designs evaluated during the process of optimisation may not be practically feasible even though they are mathematically feasible. This issue is of great importance in solving practical problems of design optimisation. III. PROBLEM FORMULATION The optimization techniques in general enable designers to find the best design for the structure under consideration. In this particular case, the principal design objective is to minimize the total cost of structure, after full filling all the requirements according to IS456: 2000, and additional requirements according to IS13920: 1993 in other case. The resulting structure, should not only be marked with a low price but also comply with all strength and serviceability requirements for a given level of applied load. The reinforced cement concrete doubly reinforced beam subjected to imposed load is taken in this present research work, the cost optimisation and comparison between with ductile detailing and without ductile detailing is made for both the structural elements. All the design variables are taken as discrete variables. Design variables for doubly reinforced beam in case of without ductile detailing are:  Width of beam  Depth of beam  Diameter for main reinforcement in tension side  Number of bars in tension side  Diameter for main reinforcement in compression side  Number of bars in compression side  Diameter for shear reinforcement  Spacing for shear reinforcement Design variables for doubly reinforced beam in case of with ductile detailing are:  Width of beam  Depth of beam  Diameter for main reinforcement in tension side  Number of bars in tension side  Diameter for main reinforcement in compression side  Number of bars in compression side  Diameter for shear reinforcement  Spacing at end span (special confining reinforcement)  Spacing at centre span a) Objectives 1. Cost optimisation of doubly reinforced beam in case of with ductile detailing and without ductile detailing. 2. Cost comparison of doubly reinforced beam results between with ductile detailing and without ductile detailing. b) Optimisation of Doubly Reinforced Beam The general form of an optimisation problem is as follows  Given - Constant Parameters  Find - Design Variables  Minimize - Objective function  Satisfy - Design Constraint Constant Parameters

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Cost of concrete per m3 for M20 = C = Rs 4400/m3 Cost of concrete per m3 for M25= C = Rs 4550/m3 Cost of concrete per m3 for M30= C = Rs 4750/m3 Cost of concrete per m3 for M35= C = Rs 5000/m3 Cost of steel per kg for Fe 415 = S = Rs 45/Cost of steel per kg for Fe 500 = S = Rs 50/Cost of steel per kg for Fe 550 = S = Rs 55/Cost of Formwork per m2 = F = Rs 100/m2 Span of Beam = L = 3m, 5m, 7m, 9m Live Load = 25kN/m, 35kN/m, 45kN/m, 50kN/m, 60kN/m Effective Cover = dc= 50mm Characteristics strength of steel =fy = 415 N/mm2, 500 N/mm2, 550 N/mm2 Characteristics strength of concrete =fck = 20 N/mm2, 25 N/mm2, 30 N/mm2, 35 N/mm2 Design Variables In my problem all the variables are taken as Discrete Variables:  Design variables for Doubly Reinforced Beam without ductile detailing Width of Beam = b = x1 Depth of beam = d = x2 Diameter of bars for steel in tension zone = dia1= x3 No of bars for steel in tension zone = bars no (1) = x4 Diameter of bars for steel in compression zone =dia2= x5 No of bars for steel in compression zone= bars no (2) = x6 Diameter of bars for shear reinforcement = dia3=x7 Spacing for shear reinforcement = sv= x8 Set of discrete values for design variables: b= (225-700) step size- 25 d= (225-1000) step size- 25 dia1= (16, 20, 25) bars no (1)= (2, 3, 4, 5, 6) dia2= (16, 20, 25) bars no (2)= (2, 3, 4, 5, 6) dia3= (8, 10) sv= (180, 200, 220, 240, 260, 280, 300)  Design variables for Doubly Reinforced Beam with ductile detailing Width of Beam = b = x1 Depth of beam = d = x2 Diameter of bars for steel in tension zone = dia1= x3 No of bars for steel in tension zone = bars no (1) = x4 Diameter of bars for steel in compression zone = dia2= x5 No of bars for steel in compression zone= bars no (2)= x6 Diameter of bars for shear reinforcement = dia3= x7 Spacing at end span (special confining reinforcement) = sv1= x8 Spacing at centre span = sv2= x9 Set of discrete values for design variables: b= (225-700) step size- 25 d= (225-1000) step size- 25 dia1= (16, 20, 25) bars no (1)= (2, 3, 4, 5, 6, 7, 8) dia2= (16, 20, 25) bars no (2)= (2, 3, 4, 5, 6, 7, 8) dia3= (8, 10) sv1= (100, 110, 120, 130, 140, 150, 160, 170) sv2= (180, 190, 200, 210, 220, 230, 240, 250, 260, 270) Objective Function The objective function to be minimized for the cost of doubly reinforced beam without ductile detailing: –

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Bikramjit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 21-34

–

The objective function to be minimized for the cost of doubly reinforced beam in case of with ductile detailing: –

Design Constraints 1. Constraint on Flexural Strength When Mu ˃ Mulim, Doubly Reinforced beam is to be designed.

2. Constraint for minimum area of tension reinforcement As per clause 26.5.1.1a of IS 456-2000, tension reinforcement shall not be less than that given by the equation

This can be written as constraint – – For doubly reinforced beam design the area of tension reinforcement should not be less than

Ast= Ast1+Ast2

3. Constraint for maximum area of tension reinforcement As per clause 26.5.1.1b of IS 456-2000, the maximum area of tension reinforcement shall not exceed 0.04bD. – – 4. Constraint for area of compression reinforcement As per clause 26.5.1.2 of IS 456-2000, the maximum area of compression reinforcement shall not exceed 0.04bD, which results in the constraint equation as, – – The area of compression reinforcement for doubly reinforced beam should not be less than

5. Constraint for shear strength As per clause 40.1, 40.4 IS 456-2000 the design of shear reinforcement in the form of constraint equation written below,

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Bikramjit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 21-34

Where, Vu= shear force due to design loads b= breadth of the member d= effective depth

The value of and design shear strength of concrete are taken from Table 19 and Table 20 IS 456-2000, But, table 19 is difficult to use when design parameter has to be computerized. For this purpose it is better to express the values by a formula. The semi-empirical formula used to derive table 19 is as follows,

Where,

For fck = 20 N/mm2 the value reduced to

Shear reinforcement shall be provided to carry a shear equal to, –

6. Constraint for spacing of shear reinforcement As per the clause 26.5.1.5 of IS 456-2000 the maximum spacing of shear reinforcement measured along the axis of the member shall not be exceed 0.75d, Where, d is the effective depth of beam and in no case shall the spacing exceed 300 mm which can be stated as, – –

Figure 1: Beam Reinforcement (Special confining reinforcement)

Ductile detailing requirements for doubly reinforced beam according to IS 13920:1993. a) The member shall have preferably had a width-to-depth ratio of more than 0.3. b) The width of the member shall not be less than 200 mm. c) The depth of the member shall preferably be not less than ¼ of the clear span. d) The spacing of hoops over a length of 2d at either end of a beam shall not exceed

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Bikramjit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 21-34

e) f)

 d/4  8 times the diameter of the smallest longitudinal bar (it must not less than 100 mm).  The first hoop shall be at a distance not exceeding 50 mm from the joint face. Vertical hoops at the same spacing shall also be provided over a length equal to 2d on either side of a section where flexural yielding may occur under the effect of earthquake forces. Elsewhere, the beam shall have vertical hoops at a spacing not exceeding d/2.

IV. RESULTS Results of optimal design of doubly reinforced beam in case of without ductile detailing. I take four cases for optimisation of doubly reinforced beam. Sr.No 1

Parameters span=4m

Case-1. In this case span, fck and fy are constant, Load vary. x(1) x(2) x(3) x(4) x(5) x(6) x(7)

x(8)

cost

225

375

280

3950

225

400

300

4314

225

400

300

4697

225

375

280

4882

225

425

300

5271

225

375

280

5537

225

450

300

6401

225

525

300

7001

225

575

300

7335

225

600

300

8112

fck=25 N/mm2 fy=415N/mm2 w=25kN/m 2

span=4m fck=25 N/mm2 fy=415N/mm2 w=35kN/m

span=4m fck=25 N/mm2 fy=415N/mm2 w=45kN/m

span=4m fck=25 N/mm2 fy=415N/mm2 w=50kN/m

span=4m fck=25 N/mm2 fy=415N/mm2 w=60kN/m

span=5m fck=20 N/mm2 fy=500N/mm2 w= 25kN/m

span=5m fck=20 N/mm2 fy=500N/mm2 w= 35kN/m

span=5m fck=20 N/mm2 fy=500N/mm2 w= 45kN/m

span=5m fck=20 N/mm2 fy=500N/mm2 w= 50kN/m

span=5m

Page 26

Bikramjit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 21-34

fck=20 N/mm2 fy=500N/mm2 w= 60kN/m

Sr.No

Parameters

w=40kN/m

Case-2. In this case load, fck and fy are constant, span vary. x(1) x(2) x(3) x(4) x(5) x(6)

x(7)

x(8)

cost

225

350

260

2886

225

450

300

6748

225

650

300

13482

275

800

300

22427

225

350

260

3021

225

575

300

7335

250

725

300

14716

275

800

300

26257

fck=25N/mm2 fy=415N/mm2 span=3m 2

w=40kN/m fck=25N/mm2 fy=415N/mm2 span=5m

w=40kN/m fck=25N/mm2 fy=415N/mm2 span=7m

w=40kN/m fck=25N/mm

fy=415N/mm2 span=9m 5

w=50kN/m fck=20N/mm2 fy=500N/mm2 span=3m

w=50kN/m fck=20N/mm2 fy=500N/mm2 span=5m

w=50kN/m fck=20N/mm

fy=500N/mm2 span=7m 8

w=50kN/m fck=20N/mm2 fy=500N/mm2 span=9m

Case-3. In this case span, load and fy are constant, fck vary. Sr.No 1

Parameters span=4m w=40kN/m fy=415N/mm2 fck=20N/mm2 span=4m w=40kN/m fy=415N/mm2 fck=25N/mm2 span=4m w=40kN/m fy=415N/mm2 fck=30N/mm2 span=4m w=40kN/m fy=415N/mm2

x(1) 225

x(2) 375

x(3) 16

x(4) 6

x(5) 16

x(6) 2

x(7) 8

x(8) 280

cost 4461

225

350

260

4466

225

350

260

4528

225

350

260

4605

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Bikramjit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 21-34

Sr.No 1

fck=35N/mm2 span=6m w=45kN/m fy=500N/mm2 fck=20N/mm2 span=6m w=45kN/m fy=500N/mm2 fck=25N/mm2 span=6m w=45kN/m fy=500N/mm2 fck=30N/mm2 span=6m w=45kN/m fy=500N/mm2 fck=35N/mm2

Parameters span=4m

225

625

300

9923

225

575

300

10368

225

525

300

10096

225

500

300

10232

Case-4. In this case span, load and fck are constant, fy vary. x(1) x(2) x(3) x(4) x(5) x(6)

x(7)

x(8)

cost

225

375

280

4511

225

375

280

4488

225

350

260

4592

225

650

300

12277

225

675

300

11523

225

650

300

12046

w=40kN/m fck=25N/mm2 fy=415N/mm2 2

span=4m w=40kN/m fck=25N/mm2 fy=500N/mm2

span=4m w=40kN/m fck=25N/mm2 fy=550N/mm2

span=6m w=60kN/m fck=20N/mm2 fy=415N/mm2

span=6m w=60kN/m fck=20N/mm2 fy=500N/mm2

span=6m w=60kN/m fck=20N/mm2 fy=550N/mm2

Results of optimal design of doubly reinforced beam in case of with ductile detailing. Sr.No 1

Parameters span=4m

Case-1. In this case span, fck and fy are constant, Load vary. x(1) x(2) x(3) x(4) x(5) x(6) x(7)

x(8)

x(9)

Cost

225

400

100

200

4392

225

400

100

200

4672

225

400

100

200

5022

fck=25 N/mm2 fy=415N/mm2 w=25kN/m 2

span=4m fck=25 N/mm2 fy=415N/mm2 w=35kN/m

span=4m

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Bikramjit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 21-34

fck=25 N/mm2 fy=415N/mm2 w=45kN/m 4

span=4m

225

450

110

220

5282

225

450

110

220

5583

225

400

100

200

6079

225

450

110

210

6799

225

525

130

260

7315

225

575

140

270

7643

225

600

150

270

8404

fck=25 N/mm2 fy=415N/mm2 w=50kN/m 5

span=4m fck=25 N/mm

fy=415N/mm2 w=60kN/m 6

span=5m fck=20 N/mm2 fy=500N/mm2 w= 25kN/m

span=5m fck=20 N/mm2 fy=500N/mm2 w= 35kN/m

span=5m fck=20 N/mm

fy=500N/mm2 w= 45kN/m 9

span=5m fck=20 N/mm2 fy=500N/mm2 w= 50kN/m

span=5m fck=20 N/mm2 fy=500N/mm2 w= 60kN/m

Case-2. In this case load, fck and fy are constant, span vary. Sr.No

Parameters

x(1)

x(2)

x(3)

x(4)

x(5)

x(6)

x(7)

x(8)

x(9)

Cost

w=40kN/m

225

400

100

200

3348

225

450

110

220

7156

225

650

160

270

13768

250

750

170

270

24047

fck=25N/mm2 fy=415N/mm2 span=3m 2

w=40kN/m fck=25N/mm2 fy=415N/mm2 span=5m

w=40kN/m fck=25N/mm2 fy=415N/mm2 span=7m

w=40kN/m fck=25N/mm2 fy=415N/mm2 span=9m

Page 29

Bikramjit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 21-34

w=50kN/m fck=20N/mm

225

475

110

230

3591

225

575

140

270

7643

250

725

170

270

15059

275

800

170

270

26694

fy=500N/mm2 span=3m 6

w=50kN/m fck=20N/mm

fy=500N/mm2 span=5m 7

w=50kN/m fck=20N/mm2 fy=500N/mm2 span=7m

w=50kN/m fck=20N/mm2 fy=500N/mm2 span=9m

Case-3. In this case span, load and fy are constant, fck vary. Sr.No 1

Parameters

x(1)

x(2)

x(3)

x(4)

x(5)

x(6)

x(7)

x(8)

x(9)

Cost

span=4m

225

500

120

250

5029

225

400

100

200

4952

225

475

110

230

5164

225

400

100

200

5111

225

625

150

270

10246

225

625

150

270

10371

225

525

130

260

10426

225

500

120

250

10412

w=40kN/m fy=415N/mm2 fck=20N/mm2 2

span=4m w=40kN/m fy=415N/mm2 fck=25N/mm2

span=4m w=40kN/m fy=415N/mm2 fck=30N/mm2

span=4m w=40kN/m fy=415N/mm2 fck=35N/mm2

span=6m w=45kN/m fy=500N/mm2 fck=20N/mm2

span=6m w=45kN/m fy=500N/mm2 fck=25N/mm2

span=6m w=45kN/m fy=500N/mm2 fck=30N/mm2

span=6m w=45kN/m fy=500N/mm2 fck=35N/mm2

Page 30

Bikramjit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 21-34

Case-4. In this case span, load and fck are constant, fy vary. Sr.No 1

Parameters

x(1)

x(2)

x(3)

x(4)

x(5)

x(6)

x(7)

x(8)

x(9)

Cost

span=4m

225

475

110

230

5047

225

400

100

200

4867

225

425

100

210

5067

225

625

150

270

12659

225

675

160

270

11846

225

675

160

270

12539

w=40kN/m fck=25N/mm2 fy=415N/mm2 2

span=4m w=40kN/m fck=25N/mm2 fy=500N/mm2

span=4m w=40kN/m fck=25N/mm2 fy=550N/mm2

span=6m w=60kN/m fck=20N/mm2 fy=415N/mm2

span=6m w=60kN/m fck=20N/mm2 fy=500N/mm2

span=6m w=60kN/m fck=20N/mm2 fy=550N/mm2

Cost comparison of doubly reinforced beam results with and without ductile detailing Case-1. span=4m, fck=25N/mm2, fy=415N/mm2 6000 5000

4392

Cost (Rs)

3950

4314

4672

4697

5022

4882

5282

5271

5583

4000 3000 2000 1000 0 25

Load (kN/m) cost without ductile detailing

cost with ductile detailing

Page 31

Bikramjit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 21-34

span=4m, fck=20N/mm2, fy=500N/mm2 9000 8000 7000

7335

7315

8404

5537

6000 Cost (Rs)

6401

6079

7001

6799

8112

7643

5000 4000 3000 2000 1000 0 25

Load (kN/m) cost without ductile detailing

cost with ductile detailing

Case-2. w=40kN/m, fck=25N/mm2, fy=415N/mm2 30000 24047 22427

Cost (Rs)

25000 20000 1348213768

15000 10000 5000

6748 7156 2886 3348

0 3

span length (metres) cost without ductile detailing

cost with ductile detailing

w=50kN/m, fck=20 N/mm2, fy=500 N/mm2 30000

2625726694

Cost (Rs)

25000 20000 1471615059 15000 10000 5000

7335 7643 3021 3591

0 3

span length (metres) cost without ductile detailing

cost with ductile detailing

Page 32

Bikramjit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 21-34

Case-3. span=4m, w=40kN/m, fy=415N/mm2 5400 5164

5200

5029

Cost (Rs)

5000 4800 4600

5111

4952 4605

4528

4466

4461

4400 4200 4000 20

fck (N/mm2) cost without ductile detailing

cost with ductile detailing

span=6m, w=45kN/m, fy=500N/mm2 10500

10426

1036810371

10400 10246

10300

10232

10200 Cost (Rs)

10412

10096

10100 10000

9923

9900 9800 9700 9600 20

fck (N/mm2) cost without ductile detailing

cost with ductile detailing

Case-4.

Cost (Rs)

span= 4m, w= 40kN/m, fck=25N/mm2 5200 5100 5000 4900 4800 4700 4600 4500 4400 4300 4200 4100

5067

5047 4867 4592 4511

4488

415

500

550

fy (N/mm2) cost without ductile detailing

cost with ductile detailing

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Bikramjit Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 21-34

Cost (Rs)

span= 6m, w= 60kN/m, fck=20N/mm2 12800 12600 12400 12200 12000 11800 11600 11400 11200 11000 10800

12659

12539

12277 12046 11846 11523

415

500

550

fy (N/mm2) cost without ductile detailing

  

V. CONCLUSIONS The GA gives near about optimum results, in case of finding the optimal solution for given parameters. The GA optimizer does a good effort to minimize the overall cost in the objective function. This effort is used to reduce the amount of material since it has the higher percentage of the total cost. The GA is the good technique of discrete optimisation. In case of doubly reinforced beam design, it is 3-6% difference in cost between without ductile detailing case and with ductile detailing case. VI.

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

16. 17. 18. 19. 20. 21. 22.

cost with ductile detailing

REFERENCES

Leps M. and Sejnoha M., “New Approach to Optimization of Reinforced Concrete Beams” ,Computers and Structures 81,2003, pp. 1957–1966, science direct. Rath D.P., Ahlawat A. S., and Ramaswamy A., “Shape Optimization of RC Flexural Members”, Journal of structural Engineering, ASCE, Vol. 125, No. 2 , December 1999, pp. 1439-1446. Zammit K., “Optimal Design of A Reinforced concrete Frame”, University of Malta, June, 2003, pp.142-149 Rao S.S., “Engineering Optimization Theory and Practice”, New Age International Publisher, 2006, III Edition, pp. 29-336. Barros M. H. F. M., Martins R. A. F., “Cost Optimization of singly and Doubly Reinforced Beams with EC2-2001”, Struct. Multidisc. Optim., Springer-Verlag London limited, Vol. 30,2005, pp. 236-242. Kalyanmoy Deb, “Optimization for Engineering Design Algorithm and Problems”, PHI Publications, 2005. Ferreira C.C., Barros M.H.F. and Barros A.F.M., “Optimal Design of reinforced concrete T section in bending”, Engineering Structures, vol 25,Science Direct 2003, pp.951-964 Kanagasundaram S. and Karihaloo B.L., “Minimum cost design of reinforced concrete structures”, Structural Optimization 2, pp.173-184. Dr. Shah V. L. & Late Dr. Karve S. R., “Limit State Theory and Design of Reinforced Concrete”, Structures Publications, Year, 2005, IV Edition, pp. 27-78. Dr. Punmia B. C., Jain A.K. and Jain A. K., “Limit State Design of Reinforced Concrete”, Laxmi Publications, Year 2007, pp. 50-53 Coello C. and Farrea F. A., “Use of Genetic Algorithm for the Optimal Design of Reinforced Concrete Beams”. Park H. S., Kwon Y.H., Seo J. H. and Woo B. H., “Distributed hybrid Genetic Algorithm for structural optimization on a PC cluster”, Journal of structural Engineering, ASCE, December 2006, pp. 1890-1897. Rojas R., “Genetic Algorithm”, Neural Networks, Springer – Verlage, Berlin.1996, Leyffer S. and Mahajan A., “Nonlinear Constrained Optimization: Methods and Software”, Argonne National Laboratory 9700 South Cass Avenue Argonne, Illinois 60439. Saini B, Sehgal V. K. and Gambhir M.L., “Genetically Optimized Artificial Neural Network Based Optimum Design Of Singly And Doubly Reinforced Concrete Beams”, Asian Journal of Civil Engineering (building and housing), vol. 7, no. 6, 2006, pp. 603-619. Goble G.G., and Lapay W, “Optimum Design of Prestressed Beams”, Journal of the American Concrete Institute, Vol.68, No.9, September, 1971, pp.712-718. Chakrabarty B. K., “Models for Optimal Design of Reinforced Concrete Beams”, Computers & Structures Vol. 42, No. 3, 1992, pp. 447-451. Ceranic B. and Fryer C., “Sensitivity Analysis and Optimum Design Curves for the Minimum cost Design of Singly and Doubly Reinforced Concrete Beams”, Struct Multidisc Optim 20, pp 260–268. Prakash A., Agarwala S. K. and Singh K. K., “Optimum Design of Reinforced Concrete Sections”, Computers and Structures Vol. 30. No. 4, pp. 47-71. Sarma K. C. and Adeli H, “Cost Optimization of Concrete Structures”, Journal of Structural Engineering, Vol. 124, No.5, May, 1998. Zielinski Z. A, Long W. and Troitsky M.S., “Designing Reinforced Concrete Short-Tied Columns Using the Optimization Technique”, ACI structural Journal, Title no. 92-S60. IS 456-2000, Code of Practice for Plain and Reinforced Concrete, Bureau of Indian Standards, New Delhi.

Page 34

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net An Investigative Study on Factors Causing Job Choice of Business School Students in Private Universities of Bangladesh Sadia Tangem1, Mohammad Ishtiak Uddin2 1&2 Lecturer of Business Administration, Department of Business Administration, University of Asia Pacific, Green Road, Dhaka-1215, BANGLADESH.

__________________________________________________________________________ Abstract: Nowadays, students are very much interested to obtain business learning because of it’s high demand in today’s corporate world. But they are not properly aware for the selection of right job or career especially in Bangladesh. This paper tries to find out the factors behind job choice of business school students in private universities of Bangladesh. A total number of 80 students from different business schools of 8 private universities located in Dhaka city were selected through simple random sampling technique. To collect primary data a well structured questionnaire has been used. The data were analyzed using linear regression, coefficient analysis and SPSS 16.0 to serve the purpose of the study. The results of this research study may be very useful for business school students to advance their professional career according to appropriate job choice. Key Words: Bangladesh, Business, Job, Private University, Students. __________________________________________________________________________________________ I. Introduction There are various private and public universities which provide quality business education following the syllabus of developed nations in Bangladesh. Especially private universities in Bangladesh help students to orient them with the corporate world by organizing different career events. So, private universities in Bangladesh are growing a very lucrative place for business students. There are a lot of job openings in corporate arena of Bangladesh which will be available specifically for business school students and of course, employers prefer business school students all over the world because ultimately they can provide the quality output compare to non-business students. According to Elaine and Kenneth (1995) at present employers’ prefer graduates in business discipline but with an exposure to functional training. As a result, the demand for business school students is gradually increasing at a strong rate (Demagalhaes et al., 2011; Mohammad Emdad Hossain and Tabassum Siddique, 2012). Although economic depression of 2008 did affect the demand of business school graduates especially accounting graduates but not in all levels of organizations (Bloom and Myring, 2008). This study mainly focuses on job choice and it’s influencing factors that affects the business school students of private universities in Bangladesh. The feedback of this research study helps the employers to identify and select the best candidates for their vacant positions. It also helps the academics and career advisors to advice their graduates regarding different job openings and choices. So, for the development of a nation as a whole, it is a great challenge for employers and academics to recognize the root causes of job choice for achieving a concrete vision. II. Literature Review Literature review helped to disclose various important studies that are very much vital to business school students’ job choice. So, developing a job choice and planning the study accordingly, is the greatest starting of career journey. Basically, the concept of job choice is much broader in sense and it represents the self-selection of their job arena on the basis of their personal capability (Mohammad Emdad Hossain and Tabassum Siddique, 2012). Job opportunities in Bangladesh are very high in manufacturing, financial and telecom sectors than other sectors and those sectors require adequate business graduation to fulfill it’s demand (Chisty et al., 2007). According to Shamsuddoha and Khanam (2003) job finding opportunities are especially high for private university students who are from business schools. Another research found that job attributes are the most crucial factors for choosing a job among business school students. According to Carpenter and Strewser (1970) US students possessed some important characteristics like task nature, growth opportunities, remuneration, working atmosphere, job security, training programs and other settlements for choosing their right job. There are other several studies identified the job factors for graduates. As stated by Tandy and Moores (1992) students prioritized their career or job growth prospects as the most crucial factor for choosing early positions. Other important issues like availability and security of job, remuneration and prospects for career advancement have been recognized for desired career or job decision (Rosen et al., 1982; Cangelosi, Condie and Luthy, 1985; Kochanek and Norgaard, 1985; Reha and Lu, 1985;

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Sadia Tangem et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 35-39

Shivaswamy and Hanks, 1985; Haswell and Holmes, 1988; Gul et al., 1989; Horowitz and Riley, 1990; Felton et al., 1994). Dinc (2008) recognized family surroundings, learning atmosphere, high prospects regarding payments, career growth, work experience, knowledge and skills by using factor analysis for choosing a right career or job. As stated by Brown (2002) Group influence helps to decide the most suitable career or job for an individual. There is important three factors namely academic stream, sexual category and individuality also facilitate to recognize the business undergraduates’ or students’ desired job or career with desired multinationals and small & medium sized industries. The undergraduates from different universities who desired to involve with small and medium sized industries are studying their major in management studies stream and who desired to involve with multinationals are studying their major in other business related streams. This study also found that male undergraduates desired to involve with multinationals compared to female undergraduates and female undergraduates desired to involve with small and medium sized industries compared to male undergraduates (Moy and Lee, 2002; Huang and Sverke, 2007). There is various research studies have been carried out to present the job choice issues of business school students in developed countries but in developing countries like Bangladesh, it is nearly absent (Mohammad Emdad Hossain and Tabassum Siddique, 2012). So, there is a very urgent need to research on this issue to show the future career path of business school students in Bangladesh. III. Objectives of the study The study was developed to determine the factors behind job choice among students of business schools in private universities of Bangladesh. 1) The first objective of this study to identify the factors that most influence business school students when choosing a job. 2) The second objective is to recognize the most promising factors that have a negative relationship with business school students’ job choice. 3) To recommend specific guidelines for chosen job by students of different business schools. IV. Rationale for the study Job or career choice may have greater effect on everyone’s whole life especially for business school students. This investigative study hopefully provides valuable information regarding job choice of business school students and it would help them a lot to make right path. This could also assist academics and employers to recognize what actually encourages their job choices. It would desire that students of business school would give appropriate thinking on influential factors in their job choices. V. Hypotheses of the study H0: There is no significant relationship between factors behind job choice and job of business school students. H1: There is a relationship between factors behind job choice and job of business school students. VI. Research Methodology This study is a quantitative based study which is set during a survey. To conduct this study both primary and secondary data have been used. The primary data has been collected using a structured questionnaire having 7 featured likert scale on the basis of the objectives of this study. 8 (eight) private universities have been selected for this study purpose and among those private universities final year undergraduate and postgraduate students from business schools or departments have been chosen. Secondary data were collected from available books, journals, publications, research studies and different web sites. The target population of this study covers students from different private universities of business schools or departments in Bangladesh of different areas of Dhaka city. The sample size is 80 (n=80) considering 99% incidence rate and 95% completion rate. The sampling method used in this research was simple random sampling (lottery method) for the selection of universities and students and interviewed to serve this rationale. The collected data have been analyzed using Multiple Regression Analysis, Coefficient Analysis and Frequencies using statistical software SPSS 16. VII. Analysis and Findings of the Study This investigative study develops generally eight groups of factors affecting job choice of business school students, namely, company size, company location, flexible work schedule, service growth, company reputation, starting remuneration and benefits, opportunity for international exposure and job security. The study results show that service growth, starting remuneration & benefits, company size, company location, job security, flexible work schedule and company reputation are highly significant factors that affecting job choice of

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Sadia Tangem et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 35-39

business school students in private universities of Bangladesh. These seven factors jointly explain 73.7% of the total variation of the model. More addition of independent variables will have less impact on adjusted R square which is explained by 70.8%. The multiple coefficient of correlation (R=0.859) specifies that variables selected are highly correlated and this present the model a good fit. On the other hand, opportunity for international exposure is exceedingly insignificant factor affecting job choice of business school students in private universities of Bangladesh. [Table 1 and 1(a)] Table 1: Relationship between factors behind job choice and service of business school students Model Summary Model 1

R Square .859a

Std. Error of the Estimate

Adjusted R Square

.737

.708

.103

a. Predictors: (Constant), Job Security, Company Reputation, Starting Remuneration and Benefits, Flexible Work Schedule, Opportunity for International Exposure, Company Size, Service Growth, Company Location Table 1(a): Coefficient Analysis Coefficientsa Standardized Coefficients

Unstandardized Coefficients Model 1

B (Constant)

Std. Error -3.278

1.117

Company Size

.405

.140

Company Location

.174

.069

Flexible Work Schedule

.117

Service Growth

.307

Company Reputation Starting Remuneration and Benefits Opportunity for International Exposure Job Security

Beta

Sig. -2.933

.005

.237

2.900

.005

.241

2.511

.014

.060

.134

1.931

.057

.087

.307

3.531

.001

.019

.012

.102

1.624

.109

.316

.103

.260

3.063

.003

-.022

.037

-.042

-.588

.558

.155

.069

.177

2.240

.028

a. Dependent Variable: Job Choice

VIII. Conclusion There are various ways to present their choice against different jobs. Basically, it is their own freedom to take the decision on the basis of their chosen factors which significantly affects their job choice and this study analyzed the proper evidence for the hypothesis that there is a strong relationship between factors behind job choice and job of business school students in private universities of Bangladesh. It is clearly visible from the analysis that majority of the business school students gave a more strong response to the factors such as service growth, starting remuneration & benefits, company size, company location, job security, flexible work schedule, company reputation are affects their job choice. Although some of them think that these chosen factors help them to take the right decision regarding job choice which will ultimately lead to remarkable performance at their workplaces. IX. Recommendations Students they can keep themselves from a lot of problems when they get good guidance and advice. Academic counsellors play a major role to help them make a right decision to smooth their future path. In the light of the findings of this study, some measures are given below for business school students:  Teachers and parents should take necessary steps to organize different discussing sessions with students to identify their preferred area of interest.  University authority should organize different career counseling workshops for students to help them understand of their future job field.  Students should involve themselves with practical fields of business from the very beginning of their academic career.  Allowed to make students accountable choices on their new intellects and career goals.  Students should be set for realizing their possibilities through pursuit of their career aspirations.

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Sadia Tangem et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 35-39

References

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Bloom, R. and Myring, M. (2008) ‘Charting the Future of the Accounting Profession’, CPA Journal, 78(6), pp. 65-67. Brown, D. (2002) ‘The role of work and cultural values in occupational choice, satisfaction, and success: A theoretical statement’, Journal of Counseling and Development, 80, pp. 48-56.

[3]

Cangelosi, J.S., Condie, F.A. and Luthy, D.H. (1985) ‘The Influence of Introductory Accounting Courses on Career Choices’, Delta Pi Epsilon, 9(Summer), pp. 60-68. Carpenter, C.G. and Strewser, R.H. (1970) ‘Job selection preferences of accounting students’, The Journal of Accountancy, 9(1), pp. 84-86. Chisty, S.K.K., Uddin, M.G. and Ghosh, K.S. (2007) ‘The Business Graduate Employability in Bangladesh: Dilemma and Expected Skills by Corporate World’, BRAC University Journal, 4(1), pp. 1-8. Demagalhaes, R., Wilde, H. and Fitzgerald, R.L. (2011) ‘Factors Affecting Accounting Students’ Job Choices: A Comparison of Students’ and Practitioners’ Views’, Journal of Higher Education Theory and Practice, 11(2), pp. 32-34. Dinc, E. (2008) ‘Meslek Seciminde Etkili Faktorlerin Incelenmesi: Meslek Yuksek Okulu-Muhasebe Programi Ogrencileri Uzerine Bir Arastirma’, Kocaeli Universitesi Sosyal Bilimler Enstitusu Dergisi, 16, pp. 90-106. Elaine, M.O. and Kenneth, R.D. (1995) ‘The Position of Marketing Education: A Student Versus Employer Perspective’, Marketing Intelligence & Planning, 13(2), pp. 47-52. Felton, S., Buhr, N. and Northey, M. (1994) ‘Factors Influencing the Business Student's Choice of a Career in Chartered Accountancy’, Issues in Accounting Education, 9(1), pp. 131. Gul, F.A., Andrew, B.H., Leong, S.C. and Ismail, Z. (1989) ‘Factors Influencing Choice of Discipline of Study - Accountancy, Engineering, Law and Medicine’, Accountant and Finance, 29(2), pp. 93-101. Haswell, S. and Holmes, S. (1988) ‘Accounting Graduate Job Choice’, ICA Journal (Australia), 53(2), pp. 63-67. Horowitz, K. and Riley, T. (1990) ‘How Do Accounting Students See US’, Accountancy, (September), pp. 75-77. Hossain, E.M. and Siddique, T. (2012) ‘Career Preference of Business Graduate in Bangladesh: A Case Study of Some Selected Private Universities’, Asian Business Review, 1(1), pp. 106-108. Huang, Q. and Sverke, M. (2007) ‘Women’s occupational patterns over 27 years: Relation to family of origin, life careers, and wellness’, Journal of Vocational Behavior, 70, pp. 369-397. Kochanek, R. and Norgaard, C.T. (1985) ‘Student Perceptions of Alternative Accounting Careers - Part I’, The CPA Journal, 55(5), pp. 36-43. Moy, J.W. and Lee, S.M. (2002) ‘The career choice of business graduates: SMEs or MNCs?’, Career Development International, 7(7/6), pp. 334-347. Reha, R.K. and Lu, D. (1985) ‘What Does It Take to Be Successful in Accounting?’, Business Education Forum, (February), pp. 24-28. Rosen, L.S., Paolillo, J.G.P. and Estes, R.W. (1982) ‘An Empirical Analysis of Career Choice Factors Among Accountants, Attorneys, Engineers, and Physicians’, Accounting Review, 57(4), pp. 785. Shamsuddoha, M. and Khanam, D.M. (2003) ‘Development of Human Resources in Bangladesh: An Analysis of Institutional Supports’, Social Science Research Network, November 30, pp. 70(1)-70(8). Shivaswamy, M.K. and Hanks, G.F. (1985) ‘What do accounting students look for in a job?’, Management Accounting, 66(June), pp. 60-61. Tandy, P.R. and Moores, T. (1992) ‘What Accountants Look for in a Job’, The National Public Accountant, 37(3), pp. 28-33.

[4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]

Appendix

Statistics

Gender of Students

Degree

Age

Valid

Missing

Gender of Students

Valid

Frequency

Percent

Valid Percent

Cumulative Percent

Male Students

58.8

Female Students

41.2

100.0

Total

100.0

Degree

Valid

Frequency

Percent

Valid Percent

Cumulative Percent

BBA

78.8

MBA

21.2

100.0

Total

100.0

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Sadia Tangem et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 35-39

Age

Valid

Frequency

Percent

Valid Percent

Cumulative Percent

21-30

93.8

31-40

6.2

100.0

Total

100.0

Page 39

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Consumer Perception Regarding Eco-Friendly Fast Moving Consumer Goods in India 1

Sudhir Sachdev, 2 Vinod Mahna Research Scholar & Assistant Professor, 2Dean Academics, Manav Rachna International University, Faridabad, Haryana, INDIA. _________________________________________________________________________________________ Abstract: The environmental issues such as global warming, ozone depletion, water and air pollution, loss of species, and farmland erosion have led to the current alarming environmental crisis that threaten the environment as well as human life. There is more risk than ever before that earth is warming under “human influence”, according to a year 2007 report compiled by the UN’s Intergovernmental Panel on Climate Change (IPCC), warning that only “substantial and sustained reduction” of greenhouse gas emissions will limit the disaster of climate change. Thus, human behaviour is a key source as well as the main solution to the environmental problems. As a result, the personal consumption decision is of growing interest of firms in various fields and some firms have changed their corporate culture to be more environmentally responsible, and have developed environmentally friendly products and services to meet the demand of environmentally conscious consumers. However, despite positive forecasts, demand for environmentally friendly products didn’t grow as expected and both attitude-behavior and intention-behavior gaps emerged. Although public opinion polls consistently show that consumers would prefer to choose a green product over one that is less friendly to the environment when all other things are equal, those "other things" are rarely equal in the minds of consumers. The purpose of this study is to discover what barriers, if any, inhibit Indian consumers who want to live a “green” lifestyle from purchasing “green” household and personal care products. This research looks into and explores the influence of demographical variables and the four traditional marketing-mix elements, i.e. product, price, place and promotion on attitude and purchase intentions of consumers of various demographics on eco-friendly products. Keywords: Green Marketing, green consumer behavior, environmentally friendly products. 1

___________________________________________________________________________ I. INTRODUCTION There is a growing concern for environmental degradation and the resultant pollution all over the world. Right from 1992, Rio de Janerio Earth Summit conference, world leaders and top environmental officials have been expressing global concern over environmental issues. The widespread environmental problems in India are choking air effluence, water pollution in the vast majority of rivers, water shortages throughout the country, heaps of solid and toxic waste, acid deposition spoiling land and water, near-total deforestation, rampant over fishing, exhaustion of agricultural land and evident consumption of even highly endangered species for food and traditional medicine has endangered the ecological balance of our country. Since society becomes more anxious with the natural environment, businesses have started to adjust their behavior in an attempt to address society's "new" concerns. Some businesses have been quick to accept concepts like environmental management systems and waste minimization, and have integrated environmental issues into all organizational activities. People are conscious about the less environment friendly products due to their own welfare, which is why this issue is a very modern topic in India. This paper tries to unearth consumer attitudes and perceptions towards eco- friendly products in FMCG sector and their willingness to pay on green products. II. WHAT IS GREEN MARKETING? Green marketing is inevitable for any type of organization. According to the American Marketing Association (AMA) ‘Green Marketing’ is the marketing of products that are presumed to be environmentally safe. It incorporates a broad range of activities, including product modification, changes to the production process, packaging changes, as well as modifying advertising. Defining green marketing is not a simple task where several meanings intersect and contradict each other. Other similar terms used are ‘Environmental Marketing‘, ‘Sustainable Marketing’ and ‘Ecological Marketing’. As per Brundtland Commission (1987), ―Development that meets the needs of the present without compromising the ability of future generations to meet their own needs (Rowell, 1996).

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Sudhir Sachdev et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 40-43

Another definition is ‘Green or Environmental Marketing consists of all activities designed to generate and facilitate any exchanges intended to satisfy human needs or wants, such that the satisfaction of these needs and wants occurs, with minimal detrimental impact on the natural environment. (Polonsky 1994b). ‘It is the voluntary pursuit of any activity that encompasses concern for energy efficiency, environment, water, conservation and the use of recycled/recycled products & renewable energy‘(Confederation of Indian Industry) Industry Peattie (2001) described evolution of green marketing in three phases. First phase is termed as "Ecological" green marketing, and during this period all marketing activities were concerned to help environment problems and provide remedies for environmental problems. Second phase is "Environmental" green marketing and the focus shifted on clean technology that involved designing of innovative new products, which take care of pollution and waste issues. Third phase was "Sustainable" green marketing came into prominence in the late 1990s and early 2000. III. THE GREEN CONSUMER There is growing interest among the consumers all over the world for protection of the environment. The concern with environmental degradation has resulted in a new segment of consumers, i.e. the green consumers. These consumers have been identified as one who avoids products which are a possible danger for health, shall damage the environment during production, use materials derived from threatened species or environment, and cause unnecessary waste. The green consumers are the main motivating force behind the green marketing process. It is their concern for environment and their own well being that drives demand for eco-friendly products, which in turn encourages improvements in the environmental performance of many products and companies. Thus, for a marketer, it is important to identify the types of green consumers. Although no consumer product has a zero impact on the environment, in business, the terms ’green product’ and ‘environmental product’ are used commonly to describe those products that strive to protect or enhance the natural environment by conserving energy and/or resources and reducing or eliminating use of toxic agents, pollution, and waste. Indian literate and urban consumer is waking up to the merits of Green products. But it is still a new concept for the majority. The new green movements need to reach the masses and that will take a lot of time and effort. By India‘s customs and Ayurvedic heritage, Indian consumers do value the significance of using natural and herbal products. Indian consumer is exposed to healthy living lifestyles such as yoga and natural food consumption. In those aspects the consumers is already aware and are inclined to accept the natural/green products. India is already one of the largest economies in the world, and will continue its brisk urbanization and economic development over the next few decades. This is a cause for celebration, however, in this growing economic prosperity, and through change of the marketing mix and marketing strategies like promotion and advertising, people are guided by an unlimited desire for additional goods and are influenced by an attitude of grandiosity, of being superior, of having things under control, of improving one's position and of preferring new commodities to old ones. This over consumption on vast scale productivity puts pressure on the resources of the ecosystem. While the material indices of wealth rose, the environmental indices fell. IV. METHODOLOGY In this section the research methods used in the data collection are succinctly discussed. The authors employed questionnaire method for data collection to explore consumers’ purchasing behavior. The survey was completed in Haryana province in India and the sample size was 500. According to the World Bank, India, with per capita income of $1580, is a lower middle income group country, with 30% of India population living under poverty line. Thus, people belonging to SEC A and SEC B (socio-economic classification A and B) were interviewed. The purpose of selecting respondents from this group was to generate data from people who are well educated and have a decent purchasing power. This number of interviews enabled us to achieve theoretical saturation in our target group. Our interview strategy was to collect opinion regarding eco- friendly fast moving consumer products from consumers of different age groups, genders and income groups. The secondary data were collected from relevant journals, books and other published data. Demographically, ‘green customer,‘ our study reveals, are diversely spread along all income ranges, age brackets, education levels and various household sizes. On average green shoppers are a little older, tend to have higher income, and more education, but you will find substantial numbers of green shoppers can be found distributed across the consumer population. V. CONSUMER ATTITUDES IN INDIAN MARKET According to various research reports, shoppers are thinking green, but not always buying that way. (Mainieri, Barnett, Valdero, Unipan, and Oskamp 1997). Demographic variables offer easy and efficient ways to segment the market and capitalize on green attitudes and behaviours for marketers. We surveyed young business professionals about sustainable consumption in India. These young business people also represented young

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Sudhir Sachdev et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 40-43

Indian consumers, mainly from the middle and upper socio-economic groups. Our research revealed that awareness and understanding of sustainable consumption among consumers was low; from the demographic analysis of the sample data it was established that only half (50.45%) of the respondents have concern about the deteriorating environment, whereas 28% respondents felt that environment issues are only somewhat serious, but there are other more essential issues that need attention. This study found that older consumers (age group 50 years and above) are more concerned about the environment and therefore are potential green product consumers. Among the ecologists, a positive relationship (p value: 0.005<0.05) was found between environment concern and green purchase behaviour. 61.2% ecologists reported that they make every effort to reduce the use of plastic bags. 28% respondents reported that they considered buying eco-friendly product some time or the other, while 21.5% respondents said they hardly gave environment a thought while making their purchases. One of the reasons why older generation is more environmentally conscious than younger generations is because of the maturity that they have gained during the years. While growing up, this generation has faced many environmental issues such as global climate change and ozone depletion and acquired an environmental awareness through a sound environmental education. Accordingly, they have learned why it is important to protect the environment. As far as youth is concerned, it is common for them to shop for the sake of pleasure because of peer pressure, or because they have money, they tend to follow fashion and technological trends. This study suggests that educated Indian consumers are concerned about the environment and such proenvironmental concerns influence their green buying behaviour moderately, thereby leading to purchase of ecofriendly products. When ANOVA was applied on the Education Level versus Awareness level to check if there is a significant difference in awareness levels for all education levels {Higher secondary, Graduate, PostGraduate, Doctorate} with different Questions of Awareness, the result revealed that there is a significant difference in awareness levels for question pertaining to contribution of sustainable future for eco-friendly products (p value = 0.94). This study has found that income is positively related to environmental understanding. The most widespread justification for this belief is that individuals can, at higher income levels, put up with the marginal increase in costs associated with supporting green causes and favouring green product offerings. On the basis of this study, the researcher has concluded that out of the four demographic factors taken into consideration, only age, qualification and income has positive effect with eco-friendly behaviour of the consumers. Results of previous studies have been inconclusive regarding the effect of age on eco-friendly behaviour of consumers. This study has shown that age has a definite effect in the eco-friendly behaviour of the consumers. In regards to education, demographic profiles done in the past show that education is linked to green consumersâ&#x20AC;&#x2122; attitudes and behaviours. Most demographic profile researches done on the relationship between education and the behaviours of green consumers have been positively correlated. The results of this study are consistent with the results of the previous studies on the same subject. Gender-related studies between males and females in regards to the environment were also inconclusive. However, this study has shown that gender has no effect on eco-friendly behaviour of the respondents. Both, male and female respondents had similar views on environment conservation. On the basis of this study, it is concluded that consumers know about climate change, understand that reducing their own carbon footprint will help fight climate change, and want to join that effort. But this study also shows that consumers do not quite understand how to act on their greener impulses. There is lack of awareness among the consumers. Only 75 respondents (i.e. 15%) were able to name eco-labels/eco-certificates prevalent in India. Further, only 294 respondents (i.e. 58.8%) respondents were able to recollect advertisement of eco-friendly products they had seen on Indian media. Environmental attributes of a product are more difficult for a consumer to assess compared to other easily observable product attributes. Hopes for green products gaining market share have also been hurt by the perception that such products are of lower quality or don't really deliver on their environmental promises. Most of the respondents felt that eco-friendly products met their quality expectations (mean: 3.1; Std. Dev: 1.24); however, they exhaust quickly. Consumers said they did not buy green products because they are worried about the diminished quality of eco-friendly versions; there has been no improvement in their quality over the time. However, most of the respondents (mean: 3.65; Std. Dev: 1.24) admitted that eco-friendly products are healthier than their conventional counterparts. High price of environmentally products was cited as most important factor for not buying eco-friendly products by sampled respondents. Majority of the respondents (76.6%, mean: 3.58; St Dev: 0.94) said that most of the eco-friendly products were overpriced. Price is a critical and important factor of green marketing mix. Findings from this work also suggest that the segment of consumers willing to pay more for eco-friendly products in India may not be very big. Even in a knowledgeable segment like the one chosen for this study, willingness to pay premium received an ordinary response. Only five percent respondents are willing to pay more than 15% premium for eco-friendly products, whereas, 16.4% respondents said that they will not pay any premium for eco-friendly products.

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Having decided to buy eco-friendly products, many consumers encounter a final obstacle: They could not find them. Most of the respondents (mean: 4.02; Std. Dev: 1.44) felt that eco-friendly products were available in few stores only. Further, when ever available, they lacked in variety (mean: 3.59, Std. Dev: 1.35) VI. MANAGERIAL IMPLICATIONS In terms of managerial implication, the profile of green product purchasers provides green marketers an indication of their target consumers. The research reveals that traditional product attributes such as price, quality and availability are still the most important ones that consumers considered when making purchasing decision. In order to fulfil individual needs and wants, including ensuring customers’ satisfaction, the marketers need to make sure that their products are of high quality and competitively priced. The marketers also need to adopt a better marketing mix for their products in order to change consumers’ negative perception towards green products. Successful green marketing entails much more than simply adding an environmental attribute into a product. It is important that marketers integrate green marketing strategies carefully into the company strategic plan. When it comes to awareness regarding eco-friendly products, consumers trust eco-labels/eco-ratings the most, followed by news reports, recommendation by known people and lastly paid advertisements by the manufactures, while also looking for opinions and information posted by other consumers online. The result shows that there is a significant relationship between consumers’ attitude on government’s role and their attitude on green products. Many people have high ecological concern but have a feeling that the preservation of the environment is the prime responsibility of the government. The survey indicates the importance of government’s role in preserving the environment. This in turn will influence consumers’ outlook on the government’s role in environmental issues and their attitudes to the green products. Hence, in order to popularise eco-friendly products among masses, government agencies and corporate should work together, not in worrying about people’s attitudes but by paying attention to shaping their behaviours. VII. CHALLENGES AHEAD: Spread awareness and advantages of eco-friendly products. Majority of the people are not even aware of green products and their uses. 2. Convince customers about long term cost effectiveness of eco-friendly products First, consumers have to be aware that a eco-friendly product is available before they purchase it. Yet many of the customers don’t even know about the availability of green alternatives in many product categories. Next, consumers must be convinced that a product will achieve the objective for which it is being purchased. But many believe that green products are of lower quality than their more traditional “conventional” counterparts. Consumers must then come to a decision whether a product lives up to its green reputation. 1.

REFERENCES Arbuthnot, J. (1977), ―The roles of attitudinal and personality variables in the prediction of environmental behavior and knowledge‖, Environment and Behavior, 9, 217 –232. Kangun, N. and M.J. Polonsky (1995), Regulation of Environmental Marketing Claims: A Comparative Perspective, International Journal of Advertising, 14, 1 – 24. Mainieri, Tina; Barnett, Elaine G. (1997), ―Green buying: The influence of environmental concern on consumer behavior‖, Journal of Social Psychology, Vol. 137, Iss.2, pp. 189 –205. McCarty, J.A. and Shrum, L.J. (1994), The recycling of solid wastes: personal values, value orientations, and attitudes about recycling as antecedents of recycling behavior‖, Journal of Business Research, Vol. 30, No. 1, pp. 53 – 62. Ottman, Jacquelyn. (1995), ―Today‘s consumers turning lean and green‖, Marketing News, Vol. 29, Iss. 23, pp. 12 – 14. Peattie, Ken. (2001), ―Towards Sustainability: The Third Age of Green Marketing‖, The Marketing Review, Vol. 2 Iss. 2, pp.129 –146. Polonsky, Michael Jay. 1994a. "Green Marketing Regulation in the US and Australia: The Australian Checklist." Greener Management International 5: 44-53. Thogersen, John. (2000), ―Psychological Determinations of Paying Attention to Eco-labels in purchase decisions‖, Journal of Consumer Policy, Vol. 23, Iss. 3, pp. 285 – 315. Wells, R.P. (1990), ‘‘Environmental performance will count in the 1990s’’, Marketing News, Vol. 19, March, p. 22. Zimmer, M.R., Stafford, T.F., Stafford, M.R., 1994. Green issues: dimensions of environmental concern. J. Bus.Res. 30, 63–74 (Summer). Zsolnai, L. (2002). ‘Green business or community economy?’, International Journal of Social Economics, 29(8): 652–662.

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ISSN (Print): 22790020 ISSN (Online): 2279International Journal of Engineering, Business and Enterprise 0039 International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

Applications (IJEBEA) www.iasir.net

Analysis of Manufacturing Competency for an Automobile Manufacturing Unit Chandan Deep Singh1* and Jaimal Singh Khamba2 1

Assistant Professor, Department of Mechanical Engineering, Punjabi University, Patiala – 147002, Punjab, India 2 Professor, Department of Mechanical Engineering, Punjabi University, Patiala – 147002, Punjab, India

______________________________________________________________________________________ Abstract- Quality, cost, delivery, innovation, and responsiveness motivate most manufacturing premeditated agenda today. Latest development in industry have suggested the emergence of another route to manufacturing excellence, that is, there is an increasing focus by industry regulators and professional bodies on the need to stimulate innovation in a broad range of manufacturing competencies. Further, competencies can be described as aptitude to pertain a set of related information, skill, and ability to execute "critical work functions" in a defined work situation. The technical category encompasses competencies related to functions, processes, and roles within the business office which includes understanding and appropriately applying procedures, requirements, regulations, and policies related to specialized expertise. In this work various parameters of manufacturing competency are studied and analyzed by correlation, and response analysis of questionnaire responses. Keywords: Manufacturing Competency, Quality, Product Concept, Production Planning & Control, Process Planning, Raw Material & Equipment _______________________________________________________________________________________ I. Introduction Some scholars see “competence” as a combination of knowledge, skills and behavior used to improve performance; or as the state or quality of being adequately or well qualified, having the ability to perform a specific role. For instance, management competency might include systems thinking and emotional intelligence, and skills in influence and negotiation. The ability of companies to effectively carry out competency- based human resources management (HRM) is becoming more and more crucial for their survival. A competency based HRM system captures the differing worth of individual contributors, facilitates multiple career paths and allows flexibility in reward-related decisions, which are important to address with the changing nature of organizations. The field of competency development is growing in popularity with administrative management in businesses and agencies worldwide. One important reason to collect data and build competency models is that they are powerful decision-making tools. II. Literature Review Manufacturing Competency is often regarded as a condition for improved competitiveness (Levinthal and March, 1993). Competencies in organizations can be broadly classified as employee level and organizationallevel (Cardy and Selvarajan, 2006). The manufacturing function can be a formidable weapon to achieve competitive superiority (Dangayach and Deshmukh, 2000). By taking a disaggregated approach, the decomposed effects of core competencies on firm performance have been examined and the relative influences of all three major constituents of core competencies, marketing competencies, technological competencies and integrative competencies (Wang et.al, 2004). (Anders Drejer, 2001) formulated a framework for competence development as a research area and an area of management attention in ﬁrms. The architecture of the novel approach based on the traceability of the design activities which have its aim to assisting competency characterization through the qualitative feature of the work was presented by (Belkedi et al., 2006). (Eric Bonjour and Jean-Pierre Micaelli, 2010) stated that significant changes have been marked both in the strategic management field, with the development of competence-based management and the use of the concept of value creating network. (F. Belkadi et al., 2007) stated that design, project managers are aware of both the impact of the designers’ competencies on the project performance and of the requirement for a fast development of these competencies. The author presented the architecture of a novel approach based on the traceability of design activities which aims at assisting competency characterisation through qualitative features of the work situation in which this competency is activated. (John P. Millikin et al., 2010) examined how composition of individual capabilities within self-managed teams translates into greater effectiveness for multi-team systems (MTS) in which teams are embedded. (Justin Barnes

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Chandan Deep Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 44-51

et al., 2001) stated that the truism to say that manufacturing firms need to innovate to reach and remain at the frontier of what has been termed ‘world-class’ competitiveness. (Qing Yu Zhang et al., 2003) described a framework to explore the relationships among flexible competence, flexible capability and customer satisfaction. (Rajesh K. Singh et al., 2010) described the status of the enterprises in India and China and examined the roles of government policies and strategy development for competitiveness. (Sajee B. Sirikrai and John C.S. Tang, 2006) proposed that the aggregate performance of many firms in a particular industry can reflect the competitiveness of that industry as a whole. (Sanjib K. Dutta, 2007) critically examined the framework of one of the leading awards of India by testing the relationship between stakeholder results and enabling practices using regression analysis, structural equation model and data envelopment analysis. The results of the study revealed that the framework is used by the organizations to enhance firm level competitiveness but not as a tool to contribute to national competitiveness. (Tim R. V. Davis, 1999) determined which core competencies have the strongest influence on improving the performance of different types of service businesses. (Wang Zhi yu et al., 2006) reviewed development of quality management and competitive advantage and explored the notion of strategic status of quality as a source of sustained competitive advantage. (Yonggui Wang et al., 2004) focused on the decomposition of impacts of core competencies on firm performance. (Yu-Ting Lee, 2006) indicated that the competency development and management are widely regarded as vital tools to enhance competitiveness for organizations and proposed a method based on the rough set theory to explore high-performers’ required competencies. III. Factors Based on the literature studied, following factors have been finalized:  Product Concept  Product Design & Development  Process Planning  Raw Material & Equipment  Production Planning & Control  Quality Control IV. Analysis A. Response Analysis The response analysis was performed on the various sub scales of the manufacturing competency. Table 1: Response Analysis (Mean) of the Respondents on Product Concept S. No

2. 3.

No. of Companies Scoring Points

FACTORS

Do you have a well planned & structured Concept Generation process in your organization? Whether your company policies promote innovation?

Total No. of Responses

Total Points Scored (TPS)

Percent Points Score (PPS)

A 1 6

B 2 40

C 3 44

D 4 28

118

330

70.0

118

276

58.4

281

59.5

241

51.0

243

51.5

241

51.0

(N)

Do you feel that marketing department 20 37 57 4 118 is adequately motivated to get an idea about the new product? Whether your organization encourages 30 55 31 2 118 the deployment of inter-departmental teams to identify and create new ideas? Is your organization flexible enough 23 74 12 9 118 for making changes during operations and maintenance to satisfy customer needs? Whether your organization uses 32 55 25 6 118 centralized planning structure for idea generation? (Total Points Scored ‘TPS’ = A x 1 + B x 2 + C x 3 + D x 4)

The response analysis results showed that the product concept based on the idea that “well planned structured concept generation process” in the organization was given maximum weightage which was followed by the idea based on marketing department motivation enough to bring up new concepts and company policies to promote innovation. In last almost similar extent of weightage was given in the surveyed organization regarding developments of inter departmental relationships for new ideas, centralized planning structure development and flexibility of organization for making changes to satisfy customers. The analysis assessed that only 23.7% of surveyed respondents reported that there was implementation of “well planned structured concept generation process” in the organization whereas majorly 37.3% and 33.9% reported it either in terms of reasonably well or

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Chandan Deep Singh et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August, 2014, pp. 44-51

to some extent respectively. The product concept based on company policies to promote innovation and marketing department motivated enough to bring up new concepts, there implementation was also on the similar scale in the organization as reported by the respondents. Table 2: Response Analysis (Mean) of the Respondents on Product Design and Development S. No

1. 2.

3. 4. 5. 6. 7

No. of Companies Scoring Points

FACTORS

A 1 25

B 2 20

C 3 37

D 4 36

Total No. of Responses (N)

Whether your organization has an effective 118 Design Technology Program (CAD)? Whether your organization uses 12 78 25 3 118 computerized technology for Analysis purposes? Whether the design program includes 23 75 16 4 118 procedures like Product Life Cycle? Whether the design program includes 25 38 48 7 118 Aesthetics and Ergonomics of the product? Does your organization use simulation and 24 59 28 7 118 modeling for analyzing designs? Does your organization track Design & 8 46 51 13 118 Development program costs? What percentage of the designing is done 45 23 31 19 118 with the aid of computer? (Total Points Scored ‘TPS’ = A x 1 + B x 2 + C x 3 + D x 4)

Total Points Scored (TPS)

Percent Points Score (PPS)

320

67.8

255

54.0

237

50.2

273

57.8

254

53.8

305

64.5

260

55.1

The response analysis results showed that the product design and development concept based on the idea that “implementation of effective design technology program” in the organization was given maximum weightage which was followed by the idea based on tracking design and development costs and simulating and modeling for the analysis of the products. In last almost similar extent of weightage was given in the surveyed organization regarding inclusion of aesthetics and ergonomics in product designing and usage of product life cycles, while least weightage was on the usage of computerized technology for analysis. The analysis assessed that only 30.5% of surveyed respondents reported that there was implementation of “effective design technology program (CAD)” in the organization whereas majorly 31.4% and 21.2% reported it either in terms of reasonably well or not at all respectively. The product design and development concept based on usage of computerized technology for analysis, usage of product life cycles, there implementation was also on the similar scale in the organization as reported by the respondents. It was analyzed that majorly 66.1% and 63.6% of surveyed respondents reported their implementation on the scale of up to some extent while 10.2% and 19.5% reported no implementation at all in their organizations whereas 21.2% and 13.6% of the respondents reported it to be at reasonably amount of implementations. Table 3: Response Analysis (Mean) of the Respondents on Process Planning S. No

1. 2.

No. of Companies Scoring Points

FACTORS

Whether your organization has an effective Process Planning program? Does your organization apply Group Technology?

Total No. of Responses

B 2 45

C 3 29

D 4 32

118

317

67.2

118

240

50.8

118

265

56.1

245

51.9

284

65.7

255

54.0

297

63.4

234

49.6

(N)

Does your organization posses a mechanism for material and machine selection?

Whether the planning software is updated & 28 56 31 3 118 reviewed periodically in accordance with technological changes? Does your organization track Process 11 28 59 10 108 Planning costs? Does your organization prefer integration of 24 51 43 0 118 different departments? Does your organization particularly take into 4 57 45 11 117 account finishing and assembly of the product? What percentage of the process planning is 42 45 22 9 118 done with the aid of technology? (Total Points Scored ‘TPS’ = A x 1 + B x 2 + C x 3 + D x 4)

6. 7

Percent Points Score (PPS)

A 1 12

Total Points Scored (TPS)

The response analysis results showed that the process planning concept based on the idea that “implementation of effective design technology program” in the organization was given maximum weightage which was followed

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by the idea based on tracking process planning costs and simulating and taking account of finishing and assemble of products. In last almost similar extent of weightage was given in the surveyed organization regarding usage of mechanism for material and machine selection and preferences to departments integration, while least weightage was on the software based for planning regularly updated and reviewed with technological advancement and group technology. On the issue based on the process planning regarding higher usage of process planning with computer aid, it was evident that there was 35.6% of the organization using it less than 25.0% while 38.1% reported it to be in range of 25.0 – 50.0%. Also 18.6% the organization reported computer usage up to 50.0- 75.0% and least 7.6% organization was using it for more than 75.0% of the process. The analysis showed 27.1% of surveyed respondents reported that there was great implementation of “effective process planning program” in the organization whereas majorly 38.1% and 24.7% reported it either in terms of to some extent or reasonably well respectively. 71.2% of the organization reported to the implementation of usage of mechanism for material and machine selection, at some extent while 20.3% were using it at reasonable level. Table 4: Response Analysis (Mean) of the Respondents on Raw Material and Equipment S. No

1. 2. 3. 4.

No. of Companies Scoring Points

FACTORS

Does your organization use ERP Software for record keeping? Whether your organization has its own Transportation? Whether your organization has enough warehouses for Inventory storage? Whether the three departments (marketing, designing and production) are synergistically involved in equipment selection decisions?

Total No. of Responses

Total Points Scored (TPS)

Percent Points Score (PPS)

A 1 18

B 2 31

C 3 35

D 4 34

118

321

68.0

118

237

50.2

118

275

58.2

118

263

55.7

270

57.2

(N)

Does your organization have sufficient 14 58 44 2 118 automated equipment with appropriate process capabilities to meet market demands? (Total Points Scored ‘TPS’ = A x 1 + B x 2 + C x 3 + D x 4)

The response analysis results showed that the process of raw material and equipment based on the idea that “usage of ERP software for record keeping” was given the maximum weightage in the organizations which was followed by the thrust to the ideas based on existences of warehouse facility for inventory storages, availability of sufficient automated equipment with processing capabilities equivalent to market demands and synergistically involvement of marketing, designing and production department in equipment selection. The least preference was given to the issue of existences of transportation facility. The “usage of ERP software of record keeping” in the organization was on great extent by the 28.8% while 29.7% and 26.3% of the organization reported its usage at either reasonable or at some extent level respectively. Table 5: Response Analysis (Mean) of the Respondents on Production Planning and Control S. No

2. 3. 4. 5. 6 7

No. of Companies Scoring Points

FACTORS

Whether your organization has Computerized Manufacturing Systems (CAM)? How much do you exert to get precise and accurate dimensions? Does your organization prefer GREEN MANUFACTURING? Does your organization track Production Planning & Control program costs?

Total No. of Responses

Total Points Scored (TPS)

Percent Points Score (PPS)

A 1 18

B 2 49

C 3 41

D 4 10

118

279

59.1

118

311

65.9

118

246

52.1

118

287

60.8

149

31.5

147

31.1

236

50.0

(N)

What percentage of the work is done with the 91 25 0 2 118 help of robots? What is the percentage of maintenance hours 90 27 1 0 118 in relation to total working hours? To what Extent Hydraulic and Pneumatic 40 51 14 13 118 systems are employed in your organization? (Total Points Scored ‘TPS’ = A x 1 + B x 2 + C x 3 + D x 4)

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The response analysis results showed that the process of production planning and control based on the idea that “exertion on accurate and precise results” was given the maximum weightage in the organizations which was followed by the thrust to the ideas based on tracking costs of the production planning and control programs and computerized manufacturing systems. The least preferences was given to the issue of green manufacturing. The analysis showed that regarding the process based on production planning and control i.e. help of robots and maintenances hours in relation to working hours, 77.1% and 76.3% of the organization reported their implementation for lesser than 25.0% of the times in the organization while 21.2% and 22.9% of the organization reported their implementation for 25.0 - 50.0% of the times. Similarly, on the implementation of hydraulic and pneumatic systems in the organization 43.2% organization reported their usage for 25.0 – 50.0% of the times while 33.9% of the tem reported their usage for lesser than 25.0% of the times. Table 6: Response Analysis (Mean) of the Respondents on Quality Control S. No

1. 2. 3. 4.

5. 6

No. of Companies Scoring Points

FACTORS

Whether your organization, test products under actual conditions? Does your organization carry out Life Cycle Analysis of the Product?

Total No. of Responses

Total Points Scored (TPS)

Percent Points Score (PPS)

A 1 8

B 2 14

C 3 64

D 4 32

118

356

75.4

118

249

52.7

269

57.0

219

46.4

156

33.0

212

44.9

(N)

Does your organization use technology to 30 37 39 12 118 analyze quality? Does your organization issue 41 53 24 0 118 computerized Quality Control instructions? Up to what Extent, the product needs to 86 28 2 2 118 be re-processed after inspection? To what Extent your organization, invest 51 42 23 2 118 on Quality Control & Inspection fraction as compared to total production cost? (Total Points Scored ‘TPS’ = A x 1 + B x 2 + C x 3 + D x 4)

The response analysis results showed that the process of quality control based on the idea that “tests product materials under actual conditions” was given the maximum weightage in the organizations which was followed by the thrust to the ideas based on technology to analyze quality and life cycle analysis of the products. The least preferences were given to the issue of issuances of the computerized quality control instructions. The analysis showed that regarding the process based on quality control i.e. reprocessing of the products after inspection, 72.9% of the organization reported their implementation for lesser than 25.0% of the times in the organization while 23.7% of the organization reported their implementation for 25.0 - 50.0% of the times. On contrary, on invest on quality control & inspection fraction as compared to total production cost in the organizations 43.2% of them were using it less than 25.0% of the times while 35.6% of the organization was using it for the 25.0 – 50.0% of the times. B. Correlation Analysis Correlation analysis was performed in this section, the purpose was to identify the relationship between each statements within each parameters of the manufacturing competencies. Moreover, the direction of perception was measured by using correlation by assessing statements as all were measured on the same scale. The correlation process was Karl Pearson Correlation with significances level 0.05. Table 7: Karl Pearson Correlation Matrix for the Product Concept Product Concept -1 Product Concept -2 Product Concept -3 Product Concept -4 Product Concept -5 Product Concept -6

Product Concept -1 1 .387** .592** .401** .171 .453**

Product Concept - Product Concept Product Concept Product Concept Product Concept 2 -3 -4 -5 -6 .387** .592** .401** .171 .453** ** ** 1 .569 .507 .174 .597** .569** 1 .541** .292** .525** .507** .541** 1 .412** .614** .174 .292** .412** 1 .393** .597** .525** .614** .393** 1

The correlation analysis results showed that the product concept based on the idea that “well planned structured concept generation process” in the organization was well positively correlated with the other product concepts like “company policies to promote innovation, marketing department motivated enough to bring up new concepts, developments of inter departmental relationships for new ideas and centralized planning structure development ” and similar of the results was evident with the product concept idea i.e. company polices for

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innovations. As above discussed both product concepts was not correlated with the product concept of “flexibility of organization for making changes to satisfy customers”. Table 8: Karl Pearson Correlation Matrix for the Product Design and Development Product Design& Dev – 1 Product Design& Dev – 2 Product Design& Dev – 3 Product Design& Dev – 4 Product Design& Dev – 5 Product Design& Dev – 6 Product Design& Dev – 7

Product Design & Product Design Product Design & Product Design & Product Design Product Design Dev - 2 & Dev - 3 Dev - 4 Dev – 5 & Dev - 6 & Dev – 7 .859** .445** .449** .627** .595** .530** 1 .403** .420** .641** .472** .549** ** * ** ** .403 1 .236 .484 .522 .454** .420** .236* 1 .566** .504** .424** .641** .484** .566** 1 .558** .495** ** ** ** ** .472 .522 .504 .558 1 .534** .549** .454** .424** .495** .534** 1

On the assessment of the inter correlation between the statements based on the ideas regarding product design and development i.e. existences of effective design technology program (CAD), higher usage of designing with computer aid, usage of computerized technology for analysis, usage of product life cycles, inclusion of aesthetics and ergonomics in product designing, usage of simulation and modeling for analyzing designs and tracking design and development costs, it was evident that there was existences of strong and significant positive inter correlation between the each ideas based on product design and development. Table 9: Karl Pearson Correlation Matrix for the Process Planning Process Planning – 1 Process Planning – 2 Process Planning – 3 Process Planning – 4 Process Planning – 5 Process Planning – 6 Process Planning – 7 Process Planning – 8

Process Planning - 2 .821** 1 .693** .518** .550** .572** .692** .300**

Process Planning - 3 .721** .693** 1 .526** .589** .645** .506** .297**

Process Planning - 4 .364** .518** .526** 1 .475** .391** .316** .527**

Process Planning - 5 .468** .550** .589** .475** 1 .437** .456** .559**

Process Planning - 6 .629** .572** .645** .391** .437** 1 .417** .187*

Process Process Planning – 7 Planning – 8 .563** .245** .692** .300** .506** .297** ** .316 .527** .456** .559** ** .417 .187* 1 .255** .255** 1

On the assessment of the inter correlation between the statements based on the ideas regarding process planning i.e. existences of effective process planning program, higher usage of process planning with computer aid, usage of group technology, usage of mechanism for material and machine selection, software based for planning regularly updated and reviewed with technological advancement, tracking process planning costs, preferences to departments integration and taking account of finishing and assemble of products, it was evident that there was existences of strong and significant positive inter correlation between the each ideas based on process planning. Table 10: Karl Pearson Correlation Matrix for the Raw Material and Equipment Raw Material & Equipment – 1 Raw Material & Equipment – 2 Raw Material & Equipment – 3 Raw Material & Equipment – 4 Raw Material & Equipment – 5

Raw Material Equipment - 2 .182* 1 .619** .147 .366**

& Raw Material Equipment - 3 .246** .619** 1 .383** .484**

& Raw Material Equipment - 4 .709** .147 .383** 1 .550**

& Raw Material Equipment - 5 .561** .366** .484** .550** 1

The correlation analysis results showed that the process of raw material and equipment based on the idea that “synergistically involvement of marketing, designing and production department in equipment selection” in the organization was well positively correlated with the other process concepts of raw material and equipment like “usage of ERP software of record keeping, existences of warehouse facility for inventory storages and availability of sufficient automated equipment with processing capabilities equivalent to market demands ” but it was not correlated with the process concept based on the raw material and equipment of “existences of transportation facility”. Table 11: Karl Pearson Correlation Matrix for the Production Planning and Control Prod Plan & Control – 1 Prod Plan & Control – 2 Prod Plan & Control – 3 Prod Plan & Control – 4 Prod Plan & Control – 5 Prod Plan & Control – 6 Prod Plan & Control – 7

Prod Plan Control - 2 .569** 1 .519** .394** -.260** .470** .484**

& Prod Plan Control - 3 .330** .519** 1 .267** -.010 .508** .557**

& Prod Plan Control - 4 .533** .394** .267** 1 -.195* .510** .436**

& Prod Plan Control - 5 -.170 -.260** -.010 -.195* 1 -.278** -.106

& Prod Plan Control - 6 .447** .470** .508** .510** -.278** 1 .410**

& Prod Plan Control – 7 .655** .484** .557** .436** -.106 .410** 1

The correlation analysis results showed that the process of production planning and control based on the idea that “maintenance hours in relation to working hours in organization” was well negatively correlated with the

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other process concepts based on production planning and control like “usage of computerized manufacturing system, precise and accurate process, usage of robots and usage of hydraulic and pneumatic systems ” but it was not correlated with the process concept based on the production planning and control of “green manufacturing and cost tracking of production planning and control programs”. Table 12: Karl Pearson Correlation Matrix for the Quality Control Quality Control – 1 Quality Control – 2 Quality Control – 3 Quality Control – 4 Quality Control – 5 Quality Control – 6

Quality Control Quality Control - Quality Control Quality Control - Quality Control -1 2 -3 Quality Control - 4 5 6 1 .672** -.204* .561** .379** .591** .672** 1 .029 .668** .567** .646** -.204* .029 1 .080 .277** -.069 .561** .668** .080 1 .644** .716** .379** .567** .277** .644** 1 .613** ** ** ** ** .591 .646 -.069 .716 .613 1

The correlation analysis results showed that the process of quality control based on the idea that “testing of products under actual conditions, life cycle analysis of the products, usage of technology to analyze quality, investment in quality control and inspection in references to total production cost and issuances of computerized quality control instructions” was well positively correlated with each other i.e. they had strong and significant positive inter correlation between each other. It was only with the one of the process of quality control i.e. reprocessing of products after inspection, it was evident that it was negatively correlated with the other process of quality control i.e. testing of products under actual conditions and investment in quality control and inspection in references to total production cost, whereas with rest of the process of quality control there was no evidences of any relationship between them. V. Conclusion From above analysis it is concluded that the parameters of Manufacturing Competency are highly correlated and they have a high internal consistency. Thus, the questionnaire is much reliable.                    

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net The human resources policy of the of Health Ministry in Angola - from current practice to the desired praxis 1

Pedro J. M. Gomes1; Pedro F. Franque1. Department of Vocational Guidance in Higher Institute of Health Sciences /Agostinho Neto University Address Av January 21, Next to Clinic Multiperfil, Luanda / Angola

Abstract: Poor countries assume dramatic indicators regarding the institutional capacity in the health sector. In this sense, planning and good management of human resources for health it is essential to solve problems linked to this issue under study. In the present study we focus on the analysis of Human Resources policy of the Department of Health in Angola because there is a substantial difference between the desired and actual practice praxis. Is observed in the study that is taking great strides to improve human resources for health in Angola, however there is a lot to improve. The path must be based on overcoming the challenges related to HR issues in health care in order to increase the coverage and attachment of professional teams to ensure the delivery of health services appropriately and equitably; guarantee competences and skills for the workforce in health; increase the performance of the professionals in the defined objectives and strengthening the capacity for planning and management of human resources in the health sector [1]. Keywords: Angolan health, human resources policy, health management, motivation, communication, resilience, moving professionals I. Introduction In the particular field of health, the World Health Organization (WHO) declared that "health is a state of complete physical, mental and social wellbeing and not merely the absence of disease or disability [2]. This definition somehow contemplates the possibility of differentiating between positive and negative health and their approach invites to a reflection that is still needed. The same authors state that the World Federation for Mental Health in 1962 defined health as the best state possible with the existing conditions. Still claim that in 1986, in Ottawa, the first official meeting of the WHO health promotion concluded that â&#x20AC;&#x153;health is a resource for daily life, not the objective of living. It is a positive concept emphasizing social and personal resources, as well as physical capacitiesâ&#x20AC;?[2]. In this sense there is an emphasis on training health professionals in Angola, and after independence, the country had only the of nurses trained on primary and secondary level. Were formed the first 6 nurses with a Bachelor's Degree in Nursing School (ESSE), current ISCISA of Agostinho Neto University in 2001. For example, given the expansion of higher education in Angola and increment of courses healthcare, higher education in nursing grown, the course currently being ministered in public institutions in 11 provinces, with the degree of Bachelor. [3] With regard to the management of Human Resources (HR) in Angola, the Ministry of Health of Angola, called MINSA, is the organ of the central administration state that executes, oversees and supervises the national health policy. Fits the this Ministry (1) prepare and propose a national health policy; (2) ensuring their correct implementation, monitoring and periodic review; (3) promote the health development of the country in coordination with national partners and related sectors of the national and international communities; (4) promoting the monitoring and combating endemic-epidemic diseases; (5) promote the health of the general population, in particular of vulnerable populations, especially children and women, to take measures necessary to ensure the fairness and accessibility to health care; (6) develop programs to solve specific health problems and submit them for approval by the Council of Ministers; (7) promote the development of human resources, participating in the planning, training and monitoring the performance of health professions in collaboration with other relevant institutions; (8) coordinate and guide the provision of health care to the national health system level, taking measures for the constant increase of their quality; (9) promoting the lifestyle, the environment and healthy nutrition, disseminating knowledge for positive behavior modification; (10) ensuring the implementation of national and international health legislation and other legislation of interest to public health; (11) promote and coordinate social and resource for the development of health mobilization; (12) Promote and implement appropriate health technologies, particularly in the areas of infrastructure, pharmaceutical, medical-surgical and non-medical means; (13) issuing the authorization or the withdrawal of the national pharmaceutical market, pharmaceutical and herbal; (14) encourage research in the area of health and their use for improving the health status of the population; (15) promote, in partnership with other agencies, legal medicine; and (16) perform other functions as may be affected. [4]

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In the present study, we aim, through intensive, reliable literature review, questioning the human resources policy of the Ministry of Health in Angola are in practice what the studies and law claiming to be. However, as can be seen below in the various studies and information analyzed, there is a difference between the policy of Human Resources (HR) of the Ministry of Health of Angola and the desired actual praxis. II. Angola and the healthcare system Angola is situated in southern Africa, has an area of 700km Â˛ 1,246 and a coastline of 1.600km from north to south Its population is estimated at 16.500.00 habitants, distributed in 18 provinces; 164 districts and 532 communes (parishes) mostly young. The economic situation is characterized by high levels of economic growth since 2002, with the end of the armed conflict. Its economy is dependent on oil (55% GDP) and diamonds. Its population is mostly poor, with 61% of the population living below the poverty line, 26% of the population lives in extreme poverty. The country is located in 160th place in accordance with the International Diploma in Humanitarian Assistance (IDHA), the set of 173 countries and the average life expectancy is 46 years. The Angolan epidemiological picture is dominated by transmitted diseases such as malaria, diarrheal diseases, acute respiratory infections, tuberculosis, trypanosomiasis (sleeping sickness), vaccine-preventable diseases such as measles and tetanus, and others. Malaria remains the leading cause of death and the prevalence of HIV Angola is less than 5%. From 1975 to 1992, the National Health System (NHS) Angola, was based on the principles of universality and gratuitousness of primary health care. Since 1992, with the approval of 21-B/92 Law, Law of the NHS, the Angolan state no longer has exclusivity providing of health services and admits the share of users, with payment of prescription charges. Currently, health care is provided by the Private Sector. In terms of infrastructures, the network of health care consists of 1,721 health units, with eight central hospitals, 32 provincial hospitals, 228 district hospitals and 1,453 clinics. At the moment, Angola has 995 Angolan doctors and 1,273 expatriate doctors, totaling 2,268 medical doctors. In terms of medications, stocks are constant breaking due to poor planning and purchases dispersed by various companies not affected to the Ministry of Health [5]. Also, Connor, Averbug and Miralles [6] note that the coverage of basic health services increased by 30-42% since 2005. Public financing of primary care health units grew more that any other category. The geographical access increases through the renovation and construction of health centers, in many cases built based on provincial health maps and some experiences using private services to reach population. The quality of the services is still below expectations due to issues related to human resources, lack of essential goods and irregular financing of recurrent costs. In Angola, was inherited from the colonial health system serve almost exclusively the settlers and was not adequate to meet the health needs of the local population. The long war that started after independence has stopped the development of an appropriate system of health until a few years ago, when the war finally ended. After decades of destruction, in the first years of peace there was a rush to invest in the health sector. This investment not necessarily agreed with the priorities of the population health, because it was done without much information or planning. In recent years, major developments in the government of Angola and donations support began pulling the health system in Angola for a more informed and systematic strategy, currently being organized in the Health service as showed in Figure [6]: Figure 1: Levels of Health Care in Angola

As can be observed, the system of providing health care is divided among three levels of health care, based on the strategy of primary health care. The first level or primary health care, represented by the posts, health centers, district hospitals, nursing stations and doctors' offices, is the first point of contact of the population with the health system. The secondary or intermediate level, represented by provincial and general hospitals, is the reference level for the units of the first level. The tertiary or national level, represented by differentiated and specialized hospitals, is the reference level for health facilities in the secondary level. The provision of health care is taken by the public, private and traditional medicine industries. [7]

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III. The distribution of health workers in Angola Health care in Angola are provided mainly by the public sector including the National Health Service (NHS), the health services of the Angolan Armed Forces (FAA) and the Ministry of Interior. It also includes public companies, such as SONANGOL, EDIAMA, among others. In general terms, the public sector is the main provider of health care at the national level. Despite the universality principle that guides the NHS, the system lacks the capacity and structure that allows widespread access of the population to health care. However, increasingly, the private sector has been participating in the market. With the approval of the Law on the National Health System (Law 21-B/92), is permitted to private sector provision of health care, having been established, similarly, the notion of sharing of users through user fees. The private sector, although important, is confined to major urban centers of the country. Prices (not regulated) health care limits the accessibility of the population to the lucrative private sector. Since 2005, all major companies provide some type of health coverage for their employees, whether in clinics, outpatient own company or independent contractors hospital facilities. This coverage extends to dependents of employees and even to employees of third sector. [8] The time of the armed conflict in Angola (1997-2001), the international community has an important role in health financing, particularly in the purchase of medicines and vaccines. After this period, support turned to training and the NHS to combat endemic diseases such as HIV/AIDS, malaria and tuberculosis. Table 1 illustrates what's new since 2005 in health financing. Angola is in a favorable position relative to sub-Saharan Africa region especially with regard to several important indicators relating to health financing. [8] Table 1: Health Financing in Angola since 2005 [9] 2005 (2000-2005) Limited public resources to primary care (25% of total expenditure on health in 2002)

2010(2003-2010) Public expenditure on primary care grew 415% to 33% of total public spending on health (2005)

70-80% budget implementation (2002-02)

62-75% budget implementation (2003-05)

Infrastructure investments without any criteria (2005)

Infrastructure investments driven "health maps" detailed 11 provinces (2008-2010)

Government expenditure on health is only 4-6% of total expenditure (2000-2002)

Government spent on health only 4.7% of total expenditures (200306)

Provinces administer budgets of operating expenses at the level of primary care (2005)

Transition of the budgetary management of operational expenses for municipal primary care levels (2008-10)

Patients pay rate of use in some of the units of primary care (2005)

Eliminated the payment of the use in primary care units (2008)

No plan for public or private health insurance (2005)

Emergence of private health insurance plans (2009)

Marchi et al. [10] reported that Angola is one of the African countries that has the highest rates of mortality and devastating shortage of human resources in health care, including nursing. The World Health Organization encourages and implements technical cooperation initiatives for the training and development of human resources in health and education, aiming at the development of African countries. These authors developed a study that aimed to identify the perceptions of nurses bound to Nursing Training Schools regarding the challenges for nursing education in Angola. According to the article by Oliveira and Artmman [11], the most important endemic diseases in Angola are malaria, tuberculosis, HIV, leprosy, among others, and has a high infant mortality rate. From the point of view of HR, there is a high concentration of nurses and other health professionals in capital, representing an asymmetry in the distribution of professionals in the Angolan provinces. With regard to health care services, is a degraded system, a result of years we've been at war and under-investment in this area. The authors suggest, from the point of view of the efforts to improve the quality there is a better allocation of human resources, which is developed relevant and appropriate to this country legislation that improves the health network, which motivate the workers, who if wages increase, which can invest in public transport (for better access to health services), there is investment and greater investment in health. Thus, "Despite the still weak performance of health systems in Africa, the gradual implementation of regionalization, which consider the realities and political-administrative, epidemiological, economic, cultural, managerial, cognitive local circumstances, according to the experiments analyzed, including African countries could represent a suitable strategy for improving access to health care, particularly for economically disadvantaged groups. In addition, it could strengthen the decentralization process underway. The Report of the great inequalities in Health Services in Angola [12] points out that the distribution of skilled health personnel in Angola be quite uneven. Most health facilities in Uige has neither a general practitioner or a specialist nurse. All health centers in Luanda have visited a lab technician and 80% have a pharmacist. In Uige, only 35% had a laboratory technician and 30% had a pharmacist. In fact, all health facilities need a stethoscope

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and a thermometer to assist in the diagnosis of diseases, and the premises in Luanda there is this basic equipment, while 30% of facilities in Uige not have a single stethoscope and 15% did not have a thermometer. Medicines and vaccines seem to miss, both in Luanda and Uige, but again, the situation is considerably worse in Uige. In turn, the antibiotics were found in stock at 75% of facilities in Luanda, but only half of the facilities in Uige. Similarly, anti malarial drugs were depleted 20% of the units in Luanda, while the rate of stock-out was 35% in the units Uige. The stock-out rates were also high for essential childhood vaccines. Finally, 55% of health facilities in Uige did not have all these vaccines in storage, while in Luanda the percentage was 25%. In the study developed by Oliveira and Artmann [11], the authors conclude that the conditions referred by doctors for accepting to work in the cities of the interior, for a time not exceeding two years, and influence the turnover and retention of professionals in the areas rural are: (a) professional factors - nature of work, job satisfaction, working conditions, compensation, opportunities for professional growth, physical accommodations, among others; (b) social factors - personal and family characteristics; (c) External factors related to the community and its geographical location. The problem of inequitable distribution of health RH, leading to low supply of services in areas far from urban areas is a phenomenon of world order, despite the specificities related to different realities. In this sense, different strategies have been implemented by different countries in an attempt to guarantee access to health services for people in rural and remote areas. The authors analyzed some strategies and it is considered that the experience of the Work Programme Internalization of Health (PITS) of Brazil is the most interesting for the reality of Cabinda as it combines aspects of intrinsic and extrinsic motivation should be adjusted to the objectives and local economic and socio-cultural specificities. Extrinsic factors refer to wages, social benefits, type of leadership or supervision, physical working conditions, organizational policies, climate of relations between management and individual and internal regulations. Intrinsic factors are related to the content of the post or nature of the tasks which the individual performs and encompass feelings of self-fulfillment, personal growth and professional recognition. A. How prompt professionals to move to other areas of Angola? Currently organizations go through a period of crisis worldwide, communication and motivation play a key role within organizations, with a view to the involvement of employees and improvement of individual performance to the achievement of organizational objectives. It is essential to motivate the employee and “break paradigms and transform the company into a place where the employee feels valued is the first step”. In fact, companies should create and maintain the internal environment in which people can become fully involved in order to achieve the organization's objectives Thus, it is expected that with this principle exists communication between different levels of the organization, as well as increased motivation of employees to the organization's goals are met, taking into account the needs of all [13]. Moreira and Soares claim that there must be involvement of people: people from all levels of the organization are its essence and its involvement enables their abilities and skills are used to the company. This principle leads to increased commitment and motivation of employees, as well as their creativity. It is expected a greater understanding on the part of employees about how important the contribution of each individual to the organization. As refers Pereira and Favero [14] the day-to-day practice of health, the activities require highly interdependent, and the motivation emerges as a fundamental aspect in the search for greater efficiency and hence higher quality of care in health provided, coupled with worker satisfaction. Indeed, institutions should create and maintain the internal environment in which people can become fully involved in order to achieve the organization's goals. Another factor is the importance of communication; communication, internal and external, has a great relevance, strengthening the bond between the individual and organizational goals [15]. Valladares and Son mention that "the open and institutionalized communication between organizational members in their different areas of expertise or strategic business units, allows working channels of communication embodying horizontal flows of knowledge. It is worth mentioning that the existence of communication channels enhances learning. Thus, should be encouraged not only by technological or formal means of information exchange, but also through informal contacts between people”. To Barlach, Limongi, Franca and Malvezzi [16] “the term resilience in the context of work in organizations refers to the existence - or construction - adaptive resources in order to preserve the healthy relationship between humans and their work in changing environment, permeated by numerous forms of ruptures”. Thus, for the Angolan health professionals, professional resilience produces self-protection capability, risk taking and proceed with the reflective knowledge of self. [17] Finally, the technical expertise of professional undergo a thorough knowledge in a specific field of activity of health, taking into account the human responses to the processes of life and health problems, which show high levels of clinical judgment and decision making, translated a set of specialized expertise covering a field of intervention. [18] IV. Conclusion The new Angolan health professionals usually begin their practice in acute care in hospitals, where his work is characterized by time constraints with high security risks for patients, and layers of complexity, and difficult

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environments. The retention of experienced professionals is central to patient safety that because young professionals spend a significant amount of time to learn their place in the social structure. With positive experiences, they begin to feel more competent with skills and relationships and become increasingly aware of discrepancies between their professional ideas and their actual experiences in the workplace. Thus, for health professionals, professional resilience produces self-protection capability, risk taking and proceed with the reflective knowledge of self. [17] In summary, organizational communication and motivation of health of any Angolan institution will have greater chances of getting desired results without taking into account human capital as having a key role in the success of strategic planning. Thus, possible deviations that hinder the achievement of established goals will be avoided. Organizational communication allows you to create a link between the Angolan institutions, take them to integrate with the other and this is only possible through communication and communication. It should be understood that the MoH of Angola must employ the various resources dedicated communication to achieve their goals and understand certain situations, since he must know how to think, act and needs of its employees (internal and external). Similarly, the policy of the MoH should play an important role in promoting more advanced cultures of quality at all levels of the organization and lead the process of change, both in human resources and strategy, as well as promote its philosophy of management, set goals and create an organizational structure capable of achieving the proposed objectives in Angolan health. References [1] [2] [3] [4] [5 ] [6] [7] [8]

[9] [10] [11] [12] [13]

[14] [15] [16] [17] [18]

C. Pierantoni, T. Varella and T. França, “Recursos humanos e gestão do trabalho em saúde: da teoria para a prática”, Observatório de Recursos Humanos em Saúde no Brasil. Estudos e Análises – Vol. 2, 51-88, in press. C. Vázquez and C. Hervás. La Ciencia del Bienestar - Fundamentos de una Psicología Positiva. Madrid: Alianza Editorial, 2009. E. Silva, Universidade Agostinho Neto. Quo vadis?, Luanda: Kilombelombe, 2012. Ministério da Saúde de Angola, http://www.minsa.gov.ao/Institucionais/Atribuicoes.aspx A. Queza, “Sistema de Saúde em Angola: Uma Proposta à Luz da Reforma do Serviço Nacional de Saúde em Portugal”, Tese de Mestrado Integrado em Medicina, Universidade do Porto, Faculdade de Medicina, 2010, p.3. C. Connor, D. Averbug and M. Miralles, “Angola Health System Assessment 2010”. Bethesda, MD: Health Systems 20/20, Abt Associates Inc. OMS, “Estratégia de Cooperação, Resumo”, http://www.who.int/countryfocus/cooperation_strategy/ccsbrief_angola_po.pdf C. Simões, J. Pinho, M. Cabral., and P. Veiga, Internacionalização do Setor da Saúde Nacional. Mercados em Análise: ANGOLA, aicep Portugal Global, Caderno Suplementar 2, 2010, http://www.portugalglobal.pt/pt/biblioteca/livrariadigital/cadernoangola.pdf USAID, “President’s malaria initiative -Malaria Operational Plan — FY2011 Angola”, 2010a,http://www.pmi.gov/countries/mops/fy11/angola_mop-fy11.pdf [último acesso: junho 2012]. L. Marchi-Alves, C. Aventura, M. Trevizan, A. Mazzo, S. Godoy and I. Mendes, “Challenges for nursing education in Angola: the perception of nurse leaders affiliated with professional education institutions”, Human Resources for Health, 2013, pp. 11-33. M. Oliveira and E. Artmann, Características da força de trabalho médica na Província de Cabinda, Angola. Cad. Saúde Pública [online]. 2009, vol.25, n.3, pp. 540-550, http://dx.doi.org/10.1590/S0102-311X2009000300009. O. Maestad, M. Frøystad and N. Villamil, “Grandes Desigualdades Regionais nos Serviços de Saúde em Angola”, Angola Brief, may 2011, vol.1, n.º 4. M. Moreira and C. Soares, C., Motivando e retendo talentos nas organizações. disponível no site https://www.google.pt/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CDEQFjAA&url=http%3A%2F%2Fw ww.aedb.br%2Fseget%2Fartigos04%2F166_Motivando%2520e%2520Retendo%2520Talentos%2520nas%2520OrganizacoesSEGET-RESENDE.doc&ei=MTZcUvzqN-Sv7Qa0n4D4CA&usg=AFQjCNF-B8k4haTaW6BQ4TOEnCIYC3HYA&sig2=pWnnD6gbTGlFLdctKFbGAw&bvm=bv.53899372,d.ZGU M. Pereira and N. Fávero, “A motivação no trabalho de equipe de enfermagem”. Revista Latino-Americana de Enfermagem, vol. 9, n.º 4, 2001, pp-7-12. A. Valladares and J. Filho, “Gestão contemporânea de negócios: dimensões para análise das práticas gerenciais à luz da aprendizagem e da participação organizacionais”. Revista FAE, vol. 6. n.º 2, 2013, pp.85-95. L. Barlach, A. Limongi-Franca, and S. Malvezzi. “The concept of resilience applied to work in organizations”. Interamercian Journal of Psychology vol. 42, n.º 1, 2008, pp. 101-112. ISSN 0034-9690. H. Hodges, A. Keeley and P. Troyan, “Professional Resilience in Baccalaureate-Prepared Acute Care Nurses: FIRST STEPS. Nursing Education Perspectives, vol. 29, n.º 2, 2008,pp. 80-89. Ordem dos Enfermeiros, Código Deontológico (Inserido no Estatuto da OE republicado como anexo pela Lei n.º 111/2009 de 16 de setembro), 2010, p.2, http://www.ordemenfermeiros.pt/legislacao/Documents/LegislacaoOE/CodigoDeontologico.pdf

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Investigating Location Differences in Influence of Parameters on Organizational Role Stress among IT Sector Employees 1

Deepa Mohan, 2Sudarsan N Ph.D. Scholar, Department of Mechanical Engineering, National Institute of Technology Calicut, Kerala, India. 2 Professor and Head, School of Management Studies, National Institute of Technology Calicut, Kerala, India.

Abstract: Organizational Role Stress (ORS) experienced at workplace has been recognized to have adverse impact on employee performance in any organizational sector. This paper attempts to investigate on possible differences in the extent to which different parameters such as emotional intelligence, organizational commitment, attitude to change and socio-demographical characteristics influence ORS experienced by Information Technology sector employees working in different locations. The statistical analysis of the survey carried out among employees located in three major cities of South India reveals subtle differences in the extent to which different parameters considered influence ORS, that can possibly be attributed to the differences in employee diversity, regional lifestyles and values. Keywords: Organizational Role Stress, Emotional Intelligence, Organizational Commitment, Attitude to change, Socio-demographical characteristics and Information Technology sector

I. Introduction Modern organizations can persevere in the dynamic, competitive environment of today only if they make use of the full potential of each employee. Dynamic business environment at workplace often results stress related to the role performed by the employee in the organization. This role stress is one of the important determinants of successful adjustment and subsequent performance of an employee. The stress induced due to roles performed by individuals as employees has been a potent organizational stressor [1, 2]. Such stress can contribute to various dysfunctional outcomes for the organization like job related tensions, job dissatisfaction, lower performance, etc. [3]. Some of the stressors identified in any work environment, that has a major influence on employee performance include, work overload, unsupportive relationship, work life imbalance, poor communication, poor working conditions and changes in organizational process. Towards this, Emotional Intelligence has been found to be very effective in preventing stress among employees [4, 5]. India with its strategic positioning has evolved into a major destination for Information Technology (IT) based organizations and has been recognized to be among the top ten high stress workplaces [6]. Estimating the level of role stress among employees in such industries undoubtedly is important in alleviating factors influencing the stress but also will be of great help in devising coping strategies. This paper is devoted to, presenting the details of investigation carried out towards identifying major factors that can be important in estimation of role stress levels among employees particularly engaged in the Indian IT sector. Consequent section presents recent and relevant studies undertaken in this direction along with definitions of the parameters employed in the study. The study methodology, description of the instrument developed and its validation are presented in the subsequent section. Analysis of the data collected and major interpretation of the results are discussed in the penultimate section with a concluding section summarizing the major findings from the study and possible scope for future studies. II. Review of Literature This study mainly focuses on estimating the level of Organizational Role Stress (ORS) among the employees particularly in IT sector owing to the unique work environment prevailing. Pestonjee had identified 3 important sectors of life from which stress originates namely as, (i) organizational & job sector (ii) social sector and (iii) intra-psychic sector [7]. Organizational/ job stress has been defined in terms of a misfit between skills & abilities of a person and the demands of his/her job. The concept of organizational/job stress falls under the umbrella of a broader concept known as role stress. Role stress refers to the conflict and tension due to the roles being enacted by a person at any given point of time. Enacted in the context of organizations, such role stresses are called organizational role stress. The stress induced due to roles performed by individuals as employees had been a potent organizational stressor, the outcomes of which have been found to be costly to the organization [8]. One of the pioneers of research on organizational role stress, Pareek had reiterated that the performance of a role in an organization had built in potential for conflict due to which stress may start rearing its head [9]. Pareek identified ten different types of organizational role stressors. They are described here briefly. i. Inter-role distance (IRD): Conflict between organizational and non-organizational roles. ii. Role stagnation (RS): The feeling of being stuck in the same role.

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iii. iv. v. vi. vii. viii. ix. x.

Role expectation conflict (REC): Conflicting expectations and demands between different role senders. Role erosion (RE): The feeling that functions that should belong to the respondent’s role are being transformed/performed or shared by others. Role overload (RO): The feeling that more is expected from the role than the respondent can cope with. Role isolation (RI): Lack of linkages between the respondent’s role and that of other roles in the organization. Personal inadequacy (PI): Inadequate knowledge, skills or preparation for a respondent to be effective in a particular role. Self-role distance (SRD): Conflict between the respondent’s values/self-concepts and the requirements of his or her organizational role. Role ambiguity (RA): Lack of clarity about others’ expectations of the respondent’s role, or lack of feedback on how others perceive the respondent’s performance. Resource inadequacy (RIn): Non availability of resources needed for effective role performance.

According to Salovey and Mayer, Emotional Intelligence (EI) is an "ability to monitor one's own and others' feelings and emotions, to discriminate among them and to use this information to guide one's thinking and actions" [10]. EI guide us to respond appropriately to different stressors and is one of the essential factors. Ayranci studied the concepts of EI and stress, and conducted a study of the relationship between these two variables [11]. The nurses who worked at some of the private and governmental hospitals in Turkey were studied, and this study identified a significant relationship between EI and stress. The study of Riaz and Khan was set out to find the impact of EI and stress on 150 faculty members of graduate teaching sector in Pakistan [12]. The results indicated that EI has a weak negative relation with stress. Porter et al. defined Organizational Commitment (OC) as the relative strength of an individual’s identification and involvement in a particular organization [13]. (i) Affective commitment (AC) refers to employees’ emotional attachment, identification with, and involvement in the organization. Employees with a strong affective commitment stay with the organization because they want to. (ii) Continuance commitment (CC) refers to employees’ assessment of whether the costs of leaving the organization are greater than the costs of staying. Employees who perceive that the costs of leaving the organization are greater than the costs of staying remain because they need to. (iii) Normative commitment (NC) refers to employees’ feelings of obligation to the organization. Employees with high levels of normative commitment stay with the organization because they feel they ought to. Mohamadkhani and Lalardi established explicit relationship between emotional intelligence and organizational commitment in his study of the hotel staff in 5-Star hotels of Tehran, Iran [14]. There is evidence in the change management literature identifying the role of organizational commitment in a change context. Cameron determined the levels of employee commitment and employee perceptions of the planned organizational changes [15]. The research was conducted in a large telecommunication organization in South Africa with over 20,000 employees The results collected for this research indicated positive correlations between Affective Commitment and employee attitudes and perceptions of change. The results also suggested that higher levels of Affective Commitment are associated with more positive perceptions of change. The present study examines the level of organizational role stress (ORS), emotional intelligence (EI), organizational commitment (OC), employee attitude to change (ATOC) and socio-demographical characteristics among employees and also examines the role of EI, OC, ATOC and socio demographical characteristics on ORS level of employees. III. Questionnaire Design The questionnaire for the investigation was developed by means of instruments established through previous researches. The organizational role stress (ORS) scale, which was developed and standardized by Pareek to measure the role stress, had been used in this study [16]. The ORS instrument comprised of 50 items to measure 10 different types of role stressors (5statements for each role stressor) assessable on a five point likert scale. The EI level was measured with The Emotional Intelligence Scale developed by Schutte et al. comprising 33 items classified into four dimensions namely (i) perception of emotion (POFE) evaluated by 10 items; (ii) managing others emotions (MOTE) by 8 items, (iii) managing own emotions (MOWE) by 9 items and (iv) utilization of emotion (UOFE) by 6 items [17]. Instrument developed by Allen and Meyer with 18 items classified in to three dimensions, namely: (i) affective (AC); (i) continuance (CC); and (iii) normative (NC) assessable with five point likert scale for measurement of OC was employed in this study [18]. Five items that measure attitudes to change (ATOC) were chosen from the instrument Attitudes to Change Questionnaire (ACQ) developed by Vakola et al. [19]. Employee details on the socio-demographical characteristics had been compiled through additional 23 items. The socio-demographical characteristics of the employees were categorized as personal attributes that comprising age, gender, family status, number of children, educational qualification, native place, number of family members and earning members and annual income; job attributes that includes number of days leaves availed, total experience, job overtime, salary satisfaction, challenging nature of work, recognition and

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appreciation for employee contribution and effective skill application and environmental attributes such as experiencing organizational change. Thus the instrument developed for the study comprised a total of 129 items essentially derived from well established instruments reported in the literature. IV. Data Collection and Analysis The population chosen for the study had been drawn from individuals employed in IT sector, essentially working in different places in the state of Kerala, Chennai and Bangalore located in the southernmost part of India. Study sample had been chosen through random sampling technique. The survey conducted through personal interview yielded a total of 759 (that includes 472 from Kerala, 158 from Bangalore and 129 from Chennai), duly completed questionnaires from a total of 850 distributed resulting in a response rate of 89.29%. The minimum sample size needed for this study estimated based on Bill Godden [20], recommends 384 for estimated population size of two hundred thousand having 50% response distribution with 5% margin of error and at 95% confidence level. The estimate was subsequently verified through software developed by Raosoft. Inc. [21]. Data collected in this investigation were analyzed using Statistical Package for Social Sciences (SPSS version 17.0). The results obtained from the analysis and inferences derived are presented below. Initially, the internal consistency of the instrument developed for the study was evaluated through Cronbach’s alpha test that demonstrated excellent reliability. The values of Cronbach’s alpha test obtained for different parameters used for evaluation as from data obtained from all the three regions are presented in table 1. Table 1: Cronbach’s alpha value No.

Variables IRD RS REC RE RO RI PI SRD RA RIn ORS MOTE MOWE POFE UOFE EI AC CC NC OC ATOC

Chronbach’s alpha Bangalore 0.81 0.78 0.75 0.69 0.82 0.76 0.79 0.74 0.77 0.76 0.96

Chennai 0.80 0.76 0.72 0.67 0.71 0.70 0.71 0.70 0.71 0.73 0.94

Kerala 0.80 0.72 0.78 0.67 0.78 0.75 0.68 0.70 0.82 0.76 0.95

0.67 0.73 0.72 0.69 0.87 0.66 0.74 0.77 0.79 0.81

0.69 0.73 0.69 0.71 0.86 0.79 0.77 0.74 0.81 0.82

0.68 0.69 0.73 0.68 0.85 0.69 0.71 0.82 0.84 0.74

Kaiser-Meyer-Olkin (KMO) and Bartlett's Test measure the strength of relationship among variables. Bartlett's test of sphericity tests the hypothesis that the correlation matrix is an identify matrix; i.e. all diagonal elements are 1 and all off-diagonal elements are 0, implying that all of the variables are uncorrelated. If the significant value for this test is less than our alpha level, we reject the null hypothesis that the population matrix is an identity matrix. The sinificant value for this analysis leads us to reject the null hypothesis and establish that there exist correlations between the parameters and the data set is amenable for factor analysis. The KMO measures the sampling adequacy and it compares the values of correlations between variables and those of the partial correlations. If the KMO index is more than 0.6, the sample size is adequate and it is feasible for factor analysis. The results of KMO and p values of Barelett’s test of different parameters presented in the table.2 indicate that the sample size is adequate and it supports factor analysis. Table 2: Results of KMO and Bartlett’s test Variables EI

ATOC

ORS

Location Bangalore Chennai Kerala Bangalore Chennai Kerala Bangalore Chennai Kerala Bangalore Chennai Kerala

KMO measure of sampling adequacy 0.80 0.74 0.84 0.80 0.76 0.86 0.70 0.75 0.71 0.88 0.82 0.89

Bartlett’s test of sphericity 1712.150 1207.443 3122.350 926.316 763.390 2724.135 272.007 215.882 370.928 4693.288 3502.536 7169.566

Significance 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

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The level of EI, OC, ATOC and ORS among the employees of Bangalore, Chennai and Kerala was estimated. Tables 3a, 3b and 3c present the comparison of mean value of EI, OC, ATOC and ORS levels of IT employees pertaining to the three different locations. Table 3a: Comparison of EI Location

MOTE (max:10)

MOWE (max:10)

POFE (max:10)

UOFE (max:10)

EI (max:10)

7.31 7.70 7.5

7.24 7.80 7.62

6.90 7.43 7.1

7.49 7.82 7.67

7.20 7.67 7.45

Bangalore Chennai Kerala

Table 3b: Comparison of OC and ATOC Location Bangalore Chennai Kerala

AC (max:10)

CC (max:10)

NC (max:10)

OC (max:10)

ATOC (max:10)

5.92 6.31 6.51

6.07 6.58 6.14

6.10 6.28 6.28

6.02 6.38 6.31

7.15 7.53 7.07

Table 3c: Comparison of ORS Location

IRD (10)

RS (10)

REC (10)

RE (10)

RO (10)

RI (10)

PI (10)

SRD (10)

RA (10)

RIn (10)

ORS (10)

Bangalore Chennai Kerala

5.59 6.11 6.185

5.70 6.26 5.865

5.97 6.58 5.87

5.25 6.62 6.735

6.15 5.64 6.055

5.56 5.75 6.225

5.67 5.61 6.39

5.48 6.09 4.785

5.66 5.28 6.47

4.98 5.31 6.05

5.29 5.83 6.065

It can be observed from tables 3a, 3b and 3c that among EI dimensions, utilization of emotion was found to be high in all the three places. Therefore the employees have the ability to harness emotions to facilitate various cognitive activities such as thinking and problem solving. The self management of emotion being high in Chennai and Kerala, it can be attributed to the organizational culture prevailing and high educational qualifications of the incumbents. Among the types of OC, all the three are only moderately high indicating lesser loyalty of the employees towards the organization as well as to the work and may well explain the relatively high attrition rate prevailing in the industry as a whole. Among the different components of ORS, it can be observed that the employees in this sector essentially demonstrate relatively high role erosion followed by role ambiguity and personal inadequacy in Kerala as compared to other stressors indicating low organizational support in these directions. This can be explained by the fact that in most of the organizations from which data were collected, belong to high employment category and hence the management intervention towards individual employee needs being limited. However, in Chennai, the results reveal high role erosion followed by role expectation conflict and role stagnation indicating diminished employee involvement, undue expectations from job, and high performance pressure, While, Bangalore reveals, high role overload, followed by role expectation conflict and role stagnation indicating high performance pressure from superiors and lower perceived opportunity for career progression. Overall, the results can be summarized that the employees in this sector experience relatively high role erosion followed by role expectation conflict and role overload. As a second stage of the study, inter relationship between the parameters including EI, OC, ATOC, ORS and other socio-demographic characteristics were established. Initially, multi-collinearity between the pairs of parameters was checked. Step wise regression was employed using components of EI & OC, ATOC and sociodemographical characteristics as the independent parameters and ORS being the dependant variable. A total of 18 independent variables were examined for the degree of influence over ORS and the results obtained are presented in table.4. It is observed that only 12 out of the 18 parameters chosen to be independent variables, in particular, UOFE, AC, CC and ATOC exhibit significant influence over ORS experienced by the employees belonging to all the three regions selected for the study. From the table.4, it is explicit that ORS depends on MOWE, age and working hours apart from the commonly influencing variables indicated above, among employees in Bangalore. The sample drawn from this region demonstrates a diverse work force constituting employees from different parts of the country comprising 65percent of males. It can be observed that stress levels as well as commitment are low among these employees owing to the life style and work culture prevailing in the region. The sample drawn from Chennai, it can be observed that ORS additionally depends on MOTE and no. of earning members. The sample had a slightly diversified population with about 70% had been native of the region and dominated by males. The region characterized to exhibit higher stress level as compared to Bangalore, can be attributed to higher affliction to family values with lesser relaxation opportunities. The regression model for sample drawn from Kerala reveals that POFE, no. of family members, annual income of the employees and working hours have an influence on ORS in addition to the common parameters. The sample comprised a very high number of native employees

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characterized by highly conservative life style. It can be observed that the employees exhibit higher stress levels, with increased working hours and work extending over family. The results clearly reveal the regional characteristics of the employees, and their natural ability to cope up with stress experienced in work. Identification of the parameters influencing in different regions will go a long way in understanding the culture, life style pattern and values prevailing in the region attributing to work related stresses can be emphasized during the periodical training of the employees in the sector, ensuring minimizing stress experienced by the employees that can result in enhanced commitment levels. V. Conclusion Although this research was conducted among employees belonging to the IT sector in South India, the research was not specific to this sector. The high Cronbach’s alpha values indicate a high internal validity of the questionnaire developed and used for the study. The results obtained are valuable in estimating the level of OC, EI, ATOC and ORS among the employees. In general, the qualification levels of the incumbents are relatively high resulting in a good EI values and also exhibits moderately high ORS. The study reveals the dynamic nature of the IT sector with frequent occurrences of the changes in the organizational process that has larger impact on the employees in the form of ORS. The results also indicate that there exists minimal organizational intervention to minimize the stress among the employees emphasizing on the need for reorientation of employee training. The study results are only indicative as the data pertains to a smaller section of employees belonging to a smaller region of the country embracing a unique value set. The results need to be validated through similar studies carried out in the different parts of India that is a confluence of diversified cultures. Similar studies can also be undertaken among other industrial clusters that will not only help the organizations in enhancing employee performance and commitment towards work and organization but also will act as a definite booster for effective Human Resource Development. Table.4 Step-wise regression analysis Bangalore Independent variables B

Constant -45.01 23.42 MOTE MOWE

2.224

.312

Adj. R square

Sig.

0.84

160.37

0.00

11.93

27.15

1.561

.761

.334

2.18

.692

.513

Adj. R square

Sig.

0.704

99.36

0.00

ATOC Age

1.672

.639

.185

1.46

.504

.197

0.79

0.02

24.33

7.023

0.84

0.78

0.02

0.84

Earning Members

21.45

16.02

6.901

Adj. R square

0.690

Sig.

131.89 0.00

1.1

.34

.21

1.92

.469

.265

-2.609

.355

-.33

3.39

.369

.413

3.002

.522

.265

10.34

4.908

.055

10.12

3.506

.077

21.74

6.528

.086

.133

Family Members

Annual income Working hours

-78.85 29.29

POFE

.505

UTOE AC

Kerala

Chennai

9.197

.114

.077

Dependent Variable: Organizational Role Stress References [1] [2] [3] [4] [5] [6]

R.L. Kahn, D. M. Wolfe, R.P. Quinn, J.D. Snoeck & R.A. Rosenthal, “Organizational Stress studies in role conflict and ambiguity,” New York: Wiley, 1964. Srivastava, A. K., “A study of the role stress-mental health relationship as a moderator by adopting coping strategies,” Psychological Studies, 3, 1991, 192–197. Behrman, Douglas N. & Perreault, William D.,"A Role Stress Model of the Performance andSatisfaction of Industrial Salespersons," Journal ofMarketing, 1984, 48, 9-21 Salovey, P., Stroud, L.R., Woolery, A., & Epel, E.S., “Perceived emotional intelligence, stress reactivity and symptom reports: further explorations using the Trait Meta-Mood Scale,” Psychology and Health, 2002, 17, 611-627. Ciarrochi, J.V., Deane, F., & Anderson, S., “ Emotional intelligence moderates the relationship between stress and mental health,” Personality and Individual Differences, 2002, 32, 197-209. http://www.hindu.com/2007/10/24/stories/2007102463470300.htm

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[7] [8] [9] [10] [11] [12] [13] [14] [15]

[16] [17] [18] [19] [20] [21]

D. M. Pestonjee, “Stress and coping-the Indian Experience,” Second edition, Sage Publications India Private Ltd., New Delhi, 1992. Fisher, D. Cynthia, & G. Richard, “A meta analysis of the correlates of role conflict and ambiguity”, Journal of Applied Psychology, 1983, 68, 320-33. U. Pareek, “Making organizational roles effective,” Tata McGraw- Hill, New Delhi, 1993. Salovey, P. & Mayer, J. D., “Emotional Intelligence,” Imagination, Cognition, and Personality, 1990, 9, 185-211. A. Riaz, S. Khan, “Relationship of emotional intelligence and stress at workplace: Taking in perspective the public and private sector universities of Peshawar,” European Journal of Economics, Finance & Administrative Sciences, 2012, Issue 46, 88.SOU E. Ayranci, “Analysis of the relationship between emotional intelligence and stress caused by the organization: a study of nurses,” Business Intelligence Journal, 2012, vol.5 (2). Porter, L. W., William J. C., & Frank J. S., “Organizational commitment and managerial turnover: A Iongitudinal study,” Organizational Behaviour and Human performance, 1976, 15, 87-98. Mohamadkhani, K., & Lalardi, N. M., “Emotional Intelligence and Organizational Commitment between the Hotel Staff in Tehran, Iran,” American Journal of Business and Management, 2012, 1(2), 54-59. Cameron, M. V., “The Relationship between Employee Attitudes towards Planned Organizational Change and Organizational Commitment: An Investigation of a Selected Case within the South African Telecommunications Industry,” Dissertation, Cape Peninsula University of Technology, South Africa, 2010. Pareek,Udai and Purohit, Surabhi, “Training instrument in HRD and OD,” New Delhi: Tata McGraw-Hill,2010. Schutte, N. S. et al., “Development and validation of a measure of emotional intelligence - Personality and Individual Differences,” 1998, 167-177. Allen, N. J and Meyer, J. P., “The Measurement and Antecedents of Affective, Continuance, and Normative Commitment to the Organization,” Journal of Occupational Psychology, 1990, 63, 1-18. Vakola, M. et al., “The role of Emotional Intelligence and Personality Variables on Attitudes toward Organizational Change,” Journal of Managerial Psychology, 2004, 19(2), 88-110. http://williamgodden.com/samplesizeformula.pdf http://www.raosoft.com/samplesize.html

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Review on Concrete Subjected to Elevated Temperature Maya T M, PG Student Department of Civil Engg, M A College of Engg. Kothamangalam M.G University Kerala India

Nivin Philip, Asst Professor Department of Civil Engg, M A College of Engg. Kothamangalam M.G University Kerala India

Dr. Job Thomas, Asst Professor Department of Civil Engg, School of Engg. Thrikkakkara CUSAT Kerala India

Abstract: The main objective of this paper is to assess the effect of elevated temperatures on concrete. Studies are mainly based upon the comparison of the mechanical properties of concrete subjected to temperature and tested at ambient temperature. Type of concrete, replacement of aggregates and cement, duration of curing, maximum temperature, time of exposure to temperature, type of cooling are the major factors which influenced the properties of concrete after temperature exposure. In each study, the test results showed that there was a significant loss of weight and mechanical properties after temperature exposure. Also relative strength of concrete decreased as the exposure temperature increased. The replacement of aggregates with certain waste materials was justified, not only in terms of increased fire resistance than normal aggregate, but also in terms of responsible waste disposal. Keywords: Aggregates; Elevated; Mechanical properties; Recycling; Replacement; Temperature I.

Introduction

During a fire, concrete material in structures is likely exposed to high temperatures. During these exposures the mechanical properties such as strength, modulus of elasticity and volume stability of concrete are significantly reduced resulting in undesirable structural failures. Therefore, the properties of concrete retained after a fire are of great importance the load carrying capacity and the serviceability of buildings. The chemical composition and physical structure of the concrete change considerably when exposed to high temperature. Concrete is a composite material produced from aggregates, cement, and water. The thermal conductivity of concrete must be considerably influenced by the thermal conductivity of aggregates as aggregates represent a considerable proportion of volume in the concrete. With the use of different aggregates, the strength degradations of concretes are not the same under high temperatures which is due to different mineral structure of the aggregates. There is accumulation of water occurring at the paste-aggregate interface which creates porous zone in which cracking will be initiated and thus aggregate-matrix interface can therefore be considered as the â&#x20AC;&#x153;weak linkâ&#x20AC;? of ordinary concrete. And hence the selection of the type of the aggregate for the concrete also plays an important role and the aggregate should be able to sustain high temperature without much affecting the mechanical properties of concrete. Also recycling construction material plays an important role to preserve natural resources. Sustainable reuse of waste materials reduces the environmental impact by recycling materials generated during building construction, demolition and renovation. The construction field is in real need for the alternatives for the concrete due to depleting nature of natural resources. The use of recycle aggregates and solid wastes from construction and demolition waste is showing a prospective application in construction and as alternative to primary and natural aggregate. II.

Research Development

The first recorded mixing of crushed brick and tile with Portland cement was in Germany from 1860 for the manufacture of concrete products. Systematic investigations have been carried out since 1928 on the effect of the cement content, water content, and grading of crushed brick and tile aggregates. Crushed brick and tile aggregate concretes have relatively lower strength at early ages than normal aggregate concrete. The compressive strength of crushed brick and tile aggregate concretes are approximately 7% lower compared to concrete made with natural aggregates. But as roof tiles are made at high temperatures, it was an assumption

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that it may result in good fire protection and usually these roof tile aggregate concretes are used in casing of steel structures as a shield. Husem (2006) examined compressive and flexural strengths of ordinary and high-performance microconcrete which were exposed to high temperatures and cooled in air and water. For that according to the mix proportioning, they casted the specimens and after 28 days specimens were tested. Tests were performed at five different temperatures, 200, 400, 600, 800 and 1000 °C. They were removed 1 h after the desired temperature was reached. For each mix 36 samples were casted, in that 24 were subjected to temperature, in which 12 were cooled in water and others at room temperature, next 12 were tested at 23 °C. Experimental results indicated that concrete strength decreases with increasing temperature, and the decrease in the strength of ordinary concrete is more than that in high-performance concrete. Studies showed that experimental samples have been damaged to a great extent and they have lost their compressive strengths if high-performance concrete was cooled in water after being exposed to the temperature of 800 °C, and ordinary concrete was cooled in water after being exposed to the temperature of 600°C. Terro (2006) studied about the effect of replacement of fine and coarse aggregates with recycled glass on the fresh and hardened properties of Portland cement concrete at ambient and elevated temperatures was studied. Percentages of replacement of 0–100% of aggregates with fine waste glass, coarse waste glass, and fine and coarse waste glass were considered. Soda-lime glass used for bottles was washed and crushed to fine and coarse aggregate sizes for use in the concrete mixes. Accordingly specimens were casted and cured for 28 days. Author heated the samples for 7-10 h in a furnace which can reach up to 1000 °C and left for cooling 15 h in furnace and 7 h in air before testing. Results showed that Concretes made with 10% aggregates replacement with waste glass possesses higher compressive strength than normal concrete at temperatures above 150 °C. The ratio of strength at elevated temperatures to that at ambient temperature for concretes made with waste glass was higher that of the normal concrete at temperatures above 150 °C. In general, concretes made with 10% aggregates replacement with waste glass had better properties in the fresh and hardened states at ambient and high temperatures than those with larger replacement percentages. Arioz (2007) investigated the effect of elevated temperatures on the physical and mechanical properties of concrete mixtures produced by different water/cement ratios and different types of aggregates. Four different concrete mixtures were prepared by using ordinary Portland cement, crushed limestone aggregate and siliceous river gravel. Casted cubes were cured for 28 days, air-dried for 6 days and oven-dried for 24 h. Specimens were subjected to elevated temperatures 200, 400, 600, 800, 1000 and 1200 °C for 2h and they were stored at room temperature for 2h until testing. From visual observation, it was noticed that the surface cracks became visible when temperature reached 600 °C. Cracks were pronounced at 800 °C and increased extremely at 1000°C. At 1200 °C specimens were completely damaged also weight of concrete significantly reduced as temperature increased and this reduction was gradual upto 800 °C but sharp reduction was observed beyond 800 °C. Relative strength of concrete reduced with increased temperature exposure and this effect was more pronounced for concrete mixes produced by river gravel aggregates and this was attributed to siliceous composition of river gravels. Chen et al (2009) investigated the compressive and split tensile strengths of concrete cured for different periods and exposed to high temperatures. Accidental fires are known to occur during construction, causing concrete to be exposed to high temperatures when it is at an early stage. After casting, they were tested after different curing ages, 1, 3,7,14 and 28days. The exposure temperatures were 200, 400, 600, 800 and 1000°C and these temperatures were maintained for 3h. The specimens were cooled both by air cooling and water spray cooling and cooling period varies between 20 min and 3h. After cooling the specimens were kept for another 28 days before the tests were conducted. Author observed that, for early-age concrete, 80–90% of its initial strength was recovered after exposure to high temperatures up to 800 °C also the recovered strength was higher than control specimens when temperature was only 200 or 400 °C. The order of the recovered strengths of the hightemperature exposed specimens was 3days > 7 days > 14days > 1 day. But while temperature reached than 1000 °C, the recovered strength of early age concrete was lower than concrete aged for 28 days. Compared with the compressive strengths a greater decrease was shown in the splitting tensile strength due to the more destructive microcrack and brittle microstructure formation that resulted from the tensile stress. For temperature being below 800 °C, for early age concrete, the recovered strength of specimens cooled by sprayed water was higher than that of specimens cooled by air, while for concrete specimens aged for 28days, the converse was true. In the case of the maximum temperature being above 1000 °C, the recovered strength of all specimens cooled by air was higher than that of water-sprayed specimens. Culfik and Ozturan (2010) investigated the effect of high temperatures, up to 250 °C, on mechanical properties of normal and high strength concretes with and without silica fume. They determined residual compressive and splitting tensile strength and static modulus of elasticity of the specimens by heating up to elevated temperatures (50, 100, 150, 200, 250 °C) without loading. For normal strength concrete compressive

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and splitting tensile strengths and static modulus of elasticity reduction started at 100 °C but by using silica fume heat resistance of normal strength concrete was increased. And also the residual strength properties of high strength concretes were higher in comparison with normal strength concretes for all heating cycles, also high strength concrete specimens with silica fume showed no decrease in residual compressive and splitting tensile strengths for any temperature regime. Demirel and Kelestemur (2010) investigated the effect of elevated temperature on the mechanical and physical properties of concrete specimens obtained by substituting cement with finely ground pumice at proportions of 5%, 10%, 15% and 20% by weight. The specimens were heated in an electric furnace up to 400, 600 and 800°C and kept at these temperatures for one hour. The unit weight of the concrete decreased when it was exposed to elevated temperature. This finding was due to the release of bound water from the cement paste and the occurrence of air voids in the concrete. The highest weight loss occurred in specimens with finely ground pumice and silica fume that were subjected to 800°C. The reduction in the compressive strength of concrete was significantly larger for samples exposed to temperatures higher than 600 °C. They concluded that this result was due to the lost water of crystallization resulting in a reduction of the Ca (OH) 2 content, in addition to the changes in the morphology and the formation of microcracks. Netinger et al (2011) studied about the effect of high temperatures on the mechanical properties of concrete made with different types of aggregates. They conducted the study on the presumption that all materials formed at high temperatures and usable as aggregates can improve the fire resistance of concrete. And materials used as fire resistant aggregates include diabase, steel slag, crushed bricks and crushed tiles. For the experiment all the mixtures were prepared with same water cement ratio 0.5 and the casted specimens were demoulded after 24hrs. The specimens were subjected to temperatures (200, 400, 600, 800 and 1000 °C) for 1.5h after 28 days curing. In this research the authors found that diabase ensures good mechanical properties of concrete at room temperature and better mechanical properties at temperatures up to 600 °C in comparison with the river aggregate and dolomite mixtures. And concrete made with brick industry waste showed satisfactory mechanical properties at room temperature, better fire resistance than the one made with river aggregates, and only slightly lower fire resistance than the one made with dolomite. Steel slag concrete shows similar fire resistance to the river aggregate mixture upto 400 °C, and much improved fire resistance at high temperature ranges. The fire resistance of these mixtures was lower than the fire resistance of the dolomite mixtures up to 800 °C, above which it increases considerably. According to these observations it can be concluded that the replacement of a river aggregate in concrete with steel and brick industry waste material can not only be justified in terms of fire resistance, but also in terms of waste management. Vieira et al (2011) investigated the residual mechanical performance (compressive and splitting tensile strengths and elasticity modulus) of concrete made with recycled concrete coarse aggregates after exposure to high temperatures. For this four concrete mixes were produced: a conventional reference concrete and three concrete mixes with replacement rates of 20%, 50% and 100% of natural coarse aggregates by recycled concrete coarse aggregates. They casted specimens and cured for 28 days and the specimens were moved to a dry chamber for 21days. Specimens from all types of concrete, besides being tested at ambient temperature (about 20 °C), were subjected to the following three temperatures for a period of 1 h: 400 °C, 600 °C and 800 °C at age 49 days and, after cooling down to ambient temperature, 4 days later they were finally subjected to mechanical testing. The results showed that in spite of higher porosity and the different thermal properties of the matrixaggregate interface of recycled concrete coarse aggregate when compared to reference concrete, the incorporation recycled aggregates does not influence the thermal response of the material. Accordingly, in terms of post-fire residual mechanical properties there are no limitations to the structural use of recycled aggregate concrete when compared with conventional concrete. Xing et.al (2011) conducted an experimental study on concrete composed of three different types of aggregates: semi crushed silico-calcareous, crushed calcareous and rolled siliceous. For each aggregate type, two water/cement ratios (W/C), 0.6 and 0.3 are studied. Concrete specimens were subjected to 300, 600 and 750 °C heating–cooling cycles. They found that the residual mechanical behaviour varied depending on the aggregates. The silico-calcareous concrete presents severe cracking, and significant mechanical strength loss between 300 and 600 °C. Residual compressive strength of normal concrete of silico-calcareous concrete is 3 to 5 times lower than that of normal concrete of siliceous concretes and normal concrete of calcareous concrete respectively. The thermal instability of flints contained in silico-calcareous aggregates explains the higher damage of silico-calcareous concretes with the temperature increase. For calcareous concrete, lime coming from the decarbonation of calcite (between 600 °C and 750 °C) reacts with ambient humidity and forms Portlandite by multiplying its volume by 2.5 and leads to concrete disintegration. With a lower W/C ratio, the porosity of the paste–aggregate interface zone decreases, and bond strength between paste and aggregate was improved. Yuksel et al (2011) examined the Influence of high temperature on the properties of concrete containing nonground granulated blast-furnace slag (GBFS) and coal bottom ash (BA) as fine aggregate. GBFS and BA were

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partially replaced with fine aggregate in different series. They casted specimens according to the mixes and cured in water for 28 days. Author’s assessed losses in weights, variation of compressive strength and dynamic modulus of elasticity of concrete specimens before and after high temperature exposure. The first three specimens were exposed to high temperature; the remaining three specimens were kept in laboratory conditions. Specimens were subjected to high temperature of 800°C at the age of 90 days and they were cooled for 1 h in furnace itself, and then completely cooled down for 24 h in air.. Results showed that loss in weight due to high temperature effect was independent from the replacement ratio of GBFS or BA. Residual compressive strengths of specimens were lower than reference concrete strength for all series. BA showed better performance than GBFS as a replacement material for fine aggregate in terms of residual compressive strength. And concretes containing GBFS or BA performed similar or better properties as normal concretes with respect to investigated properties. Thus they concluded that concretes containing GBFS or BA can be used in practical field where the concrete will subject the high temperature effect. Cakır and Hızal (2012) prepared Self consolidating lightweight concrete (SCLWC) mixtures by using two different lightweight coarse aggregates and by replacing normal weight crushed coarse limestone aggregate. All the mixtures were exposed to 300, 600 and 900 °C, respectively after being pre-dried for 24h at 105°C. Compressive strength and modulus of elasticity of the mixtures are affected from both the type of the aggregate and w/c ratio, while splitting tensile strength is mainly affected by the type of the aggregate, alone. They concluded that type and porosity of the aggregates and w/c ratio of the mixtures were the main factors that affect the porosity and thus, water absorption capacity of self-consolidating lightweight concrete. Also it was concluded that, concrete porosity adversely affects the resistance of self consolidating lightweight concretes to elevated temperatures. Even though the specimens were pre-dried at 105 °C for 24h before the exposure to elevated temperature, the pores in hardened concrete still acting as water reservoirs during exposure was attributed to be the main reason for exposure cracks. Mathew and Paul (2012) conducted study on performance of laterized self compacting concrete under elevated temperatures. They evaluated mechanical properties such as compressive and splitting tensile strengths and elasticity modulus after fire. After the casting the specimens were kept in curing tank for 27 days and kept at room temperature for 24 h. On 28th day they were heated to temperature levels (200°C, 400 °C and 600°C). after heating, one set of specimens were allowed to cool to room temperature by air and another set by sprinkling water on its surface immediately after removing from the furnace. The entire specimens were allowed to cool for 24 h and tested on 29th day of casting. The compressive strength of laterized self compacting concrete reduced with increase in temperature and showed comparatively less strength reduction against self compacting concrete and air cooled specimens showed slightly higher strength than water cooled. Combined effect of both type of aggregate and fly ash content prevented the explosive spalling up to a temperature 800 °C. They concluded that laterized self compacting concrete could be considered as substitute fire protection material for conventional concrete. Pathak and Siddique (2012) investigated the influence of fly ash as partial replacement of cement, and spent foundry sand as partial replacement of sand on the properties of SCC at different temperatures. Four mixes were prepared, accordingly the specimens were cast and left for 24 h and kept for curing. The specimens were heated to different temperatures 100, 200 and °C for 1h and cooled to room temperature. They examined mechanical properties like compressive strength test, split tensile strength and modulus of elasticity test at ages of 28, 91 and 365 days. They found that the compressive strength, splitting tensile strength and modulus of elasticity increased with a decrease in the percentage of the fly ash and the water to cementitious materials ratio. Results also showed that all the properties increased with age and with replacement of fine aggregate with spent foundry sand. There was a little improvement in compressive strength within the temperature range of 200 to 300 °C as compared to 27 to 200 °C and the slight increase in strength attributed to a modification of the bonding properties due to rehydration of the paste by the migration of water in the pores. But the rate of splitting tensile strength and modulus of elasticity loss was higher than that of the compressive strength loss at elevated temperatures and with the increase in percentage of fly ash. Porosity of the concrete specimens was increased with increase in temperature and increase in fly ash content due to the expansion of the pores diameters lead to increase in permeability. Ergun et al (2013) assessed the effects of elevated temperatures and cement dosages on the mechanical properties of concrete. The concrete specimens were tested to failure to study the variation of the residual compressive strength, residual flexural strength and velocity of ultrasonic transmission with temperatures and cement dosages. Two concrete test series were cast and prepared, Series-I a cement dosage of 250 kg/m3 was used while for Series-II the cement dosage was set at 350 kg/m3. After casting the concrete specimens were cured in lime saturated water for 27days and the specimens were subjected to temperatures of 100, 200, 400, 600 and 800°C. After attaining the targeted temperature, it was maintained for 45 min and after which they were subjected to respective tests. The results indicated that the dehydration of CSH phase in cement paste of

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concrete exposed to high temperature caused the deterioration in transition zone. Deterioration of the interface increased with the increase in the cement dosage. They concluded that, although the variation of cement dosage affects the strength and durability of concrete at ambient temperature, it does not affect the relative residual strength of concrete exposed to high temperature. The relative residual compressive and flexural strength– temperature relationships of concrete were found not to depend on the cement dosages used. The velocity of ultrasonic transmission test is found to be an effective tool to assess the degree of damage in concrete structure exposed to fire. Karakoc (2013) studied about the residual compressive strength of concrete with expanded perlite aggregate and pumice aggregate after it was exposed to high temperature. The lightweight aggregates were first mixed, with water needed for dry surface saturated for half an hour before blending. After casting the moulds were removed after 24 h and kept for water curing for 28 days. Before testing the samples were subjected to a temperature of 700 °C for 1 h after reaching peak temperature and then the specimens were allowed to cool down by three cooling regimes. First cooling regime was furnace cooling. Second cooling regime was water cooling, i.e., the hot specimens at peak temperature was taken out of the furnace and immediately immersed in a fresh water tank. Last cooling regime was natural cooling, i.e., the hot specimens at peak temperature was taken out of the furnace and hold on laboratory conditions. The compressive strength of the concrete samples that were not exposed to high temperature and those that were subjected to high temperature were tested. Results showed that the compressive strength of concrete cooled in water cooling after being exposed to the effect of different mixture with 10%, 20% and 30% expanded perlite aggregate and pumice aggregate is higher than that cooled in natural and furnace. The compressive strength of concrete cooled in water, furnace and natural cooling decreased by an average of 78%, 81% and 83%, respectively, when compared to control samples. It was concluded that natural cooling caused only a bit more deterioration in strength than in the case of furnace cooling. Marques et al (2013) investigates the effects of elevated temperatures on the residual mechanical performance of concrete in terms of thermal response and residual mechanical behaviour produced with recycled rubber aggregate. For the purpose, four types of concrete were produced: a reference concrete and three concrete mixes in which 5%, 10% and 15% of the total aggregate volume of natural aggregate were replaced by recycled rubber aggregate, using both fine and coarse particles. Specimens were cast and placed in a wet curing chamber after 24 h for 74 days and transferred to a dry chamber for 41 days. Specimens were exposed to heat at the age of 115 days and, 4 days later, after cooling down to ambient temperature, they were finally subjected to mechanical testing. The exposure temperatures were 400, 600 and 800 °C and the mechanical properties studied include compressive strength and split tensile strength. They obtained the results that, for exposure temperatures of 400 °C and 600 °C the loss in performance for recycled rubber aggregate concrete was roughly similar to that of normal concrete, but the recycled rubber aggregate with 15 % replacement suffered a steeper loss at 600 °C and large decline of residual strength took place at 800 °C and this is due to the decomposition of rubber aggregates at high temperatures. The loss in split tensile strength was greater in recycled aggregate concrete than in normal concrete. Despite the decrease in residual mechanical properties for recycled rubber aggregate concrete when compared to normal concrete after thermal exposure at higher temperatures (800°C), the relative reduction in performance is not relevant enough to prevent it from being used in structural applications, provided that low replacement rates are used. Netinger (2013) investigated the use of slag as fire-resistant aggregate. Three groups of concrete mixes were prepared with the same cement content (400 kg/ m3) and water/cement ratio (0.43). Reference mixture was prepared with dolomite and other 2 mixes was prepared by slag as coarse aggregate. In one mix poly propylene fibres were included to reduce the possible slag expansion and to improve fire resistance and 20 % replacement of cement with fly ash to reduce thermal incompatibility between slag and Portland cement. Prisms were casted for testing and placed in water tank for 7 days and moved to a storage chamber for 28 days and kept in laboratory for another 28 days. After 56 days of curing the specimens were exposed to temperatures of 100, 200, 400, 600 and 800 °C for 1h. By the increase in temperature flexural strength of the specimens decreased. Reduction in compressive strength was less observable than reduction in flexural strength for all specimens. Modulus of elasticity value increased up to a temperature 100 °C after which it decreased. Weight loss was similar for all the specimens up to 100°C, due to evaporation of water. But when reached its maximum temperature (800 °C) dolomite specimen retained 78 % of initial weight and slag specimen retained 86 % while slag with fibres retained 84 % of initial weight. According to the investigation the authors concluded that the analysed slag can be considered as an aggregate for good fire resistance but it does not provide satisfactory concrete fire resistance if combined with Portland cement. This can be explained by a high coefficient of slag thermal expansion that causes the aggregate–cement paste contact to deteriorate during heating, which thus damages concrete integrity.

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Table I: Details of work done by Authors Author

Type of concrete/Replacement

Curing

Temperature

Time of

Type of

exposure (h)

cooling

21 days in water Husem

Water and air

Ordinary and High performance

rest 7 days at room

200, 400, 600, 800

concrete

temperature

and 1000 °C

Left for Terro

Fine and coarse aggregates with

28 days curing in

recycled glass

95 % RH

1000 °C

7-10

cooling 15 h in furnace and 7 h in air before testing

Arioz

Ordinary Portland cement,

28 days, air-dried

200, 400, 600, 800,

crushed limestone aggregate and

for 6 days

1000 and 1200 °C

1, 3,7,14 and 28

200, 400, 600, 800

days

and 1000°C

Natural cooling 2

for 2h

siliceous river gravel.

Chen

Normal concrete

Water and air 3

Culfik and

Normal and high strength

50,100,150,200 and

Ozturan

concretes with and without silica

250 °C

Cooled in 3

400, 600 and 800°C

28 days

200, 400, 600, 800

1.5

furnace

fume Demirel and

Substituting cement with finely

Kelestemur

ground pumice at proportions of 5%, 10%, 15% and 20% by weight Fire resistant aggregates include

Netinger

diabase, steel slag, crushed bricks and crushed tiles

Vieira et al

Natural cooling

and 1000 °C

Recycled concrete coarse

28 days and moved

aggregates

to a dry chamber

Natural cooling 400 °C, 600 °C and

for 21days

800 °C

150, 300, 450 600

for 4 days

Natural cooling

Semi crushed silico-calcareous, Xing et.al

crushed calcareous and rolled siliceous

Yuksel et al

Non-ground granulated blast-

Cured in water for

furnace slag (GBFS) and coal

28 days and kept in

bottom ash (BA) as fine

Cakır and Hızal

and 750 °C Cooled for 1 h in furnace and

800°C

air until 90 day

cooled for 24 h

aggregate

in air

Self consolidating lightweight

Natural cooling

concrete

28 days

Mathew and

Laterized self compacting

27 days and kept at

Paul

concrete

room temperature

300, 600 and 900 °C

200°C, 400 °C and

Water and air

100, 200 and 300°C

Natural cooling

100, 200, 400, 600

45 min

Natural cooling

Cored in water for

for 24 h. Pathak and

Fly ash with cement, and spent

28, 91, and 365

Siddique

foundry sand with sand

days.

Ergun et al

Different cement dosages

27days

600°C

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Maya et al., International Journal of Engineering, Business and Enterprise Applications, 9(1), June-August., 2014, pp. 63-69

and 800°C Natural, water Karakoc

Perlite aggregate and pumice

28 days

700 °C

cooling

aggregate 74 days in wet Marques et al

Recycled rubber aggregate

and furnace

chamber and 41

Natural cooling for 4 days

400, 600 and 800 °C

days in dry chamber 7 days in water Netinger et al

Slag as fire-resistant aggregate

tank, 28 days in

373, 473, 673, 873

storage tank

and 1073 K

III.

Natural cooling

Conclusions

In this paper a survey on how high temperatures effects the concrete strength is carried out. Most of the authors concluded that due to temperature exposure there will be a drastic reduction in the mechanical properties of concrete. The study about the influence of aggregates on thermal behavior showed that permeability of aggregates plays an important role in the thermal stability. Also the residual mechanical properties of concrete after exposure to temperature varied according to the type of aggregates. When normal aggregates are replaced by fire resistant aggregates the concrete was not only justified in terms of fire resistance but also in terms of waste management. References [1]

Metin Husem, The effects of high temperature on compressive and flexural strengths of ordinary and high-performance concrete, Fire Safety Journal, vol. 41, pp. 155–163, 2006.

[2]

Mohamad J. Terro, Properties of concrete made with recycled crushed glassat elevated temperatures, Building and Environment, vol. 41, pp. 633–639, 2006.

[3]

Omer Arioz, Effects of elevated temperatures on properties of concrete, Fire Safety Journal, vol. 42, pp. 516–522, 2007.

[4]

Bing Chen, Chunling Li and Longzhu Chen, Experimental study of mechanical properties of normal-strength concrete exposed to high temperatures at an early age, Fire Safety Journal, vol. 44, pp. 997–1002, 2009.

[5]

Mehmet Sait Culfik and Turan Ozturan, Mechanical properties of normal and high strength concretes subjected to high temperatures and using image analysis to detect bond deteriorations, Construction and Building Materials, vol. 24, pp. 1486– 1493, 2010.

[6]

Bahar Demirel and Oguzhan Keles-temur, Effect of elevated temperature on the mechanical properties of concrete produced with finely ground pumice and silica fume, Fire Safety Journal, vol. 45, pp. 385–391, 2010.

[7]

Ivanka Netinger, IvanaKesegic and IvicaGuljas, The effect of high temperatures on the mechanical properties of concrete made with different types of aggregates, Fire Safety Journal, vol. 46, pp. 425–430, 2011.

[8]

J.P.B. Vieira, J.R. Correia and J. de Brito, Post-fire residual mechanical properties of concrete made with recycled concrete coarse aggregates, Cement and Concrete Research, vol. 41, pp. 533–541, 2011.

[9]

Zhi Xing, Anne-Lise Beaucour, Ronan Hebert, Albert Noumowe and Béatrice Ledesert, Influence of the nature of aggregates on the behaviour of concrete subjected to elevated temperature, Cement and Concrete Research, vol. 41, pp. 533–541, 2011.

[10]

Isa Yuksel, Rafat Siddique and Omer Ozkan, Influence of high temperature on the properties of concretes made with industrial by-products as fine aggregate replacement, Construction and Building Materials, vol. 25, pp. 967–972, 2011.

[11]

Ozge Andic-Cakır and Selim Hızal, Influence of elevated temperatures on the mechanical properties and microstructure of self consolidating lightweight aggregate concrete, Construction and Building Materials, vol. 34, pp. 575–583, 2012.

[12]

George Mathew and Mathews M. Paul, Mix design methodology for laterized self compacting concrete and its behaviour at elevated temperature, Construction and Building Materials, vol. 36, pp. 104–109, 2012.

[13]

Neelam Pathak and Rafat Siddique, Effects of elevated temperatures on properties of self-compacting-concrete containing fly ash and spent foundry sand, Construction and Building Materials, vol. 34, pp. 512–521, 2012.

[14]

Ali Ergun, Gokhan Kurklu, M Serhat Baspınar and Mohamad Y.Mansour, The effect of cement dosage on mechanical properties of concrete exposed to high temperatures, Fire Safety Journal, vol. 55, pp. 160–167, 2013.

[15]

Mehmet Burhan Karakoc, Effect of cooling regimes on compressive strength of concrete with lightweight aggregate exposed to high temperature, Construction and Building Materials, vol. 41, pp. 21–25, 2013.

[16]

A.M. Marques, J.R.Correia and J.deBrito, Post-fire residual mechanical properties of concrete made with recycled rubber aggregate, Fire Safety Journal, vol. 58, pp. 21–25, 2013.

[17]

Ivanka Netinger, DamirVarevac, Dubravka Bjegovic and Dragan Moric, Effect of high temperature on properties of steel slag aggregate concrete, Fire Safety Journal, vol. 59, pp. 1–7, 2013.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Global Talent Management Strategies for High Performance Culture Dr. A.Narasima Venkatesh Senior Assistant Professor - Department of Human Resources R.V. Institute of Management, Accredited by NAAC with ’A’ Grade, Affiliated to Bangalore University CA-17, 36th Cross, 26th Main, 4th ‘T’ Block, Jayanagar, Bangalore-560 041, INDIA. ______________________________________________________________________________________ Abstract: In today’s globalised business scenario, organizations have to persistently invest in human capital by formulating appropriate talent management strategies. Organizations should shift their focus from reactive to proactive approach and be supposed to work hard to harness talent in their organizations. Furthermore, common understanding of its meaning is required among all the stakeholders within the organization before formulating and implementing talent management strategies. Organizations need to appreciate the valuable contribution of employees at all levels not restricting merely to succession planning for top level leadership positions. To gain competitive advantage, necessary efforts to be taken to embed the talent mindset all through the organization, initiating with the CEO of the organization. Senior HR managers’ also play a critical role as talent management facilitators, to design policies where organization’s culture supports talent as it is in reality the only true competitive advantage for developing and sustaining high performance culture in an organization. Keywords: Talent Management Strategies, Talent mindset, Competitive Advantage, High Performance Culture. _________________________________________________________________________________________ I. Introduction In the competitive world, many organizations recognize that they must possess the best talent so as to succeed in the fierce competition and ever more complex global economy. In order to accomplish organization goals, it needs to attract, identify, select, develop and retain talented people and they have to manage talent as a significant resource to realize the best promising results. As such, today, Talent Management has become a strategic imperative for many organizations as it differentiates an organizational excellence when it develops and sustain into high performance culture. The current trends in business, technological and social changes entails employers to transform their approach towards talent management in order to compete successfully in the increased globalised business environment. II. Changing Business Landscape Today there is an incredible change in which the employers and employees function in the ever changing business and social environment. Let us see some of the key factors of this change as below: A. New Business Models Due to globalization many business models are constantly evolved with lot of hierarchical changes thus leading to boundary less environment. In the midst of only few entry barriers to start new businesses, the exponential growth of companies like Google, Snapdeal, Flipkart, Face book, Myntra (later merged with Flipkart in May 2014) etc., continue to motivate many business leaders, managers and entrepreneurs. B. HR - Strategic Business Partner HR manager’s role in multinational organizations is drastically changing as they need to operate their business in the international HR context. These days they need to take care of various specific Human Resource functions like expatriate and repatriate training, cultural training, language training, providing translation support to employees when required, visa and tax compliance issues, providing occupational safety and health, designing appropriate compensation packages for expatriate employees, providing education support to the children of expatriate employees etc may pose unique yet complex challenges for HR Professionals. C. Technological Changes Most businesses now operate their business using various technologically advanced applications/software and hardware systems. There is remarkable change the way in which many businesses operates today, as a considerable chunk of business transactions do take place through e-commerce portals like Snapdeal, Myntra, Amazon, e-bay, Flipkart etc. In addition to that various business models are able to procure their new customers/consumers through social media sites like Facebook or to reach wider target audience and sometimes these social media sites immensely help to promote their business brands. New technologies like cloud based technologies help various businesses to ease their operations with greater reach and speed.

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D. Workforce Diversity The complexity involved in social and demographic characteristics of today’s workforce cannot be under estimated as there are three and at times even four generations are working in the same organization from ‘Veterans’ (born prior to 1946) and ‘Baby Boomers’ (born between 1946 and 1964) to Generation ‘X’ (born between 1965 and 1979) and Generation ‘Y’ (born between 1980 and 1995).This complex nature of workforce diversity results in interesting possibilities but also cautions the need for creating value proposition that fulfills the aspirations and values of each generation as there will be differences in values and priorities across different generations as shown in figure 1. III. Global Talent Management Strategies A. Strategic Alignment In order to gain competitive advantage, organizations should constantly formulate talent management strategy in alignment with overall corporate strategy. Human capital goals for the year should be set whenever each major business unit of the organization sets its annual business goal and appropriate steps to be taken to embed these set human capital goals within the formulated business plan. System has to be formed to track the alignment of human capital goals and goal of business unit and the presence of process should make sure that human capital goals are fixed in accordance with the overall corporate strategy. B. Systems Consistency Greater care to be taken to ensure that talent management practices are implemented not in isolation but there exist consistency between systems. There should be a system in place to monitor that formulated metrics and process should be meaningful. For instance, if there is significant investment made by the organization in the area of training and development of employees in turn it should be reflected in career development, performance management and ultimately employee retention. The HR department should also monitor continuously that established systems are complementing each other and ensure that not functioning in isolation. C. Cultural Fit Successful organizations believe it is their corporate culture that acts as a source of competitive advantage in a sustainable manner. Having said so, they deliberately make consistent efforts to infuse their core values into whole talent management process. Focus should be more on employee-employer cultural fit rather than emphasizing only on job related skills and work experience. Organizations should formulate specific assessment methods to judge applicant’s personality and values to determine whether he/she will be a proper cultural fit. Figure 1: Global Talent Management Strategies

Strategic Alignment Systems Consistency

Employer Branding Global Talent Management Strategies Top Management Involvement

Cultural Fit

D. Top Management Involvement Talent management processes need to get support from top management and line managers too. Sense of ownership to be created in senior managers’ mind that placing right person in critical leadership role is not the sole responsibility of HR manager alone but also the responsibility of leaders of respective functions or departments. Line managers can act as coach or mentor and should provide right kind of opportunities like job

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shadowing, challenging assignments, cross functional projects to talented employees for getting wider exposure which will help the employees shape their career prospects. E. Employer Branding Conscious efforts need to be taken to differentiate the organization from their competitors as attraction of talent becomes easy once the organization is able to market itself to people who can meet its talent requirements. Employer branding can be done by stressing the opportunities for long term career growth and internal promotion in the employee advertisement of in the interview process. If the organization is operating in a global context, organizations can highlight their corporate social responsibility activities both locally and globally to attract and retain talent or establishing tie-ups with top universities and colleges globally and providing internship opportunities to students and allowing them to work in projects with two to three months duration thereby making the company more visible and attractive to potential candidates thereby tapping the talent pool available both inside and outside India. IV. Conclusion Talent management by no means is an immediate concern than it is right now. Thoughtful planning has to be done in order to formulate a sound talent strategy which will firmly connect the organization’s overall business strategies and needs. Moreover appropriate steps need to be taken to ensure talent management be embedded in an organization’s culture and practices. A thorough analysis of the organizational situation recognizes the current state of talent management strategies followed within a company. It should identify who owns talent management, nature of support given by senior leadership, systems in place to support individual initiatives and HR’s role in implementing the strategies. Then only it is possible for talent management to become effective and sustainable. References [1] [2] [3] [4] [5]

Chris Ashton, CRF Publishing and Lynne Morton, “Managing Talent for Competitive Advantage”, Performance Improvement Solutions, Volume 4 Issue 5 July/August 2005 Harrier Human Capital, “Total Talent Management”, www.harrierhc.com E.G. Chambers, M. Foulon, H. Handfield-Jones, S.M. Hankin and E.G. Michaels, “The War for Talent”, McKinsey Quarterly 3 (1998): 44-57. R.E. Lewis and R.J. Heckman, “Talent Management: A Critical Review,Human Resource Management”, Review 16 (2006): 139-154. Guntar K Stahl, Ingmar Bjorkman, Elain Farndale, Shad S. Morris, Jaap Paauwe, Philip Stiles,Jonathan Trevor, Patrick Wright, “Six Principles of Effective Global Talent Management”, MIT Sloan Management Review, Winter 2012 Vol.53 No.2

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net ROLE OF CRM IN BUSINESS SECTOR Dr. T.Vijayaragavan Assistant Professor (Sr.G), Department of Humanities PSG College of Technology, Coimbatore - 641 004, Tamilnadu, India. __________________________________________________________________________________________ Abstract: Every business organization needs customers for sustainable and to maintain customer satisfaction with technology rich and information based society. CRM plays a very big role to treat all of this information. It is a customer-focused business strategy designed to optimize revenue, profitability, and customer loyalty. By implementing a CRM strategy, an organization can improve the business processes and technology solutions around selling, marketing and servicing functions across all customer touch-points through Web, e-mail, phone, fax, and in-person. To realize the benefits of CRM, it is important to have an integrated solution across all customer information systems and tying together the front and back offices for a complete view of customers in order to provide them a better and comfortable service. This paper highlights the impact of CRM in business sector in different segment. Key Words: CRM, Applications, Features, Challenges, Impact and Business sector __________________________________________________________________________________ I. Introduction Customer relationship management (CRM) is a widely implemented model for managing a company’s interactions with customers, clients and sales prospects. It involves using technology to organize, automate, and synchronize business processes principally sales activities, marketing, customer service and technical support. Customer relationship management describes a company-wide business strategy including customer-interface departments as well as other departments. In general, CRM is a more efficient automated method used to connect and improve all areas of business to focus on creating strong customer relationships. CRM helps to create time efficiency and savings on both sides of the business spectrum. Through correct implementation and use of CRM solutions companies gain a better understanding of their strongest and weakest areas and how they can improve upon these. Therefore, customers get better products and services from their choices. This paper an attempt has been made to understand the impact of CRM in business sector in different segment. II. Statement of the problem Today, many businesses manage different aspects of customer relationships with multiple information systems. Growing businesses face a range of challenges. Recognizing and overcoming the common pitfalls in business is to continue to grow and thrive. Crucially, you need to ensure creating sustainable growth for the future. Every business needs to be alert to create new opportunities. Everyday problem-solving competency among today’s business leaders is also challenge to their ability. The major challenge of every business is to admit new customers, retain existing customers and buy more in greater quantity. In order to claim that business need to establish comfortable system to move forward and flourish like anything for its stability. III. Objectives of the study The researcher has established some of important objectives in this study area. The following are listed here. To study the important features of CRM in business To aware the applications of CRM in business To know the challenges of CRM while implementation To understand the impact of CRM in various segments of business IV. Significance of the study Customer Relationship Management (CRM) is the strongest and the most efficient approach in maintaining and creating relationships with customers. The strongest aspect of CRM is very cost-effective. The advantage of decently implemented CRM system is very less need of paper and manual work which requires lesser staff to manage and lesser resources to deal with. The technologies used in implementing a CRM system are also very cheap and smooth as compared to the traditional way of business. Efficiently dealing with all the customers and providing them what they actually need increases and finally customer satisfaction. This increases the confidence level and chance of getting more business which ultimately enhances turnover and profit.

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V. Need for the study In todayâ&#x20AC;&#x2122;s commercial world practice of dealing with existing customers and thriving business by getting more customers into loop is predominant and is mere a dilemma. Installing a CRM system can definitely improve the situation and help in challenging the new ways of marketing and business in an efficient manner. Hence in the era of business every organization should be recommended to have a full-fledged CRM system to cope up with all the business needs. CRM is a strategy which is customized by an organization to manage and administer its customers and vendors in an efficient manner for achieving excellence in business. VI. Methodology This research study is based on secondary data only. The researcher has been collected the relevant data for this study from some important sources like books, thesis, dissertation, journal, magazine, reports, bulletin and various websites. VII. Applications of CRM in business CRM is a strategic concept where processes are complex. Now-a-days, it becomes important for business. It provides lot of benefits for business people. The following are the applications of CRM in business. A. E-commerce web store fronts website These are converging into a customer-centric solution, which allows organizations to interact with, sell to, and service customers through all channels. Consumers are major users of this business functions. B. Channel Management/Partner Relationship Management CRM extends its benefit to the needs of extended selling channels such as distributors and value-added resellers. PRM applications enable companies to distribute leads and manage promotions outside the enterprise sales team. Business Partners are major users of this business functions. C. Sales Automation It provides sales professionals with access to critical customer information and tools that enhance their ability to effectively sell as well as manage their time. For example: contact management, calendaring functions, forecasting tools, configuration models. The sales department uses this business function. D. Marketing Automation It supports marketing departments with campaign management, lead generation, and data mining tools. Closedloop lead management is one of the most important functions of marketing automation and relies on integration with the CRM data repository and related applications. This function is highly convenient to marketing department for cross-promotion, advertising and direct marketing. E. Customer Service It enables the enterprise to effectively and efficiently address customer questions, problems or issues. While customer satisfaction is the primary goal, many organizations are seeking to increase revenues while providing customer service through "cross-selling". The customer service department uses this business function to provide quality service to customers. VIII. Features of CRM in business A. Customers Needs An organization can never assume what actually a customer needs. Hence it is extremely important to interview a customer about all the likes and dislikes so that the actual needs can be ascertained and prioritized. B. Customers Response Customer response is the reaction by the organization to the queries and activities of the customer. Dealing with these queries intelligently is very important as small misunderstandings could convey unalike perceptions. Success totally depends on the understanding and interpreting these queries and then working out to provide the best solution. C. Customer Satisfaction Customer satisfaction is the measure of how the needs and responses are collaborated and delivered to excel customer expectation. In todayâ&#x20AC;&#x2122;s competitive business marketplace, customer satisfaction is an important performance exponent and basic differentiator of business strategies. D. Customer Loyalty Customer loyalty is the tendency of the customer to remain in business with a particular supplier and buy the products regularly. To continue the customer loyalty the most important aspect of an organization is to focus on customer satisfaction. E. Customer Retention Customer retention is a strategic process to keep or retain the existing customers and not letting them to diverge or defect to other suppliers or organization for business. More is the possibility to retain customers the more is the probability of net growth of business.

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F. Customer Complaints Always there exists a challenge for suppliers to deal with complaints raised by customers. Normally raising a complaint indicates the act of dissatisfaction of the customer. There can be several reasons for a customer to launch a complaint. Handling these complaints to ultimate satisfaction of the customer is substantial for any organization and hence it is essential for them to have predefined set of process in CRM to deal with these complaints and efficiently resolve it in no time. G. Customer Service In an organization Customer Service is the process of delivering information and services regarding all the products and brands. Customer satisfaction depends on quality of service provided to him by the supplier. IX. Challenges of CRM implementation A. Defining clear objectives The organization should have a clear set of objectives to achieve through the CRM. These objectives need to be listed and defined as measurable metrics. Without doing so, the company canâ&#x20AC;&#x2122;t assess the benefits or the ROI of the CRM system. B. Appointing a core CRM team The CRM initiative is not an IT project. A core CRM team should be formed in addition to the participation from top management, senior executives, customer service, IT and end-users. Only after the requirements are clear should they be handed over to IT for implementation. C. Defining the processes It is important for the processes to be clearly defined and enforced in order to set up the CRM project for success. One good practice is to create a central repository, accessible to all, which stores all the process definitions. This allows the document to be available for referencing by anyone using the system. Key processes that need to be defined from the start of change management process, feature re-evaluation process, etc. also, clear security measures with access management need to be in place to make sure that important data is not accessible by those who shouldnâ&#x20AC;&#x2122;t be accessing it. D. Managing the application Once the CRM has been rolled-out, it is important to re-align the work culture of the teams around it. The business operation should properly map with the CRM application. This also means that end users should perform day-to-day operations through the CRM application by default and not optionally. E. Finding the right partner The rate of CRM success considerably goes up with the right solution partner. Ideally select a partner who can do both, strategy & implementation. It is important that your partner shares the risks of your implementation. Working with a vendor who understands local work culture, technology limitations and listens to the employees, are ideal. X. Impact of CRM in different segments of business A. Consumer Portal A web-based merchant solution that includes capabilities for configuration and pricing and availability checks, and allows consumers to buy products and services, trigger orders, and check order status online. It is customercentric website with e-commerce web store fronts providing one step e-tailing, bidding and auctioning solutions. It also has Consumer Specific e-room, Newsgroup & chat to enable cross functional sales guiding a reality. B. Business Partner Portal A web-based partner relation management solution enables companies to distribute leads and manage promotions outside the enterprise sales team. This portal is aimed at resellers, dealers, distributors, and agents etc. It also has business partner support e-room, newsgroup & chat to share information & documents in real time. C. Sales Dept. Employees Portal A web-based sales management solution enables companies to do sales & marketing automation with contact management using customer register, sales forecast management, sales order management, production & material planning management, and available to promise dates, marketing events management with calendaring. It also collects and manages sales calls & sales reports from field sales representatives. It also has sales support e-room, newsgroup & chat to support sales function in collaborative manner. D. Customer Support Portal A web-based expert system that provides answers to customized questions. Consumers can provide operation or maintenance specific answers. To field service person it can provide technical details about repairing the product. It also has customer support e-room, newsgroup & chat to share documents & ideas. XI. Conclusion

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CRM describes a companywide business strategy including customer interface departments as well as other departments. Consistency in customer satisfaction is the key for success of any business. It needs to track and analyze those interactions in a systematic and organized fashion in order to build everlasting customer relationship which translates to long-term success. Building customer relationships seem simple and however businesses around the world have struggled with it. CRM customers are also demanding more and more knowledge management functionality. Essentially, in the E-business economy, you need to deliver customer organizational knowledge on demand anytime and anywhere. In short, the future of CRM is bright indeed. CRM will become deeply ingrained as a business strategy for most of the companies. Technology will evolve while technical and organizational challenges are overcome. Much will change in the years ahead, but one thing is certain i.e., CRM is a journey, not a destination, and customers have their hands on the road map and the steering wheel. XII. References [1] [2] [3] [4] [5] [6]

Carol J. Kerr, Kristin L. Anderson (2002) “Customer Relationship Management” The McGraw-Hill Company. Urvashi Makkar & Harinder (2011), “Customer Relationship Management”, Edited by Kumar Makkar. FrancisButtle (2012), “Customer Relationship Management: Concepts and Technologies” Routledge, Taylor & Francis Group. M.Rajaram and V.Palanisamy (Jan-Mar 2012), “Customer Relationship Management Practices of new generation private banks in Cuddalore District”. Rathinam Journal of Management, Vol.1, No.2, PP. 12-18. S.N.Nadeem and K.Ramachandra (July 1, 2012), “Customer Relationship Management in Banking”, Southern Economist, Vol 51, N0.9, PP.41-45. M.M.Kumthekar and Pratibha Joshi (Sep1, 2012) “CRM – A Contemporary Strategic management Technique”, Southern Economist, Vol 51, N0.9, PP.13-17.

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