Management & Change by IILM Institute for Higher Education

Management & Change The Journal of the IILM Institute for Higher Education (Listed in Cabell’s Directory of Publishing Opportunities, Texas, USA)

Editor Dr. Sangeeta Chopra Associate Professor Organisational Behaviour & Human Resource IILM Institute for Higher Education management.change@iilm.edu

EDITORIAL ADVISORY BOARD Ahmed, Abad Ex-Pro-Vice Chancellor, University of Delhi, Delhi. Balachandran, V. Bala Distinguished Professor, J.K. Kellogg Graduate School of Management, Northwestern University, Evanston, Illinois. Baxi, Chetan Former Dean, Research, Management Development Institute, Gurgaon. Chatterjee, N.R. Dean Emeritus, IILM Institute for Higher Education, New Delhi. Coree, Joseph Professor, Robert Morris College, Pittsburgh, USA. Ghosh, Avijit Professor, Stern School of Business, New York University, USA. Jain, P.K. Professorof Finance, Dept. of Management Studies, Indian Institute of Technology, New Delhi & Former President, GIFT Society. Joshi, J. Rama Professor, Shri Ram Centre for Industrial Relations, New Delhi. Khan, M.Y. Ex-Professor, Dept.of Financial Studies, University of Delhi, Delhi. Mamkoottam, K. Professor, Faculty of Management Studies, University of Delhi, Delhi. Mukherji, Badal Professor TERI University & Former Director, Delhi School of Economics, Delhi. Nair, N.K. Professor Shri Ram Centre for Industrial Relations, Human Resources, Economic & Social Development, New Delhi. Panchmukhi, V.R. Ex-Chairman, Indian Council of Social Science Research. Pandey, I.M. Chairman, Academic Council, Pearl School of Business, Sector 32, Gurgaon. Pandit, V.N. Ex-Professor, Delhi School of Economics, University of Delhi, Delhi. Sheth, N.R. Ex-Director, Indian Institute of Management, Ahmedabad. Singh, J.D. Director, Jaipuria Institute of Management, Noida. Szell, Gyorgy Professor, University of Osnabruck, Germany. Vrat, Prem Professor Emeritus, MDI Gurgaon, Former Vice-Chancellor, U.P. Technical University, Lucknow and Former Director, IIT Roorkee. Manuscript Submission Contributions are invited in diverse areas of management from interested authors. In each issue of the journal it is normally planned to include research papers, case studies, original conceptual papers/perspectives, short communications, management cases and book reviews. For contributors guidelines, authors may refer to the inside back cover. Enquiries should be electronically made to the Editor, Management & Change, IILM Institute for Higher Education at e-mail management.change@iilm.edu Frequency and Subscriptions Management & Change is published bi-annually i.e. twice a year (No.1: Summer; No.2: Winter). Annual subscription rates are as follows: Within India – Institutional: Rs. 750; Individual: Rs. 500 Overseas – Asian Countries: $50; Other Countries: $150 (Air mail) Demand Draft should be drawn in favour of: IILM Institute for Higher Education, payable at New Delhi. Advertisement rates full page Rs. 20,000; half page Rs. 10,000. Editorial/Subscription Information For editorial queries, please write to the Editor, Management & Change, IILM Institute for Higher Education, Tel: 91-11-40934335, Fax: 91-11-40934339, E-mail: management.change@ iilm.edu For subscription related queries please contact Editorial Coordinator (aarti.sharma@iilm. edu, shipra.jain@iilm.edu). Order for print copies to be made at management.change@iilm.edu Online version for 2011 and later issues are being made available through IILM website (www.iilm.edu) on free downloadable basis. Copyright @ 2012 IILM Institute for Higher Education. All Rights Reserved.

Chronology of Editorial Team of ‘Management & Change’ Volume & Issue (Year)

Editor

Associate Editor

Vol. 1 No. 1 (1997) Vol. 1 No. 2 (1997) Vol. 2 No. 1 (1998)

Prof. Debi S. Saini Prof. Debi S. Saini Prof. Debi S. Saini

Sami A. Khan Sami A. Khan Sami A. Khan

Vol. 2 No. 2 (1998)

Prof. Debi S. Saini

Sami A. Khan

Vol. 3 No. 1 (1999) Vol. 3 No. 2 (1999)

Prof. Debi S. Saini Prof. Debi S. Saini

Sami A. Khan Sami A. Khan

Vol. 4 No. 1 (2000)

Prof. Gautam Bhattacharyya

Vol. 4 No. 2 (2000)

Prof. Gautam Bhattacharyya

Editorial Coordinator

Prof. Gautam Bhattacharyya Vol. 5 No. 2 (2001) Prof. Gautam Bhattacharyya Vol. 6 No. 1 (2002) Prof. Gautam Bhattacharyya Vol. 6 No. 2 (2002) Prof. Gautam Bhattacharyya Vol. 7 No. 1 (2003) Dr. Irfan A. Rizvi Vol. 7 No. 2 (2003) Dr. Irfan A. Rizvi Vol. 8 No. 1 & 2 (2004) Dr. Irfan A. Rizvi Vol. 9 No. 1 (2005) Dr. K.M.Mital Vol. 9 No. 2 (2005) Dr. K.M.Mital Vol. 10 No. 1 (2006) Dr. K.M.Mital Vol. 10 No. 2 (2006) Dr. K.M.Mital Vol. 11 No. 1 (2007) Dr. K.M.Mital Vol. 11 No. 2 (2007) Dr. K.M.Mital Vol. 12 No. 1 (2008) Dr. K.M.Mital Vol. 12 No. 2 (2008) Dr. K.M.Mital Vol. 13 No. 1 (2009) Dr. K.M.Mital Vol. 13 No. 2 (2009) Dr. K.M.Mital Vol. 14 No. 1 (2010) Dr. K.M.Mital

Zafar H. Anjum Zafar H. Anjum Zafar H. Anjum Lincy Sebastian Yusuf Siddiqui Zafar H. Anjum Lincy Sebastian Yusuf Siddiqui Zafar H. Anjum Lincy Sebastian Yusuf Siddiqui Sami A. Khan Zafar H. Anjum Lincy Sebastian Yusuf Siddiqui Zafar H. Anjum Lincy Sebastian Yusuf Siddiqui Yusuf Siddiqui

Yusuf Siddiqui

Prof. M.K. Moitra Prof. M.K. Moitra Dr. Siri D. Vivek Dr. Rajesh Pilania Dr. Rajesh Pilania -

Vol. 14 No. 2 (2010)

Yusuf Siddiqui Yusuf Siddiqui Johnson E.P Johnson E.P Johnson E.P Johnson E.P Johnson E.P Johnson E.P Johnson E.P Johnson E.P Johnson E.P Arun Thomas Arun Thomas Ms. Deepa Khanna Ms. Sarla Rawat Ms. Deepa Khanna Ms. Sarla Rawat Ms. Deepa Khanna Ms. Shipra Jain Ms. Aarti Sharma Ms. Shipra Jain

Vol. 5 No. 1 (2001)

Dr. K.M.Mital

Vol. 15 No. 1&2 (2011) Dr. P. Malarvizhi

Mr. George Skaria

Vol. 16 No. 1&2 (2012)

Dr. Sangeeta Chopra

ACKNOWLEDGEMENT TO REFREES Following management professionals acted as referees for contributions made for Management & Change, Vol. 16 No. 1 & 2 (2012). Management & Change acknowledges their valuable comments and suggestions for improving papers included in the following issue. Management & Change, Vol. 16 No. 1 & 2 Dr. P. Malarvizhi

Head of Knowledge and services, Anchor Faculty – Accounting and Finance, EMPI Business School, Chattarpur, New Delhi – 110074. Former ProfessorAccounting and Finance, IILM Institute for Higher Education, 3 Lodhi Institutional Area, New Delhi - 110003

Dr. Shuchi Agrawal

Director, Undergraduate Business School and Professor, Organisational Behaviour and Human Resource Management, IILM Institute for Higher Education, 3 Lodhi Institutional Area, New Delhi - 110003

Dr. Sudhir Naib

Professor, Organisational Behaviour and Human Resource Management, IILM Institute for Higher Education, 3 Lodhi Institutional Area, New Delhi – 110003

Management & Change VOLUME 16

NUMBER 1 & 2

2012

ARTICLES Gender Stereotyping and Stress: Examining the Role of Self Efficacy as a Mediator Among Women Executives in Call Centres

Monica Verma Kanika T. Bhal Prem Vrat

Impact of Foreign Capital Flows on Economic Growth: Empirical Evidence from India

Minakshi Paliwal Sumanjeet Singh

Index of Professionalism in Financial Decisions Shveta Singh P. K. Jain Surendra S. Yadav

Exploring The Demographic Differences in Adoption of Mobile Marketing in India

Jaydeep Mukherjee

Emotional Labour in the Education Industry

Farah Naqvi

Dr. Sangeeta Chopra

Associate Professor, Organisational Behaviour and Human Resource Management, IILM Institute for Higher Education, 3 Lodhi Institutional Area, New Delhi – 110003

Intellectual Capital and Corporate Performance Aparna Bhatia of Indian Pharmaceutical Industry: A Panel Khushboo Aggarwal Data Analysis

Mr. Sharad Gupta

Associate Professor, Marketing, IILM Institute for Higher Education, 3 Lodhi Institutional Area, New Delhi-110003

Drivers of Human Resource Outsourcing in the Surya Narayan Mohapatra 131 Indian Banking Sector: An Empirical Study

Ms. Deepa Bhaskaran

Assistant Professor and Deputy Area-Chair, Economics and Strategy, IILM Institute for Higher Education, 3, Lodhi Institutional Area, New Delhi -110003

Modeling The Emerging Market Online Social Network Adoption Behavior: Evidence from India

Dr. Anjali Malik

Professor, Marketing and Sales, IILM Institute for Business and Management, DLF Golf Course Road, Sector-53, Gurgaon-122003

Dr. Saima Rizvi

Assistant Professor, Finance and Accounting, IILM Institute for Business and Management, DLF Golf Course Road, Sector-53, Gurgaon-122003

Jaydeep Mukherjee Anandan Pillai

101

149

BOOK REVIEWS S.K.Kulshrestha (2012), Urban and Regional Planning in India- A Handbook for Professional Practice reviewed by Dr. (Prof.) Sudhir Naib.

Contributors Monica Verma

Kanika T. Bhal

Associate Professor, School of Management, Inderprastha Engineering College, 63, Site IV, Industrial Area, Sahibabad, Ghaziabad (U.P.), India. E-mail: monicadev00@gmail.com Professor and Head, Department of Management Studies, Indian Institute of Technology, Delhi -110016, India. E-mail: kanika@dms.iitd.ernet.in

Prem Vrat

Vice Chancellor, ITM University, HUDA, Sector 23 A, Gurgaon, Haryana â&#x20AC;&#x201C; 122017, India. E-mail: premvrat@gmail.com

Minakshi Paliwal

Assistant Professor, Department of Commerce, Ramjas College, University of Delhi, Delhi-110007, India. E-mail: minakshicfa@yahoo.com

Sumanjeet Singh

Assistant Professor, Department of Commerce, Ramjas College, University of Delhi, Delhi-110007, India. E-mail: sumanjeetsingh@gmail.com

Shveta Singh

Assistant Professor, Department of Management Studies, IIT Delhi, New Delhi -110016, India. Phone: +91-11-26596303. E-mail: shvetasingh@dms. iitd.ernet.in

P. K. Jain

Professor, Department of Management Studies, IIT Delhi, New Delhi-110016, India. Phone: +91-1126591199. E-mail: pkjain@dms.iitd.ac.in

Surendra S. Yadav

Professor, Department of Management Studies, IIT Delhi, New Delhi-110016, India. Phone: +91-1126591242. E-mail: ssyadav@dms.iitd.ac.in

Jaydeep Mukherjee

Associate Professor (Marketing), Management Development Institute, M.G. Raod, Sukhrali, Gurgaon â&#x20AC;&#x201C; 122001, India. Phone: 0124-4560381, Mobile: 9312403442. E-mail: jmukherjee@mdi.ac.in

Farah Naqvi

Assistant Professor.-OB/HR, IBS Hyderabad, Academics Block, Dontanapalli, Shankarpalli road, R.R. district, Hyderabad - 501203, Andhra Pradesh, India. E-mail: frh_naqvi @yahoo.com

Aparna Bhatia

Assistant Professor, Department of Commerce and Business Management, Guru Nanak Dev University, Amritsar -143001, Punjab, Inida. Phone: 09914115109. E-mail: aparnamohindru@yahoo.co.in

Khushboo Aggarwal

Research Fellow, Department of Commerce and Business Management, Guru Nanak Dev University, Amritsar-143001, Punjab, India. Mobile: 09815723594. E-mail: khushboo9983@yahoo.co.in

Surya Narayan Mohapatra Associate Professor, Finance Department, IBS, Hyderabad, F-209, Academics Block, Dontanapalli, Shankarpalli road, R.R. district, Hyderabad - 501203, Andhra Pradesh, India. Moblie: 9912638453, 08417 236660, Extn. 6209 E-mail: suryan.mohapatra@gmail.com Anandan Pillai

FPM Scholar (Marketing), Management Development Institute, M.G. Raod, Sukhrali, Gurgaon-122001, India. E-mail: anandan1982@gmail.com

From the Editor’s Desk MANAGEMENT EDUCATION - A VIEWPOINT Changes in the social, cultural framework, and even more so in the economic order, have pushed education system and higher education in particular, into a new environment where quality plays an important role. Business dictionary.com (2013) defines ‘Quality’ as a measure of excellence or a state of being free from defects, deficiencies and significant variations. Recently, a study published in TQM journal, reported that service quality, specifically in the higher education setting, comprises seven dimensions viz. input quality, curriculum, academic facilities, industry interaction, interaction quality, support facilities and non academic processes (Jain et al., 2013). Of late, TQM has become the emerging philosophy in management. Its newness lies in its practice and experiments done in professional education institutions 1 (Agarwal et al., 2011). Developed countries such as Japan, USA and UK have already benefitted from using TQM principles in the field of professional education. India has used it in some of its professional educational institutions, and significantly, it has directly impacted the quality of professional education in those specific institutions (ibid.). The much-evident, slow infusion of ‘quality inspired lessons’ in management education, is something of a concern for academic leaders. A recent study, in a startling revelation, brought out that ‘to inculcate new knowledge’ or ‘to build skills’, the business educators at the MBA level were not using research knowledge (Klimoski & Amos, 2012). Conclusion was drawn that management education did not put in sufficient / required effort; it did not engage in evidence-based teaching2 . Its appalling, in that it inspires a sense of deja vu, as, just over 50 years ago, two key reports commissioned by Ford Foundation and Carnegie Foundation respectively 3 , went public in criticizing the rigor of business school programs, the caliber of business students, and the quality of teaching (ibid.). These reports gained visibility at their time, and argued, going to the extent of pressing for - a more analytic and theory based approach. A current analysis of management education research carried out in India, revealed that most research carried out, is primarily of an academic nature, and rare attempts are made in direction of studies with policy implications (Sahoo, 2012). 1. 2.

Historically, TQM has been used in business and industry. The researchers based their finding, with reference to concepts of ‘leadership’ and ‘leadership development’. Further, they claimed, not much effort was being put in to transform students into leaders. The reports were famously referred to, as the “Gordon and Howell” report (Gordon and Howell, 1959) and the Pierson Report (Pierson, 1959). ix

Seemingly, the incomprehensible, and, a not so favourable shift in focus, was affected, in some measure, by budget cuts imposed on the education sector in early 1991, as part of the economic reforms package - which acutely affected higher management education (ibid.). The history of modern system of education goes back 150 years ago. The journey from education to learning or to reap the experience curve advantage is chequered. Various 20th century researches dating back to the 1930s have shared this view (for instance Wright, 1936; Hirschmann, 1964 and Ghemawat, 1985). At the same, Management research in India can no longer be considered in its infancy. The main issues and challenges facing Management Education are information technology, globalisation, the role of faculty, competition and business model performance (Howard et al., 2013). Some Institutes have infused, quite actively, the quality ethos in their practice. As per a recent 2012 report, the Indian School of Business has as many as 100 foreign based faculty members4 delivering the same modules which they teach at Kellogg, Wharton, London Business School etc. One may infer that the phrase ‘quality of education’ is a difficult one to discuss in a concrete way, however strands of initiatives can lead us in the desired direction. Pertinently, Feigenbaum professes that in the ‘invisible competition’ between the Institutes, the quality of education emerges as the key factor. The supporting argument is that the quality of products and services thus produced is defined by the action, decision-making and thoughts of managers, engineers, workers and faculty members. Globalisation, privatisation, internationalisation are all simultaneous occurrences in higher management education. These phenomenal changes, heading towards redefining professional management education, are nothing but a reflection of how business environment has been similarly affected by these macro-moves. There is a need to delve deep into the intricacies of the environmental dynamism to analyze its impact on the system of higher education in the country (Singh and Tripathy, 2006). Strategic challenges such as growth in number of institutions that offer business education, internationalization of the market and the players and increased pressure for external validation and accreditation, have put pressure on this sector, which as it is remains plagued with over expectation and also blamed for perceived under delivery. Additionally there is some conflict 4.

The foreign faculty invited at ISB spends a few weeks at the institute to deliver few targeted courses, that are taught at foreign business schools. For details refer Sahoo(2012). x

within the academic community about the goals and behaivours, on which academic leaders should focus (Kalargyarou et al., 2012). While understandably, India was a late starter in modernization of Industry (in general), it is a front-runner in the emerging knowledge based New Economy. Professional Education and Management education in particular, that is increasing in dimensions largely due to the favourable and permissive policies (also business conditions), needs to be shaped by TQM principles to ensure quality in teaching and learning. Based on the above observations and insight, we can implement new policy and strategies for management education. REFERENCES Agarwal, P.K., Kumar, P., Gupta, S., Tyagi, A.K.(2011) “Implementing total quality Management in Professional Educational Institutes in India”, Advances in Management, 4(4): 18 - 22. Businessdictionary.com 2013, WebFinance Inc., accessed 21 June 2013, http://www.businessdictionary.com/definition/quality.html. Feigenbaum (1993) Total Quality Control: Engineering and Management. Ghemawat, P. (1985) Building strategy on the experience curve, McGraw-Hill, New York. Gordon, R.A., & Howell, J.E. (1959) Higher education for business, Columbia University Press, New York. Hirschmann, W. (1964: Jan-Feb) Profit from the learning curve, Harvard Business Review. Jain, R., Sahney, S., & Sinha G. (2013) “Developing a scale to measure students’ perception of service quality in the Indian context”, TQM Journal, 25(3): 276-294. Kalargyrou, V., Pescosolido, A.T., Kalargiros, E.A. (2012) “Leadership skills in Management Education”, Academy of Educational Leadership Journal, 16(4): 39-63. Klimoski, R. & Amos, B. (2012) “Practicing Evidence-based Education in Leadership Development”, Academy of Management Learning and Education, 11(4): 685-702. xi

Pierson, F.C. (1959) The education of American businessmen, McGrawHill, New York. Sahoo, K. (2012) “Present Scenario of Management Education in India”, SIES Journal of Management, 8 (1): 74-82. Singh, K.P. & Tripathi, S.K. (2006) “150 years of Higher Education in India and Emerging Environmental Challenges : Towards inventing the Future”, University News, Special Issue, November, Association of India Universities, New Delhi (India). Thomas, H., Thomas, L., Wilson, A. (2013) “The unfulfilled promise of management education (ME): the role, value and purposes of ME”, Journal of Management Development, 32(5): 460- 476. Wright, T.P.(1936) “Factors Affecting the Cost of Airplanes”, Journal of the Aeronautical Sciences, 3(4): 122-128.

IILM Institute for Higher Education

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Dr. Sangeeta Chopra

GENDER STEREOTYPING AND STRESS: EXAMINING THE ROLE OF SELF EFFICACYAS A MEDIATOR AMONG WOMEN EXECUTIVES IN CALL CENTRES Monica Verma1

Kanika T. Bhal2

Prem Vrat 3

Recently, call centres have emerged as the ‘most sought after’ work places for Indian women. Researches indicate that call centres have completely transformed the status and personality of Indian women, but women in call centres still feel stressed. It has been found that gender stereotyping is one of the reasons of stress among women executives. Different call centres have taken various measures to make their organizations stress-free but still women report of stress due to gender stereotyping. The study was aimed at finding out the relationship between gender stereotyping and stress and to determine the effect of self efficacy on the relationship between the two. The study was conducted on the data collected from 138 women employees of various call centres in NCR. Random sampling was used to collect the data. Multiple regression was applied to test the model fit using SPSS version 17.0. The results indicated that self efficacy totally mediated the relationship between gender stereotyping and stress. Keywords: Gender Stereotyping, Self Efficacy, Stress, Call Centres, Women. INTRODUCTION It is well documented that the economy of India has undergone a major transformation in recent years. A fundamental feature of this change has been the increase in the number of jobs in the service sector especially the 1.

2. 3.

Monica Verma, Associate Professor, School of Management, Inderprastha Engineering College, 63, Site IV, Industrial Area, Sahibabad-201005, Ghaziabad (U.P.), E-mail: monicadev00@gmail.com Kanika T. Bhal, Professor and Head, Department of Management Studies, Indian Institute of Technology, Delhi-110016, E-mail: kanika@dms.iitd.ernet.in Prem Vrat, Vice Chancellor, ITM University, HUDA, Sector 23 A, Gurgaon122017 (Haryana). E-mail: premvrat@gmail.com

2 Gender Stereotyping and Stress: Examining ...

IT-BPO industry, to the extent that the service industry now dominates employment. As this general shift towards the service has taken place, there has been a considerable increase in the number of women participating in the labour market, not only in India but the world over. In India, the number of women professionals over the years was estimated to reach 31 percent in FY 2009 (NASSCOM, 2011) whereas research on call centres in UK has indicated that women make up the majority of call centre workers, reportedly accounting for around 70 percent of employees (Austin Knight/ Calcom, 1997; Mitial, 1998; Belt et al., 1999). The reasons for the feminization of IT-BPO industry are of course manifold and highly complex. A number of authors have observed that females are increasingly in demand because women are believed to naturally possess in abundance many of the social skills required by employees in the service based economy (Adkins, 1995; McDowell, 1997; Woodfield, 1998; Bradley et al., 2000). This is particularly the case in so-called ‘interactions service occupations’, in which the main emphasis is on ‘voice to voice’ or ‘face to face’ contact with people (Leidner, 1991; Adkins, 1995; Taylor, 1998). It has been claimed by employers that women are more suited to the work than men in call centres because they are more likely to possess the appropriate social skills, particularly the ability to ‘smile down the phone’ (Marshal & Richardson, 1996). Shanker (2008) also indicated that women are considered to be apt for call centres due to their feminine voices. Ng & Mitter (2005) in their research concluded that call centres have emerged as the “most sought after” work places for Indian women in recent times. In India, the mobility, spending power, decision-making, etc of women have been traditionally controlled by men, but call centres have helped women to become more assertive. The skills ranging from soft skills like communication, listening and interpersonal skills to technical competency have given the women a sense of confidence (Ng & Mitter, 2005). Attempts made by a few scholars (Gayathri & Antony, 2008) to study the impact of call centres on the lives of women explained how the growth of call centres in India has contributed to social equity of women. Relatively, little empirical research has been carried out that focuses on the implications of increased women labour force in BPO industry on gender relations. As Belt et al., (2002) put it that it is not known that how feminine social skills are being used by employers in the BPOs and to what extent are these skills valued, acknowledged and rewarded? Feminist researchers have long argued that skill definitions are ‘saturated with gender bias’ and Management & Change, Volume 16, Number 1 & 2 (2012)

Monica Verma, Kanika T. Bhal & Prem Vrat 3

as a result women’s work has traditionally not been defined as ‘skilled work’(Phillips & Taylor, 1980). Some other researches indicated that call centres merely represent the latest manifestation of a long established trend in which women are recruited to routine skilled work (Game & Pringle, 1984; Bradley, 1986). In addition to this, Belt et al., (2002) found marked differences within the case study call centres in Europe. Men dominated the specialized technical support roles in the computer services call centres such as on software ‘help-desks’ whereas women concentrated on the whole in the customer service roles. Bradley (1986) asserted that both employers and managers viewed male and female labour differently and also used them differently. There is a perception that though call centres have completely transformed the status and personality of Indian women, yet women in call centres feel stressed. However there is no empirical or research based evidence available which proves the above point. Since stress has dysfunctional consequences on workers, an understanding of the effects of workplace elements on job stress is likely to help organizations in creating a ‘low-stress’ environment and assist employees in coping with stress. Keeping this view in mind, an attempt has been made in this study to explore the relationship between gender stereotyping and stress. Brief & Aldag (1981) implied that one of the immediate effects of job related stressors may be to lower ones’ level of self efficacy. Reduction in self efficacy beliefs, in turn may lead to job related strains. According to this viewpoint, the study also tries to examine the mediating effect of self efficacy on the relationship between gender stereotyping and stress. This is followed by literature review on gender stereotyping as a cause of stress, stress itself and the role of self efficacy as a mediator. In the next section, we understand the concept of single mediator model followed by research methodology and data analysis. Discussion and limitations are included in the last section. LITERATURE REVIEW Stress Stress can be triggered by a large number of variables relating to the environment, the organization and the individual (Robbins & Judge, 2007). Parker & DeCotiis (1983) defined stress as an awareness or feeling of personal dysfunction resulting from perceived conditions at the workplace, as well as one’s psychological and physiological reactions to these uncomfortable or undesirable conditions. For employees, the climate of the Management & Change, Volume 16, Number 1 & 2 (2012)

4 Gender Stereotyping and Stress: Examining ...

organization plays a major role in causing job stress. This is advocated by a number of researchers. Organizational climate has been proposed as a contributor to stress (Hemingway & Smith, 1999; Zeffane & McLoughlin, 2006). A favourable evaluation of the work environment will lead to lower stress, whereas an unfavourable psychological atmosphere perceived by the employees will result in higher stress. Our purpose in this study was to examine whether gender stereotyping which creates an unfavourable organizational climate serves as direct antecedent of job stress. We have focused on gender stereotyping because it represents the general practice that pervades many organizations as advocated by a number of researchers (Kanter, 1977; Truman & Baroudi, 1994; Kramer & Lambert, 2001; Von Hellens et al., 2001). In an industry that is volatile, has high attrition rate, employees’ perceptions of their organization’s climate are important (Akbulut, Kuzu, latchem & Odabasi, 2007). Gender Stereotyping and Stress The process of categorizing an individual into a particular group and assigning a set of traits to the individuals on the basis of the group membership is termed as stereotyping (Davidson & Cooper, 1992). Both organizational structure and processes are embedded with gender (Acker, 1990; Halford et al., 1997) and everyday language and activity create, produce and reproduce gender images (Gavey, 1989). According to Halford et al., (1997), “organizational designs, practices and cultures are constructed within economic, social and cultural processes that are always already gendered”. A survey on Chinese students majoring in management revealed that women are described as more incompetent, slower, weaker, more follower-thanleader, more lenient, more democratic, less active and more friendly than male managers (Frank, 2001). These ‘more’ or ‘less’ affect women’s working styles and their stress levels. Kanter (1977) revealed that in a male-dominated organization, men tended to exaggerate the culture differences between men and women and persuaded women to perform stereotypic gender roles. According to a research by Darity & Mason (1998), “stereotypes or prejudice is the base for sorting men and women into different occupations by employers”. Kramer and Lambert (2001) concluded that women are more likely to be directed into occupations with low pay and with no promotion ladders, while men are more likely to be channeled into occupations with high pay and promotion opportunities. This is supported by earlier researches also. Rosenberg et Management & Change, Volume 16, Number 1 & 2 (2012)

Monica Verma, Kanika T. Bhal & Prem Vrat 5

al., (1993) found that women in the law profession report less discrimination at the “front door” (hiring and recruiting) but more discrimination on the job (salary, promotion and job assignment). Truman & Baroudi (1994) in their research on gender differences in IT careers, found that women received lower salaries than men even when job level, age, education and work experience were controlled. Also, women are perceived to have less favourable chances for promotion than men (Igbaria & Baroudi, 1995). These gender differentials in salary and promotions have been explained by Sheinin (1989) and others (Barinaga, 1992; Konrad & Cammings, 1997) by suggesting one factor i.e., the variety of roles that women assume – wife, mother and caretaker – during peak periods of their professional and academic careers. These factors often result in self-selection into gender-typed professions and positions within professions (Ragins & Sundstrom, 1989). It has been suggested by Gefen (2000) that genderrelated differences and stereotypes are so strong that many societies have predetermined communication styles that are expected of men and women. Von Hellens et al., (2001) reported that women confirmed that they receive patronizing and offensive treatment from male peers, although not to the extent that it would cause them to leave the industry. Von Hellens & Nielsen (2001) in their study on Australian population suggested that negative experiences typically engendered a coping response from women ICT professionals, rather than outright withdrawal from the industry. Women tend to be stereotyped as staff persons who do not take risks rather than as line persons, whereas men are innovators and designers (D’Agostino, 2003). One of the factors suggested by Ahuja (2002) causing barriers to female minor positioning in the IT industry is established discriminatory practices along with old boy’s network and lack of female role models and mentors. Similarly, one of the four obstacles identified by Lui & Wilson (2001) restricting the career of female managers employed in IT is gender stereotypes and attitudes, in addition to family responsibility, working time constraints and lack of confidence. Thus, these sanctions in the form of gender stereotypes may result in gender-inappropriate behaviour (Konrad & Linehan, 1999; Scandura & Baugh, 2002). On the basis of the literature, it can be conjectured that gender stereotyping can be stressful and could become a source of leaving the organization for women professionals of ITES sector. Therefore, we propose that H1: There will be a positive relationship between gender stereotyping and stress among the women employees of the call centres. Management & Change, Volume 16, Number 1 & 2 (2012)

6 Gender Stereotyping and Stress: Examining ...

Self Efficacy, Gender Stereotyping and Stress Bandura (1977, 1978) defined self efficacy as a person’s beliefs about whether he/she can successfully perform a task. It involves either effort or ability. According to Bandura (1977), self efficacy beliefs are determined primarily by ‘inactive mastery’, which depends on both perceived and actual prior task performance. Lately, some organizational researches have begun to show some consistent relationships between self efficacy beliefs and task performance. Barling & Beattie (1983) showed that strong self efficacy beliefs were associated with high levels of sales performance. Also, Taylor, et al., (1984) found a similar relation between self efficacy beliefs and faculty research productivity. A few studies have found that self efficacy is affected by objective, structural and occupational conditions, such as social class, occupational prestige, work conditions, autonomy, earnings and employment status (Downey & Moen, 1987; Gecas & Seff, 1989). Gecas & Seff (1989) found that social class and occupational prestige affected self efficacy through work conditions, such as work complexity, routinization and supervision. Downey & Moen (1987) showed that higher earnings increased women’s and men’s sense of personal control. Of late, some researches have been conducted to explain the role of self efficacy in areas other than job performance. The study of work related stress, in particular, has been conducted under the assumption that employees are rather passive recipients of stressful organizational conditions (Jex & Gudanowski, 1992). According to Beehr & Newman (1978), environmental stressors interact with characteristics of the individuals to produce stress reactions. According to this model, self efficacy beliefs can best be conceptualized as a moderator variable. It might be predicted that individuals who do not believe that they will be able to carry out their job responsibilities (low levels of self efficacy) would view organizational stressors as being more threatening and show some positive reactions than those who are more confident (high levels of self efficacy). In the same way, Jex & Bliese (1999) surmised that individuals with high self efficacy try to manage stressors, whereas those with low self efficacy have a tendency to negatively react to them. Moreover, Jex et al., (2001) argued that stressors would be more threatening to individuals who do not perceive themselves as having the capability to perform their tasks. However, Brief & Aldag (1981) concluded that one of the immediate Management & Change, Volume 16, Number 1 & 2 (2012)

Monica Verma, Kanika T. Bhal & Prem Vrat 7

effects of job related stressors may be to lower one’s level of self efficacy. These reductions in self efficacy, in turn may lead to job related strains. This viewpoint attributes self efficacy as a mediating variable. Gecas (1991) pointed out that when the sense of efficacy of people is threatened, they are motivated to maintain, protect and enhance their self efficacy. They do so either by leaving the social context that threatens their self efficacy or diluting the significance of the social context. The reduction in self efficacy may facilitate women to leave “voluntarily” the hostile environment in order to prevent further erosion of their self efficacy. The deterioration of self efficacy can also increase women’s involuntary job turnover by impairing their job performance and work motivations. Thus, we argue that in call centres, workplace stressor such as gender stereotyping would reduce the self efficacy of women employees. This reduction in self efficacy makes it difficult for the females to cope up with stress. On the other hand, those who are persistent in their efforts towards achieving their goals (high self efficacy) are likely to use effective coping strategies, which in turn will help them to adapt to workplace stressor, i.e, gender stereotyping. On the basis of the literature that stress levels differ according to one’s belief about oneself, it can be conjectured that, H2: There will be a negative relationship between Gender Stereotyping and Self Efficacy among women professionals in call centres. Also, H3: The positive relationship between gender stereotyping and stress will be mediated by self efficacy. MEDIATOR MODEL There is an increasing trend of assigning significance to single mediator models among the researchers in social sciences. In these models, the effect of an antecedent is transmitted to a consequence through a mediator (James & Brett, 1984). According to Baron & Kenny (1986), a variable functions as a mediator when it meets the following conditions: Condition 1:

Independent variable leads to dependent variable Drawing analogy to our proposed model, gender stereotyping leads to stress. Management & Change, Volume 16, Number 1 & 2 (2012)

8 Gender Stereotyping and Stress: Examining ...

Condition 2:

Independent variable leads to mediating variable. In our model, gender stereotyping leads to self efficacy

Condition 3:

Mediating variable leads to dependent variable rendering the previously significant relation between the independent and dependent variables no longer significant. In our model, self efficacy leads to stress rendering the previously significant relation between gender stereotyping and stress as non significant.

The conceptual model based on the model of Baron & Kenny (1986) is as given under: Figure 1:

A Socio-Psychological Model of Gender Stereotyping and Stress with Self Efficacy as a Mediator (a) denotes direct relationship between gender stereotyping and stress (b & c) denote mediated relationship between gender stereotyping and stress a (+)

Gender Stereotyping

b (-)

c (-)

Self Efficacy

Stress

METHODOLOGY The sample was drawn from Business Process Outsourcing organizations located in National Capital Region of Delhi, with follow-up over the course of a 12-month period from January 2011 to December 2011. The survey was administered to the women employees of six call centres. The sample consisted of 138 women employees responsible for different processes in the call centres. The break up of the number of respondents from each call centre is given in Appendix - A. The names of the call centres have not been revealed as confidentiality was promised to them. Company officials were hesitant to allow us to gather information on the survey questionnaire, Management & Change, Volume 16, Number 1 & 2 (2012)

Monica Verma, Kanika T. Bhal & Prem Vrat 9

for they believed that our questionnaire might influence the respondents. However, assurance of confidentiality was presented both to the respondents as well as to the officials. Demographic profile of the respondents is shown in appendix. As can be seen from the appendix, a significant number of them were between 22-26 years of age and had worked in the organizations for 0-3 years. Most of the respondents (82) were post-graduate. The sample was composed of 47 married and 91 unmarried women. MEASURES Measures used in this study were both developed as well as adapted from previous researches. A brief description of each measure is provided below: Gender Stereotyping Gender Stereotyping was assessed by developing a scale adapted from the questionnaire of the study undertaken by Nasscom and IIM, Ahmedabad (2008). The scale consisted of seven items (eg. My experience is that when there is an assignment in a new area of work the company is more likely to hand it to a man, My male colleagues within the organization are skeptical of performance of women, Women in my organization need to work harder than men to prove themselves). All the items in the scale were measured using a five point rating scale from strongly disagree (1) to strongly agree (5). A five response option was included for respondents to indicate their agreeableness to each statement. The internal reliability of the scale in this study was satisfactory with Cronbach’s coefficient alpha as 0.826. Stress Stress was measured with four burnout and anxiety related items (eg. I feel emotionally drained by my job, I feel tense at my job) adapted from the scale developed by Tate et al., (1997). Participants indicated on a six-point scale the degree to which they experienced each of these symptoms. Firth et al (2004) reported an internal reliability co-efficient of 0.87 and Siong et al., (2006) indicated Cronbach’s alpha as 0.90 for this scale. In this study, Cronbach’s alpha = 0.775. Self Efficacy Self efficacy was measured using the scale developed by Sherer et al., (1982) consisting of seventeen items. The original scale was modified by Management & Change, Volume 16, Number 1 & 2 (2012)

10 Gender Stereotyping and Stress: Examining ...

Bosscher & Smit (1998) and was reduced to twelve item version consisting of three sub-scales. The persistence parameter of self efficacy consisting of four items was used in this study. The Cronbach’s coefficient alpha was found to be 0.64 in the study conducted by Bosscher & Smit (1998). For this study, the internal reliability was satisfactory with a = 0.790. Control Variables We controlled for experience, age and marital status as these variables might affect self efficacy and stress level of women employees in call centres. ANALYSIS AND RESULTS To test our hypotheses regarding the effects of control variables, i.e., experience, age and marital status and gender stereotyping on stress, we conducted multiple regression analysis with gender stereotyping as a predictor variable and stress as a criterion variable respectively. Using the enter method, a significant model emerged: F (4,133) = 4.972, p < 0.005. The results for condition 1 appear in Table 1. Experience and gender stereotyping were significantly related to stress (ß = 0.396; p < 0.001 and ß = 0.202; p < 0.05 respectively), thus offering support for H1. Age and marital status had no significant relationship with stress (p = 0.05 & 0.697 respectively). Table 1 Relationship between control variables, gender stereotyping and stress. Condition 1 Unstandardized Coefficients Model 1

Std. Error

Standardized Coefficients Beta

Sig.

(Constant)

.286

.412

.695

.488

Experience

.457

.116

.396

3.955

.000

Age

-.230

.120

-.217

-1.918

.057

Marital Status

-.081

.207

-.039

-.390

.697

Gender Stereotyping

.300

.122

.202

2.457

.015

Dependent Variable: Stress

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Monica Verma, Kanika T. Bhal & Prem Vrat 11

The results of the regression of gender stereotyping on self efficacy (condition 2) are shown in Table 2. A significant model emerged with F (4,133) = 17.014, p < 0.001. Gender stereotyping was significantly related to self efficacy (ß = -0.423, at p < 0.001). Among the control variables, age was found to be significantly related to self efficacy (ß = 0.399, p < 0.001). Thus, H2 was accepted. On the other hand, experience and marital status did not support our hypotheses (p = 0.855 and 0.388 respectively). Table 2 Relationship between control variables, gender stereotyping and self efficacy. Condition 2 Unstandardized Coefficients Model 1

Std. Error

Standardized Coefficients Beta

Sig.

14.119

.00

(Constant)

4.132

.293

Experience

-.015

.082

-.016

-.183

.855

.343

.085

.399

4.034

.000

Marital Status

-.127

.147

-.075

-.867

.388

Gender Stereotyping

-.512

.087

-.423

-5.890

.000

Age

Dependent Variable: Self Efficacy

We used regression based mediation analysis (Baron & Kenny, 1986) to test hypotheses 3 which suggested that self efficacy (persistence) of women employees in the call centres mediated the relationship between gender stereotyping and stress. A significant model emerged: F (5,132) = 5.078, p < 0.001. Table 3 reveals significant linkages between gender stereotyping, self efficacy and stress (condition 3). Self efficacy was found to be significantly related to stress, mediating the relationship between gender stereotyping and stress (ß = -0.217, p < 0.05). The relationship between gender stereotyping and stress became non-significant (ß = 0.110, p = 0.228). Among the control variables, experience was significantly related to stress (ß = 0.393, p < 0.001) whereas age and marital status became non significant. Thus, the results supported H3.

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12 Gender Stereotyping and Stress: Examining ...

Table 3: Relationship between gender stereotyping, self efficacy and stress showing the mediation effect. Condition 3 Unstandardized Coefficients Model 1

Std. Error

(Constant)

1.388

.642

Experience

.453

.114

Age

-.138

Marital Status

Standardized Coefficients Beta

Sig.

2.163

.032

.393

3.977

.000

.125

-.131

-1.105

.271

-.115

.205

-.055

-.561

.576

Gender Stereotyping

.164

.135

.110

1.212

.228

Self Efficacy

-.267

.120

-.217

-2.217

.028

Dependent Variable: Self Efficacy

DISCUSSION The study provides tentative evidence for our proposed research model which uses self efficacy to link gender stereotyping to stress. Our findings suggest several conclusions. Firstly, gender stereotyping is positively related to stress. As the gender stereotyping increases, the stress among women employees also increases. This is supported by a number of earlier researches also. Gefen (2000) and Trauth (2002) talked about stress due to gender related differences and higher standards set for women in ICT industry respectively. Secondly, gender stereotyping and self efficacy are significantly related to each other. As the gender stereotyping increases, self efficacy tends to decrease. This implies that gender stereotyping has negative connotation. The increased gender stereotyping in the organization tends to lower the self efficacy of women which results in increased levels of stress. The lowering of self efficacy might also affect the performance of women employees. This is supported by the research of Barling & Beattie (1983) who showed that strong self efficacy beliefs were associated with high levels of sales performance. Taylor et al., (1984) found a similar relation between self efficacy beliefs and faculty research productivity. The reduction in the levels of self efficacy might also imply a reduction in the commitment level of women employees towards the organization which might further lead to an intention to quit. In a study by Moore (2002), high self efficacy (assessed specifically among nurses in relation to their professional abilities), was associated with reduced intention to quit. Stumpf et al., (1987) also Management & Change, Volume 16, Number 1 & 2 (2012)

Monica Verma, Kanika T. Bhal & Prem Vrat 13

proposed that reductions in self efficacy beliefs lead to more emotion-focused coping, which is generally not as successful as problem-focused coping (Lazarus & Folkman, 1984). Thirdly, it was found that self efficacy plays a mediating role on the relationship between gender stereotyping and stress. As the self efficacy increases, stress decreases, rendering the relationship between gender stereotyping and stress as non-significant. There is total mediation by self efficacy on the relationship between gender stereotyping and stress. This implies that women who are persistent in their efforts i.e., have high self efficacy will not be affected by gender stereotyping and hence, will not be stressed. This might lead to increased level of performance and commitment towards the organization. With reference to control variables, experience was found to be significantly related to stress in condition 1 and condition 3 whereas condition 2 exhibited the significant relation of age with stress. In both the cases, marital status had no effect on stress. CONCLUSION This investigation fills an important gap in the literature on call centres by developing theory about how self efficacy plays the role of a mediator between gender stereotyping and stress in call centres. Our finding of the research is in accordance with Bandura’s (1995) theory which states that self efficacy affects the perception of external demands and mediates the relation between external stressors and psychological stress. As previous studies have shown that occupational conditions such as occupational prestige, work conditions, autonomy, earnings and employment status shape self efficacy (Downey and Moen, 1987; Gecas and Seff, 1989; O’Brien and Feather, 1990), our study gives an understanding of how gender stereotyping in call centres deteriorates the self efficacy of women employees. Reduced levels of self efficacy not only increase the levels of stress but also adversely affect the performance and commitment of women employees. This in turn may increase women’s turnover adding to the already high attrition rate of employees in call centres. Thus, call centres can take appropriate measures and design training programs to improve the self efficacy of female employees so that they develop the ability to cope up with stress in call centres.

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14 Gender Stereotyping and Stress: Examining ...

APPENDIX - A Breakup of the number of respondents S. No.

Name of the call centre

Location of the call centre

No. of respondents

Noida

Gurgaon

APPENDIX - B Demographic profile of the respondents Element

Frequency

Percentage (%)

Age (years old) 18-22

10.14

22-26

48.55

26-30

23.19

30-34

13.04

More than 34

05.07

0-3

65.94

3-6

18.12

6-9

11.59

More than 9

04.35

Graduate

23.19

Post Graduate

59.42

Professional

15.22

Others

02.17

Unmarried

65.94

Married

34.06

Experience (years)

Qualification

Marital Status

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Monica Verma, Kanika T. Bhal & Prem Vrat 15

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Mitial (1998), European Location Study: Call Centres in the UK, Republic of Ireland, Belgium and the Netherlands (Wrexham: Mitial) in Belt, V., Richardson, R., and Webster, J. (2002), “Women, Social Skill and Interactive Service Work in Telephone Call Centres”, New Technology, Work and Employment, 17 (1): 20-34 Moore, K.A. (2002), “Hospital Restructuring: Impact on Nurses Mediated by Social Support and a Perception of Challenge”, Journal of Health and Human Services Administration. Nasscom (2011) “Impact of the IT-BPO Industry in India: A Decade in Review”. Retrieved from http://www.nasscom.in Nasscom-IIMA Survey (2008) “Crossing the Digital Barrier”, retrieved from http://www.nasscom .in on December 24, 2009 Ng. Cecilia., and Mitter. S. (2005) “Gender and the Digital Economy: Perspectives from the Developing World” in “New found Freedom for Indian Women with BPOs” retrieved from http://www.expressindia.com/ news/fullstory.php?newsid=60025 on November 23, 2011. O’Brien, G.E. and Feather, N.T. (1990) “The Relative Effects of Unemployment on the Affect, Work Values and Personal Control for Adolescents”, Journal of Occupational Psychology, Vol. 63, pp. 15165 Parker, D.F., and DeCottis, T.A. (1983) “Organizational Determinants of Job Stress”, Organizational Behaviour and Human Performance, Vol. 32, pp. 160-177. Phillips, A. and B. Taylor (1980), “Sex and Skill: Notes Towards Feminist Economics”, Feminist Review, Vol. 6, pp. 79-88 Ragins, B.R., and Sundstrom, E. (1989) “Gender and power in Organizations: a Longitudinal Perspective”, Psychological Bulletin , Vol. 105, pp. 51-88. Robbins, S.P., and Judge, T.A. (2007) Organizational Behaviour, 12th edition, New Jersey; Pearson Education in Nasurdin, A.M., Ramayah, T., and Beng, Y.C. (2009), “The Impacts of Structure, Climate and Self Efficacy on Stress: A Malaysian Survey”, Asia Academy of Management Journal, Vol. 14(1), pp. 59-79. Management & Change, Volume 16, Number 1 & 2 (2012)

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Rosenberg, J., Perlstadt, H., and Phillips, W. (1993) “Now That We are here: Discrimination, Disparagement, and Harassment at Work & the Experience of Women Lawyers”, Gender and Society, Vol. 7(3), pp. 415-33. Scandura, T.A., and Baugh, S.G. (2002) Advancing Women’s Careers: Research & Practice, Blackwell Publishers, London in Ismail, M. (2008), “Barriers to Career Progression faced by Women”, Gender in Management: An International Journal, Vol. 23(1), pp. 51-66. Shanker, D. (2008) “Gender Relations in It Companies: An Indian Experience”, Gender, Technology & Development, Vol. 12 (2), pp. 185-207. Sheinin, R. (1989) “Women as Scientists: their Rights and Obligations”, Journal of Business Ethics, Vol. 81 (3), pp. 11-55. Sherer, M., Maddux, J.E., Mercandante, B., Prentice-Dunn, S., Jacobs, B., and Rogers, R.W. (1982) “The Self Efficacy Scale: Construction and Validation”, Psychological Reports, Vol. 51(2), pp. 663-671. Siong, Z.M.B., Mellor, D., Moore, K.A., and Firth, L. (2006) “Predicting Intention to Quit in the Call Centre Industry: Does the Retail Model Fit?”, Journal of Managerial Psychology, Vol. 21(3), pp. 231-243. Stumpf, S.A., Brief, A.P. and Hartman, K. (1987) “Self Efficacy Expectations and Coping with Career related Events”, Journal of Vocational Behaviour, Vol. 31, pp. 91-108 Tate, U., Whatley, A. and Clugston, M. (1997) “Sources and Outcomes of Job Tension: A Three Nation Study”, International Journal of Management, Vol. 3, pp. 350-358. Taylor, S. (1998) “Emotional Labour and the New Workplace”, in P. Thompson and C. Warhurst (eds.), Workplaces of the Future (London: Macmillan) in Belt, V., Richardson, R., and Webster, J. (2002), “Women, Social Skill and Interactive Service Work in Telephone Call Centres”, New Technology, Work and Employment, Vol. 17 (1), pp. 20-34 Taylor, M.S., Locke, E.A., Lee, C. and Gist, M. (1984) “Type A Behaviour and Faculty and Research Productivity: what are the Mechanisms?”, Management & Change, Volume 16, Number 1 & 2 (2012)

22 Gender Stereotyping and Stress: Examining ...

Organizational Behaviour and Human Performance, Vol. 34, pp. 402-418. Trauth, E.M. (2002) “Odd Girl Out: An Individual Differences Perspective on women in the Information Technology Profession”, Information Technology and People, Vol. 15(2), pp. 98 118. Truman, G.E., Baroudi, J.J. (1994) “Gender Differences in the Information Systems Managerial Ranks: An Assessment of Potential Discriminatory Practices”, MIS Quarterly, Vol. 18, pp. 129-41. Von Hellens, L., Nielsen, S. (2001) “Australian Women in IT”, Communications of the ACM, Vol. 44(7), pp. 46-52. Von Hellens, L., Nielsen, S., Trauth, E.M.(2001) “Breaking and Entering the Male Domain: Women in the IT Industry”, Proceedings of the 2001 ACM SIGCPR Conference on Computer Personnel Research, Special Interest Group on Computer Personnel Research, Annual Conference, available at http://portal.acm.org/citation.cfm Woodfield, R. (1998) “Working Women and Social Labour”, RUSEL, Working paper no. 33, Department of Politics, University of Exeter in Belt, V., Richardson, R., and Webster, J. (2002), “Women, Social Skill and Interactive Service Work in Telephone Call Centres”, New Technology, Work and Employment, Vol. 17 (1), pp. 20-34 Zeffane, R., and McLoughlin, D. (2006) “Cooperation and Stress: Exploring the Differential Impact of Job Satisfaction, Communication and Culture”, Management Research News, Vol. 29(10), pp. 618-631.

Management & Change, Volume 16, Number 1 & 2 (2012)

IMPACT OF FOREIGN CAPITAL FLOWS ON ECONOMIC GROWTH: EMPIRICAL EVIDENCE FROM INDIA Dr. Minakshi Paliwal1

Dr. Sumanjeet Singh2

The role of foreign capital in economic growth is much discussed nowadays but remarkably little analysed. The basic objective of this paper is to investigate the causal long run relationship between FCIs (Foreign Capital Inflows) and the economic growth of India. The FCIs-growth linkage assumes that the foreign capital inflows provide a significant amount of contribution to the economic growth. We examine the linkage using a model on the basis of the source of financing available to an economy i.e. domestic capital and foreign capital. The model is then analysed using co-integration test and Error Correction Model (ECM) technique. The paper concludes that there is a bidirectional causality between FCI and GDP; GDCF (Gross Domestic Capital Formation) and FCI but there is a unidirectional causality between the GDCF and GDP. Keywords: Foreign Capital, Economic Growth, Gross Domestic Capital Formation. JEL Codes: F3; F4; O4; O40 The relationship between financial openness and economic growth remains the subject of heated controversy (Edwin, 1950; Lucas, 1990; Rodrik and Andres, 1999; Joshua et al., 2010; Albuquerque, 2003 and Pierre and Olivier, 2009). While there is a broad agreement among economists on the desirability of open trade in goods, there is much disagreement with respect to the virtues of financial openness (Mohanty, 2012). In theory, free flow of capital across borders should promote investment and growth in recipient countries. In practice, however, the international historical experience fails to yield convincing evidence of a positive relationship between foreign capital flows 1.

Dr. Minakshi Paliwal, Assistant Prof., Department of Commerce, Ramjas College University of Delhi, Delhi-110007. E-mail: minakshicfa@yahoo.com

Dr. Sumanjeet Singh, Assistant Prof., Department of Commerce, Ramjas College, University of Delhi, Delhi-110007. E-mail: sumanjeetsingh@gmail.com

24 Impact of Foreign Capital Flows on Economic Growth: Empirical...

and growth3 (Joshua et al., 2010). Added to this, liberalization of the shortterm capital account has invariably been associated with serious economic and financial crises in Asia and Latin America in the 1990s (Pablo, 2004; Sethi, 2011; Haussman and Velasco, 2002 and Calvo et al., 2002). Since Mexico in 1995 and on to Thailand, Indonesia, Korea, Russia, Brazil, Argentina and Turkey, all recent crises have tended to be â&#x20AC;&#x153;financial crises,â&#x20AC;? that is capital-account dominated. They were accompanied by large shifts in interest rate spreads between emerging markets and world financial centers, and by sharp exchange rate movements (SĂŠrgio, 2001). Thus, there is an ongoing debate on the pros and cons of Capital inflows (Feldstein and Charles, 1990; Manzocchi and Philippe, 1997) i.e. is there any strong positive connection between foreign capital inflows (FCIs) and growth? Evidence on this very important question is far from unambiguous, with China lending support and Brazil negating it (Silkdar, 2006). Since 1994, Brazil has attracted enormous foreign direct investment (FDI) from developed countries, but neither the growth rate nor the export prospects have shown commensurate results. Theoretical and empirical research on the role of foreign capital in the growth process has generally yielded conflicting results (Waheed, 2004). The study by Carkovic and Levin (2002) failed to find strong evidence of a positive correlation between FDI inflows and output growth. Kose et al., (2006) find little robust evidence for long-run growth benefits from global capital inflows. They suggest that international financial integration brings collateral benefits-greater financial development and better investments-but these do not necessarily or immediately translate into superior growth outcomes. Prasad et al., (2006) go one step further. They emphasize the negative correlation between growth and capital flows in developing countries, and conclude that international capital may even hurt economic growth in poor countries. Conventionally, the two-gap approach justifies the role of foreign capital for relaxing the two major constraints to growth (Sethi, 2010; Chenery and Burno, 1962; Mckinnon, 1964). In the neoclassical framework, however, capital neither explains differences in the levels and rates of growth across countries nor can large capital flows make any significant difference in the growth rate that a country could achieve (Krugman, 1993). In the subsequent resurrection of the two-gap approach, the emphasis has generally laid on the preconditions that could make foreign capital more productive in developing countries. The important preconditions comprised presence of surplus labour and excess productive demand for foreign exchange. With the growing influence of the new growth theories in the second half of the 3.

Even strong proponents of free trade such as Jagdish Bhagwati have expressed scepticismabout the gains from unfettered trade in financial assets.

Management & Change, Volume 16, Number 1 & 2 (2012)

Dr. Minakshi Paliwal, Dr. Sumanjeet Singh 25

1980s that recognized the effects of positive externalities associated with capital accumulation on growth, the role of foreign capital in the growth process assumed renewed importance (Sethi, 2010). Fitz Gerald (1998) theoretically argues that higher capital inflows lower interest rates, which help increase investment and economic growth. In their attempt to measure the link between growth and capital inflows into India, Marwah and Klein (1998) concluded that for every one percentage growth point, 0.351 is generated by the growth of domestic and foreign capital nested together, 0.569 by Labor and 0.08 by imports. The contribution of the two types of capital to the growth in productivity can be allocated in proportion to their respective weights in the total nest. A recent study of Joshua et al., (2011) concluded that FDI flows- both inflows and outflows-are positively associated with economic growth, especially when exchange rates are stable and monetary policy independent. In contrast, portfolio investment and the growth of equity investment associations with growth are statistically insignificant (Joshua et al., 2011). Rachidi and Saidi, (2011) analyzed the effect of foreign direct investment and portfolio investment in both developed and developing countries. The panel data covers the period of 1999-2009 and comprises of 100 countries. Popular methods of poled, random effect and fixed effect models have been used in the study. Results suggested that FDI has a significant positive impact on real per capita growth. Also no evidence was found that portfolio investment enhances output growth in developing countries. However this is positive and significant for developed countries, when the GMM (Generalized Method of Moments) estimator is used. In random effect the coefficient of FDI remains positive but statistically insignificant, and the portfolio investment remains negative and insignificant for all the countries. METHODOLOGY With this background, our aim is to examine the impact of foreign capital flows FCIs, which include FDI and FPI on economic growth of India 4 . India, being one of the fastest growing emerging markets in the world makes an interesting case to examine the role of capital flows in its growth5 . Also it has been an economy with a very high proportion of domestic saving in its 4.

The literature regarding the impacts of Foreign Capital Inflows on economic growth in developing countries is extensive. However, we noticed a lack of studies that analyse both FDI and FPI in the same model, hence comparing their impact within the same setting. Since FDI and FPI are the two major sources of external capital we believe such a comparison is necessary. Management & Change, Volume 16, Number 1 & 2 (2012)

26 Impact of Foreign Capital Flows on Economic Growth: Empirical...

total investment. Thus it is worth examining the role of capital flows in the Indian economy. India has been receiving significant amounts of foreign capital since the beginning of 1990s. The reference period for this study is 1992-2010. Prior to this sample period, the Indian economy witnessed very little amount of the capital flows on account of the capital account restrictions and relatively closed econnomy. After the balance of payment crisis in 1990-91, the restrictions were gradually removed and subsequently, India attracted huge amount of foreign capital over time during the sample. Hence it is worth examining the role of this sudden spurt in capital flows in the economic growth, investment, savings and the capital formation. Different type of studies were undertaken in order to understand the impacts of foreign capital inflows (FCIs) on the economic growth (Aghion et al., 2006; Rodrik, 2006; Lane et al., 2002; Henry, 2006; Borensztein et al., 1998; Tressel and Thierry, 2007; Bekaert et al., 2005). Most of the studies have focused on the impact of foreign direct investment on economic growth (Figlio and Blonigen, 2000; Alfaro et al., 2004; Ramachandran, and Shah, 1997; Djankov and Hoekman, 1998; Aitken and Harrison, 1999 and Borensztein et al., 1998). Few studies were focused on foreign aid (Boone, 1996; Knack, 2001; Dalgaard et al., 2001 and Easterly et al., 2004). And, some of the studies focused on the impact of FCI on the domestic savings, investments and capital formation. 5.

India’s growth trajectory over the last decade has thrown up a direct link between capital flows and GDP expansion. While domestic consumption is a big growth booster, nearly 20 per cent of the country’s growth has been fuelled by capital flows-both portfolio and foreign direct investment. For instance, in financial year 2000-2001, while GDP grew 4.4 per cent year-on-year, capital flows stood at US$6.5 billion. As foreign investors saw the emergence of a dynamic entrepreneurial class and a hungry middle class, they were willing to pay a premium for economic growth that promised to breach the nine per cent level. However, the worrying bit is that India is heavily reliant not on the more sticky FDI, but the more volatile FII inflows. Over the last three years, of the total capital inflows of US$120 billion, 61.8 per cent have come from non-FDI sources. According to Morgan Stanley, over the last 12 months, as portfolio equity inflows have slowed, dependence on debt-creating inflows has shot up. The share of debt-creating inflows is expected to rise to 65 per cent in FY2012, from 44 per cent in the 10-year period of FY2001-10. Deutsche Bank’s global research team says capital flow volatility is not only becoming a source of stress to the external account, it’s impacting growth as well. When flows dry up, they impact investment, which, in turn, has adverse implications for growth. Indeed, in recent years, India’s growth trajectory has begun to move hand-inhand with capital flows.

Management & Change, Volume 16, Number 1 & 2 (2012)

Dr. Minakshi Paliwal, Dr. Sumanjeet Singh 27

While some other researchers paid much attention to study the impact of FCI on the debt burden, GDP growth rate etc (Razin et al., 1998; Mohan, 2008; Joshi, 2007; Marc and Gail, 2005), Some other studies focused upon the impact of FCI on the different sectors of the economy like the agricultural sector, energy and the industrial sectors, and social sectors (like health and education etc.). It is difficult to analyze the effect of foreign capital inflows on all the sectors in a single study and as described earlier the major objective of this paper is to analyze the impact of foreign capital inflows (FCIs) on the growth rate of India. Therefore, the researcher narrows down the analysis to only the impact of FCIs on GDP growth. But, GDP growth of an economy depends upon the various factors like saving, investment and capital formation. The general objective of this study is to examine the relationship between FCIs and economic growth in India using recent advancement in time-series techniques. The specific objectives are to identify factors affecting economic growth in the Indian economy and test the co-integration relationship between a few variables affecting GDP growth in India. A total of 18 observations over the period of the study 1992-2010 have been used for analyzing the relationship. The data for the study have been taken from the Handbook of Statistics on Indian Economy published by RBI (Reserve Bank of India). MODEL The FCIs-growth linkage assumes that the foreign capital inflows provide a significant amount of contribution to economic growth. There are a number of factors which contribute to GDP growth of any country including consumption, investment, domestic and foreign capital etc. Among all these factors some factors play a very vital role. Therefore, the researcher observes the relationship between the GDP and some factors, which contribute to the GDP. We assume a production function in the form of (1) Y = f (L, K) Where, Y represents real aggregate output, K is the capital and L is the land. In this production function the researcher assumes that the land is the fixed factor because the researcher applies the above production function to an economy i.e. India. And K (Capital) is divided into two parts that is domestic capital and international capital. Output is measured in terms of GDP growth. As the domestic capital data was not available the researcher used the Gross domestic capital Formation (GDFC) as a proxy variable to the Domestic Capital. Therefore, the production function becomes Management & Change, Volume 16, Number 1 & 2 (2012)

28 Impact of Foreign Capital Flows on Economic Growth: Empirical...

(2) GDPFC = f (FCIs, GDCF) By total differentiation of the equation (2) with respect to time and division of both the sides of resulting time derivative by GDP, we can specify the linear growth model of the form: (3)

& + α GDCF & GDP& = α + α1 FCIs 2

Where a variable with a dot over it indicates its first derivative, i.e. dY/dt; and a’s are the respective elasticities. For the application of multivariate cointegration techniques, the equation (3) can be represented in the following linear logarithmic regression form, (4) Where, L represents the natural logarithms of the variables and e the stochastic error term. As the first difference reflects the rate of change of each variable, equation (4) can be used to examine both the short and the long run relationship between the economic indicators. The investigation of the long run relationship between LGDP, LFCIs, LGDCF in a co-integration framework begins with an examination of the properties of the data. If the variables are integrated of order one, the determination of the co-integration rank using Johansen and Juselies (1990) maximum likelihood co-integration procedure follows. Once a long-run equilibrium relationship is established, Granger causality is then tested using the error correction set up of Engle and Granger (1987). INTEGRATION PROPERTIES OF THE DATA The basic objective of this paper is to investigate the long run relationship between FCIs and economic growth of India. To examine the same, the researcher employed a co-integration test and Error Correction Model (ECM) technique. However, the prime requirement of this technique is to test the order of integration and that has been done through unit root test only. Therefore the researcher first highlights the concept of unit root test and then the co-integration test and ECM technique. UNIT ROOT TEST The researcher can test the stationarity of a variable by using the Augmented Dicky-Fuller ADF6 test and the Phillips-Perron (PP) test. The testing procedure for the ADF test is the same as for the Dickey–Fuller test but it is applied to the model: Management & Change, Volume 16, Number 1 & 2 (2012)

Dr. Minakshi Paliwal, Dr. Sumanjeet Singh 29

(5)

∆yt = α + βt + γ yt −1 + δ 1∆yt −1 +δ p − 1∆yt− p+ 1+ ∈t ,

Where a is a constant, β the coefficient on a time trend and p the lag order of the autoregressive process. Imposing the constraints α = 0 and β = 0 corresponds to modelling a random walk and using the constraint β = 0 corresponds to modelling a random walk with a drift. By including lags of the order ρ (greek for ‘rho’) the ADF formulation allows for higher-order autoregressive processes. This means that the lag length p has to be determined when applying the test. One possible approach is to test down from high orders and examine the t-values on coefficients. An alternative approach is to examine information criteria such as the Akaike information criterion (AIC), Bayesian information criterion (BIC) or the Hannan-Quinn information criterion (HQIC). We use this alternative approach of determining the lag length based on AIC. The unit root test is then carried out under the null hypothesis ? = 0 against the alternative hypothesis of ? < 0. Once a value for the test statistic is computed it can be compared to the relevant critical value for the Dickey– Fuller Test.

(6)

DFτ =

) γ ) SE (γ )

If the test statistic is less (this test is non symmetrical so we do not consider an absolute value) than (a larger negative) the critical value, then the null hypothesis of ? = 0 is rejected and no unit root is present. One advantage of ADF is that it corrects for higher order serial correlation by adding lagged difference term on the right hand side. One of the important assumptions of DF test is that error terms are uncorrelated, homoscedastic as well as identically and independently distributed (iid). Phillips-Perron (1998) has modified the DF test, which can be applied to situations where 6.

ADF is an augmented version of the Dickey–Fuller test for a larger and more complicated set of time series models. The augmented Dickey–Fuller (ADF) statistic, used in the test, is a negative number. The more negative it is, the stronger the rejections of the hypothesis that there is a unit root at some level of confidence. Management & Change, Volume 16, Number 1 & 2 (2012)

30 Impact of Foreign Capital Flows on Economic Growth: Empirical...

the above assumptions may not be valid. Another advantage of the PP test is that it can also be applied in frequency domain approach, to time series analysis. The derivations of the PP test statistic are quite involved and hence not given here. The PP test has been shown to follow the same critical values as that of the DF test, but has greater power to reject the null hypothesis of a unit root test. CO-INTEGRATION AND THE ERROR CORRECTION MODEL The central concept of the co-integration test is the specification of models, which include the long run movements of one variable relative to others. In other words, it clarifies the existence of the long term equilibrium relationship between the two variables. If the time series variables are non-stationary in their levels, they can be integrated with integration of order one, when their first differences are stationary. These variables can be co-integrated as well, if there are one or more linear combinations among the variables that are stationary. If these variables are being co-integrated, then there is a constant long-run relationship among them. The co-integration test was first introduced by Engel and Granger (1987) and then developed and modified by Stock and Watson (1998), Johansen (1988) and Johansen and Juselius (1990). The test is very useful in examining the long run equilibrium relationships between the variables. Consider an unrestricted Vector Auto Regression (VAR) model represented by, p

(7)

Yt = µ + ∑ ΠκYt − k + εt k =1

Where, et is p dimensional Gaussian error with mean zero and variance matrix ?, Y t is an (n×1) vector of I(1) variables, and µ is an (n×1) vector of constants. As Y t is assumed to be non-stationary, and equation (7) could be rewritten in the first difference notation reformulated in error correction form, p −1

(8)

∆Yt = µ + ∑ Πκ ∆Yt − k + ΠYt − 1 + εt k =1

Management & Change, Volume 16, Number 1 & 2 (2012)

Dr. Minakshi Paliwal, Dr. Sumanjeet Singh 31

Where Π k = I – ( Π 1 -….- Π k ) ; and Π = I – ( Π 1 ,……., Π p ). Since et is stationary, the rank r of the long-run matrix determines how many linear combinations of Yt are stationary. If the co integrating rank r-0 so that Π =0, the equation (8) is similar to a traditional first differenced VAR model. With 0 > r > n, there is r co-integrating vectors or r stationary linear combinations of Yt where Π = αβ ', where both a and β are (n×r) matrices. The co-integrating vector β has the property that β ' Yt is stationary although Yt is non-stationary. The co-integrating rank r can be tested with statistics such as maximum eigenn value (?max) test and trace test. The asymptotic critical values are in Johansen and Juselius (1990) and Osterwald-Lenum (1992). The results of VAR models are sensitive to lag length choice (Boswijk and Frances,1992). They suggest the use of Johansen's approach to determine the different lag lengths and to base the final and the significance of parameters of higher lags. A VAR model incorporating two lags of each variable is selected from the test applied. GRANGER CAUSALITY TESTS FROM ERROR CORRECTION MODEL In order to test whether a long run growth relationship was established in the model and the relationship will hold given the short-run disturbances, a dynamic error correction model was used based on the co-integration relationship. For this purpose the lagged residual error derived from the cointegration vector was incorporated into the general error model. This leads to the specification of an error correction model. The presence of one cointegrating relationship permits the use of an Engle and Granger’s (1987) error correction model in equation (8) for the two variables is written in equation (9),

Management & Change, Volume 16, Number 1 & 2 (2012)

32 Impact of Foreign Capital Flows on Economic Growth: Empirical...

In the equation (9), m is the lag length and ECt-1 is the error correction term. The coefficient of the EC contains information about whether the past values of variables affect the current value of the variables under the study. The size and the statistical significance of the coefficient of the error correction model measure the tendencies of each variable to return to equilibrium. For example if ?1 in equation (9) is statistically significant it means that LGDP responds to disequilibria in its relations with exogenous variables. According to Choudry (1995), even if the coefficients of the lagged changes of the independent variables are not statistically significant, Granger causality can still exist as long as ? is significantly different from zero. The short-run dynamics are captured through individual coefficients of the first difference terms. ESTIMATION AND RESULTS Before applying the co-integration test and Error Correction Model, the researcher first establishes the maximum integration order (dmax) of the variables by carrying out an Augmented-Dickey Fuller(ADF) test and Dickey-Fuller(DF) test on the FCIs , GDP and GDCF series at their log levels and their log differentiated forms. The results of the various unit root test are shown in Table 1. Table 1 Result of Dickey-Fuller test and Augmented-Dickey Fuller test at log levels and log Differentiated forms of the Variables At Level Variable

Without Trend DF

With Trend

ADF

LGDP

1.414359

2.539034

-0.687078

2.329148

LFCI

-0.486895

-2.135845

-3.704518*

-4.673619*

LGDCF

-0.054677

1.200005

-1.109620

-0.829526

At First different Variable DLGDP DLFCI DLGDCF

Without Trend

With Trend

ADF

-1.300438

-0.781012

-2.522873**

-1.888222**

-2.594024**

-4.069724*

-4.110289*

-3.801568**

-3.815589*

-3.708341**

-4.382117*

-4.122364**

*Mckinnon Critical values at 1% level of significance **Mckinnon Critical values at 5% level of significance Notations: LGDP= Natural log of GDP Management & Change, Volume 16, Number 1 & 2 (2012)

ADF

Dr. Minakshi Paliwal, Dr. Sumanjeet Singh 33 LFCI= Natural log of FCI LGDCF= Natural log of GDCF DLGDP= First Difference of LGDP DLFCI = First Difference of LFCI DLGDCF= First Difference of LGDCF

The results presented in table 1 show the values of DF and ADF unit root test at a level and at their first difference of the different variables in their natural logarithms forms. It was found that all the variables i.e. GDP, FCI and GDCF are non-stationary at their level without trend values whereas FCI was found to be stationary when the trend is allowed in the series. But all the variables are found to be stationary at their first difference and GDP becomes stationary when the trend is allowed in the series. This indicates that all the series are integrated of order one [i.e. I (1)] and the trend is allowed in the co-integrating series. Hence, it confirms the possibility of a long run relationship between the variables. To explore the long run (co-integrating) relationship among the variables the researcher applies the Johansen and Juselius approach of co-integration. Both the dependent and independent variables in the co-integrating regression model are in the natural logarithmic form which means that this kind of regression models are in the natural logarithmic form which further illustrates that this kind of regression is of double-log or log-linear form. The results of the co-integration test are the eigen values; and trace statistics and presented in table 2: Table 2 Results of Co-integration Test (Using Johansen & Juselius Approach) Panel 2 (a) Hypothesized No. of CE(s)

Eigen Value

Trace Statistics

5% critical value

p-value

None*(r=0)

0.846781

53.10363

42.91525

0.0036

At most 1(r=1) At most 2(r=2)

0.651445

21.21357

25.87211

0.1706

0.176259

3.296289

12.51789

0.8396

Note: *denotes rejection of the hypothesis at 5% level of significance. Trace Test denotes one co-integrating equation at 5% level of significance

Management & Change, Volume 16, Number 1 & 2 (2012)

34 Impact of Foreign Capital Flows on Economic Growth: Empirical...

Panel 2(b) Hypothesized No. of CE(s) None*(r=0) At most 1(r=1)

Eigen Value 0.846781 0.651445

Max-Eigen Statistic 31.89006 17.91729

5% critical value 25.82321 19.38704

p-value

At most 2(r=2)

0.176259

3.296282

12.51798

0.8396

0.0070 0.0807

Note:*denotes rejection of the hypothesis at 5% level of significance. Max-Eigen Test denotes one co-integrating equation at 5% level of significance. r denotes the number of co-integrating equations.

The above tables indicate the results of the co-integration test in the two panels i.e. panel 2(a) shows the results of Trace statistics and panel 2(b) shows the results of Max-Eigen statistics. Both testing strategies begins with r=0. Using both the Trace and Max-Eigen test statistics, one can reject r=0 against the alternative r=1 and r=2 but fails to reject the hypothesis of existence of more than one stationary linear combination. In other words, these tests indicate the presence of a long run equilibrium relationship among the variables. As a result, an error correction model is constructed to determine the direction of causality. The results of the Error Correction Model are shown in Table 3: Table 3 Results of Error Correction Model Variables

Coefficient

Standard Error

t-statistics

-0.327452

0.14015

2.33650

CEI

-0.973395

0.44247

-2.19993

∆ LGDP(-1)

-1.700330

1.06993

-1.58920

∆ LGDP(-2)

-1.692765

1.18634

-1.42687

∆ LFCI(-1)

-0.036000

0.01746

-2.06135

∆ LFCI(-2)

-0.009980

0.01999

-0.49926

∆ LGDCF(-1)

0.112025

0.22965

-0.48780

∆ LGDCF(-2)

0.022039

0.19422

R-squared

0.732260

S.E. of equation

0.041306

Adj R-squared

0.697988

F-Statistics

3.125682

Management & Change, Volume 16, Number 1 & 2 (2012)

0.11347

Dr. Minakshi Paliwal, Dr. Sumanjeet Singh 35

The results present in table 3 indicate the statistics of the error correction model. The result of the error correction model confirms that a long-run causal flow runs from changes in FCIs, Capital formation and GDP. This is revealed by the estimated coefficient (?) of the error correction term (CEI) which is negative, as expected and statistically significant in terms of its associated t-value. The changes in lagged capital formation have positive and significant effects on real GDP growth. However, FCI exerts significant negative, but diminishing effect on the economic growth rates. This is revealed from the negative sum of the coefficients of subsequent lagged FCI values. The reason behind the negative relationship between FCI and economic growth rates is probably due to high amounts of Foreign Institutional Investments (FII) in foreign capital inflows and FII is highly volatile in case of India. The adjusted R2 at 0.6979 shows a high explanatory power of the model. The F statistics at 3.1256 suggest that moderate interactive feedback effect exists within the system. The significance of F statistics further indicates Granger causality among variables. To find out the direction of causality the results of granger causality test is shown in table 4. The optimum number of lags is determined by the AIC criterion. Table 4 Results of Granger causality tests Null Hypothesis

F-statistics

p-value

FCI does not Granger Cause GDP

72.4491*

0.0000

GDP does not Granger Cause FCI

3.32409**

0.0705

GDCF does not Granger Cause GDP

23.4609*

0.0001

GDP does not Granger Cause GDCF

1.57726

0.2618

GDCF does not Granger Cause FCI

3.48973**

0.0632

FCI does not Granger Cause GDCF

3.85156**

0.0503

No. of length specified by AIC criterion

Conclusion: FCI<=>GDP, GDCF=>GDP, GDCF<=>FCI Note: <=> Bidirectional causality, => unidirectional causality *significant at 1% level **significant at 10% level. Source: self computed

In table 4, F-statistics indicates that the null hypothesis, GDP does not granger because GDCF, cannot be rejected and all other null hypothesis can be Management & Change, Volume 16, Number 1 & 2 (2012)

36 Impact of Foreign Capital Flows on Economic Growth: Empirical...

rejected at 1% and 10% level of significance. The result shows that there is a bi-directional causality between FCI and GDP; it means that the any change in FCI causes GDP and vice-versa. Similarly, there is a bi-directional causality between GDCF and FCI. But there is a unidirectional causality between GDCF and GDP, it means GDCF causes GDP but GDP does not cause GDCF. In other words, there is statistical evidence that any forecast about the GDP growth of India depends on the movement of FCIs and GDCF but any forecast about the GDCF does not depend on the GDP growth. CONCLUSION This paper empirically examines the linkage between FCIs, Gross domestic capital formation and GDP growth in the context of India by using time series data for the period 1992-2010. Prior to this time period, the Indian economy witnessed very little amount of capital flows on account of the capital account restrictions and relatively closed economy. After the balance of payment crisis in 1990-91, the restrictions were gradually removed and subsequently, India attracted huge amount of foreign capital overtime during the sample period. Hence it is worth examining, the role of this sudden spurt in capital flows in the economic growth, investment, savings and the capital formation. The long run relationship is examined using the co-integration analysis. The Johansen co-integration results established the long run relationship between the variables. The results show that GDCF contributes positively to the GDP but FCI after liberalization contributes negatively to GDP because of the increase in highly risky and volatile portfolio investment. The significance of CEI and F-statistics indicates a causal and long term relationship between the variables and supports the result of Johansen cointegration results. The granger causality test found the causality between GDP growth, FCIs and GDCF. There is a bidirectional causality between FCI and GDP; GDCF and FCI but there is a unidirectional causality between the GDCF and GDP. Moreover, we can conclude that FCI has a negative effect on the growth rates of India. Thus, the findings of this study are, for the most part, inconsistent with findings of previous studies on the effects of FCI on economic growth. But we can not, by this conclude that FCI is not good in promoting development entirely, since it requires further exploration on its effect on other development indicators. Hence, even if our regression model indicates a negative effect of FCI on economic growth, we cannot exclude the fact that the obtained results can stem from a possible bias in the selection Management & Change, Volume 16, Number 1 & 2 (2012)

Dr. Minakshi Paliwal, Dr. Sumanjeet Singh 37

of variables in the model. Therefore, based on these arguments, FCI can not be completely discarded as a necessary capital flow for developing nations. In fact, from the policy perspective, the evidence convincingly suggests that countries that are successful in attracting foreign capital especially FDI can finance more investments and grow faster than those that deter foreign capital. Further, negative relationship between FCI and Growth Rate can be associated with high volatility in foreign capital in India. During the study, we investigated that foreign capital flows were highly volatile especially in the 1998-1999, 2004-06 and 2008-2009 fiscal. In most cases, it was primarily due to high volatility in foreign institutional investment. For instance, during the crisis ridden year of 2008-2009, FIIs pulled out US$ 9.77 billion of portfolio investment from Indian equity markets7 . Yet they have been quick to return in 2010. In just the first four months of the fiscal year, they have nearly made up for the exit, reinvesting 87% of the amount they pulled out. Such volatile foreign capital creates instability in capital inflows. The instability of capital inflows may retard economic growth and structural development; when there is sudden increase in capital flows, it leads to increase in real exchange rate, inflationary pressures and deterioration in current account. But, the sudden decline of capital flows could push the country into insolvency or drastically lower the productivity of existing capital stocks and affect many macroeconomic variables like exchange rate, interest rate, forex reserves and domestic monetary conditions etc. Moreover, in high volatility conditions reliance on foreign aid is greater, which has had a poor record of accelerating growth. On the other hand, low volatility conditions are more conducive to investment. They thus tend to attract more private capital flows, and in that setting, private capital can undertake more medium term and riskier investments enhancing growth. Thus, at the policy front, it is suggested that government should take more vibrant steps to reduce the volatility of foreign capital flows especially FII flows in India. REFERENCES Aghion, Philippe, Diego Comin, and Peter Howitt (2006) “When Does Domestic Saving Matter for Economic Growth?” Working Paper 12275. Cambridge, Mass.: National Bureau of Economic Research. Aitken, B. and A. E. Harrison (1999) “Do Domestic Firms Benefit from 7.

Yet they have been quick to return in 2010. In just the first four months of the fiscal year, they have nearly made up for the exit, reinvesting 87% of the amount they pulled out (CLSA Asia-Pacific Markets). Management & Change, Volume 16, Number 1 & 2 (2012)

38 Impact of Foreign Capital Flows on Economic Growth: Empirical...

Direct Foreign Direct Investment? Evidence from Venezuela”, The American Economic Review, 89(3):39-54. Albuquerque, Rui, (2003) “The Composition of International Capital Flows: Risk Sharing Through Foreign Direct Investment,” Journal of International Economics, 1(2):353-383. Alfaro, Laura, Chanda, Areendam, Kalemli-Ozcan, Sebnem and Sayek, Selin (2004) “FDI and Economic Growth: The Role of Local Financial Markets.” Journal of International Economics, 64:89-112. Bekaert, Geert, Campbell, R. Harvey and Christian, Lundblad (2005) “Does Financial Liberalization Spur Growth?” Journal of Financial Economics, 77 (1):3-55. Boone, Peter (1996) “Politics and the Effectiveness of Foreign Aid,” European Economic Review, 40 (2):289-329. Borensztein, Eduardo, José De Gregorio, and Jong-Wha Lee (1998) “How Does Foreign Direct Investment Affect Economic Growth?” Journal of International Economics, 45 (1):115-35. Borensztein, Eduardo, De Gregorio, Jose and Lee, Jong-Wha (1998) “How Does Foreign Direct Investment Affect Economic Growth?” Journal of International Economics, 45 (1):115-35. Calvo, G., Izquierdo, A. and Talvi, E. (2002) “Sudden Stops, the Exchange Rate and Fiscal Sustainability: Argentina’s Lessons,” Working Paper, No. 469, July, Inter-American Development Bank. Carkovic, Maria and Levine, Ross (2002) “Does Foreign Direct Investment Accelerate Economic Growth?”, U of Minnesota Department of Finance Working Paper, accessed on http://papers.ssrn.com/sol3/ papers.cfm?abstract_id=314924 Chenery and Bruno (1962) “Development Alternatives in an Open Economy: The Case of Israel”, Economic Journal,. 72 (285):79-103. Choudry, T. (1995) “Long-run Money Demand Function in Argentina During 1935-1962: Evidence from Cointegration and Error Correction Models”, Applied Economics, 27 (13):661-67. Management & Change, Volume 16, Number 1 & 2 (2012)

Dr. Minakshi Paliwal, Dr. Sumanjeet Singh 39

Dalgaard, Carl-Johan and Henrik, Hansen (2001) “On Aid, Growth and Good Policies”, Journal of Development Studies, 37 (6):17-41. Djankov, S. and B. Hoekman (1998) “Foreign investment and productivity growth in Czech enterprises”, Policy Research Working Paper 2115, The World Bank, Washington, D.C. Easterly, William, Ross, Levine, and David, Roodman (2004) “Aid, Policies, and Growth: Comment,” American Economic Review, 94 (3):774-780. Edwin, P. Reubens (1950) “Foreign Capital in Economic Development: A Case Study of Japan”, The Milbank Memorial Fund Quarterly, 28 (2):173-190. Feldstein, Martin and Charles, Horioka (1990) “Domestic Saving and International Capital Flows,” Economic Journal, 358 (21):314–29. Figlio, David and Blonigen, Bruce (2000) “The Effects of Direct Foreign Investment on Local Communities.” Journal of Urban Economics, 48 (2):338-63. Griffin, Keith (2009) “Foreign Capital, Domestic Savings and Economic Development”, Oxford Bulletin of Economics and Statistics, Vol. 32, No. 2, pp 99-112. Haussman, R. And Velasco, A. (2002) “Hard Money’s Soft Underbelly: Understanding the Argentine Crisis,” mimeo, July, Kennedy School of Government - Harvard University, Cambridge, Mass. Henry, Peter, Blair (2006) “Capital Account Liberalization: Theory, Evidence, and Speculation.” Working Paper 12698. Cambridge, Mass.: National Bureau of Economic Research. Joshi, Himanshu (2007) “The Role of Domestic Savings and Foreign Capital Flows in Capital Formation in India”, Reserve Bank of Indian Occasional Paper, Vol. 28, No. 3. Joshua , Aizenman, Yothin, Jinjarak, and Donghyun, Park (2011) “Capital flows and economic growth in the era of financial integration and crisis, 1990–2010”, NBER Working Paper No. W17502. Management & Change, Volume 16, Number 1 & 2 (2012)

40 Impact of Foreign Capital Flows on Economic Growth: Empirical...

Joshua Aizenman, Yothin, Jinjarak and Donghyun, Park (2010) “Capital flows and economic growth in the era of financial integration and crisis, 1990–2010”, accessed on http://www.voxeu.org/index.php?q=node/7172 Joshua , Aizenman, Yothin, Jinjarak, and Donghyun, Park (2011) “Capital flows and economic growth in the era of financial integration and crisis, 1990–2010”, VOX, accessed on http://www.voxeu.org/article/capitalflows-and-growth-1990-2010 Knack, Stephen (2001) “Aid Dependence and the Quality of Governance: Cross-Country Empirical Tests,” Southern Economic Journal, Vol. 68, No. 2, pp 310-329. Kose, A., Prasad, E., Rogoff, K., and Wei, S-J. (2006) “Financial Globalization: A Reappraisal,” International Monetary Fund Working Paper No. 06/189. Krugman, P. (1993) “On the Relationship between Trade Theory and Location Theory”, Review of International Economics, 1 (2):11-22. Krugman, P. (1993a) “On the Relationship between Trade Theory and Location Theory”, Review of International Economics, 1 (2):11-22. Lane, Philip R., and Gian, Maria Milesi-Ferretti (2002) “Long-Term Capital Movements.” NBER Macroeconomics Annual 2001, edited by Ben S. Bernanke and Kenneth S. Rogoff. MIT Press. Lucas, Robert (1990) “Why Doesn’t Capital Flow from Rich to Poor Countries,” American Economic Review, 80 (3):92-96. Manzocchi, Stefano and Philippe Martin (1997) “Are Capital Flows Consistent with the Neoclassical Growth Model? Evidence from a Cross-section of Developing Countries,”, Economie Internationale, 72 (1):7–24. Marc Labonte and Gail E. Makinen (2005) “The National Debt: Who Bears Its Burden?”, CRS Report for Congress, Order Code RL30520. Marwah, K. and L. Klein (1998) “Economic Growth and Productivity Gains from Capital Inflow: Some Evidence for India”, Journal of Quantitative Economics, January. Management & Change, Volume 16, Number 1 & 2 (2012)

Dr. Minakshi Paliwal, Dr. Sumanjeet Singh 41

Mohan, Rakesh (2008) “The Growth Record of the Indian Economy, 19502008: A Story of Sustained Savings and Investment” Reserve Bank of India Bulletin, March . Mohanty, Deepak (2012) “Global Capital Flows and Indian Economy: opportunities and Challenges”, paper presented at the Annual Technical and Entrepreneurship Festival of the Indian Institute of Technology Kanpur (IITK), Kanpur, 28 January 2012. Pablo, Bustelo (2004) “Capital Flows and Financial Crisis: A Comparative Analysis of East Asia (1997-1998) and Argentina (2001-2002)”, Working Paper No. 2004-017, Complutense University of Madrid, Faculty of Economics. Pierre-Olivier, Gourinchas and Olivier, Jeanne (2009) “Capital Flows to Developing Countries: The Allocation Puzzle”, Peterson Institute of International Economics, Working Paper No. 9. Prasad, E., Rajan, R., and Subramanian, A. (2006) “Patterns of International Capital Flows and their Implications for Economic Development,” presented at the symposium, “The New Economic Geography: Effects and Policy Implications,” The Federal Reserve Bank of Kansas City, Jackson Hole, Wyoming, August 24-26. Rachdi, H., & Saidi, H. (2011) “The impact of Foreign Direct Investment and Portfolio Investment on Economic Growth in Developing and Developed Economies”, Interdisciplinary Journal of Research in Business. , I (6):10-17. Ramachandran, V. and Shah, M. K. (1997) “The effects of foreign ownership in Africa: evidence from Ghana, Kenya and Zimbabwe”, RPED Paper No. 81, The World Bank, Washington, D.C. Razin,A.; E. Sadka, C. and Yuen (1998) “Capital Flows with Debt- and Equity-Financed Investment: Equilibrium Structure and Efficiency Implications”, IMF Working Paper WP/98/159; November 1998. Rodrik, Dani and Andres, Velasco (1999) “Short-term Capital Flows,” Annual Bank Conference on Economic Development, Washington: World Bank. Management & Change, Volume 16, Number 1 & 2 (2012)

42 Impact of Foreign Capital Flows on Economic Growth: Empirical...

Rodrik, Dani. (2006) “Capital Account Liberalization and Growth: Making Sense of the Stylized Facts.” Remarks at the IMF Center Economic Forum: How Does Capital Account Liberalization Affect Economic Growth? Washington, November 10. Sérgio, Pereira, Leite (2001) “International Capital Flows: A Challenge for the 21st Century”, IMF, accessed on http://www.imf.org/external/np/ vc/2001/070101.htm Sethi, Narayan (2010) “Economic Reforms, Capital Flows and Macro Economic Impact on India”, accessed on http://www.igidr.ac.in/money/ mfc_10/Narayan%20Sethi_submission_51.pdf Sethi, Narayan (2011) “Effects of Foreign Capital Inflows on Economic Growth of India: An Empirical Analysis”, Asian Economic Review, 53 (2):249-268. Sikdar, Soumyen (2006) “Foreign Captial Flows into India: Determinants and Manmagement”, INRM Policy Brief, 4, Asian Development Bank. Tressel, Thierry, and Thierry, Verdier (2007) “Financial Globalization and the Governance of Domestic Financial Intermediaries.” Working Paper 07/47. Washington: International Monetary Fund. Waheed, Abdul (2004) “Foreign Capital Inflows and Economic Growth of Developing Countries: A Critical Survey of Selected Empirical Studies”, Journal of Economic Cooperation, 25 (1):1-36.

Management & Change, Volume 16, Number 1 & 2 (2012)

INDEX OF PROFESSIONALISM IN FINANCIAL DECISIONS Dr. Shveta Singh1

Prof. Surendra S. Yadav 2

Prof. P K Jain3

The financial performance of an organization is generally measured by the following parameters – profit, cash flows, balance sheet strength, risk management, valuation and owners’ net worth (Stern, 2012). Apart from the numbers that the above parameters generate, human psychology and the merit/demerit associated with financial decision-making also play a vital role in determining eventual corporate success (Bondt and Thaler, 1995).The paper is primarily based on the research monograph (under publication) titled “Financial Management Practices: An Empirical Study of Indian Corporates” (ISBN 978-81-322-0989-8) by Springer. The study covers virtually all the major aspects of financial management, viz., capital budgeting, capital structure, dividend policies, working capital, corporate governance, global finance and risk management.This paper sets out to rate the respondent companies based on the quality of their decision-making vis-a-vis sound finance theory on aspects of capital budgeting (CB), capital structure (CS), working capital management (WC), dividend policy (D), corporate governance (CG), global finance and risk management (RM). It develops an index, as a measure of professionalism in the area of financial management, as practised in the sample companies in India, in an attempt to understand the extent of professionalism behind the decisions. Keywords: Professionalism, Index, Decision-making. 1.

Shveta Singh, Assistant Professor, Department of Management Studies, IIT Delhi, New Delhi -110016, India. Phone: +91-11-26596303. E-mail: shvetasingh@dms. iitd.ernet.in Surendra S. Yadav, Professor, Department of Management Studies, IIT Delhi, New Delhi-110016, India. Phone: +91-11-26591242. E-mail: ssyadav@dms.iitd.ac.in P. K. Jain, Professor, Department of Management Studies, IIT Delhi, New Delhi-110016, India. Phone: +91-11-26591199. E-mail: pkjain@dms.iitd.ac.in

44 Index of Professionalism in Financial Decisions

INTRODUCTION The survival and long-term success of firms is influenced by their sound financial management policies and decisions. The subject assumes greater significance now (than ever before) for the business enterprises, in view of the present dynamic and turbulent business environment. Eric Hoyle (1980) defines professionalization of any activity/decisionmaking as having the following aspects - long period of training, qualified membership, management control and continuous improvement of knowledge and skills of the practitioners. The decision-makers in the sample companies fulfil all the above criteria. It is obviously then expected that corporate performance would be better if activities are carried out in a professional manner, that is, by employing a systematic and sound knowledge in practice. The objective of this paper is to rate the respondent companies on the quality of their decision-making vis-a-vis sound finance theory on aspects of capital budgeting (CB), capital structure (CS), working capital management (WC), dividend policy (D), corporate governance (CG), global finance and risk management (RM). It develops an index as a measure of professionalism in the area of financial management, as practised in the sample companies in India, in an attempt to understand the extent of professionalism behind the decisions. A corporate would be called professional if its management practices are consistent with the systematic body of knowledge and tools and techniques of sound theory. This means that the professional enterprises would not follow arbitrary/ad-hoc or rule-of-thumb approach in taking decisions. The extent of professionalism practised in the sample companies, in the areas mentioned is examined in the subsequent sections. For better exposition, the paper has been divided into three sections. Section I contains the detailed methodology used for the creation of the index. Section II lists the discussions based on the scores obtained in each category of financial decision-making - by the respondent companies, and section III contains the concluding observations. SECTION I METHODOLOGY The BSE 200 index of the Bombay Stock Exchange (BSE) comprises the top Management & Change, Volume 16, Number 1 & 2 (2012)

Dr Shveta Singh, Prof Surendra S. Yadav, Prof P K Jain 45

200 companies listed with the Bombay Stock Exchange, based on their market capitalization. The selected sample comprised 84.32 per cent of the total market capitalization of the Bombay Stock Exchange, as on April 1, 2010 (source: bseindia.com). Out of these 200 companies, 34 companies were engaged in the financial sector (as on April 1, 2010, the date of sample selection). Therefore, the scope of this study is limited to the 166 non-financial BSE 200 companies engaged in manufacturing and service rendering businesses. This universe was chosen basis the convenience in accessing the required data required and on the assumption that it would be an accurate representation of the largest firms in India. Small businesses tend to use naĂŻve methods rather than the ones prescribed in financial theory (Block, 2005; Danielson and Scott, 2006), hence the focus on large firms. Also, selecting the population as large firms with a similar sampling frame to previous studies facilitated comparison with these studies. The research instrument for primary data consisted of a questionnaire. Minor problems with language and interpretation in some questions were addressed in the pre-test. Questions designed were simple and specific relating to objectives, policies and techniques relating to various aspects of financial management. Opinion-based and subjective information was kept to a minimum in order to keep the study more objective and scientific. The questionnaire (along with covering letter) was sent by courier to the CFO/Finance Manager/ Director Finance of each of the 166 companies. At the same time, an attachment file of the copy of the questionnaire was also e-mailed (along with the covering letter) so that in case the respondent faces a problem in the physical delivery of the questionnaire, he/she can download the questionnaire from the file attached. Subsequently, the questionnaire was re-mailed to the non-responding companies for follow-up, in order to maximize the response rate. It was indicated to the CFOs that the individual responses would be kept strictly confidential and only aggregate generalizations would be published. The initial response was poor; only a few companies (eight) responded. Subsequently two reminders (both through post and email) were sent to the remaining (non-responding) companies. Personal contacts were also established with the companies located in and around Delhi. This part of the analysis is based on 31 responses received out of 166 after 2 reminders (a response rate of 18.67 per cent). Prima-facie, the response rate may be seen as low; however, the number of respondents and the response rate are similar to previous studies using a Management & Change, Volume 16, Number 1 & 2 (2012)

46 Index of Professionalism in Financial Decisions

similar method (Jain and Kumar, 1997; Jain and Yadav, 2000; Jain and Yadav, 2005). Also, considering that the survey was addressed to time-constrained CFOs, as well as the commercial sensitivity of some of the requested information, we had no option but to rely only on 31 responses for the present study; the findings of the present research should, therefore, be viewed in the light of this limitation of primary data. It is pertinent to state here that the authors have conducted three more studies in the past (Jain and Kumar, 1997; Jain and Yadav, 2000 and Jain and Yadav, 2005) spanning the period 1991-2003. An effort has been made to link the findings of these studies with the current one, with the aim to establish trends (if any) in certain aspects of financial decision-making over the past two decades (to provide a broader perspective). The basic methodology for preparation of this index has been taken from an earlier study of the public sector enterprises in India (Jain and Yadav, 2005). However, substantial modifications have been introduced in the questionnaire used for this study to reflect the emerging areas of research in financial management (for example, the usage of real options in capital budgeting) and evaluate them in terms of present decision-making. Also, relatively recent aspects like corporate governance and risk management were included in the questionnaire. The questionnaire used in the survey was prepared with items pertaining to six practices of financial management. These were: capital budgeting (CB), capital structure (CS), working capital management (WC), dividend policy (D), corporate governance (CG), global finance and risk management (RM). The questionnaire was exploratory in nature with certain questions in each section directly enquiring about the practice followed, in regard to that specific financial decision. Not all of these questions have responses that can directly be connected to good decision-making. For instance, item 2, viz., â&#x20AC;&#x153;In the past decade, the capital expenditure of your company has mainly constituted of outlays onâ&#x20AC;?, entails a choice dependent on the companyâ&#x20AC;&#x2122;s strategy. Similarly, questions that did not indicate directly a good/bad financial decision were not taken up for the creation of the index. This applies to all sections of the questionnaire comprising of 70 questions in all. As a result of this exercise, five out of nine items were picked up for the creation of the index from the capital budgeting section and so on. For items in each category used for the development of index, refer to questionnaire provided in Appendix 2. Management & Change, Volume 16, Number 1 & 2 (2012)

Dr Shveta Singh, Prof Surendra S. Yadav, Prof P K Jain 47

For each financial decision, a number of alternative practices are possible – each one of them varying in terms of theoretical soundness. For example, in working out the cost of capital, weighted average cost of long-term finance is considered superior to other practices/methods. So, maximum score is assigned to the item relating to the cost of capital if the response given by a particular enterprise shows that it uses the weighted average cost of capital. On the other hand, minimum score is assigned in case the enterprise responds that its cost of capital is decided by the top management in an ad-hoc manner. In this way, for each item, a maximum and a minimum score is assigned. The scores awarded to each response was on a scale of 0-5, with ‘5’ being awarded to the response most in tune with sound financial theory and ‘0’ being assigned to the response completely against sound theory. Then, the total score obtained by that enterprise relating to a specific financial management category, say, capital structure decision, is divided by the maximum score that could be obtained if the company practised only the best methods under that category. The ratio thus obtained is multiplied by 100 in order to get an IPF (index of professionalism in financial management) for that company, in that category of financial management practice. To illustrate further, there are four items in capital structure (CS) category. Suppose the score of a company on item i (i varying from 1 to 4) is Si while maximum obtainable score on this item is Sim (maximum score). Then IPF (CS) for this enterprise: IPF (CS) = ( Σ Si / Σ Sim) * 100 Thus a set of six indices each has been constructed for each company. These are IPF (CB) for capital budgeting, IPF (CS) for capital structure, IPF (WC) for working capital, IPF (D) for dividend policy, IPF (CG) for corporate governance and IPF (RM) for risk management. Then an average value of IPF (CB), IPF (CS), IPF (WC), IPF (D), IPF (CS) and IPF (RM) is determined for all responding companies taken together (Table 1). Under each financial management practice, an average, as worked out, is given in the lowest row of the table. The average is based on the number of responding companies. For example, calculation of AvIPF (CB) is based on the responses of 29 companies. Abbreviations used in this chapter are all given in Appendix 1. Management & Change, Volume 16, Number 1 & 2 (2012)

48 Index of Professionalism in Financial Decisions

Table 1 also contains the average value of index for the sample companies as a whole, under each category of financial management practices (given the last row). Table 1 Professional Index Values for Each Sample Company (in Percentages) Company IPF (CB)

IPF (CS) IPF (WC)

IPF (D)

IPF (CG)

IPF (RM)

65.00

32.00

64.00

60.00

87.50

22.86

100.00

64.00

80.00

100.00

87.50

51.43

60.00

64.00

100.00

20.00

93.75

31.43

100.00

84.00

92.00

100.00

75.00

28.57

70.00

48.00

76.00

100.00

76.25

60.00

75.00

80.00

100.00

60.00

93.75

31.43

100.00

80.00

64.00

100.00

88.75

48.57

95.00

48.00

100.00

87.50

54.29

55.00

68.00

100.00

93.75

40.00

100.00

44.00

100.00

83.75

51.43

65.00

64.00

100.00

93.75

57.14

55.00

68.00

64.00

100.00

57.14

70.00

60.00

100.00

87.50

40.00

60.00

64.00

100.00

81.25

48.57

90.00

64.00

80.00

100.00

81.25

51.43

75.00

64.00

100.00

87.50

100.00

80.00

92.00

100.00

87.50

51.43

80.00

44.00

100.00

87.50

48.00

100.00

60.00

81.25

51.43

60.00

52.00

100.00

87.50

34.29

100.00

60.00

56.00

60.00

87.50

25.71

45.00

68.00

72.00

100.00

68.75

51.43

80.00

68.00

80.00

68.75

25.71

75.00

80.00

100.00

93.75

42.83

75.00

44.00

95.00

31.43

Management & Change, Volume 16, Number 1 & 2 (2012)

Dr Shveta Singh, Prof Surendra S. Yadav, Prof P K Jain 49 26

44.00

100.00

81.25

100.00

48.00

100.00

93.75

37.14

75.00

64.00

72.00

100.00

57.50

20.00

100.00

44.00

84.00

100.00

75.00

62.86

79.42

60.86

79.29

91.11

84.96

42.64

45-100

32-80

44-100

20-100

57.50-100

20-62.86

AvIPF (C) Range

Note: In the calculation of AvIPF (C), only the companies that have responded to more than 50 per cent of the questions in a particular category have been included in the calculation of the average; a â&#x20AC;&#x2DC;-â&#x20AC;&#x2DC; denotes the companies not meeting the above criterion.

Finally, an overall aggregate single average index has been calculated as follows: AvIPF (AG) = (AvIPF (CB) + AvIPF (CS) + AvIPF (WC) + AvIPF (D) + AvIPF (CG) + AvIPF (RM)) / 6 As per Table 1, the category aggregate scores (AvIPF (C)) are the highest for the dividend policy category (for the respondent companies) at 91.11 per cent. Finally, the AvIPF (AG) for all the respondent companies for all the categories taken together is 73.05 per cent. This is encouraging as the majority of the respondent companies seem to be following sound financial management practices, based on financial theory, in all areas of financial management, undertaken in the study. SECTION II DISCUSSIONS It is observed that for sample companies the IPF (CB) varies from as low as 45 to 100. However, an average of 79.42 is quite high, signifying that, in nearly 80 per cent companies, sound capital budgeting practices are in place. The average is higher than the IPF (CB) of 70.47 reported by Jain and Yadav (2000) in their study of private sector enterprises over the period 1991-1998 and that of 76.80 noted by Jain and Yadav (2005) in their study of Indian public sector undertakings, indicating growing professionalism amongst companies with regard to their capital budgeting decisions. Similar observations can be made for different categories of financial management practices. Management & Change, Volume 16, Number 1 & 2 (2012)

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It is seen that average index values are generally above 75 for all categories except for capital structure decisions (60.86) and risk management (42.64). This is surprising as the sample companies are amongst the largest and well-established companies in the country and have access to various sources of finance enabling them to follow sound capital structure practices and also employ more risk management tools and techniques. Further, Jain and Yadav (2000) reported an IPF (CS) of 76.54 for private sector enterprises and the public sector undertakings studied by Jain and Yadav (2005) that reported an IPF (CS) of 74.57. Comparatively, sample companies have reported a dismal performance. Similarly, the IPF (WC) of 79.29 is lower than the IPF (WC) of 84.96 reported by the sample private sector companies over 1991-1998 (Jain and Yadav, 2000) and the IPF (WC) of 88.32 reported by the public sector undertakings (Jain and Yadav, 2005). However, it is pertinent to note that the two indices and their valuations are not entirely comparable as the questions and their numbers varied for each category in all the three questionnaires. Also, the additional categories of corporate governance and risk management and the overall methodology in the creation of this index had minor modifications from the ones used by Yadav and Jain (2000; 2005). Hence, any comparisons, in this regard, should be viewed in the light of the aforementioned. By and large, the index values are generally high for capital budgeting, working capital, dividend policy and corporate governance. The averages are above 75. This indicates the sample companies are paying close attention to aspects like investments, liquidity, inventory, receivables, investors and corporate legislations. Of course, this and other results have to be taken with a pinch of salt since the calculations are based on a small number of enterprises, i.e., 29. The aggregate professional index value (73.05) indicates that the sample companies, especially those that have responded to the questionnaire, are following good financial management practices. SECTION III CONCLUDING OBSERVATIONS What has been described and discussed above is an attempt to develop an index of professional practices relating to financial management. The index Management & Change, Volume 16, Number 1 & 2 (2012)

Dr Shveta Singh, Prof Surendra S. Yadav, Prof P K Jain 51

has been developed on the basis of the responses received to a questionnaire sent to all the 166 sample companies. Though the number of responses received and used, being 29, was not very high, it can be considered a fairly good representation of the sample. In conclusion, it can be said that sample companies are using sound financial management practices in a great measure. Needless to say, there is a greater scope for improving professionalism in some categories of financial management practices than others. However, there is a greater scope for improving professionalism in some categories of financial management practices (like capital structure and risk management) than others. The findings of this paper have implications/relevance for financial management students/researchers and in particular, practitioners, who may utilize the methodology to evaluate the extent of professionalism, underlying their decisions, by comparing them with, sound financial theory. REFERENCES Bombay Stock Exchange (BSE) website (2010) http://www.bseindia.com/ about/abindices/bse200.asp Accessed on April 1. Bondt, Werner, F. M. De and Thaler, Richard H. (1995) “Financial DecisionMaking in Markets and Firms: A Behavioral Perspective”, Chapter 13 – Handbook in OR & MS, Vol. 9.R. Jarrow et al., Eds., Elsevier Science B.V. Hoyle, Eric (1980) “Professionalization and De professionalization in Education,” World Year Book of Education, Kogan Page Ltd. London, p. 42. Jain, P. K. and Yadav, Surendra S. (2000) “Financial Management Practices in Select Private Corporate Enterprises – A Comparative Study of India, Thailand and Singapore,” Hindustan Publishing Corporation, India. Jain, P. K. and Yadav, Surendra S. (2005) “Financial Management Practices – A Study of Public Sector Enterprises in India,” Hindustan Publishing Corporation (India). Stern, David (2012) “7 Ways to Measure Financial Performance”, http:// davidsterncfo.wordpress.com/learning/7-ways-to-measure-financialperformance/, Accessed on July 19, 2012. Management & Change, Volume 16, Number 1 & 2 (2012)

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Yadav, Surendra S. and Jain, P. K. (2000) “Professionalism in Financial Management: Construct of an Index”, Management & Change, Vol. 4, No. 2, pp. 287-308. Yadav, Surendra S. and Jain, P. K. (2005) “Financial Management Practices in Public Enterprises: Development of an Index of Professionalism”, Management & Change, Vol. 9, No. 1, pp. 1-34. APPENDIX 1 ABBREVIATIONS WITH THEIR EXPANSIONS IPF (CB) AvIPF (CB) IPF (CS) AvIPF (CS) IPF (WC) AvIPF (WC) IPF (D) AvIPF (D) IPF (CG) AvIPF (CG) IPF (RM) AvIPF (RM) IPF (C)

: Index of Professionalism with regard to Capital Budgeting (CB) Practices in Sample Company; : Average Index of Professionalism with regard to Capital Budgeting (CB) Practices for the Sample as a whole; : Index of Professionalism with regard to Capital Structure (CS) Practices in Sample Company; : Average Index of Professionalism with regard to Capital Structure (CS) Practices for the Sample as a whole; : Index of Professionalism with regard to Working Capital (WC) Practices in Sample Company; : Average Index of Professionalism with regard to Working Capital (WC) Practices for the Sample as a whole; : Index of Professionalism with regard to Dividend (D) Practices in Sample Company; : Average Index of Professionalism with regard to Dividend (D) Practices for the Sample as a whole; : Index of Professionalism with regard to Corporate Governance (CG) Practices in Sample Company; : Average Index of Professionalism with regard to Corporate Governance (CG) Practices for the Sample as a whole; : Index of Professionalism with regard to Risk Management (RM) Practices in Sample Company; : Average Index of Professionalism with regard to Risk Management (RM) Practices for the Sample as a whole; : Aggregate Value of the Index of Professionalism for all Companies for one Category of Financial Management Practice;

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IPF (AG)

: Aggregate Value of the Index of Professionalism for all Companies and all Financial Management Practices Combined. APPENDIX 2

QUESTIONNAIRE FOR THE CALCULATION OF INDEX I â&#x20AC;&#x201C; Items related to Capital Budgeting (CB) Practices 1. How many year(s) ahead do you plan for capital expenditure? a. [ ] For next one year only b. [ ] For next five years c. [ ] For next ten years d. [ ] As and when the opportunity takes place e. [ ] Any other (please specify) __________________ 2. Does your company ever forego any expected profitable investment opportunity because of paucity of financial resources? Yes [ ] No [ ] 3. (A) Please identify capital expenditure evaluation technique(s) used in your company a. [ ] Accounting rate of return on investment b. [ ] Payback period Discounted cash flow techniques i [ ] Net present value ii [ ] Internal rate of return iii [ ] Profitability index/Present value index d. [ ] Any other (please specify) __________________ (B) Is your company using the following techniques? a. [ ] Real options Yes [ ]

No [ ]

4. Please state method(s) followed to incorporate project risk into your investment decision a. [ ] Shorter payback period for risky projects b. [ ] Higher cut-off rate for risky projects c. [ ] Sensitivity analysis d. [ ] Any other (please specify) _______________ II â&#x20AC;&#x201C; Items related to Capital Structure Decisions Management & Change, Volume 16, Number 1 & 2 (2012)

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1. During the course of capital expenditure projects, does your company opt for sound capital structure to ensure a low cost of capital for the project? Yes [ ] No [ ] 2. (A) Which method do you use to determine cost of capital? a. [ ] Weighted average cost of long - term sources of finance b. [ ] Marginal cost of additional funds raised to finance new asset c. [ ] Decided by the top management d. [ ] Any other (please specify) _________________ 3. In your opinion the ratio of debt to equity should be maintained less than 1, 1:1, 2:1, 3:1or greater than 3? 4. If your firm prefers to have predominantly more equity, the reason(s) could be a. [ ] Firm is not under obligations to pay dividends. b. [ ] There is flexibility in paying dividends. c. [ ] Equity is easy to raise. d. [ ] Any other (please specify) ________________ 5. Cost of retained earnings in your company is equivalent to [ ] Cost of equity capital [ ] Opportunity cost of using these funds by company [ ] Opportunity cost of using these funds by equity - holders [ ] No cost is considered [ ] Any other (please specify)_______________ III â&#x20AC;&#x201C; Items related to Working Capital Management 1. Which of the following forms the basis for working capital determination? a. [ ] Percentage of budgeted production b. [ ] Percentage of budgeted sales c. [ ] Length of operating cycle d. [ ] Determination of individual components of current assets and current liabilities (based on raw material holding period, debtors collection period, creditors payment period and so on) e. [ ] Any other (please specify) _______________ 2. Please state your companyâ&#x20AC;&#x2122;s policy regarding financing of working capital a. [ ] Mainly from long-term sources b. [ ] Mainly from short-term sources Management & Change, Volume 16, Number 1 & 2 (2012)

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c. [ ] Temporary/seasonal needs from short-term sources and only for period needed d. [ ] Permanent needs from long-term sources and temporary/ seasonal needs from short–term sources e. [ ] Any other (please specify) _________________ 3. How do you manage emergency requirements of cash? (Arising due to unexpected events or to exploit an opportunity) a. [ ] Always maintain minimum cash balance over and above the required amount b. [ ] Bank overdraft c. [ ] Utilization of cash credit limit from bank d. [ ] Discount bill receivables e. [ ] Have special arrangements with some lending agency for such purposes f. [ ] Sell marketable securities g. [ ] Raise loan against warehouse receipt h. [ ] Any other (please specify) ______________ 4. Is risk analysis of customers made before granting credit? Yes [ ]

No [ ]

5. Is the ageing-schedule of debtors prepared?

Yes [ ]

No [ ]

1. Does your company follow a stable dividend policy? Yes [ ]

No [ ]

2. Does your company follow a constant payout ratio? Yes [ ]

No [ ]

IV – Items related to Dividend Policy

V – Items related to Corporate Governance 1. Does your company have an internal team dedicated to corporate governance? Yes [ ] No [ ] 2. Has the company been assessed for its Corporate Governance practices by any rating agency like CRISIL or ICRA etc. Yes [ ] No [ ] 3. Does the company publish its annual report within stipulated time of six Management & Change, Volume 16, Number 1 & 2 (2012)

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months after the end of the financial year? Always [ ] Mostly [ ] Occasionally [ ] Sometimes [ ] Never [ ] 4. Does the company publish/announce semi- annual reports within one month of the end of the half-year? Always [ ] Mostly [ ] Occasionally [ ] Sometimes [ ] Never [ ] 5. Does the company publish/announce quarterly reports within one month of the end of the quarter? Always [ ] Mostly [ ] Occasionally [ ] Sometimes [ ] Never [ ] 6. Does the company consistently disclose material sensitive information to stakeholders? Always [ ] Sometimes [ ] Never [ ] 7. Are the statutory auditors of the company unrelated to the top management of company? Yes [ ] No [ ] 8. Is there a whistle â&#x20AC;&#x201C; blower policy in your company? Yes [ ]

No [ ]

9. Is there an investorsâ&#x20AC;&#x2122; grievance cell in your company? Yes [ ]

No [ ]

10. Do the CEO and CFO of your company establish and maintain internal controls and implement remediation and risk mitigation towards deficiencies in internal controls? Yes [ ] No [ ] 11. Does your company submit a quarterly compliance report on corporate governance to the stock exchange where it is listed in the prescribed form? Yes [ ] No [ ] 12. Does your annual report contain a separate section on corporate governance with a detailed compliance report? Yes [ ] No [ ]

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13. Does your company obtain a certificate either from auditors or practicing company secretaries regarding compliance of conditions as stipulated in clause 49 and annex the same to the directorâ&#x20AC;&#x2122;s report? Yes [ ] No [ ] 14. Does your company have a committee on corporate governance as per clause 49? Yes [ ] No [ ] 15. Does your company have the mandatory audit committee as per clause 49? Yes [ ] No [ ] 16. Does your company have the remunerations committee as per clause 49? Yes [ ] No [ ] VI â&#x20AC;&#x201C; Items related to Risk Management 1. What are some of the steps your company takes to mitigate its financial risk? a. [ ] Keep the debt/equity ratio close to the industrial benchmark. b. [ ] Make conscious efforts to keep the financial leverage as low as possible by reducing debt in the capital structure. c. [ ] Have internal control ratios like cash-flow return on investment. d. [ ] Make conscious efforts to keep the interest coverage ratio as high as possible. e. [ ] Make extensive use of financial derivatives. f. [ ] Examine tax consequences of cross border activities and incorporate it in financial planning. g. [ ] Any other (please specify) _____________ 2. What are some of the steps your company takes to mitigate its business/ operational risk? a. [ ] Use adequate insurance coverage against fixed asset loss. b. [ ] Use leasing/hire-purchase arrangements to keep fixed cost investment as low as possible. c. [ ] Examine components like transfer pricing, excise duties etc as consequences of cross border activities and incorporate it in operational planning. d. [ ] Review acquisitions and handle disposal/liquidation of business components/joint ventures. e. [ ] Budgets are regularly monitored and reallocated in line with revised risk/resource needs. Management & Change, Volume 16, Number 1 & 2 (2012)

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[ ] There is a strong and conscious effort to focus on variable-cost - dominated ventures and strategies. g. [ ] Any other (please specify) _____________ 3. If operating risk is high, does your company make a strong effort to reduce financial risk (or vice-versa) in order to keep the overall risk low? Yes [ ] No [ ] 4. Indicate the order of preference as to which of the following precautions could help in minimizing the political risk in international operations. (1 for most important, 2 for next preference and so on) [ ] Incorporating a risk premium in the cost of capital. [ ] Integrating products of the host country in your business. [ ] Taking loans from the financial institutions of the host country. [ ] Increasing the number of the host country employees. [ ] Creating joint ventures with an enterprise of the host country. [ ] Any other (please specify) _______________ 5. For managing exchange rate risk, do you use the following technique(s)? Yes No Leads and lags [ ] [ ] Netting [ ] [ ] Back to back swap [ ] [ ] Re-invoicing through a centralized system [ ] [ ] Risk sharing [ ] [ ] Any other (please specify) ______________ 6. In case of anticipated depreciation of local currency, which of the basic hedging strategies are used by your company? (Please tick mark) [ ] Buy foreign currency forward. [ ] Reduce levels of local currency cash and marketable securities. [ ] Reduce local-currency receivables. [ ] Delay collection of hard currency (appreciating currency) receivables. [ ] Borrow locally. [ ] Delay payments of local currency payable. [ ] Speed up dividend and other remittances to parent. [ ] Invoice exports in foreign currency and imports in local currency. 7. In case of anticipated appreciation of local currency which of the basic hedging strategies used by your company? (Please tick mark) Management & Change, Volume 16, Number 1 & 2 (2012)

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[ ] Sell foreign currency forward. [ ] Increase levels of local-currency cash and marketable securities. [ ] Relax local-currency credit terms (i.e. increase local currency receivables) [ ] Speed up collection of soft currency (depreciating currency) receivables. [ ] Reduce local borrowing. [ ] Speed up payments of local currency payable. [ ] Delay dividend and other remittances to parent. [ ] Invoice exports in local currency and imports in foreign currency.

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EXPLORING THE DEMOGRAPHIC DIFFERENCES IN ADOPTION OF MOBILE MARKETING IN INDIA Jaydeep Mukherjee1 With rapid proliferation of the mobile telephony and technology, marketing through Mobile media has become a critical tool for carrying out marketing in India. It offers unparalleled reach and access to consumers. However, mobile based marketing is different from traditional marketing as this medium is an extremely personal device which remains connected with the user, through day and night. Thus, customizing the mobile marketing communication for the specific consumer is of critical importance. Literature gives us evidence to hypothesize that Age and Gender, the commonly used variables for segmenting the market, tend to affect consumers’ intention to buy through mobile. This research was conducted to provide insights which could helpmarketers design gender and age specific mobile marketing campaigns. This research was conducted through internet based survey among 353 respondents, who had easy access to internet and most of them owned smart phones. The analysis of the data collected through the survey was carried out through ANOVA to study the impact of Age and Gender on Consumer’s ‘Privacy’ Concerns, Consumer’s ‘Security’ Concerns, the Influence of Peers and the Purchase Intention. The research provided empirical evidence that in India, women perceive less security concern as compared to men, and peer group influence was significantly more in case of older respondents (above 41 years of age) while using mobile for buying online. It also identifies the need for further research to help the marketers understand the specific drivers of higher security concern among Indian men as compared to women and its consequences for mobile based marketing initiatives. 1.

Jaydeep Mukherjee , Associate Professor (Marketing), Management Development Institute, M.G. Raod, Sukhrali, Gurgaon -122001, India. Phone: 0124-4560381, Mobile: 9312403442. E-mail: jmukherjee@mdi.ac.in

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Keywords: Mobile Marketing in India, Influence of Gender and Age on Mobile Marketing, Peer Group Influence, ‘Security’ in Mobile Marketing. INTRODUCTION Mobile devices, applications and services have become integrated to people’s daily lives, at a personal and professional level. The integration of the Internet, mobility and communications at the device, service and transport level has created a new set of business opportunities. Marketers need to identify and reach their target audience through various innovative methodologies and technologies which integrate mobile and internet. With the inherent characteristics of high reach and personal involvement of consumers, as well as the rapid proliferation of mobile technology, marketing through Mobile media has become a critical tool for carrying out marketing. It has also led to development of a new ecosystem of marketing. . The marketers need to adapt to this medium of marketing in order to survive and grow (Watson et al., 2002). Mobile marketing has been defined as a marketing activity conducted through the ubiquitous network to which a consumer is always connected using a personal mobile device. Mobile technology offers opportunity for direct communication with consumers, anytime and anyplace. As mobiles are extremely personal devices, their use as a communication medium has to be understood well for effectively harnessing its full potential as a marketing medium. Thus it can be said that mobile based marketing communication is much more personal in nature, which requires customer and context specific offers. Personalization, ubiquity, interactivity and localization – are a few of the attributes inherent to Mobile Marketing which generate significant potential for commercial communication. It is an ideal method for individualized and dialogue-oriented communication. Mobile Social media is a group of mobile marketing applications that allow the creation and exchange of usergenerated content. In addition, mobile phone lends itself to enlarging a campaign’s reach through viral effects. A viral effect develops if recipients of advertising messages forward these to further recipients who were earlier not the initial target group of the campaign, but would certainly be more influenced than the former as the campaigns have flown to them through their friends and family (Okazaki 2009). Management & Change, Volume 16, Number 1 & 2 (2012)

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Newly emerging markets in Asia are becoming dominant nations online, having greatest numbers of users, despite lower levels of adoption. The population is more enthusiastic about new technology (Dutta, Dutton and Law 2011). India is a very big market and extremely fragmented. Mobile penetration rate is very high and hence marketing initiatives could be harnessed very effectively through mobile. The practical issue faced by most marketers using mobile as a medium, is that, mobile is a personal device, hence marketing through this medium has to be personalized. The marketers are able to access broad demographic variables like age, sex, household income. Thus, there is need to find out if these demographic variables affect the consumers’ acceptance of mobile marketing initiatives. Consumer behavior in the technology mediated environment depends on the culture as well as the individual specific variables (Moller & Eisend, 2010). Since there are no India specific studies on the impact of demographic variables like age and gender on the consumer’s acceptance of marketing through mobile medium, there is a need for researching the topic. LITERATURE SURVEY Technology Enabled Marketing & Mobile Marketing Interactivity is a hallmark of the paradigm shift in both marketing and communication in the mobile media. Marketing communications have been converted to a two way process from a one-way process, through the interactive media (Haghirian & Madlberger, 2005). In the interactive medium, marketing is viewed as an integrated exchange process by which an organization uses the understanding of technology, customer behavior and other resources to create and manage customer value. The desired results are building collaborative relationships with the consumer and enhancing shareholder value through products/service offerings. Relevant brand messages and ideas communicated and delivered to the right customers through appropriate channels and contact points at appropriate times is termed as Interactive Marketing Communication (IMC) (Venkatesh and Malthouse, 2006). The effectiveness of the customer relationship initiatives of firms is enhanced by different media elements; this aspect is also addressed by the IMC. The addition of ‘Interactive’ marketing domain to the ‘Integrated’ aspect of marketing has successfully brought together myriads of customers, messages and media touch points. In today’s world which is Management & Change, Volume 16, Number 1 & 2 (2012)

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so customer oriented and data driven, interactive and integrated marketing has made traditional concept of mass communication redundant and a little meaningless in being sufficient in developing relationships with customers (Zahay et al., 2004). Targeted marketing communications has been developed by the marketers to build customer trust and relationship in niche markets (Peltier, Drago and Schibrowsky 2003). The aspect of personalization has been made possible by the developments in Information technology which allows customer specific communication (Hoffman & Thomas, 1996). During the last decade, a lot of changes have been observed in the marketing environment across markets. A lot of fragmentation has been noticed by the marketers in the mass market which has led to changes in thought process of the marketers - to move away from mass marketing to a more personalized consumer experience. The growth of technology has enabled marketers across the globe to communicate with their consumers in a more personalized way. They can reach their consumers anytime, anywhere, with ease and almost in real time, increasing the possibilities and scope of marketing initiatives and communication. Using highly targeted and interactive media is the key used by the marketers in todayâ&#x20AC;&#x2122;s competitive environment. Content delivery and direct response channel in integrated campaigns along with traditional media such as a TV, radio, and print, or as a standalone medium, are a few types of mobile marketing instances, which positions mobile media as a strong advertising and direct marketing tool (Tappey and Woodside, 2005). An increasing amount of money is being spent on the marketing activities through mobile media, owing to the effects of mobile advertising on the companies. For several years, mobile advertising has been exploited successfully by the big global brands like Coca-Cola, Adidas, Nike, Pepsi, McDonaldâ&#x20AC;&#x2122;s, Disney etc. The effectiveness of the advertising through SMS as a brand vehicle and in stimulating consumerâ&#x20AC;&#x2122;s response has been proved by the previous studies (Barwise & Strong, 2001). Delivery of personalized, context and location based messages to a specific target audience is allowed by the mobile devices (Sultan & Rohm, 2005). Thus mobile marketing opens up a unique opportunity for marketers to engage with their consumers, build long term relationship and achieve the ideal situation of being able to deal with every consumer on a one-to-one basis. Management & Change, Volume 16, Number 1 & 2 (2012)

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Impact of Demographic Differences Donthu and Garcia (1999) found significant differences between online shoppers and non-shoppers in terms of socio-demographic, behavioral and attitudinal variables. Age Literature pertaining to the effect of age differences indicates that the elderly individuals and younger adults process information in different ways. Age differences result in complex set of changes in individualsâ&#x20AC;&#x2122; sources of information, ability to learn, and susceptibility to social influence (Phillips & Sternthall, 1997). Exploratory research indicated that more aged consumers are heavy users of mass communication sources for information. They concluded that the elderly rely more heavily on newspapers and salespersons, for marketing information compared to other generational groupings (Martin et al., 1976). The elderly people tend to use friends, personal experience, price comparisons, and mass media as information sources in making marketing decisions (Beardon & Mason, 1978). The literature has very divergent findings about relevance of age in technology adoption. For technology adoption, age matters (Morris and Venkatesh, 2000) while other research findings suggested that demographic factors no longer impacted the perception of e-commerce benefits and ecommerce adoption (Lee, 2010). In light of these differences, there is a need to examine different cultures and environments to find out if there is any difference across different age groups. Gender Male consumers showed a more favorable attitude towards mobile advertising than female consumers. Younger consumers valued advertising messages via mobile devices to a higher extent than older consumers and they also showed a more positive attitude toward them (Haghirian and Madleberger, 2005). While males make their decisions through the use of heuristic devices, such as the credibility of the sender and the attractiveness of the sender and/or message, females make their decisions based on the content of the Management & Change, Volume 16, Number 1 & 2 (2012)

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cognitive and affective cues provided in the marketing communication messages (Erdogan, 2002). Women had much more positive attitudes toward shopping in general. Women’s ratings of catalogue and store shopping were significantly more positive than men’s ratings. However, the opposite was found to be true for Internet shopping (Alreck & Settle, 2002). Though there was a high level of concern overall, regardless of gender, only minor or insignificant gender-based variations were detected (Kolsaker and Payne, 2002) . The gender differences significantly affect new-technology based decision making processes as suggested in previous research. Women are ready to accept information technology and are more influenced by its ease of use (Van Slyke et al.,2002; Venkatesh et al.,2000). There may be a gender difference in the degree to which, as well as the way in which adolescents are influenced by peers (Steinberg and Silverberg, 1982). Findings in researches have also indicated that gender does cause difference in information processing strategies (Altizer, Sen and Tegarden, 1993), attitude toward technology (Chiu, Lin & Tang, 2005) and shopping behavior. So, it would be interesting to understand, if the concern for ‘Privacy’ while making an online transaction also varies with gender. Consumer ‘Privacy’ Concerns Every online transaction requires a certain level of personal information sharing between the consumer and the firm through a third party i.e. the network provider. Personal information is necessary to perform any kind of e-commerce activity and when consumers have more ‘privacy’ concerns, the outcome is retreatment from performing online purchases (Yazdanifard, Edres & Seyedi 2011). ‘Privacy’ was defined as “the rights of individuals and organizations to determine for themselves when, how and to what extent information about them is to be transmitted to others” (Grandinetti, 1996; Martin , 1973) The Consumer ‘Privacy’ is one of the critical factors that influences a consumer’s decision of buying using Mobile Marketing as a medium. The concern that tops the list for Internet users is the ‘Privacy’; it is also the top reason why online shopping is avoided by the people who do not use it (Udo, 2001). Management & Change, Volume 16, Number 1 & 2 (2012)

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Permission based advertising is a very critical factor that affects consumers’ perception towards accepting mobile marketing. It means that consumers agree to receive the SMS/MMS advertisements by their free will, by opting for a scheme with the mobile network provider rather than simply being exposed to it without his or her free will. Recipients of the message who could be the potential consumers have opted for these advertisements, that can bring a better value for advertiser’s money (Martin et al., 2003). Consumer ‘Security’ Concerns One of the pivotal factors of concern in online marketing is ‘Security’. The increased level of technological advancements has made the consumerdetails collected by any marketer over the internet, vulnerable to being accessed by various parties and thus ‘Security’ becomes a bigger concern while making any decision to communicate personal information, even where there is no direct transaction. This concern seems to be bringing the consumer at a risk of incurring losses because of mishandling of information by any of the parties. Increasing concerns on security risks has become a global issue (Ackerman and Davis, 2011). The deterioration of confidence that consumers hold in online mode of transactions has got weakened owing to the ‘Security’ issues, as the technological advancements enable more public access (Yazdanifard, Al-Huda Edres and Seyedi, 2011). Thus, it is required to understand whether the consumer’s ‘Security’ Concerns really have any effect on the intention to purchase. The server platforms and the payment systems do bring a sense of insecurity among the consumers and have often led to a negative impact on consumers’ intention to buy as per the marketers. ‘Security’ has always been perceived by most of the marketers as a significant barrier to the emergence of consumer’s interest in making online buying (Anckar and D’Incau, 2002). The studies indicate a widespread apprehension about the security of mobile devices and their ability to protect pertinent information relayed in a financial transaction. Majority of users were unconvinced about mobile devices being safe for shopping and banking. “Despite unprecedented growth in the number of cell phone users and the advancement of mobile technologies, telecom providers, online retailers, and financial institutions seem unable to convince consumers worldwide that a secure platform exists for conducting online mobile transactions” Management & Change, Volume 16, Number 1 & 2 (2012)

68 Exploring the Demographic Differences in Adoption...

(Blue, 2008). ‘Security’ is a key integral issue for users, and hence worth becoming a part of this study. Influence of Peers Following an old cliché, ‘Human Beings are social animals’ does make a clear presentation of the fact that their behaviors and actions are significantly influenced by the people surrounding them. However, consumers will always belong to several groups simultaneously and some will be more influential than others. They get influenced by high-status groups and role models, with purchasing decisions aimed towards achieving greater closeness to a group. The emergence of online communities and social networks can often increase the size and geographical distribution of the groups that can influence consumer behavior. Peer group influence in some situations works only for adults and the effects are moderated by gender. Thus marketing initiatives have to take the variables of age and gender into consideration (Nuttall and Tinson, 2005). Consumers Intention to Buy Perceived usefulness of online purchase can be defined as the prospective consumer’s subjective probability that using the internet will efficiently facilitate his or her purchasing (Chiu et al.,2005). (Childers et al.,2001) in their empirical study also found that usefulness is the primary determinant of behavioral intention to use a technology, with ease of use and enjoyment acting as secondary determinants. (Moe, 2003) related the online search behaviors to purchase intention, and made interesting findings indicating that online search mode is closely related to purchasing intention. THE RESEARCH PROBLEM A detailed literature review was conducted to understand the existing linkages between the various factors that actually have influence on the purchase intention of the consumers. A list of factors identified for this study was Influence of Peers, Consumers ‘Security’ & ‘Privacy’ concerns, and their impact on the purchase intention for different demographic parameters. Thus, on the basis of literature search, we modeled the consumers’ intention to buy through mobile medium as a function of Management & Change, Volume 16, Number 1 & 2 (2012)

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consumers’ perception of the mobile marketing communication and the consumers’ concerns linked to the same. Diagram of the proposed model is given in Exhibit1. The literature gives us evidence to hypothesize that Age and Gender, the commonly used variables for segmenting the market, tend to affect consumers’ intention to buy through mobile. Thus, the following hypotheses were tested: H1.a: There is no difference between the Consumer ‘Privacy’ Concerns across different Ages. H1.b: There is no difference between the Consumer ‘Privacy’ Concerns across genders. H2.a: There is no difference between the Consumer ‘Security’ Concerns across different Ages. H2.b: There is no difference between the Consumer ‘Security’ Concerns across genders. H3.a: There is no difference between the Influence of Peers across different Ages. H3.b: There is no difference between the Influence of Peers across genders. H4.a: There is no difference between the Consumers’ Purchase Intention across different Ages. H4.b: There is no difference between the Consumers’ Purchase Intention across genders. METHODOLOGY The instrument for data collection: The variables under study, influence of peers, security concerns, ‘privacy’ concern, and purchase intention were measured with a scale derived from literature. The age and gender data was collected using a direct question. Thus the validity of the measures was achieved conceptually through literature. The reliability of the constructs was pilot tested. An online survey Management & Change, Volume 16, Number 1 & 2 (2012)

70 Exploring the Demographic Differences in Adoption...

was conducted using this questionnaire to collect the data from 58 respondents. Based on the results of the reliability test, few of the scale items were modified. The final questionnaire along with the source of questions is given in Exhibit 2. The final data was put through a reliability analysis using SPSS. The items that were reducing the reliability figures were dropped and the final analysis was done with items for which Cronbach alpha figures were more than the acceptance range of 0.6 (Malhotra 2004). The final Cronbach alpha figures of the constructs are given below in Table1 Table 1 Chronbach alpha figures Construct

Cronbach’s alpha

Influence of peers

0.6488

Security Concerns

0.7306

‘Privacy’ concerns

0.7038

Purchase Intention

0.7037

Data Collection: The data has been collected from a group of people majorly from the IT and manufacturing industry. The survey has been conducted through internet based survey among respondents who had easy access to internet and most of them owned smart phones. Data Analysis: The data so collected from the 353 respondents was analyzed using SPSS. The male respondents were 71% while rest 29% were female. 52% of the respondents were in the age of 20-30 years, 37 % in 30-40 years and rest 11 % were more than 40 years of age. Hence the data captured the demographic variation that we wished to study. RESULTS The analysis of the data collected through the survey was carried out through use of SPSS 10 software. ANOVA was used to find out if there is significant variation in the Consumers’ ‘Privacy’ Concerns, Consumers’ ‘Security’ Concerns, the Influence of Peers and the Purchase Intention across ‘Age’ and ‘Gender’. The summary of the results from ANOVA tables is given below in Table2 and Table 3:

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Table 2 Age-wise Summary of Mean and ANOVA of Dimensions of Mobile Usage Dimensions Mobile Usage Consumer ‘Privacy’

21 to 30 years (N=183)

31 to 40 Above 41 years years (N=129) (N=41)

F value

P value

3.765

3.6357

3.5976

1.568

.210

Influence of peers

3.3082

3.1473

3.5366

6.332

.002**

Purchase Intention

3.8317

3.7798

3.7073

0.698

.498

Security Concern

2.8743

3.7.73

3.0488

0.916

.401

Table 3 Gender wise Summary of Mean and ANOVA of Dimensions of Mobile Usage Dimensions Mobile Usage

Male (N=249)

Female (N=104)

F value

P value

Consumer ‘Privacy’

3.7008

3.6923

0.10

.922

Influence of peers

3.3036

3.2096

1.561

.212

Purchase Intention

3.8233

3.7385

1.258

.263

Security Concern

3.0753

2.5793

26.441

.000**

Note: i) All figures, except F-values and p-values are mean values. ii) *Value is significant at 5% level with degree of freedom (df) = 2/352 iii) **Value is significant at 1% level with degree of freedom (df) = 2/352

Since there were significant differences across Age for the Influence of Peers, Duncan’s post hoc analysis was done to specify the specific age groups where the difference was significant. The results are given below in Table 4.

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Table 4: Results of post hoc analysis of Influence of peers and age Influence of peers Duncan’s test results Age in years

Subset for alpha = 0.05 1

31-40

129

3.1473

21-30

183

3.3082

Above 41

Sig.

3.5366 0.111

1.000

Means for groups in homogeneous subsets are displayed.

Based on the tables, we can conclude that the empirical data supported the entire set of hypotheses, except H2.b and H3.a. H2.b: There is no difference between the Consumer ‘Security’ Concerns across genders - Rejected. Thus the results demonstrated that the security concerns of men (mean of 3.0753) were significantly more than that of women (mean of 2.5793). H3.a: There is no difference between the Influence of Peers across different Ages – Rejected. The Duncan test revealed that the influence of peer was not significantly different among the age group of 21-30 years and 31-40 years (mean of 3.1473 and 3.3082 respectively). However, the influence of peers was more in case of age of above 41 years (mean of 3.5366) and was significantly more from compared to the other age groups. DISCUSSIONS AND CONCLUSIONS Mobile marketing is a very important channel for marketers in communicating with the consumers. Consumers increasingly prefer to be dealt with in a personalized manner and mobile technology provides a unique possibility of meeting this requirement cost- effectively. Marketers are coming up with the mobile applications or portals to promote their products while dealing with consumers of varying age and gender. The Age and gender data are verified before assigning the mobile numbers in India (provided through mandatory identity proof), and hence can be considered to be authentic. This data is also easily available with the Management & Change, Volume 16, Number 1 & 2 (2012)

Jaydeep Mukherjee 73

mobile marketeer, which they often use for targeting their communication. Thus these two demographic variables, namely age and gender are critical in building strong and successful relationship through mobile marketing initiatives, which can be practically implemented also. Culture of user is significant in determining the effectiveness of the communication carried out in a computer mediated environment (Kim 2008). There are many researches already conducted in predominantly type I culture (individualistic, weak uncertainty avoidance, low long term orientation), while not much has been done with respondents from type II culture (collectivist, strong uncertainty avoidance, high long term orientation). This research provides empirical evidence on one of the large markets which has type II culture, specifically the Indian consumers, in the context of mobile marketing initiatives. There is evidence in literature that women perceived higher risk in the online purchase environment (Janda 2008). Singapore, which has cultural resemblance to India, shows no difference on security concern based on gender (Hui and Wan 2007). However, this study shows an exactly opposite trend among Indian consumers. Previous research on Indian context also suggests that females perceive significantly greater â&#x20AC;&#x2DC;privacyâ&#x20AC;&#x2122; as well as security in the computer mediated environment (Gupta and Bansal 2011). The finding that in India, women perceive less security concern as compared to men, could be due to various reasons, which are not really linked to one another. It could be because of:a. greater trust in security mechanisms in the electronic medium b. lack of understanding of the possible security threats in the electronic medium c. men experience relatively greater anxiety as compared to women Since the quantitative research did not give any pointers to the specific explanation, the literature was referred, to figure out the possible explanations. The following could possibly explain the empirical findings: Online trust affects online shopping behavior differently among genders and there is difference between how men and women develop the trust (Awad and Rogowsky, 2001). There are several gender specific trust regions and two gender independent trust regions in the human brain. The differences in gender specific trust regions in human brain can explain the difference in trust formation and consequent security concerns (Riedl, Hubert and Kenning, 2010). Higher levels of anxiety leads to higher levels of security concern (Li Management & Change, Volume 16, Number 1 & 2 (2012)

74 Exploring the Demographic Differences in Adoption...

and Zhang, 2009). Thus there are evidences in the literature, about the possible explanations for this interesting finding. However,which specific concepts explain the empirical evidence is yet not established and needs further research. The study clearly demonstrated that peer group influence is significantly more in case of older age group (above 41 years) in India. This finding corroborated the earlier research, which demonstrated that for younger people, adoption of technology depends on their attitude towards technology, while for older people it depends on subjective norms and perceived behavior control (Morris and Venkatesh, 2000; Nuttall and Tinson, 2005). Hence, the Indian situation is no different from other contexts. The behavior could be explained quite logically as: the population above 40 years is less confident of using new technology and their response to mobile marketing initiatives would also be influenced by peers. Thus, this research has very clear takeaways for the marketers who want to adopt mobile phones as a medium of marketing communication in India. a. Mobile based marketing campaign could be very effectively customized for their target market using the gender and age as variables. Since impact of age and gender are not observed on purchase intention and â&#x20AC;&#x2DC;privacyâ&#x20AC;&#x2122; concern, the mobile marketing communication could be quite uniform in many situations. b. Security concern of women is less than men, and hence women could be targeted more effectively to engage in internet enabled mobile technology based purchase. Also, less money and focus should be on themes which try to alleviate the security concern in marketing communications, that are targeted at women. c. The older age group could be prodded to adopt the mobile based transaction by using the peer group influence mechanisms. The marketing process would be to figure out the active users in the age group of 41 years and above, and provide incentive to them to induce new peer group members to the concepts of mobile purchases. LIMITATIONS AND AREAS OF FUTURE RESEARCH This research had two major limitations. First, the sampling procedure followed was convenience sampling. Second, the number of respondents Management & Change, Volume 16, Number 1 & 2 (2012)

Jaydeep Mukherjee 75

were limited to internet using population only, leading to some concerns about the sample being representative of population. Hence there could be questions about the generalizability of the findings. The research focused on the variation in the age and gender only. Thus other demographic factors like household income, and psychographic variables like personality, lifestyles etc. would be worthwhile to study and evaluate for their effect on the consumers’ intention to buy through mobile. There is scope of qualitative research to help the marketers understand the specific drivers of higher security concern among Indian men as compared to women and its consequences. Exhibit 1 Framework for consumer’s intention to buy through mobile medium Consumer Privacy

- ve

- ve Purchase Intention through Mobile Marketing

Security

+ve Influence of Peers

Exhibit 2 Costruct and its measurement Construct

Question

Adapted from

Consumer ‘Privacy’

My online transaction data is kept confidential Udo G. J. by the site (2001

Consumer ‘Privacy’

Marketers are normally able to find out personal information about Online-shoppers

Consumer ‘Privacy’

Web is full of electronic junk mail sent by marketers Management & Change, Volume 16, Number 1 & 2 (2012)

76 Exploring the Demographic Differences in Adoption... Consumer ‘Privacy’

I believe the information I share with online companies will not be shared with other companies.

Consumer ‘Privacy’

Online companies will keep confidential what they learn about me from my activities on their site.

Influence of Peers

I normally purchase the same products that my Nuttall and peers purchase Tinson (2005)

Influence of Peers

I would often take help from others in choosing the products that I buy

Influence of Peers

I am guided by what others are buying and using

Influence of Peers

I frequently depend on suggestions from friends for buying a product

Influence of Peers

It is important that others like the products that I buy.

Purchase Intention Purchase Intention

It is easier to compare and select the right products Moe (2003) in the online media. and Chiders Online purchases are more convenient than real (2001) world purchases.

Purchase Intention

We can get better deals in online than in real world purchases.

Purchase Intention

Given a choice I would prefer to buy online

Purchase Intention

I am comfortable with making online purchases.

Security Concerns

The Mobile/Web is a secure means through which Ackerman and Davis to send sensitive information. Overall, the Mobile/Web. is a safe place to transmit (2011)

Security Concerns

sensitive information.

Security Concerns

I would feel secure sending sensitive information across the Mobile/Web.

Security Concerns

I would feel totally safe providing sensitive information about myself over the Mobile/Web.

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Jaydeep Mukherjee 77

REFERENCES Ackerman, M.S. and Davis D.T. (2011) “Privacy and Security Issues in ECommerce” accessed from http://econ.ucsb.edu/~doug/245a/Papers/ ECommerce%20Privacy.pdf Alreck, P. and Settle, R. (2002) “Gender Effects on Internet , Catalog and Store Shopping”, Journal of Database Marketing , Volume 9, Number 2, 1 January 2002 , pp. 150-162(13) Altizer, M., Sen, T.K. and Tegarden, D.P. (1993) “Gender Differences in Decision Making” Department of Accounting Anckar, B. and D’Incau, D. (2002) “Value Creation in Mobile Commerce: Findings from a Consumer Survey”, Journal of Information Technology Theory and Application, vol. 4, no. 1, pp. 43-64. Awad, N.F. and Rogowsky, A. (2001) “Establishing trust in electronic commerce through online word of mouth: An examination across genders”, Journal of management Information Systems, Vol. 24, No. 4, pp 101-121. Barnes, S.J. and Scornavacca, E. (2004) “Mobile marketing: the role of permission and acceptance”, International Journal of Mobile Communications, 2(2), 128-139. Barwise, P. and Strong, C. (2002) “ Permission-based mobile advertising”, Journal of Interactive Marketing, pp. 14-24. Beardon, W. O. and Mason, J. B. (1978) “ Elderly Use of In-Store Information Sources in Supermarket Purchase”, Journal of Retailing, Vol. 55, Issue 1, pp. 79- 92. Blue P. (2008) “Banks, telcos and retailers must collaborate to increase adoption of mobile payments”, http://www.unisys.com/unisys/news/ detail.jsp?print=true&id=5100044 Childers, T.L., Carr, C.L., Peck, J. and Carson, S. (2001) “Hedonic and utilitarian motivations for online retail shopping behavior”, Journal of Retailing, 77, pp. 511-535

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Chiu Y., Lin C.P. and Tang, L. L. (2005) “Gender differs: assessing amodel of online purchase intentions in e-retail service”, International Journal of Service Industry Management, 16(5), pp. 416-435 Donthu, N. and Garcia, A. (1999) “The Internet Shoppers’’, Journal of Advertising Research, Vol. 39, 52-58. Duncan, T. and Sandra, E. M. (1998) “A Communication-Based Marketing Model for Managing Relationships,” Journal of Marketing, Vol. 62 Issue 2, p1-13. Dutta, S., Dutton, W.H., and Law, G. (2011) “The new internet world: A global perspective on freedom of expression, ‘privacy’, trust nad security online”, INSEAD Working Paper, 2011/89/TOM. Erdogan, K. (2002) “The impact of gender in marketing communications: the role of cognitive and affective cues”, Journal of Marketing Communications , Vol. 8 Issue 4, p257-275. 19p. Gupta, K.K. and Bansal, I. (2011) “Effect of demographic variables in customer perceived internet banking service quality: Empirical evidence from India”, Paradigm, Vol XV, No. 1 & 2, pp. 83-92. Haghirian, P. and Madlberger, M. (2005) “Consumer Attitude Toward Advertising Via Mobile Devices – An Empirical Investigation Among Austrian Users”, Proceedings of the European Conference on Information Systems, Regensburg, Germany, May 2005. Hui, T.K. and Wan, D. (2007) “Factors affecting internet shopping behavior in Singapore: Gender and Educational issues”, Internatrional Journal of Consumer Studies, Vol. 31, pp. 310-316. Janda, S. (2008) “Does Gender moderate the online concerns on purchase likelihood” Journal of Internet Commerce, Vol 7, Issue 3, pp. 339-358. Kim, D. J. (2008) “Self perception-based versus transference-based trust determinants in computer mediated transactions: A cross cultural comparison study”, Journal of management Information Systems, Vol. 24, No. 4, pp. 13-45. Kolsaker, A. and Payne, C. (2002) “Engendering trust in e-commerce: a Management & Change, Volume 16, Number 1 & 2 (2012)

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study of gender-based concerns”, Marketing Intelligence & Planning, Vol. 20 Issue: 4, pp.206 – 214. Lee, J.W. (2010) “The roles of demographics on the perceptions of electronic commerce adoption”, Academy of Marketing Studies Journal, Volume 14, Number 1, 2010. Li, S. and Zhang, C. (2009) “An empirical investigation of information ‘privacy’ concern and its antecedents and consequences”, Northeast Decision Sciences Institute Proceedings, pp. 344-349. Malhotra, N. K. (2004) Marketing Research: An Applied Orientation, New Jersey: Pearson Education Inc. Mark, S. A., Donald, T. and Davis, Jr. ‘Privacy’, “Security Issues in ECommerce. New Economy Handbook”, econ.ucsb.edu/~doug/245a/ Papers/ECommerce%20Privacy.pdf. Martin, B. A. S., Van Durme, J., Raulas, M., & Merisavo, M. (2003) “Email Advertising: Exploratory Insights from Finland”, Journal of Advertising Research, 43 (September), pp. 293-300. Martin, C. (1976) “A Transgenerational Comparison: The Elderly Fashion Consumer in the Elderly Consumer”, Advances in Consumer Research, Vol. 3, Issue 1, p453-456. Mobile Marketing Association (2008), “2008 mobile attitude & usage study” accessed from http://www.mobilemarketer.com/cms/lib/2476.pdf Moe, W. W. (2003) “Buying, Searching, or Browsing: Differentiating between Online Shoppers Using In-Store Navigational Clickstream”, Journal of Consumer Psychology, Vol. 13 Moller, J. and Eisend, M. (2010) “A Global Investigation into the Cultural and Individual Antecedents of Banner Advertising Effectiveness”, Journal of International Marketing, Vol 18, No 2, 80-98. Morris, G.M. and Venkatesh, V. (2000) “Age differences in technology adoption decision: Implications for a changing work force”, Personnel Psychology, Vol. 53, pp. 375-402 Management & Change, Volume 16, Number 1 & 2 (2012)

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Nuttall, P. and Tinson, J. (2005) “Exploring peer grouping influence”, The marketing Review, Vol. 5, pp. 357-370. Okazaki, S. (2009) “The Tactical Use of Mobile Marketing: How Adolescents’ Social Networking Can Best Shape Brand Extensions”, Journal of Advertising Research, Vol. 49 Issue 1, p12-26. Parissa, H. and Madlberger, M. (2008 ) Consumer Attitude toward Advertising via Mobile Devices – An Empirical Investigation among Austrian Users http://citeseerx.ist.psu.edu/viewdoc/ summary?doi=10.1.1.99.9927. Peltier, J., Drago, W. and Schibrowsky, J. (2003) “Virtual Communities and the assessment of online marketing education”, Journal of Marketing Education, 25(3), 260-276. Phillips, L. W. and Sternthall, B. (1977) “Age Difference in Information Processing: A perspective on the Aged Consumers”, Journal of Marketing Research, Nov77, Vol. 14 Issue: 4, pp. 444-457. Riedl, R., Hubert, M. and Kenning, P. (2010) “Are there neural gender differences in online trust? An fMRI study on perceived trustworthiness of e-bay offers”, MIS Quarterly, Vol 34, No 2, pp 397-428 Steinberg, L. and Silverberg, S. (1982) “The Vicissitudes of Autonomy in Early Adolescence”, Child Development, pp.841-851. Sultan, F. and Rohm, A. (2005) “The Coming Era of ‘Brand in the Hand’ Marketing,” MIT Sloan Management Review, 47 (1), 83-90. Trappey, R.J. and Woodside, A.G. (2005) “Consumer responses to interactive advertising campaigns coupling short-message-service direct marketing and TV commercials”, Journal of Advertising Research, 45(4), p.p. 382 -401. Udo, G. J. (2001) “Privacy’ and security concerns as major barriers for ecommerce: a survey study”, College of Business Administration, University of Texas at El Paso. Van Slyke, C., Comunale, C. and Belanger, F. (2002) “Gender differences in perceptions of Web-based shopping”, Communications of the ACM, 45(7), 82-86. Management & Change, Volume 16, Number 1 & 2 (2012)

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Van Slyke, C., Belanger, F. and Comunale C. (2004) “ Factors influencing the adoption of web-based shopping: the impact of trust”, Database for Advances in Information Systems, 35(2). Venkatesh, S. and Malthouse, E. C. (2006) “Moving Interactive Marketing Forward”, Journal of Interactive Marketing Winter2006, Vol. 20 Issue 1, p2-4. Watson, R. T., Berthon, P., Pitt, L.F. and Zinkhan, G., M. (2004) “Marketing in the age of the network: From marketplace to U-space”, Business Horizons, Vol. 47, Issue 6, pp33-40. Yazdanifard, R., Edres, N., and Seyedi, A. (2011) “Security and ‘Privacy’ Issues as a Potential Risk for Further Ecommerce Development”, Singapore: IACSIT Press. Zahay, D., Peltier, J., Shultz, D.E. and Griffin, A. (2004) “ The Role of Transactional Versus Relational Data in IMC Programs: Bringing Customer Data Together”, Journal of Advertising Research, Vol. 44 Issue 1, p3-18. 16p. Zinkhan, G. M. and R. T., Watson (1996) “Advertising trends: innovation and the process of creative destruction”, Journal of Business Research, 37 (3):163-171.

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Management & Change, Volume 16, Number 1 & 2 (2012)

EMOTIONAL LABOUR IN THE EDUCATION INDUSTRY Farah Naqvi1 Emotional labor concerns emotions in organizational life and organizational attempts to control and direct how employees display emotions to customers. Teaching is a profession that requires almost constant interaction with students involving a high level of emotional labor. This includes such behavior as surface acting, deep acting and suppression of emotion. This paper reviews literature on emotional labor and aims to study the different constructs of emotional labor in the teaching profession. Data was collected from teachers teaching at the graduate and postgraduate level in reputed colleges. It discusses the association between the variables of emotional labor, emotional exhaustion and job satisfaction. Keywords: Emotional labor, Emotional exhaustion, Job satisfaction. INTRODUCTION Although emotions have long been a topic of interest to sociologists and psychologists (Clark, 1992; Hochschild, 1983; Thoits, 1990), the display of emotions in organizations has become a topic of greater interest to organizational scholars during the past several years (Ashforth & Humphrey, 1995; Fineman, 1993). Increased competition among service providers, along with overall growth in the service economy, has forced organizations to focus greater attention on the nature and quality of services provided to customers and clients (Schneider & Bowen, 1995; Zeithaml, Parasuraman, & Berry, 1990). Though underresearched, yet a critical aspect of the literature - “emotional labor” concerns the emotions in organizational life and the organizational attempts to control and direct employees’ display of emotions to customers (Hochschild, 1990). Emotional labor can be described as the effort, planning, and control needed to express organizationally desired emotions, during interpersonal transactions. Consequently, emotional 1.

Dr. Farah Naqvi , Assistant Prof.-OB/HR, IBS Hyderabad, F- 209, Academics Block, Dontanapalli, Shankarpalli road, R.R. district-501203, Hyderabad, Andhra Pradesh, , Email: frh_naqvi@yahoo.com

84 Emotional Labour in the Education Industry

experience and expression can be and often are subject to external direction, enhancement, and suppression (Ashforth & Humphrey, 1995; Hochschild, 1990; Kemper, 1990; Thoits, 1990).Even if there is congruence between the individual’s felt emotions and the organizationally desired emotions, there will still be some degree of effort required in expressing emotions though certainly there will be less effort required in such a situation. In most work settings, it does indeed take “labor” to display organizationally desired emotions, because felt emotion must still be translated into appropriate emotional displays. Teaching is a profession with a high level of stress and a high level of burnout (Johnson, Cooper, Cartwright, Donald,Taylor, & Millet, 2005)). Teachers have to face varied demands from management, students and parents (Smyth et al.,2000). In this sense, teachers may also need to perform emotional labor (Hebson et al., 2007). This may also affect their job performance, commitment and enthusiasm (Husheger et al., 2010; Naring et al., 2006; Philipp and Schupbach, 2010). However, some researchers disagree that emotional labor is negative to teaching, because they find emotional labor may contribute to teachers’ job satisfaction, commitment, and effectiveness (Hargreaves, 1998b; Isenbarger and Zembylas, 2006; Mack, 2008). In these senses, it is necessary to understand the emotional labor of teaching, if we want to promote the quality of education in our societies. It is in this context this research tries to study the emotional labor of teachers as to the variety, frequency and intensity of emotions expressed by them and how the usage of two different forms of emotive effort influences other variables like emotional exhaustion and job satisfaction. EMOTIONAL LABOUR IN TEACHING JOB Until a decade ago, the role of emotion in teachers’ work, teaching and student learning was largely unexplored. Notably, Boler (1997) provided the first overview of the conceptualization of teacher emotion through a review of past approaches to reason and emotion within different paradigms. Most relevant to the concerns of this paper was the argument put forth by Nias (1996) that because teaching involves human interaction, it must have an emotional dimension.Since pedagogy relies on the relationship between teacher, student, and knowledge of a changing world (Lusted, 1986), it inevitably involves both emotion and identity negotiation. Teaching is a profession that involves a high level of emotional labour. Furthermore, teachers are expected to ensure the orderly conduct of Management & Change, Volume 16, Number 1 & 2 (2012)

Farah Naqvi

classes throughout the day, every working day. In order to perform these tasks adequately, teachers have to show or exaggerate some emotions (Ogbonna & Harris, 2004) and minimize or suppress the expression of other emotions. Teachers consider the faking of emotions to be stressful (Ogbonna & Harris, 2004), but the exact consequences on their job-related well-being, following strict regulation of their emotions are not fully known. Hochschild was the first to note that, especially in service jobs, employees are often required to show certain emotions in order to please the customer. Similar to other service organizations (Rafaeli 1989; Sturdy 1998), changes to the nature of academic work such as quality assessments taken from students, research assessment exercises and reviews, have provided tangible and comparable measures of ‘lecturer quality’, on basis of which management has been further able to tighten its control over the academic labour process. Of particular contemporary relevance are the increasing demands from students, who as ‘customers’ in an increasingly ‘enterprise culture’ (Knights et al. 1994; du Gay 1996) are ever more aware of their ‘rights’ to demand greater levels of service. However, few studies have explored the response of academicians and the ways in which they seek to cope with the comprehensive changes to their labour process and the diverse nature of frequently conflicting demands that these have imposed. Emotional rules of teaching may be implicit and disguised as teacher professionalism, that constrains teachers’ emotional activities (Zembylas, 2002b, 2005). According to Zembylas’ (2005) study, a generally emotional rule of teaching is to avoid expressing too strong and too weak emotions. More specifically, Winograd’s (2003) self-study reveals five emotional rules of teaching: (1) to love and to show enthusiasm for students; (2) to be enthusiastic and passionate about subject matter; (3) to avoid the display of extreme emotions like anger, joy and sadness; (4) to love their work; and (5) to have a sense of humor and laugh at their own mistakes and the peccadillos of students. If teachers cannot manage their emotions appropriately according to the rules, they will be treated as unprofessional (Zembylas, 2002b, 2005). As a result, teachers need to perform emotional labor. This calls for further investigation into the nature and the outcomes of the emotional labor of teaching. DIMENSIONS OF EMOTIONAL LABOR Emotional labor construct can be conceptualized along the following dimensions: Management & Change, Volume 16, Number 1 & 2 (2012)

86 Emotional Labour in the Education Industry l

Frequency of Emotional Display

Frequency of emotional display has been the most examined component of emotional labor; most previous research, in fact, has focused on the frequency of interaction between service providers and clients as the key dimension along which jobs can be arrayed in terms of emotional labor (Wharton & Erickson, 1993). The more often a work role requires socially appropriate emotional displays, the greater the organization’s demands for regulated displays of emotion will be. l

Intensity and Duration of emotional display

Emotional intensity refers to how strongly or with what magnitude an emotion is experienced or expressed. Frijda, Ortony, Sonnemans, and Clore (1992) argued that it is the intensity of the expressed emotion, more than any other factor, that determines whether clients and customers change their behavior during service interactions, because people may be convinced or intimidated by the perceived intensity of service providers’ emotions. Sutton and Rafaeli’s (1988) and Rafaeli’s (1989) work with convenience store clerks suggests that short interactions with customers often involve highly scripted interaction formats—a simple thank you, perhaps a slight smile. This finding implies that the level of effort required for emotional displays of short duration is quite minimal. Conversely, emotional displays of longer duration should require more effort and thus more emotional labor. The longer the emotional displays go on, the more likely they will become less scripted; consequently, longer emotional displays require greater attention and emotional stamina (Hochschild, 1983). l

Variety of Emotions Required To Be Expressed

Emotional displays in organizations have been characterized as positive, neutral, or negative in nature (Wharton & Erickson, 1993). The greater the variety of emotions to be displayed, the greater the emotional labor of role occupants will be. Service providers who must alter the kinds of emotions expressed to fit specific situational contexts have to engage in more active planning and conscious monitoring of their behavior. Given the dynamic nature of many service encounters, it is not surprising to find that different sets of occupational and organizational display rules are sometimes utilized as the demands of a given transaction change (Sutton, 1991). Similarly, some jobs (such as those of teachers) often require frequent changes of emotions that are displayed - positive emotions to build enthusiasm, negative Management & Change, Volume 16, Number 1 & 2 (2012)

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emotions to support discipline, and neutrality of emotions to demonstrate fairness and professionalism. EMOTIONAL LABOR EFFORTS Service employees perform emotional labor using different acting techniques. One is “surface acting” where service providers alter their outward appearance to simulate the required emotions—emotions that are not necessarily privately felt. The second acting mechanism is “deep acting.” Deep acting occurs when employees change not only their physical expressions, but also their inner feelings. The two key forms of engaging in emotional labour are elaborated below; how these may influence job satisfaction and emotional exhaustion is the prime objective of this research. Surface acting: Throughout the literature on emotional labor, surface acting has been considered as a superficial response to emotional labor requirements, when emotional dissonance was experienced in the process. Individuals are aware of the organizational rules of emotional display, but in the case of surface acting, they choose to display only those emotions that are necessary for the employee-customer interaction. There is no attempt by the employee to genuinely feel the required emotion (Hochschild, 1983; Ashforth & Humphrey, 1993) Deep acting: Deep acting is the cognitive adaptation of feeling organizationally required rules of emotional display. Humphrey (1993) considered deep acting to be consistent with the employee having more concern for the customer, in that, active deep acting requires more emotive effort than surface acting. Even though deep acting requires more emotive effort, empirical evidence has shown that it is the less stressful method of employing emotional labor. Totterdell and Holman (2003) found that deep acting was associated with quality performance and that it was not associated with emotional exhaustion. Brotheridge and Grandey (2002) reported evidence that deep acting was negatively associated with the depersonalization dimension of burnout and positively associated with the personal accomplishment dimension. Emotional labor, emotional exhaustion and job satisfaction: Emotional exhaustion is the state of depleted energy caused by excessive emotional demands made on people interacting with customers or clients (Saxton, Phillips & Blakeney, 1991), and it involves “feelings of being emotionally overextended and exhausted by one’s work” (Maslach, Jackson Management & Change, Volume 16, Number 1 & 2 (2012)

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& Leiter, 1996). Research by Wharton (1993) has shown that jobs requiring emotional labor do not place employees at greater risk of emotional exhaustion than other jobs. Emotional exhaustion is predicted via emotional labour, based on the argument that emotional dissonance arising out of deep acting is a type of role conflict and role conflict has been shown to be a key antecedent of emotional exhaustion. Research by Kruml and Geddes (2000) gives the notion that employees who engage in surface acting are more emotionally exhausted than those who adhered to display rules by deep acting. The research on the relationship between emotional labor and job satisfaction has also found both positive (Adelmann, 1995; Wharton, 1993) and negative relationships (Abraham, 1998). These findings may be explained by the method of emotional labor undertaken, for instance, surface acting may lead to feelings of inauthenticity and consequently job dissatisfaction. Second, job satisfaction link to emotional labour is predicted through person – environment fit theory, which suggests that not all workers would find the requirement to express organizationally desired emotions satisfying or dissatisfying. OBJECTIVES : The primary objective of the study is to gain an insight into the emotional labor processes as experienced by teachers with respect to frequency, intensity and variety of emotional display and study the association between the two forms of emotional labour(surface and deep acting) with job satisfaction and emotional exhaustion. MATERIAL AND METHODS: Measures: Emotional Exhaustion (Maslach and Jackson (1986): Nine items comprise the emotional exhaustion subscale of the Maslach Burnout Inventory. The measure assesses how often respondents report feeling the symptoms of emotional exhaustion at work. A sample item is “I feel emotionally drained at work.” Higher scores on this measure suggest high levels of emotional exhaustion. Brotheridge and Grandey (2002) report high internal consistency reliability for this subscale (alpha = 0.91). Job satisfaction subscale of Michigan organizational assessment questionnaire (Cammann, Fichman, Jenkins and Klesh 1979 ):This measure consists of three items that assess overall job satisfaction. A sixpoint Likert response scale is used where “1” corresponds to strongly Management & Change, Volume 16, Number 1 & 2 (2012)

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disagree and “6” corresponds to strongly agree. A higher score indicates overall satisfaction with the job. Grandey (2003) reports a high alpha of 0.93 for this subscale. Emotional labour (Brotheridge and Lee 1998 ):This scale is comprised of subscales that measure the six dimensions of emotional labor. The dimensions are measured on a five-point Likert response scale (1 = never and 5 =always). Participants are asked to answer items in response to the stem question, “On an average day at work, how often do you do each of the following when interacting with customers?” Higher scores on each of the subscales represent higher levels of the dimension being assessed. Brotheridge and Lee (2002) report good combined coefficient alpha for the role characteristics (frequency, intensity and variety) subscales (a= 0.71), as well as for the deep acting and surface acting subscales (a= 0.89, a =.86). Participants: A sample of sixty teachers participated in the study. This included teachers teaching at the graduate and post graduate level. Data was collected from five reputed and well established colleges. The sample includes both male and female participants. 38% of the respondents were female while rest 62% were male. Majority of the respondents fell between the age group of thirty two to forty five. Their job comprised of primarily designing the course structure, delivering the designed curriculum, grading and also some administrative work like ensuring smooth conduct of examination, maintaining discipline etc. RESULTS AND DISCUSSION: (I) Emotional labour experience of teachers with respect to frequency, intensity and variety of emotions to be displayed: (i) Emotional labour-frequency

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Graph 1- Frequency of emotional labor performed by teachers Explanation: The more often a work role requires socially appropriate emotional displays, the greater the organizationâ&#x20AC;&#x2122;s demand for regulated displays of emotion will be. The questionnaire had three items corresponding to the frequency dimension of emotional labour, on a five point rating scale. As seen in graph 1 the ratings of majority of the teachers fall between eight to ten indicating moderate levels of the dimension. (ii) Emotional labour-Variety

Graph 2- Variety of emotional labor performed by teachers Explanation: Given the dynamic nature of encounters, it was expected that teachers will score on the higher side on this dimension. Teaching requires frequent changes of emotions that are displayed - positive emotions to build enthusiasm, negative emotions to support discipline, and neutrality of emotions to demonstrate fairness and professionalism, thus highlighting the need to express variety of emotions. As seen in graph 2 all the rankings were ten or above, majority rating between 12 and 15 - indicating high presence of variety of emotions to be displayed in the teaching profession. (iii) Emotional labour-Intensity

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Graph 3- Intensity of emotional labor performed by teachers Explanation: Teaching involves close human interaction with the students, hence emotional dimension and emotions are not separate from teaching and learning. The longer the emotional displays go on, the more likely they will become less scripted; consequently, longer emotional displays will require greater attention and emotional stamina (Hochschild, 1983).As such it was expected that teachers would rate highly on this dimension of intensity of emotions which was confirmed by the analysis. With the ratings ranging from two to ten, more than 95 percent of the respondents had scores between eight to ten - scoring on the higher side of this dimension. (II) HYPOTHESES TESTING The proposed hypotheses listed below were tested and the results are detailed below: H1: There is no significant association between Surface acting and emotional exhaustion H2: There is no significant association between Deep acting and emotional exhaustion H3: There is no significant association between Surface acting and job satisfaction H4: There is no significant association between deep acting and job satisfaction Association between emotional labour and emotional exhaustion: Erickson (1997) found the relationship between emotional labor and wellbeing is not as straightforward as was first proposed by Hochschild. Ericksonâ&#x20AC;&#x2122;s data showed that the effect of emotional labor on well-being was dependent upon job autonomy; individuals with high job autonomy suffered fewer negative effects of emotional labor than did those with low job autonomy. Correlation was calculated to study the association between emotional labour dimensions of surface acting, deep acting and emotional exhaustion. H1: There is no significant association between Surface acting and emotional exhaustion Management & Change, Volume 16, Number 1 & 2 (2012)

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H2: There is no significant association between Deep acting and emotional exhaustion Result: Table 1 Correlation between surface acting and emotional exhaustion

Emotional exhaustion

Surface acting

Pearson Correlation

.347(**)

Sig. (2-tailed)

.007

.347(**)

.007

N Surface acting

Pearson Correlation Sig. (2-tailed) N

** Correlation is significant at the 0.01 level (2-tailed)

Table2 Correlation between deep acting and emotional exhaustion

Emotional exhaustion

Deep acting

Pearson Correlation

-.120

Sig. (2-tailed)

.362

Pearson Correlation

-.120

Sig. (2-tailed)

.362

N Deep acting

In the findings of this study Surface acting was found to be positively correlated with emotional exhaustion which was significant at 0.01 level (Table 1).Deep acting was negatively correlated with emotional exhaustion (Table 2).However this correlation was not found to be significant indicating no strong link between teachers performing deep acting and the emotional exhaustion experienced by them. Research by Kruml and Geddes (2000) supports the above hypotheses because they found that employees who engaged in surface acting were more emotionally exhausted than those who adhered to display rules by deep acting. Management & Change, Volume 16, Number 1 & 2 (2012)

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Association between emotional labour and Job satisfaction: H3: There is no significant association between surface acting and job satisfaction H4: There is no significant association between deep acting and job satisfaction There is a wide discrepancy in the literature exploring the relationship between emotional labour and employee well-being. Adelmann (1995) for example, found no relationship between emotional labour and job outcomes in a study of table servers, whereas Wharton (1993) found that emotional labour actually enhanced job satisfaction. The relationship between emotional labour and job outcomes appears to be further complicated by the interaction of emotional labour with other work conditions such as job autonomy, job involvement, self-monitoring, and organizational identification (Adelmann, 1995; Wharton, 1993). In this research study done for the role of teachers, negative relationship was found between surface acting and job satisfaction, significant at 0.01 level (Table 3).This indicated that surface acting by teachers in their role has a negative impact on the job satisfaction level. Significant correlation of .385 was found between deep acting and job satisfaction (Table 4). This could be so because as Ashforth and Humphrey (1993) suggested that performing â&#x20AC;&#x2DC;deep actingâ&#x20AC;&#x2122; may actually make interactions more predictable and help workers to avoid embarrassing interpersonal problems. This understanding, in turn, should help reduce stress and increase satisfaction. Table 3 Correlation between Surface acting and Job satisfaction Job satisfaction Surface acting

Job satisfaction

Surface Acting

Job satisfaction

Pearson Correlation

-.422(**)

Sig. (2-tailed)

.001

-.422(**)

.001

N Job satisfaction

Pearson Correlation Sig. (2-tailed) N

** Correlation is significant at the 0.01 level (2-tailed). Management & Change, Volume 16, Number 1 & 2 (2012)

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Table 4 Correlation between deep acting and job satisfaction

Job satisfaction

Deep acting

Pearson Correlation

.385(**)

Sig. (2-tailed)

.002

.385(**)

.002

N Deep acting

Pearson Correlation Sig. (2-tailed) N

** Correlation is significant at the 0.01 level (2-tailed).

CONCLUSION AND IMPLICATIONS In the literature, it is commonly agreed that teaching involves emotional labor, because teachers’ emotional activities are governed by the emotional rules of teaching (Winograd, 2003; Zembylas, 2002a, 2005). According to the emotional rules, teachers need to control and manage their emotions by surface acting and deep acting. Therefore, some researchers refer the emotional labor of teaching to these two emotion management strategies and find that the emotional labor of teaching may be alienating to teachers (e.g. Çukur, 2009; Hu¨lsheger,et al.,2010; Na¨ring, et al., 2006; Philipp and Schu¨pbach,2010). On the other hand, other researchers argue that the emotional labor of teaching is not alienating, because it contains use-value, including love, care, and passion (e.g.Hargreaves, 1998a, 1998b, 2000; Intrator, 2006;Isenbarger and Zembylas, 2006).In fact, it is difficult operationally to separate emotional labor and emotion work in teaching. As Oplatka (2007) demonstrates, the emotion management of teaching may happen in both the public life (e.g. emotion management in classroom teaching for a wage) and the private life (example emotion management in the friendship between teachers and students outside classroom) of teachers. It is incorrect to say that teaching does not involve emotional labor. Rather, it is found that the current state of investigation into ‘emotional labor of teaching’ is insufficient. Indeed, many commentators have observed that academics have traditionally concentrated on investigating the work of other occupational groups, and, in so doing, they have tended to neglect their own labour process (Miller 1991; Willmott 1995; Oshagbemi 1996). Thus, despite Hochschild’s (1983) claim that university lecturers emotionally labour, to date, this aspect Management & Change, Volume 16, Number 1 & 2 (2012)

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into the academic labour process remains woefully underexamined. In addition to contributing to the growing literature on emotional labour, this study aims to respond to the recent calls for greater investigation of academic labour processes (Oshagbemi 1996). This is especially pertinent in the university context, where lecturers undertake a wide range of disparate tasks (for example, teaching, research, administration, management, and student counselling) - each requiring varying degrees of emotional display over an extended period. Interestingly, the intensification of academic work is not merely a consequence of the demands made by the government and their university managers. This research provides insight into the dimensions of emotional labor like surface acting and deep acting, studies its association with the individual variables of emotional exhaustion and job satisfaction-specifically relating to teachers. It also provides insight into the other dimensions of emotional labour like frequency, intensity and variety of emotions to be displayed in teaching job .It was found that teaching job requires high intensity and variety of emotions to be displayed. This research confirmed the central role of emotional labour variables in the experience of emotional exhaustion and satisfaction at work for teachers. In a highly emotion-charged environment, employees’ emotional expression is significant in determining customers’ perception of service quality. Therefore, it is important for employers to monitor the emotional labour performed by their employees. In addition to the significance of this knowledge for theories of emotion and emotional labour, such information will have the practical benefit of suggesting modifications to recruitment, training, and job design that may facilitate better handling of problems. Further exploration of the possible consequences of emotional labor in general, is needed. Research on other outcomes, such as job related self-esteem and anxiety, may also be useful. REFERENCES Abraham, R. (1998) “Emotional dissonance in organizations: Antecedents, consequences, and moderators. Genetic,” Social, and General Psychology Monographs, 124 (2), 229 246. Adelmann, P. K. (1995) “Emotional labor as a potential source of job stress. In S. L. Sauter L. R. Murphy (Eds.),” Organizational risk factors for job stress (pp. 371 – 381). Washington, DC: American Psychological Association. Ashforth, B. E., & Humphrey, R. H. (1993) “Emotional labor in service Management & Change, Volume 16, Number 1 & 2 (2012)

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roles: The influence of identity.” Academy of Management Review, 18(1), 88-115. Ashforth, B. E., & Humphrey, R. H. (1995) “Emotion in the workplace: A reappraisal. Human Relations.” 48: 97-125. Boler, M. (1997) “The risks of empathy: Interrogating multiculturalism’s gaze. Cultural Studies,” 11(2), 253-273. Brotheridge, C. M. & Grandey, A. A. (2002) “Emotional labor and burnout: Comparing two perspectives of ‘people work’.” Journal of Vocational Behavior, 60, 17-39. Brotheridge, C. M. & Lee, R. T. (2002) “Testing a conservation of resources model of the dynamics of emotional labor.” Journal of Occupational Health Psychology, 7 (1), 57-67. Brotheridge, C.M. & Lee, R.T. (1998) “On the dimensionality of emotional labor: development and validation of an emotional labor scale.” Paper presented at the First Conference on Emotions in Organizational Life, San Diego State University, San Diego, CA, August 7-8, 1998 Clark, M. S. (Ed.). (1992) Review of personality and social psychology: Emotion and social behavior, vol. 14. Newbury Park, CA: Sage. Çukur CS (2009) “The development of the Teacher Emotional Labor Scale (TELS): Validity and reliability.” Educ.l Sci. Theory and Practice, 9(2):559-574. Du Gay, Paul (1996) “Consumption and identity at work. The Emotions of control: A qualitative exploration of environmental regulation.” Human Relations 52/5: 631–663. Erickson, R.J. and Wharton, A.S. (1997) “Inauthenticity and depression: assessing the consequences of interactive service work”, Work and Occupations, Vol. 24 No. 2,pp. 188 213. Frijda, N. H., Ortony, A., Sonnemans, J. & Clore, G. L. (1992) “The complexity of intensity: Issues concerning the structure of emotion intensity.” In M. Clark (Ed), Review of personality and social psychology, Vol. 13: 60-89. Newbury Park, CA: Sage. Management & Change, Volume 16, Number 1 & 2 (2012)

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Fineman, S. (1993) Emotion in organizations. Newbury Park, CA: Sage. Grandey, A. A. (2003) “The show must go on”: Surface acting and deep acting as determinants of emotional exhaustion and peer- rated service delivery. Academy of Management Journal, 46(1), 86-96. Hargreaves A (1998b) “The emotional practice of teaching.” Teaching and Teacher Education, 14(8), 835-854 Hargreaves A (1998a) “The emotional politics of teaching and teacher development: With implications for educational leadership.” Int. J. Leadership in Education, 1(4), 315-336. Hargreaves A (1998b) “The emotional practice of teaching.” Teaching and Teacher Education, 14(8), 835-854. Hargreaves A (2000) “Mixed emotions: Teachers’ perceptions of their interactions with students.” Teaching and Teacher Education,16,811826 Hebson G, Earnshaw J, Marchington L (2007) “Too emotional to be capable? The changing nature of emotion work in definitions of ‘capable teaching’.” Journal of. Education Policy, 22(6):675-694. Hochschild, A. ( 1983) The managed heart. Berkeley: University of California Press. Hochschild, A. ( 1990) “Ideology and emotion management: A perspective and path for future research.” In T. Kemper (Ed.), Research agendas in the sociology of emotions: 180 203.Albany: State University of New York Press. Hulsheger UR, Lang JWB, Maier GNW (2010) “Emotional labor, strain, and performance: Testing reciprocal relationships in a longitudinal panel study.” Journal of Occupational Health Psychol. 15(4), 505-521 Isenbarger L, Zembylas M (2006) “The emotional labour of caring in teaching.” Teaching and Teacher Education, 22(1),120-134 Intrator SM (2006) “Beginning teachers and the emotional drama of the classroom.” J. Teacher Educ. 57(3),233-239. Management & Change, Volume 16, Number 1 & 2 (2012)

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Johnson, S., Cooper, C., Cartwright, S., Donald, I., Taylor, P., & Millet, C. (2005) “The experience of work-related stress across occupations.” Journal of Managerial Psychology, 20, 178-187. Kemper, T. D.(1990) “Themes and variations in the sociology of emotions.” In T. D. Kemper (Ed.), Research agendas in the sociology of emotions: 1-25. Albany: State University of New York Press. Knights, David, Andrew Sturdy, and Glenn Morgan (1994) “The consumer rules? An examination of the rhetoric and “reality” of marketing in financial service’.” Marketing 28/3: 42–54. Kruml, S. M. & Geddes, D. (2000) “Exploring the dimensions of emotional labor: The heart of Hochschild’s work.” Management Communication Quarterly, 14 (1), 8-49. Lusted, D. (1986) “Why pedagogy?” Screen, 27(5), 2-14. Mack N (2008) “Energy and enthusiasm: Don’t start the school year without them.” English J. 98(1):18-25. Oplatka I (2007) “Managing emotions in teaching: Toward an understanding of emotion displays and caring as nonprescribed role elements.” Teachers College Record, 109(6):1374-1400. Miller, K.J, and Koesten, J. (2008) “Financial feeling: An investigation of emotion and communication in the workplace.” Journal of Applied Communication Research,36, 8-32. Maslach, C., Jackson, S. E., & Leiter, M. P. (1996) “Maslach Burnout Inventory Manual (3rd ed.).” Palo Alto, CA: Consulting Psychologists Press, Inc. Maslach. C. (1982) “Bumout: The cost of caring.” Englewood Cliffs, NJ: Prentice Naring Gr, Brie¨t M, Brouwers A (2006) “Beyond demand-control: Emotional labour and symptoms of burnout in teachers.” Work and Stress, 20(4):303-315. Nias, J. (1996) “Thinking about feeling: The emotions in teaching.” Cambridge Journal of Education,26(3), 293-306 Management & Change, Volume 16, Number 1 & 2 (2012)

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Ogbonna, E., & Harris, L. C. (2004) “Work intensification and emotional labour among UK university lecturers: An exploratory study.” Organization Studies, 25, 1185_1203. Oshagbemi, Titus (1996) “Job satisfaction of UK academics”. Educational Management and Administration 24/4: 389–400. Philipp A, Schu¨pbach H (2010) “Longitudinal effects of emotional labor on emotional exhaustion and dedication of teachers. J. Occupational Health Psychol.” 15(4):494-504. Rafaeli, A., & Sutton, R. I. (1987) “Expression of emotion as part of the work role.” Academy of Management Review, 12: 23-37. Rafaeli, Anat (1989) “When cashiers meet customers: An analysis of the role of supermarket cashiers”. Academy of Management Journal 32: 245–273. Saxton, M. J., Phillips, J. S., & Blakeney, R. N. (1991) “Antecedents and consequences of emotional exhaustion in the airline reservations service sector.” Human Relations,44, 538- 595. Schneider, B., & Bowen, D. E. (1995) “Winning the service game. Boston, MA: Harvard Business School Press.” Smyth J, Dow A, Hattam R, Reid A, Shacklock G (2000) “Teacher’s work in a globalizing economy. London: The Falmer Press.” Pp. 15-5 Sturdy, Andrew(1998) “Customer care in a consumer society: Smiling and sometimes meaning it?” Organization 5/1:27–53. Sutton, R. I. (1991) “Maintaining norms about expressed emotions: The case of bill collectors.” Administrative Science Quarterly. 36: 245-268. Thoits. P. A. (1990) “Emotional deviance: Research agendas. In T. Kemper (Ed.), research agendas in the sociology of emotions: 180-203.” Albany: State University oi New York Press. Totterdell, P. & Holman, D. (2003) “Emotion regulation in customer service roles: Testing a model of emotional labor.” Journal of Occupational Health Psychology, 8 (1), 55-73. Management & Change, Volume 16, Number 1 & 2 (2012)

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Wharton, A. S., & Erickson, R. J. (1993) “Managing emotions on the job and at home: Understanding the consequences of multiple emotional roles.” Academy of Management Review,18: 457-486. Wharton. A. S. (1993) “The affective consequences of service work.” Work and Occupations.20: 205-232. Winograd K (2003) “The functions of teacher emotions: The good, the bad, and the ugly.” Teachers College Record, 105(9):1641-1673. Willmott, Hugh (1995) “Managing the academics: Commodification and control in the development of university education in the U.K.” Human Relations 48/9:993–1027. Zeithaml, V. A.. Parasuraman. A., & Berry. L. L. (1990) “Delivering quality service: Balancing customer perceptions and expectations.” New York: Free Press. Zembylas M (2002a) “Constructing genealogies of teachers’ emotions in science teaching.” J.Res. in Sci. Teaching, 39(1), 79-103. Zembylas M (2005) “Discursive practices, genealogies, and emotional rules: A poststructuralist view on emotion and identity in teaching.” Teaching and Teacher Education, 21(8),935-948

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INTELLECTUAL CAPITAL AND CORPORATE PERFORMANCE OF INDIAN PHARMACEUTICAL INDUSTRY: A PANEL DATA ANALYSIS Dr. Aparna Bhatia1

Khushboo Aggarwal2

The purpose of the present paper is to study the extent of growth in INTELLECTUAL CAPITAL (IC) in Indian pharmaceutical industry over a period of time. Further, the study attempts to examine the impact of INTELLECTUAL CAPITAL (IC) on the performance of Indian Pharmaceutical Industry. An empirical study involving the impact of Intellectual Capital, specifically taking RETURN ON ASSETS (ROA) and RETURN ON EQUITY (ROE) as the performance variables has been carried out on data collected from CMIE database, Prowess. VALUE ADDED INTELLECTUAL CAPITAL (VAIC) has been calculated on a select sample of 50 pharmaceutical companies for a period of eleven years, from 2001-2011. Panel regression models have been used on the data for analysis. Results indicate that Intellectual Capital has grown rapidly over a period of time. Also, profitability and intellectual capital are positively associated. However, Value Added Capital Employed (VACA) has been found to be the most significant factor affecting the performance of the firms. A more detailed study may be carried out by taking the major knowledge-intensive industries with cross-section analysis, to have better assessment of the results. Keywords: Intellectual Capital, Pharmaceutical Industry, VAIC, Panel Regression, Profitability. INTRODUCTION The dawn of knowledge and information industry has changed the mechanism 1.

Dr. Aparna Bhatia, Assistant Professor, Department of Commerce and Business Management, Guru Nanak Dev University, Amritsar -143001. E-mail: aparnamohindru@yahoo.co.in Khushboo Aggarwal, Research Fellow, Department of Commerce and Business Management, Guru Nanak Dev University, Amritsar-143001. E-mail: khushboo9983@yahoo.co.in

102 Intellectual Capital and Corporate Performance of Indian...

of companies. Earlier the focus of companies was on the optimum utilization of physical and tangible assets, but now companies create their competitive advantage through the effective use of manpower and other intangible assets owned by them. Intangible assets like Intellectual Capital (IC), trademarks, brands, patents, know-how, innovation, Research and Development (R&D) expenditure, customer base, networks, organization structure etc. are the drivers and roots of the company’s value (Edvinnsson and Malone, 1997; Stewart, 1997; Tseng & Goo, 2005). Intangible assets have become an imperative part of a company’s performance and success. It has been recognized that intangible assets, especially Intellectual Capital (IC) are the key to attaining competitive advantage for the firms (Segelod, 1998; Chen et al., 2005; Edvinsson and Malone, 1997; Bismut and Tojo, 2008; Stewart, 1997). The intellectual capital of a firm plays a significant role in the modern approach of value creation. The importance of intellectual capital has increased with the rise of technologies such as internet, shift to networked organizations and the emergence of a global society. This requires new ways of thinking about the unmatched opportunities and challenges that we encounter. However, thinking is not possible without human beings. It is human beings who provide businesses with experience and expertise, educational qualifications and occupational competencies. Human beings are also the source of intellectual property while being the mainspring of corporate culture and knowledge networks (Fincham and Roslender, 2003). In 1990s IC was first introduced in the business context. Various definitions of Intellectual Capital are found in literature. Brooking (1996) defined intellectual capital as the term given to the “combined intangible assets of market, intellectual property, human-centered and infrastructure – which enables the company to function”. Roos et al. (1997) defines IC as “the sum of knowledge of company’s members and practical translation of this knowledge like trademarks, patents and brands”. Stewart (1999) says Intellectual Capital is “knowledge, information, intellectual property, experience – that can be put to use to create wealth”. Similarly, Harrison and Sullivan (2000) describe IC as “knowledge that can be converted into profit”. Chartered Institute of Management Accountants (CIMA), 2001, defines intellectual capital as “possession of knowledge and experience, professional knowledge and skill, good relationship, and technological capacities, which when applied will give organization competitive advantage”. Bouteiller, (2002) suggested that “Intellectual Capital – is a developmental knowledge that is human, structural, and customer-based, and needs to be Management & Change, Volume 16, Number 1 & 2 (2012)

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103

aligned with the corporate strategy and formalized / packaged in some way.” Even Salleh and Selamat (2007) described IC as the aggregation of human capital, structural capital and customer capital”. However, they all agree that intellectual capital is a sum of all intangible assets, including knowledge (part of human capital), structural capital, relational capital, organizational capital, internal capital, and external capital. To summarise, IC may be referred to as the sum of knowledge within an organisation, which involves value creation and gives competitive advantage to business organisations. This paper is divided into five sections; first section covers the introduction, second section covers review of literature. Research methodology is a part of third section. Results and analysis are discussed in section four, and section five concludes the paper findings. The purpose of the present paper is to study the extent of growth in Intellectual Capital (IC) in Indian pharmaceutical industry over a period of time. Further, the study attempts to examine the impact of Intellectual Capital (IC) on the performance of Indian Pharmaceutical Industry. Such a study would be very useful for a developing country like India because being a highly populous country it has large pool of potential human capital. It would also be useful for academicians and managers as they may enhance the profitability of companies by proper utilization of intellectual capital. As the paper suggests a positive and significant impact of Intellectual Capital on Profitability, it recommends the companies to manage and monitor their Intellectual Capital, in order to stand to gain. REVIEW OF LITERATURE Since in the present decade, Intellectual Capital is considered as one of the critical success factors, many authors have examined the role of Intellectual Capital in companies’ progress - both in the developed as well as the developing nations. In developed countries like UK (Zeghal & Maaloul, 2010) and Hong-Kong (Chu et al, 2011) Value Added Intellectual Capital (VAIC) was found to have a positive impact on the performance variables - as measured by Return on Assets, Market to Book Value Ratio and Return on Equity. In both these studies, Capital Employed (VACA) was found to be the key factor in predicting the performance of firms. Similarly, in USA, Guo et al, 2012 also found that VAIC had a positive impact on financial performance and also Human Capital (VAHU) was identified as the major sub-component affecting performance. Even in developing countries such as South Africa (Firer and Williams, 2003), Malaysia (Gan and Saleh, 2008), Jakarta (Razafindrambinina and Management & Change, Volume 16, Number 1 & 2 (2012)

104 Intellectual Capital and Corporate Performance of Indian...

Anggreni, 2008), Taiwan (Chen et al., 2005; Wang and Chang, 2005; Wang, 2008; Wang, 2011), Singapore (Tan et al., 2007) and Malaysia (Ting and Lean, 2009) VAIC was found to have positive relation with the financial performance. In majority of the developing countries VACA had a major impact among all the components of VAIC. However, in Iran (Ahangar, 2011) and Serbia (Komnenic & Pokrajic, 2012), Human Capital (VAHU) played a major role in enhancing the performance of the firms. In Finland (Kujansivu and Lonnqvist, 2005), no relationship was found between intellectual capital and performance. With specific reference to India (Kamath, 2008); Ghosh & Mondal, 2010; Pal & Soriya, 2012; Bhanawat & Bhanawat, 2012), all the studies reviewed found a positive association of VAIC with the financial performance. A brief review of few studies on VAIC and performance is given in Table 1. Research gap identified The evolution of Indian economy from production to knowledge stage has led to increasing importance of intangible assets. Several researchers have tried to measure IC and its relationship with the corporate performance (Firer and Williams, 2003; Chen et al., 2005; Kujansivu and Lonnqvist, 2005; Wang and Chang, 2005; Ghose and Wu, 2007; Tan et al., 2007; Gan and Saleh, 2008; Razafindrambinina and Anggreni, 2008; Wang, 2008; Ghosh and Mondal, 2009; Ting and Lean, 2009; Sharabati et al., 2010; Zeghal and Maaloul, 2010; Chu et al., 2011; Ahangar, 2011; Mehralian et al., 2012; Guo et al., 2012).In comparison to developed countries like UK (Zeghal and Maaloul, 2010) and USA (Guo et al., 2012) and developing countries like Malaysia (Gan and Saleh, 2008; Ting and Lean, 2009),South Africa (Firer and Williams, 2003), Iran (Ahangar, 2011; Mehralian et al., 2012) Taiwan (Chen et al., 2005; Wang and Chang, 2005; Wang, 2008) Finland (Kujansivu and Lonnqvist, 2005), Singapore (Tan et al., 2007), only a few studies have been carried out in India (Kamath, 2008; Ghosh and Mondal, 2009; Pal and Soriya, 2012). From the review of literature it may be highlighted that intellectual capital research is in its early phase in India. India, being a developing country it has a large base of human capital. Also, none of the studies reviewed has studied the trend of growth in IC over a period of time. This study will cover this aspect at three points of time 2001, 2006 and 2011. Consequently vast scope is available for examining the Management & Change, Volume 16, Number 1 & 2 (2012)

19972001

Wang & Chang (2005)

All the listed IT firms in Taiwan

20012003

Kujansivu 11 largest & Finnish Lonnqvist industries (2005)

2001

19922002

75 publicly listed Companies in South Africa

Firer & Williams (2003)

Intellectual Capital Efficiency

Return on Equity Return on Assets Growth in revenue Employee productivity Market to Book Value

Return on Assets Asset Turnover ratio Market to Book Value

Dependent Variable

Partial Least Return on Assets Square Adjusted Return on Assets Return on Equity Adjusted Return on

Average Values

Descriptive Statistics Correlation

Correlation Linear Multiple Regression

Time Techniques Period Used

Chen et 4254 al.,(2005) observations from Taiwan

Sample Size

Authors

VAIC and its variables

VAIC

VAIC and its components R&D expenditure Advertising expenditure

VAIC and its variables

Independent Variable

Table 1 Review of Literature

The results observed that innovation capital, process capital and customer capital had a direct impact on business performance. Intellectual capital elements directly

Results indicated no clear relationship between value of intellectual capital and its efficiency of different industries.

Positive impact of Intellectual Capital on market value and financial performance. Research and Development expenditure for structural capital showed a positive effect on profitability and firmâ&#x20AC;&#x2122;s value.

Physical capital was the most important resource for increasing the intellectual capital base of South African firms.

Findings

Dr. Aparna Bhatia, Khushboo Aggarwal 105

Management & Change, Volume 16, Number 1 & 2 (2012)

20012002

20042005

Ghosh & Electronics Wu industry in (2007) Taiwan

Gan & Saleh (2008)

89 Companies listed on Malaysia Stock Exchange

20002002

Tan et 150 Companies al., (2007) listed on Singapore Stock Exchange

Correlation Regression

Descriptive Statistics Regression

One way ANOVA Correlation

Market to Book value Return on Assets Asset turnover ratio

Market-to-Book Ratio Tobin’s Q

Return on Assets Earnings Per Share Annual stock return

Stockholders’ Equity Operating Income ratio Stock price Market value

VAIC and its variables

IT intensity Information System related employees in a company R&D Intensity Patent per Employee

VAIC and its components

Physical capital was found to be most significant component among all the components.

Measures of IC were still significant explanatory variables (of firm value). The financial and IC measures affected financial analysts’ investment recommendations.

Intellectual Capital and performance were positively related. Contribution of Intellectual Capital to performance differed by industry.

affected business performance, with the exception of human capital.

106 Intellectual Capital and Corporate Performance of Indian...

Consumer Goods Companies listed on the Jakarta Stock Exchange

50 Software and 30 Pharmaceutical Companies in India

Ghosh & Mondal (2009)

4625 Observations from Taiwan

Wang (2008)

Razafindrambinina & Anggreni (2008)

25 firms listed on Bombay Stock Exchange

Kamath (2008)

2002- Multiple 2006 regression

2003- Minimum 2006 and Maximum Values Means Standard Deviation

2001- Panel Data 2008 Regression

1996- Correlation 2006 Regression

VAIC and its Return on Assets Assets Turnover ratio variables Market to Book Value ratio

Return on Assets Asset Turnover Growth in Revenue Operating Cash Flow ratio

VAIC had significant positive influence over profitability. Assets turnover ratio and company size, measured by LCAP, no consistent relationship with profitability over the study period was found.

Physical capital and Structural capital were considered the most influencing components to increase the future performance of the organizations.

The results revealed that the intellectual capital had a strong influence on the competitive advantage and market capitalization of the firm.

Operating income to VAIC and its variables total assets Return on Assets Market Capitalization Human Capital Efficiency Capital Employed Efficiency Structural Capital Efficiency

Human capital was the major component having impact on firmâ&#x20AC;&#x2122;s productivity and profitability.

VAIC and its variables

Return on Assets Asset turnover ratio Market to Book value

Dr. Aparna Bhatia, Khushboo Aggarwal 107

Management & Change, Volume 16, Number 1 & 2 (2012)

2005

Zeghal & Maaloul (2010)

Chu et 151 Companies al.,(2011) listed on Hong Kong Stock Exchange

20052008

2007

Sharabati 15 Organisaet al., tions (2010) registered on Jordian Association of Pharmaceutical Managers (JAPM) in Jordan

300 UK Companies

19972007

Financial Institutions in Malaysia

Ting & Lean (2009)

Regression

Descriptive Statistics Regression Correlation

Correlation Path Analysis

Correlation Regression

Market to Book Value Return on Assets Asset Turnover ratio Return on Equity

Operating Income/ Sales Return on Assets Market to Book Value

Innovation & Creation Research & Development Intellectual Property Rights Relations with Partners, Suppliers and Customers Knowledge about Partners, Suppliers and Customers Alliances, Licensing and Agreements

Return on Assets

VAIC and its components

VAIC Size Leverage

VAIC and its variables

Capital employed was found to be key factor in predicting business performance.

Capital employed was the main factor of financial stock market performance though it ha d negative effect on the economic performance.

The components of VAIC i.e. CEE, HCE and SCE were significantly associated with profitability of the firms. Intellectual Capital variables and sub variables had a substantive and significant relationship with the business performance. Intellectual property rights had a negative value. Relation with Partners, Suppliers and Customers was the strongest indicator of the relational capital.

The paper revealed that VAIC and ROA were positively related among Malaysiaâ&#x20AC;&#x2122;s finance sector.

108 Intellectual Capital and Corporate Performance of Indian...

20062008

20042009

31 MNCâ&#x20AC;&#x2122;s among top 300 Companies on Journal of business and finance in Serbia

Mehralian 19 firms listed et al., in Iranian (2012) Stock Exchange

Return on Assets Return on Equity Assets Turnover ratio

Analysis of Return on Assets Correlation Market to Book Value Multiple Assets Turnover ratio Regression

Regression

Return on Assets Return on Equity

Komnenic & Pokrajic (2012)

Regression

2009

Return on Assets Market to Book Value Ratio Assets Turnover ratio

Uadiale & 32 Quoted Uwuigbe Companies in (2011) Nigeria

2 0 0 1 - Panel Data 2007 Regression

1 9 8 0 - Descriptive Return on Assets 2009 Statistics Growth in revenue Multiple Regression

4407 documents in Taiwan

Ahangar Largest (2011) Companies in Iran

Wang (2011)

VAIC

VAIC and its components

VAIC and its variables

Research & Development Expenditure Intellectual Capital and its variables

Relationship between Intellectual Capital and performance was varied.

Human capital was positively associated with the performance. Structural capital and MNCâ&#x20AC;&#x2122;s profitability was partially correlated.

Intellectual Capital had a positive and significant relation with performance.

Human capital was found to be efficient factor as compared to other factors of VAIC.

The study revealed that the intellectual capital and performance were positively related.

Dr. Aparna Bhatia, Khushboo Aggarwal 109

Management & Change, Volume 16, Number 1 & 2 (2012)

105 Pharmac- 2001eutical and 2010 102 Textile industries in India

8 Pharmac2004eutical 2009 Companies in India

Bhanawat & Bhanawat (2012)

19942005

Pal & Soriya (2012)

Guo et 331 Biotech al. (2012) firms in USA

Management & Change, Volume 16, Number 1 & 2 (2012)

Patent Size Human Capital Corporate Governance

Intellectual Capital

Market to Book Value VAIC Return on Assets Assets Turnover Ratio Return on Equity Physical capacity Debt Equity Ratio Sales

Stock Return Return on Assets Return on Equity

Mean Perc- Net Operating Profit entage (%) Correlation (r) Coefficient of Variation (C.V) Probable error (P.E)

Correlation Ordinary Least Square (OLS)

Moving Averages Method of FamaMacBeth

Artificial Neural Networks (ANN)

The significant correlation was found between tangible assets and net operating profit. No significant difference was found between percentage of Intellectual Capital to market value and percentage of tangible assets to market value.

Results indicated that profitability and intellectual capital were positively associated No significant relationship was of intellectual capital with productivity and market valuation was found in both industries.

Patent was an insignificant factor for the study. Human capital played a positive role in technology innovations and financial performance.

110 Intellectual Capital and Corporate Performance of Indian...

Dr. Aparna Bhatia, Khushboo Aggarwal

111

growth of intellectual capital and its impact on performance. Hence, the present study has been conducted. RESEARCH METHODOLOGY Research objectives The main objectives of this paper are to study the extent of growth in IC of Indian pharmaceutical industry. The same has been measured using Compounded Annual Growth Rate (CAGR) over the study period. The second objective of the present paper is to examine the impact of Intellectual Capital (IC) on the performance of Indian Pharmaceutical Industry. This objective has been achieved using Panel Regression. Sample and time period The sample for the study is taken from Business Standard (BS) 1000 that lists leading companies of India on the basis of net sales. From the list of 58 pharmaceutical companies given in Business Standard (BS), 50 companies have been selected. Eight companies were dropped as complete information with respect to the variables was not available for these companies. The time period for the study is eleven years i.e. 2001-2011. However, to study the growth in investment, this time period has been split into three points of time i.e. 2001, 2006 and 2011. An intangible asset like Intellectual Capital which denotes human, structural and physical capital would take years to grow. Therefore, growth in Intellectual Capital has been studied over a gap of five years each. Overall, the span of more than a decade would be helpful to establish the consistency and predictability for research conclusions. Data Source The data is collected through secondary sources. The relevant data required for present research is collected from Electronic database â&#x20AC;&#x2DC;PROWESSâ&#x20AC;&#x2122; of the Centre for Monitoring Indian Economy (CMIE). This database was chosen because all the information required for the study was readily available in this. Methodology In order to measure the growth in Intellectual Capital, Compounded Annual Growth Rate (CAGR) was calculated. CAGR is not an accounting term, Management & Change, Volume 16, Number 1 & 2 (2012)

112 Intellectual Capital and Corporate Performance of Indian...

but is widely used, particularly in growth industries or to compare the growth rates of two investments because CAGR reduces the effect of volatility of periodic returns that can render arithmetic means irrelevant. CAGR is often used to describe the growth over a period of time of some element of the business, for example revenue, units delivered, registered users etc. For studying the impact of Intellectual Capital on performance, following variables were taken. Dependent variables Return on Assets (ROA)3 and Return on Equity (ROE)4 have been taken as the dependent variables. Independent Variables In the present study for measuring the value of IC, Pulic (1998) model has been applied. IC has been defined variedly, but the most commonly accepted defnition classifes it into human, structural and customer capital (Pulic, 1998). The first measure is that which is used to measure the efficiency of the capital employed (VACA). This is the ratio of the Value Added (VA) to the total Capital Added (CA) by the firm, VACA= VA / CA Where, VACA=Value Added Capital Coefficient for firm, VA= Value Added for the firm, CA= Book Value of the net assets for firm. The VA is measured as: VA= I+DP+D+T+M+R where VA, value added for firm computed as sum of I, interest expense; 3.

Return on Assets (ROA) is measured as the ratio of operating income to total assets of the firm. It is an indicator to measure whether the firm has been performing profitably as compared to the previous year or not.

Return on Equity (ROE) is the ratio of the net income (less preference dividends) divided by the shareholderâ&#x20AC;&#x2122;s equity as disclosed in the respective annual reports of the firm.

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DP, depreciation expenses; D, dividends; T, corporate taxes; M , equity of minority shareholders in net income of subsidiaries; R, profits retained for the year. The next step is to determine the efficiency of the human CE on the value creation of the firm. This is obtained by estimating the ratio of human capital coefficient for the firm VAHU; this is the ratio of VA of the firm to the expenditure made by the ?rm on its human capital. These expenses are refected in the salaries and wage cost of the firm in their annual reports: VAHU= VA / HC Where VAHU= Value Added Human Capital Coefficient for the firm VA= Value Added for the firm; HC= Human Cost (total salary and wage costs for the firm). The next measure captures the efficiency of the structural capital on the VA by the firm. This is the ratio of SC and VA of the firm represented as STVA. The SC is calculated as follows: SC= VA-HC Where SC= Structural Capital for the firm, VA= Value Added for the firm, HC= Human Cost (total salary and wage costs for the firm). Then the relationship is shown as: SCVA=SC/VA Where SCVA= Structural Capital Value Added for the firm, SC= Structural Capital for the firm, VA= Value Added for the firm. Therefore, VAICTM= VACA+VAHU+SCVA Where VAICTM= Value Added Intellectual Coefficient for the firm, VACA= Value Added Capital Coefficient for firm, Management & Change, Volume 16, Number 1 & 2 (2012)

114 Intellectual Capital and Corporate Performance of Indian...

VAHU= Value Added Human Capital coefficient for the firm, SCVA= Structural Capital Value Added for the firm. The VAICTM is measured using three important components, namely Value Added Capital Coefficient (VACA), Human Capital Coefficient (VAHU) and Structural Capital Value Added (SCVA), which comprehensively measures the Value Added (VA) of the ?rm by using its important resources such as human resources, customer capital and structural capital. Measuring Intellectual Capital (IC) is essential and very important in order to compare different companies and to estimate their real value or even to control their improvement year after year. It also improves the way in which companies manage their intellectual resources that generate value and give back some benefits that maximize the advantages for the company. Ante Pulic (1998, 2000) developed the “Value Added Intellectual Coefficient” (VAICTM) to measure the IC of companies. The VAICTM method is designed to provide information about the value creation efficiency of tangible and intangible assets within a company. VAICTM is considered as a “universal indicator showing abilities of a company in value creation and representing a measure for business efficiency in a knowledge-based economy” (Pulic, 1998). Kamath (2007) also confirmed that VAICTM is a management and control tool that is “designated to monitor and measure the IC performance and potential of the firm”. This measuring tool has been used in many studies (Firer and Williams, 2003; Mavridis, 2004, 2005; Goh, 2005; Mohiuddin et al.,2006; Tan et al., 2007; Yalama and Coskun, 2007; Kamath, 2008; Zeghal and Maaloul, 2010) Firer and Williams (2003) identified several advantages of using VAICTM. Firstly, VAICTMprovides a standardized and consistent basis for measurement, thereby, enabling the effective conduct of an international comparative analysis using a large sample size across various industrial sectors. Secondly, all data used in the VAICTM calculation is based on audited information and therefore, calculations are objective and verifiable. Finally, VAICTM is a straightforward technique that enhances cognitive understanding and enables ease of calculation by various internal and external stakeholders. Due to ease of calculation feature, VAICTM has enhanced the universal acceptance of many traditional measures of corporate performance such as return on assets (ROA), market-to-book value (MB). Still, IC measure has certain limitations: (a) it utilizes information associated with a select group of firms Management & Change, Volume 16, Number 1 & 2 (2012)

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(for example stock data) (b) it involves unique financial and non-financial indicators that can be readily combined into a single comprehensive measure; and/or (c) are customized to fit the profile of individual firm (Roos and Roos, 1997; Edvinsson, 1997; Sullivan, 2000). Consequently, the ability to apply alternative IC measures consistently across a large and diversified sample for comparative analysis is diminished. Control variables Four control variables are included in the analysis. Size of the firm (SIZE) is determined through natural logarithm of firm’s book value of total assets (Firer and Williams, 2003; Ghosh and Mondal, 2009; Zeghal and Maaloul, 2010; Chu et al. 2011; Wang, 20011). Age of the firm (AGE) is calculated as the difference between 2011 and the founding year of the organization (Taliyang, 2011). Leverage (LEV) is calculated as ratio of the total debt to book value of assets of the firm (Kamath, 2008; Ghosh and Mondal, 2009; Zeghal and Maaloul, 2010; Ahangar, 2011; Chu, et al. 2011) and Physical Capital intensity (PC) is measured by the ratio of a company’s fixed assets to its total assets (Firer and Williams, 2003; Ghosh and Mondal, 2009; Ahangar, 2011; Pal and Soriya, 2012). Regression models For conducting the empirical research four models have been run ROA=a+ß1 VAICTM+ß2 Lev+ß3 Size+ß4 Age+ß5 PC+µ.................... (model 1) ROE=a+ß1 VAICTM+ß2 Lev+ß3 Size+ß4 Age+ß5PC+µ................... (model 2) ROA=a+ß1 VAHU+ß2 STVA+ß3VACA+ß4Lev+ß5 Size+ß6 Age+ß7 PC+µ........... (model 3) ROE=a+ß1 VAHU+ß2 STVA+ß3 VACA+ß4Lev+ß5 Size+ß6 Age+ß7 PC+µ...... (model 4) Where, ROA= Return on Assets ROE = Return on Equity VAICTM= Value Added Intellectual Coefficient VAHU= Value Added Human Capital SCVA= Structural Capital Value Added Management & Change, Volume 16, Number 1 & 2 (2012)

116 Intellectual Capital and Corporate Performance of Indian...

VACA= Value Added Capital Coefficient Lev= Leverage PC= Physical Capital µ= Error Term RESULTS AND ANALYSIS I. Trends in growth of VAIC The first objective was to see the trend in growth of VAIC (Annexure 1). The same is presented in Table 2. As in Table 2, the Compounded Annual Growth Rate (CAGR) of 50 leading pharmaceutical companies from BS-1000 has been calculated. It was just 1.58% in 2006 as compared to the base year 2001. However, there has been a tremendous rise in investment in VAIC in the year 2011, showing a growth rate of 16.40%. The sample of the study has been taken from BS 1000 which represents the leading companies of India on the basis of net sales. This increase in growth of IC indicates that the companies are managing and paying attention to investment in human capital along with structural and physical capital. The companies seem to have realized that investment in intellectual capital helps them in generating profits. The role and importance of high-tech organizations in a knowledge-based economy is well recognized. Pharmaceutical industry is considered to be one of the most high–tech, innovative and well balanced industry in respect of human intervention and technology. It mainly relies on intellectual capital as a source of innovation and business performance. As such, when an organization increases its intellectual capital, it is expected that its performance will be enhanced. II. Impact of Intellectual Capital on Profitability For studying the impact of IC on profitability of pharmaceutical industry, panel regression has been used. For checking the stationarity of the data Harris–Tzavalis unit root test was used. This test assumes that the number of panels tends to be infinite while the number of time periods is fixed (Harris and Tzavalis, 1999). All the data was found to be stationary. Then, to have better results both fixed and random effect models are applied on the panel data. Results of both the models are checked through applying Hausman Specification Test (Hausman, 1978). If Prob < Chi2= 0.05 (i.e. Management & Change, Volume 16, Number 1 & 2 (2012)

Dr. Aparna Bhatia, Khushboo Aggarwal

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significant) then fixed effects is used. The test suggested random effect model. Table 3 presents the results of panel regression random effects model where ROA is the dependent variable. Table 3 Panel Regression with ROA as the Dependent Variable R-sq:

within = 0.1234 between = 0.4903 overall = 0.2620 Random effects u_i ~ Gaussian corr(u_i, X) = 0 (assumed) ROA

Obs per group:

min = avg = max = Wald chi2(5) = Prob > chi2 =

11 11.0 11 121.89 0.0000

Coef.

Std. Err.

P>|z|

[95% Conf.

Interval]

VAIC

.3615575

.0467452

7.73

0.000

.2699387

.4531764

Age

.2172349

.0449101

4.84

0.000

.1292128

.305257

Size

.3271033

.8905873

0.37

0.713

-1.418416

2.072622

-8.699022

3.955687

-2.20

0.028

-16.45203

-.9460173

Lev

-1.314318

.3285065

-4.00

0.000

-1.958179

-.670457

5.497523

3.75738

1.46

0.143

-1.866806

12.86185

_cons sigma_u

4.5169727

sigma_e

9.5189563

rho

.18378872 (fraction of variance due to u_i)

Assessment of Table 3 reveals that overall R2 is 26.2 percent suggesting that the variation in profitability as measured by ROA is explained by the independent variables only to the extent of 26.2% and the remaining variation is due to the impact of some other factors. Wald chi2= 121.89 [degrees of freedom (d.f) =5] which is highly significant at 1 % indicates a good model fit. The results of VAIC (P-value < 0.000) show that it has positive and significant impact on the performance (ROA) of the company after controlling for the effects of age, size, physical capital and leverage. Age and physical capital are significant in estimating the dependent variable (ROA) though age has positive effect on ROA while PC has a negative impact. Leverage too has negative and significant impact indicating that when leverage increases ROA decreases. Size is not significant in affecting ROA, though it has positive effect on ROA. This proves that as the VAIC of a company increases, ROA also increases. Management & Change, Volume 16, Number 1 & 2 (2012)

118 Intellectual Capital and Corporate Performance of Indian...

The results of this model are consistent with the empirical findings of Chen et al. (2005), Razafindrambinina and Anggreni (2008), Tan et al. (2007), Ting and Lean (2009), Sharabati et al. (2010) and Uadiale and Uwuigbe (2011) in which it is clearly revealed that there was a significant positive relationship between VAIC and ROA at 1% level of significance. Although, these studies are conducted in different countries, yet the results are same indicating that the importance of Intellectual Capital is becoming a universal phenomenon. This indicates that IC plays a major role in creating value for stockholders as well as for other stakeholders. Moreover, IC plays an important role in reducing a companyâ&#x20AC;&#x2122;s production costs. Table 4 shows the results of the model where ROE is the dependent variable. Table 4 Panel Regression with ROE as the Dependent Variable R-sq:

within = 0.1236 between = 0.0823 overall = 0.1093 Random effects u_i ~ Gaussian corr (u_i, X) = 0 (assumed) ROE

Obs per group: min avg max Wald chi2 (5) Prob > chi2

= = = = =

11 11.0 11 72.74 0.0000

Coef.

Std. Err.

P>|z|

[95% Conf.

Interval]

VAIC

.5937079

.1030524

5.76

0.000

.3917289

.7956869

Age

.1213994

.073291

1.66

0.098

-.0222482

.2650471

Size

-.2672887

1.760368

-0.15

0.879

-3.717546

3.182968

-6.32157

8.028484

-0.79

0.431

-22.05711

9.41397

Lev

-4.002179

.718725

-5.57

0.000

-5.410854

-2.593503

19.83177

7.559982

2.62

0.009

5.014477

34.6490

_cons sigma_u

5.4822499

sigma_e

21.810745

rho

.05942511

(fraction of variance due to u_i)]

Assessment of Table 4 reveals that overall R2 is 10.9 percent which does not seem enough for a good model. The value of Wald Chi2= 72.74 [degrees of freedom (d.f) =5] which is highly significant at 1 % indicates a good model fit. The results of VAIC (P-value < 0.000) show that it has positive and significant impact on the performance of the company after controlling for the effects of age, size, physical capital and leverage. Age has significant and positive impact on ROE at 10% level of significance indicating that as Management & Change, Volume 16, Number 1 & 2 (2012)

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age of the company increases, the ROE also increases. On the other hand, leverage has negative and significant impact on ROE. PC and Size have a negative impact on ROE though these are insignificant. Overall, comparison of Model 1 and Model 2 has been made in Table 5. Table 5 Significant Factors that Influence Profitability Dependent Overall R2 Variable

VAIC

AGE

SIZE

LEV

Model 1

ROA

26.2%

-**

Model 2

ROE

10.9%

+***

Significant variable where *=1%, **= 5% and ***= 10%

Model 1 with ROA as the dependent variable is a better fit model as the value of R2 is 26.2% and the number of significant variables is four as against ROE (Model 2) where number of significant variable is three and adjusted R2 is 10.9%. Hence, VAIC has more evident impact on ROA. The results coincide with the findings of Uadiale and Uwuigbe (2011) where ROE is positively and significantly correlated with intellectual capital at 1% level of significance. Similarly, Pal and Soriya (2012) also found that ROE is significantly and positively related with intellectual capital in case of pharmaceutical industry (8.594), at 5% level of significance but is negatively (-0.541) related in case of textile industry. The PC in case of Pharmaceutical industry (-1.683) and textile industry (-0.189) is negative, hence supporting the results of the present study. The result specifies that Intellectual Capital, especially in the pharmaceutical industry is gaining momentum all over. In other words, the results of VAIC demonstrate that increase in value creation efficiency affects a firmâ&#x20AC;&#x2122;s profitability. Now, to make an indepth analysis of impact of Intellectual Capital on profitability, components of VAIC have been taken i.e. VACA, VAHU and SCVA and Panel Regression has been run again. The results are shown in Table 6.

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120 Intellectual Capital and Corporate Performance of Indian...

Table 6 Panel Regression with ROA as the Dependent Variable and Components of VAIC R-sq:

within = 0.6124 between = 0.3716 overall = 0.4945 Random effects u_i ~ Gaussian corr(u_i, X) = 0 (assumed) ROA

Obs per group: min avg max Wald chi2 (7) Prob > chi2

= = = = =

11 11.0 11 789.12 0.0000

Coef.

Std. Err.

P>|z|

[95% Conf.

Interval]

VACA

8.530643

1.227876

6.95

0.000

6.12405

10.93724

VAHU

2.027139

.0913874

22.18

0.000

1.848023

2.20625

SCVA

-.038981

.1605337

-0.24

0.808

-.3536213

.2756592

Age

.2881123

.0426796

6.75

0.000

.2044617

.3717629

Size

-2.286852

.685471

-3.34

0.001

-3.630351

-.9433536

.4025307

2.931057

0.14

0.891

-5.342235

6.147296

Lev

-1.54548

.2502984

-6.17

0.000

-2.036056

-1.05490

_cons

1.250191

2.838139

0.44

0.660

-4.312459

6.812841

sigma_u

4.6831618

sigma_e

6.3731319

rho

.35063822

(fraction of variance due to u_i)

ROA is the dependent variable and components of VAIC i.e. Value Added Human Capital (VAHU), Value Added Capital Coefficient (VACA) and Structural Capital Value Added (SCVA) are the independent variables. The effects of Size, PC and Leverage have been controlled for. Overall R2 is 49.4 percent indicating a reasonably fair explanatory power of the model. The value of Wald Chi2= 789.12 [degrees of freedom (d.f) =7] which is highly significant at 1 % indicating a good model fit. The result shows that VACA and VAHU are positively and significantly related with ROA. SCVA is negatively related with ROA, though the z-value (0.808) is insignificant. 1% change in VACA will bring 8.53 percent change in ROA and similarly 1% change in VAHU will bring 2.02 percent change in ROA, when age, size, physical capital and leverage are controlled for. Age and size affects ROA positively and significantly. Leverage has a negative and significant impact on ROA. Physical Capacity (PC) has a positive impact on ROA, though it is insignificant. The result of this model indicates that out of all the components of VAIC, VACA is the most influencing factor affecting ROA. This indicates that capital employed (physical and financial) still remains Management & Change, Volume 16, Number 1 & 2 (2012)

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important for stockholders and stakeholders, through its significant role in value creation. The negative and insignificant impact of SCVA is consistent with the study of Kamath (2008) where the results of correlation showed that SCVA was negatively related with ROA. The results of Razafindrambinina and Anggreni (2008) which tested the association between Intellectual Capital and financial performance of Indonesian companies are in support of the results. The findings are similar to Ting and Lean, 2009 where VAHU has positive effect on ROA as the estimated coefficient is 10.33 and VACA also has positive association with the firmâ&#x20AC;&#x2122;s ROA as the estimated coefficient is 417.731 and is significant at 1 per cent level. SCVA has a negative effect on ROA but it is not significant. However, the outcome of this study is inconsistent with Kamath (2008) in terms that he found human capital to be the most influencing factor. However, the present study concludes that physical capital is the most important factor influencing the profitability of the companies. This inconsistency may be due to the sample size as Kamath (2008) used a sample of only 25 Pharmaceutical companies hence generalisation of results is difficult. In Table 7 ROE is the dependent variable and components of VAIC are the independent variables. Table 7 Panel Regression with ROE as the Dependent Variable and Components of VAIC R-sq:

within = 0.4068 between = 0.1551 overall = 0.3201 Random effects u_i ~ Gaussian corr(u_i, X) = 0 (assumed)

Obs per group: min avg max Wald chi2(7) Prob > chi2

= = = = =

11 11.0 11 315.49 0.0000

ROE

Coef.

Std. Err.

P>|z|

[95% Conf.

Interval]

VACA

36.64608

3.29444

11.12

0.000

30.1891

43.10307

VAHU

1.783757

.2309438

7.72

0.000

1.331115

2.23639

SCVA

-.1223844

.4315894

-0.28

0.777

-.9682842

.7235153

Age

.1130227

.0713136

1.58

0.113

-.0267494

.2527947

Size

-2.181246

1.594036

-1.37

0.171

-5.305499

.9430071

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122 Intellectual Capital and Corporate Performance of Indian... PC

-1.572677

7.162395

-0.22

0.826

-15.61071

12.46536

Lev

-6.437075

.6601189

-9.75

0.000

-7.730884

-5.143266

12.17162

6.746589

1.80

0.071

-1.051453

25.39469

_cons sigma_u

6.0417476

sigma_e

18.112447

rho

.10012719

(fraction of variance due to u_i)

In Table 7 overall R 2 is 32.0 percent indicating a reasonably fair explanatory power of the model. The value of Wald chi2= 315.49 [degrees of freedom (d.f) =7] which is highly significant at 1 % indicating a good model fit. The result shows that VACA and VAHU are positively and significantly related with ROE. This means that 1% change in VAHU will bring 7.72 percent change in ROE and similarly 1% change in VACA will bring about 11.12 percent change in ROE when age, size, physical capital and leverage are controlled for. Physical Capital (PC) has a negative impact on ROE though it is not significant. Leverage too has negative and significant impact on ROE. SCVA is negatively related with ROE, though the value is insignificant. The result of this model indicates that out of all the components of VAIC, VACA is the most influencing factor affecting ROE. This indicates that stakeholders still accept the performance of the firm in terms of tangible assets and less in terms of intangible assets.

Table 8, shows a comparison of ROA and ROE model Table 8 Significant Factors that Influence Profitability Dependent Overall VACA VAHU SCVA AGE SIZE PC LEV Variable R2 Model 3

ROA

49.4%

Model 4

ROE

32.0%

Significant variable where *= 1%

Model 3 with ROA as the dependent variable is a better fit model as the value of R2 is 49.4% and the number of significant variables is five as against ROE (Model 2) where number of significant variable is three and adjusted R2 is 32.0%. Hence, components of VAIC have more apparent impact on ROA. Management & Change, Volume 16, Number 1 & 2 (2012)

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The findings of the present study correspond with the results of Sharabati et al. (2010) who reported that the intellectual capital variables and subvariables had a substantive and significant relationship with business performance. Similarly, Firer and Williams (2003) and Razafindrambinina and Anggreni (2008) also claimed that physical capital was the most influencing components to increase the future performance of the organizations. Additionally, Gan and Saleh (2008) also claimed that physical capital efficiency was the most significant variable related to profitability among all the components. However, the results are contradictory with the findings of Ahangar (2011) who suggested that human capital was very efficient, more than structural capital and physical capital in terms of value creation efficiency. This inconsistency may be due to geographical biasedness as the present study is conducted in India and the former was conducted in Iran. Moreover, Ahangar (2011) draws analysis, based on data from a single company but the present study uses a data of 50 companies. CONCLUSION The paper analyzes the impact of IC on the performance of Indian Pharmaceutical industry for a period of eleven years i.e. 2001-2011. The sample of the study comprised top 50 pharmaceutical companies as given by BS 1000. The empirical results revealed two findings. (i) IC has grown rapidly over a period of time in pharmaceutical industry and (ii) Intellectual Capital has a significant impact on financial performance, though Physical capital is the major factor affecting the Indian Pharmaceutical industry. The findings of the study have attempted to direct our attention towards the importance of intellectual capital measurements in evaluating the performance of companies. India has a large potential of human capital. India is one of the most populous nations in the world with about 1.2 billion population. There is a need to manage people as wealth of the nation in terms of Intellectual Capital. Hence, training and development programs for employees should be arranged. Government also needs to support the knowledge intensive companies by providing them subsidies and tax exemptions. Investment in research and development should also be encouraged. Databases of intellectual capital need to be properly monitored and maintained. Overall, investment in Intellectual Capital would help firms to have competitive edge and an advantage. Management & Change, Volume 16, Number 1 & 2 (2012)

124 Intellectual Capital and Corporate Performance of Indian...

REFERENCES Ahangar, R.G. (2011) “The Relationship between Intellectual Capital and Financial Performance: An Empirical Investigation in an Iranian Company”, African Journal of Business Management, 5(1): 88-95. Anonymous Current Issues Global growth centres, a Deutsche Bank Research Report (2005), (www.dbresearch.com/prod/.../ prod0000000000190080.pdf). Bhanawat, S.S. & N. Bhanawat (2012) “Indian Pharmaceutical Industry: Measurement and Analysis of Intellectual”, Research Journal of Social Science and Management, 1(12): 1-4. Bismuth, A. & Y. Tojo (2008) “Creating Value from Intellectual Assets”, Journal of Intellectual Capital, 9(2): 228-45. Bontis, N. (2004) “National Intellectual Capital Index: A United Nations Initiative for the Arab Region”, Journal of Intellectual Capital, 5(1): 13-39. Brooking, A. (1996) “Intellectual Capital: Core Asset for the Third Millennium Enterprise”, New York, Thomas Business Press. Bouteiller, Ch. (2002) “The Evaluation of Intangibles: Advocating for an Option Based Approach”, VIth Alternative Perspectives on Finance Conference, Hamburg, August. Chen, M.C., S.J. Chang & Y. Hwang (2005) “An Empirical Investigation of the Relationship between Intellectual Capital and Firm’s Market Value and Financial Performance,” Journal of Intellectual Capital, 6(2): 159-176. Chu, S.K.W., K.H. Chan & W.W.Y. Wu (2011) “Charting Intellectual Capital Performance of the Gateway to China”, Journal of Intellectual Capital, 12(2): 249-76. CIMA (2001) Understanding Corporate Value: Managing and Reporting Intellectual Capital, Cranfield University, Publisher: Chartered Institute of Management Accountants (CIMA): 1-28. Fincham, R. & R. Roslender (2003) “The Management of Intellectual Capital Management & Change, Volume 16, Number 1 & 2 (2012)

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and its Implications for Business Reporting”, A Research Report Submitted to The Institute of Chartered Accountants of Scotland (www.icas.org.uk/res_ fincham_roselander_report.pdf). Firer, S. & S.M. Williams (2003) “Intellectual Capital and Traditional Measures of Corporate Performance,” Journal of Intellectual Capital, 45(3): 348-360. Gan, K. & Z. Saleh (2008) “Intellectual Capital and Corporate Performance of Technology-Intensive Companies: Malaysia Evidence”, Asian Journal of Business and Accounting, 1(1): 113-30. Ghosh, D. & A. Wu (2007) “Intellectual Capital and Capital Markets: Additional Evidence”, Journal of Intellectual Capital, 8(2): 216-235. Ghosh, S. & A. Mondal (2009) “Indian Software and Pharmaceutical Sector Intellectual Capital and Financial Performance”, Journal of Intellectual Capital, 10(3): 1469-930. Goh, P.C. (2005) “Intellectual Capital Performance of Commercial Banks in Malaysia”, Journal of Intellectual Capital, 6(3): 385-96. Guo, W. C., S. Shiah-Hou & W. Chien (2012) “A Study on Intellectual Capital and Firm Performance in Biotech Companies”, Applied Economics Letters, 19(16): 1603-1608. Harrison, S. & P. Sullivan Sr (2000) “Profiting from Intellectual Capital: Learning From Leading Companies”, Journal of Intellectual Capital, 1(1): 33-46. Hausman, J.A. (1978) “Specification Tests in Econometrics”, Econometrica, 46(6): 1251-71. Kamath, G.B. (2007) “The Intellectual Capital Performance of Indian Banking Sector”, Journal of Intellectual Capital, 8(1): 96-123. Kamath, G.B. (2008) “Intellectual Capital and Corporate Performance in Indian Pharmaceutical Industry,” Journal of Intellectual Capital, 9(4): 684-704. Komnenic, B & D. Pokrajcic (2012) “Intellectual Capital and Corporate Management & Change, Volume 16, Number 1 & 2 (2012)

126 Intellectual Capital and Corporate Performance of Indian...

Performance of MNCs in Serbia”, Journal of Intellectual Capital, 13(1): 106 – 119. Kujansivu, P. & A. Lonnqvist (2005) The Value and Efficiency of Intellectual Capital in Finnish Companies (www.tut.fi/units/tuta/tita/tip/ Kujansivu_Lonnqvist.pdf). Harris, R. D. F. & E. Tzavalis. (1999) “Inference for Unit Roots in Dynamic Panels Where the Time Dimension is Fixed”, Journal of Econometrics, 91: 201–226. Mavridis, D. (2004, 2005) “Intellectual Capital Drivers in the Greek Banking Sector,” Management Research News, 28(5): 43-62. Mehralian, G. (2012) “Intellectual Capital and Corporate Performance in Iranian Pharmaceutical Industry”, Journal of Intellectual Capital, 13(1): 138 – 158. Mohiuddin, M., S. Najibullah & A.I. Shahid (2006) “An Exploratory Study on Intellectual Capital Performance of the Commercial Banks in Bangladesh”, The Cost and Management, 34(6): 40-54. Pal, K. & S. Soriya (2012) “Intellectual Capital Performance of Indian Pharmaceutical and Textile Industry”, Journal of Intellectual Capital, 13(1): 120 – 137. Pulic, A. (1998, 2000) “VAIC– An Accounting Tool for IC Management”, International Journal of Technology Management, 20(5-8): 702-14. Pulic, A. (2001) Value Creation Efficiency Analysis of Croation Banks 19962000”, International Business Consulting LLC, Zagreb (www.vaicon.net). Razafindrambinina, D. & T. Anggreni (2008) An Empirical Research on the Relationship between Intellectual Capital and Corporate Financial Performance on Indonesian Listed Companies (www.lby100.com/ly/ 200806/p020080627326310290656.pdf). Roos, J., G. Roos, N.C. Dragonetti & L. Edvinsson (1997) “Intellectual Capital: Navigating the New Business Landscape”, London, Macmillan Press. Management & Change, Volume 16, Number 1 & 2 (2012)

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Salleh, A.L. & F. Selamat (2007) “Intellectual Capital Management in Malaysian Public Listed Companies”, International Review of Business Research Paper, 3(1): 266-278. Segelod, E. (1998) “Capital Budgeting in A Fast-Changing World”, Long Range Planning, 31(4): 529-541. Sharabati, A.A., S.N. Jawad & N. Bontis (2010) “Intellectual Capital and Business Performance in the Pharmaceutical Sector of Jordan”, Management Decision, 48(1): 105-31. Shaari, J. A. N., M. Khalique & A. H. B. M. Isa. (2010) “Ranking of Public and Domestic Private Sector Commercial Banks in Pakistan on the Basis of the Intellectual Capital Performance”, Proceedings of International Borneo Business Conference (BBC). Sofian, M., P. Saudah & R. Tayles (2006) “The Implications of Intellectual Capital on Performance Measurement and Corporate Performance”, Journal Kemanusiaan, 8: 13-24. Stewart, T. A. (1997, 1999) “Intellectual Capital: The New Wealth of Organizations”, Doubleday/Currency, New York, NY. Sullivan, P. (2000) “Value-Driven Intellectual Capital - How to Convert Intangible Corporate Assets into Market Value, New York. John Wiley and Sons. Taliyang, S.M. (2011) “Determinants of Intellectual Capital Disclosure among Malaysian Listed Companies”, Unpublished master’s thesis, UUM, Malaysia. Tan, H.P., D. Plowman & P. Hancock (2007) “Intellectual Capital and Financial Returns of Companies,” Journal of Intellectual Capital, 8(1): 76-95. Ting, W.K.I. & H.H. Lean (2009) «Intellectual Capital Performance of Financial Institutions in Malaysia», Journal of Intellectual Capital, 10(4): 588-99. Tseng C.Y. & Y. J. J. Goo (2005) «Intellectual Capital and Corporate Value in an Emerging Economy: Empirical Study of Taiwanese Manufacturers», R&D Management, 35(2): 187-201. Management & Change, Volume 16, Number 1 & 2 (2012)

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Uadiale, O. M. & U. Uwuigbe (2011) “Intellectual Capital and Business Performance: Evidence from Nigeria”, Interdisciplinary Journal of Research in Business, 1(10): 49- 56. Wang, J.C. (2008) “Investigating Market Value and Intellectual Capital for S&P 500”, Journal of Intellectual Capital, 9(4): 546-63. Wang, W.Y. & C. Chang (2005) “Intellectual Capital and Performance in Casual Models Evidence from the Information Technology Industry in Taiwan”, Journal of Intellectual Capital, 6(2): 222-36. Wang, M.S. (2011) “Measuring the Intellectual Capital and Their Effect on Financial Performance: Evidence from Capital Market in Taiwan,” CIBMP annual conference on Innovations in Business and Management, London, UK. Yalama, A. & M. Coskun (2007) “Intellectual Capital Performance of Quoted Banks on the Istanbul Stock Exchange Market”, Journal of Intellectual Capital, 8(2): 256-271. Young, C.S., H.Y. Su, S.C. Fang & S.R. Fang (2009) “Cross-Country Comparison of Intellectual Capital Performance of Commercial Banks in Asian Economies”, The Service Industries Journal, 29(11): 1565-79. Zeghal, D. & A. Maaloul (2010) “Analyzing Value Added as an Indicator of Intellectual Capital and its Consequences on Company Performance,” Journal of Intellectual Capital, 11(1): 39-60. Annexure 1 Table 2 Growth in VAIC Company name

2001

2006

2011

Aarti Drugs Ltd.

5.24

5.55

4.57

Abbott India Ltd.

6.63

4.98

1.33

Ajanta Pharma Ltd.

3.22

2.28

2.48

-0.11

10.59

6.1

Arvind Remedies Ltd.

3.28

4.22

6.05

Aurobindo Pharma Ltd.

2.43

3.1

1.17

Ankur Drugs & Pharma Ltd.

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129

8.87

3.19

4.3

-0.29

4.68

3.03

Cadila Healthcare Ltd.

3.71

7.05

6.62

Cipla Ltd.

2.69

2.59

2.7

Claris Lifesciences Ltd.

6.75

6.63

4.15

Dishman Pharmaceuticals & Chemicals Ltd.

3.09

3.01

5.09

Divi’S Laboratories Ltd.

8.77

6.4

5.98

Dr. Reddy’S Laboratories Ltd.

6.18

2.19

2.87

Elder Pharmaceuticals Ltd.

2.28

2.25

2.41

F D C Ltd.

3.07

3.53

3.45

Glenmark Pharmaceuticals Ltd.

2.67

2.8

3.52

Granules India Ltd.

4.98

4.01

2.47

Hikal Ltd.

9.23

4.36

3.78

Ind-Swift Laboratories Ltd.

9.84

8.92

9.26

Ind-Swift Ltd.

5.15

3.48

5.5

Indoco Remedies Ltd.

3.1

2.07

1.55

Ipca Laboratories Ltd.

1.8

1.61

2.48

J B Chemicals & Pharmaceuticals Ltd.

2.83

2.41

2.3

Jubilant Life Sciences Ltd.

3.32

3.87

3.91

21.74

17.84

18.22

Lupin Ltd.

3.55

2.85

3.38

Natco Pharma Ltd.

3.99

2.83

2.86

Biocon Ltd.

Jupiter Bioscience Ltd.

Nectar Lifesciences Ltd.

12.63

9.88

8.47

Neuland Laboratories Ltd.

3.02

3.3

3.15

Novartis India Ltd.

4.41

2.87

3.06

Orchid Chemicals & Pharmaceuticals Ltd.

6.07

5.02

4.22

Panacea Biotec Ltd.

4.69

3.81

3.31

Parenteral Drugs (India) Ltd.

4.93

2.67

3.33

Piramal Healthcare Ltd.

3.02

2.49

3.83

Plethico Pharmaceuticals Ltd.

1.78

8.71

4.52

Ranbaxy Laboratories Ltd.

3.15

1.35

3.12

Sharon Bio-Medicine Ltd.

11.55

12.24

6.47

Management & Change, Volume 16, Number 1 & 2 (2012)

130 Intellectual Capital and Corporate Performance of Indian... Shasun Pharmaceuticals Ltd.

4.09

3.92

-5.46

Strides Arcolab Ltd.

5.89

4.87

4.88

Sun Pharmaceutical Inds. Ltd.

6.32

8.06

9.08

11.11

12.68

7.96

T T K Healthcare Ltd.

0.35

-0.75

0.76

Torrent Pharmaceuticals Ltd.

4.25

1.64

3.02

2.3

3.35

2.59

Venus Remedies Ltd.

3.85

10.55

6.26

Vinati Organics Ltd.

5.38

1.92

6.45

Wanbury Ltd.

5.14

2.4

-0.65

Wockhardt Ltd.

2.48

4.81

1.02

Wyeth Ltd.

1.67

3.14

6.45

1.58%

16.40%

Surya Pharmaceutical Ltd.

Unichem Laboratories Ltd.

Growth rate (CAGR)

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MODELING THE EMERGING MARKET ONLINE SOCIAL NETWORK ADOPTION BEHAVIOR: EVIDENCE FROM INDIA Jaydeep Mukherjee1

Anandan Pillai2

In this paper a comprehensive model of ONLINE SOCIAL NETWORK (OSN) Adoption Behavior which includes technical, individual, collective and social aspects of decision making has been developed. It is very relevant for the MNCs as they have consumers from the developed as well as emerging markets, to be addressed concurrently in the online environment, where boundaries are fuzzy. The results have validated that the OSN adoption drives the perception of perceived enjoyment, social influence and usefulness, and flow experience which lead to the formation of the intention to use OSN. This finding is likely to be of immense value to the academic community and business organizations in harnessing the full potential of the OSNs. These findings may provide useful insights for designing and marketing new OSNs and modifying the functionalities and the marketing of existing OSNs. If the findings are corroborated in further research, it most likely would require culture customized solution for marketing using the OSNs. Keywords: Online Social Network, Social Media, Social Networking Websites, User Acceptance, Technology Acceptance Model. INTRODUCTION ONLINE SOCIAL NETWORK (OSN) is very popular and it has immense marketing as well as business application. Large num bers of OSNs are there 1.

Jaydeep Mukherjee, PhD, Associate Professor (Marketing), Management Development Institute, M.G. Raod, Sukhrali, Gurgaon-122001 India E-mail: jmukherjee@mdi.ac.in Anandan Pillai, FPM Scholar (Marketing), Management Development Institute, M.G. Raod, Sukhrali, Gurgaon-122001, India. E-mail: anandan1982@gmail.com

150 Modeling the Emerging Market Online Social Network...

and they have specific consumer bases. Multinational Corporations (MNCs) use the OSNs for various business objectives across their global stakeholder base. OSN advertising, which is currently $2.74 billion business, is expected to surpass $10 billion by 2013 (E-Marketer, 2011). OSNs could be harnessed across the different departments in these organizations. Research and Development could use it for listening to customer voice and to derive insights, marketing could communicate with consumers and promote its offers, sales could generate word-of-mouth references for sales leads, and customer support could use OSNs to help the customers solve their problems (Bernoff and Li 2008). The relevance of OSN as a marketing tool is going to increase for the MNCs tapping the increasingly globalized markets. In emerging markets, OSNs are effective in reaching the target audience in remote locations, and their overall popularity makes them an attractive marketing communication medium. Thus there is need for holistic understanding of the OSN Adoption Behavior (OSNAB) among consumers of the developed and emerging markets, so as to harness their full potential. Technology Adoption Model (TAM) is a commonly used model to understand the OSNAB and has been extensively researched and validated in the developed markets like USA and Europe The basic argument of TAM is that adopters develop perception about the usefulness and ease of adoption of technologies; in turn, these perceptions influence the behavioral intentions and ultimately, the actual system use (Venkatesh et.al,, 2003). OSNAB is not only about acceptance of technology, but is a social and hedonistic activity; hence TAM may not be an adequate model to comprehend the phenomenon (Pillai and Mukherjee, 2011). The model needs to be examined from both these perspectives. There are large sections of users in the emerging markets, who access the OSNs through internet browsing centers, offices, homes and educational institutions. These facilities provide easy access to a plethora of OSNs for use by the consumers. Also, most commonly used OSNs like Facebook, MySpace, Orkut are used for hedonistic purposes. Hence, OSNAB is unlikely to be a high involvement decision making process. In such cases, OSN adoption can be conceptualized as an impulse driven activity. Thus there is a high probability that the adoption of OSNs leads to perception and attitude formation about OSNs, which is very different from the TAM model. Thus the TAM model may not be the appropriate starting point for OSNAB, which needs to be tested. The adoption behavior models built in developed markets are less Management & Change, Volume 16, Number 1 & 2 (2012)

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effective when they are applied to the emerging markets unless they are adapted (Aulakh, Kobate, and Teegen 2000). Also, the social influence and cultural differences have significant impact on the technology adoption related to OSN (Vannoy and Palvia 2010; Tan, et. al, 2007). Since the social influence and cultural differences are different in emerging markets as compared to the developed markets (Calthone, Griffith, Yalcinkaya,2006), there is a need to study the OSNAB in the emerging markets. LITERATURE REVIEW Conceptual foundations which can explain the OSNAB: TAM posits that behavioral intention to use information systems is atleast in part based on perceived ease of use and perceived usefulness of the said systems (Rosen & Kluemper 2008). Perceived Enjoyment (PE) and perceived ease of use are greater predictors of intention to use a hedonistic information system as compared to perceived usefulness (Heijden, 2004). Perceived Ease of Use (PEU) is defined as “the degree to which a person believes that using a particular system would be free of effort”; it has been positively associated with perceived usefulness and behavioral intentions to use technology, in hundreds of studies (Davis, 1989). Perceived Usefulness (PU) is commonly defined as “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989). This construct is a departure from the normal day to day job usefulness construct and it has been modified to take into account the usefulness from social networking leading to social effectiveness. Theory of Reasoned Action suggests that a person’s behavioral intention depends on the person’s attitude about the behavior and subjective norms. Also behavioral intention is a good estimator of actual behavior (Fishbein &

Fig.1 Technology Adoption Model (TAM) Management & Change, Volume 16, Number 1 & 2 (2012)

152 Modeling the Emerging Market Online Social Network...

Ajzen, 1975). There are many OSN adoption models, which consider OSN adoption as a form of new technology adoption. Thus the basic structure of our framework is developed from technology adoption model, given in fig.1. However, there is need to consider other variables which are specifically relevant to the concept of OSNAB so as to make it more comprehensive. Thus it was decided to consider the various different antecedents of OSNAB given in the literature. The objective was to develop a comprehensive framework based on literature. Acar (2008) has suggested that OSNAB depends on stranger contacts, multiple presence on social networking websites, time spent online. The following specific constructs were considered for this study: Extroversion: Burgoon (1976) and McCroskey and Richmond (1990) saw extroversion as an antecedent to (un)willingness to communicate. Two of recent studies found empirical support for the proposition that extroverts communicate more with others on the internet than do introverts (Maclntyre, Babin, and Clement, 1999; Kiesler et al., 2002). Studies showed that the Internet’s anonymity and reduced cues might stimulate online self-disclosure because there is no fear of being ridiculed or rejected (Derlega, Metts, & Petronio, 1993; Pennebaker, 1989). Sheldon and Honeycott (2008) found that students who are afraid of face-to-face meetings are more likely to go on Facebook to pass time when bored or just to occupy their time. On Facebook they can interact with others without looking at each other’s face. This may be particularly appealing to introverts when trying to open up. The findings of these studies suggest that extroversion is an antecedent to self-esteem which negatively correlates with communication apprehension and positively correlates with willingness to communicate (MacIntyre et.al, 1999). Flow: Csikszentmihalyi (1990) described flow as “the holistic experience that people feel when they act with total involvement.” This construct is comprised of variables such as heightened enjoyment, curiosity, control, focused immersion and temporal distraction. OSNAB is for functional / professional use as well as for entertainment (Pillai and Mukherjee, 2011) and hence flow experience is an important element in the OSNAB (Hua and Haughton 2009). Perceived Enjoyment (PE): Since the value of a hedonic information system is the fun experienced by a user, any model that attempts to explain the use of these systems should include the construct of perceived enjoyment, which Management & Change, Volume 16, Number 1 & 2 (2012)

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is “the extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated” (Davis, Bagozzi and Warshaw, 1992). This construct would take the entertainment value of social networking websites into account ,which would directly influence the behavioral intention of social media user. Social Influence (SI): This variable tries to capture how the behavioral intention towards social media is affected by the influence of our reference group or peers or for that matter any source. Behavioral Intentions (BI): Behavioral intentions are a function of an individual’s attitude toward the behavior, the subjective norms surrounding the performance of the behavior, and the individual’s perception of the ease with which the behavior can be performed (Eagly and Chaiken 1993). Behavioral intentions consist of frequency of usage and acceptability of OSNs which would in turn be predicted from the variables defined above. Adoption is a complex behavior. It involves a number of variables. A simple index based on one variable may not give an accurate measure of adoption. This paper presents a new scale for measuring adoption behavior, taking into account the different variables involved. The construct OSNAB was measured with the help of manifest variables like size of the network, time spent online, number of contacts the person had on the OSN, and the number of different OSNs which the person used on a regular basis. So the overall model developed from existing literature was used to extend the TAM (fig. 2). Alternate conceptualization of modeling OSNAB: The limitation of TAM

Fig. 2 Extended TAM Management & Change, Volume 16, Number 1 & 2 (2012)

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based model in modeling adoption behavior has been highlighted in literature (Bagozzi, 2007) and a more comprehensive model was proposed by Venkatesh et.al, (2003). The argument rests on the premise that technology adoption is predominantly driven by personal intention. However, OSN adoption is not only really driven by personal intention only, rather, it is driven by collective intention, moreso in interdependence based cultures like the emerging markets of India, China etc. (Bagozzi,2007). Thus OSN adoption need not follow the technology adoption framework at all, especially in the emerging markets. Our conceptualization is that adoption behavior actually is a pre-requisite for the OSN usage. The actual behavior leads to experiencing the OSN and attitude formation about intention of further use. In the specific context of OSN, adoption of OSN leads to the experience of flow, social influence, perceived usefulness and perceived enjoyment. This in turn leads to the attitude formation about intention to use OSN and eventually the behavioral intention. The individual characteristic of extroversion directly impacts the behavioral intention towards OSN. The alternate model has been depicted in fig. 3. HYPOTHESES

Fig. 3 Alternate Model

Based on the above alternate model we have tested the following hypotheses in this study: H1: OSNAB has positive impact on Perceived Usefulness Management & Change, Volume 16, Number 1 & 2 (2012)

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H2: OSNAB has positive impact on Flow H3: OSNAB has positive impact on Perceived Enjoyment H4: OSNAB has positive impact on Social Influence H5: Extroversion has positive impact on Behavioral Intention. H6: Perceived Usefulness has positive impact on Behavioral Intention. H7: Flow has positive impact on Behavioral Intention H8: Perceived Enjoyment has positive impact on Behavioral Intention H9: Social Influence has positive impact on Behavioral Intention. RESEARCH DESIGN Research Methodology: We needed to study a large number of respondents and measure their responses on a scale, so as to be able to make generalizations about the relationships between the variables. This paper has followed cross sectional survey methodology as we were studying phenomenon which were not directly observable and were related to attitude. The questionnaire was designed by adapting from the existing literature and that provided the validity of the scale and the reliability was measured by using the chronbach alpha figures. Operationalization of Constructs: All the constructs were measured using scales that were previously developed and validated. The same were modified for the context of our research. The operationalization of the constructs was done by a questionnaire set in Likert scale (Refer Annexure 1 for the questionnaire). Unit of Analysis: Individual OSN user Respondents: The survey instrument was either physically administered or sent by email to 550 respondents in the age group of 18 â&#x20AC;&#x201C; 40 years (as they were expected to be the prime users of OSN). There were 293 respondents; 277 responses were complete in all respects for the analysis. The respondents were working executives with five to fifteen years of industry experience. Management & Change, Volume 16, Number 1 & 2 (2012)

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The data collection sites typically represented an all India character and hence, even though the sampling was not representative by design, the respondents were reasonably representative of the OSN user population of India. The participants represented the target audience of OSNs and were using it both for hedonistic purposes like Facebook as well and for utilitarian use like Linkedin. Procedures and Measurement: The scale of the instrument has been adapted from different academic literature; we revalidated the reliability of the final scale statistically. For reliability we checked the Cronbach alpha value obtained from normal reliability analysis with the help of SPSS software. The Cronbach alpha values obtained are enumerated in table 1. Table1 Chronbachâ&#x20AC;&#x2122;s alpha figures Variable

Standardized Alpha

Reference for construct

Extroversion

0.6384

Digman 1990

Perceived Usefulness

0.8728

Rosen & Kluemper (2008)

Perceived Ease of Use

0.7373

Segars & Grover 1993

Perceived Enjoyment

0.806

Hua & Haughton (2009)

Social Influence

0.6032

Zhang & Daughterty

Flow

0.6936

Rosen & Sherman

Behavioral Intentions Behavior

0.715

Venkatesh et al.

0.6692

Venkatesh et al.

The two models were tested using AMOS 4 software for finding the significance of the relationship and the model fitting.

FINDINGS The fit measures of both the model were found out by using the AMOS 4 software. A select set of fit measures, which are relevant for our research are given in Table 2. Table 2 Fit measures Fit Measures

Theory Model

Alternate Model

CMINDF

1.639

2.057

GFI

0.882

0.855

CFI

0.908

0.843

Management & Change, Volume 16, Number 1 & 2 (2012)

Jaydeep Mukherjee, Anandan Pillai RMSEA

0.048

0.062

NFI

0.797

0.739

RFI

0.766

0.706

IFI

0.91

0.846

TLI

0.894

0.824

157

We notice that the fit measures of the extended TAM model and the alternate model are in the acceptable range as well as comparable to each other. Hence there is merit in considering the alternate conceptualization as having similar power to explain the empirical data. The fit measures indicated that the model developed (based on literature) represented the empirical data. However, the results did not confirm all the hypotheses which were tested. For that, a regression analysis was done and their significance levels were used for hypothesis testing. The results of regression analyses have been given in table 3. Table 3 Regression Figures Theory Model Relationships

Alternate Model

Regression Significance Weights levels Standardized

Regression Weights Standardized

Significance levels

PE —> BI

0.032

0.841

0.207

0.058

PU —> BI

0.274

0.02

0.269

0.015

Flow—>BI

0.12

0.292

0.139

0.17

SI—>BI

0.592

0.02

0.437

0.011

BI—>OSNAB

0.432

Extroversion —>OSNAB

0.096

0.359

OSNAB—> Flow

0.602

OSNAB—> SI

0.888

OSNAB—> PE

0.739

OSNAB—> PU

0.771

-0.023

0.741

Extroversion—>BI

DISCUSSION Management & Change, Volume 16, Number 1 & 2 (2012)

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The data supported the hypothesis, H1, H2, H3, and H4 at p value of 0.01. However hypothesis H5, H6, H7, H8 and H9 were not found significant as p > 0.01. Though the effect of perceived usefulness, flow, perceived enjoyment and social influence on behavioral intention was found to be positive, the effect of extroversion on behavioral intention was found to be negative. In the alternate model, the relationships of OSNAB leading to the experience of flow, social influence, perceived enjoyment and perceived usefulness have been found to be significant. This could be an indicator of the fact that in an interdependent culture where the study was conducted, a lot of consumer decisions are driven by need for social conformity. In this type of culture, the OSNAB may not go through the perceived usefulness and perceived enjoyment route of Think à Feel à Act. In fact exactly opposite route of Act à Feel à Think seems to be operational. Extroversion was found to have a negative effect on the intention to use OSNs. This could be possibly explained by the understanding that extroverts would be finding it very easy to socialize in the real world environment, hence they do not show any additional activity on OSN. However, it is possible that introverts would find their expression more in the OSN. Also, the findings are comparable to many studies which have been referred to in many a literature (Acar 2008). CONTRIBUTION In this paper, a literature based comprehensive model of OSNAB, which includes technical, individual, collective and social aspects of decision making, has been developed. The model was empirically tested in context of India, an emerging market. The results have validated that the OSN adoption drives the perception of social influence and usefulness, which leads to the formation of the intention. This finding is likely to be of immense value to the academic community and business organizations in harnessing the full potential of the OSNs. It is very relevant to the MNCs as they have to address consumers from the developed as well as from emerging markets, concurrently, in the online environment. Finally, these findings may provide useful insights for designing and marketing new OSNs, modifying the functionalities and for marketing the existing OSNs. The paper essentially has two contributions: Management & Change, Volume 16, Number 1 & 2 (2012)

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The first one is that it puts forward and empirically tests the presence of an “act-feel-think” paradigm of OSNAB as distinct from the prevalent “think-feel-act” perspective which is essentially derived from the technology adoption models. The second contribution is that it confirms the impact of social influence, flow experience, and extroversion on the perceived enjoyment and perceived usefulness. It also models the linkage of the perceived usefulness, perceived enjoyment and extroversion in the formation of intention to use OSN. The above two findings have the following managerial significance: Since the OSNAB is driven by the actual experience of the prospective user, the key issue is the retention of users and not their acquisition. The best way to manage the retention would be through the perceived social influence, perceived usefulness, perceived enjoyment and perceived flow experience, and in that order. Thus it provides a tradeoff criterion in the designing of the OSNs and managing the traffic. LIMITATIONS Like any research, this study has limitations. First, we used a convenience sample of the students of a management school and employees of a business enterprise. The sites were such that it comprised people from all over India and could be considered to represent the whole country. However, we strictly can’t say the sample was representative. A study having a more representative sample would have given more reliable inferences. Second, we collected data from respondents on only a set of variables, asking them to think about their OSN experience. The study definitely was limited in scope and the entire set of variables have not been studied. Hence, the model is at best an improved model, but not complete. Thus, there is scope for further research, such as incorporating individual specific variables like technological awareness, impulsiveness, etc. This research needs to be replicated in an independent culture. It is possible that the findings of this research are also applicable in other settings. Findings of such research would be useful for designing the newer OSNs, and in their use for marketing.

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REFERENCES Acar A. (2008) “Antecedents and Consequences of Online Social Networking Behavior: The case of Facebook”, Journal of Website Promotion, Vol. 3 (1/2), pp. 62-83. Aulakh P.S., M. Kobate, and H. Teegen (2000) “Export Strategies and Performance of Firms from Emerging Economies: Evidence from Brazil, China and Mexico,” Academy of Management Journal, Vol. 43(3), pp. 342-61. Bagozzi, R.P. (2007) “The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift”, Journal of the Association for Information Systems, Vol. 8, pp. 244–254. Bernoff, J., Li C., (2008) “Harnessing the Power of the Oh-so – social Web”, MIT Sloan Management review, Spring 2008, pp. 36-42. Burgoon, J. (1976)”The unwillingness to communicate scale: Development and validation”, Communication Monographs, Vol. 13, pp. 60-69. Calthone R.J., Griffith D.A., Yalcinkaya C. (2006) “An Empirical Examination of a Technology Adoption Model for the Context of China”, Journal of International Marketing, Vol. 14, No4, pp. 1-27. Csikszentmihalyi, M. (1990) “Flow: The Psychology of Optimal Experience”, New York: Harper and Row. ISBN 0-06-092043-2 Davis, F. D. (1989) “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, Vol. 13(3), pp. 319–340. Derlega, V.L., Metts, S., Petronio, S., Margulis, S. T. (1993) Self disclosure, London: Sage. Digman, J.M. (1990) “Personality structure: Emergence of the five-factor model”, Annual Review of Psychology, Vol. 41, pp.417-440. E-Marketer (Nov 2, 2011) “Greater-Share-of-Online-Ad-Spend”, accessed on 8 th November 2011 from http://www.emarketer.com/ Article.aspx?R=1008669 Management & Change, Volume 16, Number 1 & 2 (2012)

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Eagly, A. H., & Chaiken, S. (1993) “The psychology of attitudes”, Fort Worth, TX: Harcourt Brace Jovanovich. Heijden H.D.V., (2004) “User acceptance of hedonistic information systems”, MIS Quarterly, Vol. 28, No. 4, pp. 695-704. Hua G., Haughton D. (2009) “Virtual world adoption: a research framework and empirical study”, Online Information review, Vol. 33, No 5. Fishbein, M. & Ajzen, I. (1975) “Belief, attitude, intention, and behavior: An introduction to theory and research.” Reading, MA: Addison-Wesley. Kiesler, S., Kraut, R., Cummings, J., Boneva, B., Helgeson, V., & Crawford, A. (2002) “Internet evolution and social impact”, IT & Society, Vol.1, No. 1, pp. 120-134. McCroskey J.C., Richmond V.P. (1990) “Willingness to communicate: Differing cultural perspectives”, Southern Communication journal, Vol. 56, pp. 72-77. MacIntyre, P. D., Babin, P. A., & Clement, R. (1999) “Willingness to communicate: Antecedents & consequences.” Communication Quarterly, Vol. 47, pp. 215-229. Pennebaker, J. W. (1989) “Confession, inhibition, and disease” In L. Berkowitz (Ed.), Advances in experimental social psychology, pp. 211244, New York: Academic Press. Peter Rosen and Peter Sherman (2006) “Hedonic Information Systems: Acceptance of Social Networking Websites” AMCIS 2006 Proceedings. Pillai A., Mukherjee J., (2011) “User acceptance of hedonic versus utilitarian social networking web sites”, Journal of Indian Business Research, Vol. 3, Issue: 3 Rosen P.A., Kluemper D. H., (2008) “ The impact of big five personality traits on the acceptance of social networking website”, Proceedings of the Fourteenth Americas Conference on Information Systems, Toronto, Canada, August 14th – 17th 2008. Management & Change, Volume 16, Number 1 & 2 (2012)

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Segars A. H., Grover V. (1993) “Re-Examining Perceived Ease of Use and Usefulness: A Confirmatory Factor Analysis”, MIS Quarterly, Vol. 17, No. 4, pp. 517-525. Sheldon P., Honeycott J., (2008 November) “A structural equation model for oral communication and Facebook use”, Presented at the annual meeting of the International Communication Association, Chicago, IL Tan F.B., Yan L., Urquhart C., (2007) “The effect of cultural differences on attitude, peer influence, external influence, and self efficacy in actual online shopping behavior”, Journal of Information Science and Technology, No 4, Vol. 1, pp. 4-23. Vannoy S.A., Palvia P., (2010) “The social influence model of technology adoption”, Communications of the ACM, Vol. 53, No. 6, pp 148-153 Venkatesh, V.; Morris; Davis; Davis (2003) “User Acceptance of Information Technology: Toward a Unified View”, MIS Quarterly, Vol. 27, pp. 425–478. Zhang J., Daugherty T., (2009) “Third-Person Effect and Social Networking: Implications for Online Marketing and Word-of-Mouth Communication”, American Journal of Business, Vol. 24, Issue 2, pp. 53-63

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Annexure 1: Questionnaire Survey no (for official use only*): ………….. Dear Respondent, We thank you for your interest in participating in the research project about the social media behavior. We expect the responses would help us understand the research objectives. It has 2 sections, kindly answer all the questions. Section-1 (give ticks at an appropriate place) 52 Questions S. No.

Question

Strongly Dis- Neutral Agree Strongly Disagree agree Agree

Are you inclined to keep in the background on social occasions?

I feel lonely even in a group.

While taking an important exam, I perspire a great deal.

I pay attention to details in all my activities in social networking websites.

Would you be very unhappy if you were prevented from making numerous social contacts?

Using Social networking websites enhances my social effectiveness in life.

It was very easy to learn using Social networking Websites.

Using a social media website would be fun for me.

I would be influenced by my friends to use social networking websites.

10. Without Social networking websites I would have felt my social life less easy. Management & Change, Volume 16, Number 1 & 2 (2012)

164 Modeling the Emerging Market Online Social Network... 11. I intend to increase my use of the Social networking websites in the future. 12. Do you like to mix socially with people? 13. I am concerned about what others say about me. 14. I sometimes feel my heart beating very fast during important exams. 15. I follow a schedule for engaging in social networking website. 16. Do you usually take the initiative in making new friends? 17. Using Social networking websites enhances my social life. 18. Social networking Webites are very easy to use. 19. Using a social networking website would be entertaining. 20. I can influence others to use social networking websites. 21. Most of time when you surf Social networking websites you are completely engrossed. 22. I intend to use the Social networking websites in the future. 23. Do you like to have many social engagements? 24. I need recognition to feel good about myself. 25. I usually get depressed after taking a test.

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26. I frequently make a mess of things. 27. Do you like to play pranks upon others? 28. I find Social networking websites useful in my social interactions. 29. It is very easy to become skillful with Social networking websites. 30. The social media website is a way for me to relax and have fun. 31. Even If social networking is a fad, I would like to try it too. 32. Are you usually a “good mixer with people?” 33. I’d like to take a look at unexplored Social networking websites. 34. Would you rate yourself as a happy-go-lucky individual? 35. I am truthful to myself. 36. The harder I work at preparing for a test or studying for one, the more confused I get. 37. I leave my belongings around. 38. Do you often “have the time of your life” at social affairs? 39. Using Social networking websites improves my social performance in life. 40. I clearly understand Social networking websites that I use. 41. Sometimes I feel bored while using Social networking website. Management & Change, Volume 16, Number 1 & 2 (2012)

166 Modeling the Emerging Market Online Social Network... 42. Can you usually let yourself go and have a good time at a party? 43. I like being alone sometimes. 44. I really donâ&#x20AC;&#x2122;t see why some people get so upset about appearing for examinations. 45. I often forget to put things back in their proper place. 46. Do you derive more satisfaction from social activities than from anything else? 47. Are you inclined to limit your acquaintances to a select few? 48. You have experienced that time flew, when you surf the Social networking websites? 49. You have experienced completely oblivious of your environment when surfing the internet? 50. I would feel deprived if I did not get the opportunity to use social networking website in future. 51. I seldom get completely immersed in the social networking websites. 52. I do not get influenced by my friends in my decision to use a social networking website.

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Section-2 (give ticks at an appropriate place) 6 questions The data collected would be confidential and would never be used at an individual level of analysis. 53. Are you Male or Female?

Male

Female

54. How many social networking sites you have an account in?

5 or More

55. How many social networking websites do you use at least twice a week?

5 or More

56. Please indicate number of Less than 100 to 200 to 300 to More your friends in your social 100 200 less than less than networking websitesâ&#x20AC;&#x2122; 300 than 400 account? 400 57. On an average, how much Less than 10-15 time do you spend weekly in 10 hours hours social networking?

15-20 hours

20-25 hours

58. Percentage of friends in your Less than social networking friends 20% with whom u had face to face interaction.

40 to less than 60%

60 to Above less 80% than 80%

20 to less than 40%

More than 25 hours

Your name: ...................................................................................... Age: ........................... Signature:.................................................................. Date..............................

Thanks for answering!

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Management & Change, Volume 16, Number 1 & 2 (2012)

BOOK REVIEW Urban and Regional Planning in India- A Handbook for Professional Practice By S.K.Kulshrestha, (2012) SagePublications.(299 pages, Rs 895) Urban planning is a technical and political process concerned with the control of the use of land and design of the urban environment, to ensure orderly development of settlements and communities. Distinct characteristics of urban planning from the remains of the cities of Harappa, Lothal and Mohenjo-daro in the Indus Valley Civilisation lead archaeologists to conclude that they are the earliest examples of deliberately planned and managed cities. The modern origins of urban planning lie in the movement for urban reform that arose as a reaction against the disorder of industrial cities in the mid 19th century. Physical planning or spatial planning is a general term used interchangeably to represent planning of both urban centres and regions. Physical planning is a process of formulation of the plan of a settlement or region, as the case may be, which serves as a tool in guiding the manner in which land will be used for various socio-economic and physical activities, and provision of infrastructure and development therein to be carried out by the public or private agencies. Urban and regional planning is a multidisciplinary subject and requires, in addition to planning, inputs from various other disciplines including administration, architecture, economics, engineering, environmental planning, finance, landscaping, law, sociology, urban design and urban management. Contemporary India is changing fast and urban planning has a key role to play in developing cities and towns. The exponential growth in Indiaâ&#x20AC;&#x2122;s urban population has put great pressure on public utilities in the cities. The challenge before the urban planners is to find innovative solutions to these problems and design places for people to live and work in more fulfilling way. In this context, this book assumes importance. The scope of the handbook covers (a) professional practices and procedures in public, private, and joint sectors in respect of urban and regional planning ,(b) scale of professional Management & Change, Volume 16, Number 1 & 2 (2012) ÂŠ 2012 IILM Institute for Higher Education. All Rights Reserved.

170 Book Review

fee for rendering professional services, and preparation of consultancy proposals, ,(c)essential elements of agreements/contracts, and (d) procedure of establishing different type of offices/firms. The book is structured in eleven chapters. Chapter 1 deals with basic issues like what is urban and regional planning, who is an urban and regional planner, his role and responsibilities, and various types of assignments that may be offered to him. Also recent reforms in the planning system starting with the policy document- Urban Development Plan Formulation and Implementation (UDPFI) Guidelines by the Union Government in 1996, is dealt with. The chapter concludes with obligations of spatial planners and Code of their professional conduct as given by the Institute of Town Planners, India (ITPI). Chapter 2 discusses the urban and regional planning practice at the national level starting with Planning Commission, Ministry of Urban Development & Housing, and role of public sector agencies like National Buildings Construction Corporation, National Institute of Urban Affairs, National Buildings Organisation, Housing and Urban Development Corporation, Central Pollution Control Board, etc. Further the planning practices at the district level (Zila Parishad, District Planning Committee) and at the Metropolitan level (Mumbai, Chennai, and Bangalore) are discussed. Chapter 3 deals with the role of private sector in spatial planning process. It discusses the procedure for appointment of consultants by public sector. It inter-alia deals with selection procedure of consultants, preparation of consultancy document, placing the Request for Proposal (RFP) as an advertisement in media. Further, the procedure for appointment of contractor involving preparation of tender documents, Notice Inviting Tender, receipt and processing of tenders, negotiations and acceptance of the tender, is dealt with. The issue of appointment of Developers is touched briefly in the chapter. The existing scenario of professional practice in the private sector is dealt with in chapter 4. In chapter 5, such practices are discussed from the perspective of publicprivate partnership (PPP). Here different models of PPP are discussed along with some case studies of selected joint ventures. Management & Change, Volume 16, Number 1 & 2 (2012)

Book Review 171

Taking into account the WTO-GATS, chapter 6, presents international planning practice and discusses the types of services, obligations and mode of supply of services by one country to another member country. The next two chapters deal with the scale of professional fee charged, with sample calculations for rendering the services (chapter 7), and discusses the manner in which the consultancy proposals should be prepared (chapter 8). In India, a scale of professional fee and charges is prescribed by the ITPI. A spatial planner who is a member of this professional body is required to follow the recommended scale. This has been revised in December 2011. Sample calculations of professional fee chargeable as per the scale of professional fees are given. Relevant examples of various components of consultancy proposals are also given. Agreement is the most important legal tool that expresses the intentions and commitments of the client and the consultant. It is a document that contains mutually agreed clauses that bind the consultants and clients. Chapter 9 deals with essential elements of agreements/contracts and discusses the format and contents of Consultancy agreements and Work contracts with contractors. Chapter 10 discusses the structure of formal organisation of professional office, and procedure for establishing different type of firms- unregistered, registered, NGO, and foreign collaboration. The concluding chapter 11 deals with personnel management and performance appraisal. While chapter 1 explains the basics of urban and regional planning, and deals with the professional ethics demanded from the members of the Institute of Town Planners, India, a professional body of spatial planners in the country; chapters 3, 7, 8, deal with the nuts and bolts, such as the manner in which consultancy proposals should be made, and the essential elements of consultancy agreements and work contracts. These chapters also give exhibits of such documents. This justifies the title Handbook for Professional Practice, of the book. However, the other chapters are rather theoretical in nature. Further, the relevance of the last chapter â&#x20AC;&#x2DC;Personnel Management and Performance Appraisalâ&#x20AC;&#x2122;, in this volume does not fit in. Further, in the recent times, there have been changes in the planning process. Now more citizens are calling for democratic planning processes Management & Change, Volume 16, Number 1 & 2 (2012)

172 Book Review

which allow public to make important decisions as part of the planning process. There should be participation by inhabitants in the design of the urban environment, as opposed to simply leaving all development to large scale construction firms. Many NGOs and some activists are actively pursuing the agenda of planning from the grass root level. Right to Information has led to quite a transparency at least in public sector where Urban authorities are required to provide the proposed details of Master Plans and invite public participation. There is increasing recognition that there is a need for underrepresented voices to be a part of the planning process. Urban planners have to recognise that their actions are not value-neutral. There is a need for education planners to work within the social and political context of the planning process, which goes beyond the rational planning model involving value-neutral recommendations. It would have been better if the author could have dealt with this dimension of democratising the planning process. The book will be useful as reference material for urban and regional planners in the country. Architects engaged in planning projects will also find this book useful. Dr. Sudhir Naib, Professor, Organisational Behaviour and Human Resource Management, IILM, 3, Lodhi Institutional Area, New Delhi-110003

Management & Change, Volume 16, Number 1 & 2 (2012)

Guidelines for Contributors Management & Change invites original articles, research-based papers, perspectives, short communications and management cases on topics of current interest in all areas of management. While sending contributions following ‘guidelines’ may be adhered to failing which they may not be considered for publication. Manuscripts should normally not exceed 10,000 words (20 to 40 A-4 size pages, typed double space with adequate margins on all sides and giving page numbers). Manuscripts should be submitted in duplicate with the cover page bearing only the title of the paper and author/s names, designations, name of the organisation, official addresses with city, pin code, e-mail IDs and telephone/fax numbers. Author/s name should not appear on any other page. 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For more than one publication in one year by the same author, use small (lower case) letters to distinguish them (e.g. 1980a, 1980b). Follow British spellings throughout (programme, not program). Use ‘s’ spellings instead of ‘z’ spellings. This means that words ending with‘-ize’, ‘ization’, etc., will be spelt with ‘z’ (e.g., ‘organisation’, ‘civilise’). Use of numerals: One to twelve in words, thirteen and above in figures, unless the reference is to percentages (5 percent), distance (5 km), or age (10 years old). Use 1900s and 19th century. No stops after abbreviations (JK, MBA). Use stops after initials (K.S. Singh). Use double quotes throughout. The use of single quotes to be restricted for use within double quotes, e.g., “In the words of Szell, the ‘economic question’ is today…” Quotations in excess of 45 words should be separated from the text with a line space above and below and indented on the left. Quotes should be cited accurately from the original source should not be edited, and should give the page numbers of the original publication. Italicization and use of diactricals is left to the contributors, but must be consistent, when not using diactricals, English spelling should be followed. Capitalisation should be kept to the minimum and should be consistent On receipt of the manuscript, it goes through the following stages Initial screening by the editor, to check whether there is an obvious reason to reject it, for example, if it does not fit well within the aims and scope of M&C. Once the preliminary checks are done, the manuscript is processed by utilising double blind refereeing process, meaning that the author does not get to know who reviews the manuscript and similarly, the reviewer does not know who wrote it. Depending upon the reviewers’ recommendations, the manuscript is accepted or rejected or, most likely, rewriting suggestions are given to the author, who then modifies the manuscript as per the requirement and sends a revised manuscript. Proofs Communication regarding editorial changes and proofs for correction will be sent to the first author unless otherwise indicated. On completion of the review process, the author will be informed of the status of the paper. Typically, in most cases, the entire review and acceptance process should be completed in about three months to one year. Acceptance of papers for publication shall be informed through e-mail only. Manuscripts not considered for publication will not be sent back. Authors, apart from hardcopy, should also send a soft copy of the contribution in MS word, Times New Roman Font style by e-mail to: management.change@iilm.edu. Those submitting papers should also certify that the paper has not been published or submitted for publication elsewhere. The hard copy and electronic files must match exactly. An author will receive a complimentary copy of the issue in which his/her paper appears. For Book reviews the author should mention: Name of book, complete address of the publisher, edition number, year of publication, number of pages in Roman and Arabic figures to include preliminary pages, price of the book, binding specifications such as paperback or hardback For example: Udai Pareek, Training Instruments for Human Resources Development, Tata Mc Graw Hill, 2, Raja Subodh, Mullick Square Calcutta - 700 013, first edition,1997, xi+625pp. Rs. 595, hardbound. The contributions received will be acknowledged by email. All correspondences with contributors will only be through email. Manuscripts and all editorial correspondence should be sent electronically to The Editor, Management & Change, IILM Institute for Higher Education at management.change@iilm.edu Hard copy is to be addressed to: Editor, Management & Change IILM INSTITUTE FOR HIGHER EDUCATION 3, Lodhi Institutional Area, Lodhi Road, New Delhi- 110003 INDIA Tel: 91-11-40934335 Fax: 91-11-40934339 Email ID: management.change@iilm.edu