jbm-vol-16-01

J.B.M.

Vol. 16 No. 1

Journal of Business and Management Editors

Amy E. Hurley-Hanson, Ph.D. Cristina M. Giannantonio, Ph.D.

Published by Chapman University’s Argyros School of Business and Economics Sponsored by the Western Decision Sciences Institute

WDSI

WDSI

WESTERN DECISION SCIENCES INSTITUTE The Western Decision Sciences Institute is a regional division of the Decision Sciences Institute. WDSI serves its interdisciplinary academic and business members primarily through the organization of an annual conference and the publication of the Journal of Business and Management. The conference and journal allow academicians and business professionals from all over the world to share information and research with respect to all aspects of education, business, and organizational decisions.

PRESIDENT Mahyar Amouzegar California State University, Long Beach PRESIDENT-ELECT Nafisseh Heiat Montana State University-Billings PROGRAM CHAIR/VICE PRESIDENT FOR PROGRAMS/PROCEEDINGS EDITOR John Davies Victoria University of Wellington VICE PRESIDENT FOR PROGRAMS-ELECT Sheldon R. Smith Utah Valley State College VICE PRESIDENT FOR MEMBER SERVICES David Yen Miami University of Ohio SECRETARY/TREASURER Richard L. Jenson Utah State University DIRECTOR OF INFORMATION SYSTEMS Abbas Heiat Montana State University - Billings IMMEDIATE PAST-PRESIDENT G. Keong Leong University of Nevada, Las Vegas REGIONAL VICE PRESIDENT Vijay Kannan Utah State University

Journal of Business and Management – Vol. 16, No. 1, 2010

Journal of Business and Management Volume 16, Number 1

2010

EDITORS Amy E. Hurley-Hanson, Chapman University Cristina M. Giannantonio, Chapman University

J.B.M. Journal of Business and Management EDITORS Amy E. Hurley-Hanson, Chapman University Cristina M. Giannantonio, Chapman University

EDITORIAL BOARD Nancy Borkowski Florida International University Krishna S. Dhir Berry College Sonia M. Goltz Michigan Tech University Miles G. Nicholls RMIT University Richard L. Jenson Utah State University Terri A. Scandura University of Miami Jeffrey A. Sonnenfeld Yale University Victor H. Vroom Yale University

PAST EDITORS Burhan Yavas, California State University Dominguez Hills 1993-1999 Raymond Hogler, Colorado State University 2000-2004

EDITORIAL STAFF Rosalinda Monroy, Chapman University Publications Jaclyn Witt, Editorial Assistant

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We would like to thank the many ad hoc reviewers who shared their expertise to review the manuscripts submitted to JBM over the past few years. Their time and effort greatly contributed to the Journal of Business and Management.

Hank Adler

Lori Baker-Eveleth

Chapman University

University of Idaho

David Ahlstrom

Debora J. Gilliard

The Chinese University of Hong Kong

Metropolitan State College of Denver

Ayca Altintig

Thomas E. Griffin

Chapman University

Nova Southeastern University

Susan Wills Amat

Mary L. Grimes

University of Miami

South Carolina State University

Queen E. Booker

Gary Hackbarth

Minnesota State University – Mankato

Iowa State University

William J. Carnes

Owen P. Hall Jr.

Metropolitan State College of Denver

Pepperdine University

Natasa Christodoulidou

John Hathorn

California State University Dominguez Hills

Metropolitan State College – Denver

Richard Clodfelter

J. Kline Harrison

University of South Carolina

Calloway School of Business and Accountancy

John Davies

Mary F. Hazeldine

Victoria University of Wellington

Georgia Southern University

Don W. Davis Sr.

Abbas Heiat

Penn State University

Montana State University – Billings

Jeanette A. Davy

Roger W. Hutt

Wright State University

Arizona State University – Polytechnic Campus

Kathy Lund Dean Idaho State University

Richard F. Deckro Air Force Institute of Technology

Lidija Dedi University of Zagreb

Stephanie Dellande University of New Orleans

Peter T. DiPaolo Nova Southeastern University

Rafik Z. Elias California State University Los Angeles

Grigori Erenburg Chapman University

Waithaka N. Iraki University of Nairobi

Ronald D. Johnson North Dakota State University

Stefanie K. Johnson University of Colorado – Denver

Gary M. Kern Indiana University – South Bend

Donald Kerr Griffith Business School

Steven Ko Hong Kong Polytechnic University

Austin Kwag Utah State University

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Jennifer Leonard

Karen L. Proudford

Montana State University – Billings

Morgan State University

June Lu

Sharon L. Purkiss

University of Houston – Victoria

California State University Fullerton

Christine B. Mahoney

Kendra Reed

Cleveland State University

Loyola University – New Orleans

Ronald G. McGarvey

Mooweon Rhee

RAND

University of Hawaii

Paul F. Messina

Barbara A. Ribbens

University of Texas, San Antonio

Western Illinois University

John G. Michel

Filippina Risopoulos

Notre Dame University

Sustainable University Graz

James Moore

Melissa St. James

Indiana University-Purdue University, Fort Wayne

California State University Dominguez Hills

Lewis A. Myers Jr.

University of La Verne

St. Edward’s University

Niklas Myhr Chapman University

Carmela R. Nanton Palm Beach Atlantic University

Vivek S. Natarajan Lamar University

Paul L. Nesbit Macquarie Graduate School of Management

Prashanth Nyer Chapman University

Wilhelmina N. Okunbor University of Maryland – Eastern Shore

Asbjorn Osland San Jose State University

Melanie C. Page Oklahoma State University

Mahour Mellat Parast University of Nebraska – Lincoln

Ekin K. Pellegrini University of Missouri – St Louis

Richard Peters Louisiana State University – Shreveport

Carol H. Sawyer Paul L. Schuman Minnesota State University – Mankato

Sharon Segrest University of Florida – St. Petersburg

Lois M. Shelton California State University Northridge

Sung J. Shim Seton Hall University

Ashraf I. Shirani San Jose State University

Kenneth Small Coastal Carolina University

Faye L. Smith Missouri Western State University

Richard L. Smith University of California – Riverside

James C. Spee University of Redlands

Gerald Steiner University of Graz

Ahmad Syamil Arkansas State University

Chong Leng Tan University of Idaho

Journal of Business and Management – Vol. 16, No. 1, 2010

Liz Thach

Donna Wiley

Sonoma State University

California State University East Bay

Donna Tillman

John L. Wilson

California State Polytechnic University Pomona

Nova Southeastern University

Teresa C. Tompkins

Umeå University

Timothy L. Wilson

Pepperdine University

Melien Wu

Romica Trandafir

Chung-Yuan Christian University

Technical University of Civil Engineering Bucharest

Ching Yun University

Bruce O. Treadway IPG Converting

William Tsao Chung-Yuan Christian University

Hui-Hua Ou-Yang Limao Yang Hubei University

Chen Yen Yao Shih Hsin University

Francis D. Tuggle

Jeffrey D. Young

Chapman University

Mount Saint Vincent University

Nicholas W. Twigg

Gail M. Zank

McNeese State University

Texas State University – San Marcos

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Contents Microcredit and Rural Women Entrepreneurship Development in Bangladesh: A Multivariate Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Sharmina Afrin, Nazrul Islam and Shahid Uddin Ahmed Heterogeneity in Consumer Sensory Evaluation as a Base for Identifying Drivers of Product Choice. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Oded Lowengart Group Attributional Style: A Predictor of Individual Turnover Behavior in a Manufacturing Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Laura Riolli and Steven M. Sommer Business Failure Prediction for Publicly Listed Companies in China . . . . . . . . . 75 Ying Wang and Michael Campbell Executive Compensation as a Moderator of the Innovation – Performance Relationship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Kathleen K. Wheatley and D. Harold Doty

Journal of Business and Management – Vol. 16, No. 1, 2010

Afrin, Islam and Ahmed

Microcredit and Rural Women Entrepreneurship Development in Bangladesh: A Multivariate Model Sharmina Afrin Khulna University, Bangladesh Nazrul Islam East West University Shahid Uddin Ahmed University of Dhaka

Microcredit programs have a positive socioeconomic impact on the rural female borrowers of Bangladesh. This study suggests that the microcredit programs do not help the borrowers to develop any entrepreneurial capabilities other than survival. Thus, this paper aims at identifying the factors related to the development of entrepreneurship among rural women through the microcredit programs of providers. A multivariate analysis technique (Factor Analysis) was conducted to identify the factors related to entrepreneurship development. Structural Equation Modeling (SEM) was used to identify the relationship between microcredit programs and the development of rural female entrepreneurship in Bangladesh. Results show that financial management skills are the most important factor and have a significant relationship with the development of rural women and entrepreneurship. Results also show that the group identities of the female borrowers have a significant relationship with the rural entrepreneurship development in Bangladesh. A borrower’s experience from the parents’ families and the limitation of options also lead to the development of entrepreneurship among the rural female borrowers of Bangladesh.

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About 84% of the 140 million people living in Bangladesh reside in rural areas. Half of this population is women. Men who live in the rural areas are primarily engaged in agricultural and related activities. Females however, remain idle in their houses due to a number of social and cultural barriers. They are discouraged from working outside of their homes. This situation can be attributed to the dominant patriarchal society and strong religious influence (Purdah) in Bangladesh (Ahmed et al., 1997; Cain & Khanam & Nahar, 1979). Barriers can also be attributed to the lack of access to funds, the knowledge of agro-based production technology and the market, as well as the support from other family members. Research shows that a large number of rural women in Bangladesh are compelled by macroeconomic factors to enter into the labor market. Hence, the overwhelming majority of women in Bangladesh are poor and also caught between two vastly different worlds: the world determined by culture and tradition that confines their activities inside family homesteads, and the world shaped by increasing landlessness and poverty that drive them outside into wage employment (Chowdhury, 1998).

In the last two decades, microcredit programs have been operated by government (GOs) and nongovernmental organizations (NGOs) in Bangladesh. The prime objective of these programs is to enhance the income-earning potential of female borrowers of rural families, and empower them socially and economically. This program helped rural women working in paddy husking, poultry farming, petty trading (e.g., grocery), pond aquaculture, animal husbandry, weaving, mini-garments, handicrafts, dairy farming, and plant nursery activities (which all tend to be homebased in nature). Microcredit programs substantially contribute to the socioeconomic development of the rural women in this country. Studies show that the microcredit programs have created significant positive differences in the socioeconomic lives of the rural women in Bangladesh (Hashemi, 1998; Schuler, Hashemi & Riley, 1997).

Microcredit programs have also helped the rural women to be involved in home-based economic activities, which in turn, have created enormous opportunities for them to be independent and self-sufficient. Studies also show that the involvement of rural women in home-based economic activities through microcredit programs has a positive socioeconomic impact on their lives, as well as their families. However, it is not apparent whether these programs are actually making the rural female borrowers entrepreneurial or not (Hashemi, 1998).

The positive impact of microcredit programs can be discussed in two ways. Firstly, microcredit programs create employment opportunity, increased productivity, provide economic security, give nutritional and health status, and improve the housing conditions of the rural women. The positive impact on income has increased their asset position and has created wealth for their families (Hulme & Mosely, 1998).

Secondly, microcredit programs create a significant influence on rural women in the area of social empowerment, awareness and education, self-esteem, sense of dignity, organizational and management skills, mobilization of collective strengths, etc. (Pitt & Khandaker, 1996). This positive socioeconomic change subsequently helps them to be more independent and more financially solvent in their families and localities.

Microcredit providers assert that the important impact of their programs is the sustainable development of the socioeconomic lives of rural women. But the reality is

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that the developments are hardly prolonged. Observation shows that rural women are unable to be completely self-reliant even if they are involved in microcredit programs for a long period of time (i.e., 10 to 15 years). This indicates that the credit programs are making the women more dependent on the credit provider instead of making them independent. Thus, concerns have been raised by the researchers about the sustainability of the socioeconomic developments of the rural women. These concerns are very much relevant to the development of rural women and their entrepreneurship in Bangladesh.

The development of rural entrepreneurship in Bangladesh primarily depends on the socioeconomic development of the people. It is necessary to develop rural entrepreneurship in order to foster the development of the capabilities of the borrowers. Once the rural women are self-sufficient, they will be able to initiate their own projects that result in self-independence. In order to encourage rural women’s entrepreneurship in a developing country like Bangladesh, three types of activities might be performed. These activities include stimulatory, supporting, and sustaining (Rahman, 1979, 1999; Katz, 1991a). All three types of activities are partially performed by the microcredit providers that are helping the borrowers to survive. In addition, the degree of the differences in sustainability is significant in both governmental and nongovernmental programs (Amin, 1994b).

For the development of rural female entrepreneurship, stimulatory supports are essential, as the women tend to be unaware of their capabilities. Interaction with the borrowers and the microcredit providers, as well as direct observation, education, and training in selecting products, projects, and other technoeconomic information motivate rural women to be more enthusiastic and entrepreneurial. The next step is to support the entrepreneurs and their different qualifications. Once the women are encouraged to engage in homestead economic activities, they require a different kind of support to start and run their own business. This support can be related to the supply of scarce raw materials, access to different facilities, such as fund, technology, production methods and procedures, the marketing of products, reinvestments, etc.

The question of sustainability comes at the third stage of the entrepreneurial development process. Once the business is run, rural female entrepreneurs require support for sustaining their projects in order to foster growth in the future. These sustaining activities are related to the help in modernization, diversification, additional financing for full capacity utilization, deferring repayment/interest, diagnostic industrial extension, product reservation, new adventures for marketing, quality testing, and improving services. Rural women can benefit from the credit providers by obtaining support facilities, which are helpful in order for them to increase the level of sustainability of their economic activities. Therefore, the research questions of this study are as follows: (i) (ii)

Are the rural female borrowers becoming independent by their involvement in microcredit programs?

Are they gaining any knowledge from the income-generating projects initiated by the credit?

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(iii) If not, how can the women borrowers be made entrepreneurial in operating home-based economic activities? (iv) Is there any difference in the rural female entrepreneurship development between governmental and nongovernmental programs?

This study primarily focuses on how to identify the factors related to the development of entrepreneurship among the rural women borrowers of Bangladesh. The present research also analyzes the sustainability of the socioeconomic impact on rural women, which is termed in this study as rural entrepreneurship development. The specific objectives of the study are as follows: 1. To identify and explain the factors related to entrepreneurship development through microcredit programs.

2. To test the appropriateness of the factors.

3. To develop a model related to the development of entrepreneurship among the rural women through microcredit programs.

4. To recommend a policy framework for the credit providers to develop rural women entrepreneurship in Bangladesh.

Microcredit program and the entrepreneurship development Over the last two decades, microcredit became an important tool for alleviating poverty in Bangladesh (Khandkar & Chowdhury, 1996). The overall success of microcredit programs depends not only on immediate alleviation of poverty, but also on long-term sustainability. Long-term sustainability then depends on accumulation assets (Chowdhury, 2004). In Bangladesh, the Grameen Bank started microcredit programs in 1976 as a pilot project. Now, more than 3000 nongovernmental organizations (NGOs), national commercial banks, and specialized financial institutions operate microcredit programs in Bangladesh. Such programs have proven to be a strong means to alleviate poverty through the social and economic empowerment of rural and disadvantaged women (Puhazhendhi & Badatya, 2002).

Such a group savings program can help the rural women to bring economic security into their lives (Secretary General, UN, 1998). The changing role of women shows a steady upward growth in the economic activities in Bangladesh (Arefin & Chowdhury, 2008). Studies show that female entrepreneurs are doing better in the service sector than in the manufacturing sector in Bangladesh (Begum, 2002).

Microcredit is a structured program under which microlevel loans are given to disadvantaged residents, especially to poor rural women, without collateral security.

It is a group-based and intensively supervised loan program. The uniqueness of this loan program is that there is no requirement of collateral security from the borrowers. Anyone can apply for this credit and is also eligible to receive credit. It is a small loan that varies from Tk. 1,000.00 to Tk. 10,000.00 for each borrower. The purpose of this microcredit program is to give loans for self-employment that generates income and allows them to care for themselves and their family members (Sankaran, 2005).

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There are three C’s to the microcredit program: character, capacity and capital (Yunus, 2003). Character is defined as the historical record of the borrowers such as how a borrower has handled his/her past debt obligations, his/her background, and a borrower’s honesty and ability to repay the loan. Capacity is termed as how much debt a borrower can actually handle, according to their income, and still be able to pay that debt off. Capital is all the current available assets that the borrower has that will also help him/her to repay the loan on time.

Microcredit programs have a significant impact on the income and economic security of the lives of rural women. These programs increase income and help the female borrowers to spend more in order to foster the development of their families.

Such programs also help to increase household income which in turn, improves the consumption patterns and lifestyles of the families (Hossain & Sen, 1992; Navajas et al., 2000). The access to the microcredit program for rural women improves their lifestyles through economic solvency and self-sufficiency; the single most important need of destitute women in Bangladesh (Apte, 1988). Microcredit encourages female borrowers to save for the future, which is an important source of capital accumulation for the rural families and for the economy. Increased income indirectly improves the level of education of the borrowers and the awareness about consumption and sanitation needs as well. The improvement of education among the rural borrowers helps to increase consciousness about their health and the future of the next generation. Credit programs increase productive resources for rural families and their housing conditions and also result in economic security for the borrowers.

The needs of low-income microcredit clients would be best served by highly flexible financial services that enable them to conduct frequent transactions both for small savings and for borrowing at irregular intervals (Sinha, 2003). The main objective of microcredit providers is to create self-employment opportunities for the rural unemployed women. These opportunities are largely in nonfarm related industries. Before joining microcredit programs, many borrowers were employed as day laborers. Now they are more self-sufficient and can work on their own projects, whereas previously they had very little chance to participate in economic activities under the socioeconomic conditions in Bangladesh. Microcredit programs have created the opportunity to reduce their dependency on others in their families. The immediate macroeconomic effect of microcredit is the reduction of labor supply and the raising of the wage rate, given the local demand for labor. Wages remain at the high level if the credit program induces a large demand for food and other local products. Hence, the result of microcredit programs is the increase of placement in rural areas (Ghai, 1984).

Rural wage is a reflection of rural economic conditions. The growth of selfemployment has been achieved at the expense of wage employment (Shahidur, 1998).

The self-employment of borrowers was much higher than the reduction in wage employment in rural areas. The immediate impact of microcredit is on the labor force participation rate and the total hours worked. A survey on Grameen Bank shows that microcredit programs generated new employment for about one third of its members (Hossain, 1986). Most of the new employment was created for the female borrowers.

It has also reduced the dependency ratio in the village families. Rural development is

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based on the investments that promote economic growth in rural areas. Increased farm productivity is the main emphasis for this microcredit programs (Jha, 1991). Ability and efficiency are considered here in order to denote the productivity of rural women borrowers. Through this variable, an inquiry was made to discover whether the production of goods was increased by the borrowers after the involvement in creditfinanced projects. In addition, women’s group memberships seriously shifted overall decision-making patterns from norm-guided behavior and male decision-making to a more joint and female decision-making approach (Holvoet, 2005). In Vietnam, the microcredit program has also reduced the poverty rate of the participants (Cuong, 2008). Microcredit programs have increased the agricultural productivity of small and marginal farm households. The use of high-yielding variety is higher among the borrowers, which helps them produce more products for the locality (Alam, 1988).

The nonfarming activities of Bangladesh include harvesting livestock, poultry, fisheries, trading, and shop keeping. The increase in shop keeping activities has increased the volume of trade in the rural areas. It is reported by the Grameen Bank that 46% of its total trade loans given to the trade sector went to crop trading in 1985, while 22% went to livestock and fisheries. Trading and shop keeping activities have a positive impact on the development of local markets by boosting local production and creating new market opportunities for selling those products locally (Shahidur et al., 1998). A housewife or part-time farmer can link this business to the local production and consumption, as well as outside economic activity. The less fortunate are actually able to work and increase their working days after joining the rural credit programs (Hossain, 1988).

The empowerment of women is another main purpose of microcredit programs.

Empowerment is about a change in favor for those who previously exercised little control over their lives. This change is two-sided. The first side is control over resources (financial, physical, and human), and the second is control over ideology (beliefs, values, and attitudes) (Sen, 1997). The next question is for whom are the empowerment benefits for? Such benefits are undoubtedly for the rural women in Bangladesh who are governed by the two powerful forces of patriarchy and class structures (Amin et al., 1994a). The literature on microcredit and female empowerment provides examples of a number of empowerment measures, including a borrower’s control over loan (Goetz & Gupta, 1996; Montgomery, 1996), knowledge of the enterprises accounts (Ackerly, 1995), mobility, intra-household decision making power, and general attitudes about children’s lives (Amin & Pebley, 1994b; Hashemi et al., 1996). A woman’s control over resources and incidence of domestic violence is also a factor (Naved, 1994).

Social empowerment is essential for the development of poor rural women in Bangladesh. The positive argument is that microcredit programs help rural women to be more socially empowered (Zaman, 1999; Acharya, 1994). Empowerment is characterized as the mobility of women, economic security, ability to make purchases, involvement in major household decisions, political and legal awareness, and involvement in public protest and political campaigns. Women’s participation in such programs increases their ability to visit market places for buying products, medical centers for medication, cinemas for watching movies, other homes in the village, and

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outside villages for more social relations. Participation also enhances the ability of the women to make both small and large purchases. Small purchases include small items used for daily preparation for the family (e.g., kerosene oil, cooking oil, spices), for oneself (e.g., hair oil, soap, glass, etc), or items like ice cream or sweets for the children. The large purchases are usually things like pots and pans, children’s clothing, personal clothing (e.g., Saries), and a family’s daily food.

Microcredit increases the ownership of productive assets for the women. The microcredit programs also influence legal and political awareness and participation in public campaigns. Such campaigns are often for the members themselves, the chairman, the locale, and political leaders. The longer the involvement of a woman in a credit program, the greater the likelihood will be of that woman being empowered.

She is likely to contribute more to not only her family, but to society as a whole in the long run. Credit programs enable women to negotiate gender barriers that increase the control of women over their own lives, improve their freedom in the family, and increase their persuasive power. As a result, credit programs improve the relative positions of women in their families, and in society as well.

Another positive result of microcredit programs is the improvement of nutrition and the health conditions of the rural women and their family members (Srinivasan & Bardhan, 1990; Hossain, 1986). Microcredit increases awareness about the access to modern medication facilities. Tube well water is not normally used by the rural people in Bangladesh. Things such as sanitary latrines and urinals, which to some are everyday conveniences, are a dream for the villagers. One of the major indicators of poverty is the nonavailability of such facilities. The rural credit providers usually try to address this problem in order to improve the quality of life of the rural population.

Studies show that the credit programs have even increased the daily intake of protein and calories for the rural people (Shahidur, 1996). The children of microcredit borrowers tend to have better nutritional health compared to the children of nonborrowers. Rural credit projects help increase the income of the rural women, which leads to higher food security and a better life overall. The ability to spend more on sanitation and the health care activities also is increased by the use of credit programs. Female borrowers can also improve their housing conditions from the money they earn from the credit-supported projects. This is often considered to be an insurance against rural poverty in Bangladesh.

Rural credit also increases education and awareness among the rural women. The involvement of women in income-generation activities changes their attitudes also (Ahmed et al., 1997). With the help of fellow borrowers and loan providers, women often feel the need to further their education (an education that will likely benefit their children, their husbands, and themselves). Credit programs actually increase the likelihood for female education more than for male education (Pitt & Khandaker, 1996; Kabeer, 2001). Due to the increase in income, they then are able to send their children to school also. Microcredit programs create awareness among the rural women through interactions with the group members and health workers. Because women are likely to become more educated after enrolling in a microcredit program, the use of contraception and birth control increases greatly. The exchange of ideas with others, social support for the legitimization of innovative reproductive behavior, and group interactions encourage

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rural women to use more contraception in their day-to-day lives (Amin et al., 1994a).

Microcredit in turn, decreases the level of desire for additional children in rural families.

Once a woman obtains economic security and is able to contribute to her family, she will have the freedom of mobility, freedom from domination by only the family, better control of her body, and birth control options. Mobility in the village, and being able to travel outside of the village, helps women to seek family planning information, and other types of educational assistance (Schular et al., 1997). Women earning independently and contributing to their families are less insecure and less vulnerable to the threat that abandonment by their husbands can pose. Acquiring their own money and other assets makes these women less fearful of the repercussions of having more children, should they choose to do so. Studies show that in almost all cases, the impacts of microcredit are positive in terms of returns on investments, household income, employment in the nonagricultural sector, the labor force participation rate, socioeconomic empowerment, household expenditure and consumption patterns, human capital, and fixed investments (Hossain, 1988; Rahman, 1996).

Rural entrepreneurship is a key to economic development in many countries across the globe (OECD, 1998, 2003; UN, 2004). About half of the population of Bangladesh is women who usually remain idle and unproductive within their homes. They have no method of participation in the economy and no resources for income-generating activities except taking care of their family. Thus, these women can become more productive by getting involved in economic activities. By providing stimulatory and sustaining supports, these women can be made able to initiate businesses and other income-generating projects. Hence, both the developed and developing countries are focusing more on groups such as rural women in order to engage them in incomegenerating activities (Chowdhury, 2002). Countries focus on female entrepreneurship development by demonstrating that financial assistance can lead to reduced fertility and an increase in the economic growth of the country.

Rural entrepreneurship has been defined by different scholars and has also changed over time in Bangladesh (Islam & Mamun, 2000). Studies show the shifting focus of entrepreneurial success factors. Before 1990, the focus was on personal and psychological factors, while after 1990, the focus was shifted to managerial and environmental factors. The common aspects found in the definitions are the entrepreneur, innovation, organization, value creation, opportunity taking, profit or nonprofit, growth, uniqueness, flexibility, dynamism, and risk taking propensity. These aspects can be put into overlapping typologies.

There are five different perspectives of entrepreneurship, which include: (1) an economic function, (2) a form of behavior, (3) a set of characteristics, (4) a small business, and (5) creation of wealth (Ahmed & McQuaid, 2005; Deshpande & Joshi, 2002). In almost all definitions of entrepreneurship, there is agreement that entrepreneurs behaviors include (1) initiative-taking, (2) organizing and reorganizing of social and economic mechanisms, and (3) the acceptance of risk or failure.

Entrepreneurship has a wide range of meaning and has been debated among scholars, educators, researchers and policy makers since the early 1700s when the term was first coined. The idea of entrepreneurship is an elusive concept (McQuaid,

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2002). Since the expectations and perspectives of various stakeholders are different, their views regarding enterprise, entrepreneurship and small business are also different. Rosa (1992) argued that the vagueness of enterprise definition has been to the advantage of both government and academics in the 1990s in their attempts in the UK to change the national culture. Katz (1991b) commented on this debate, saying that small business is a subset of entrepreneurship, while others argue that small business commencement is an integral part of entrepreneurship. Kearney (1996) asserted that enterprise is the capacity and willingness to initiate and manage creative action in response to opportunities, wherever they appear, in an attempt to achieve outcomes of added value. These outcomes can be personal, social, and cultural.

Typically, enterprise involves facing degrees of uncertainty as well. The associated risks are not necessarily financial, but may be physical, intellectual, or emotional.

Innovation Innovation is an important characteristic for an entrepreneur. Austrian economist Schumpeter (1949) defined entrepreneurship as focusing on innovation in four different areas such as new products, new production methods, new markets, and new forms of organization. Anyone who combines inputs in an innovative manner to generate value to the society, results in a creation of some kind of wealth. According to Schumpeter (1949), the use of new combinations defines enterprise and the individuals whose function it is to carry them out. The Industrial Revolution also added to this dimension in the entrepreneurial concept. Audretsch (1995) and Cunningham and Lischeron (1991) emphasized the innovation issue of an entrepreneur. They identified three levels of the term of entrepreneurship: (1) small firms and enterprise level, (2) new firm formation, and (3) innovation and a systemwide coordination of complex production. Innovation and system-wide coordination is also emphasized in other studies (Malechi, 1997; Casson 1990; Casson, 1999).

Behavioral and social scientists also focused on risk-taking, innovation, and initiativetaking capabilities in their definitions of entrepreneurship (Weber, 1930; Hoselitz, 1952; Chell, Haworth & Brearley, 1991, Gartner, 1988). These characteristics are related to the cognitive aspects of entrepreneurship.

Risk-taking Risk-taking is the prime factor for the success of an entrepreneur. When an entrepreneur initiates a business venture, that person has to take risk and face uncertainty. In the 18th century, the French term entrepreneur was first used by Cantillon to describe a ‘go-between’ or a ‘between-taker’ whereby they bought goods at certain prices but sold at uncertain prices and when they purchased such goods at a given price, they could not be sure what price they would be able to sell them for. So, he/she bore the risk and uncertainty of a venture, but kept the surplus after the contractual payments had been made (Ahmed & McQuaid, 2005). In 1971, Peter F. Drucker also supported the view point of Cantillon and said that risk-taking is an important characteristic of an entrepreneur. Ahmed (1981) found an entrepreneur to be a risk-taker since he/she invests money and is involved in making decisions, the success of which brings rewards; and the failure of which

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could lead to the loss of those rewards. An entrepreneur could also face the loss of their principal (i.e., invested money). Therefore, it is very logical to place risktaking as the focal point of entrepreneurship. Hence, the person who takes risks in order to establish new ventures, or who has the capability of taking moderate risks can be defined as an entrepreneur (Ahmed, 1982; 1987). A person can also be defined as entrepreneurial when they have a very strong eagerness to achieve, an idea which was emphasized by McClelland (1961). McClelland (1961) also found that achievement motivation is an important characteristic of a successful entrepreneur. The person who strives to reach the top of the success ladder by taking moderate risks is achievement and motivation-oriented. An entrepreneur should not only initiate new business ventures, but also be able to run the business efficiently. In this regard, Jean-Baptiste Say identified a few dimensions of entrepreneurship, with the ideas proposed by Cantillon: planning, supervising, organizing, and even owning the factors of production. These activities are primarily related to business management.

Opportunity-seeking Another characteristic of an entrepreneur is opportunity-seeking. Stevenson (2000) explained that entrepreneurship is an approach to management that can be defined as the pursuit of opportunity without regard to the currently controlled resources. He examined five critical dimensions of business practices: strategic orientation, commitment to opportunity, control of resources, management structure, and reward philosophy, all of which are related to entrepreneurial development.

Entrepreneurship is the pursuit of a discontinuous opportunity involving the creation of an organization with the expectation of value-creation for the participants.

The entrepreneur is the individual or team that identifies the opportunity, gathers the necessary resources, and is ultimately responsible for the performance of the organization. As a catalyst agent, an entrepreneur creates the forces of change and utilizes it in accelerating the socioeconomic value-addition of a country through resource utilization, employment generation, capital accumulation, and industrialization (Rahman, 1979; 1996). Hence, self-employment is the result of the development of entrepreneurship. Entrepreneurs create employment for themselves and for others in order to work with innovative and economic-centered projects.

People who are self-employed and have ownership of the business are called entrepreneurs (Chowdhury, 2002). They are the owners of the business enterprises as well. In this regard, women entrepreneurs are defined as conventional entrepreneurs, radical proprietors, and domestic traders (Begum, 2003).

Therefore, it is evident that some definitions of entrepreneurship are concerned with business development aspects, while some are concerned more with the behavioral aspects of the entrepreneur (Ahmed & McQuaid, 2005). Business development aspects can be defined by opportunity seeking, initiative taking for establishing new business venture, and creating wealth. While, in contrast, behavioral aspects are related to achievement motivation, risk-taking propensity, inner urge to do something valuable for oneself and for the society as a whole. Essentially, entrepreneurship is the dynamic process of creating incremental wealth, which is

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created by the individual. This can be achieved by adopting risks in terms of equity, time, and career commitment. It is the process of creating something new by devoting the time and effort, assuming the accompanying financial, psychic, and social risks, and receiving the rewards of monetary, personal satisfaction, and independence.

Hence, entrepreneurship can emerge through the actions of four factors. These are a support system, socio-sphere system, resource system, and a self-sphere system. First, a support system includes structure, organizational goals /policies, activities, technical competence, organizational climate, and style of functioning. A sociosphere system includes value orientation (which is defined by work) independence, initiative, innovations, and risk-taking norms. Third, a resource system, includes manpower, market, raw material, transport communication, other industries and enterprises, technology, and technical manpower. A self-sphere system includes motivation and skill where motivation is explained by personal efficiency, coping capability and skill is defined by a selection of product/process, project development, and by establishing and managing enterprises.

The emergence of women entrepreneurs in a society depends mainly upon various economic, social, religious, cultural, and psychological factors (Habib, Roni & Haque, 2005). The motivations for starting a business by rural women are significant and include earning an attractive source of income, enjoying a better life, the availability of loans, and general security.

One of the key factors for the development of female entrepreneurship in Bangladesh is recognition (Saleh, 1995). When activities are performed by family members or by neighbors, rural women feel encouraged to participate. Therefore, whatever rural women do, it must first be recognized by their husbands, then by the family members, then by others. The type of family in the rural areas has an impact on the development of rural women entrepreneurship. Studies show that rural women that come from a nuclear family (a family consisting of a father, mother, and their children living under the same roof) tend to become more entrepreneurial than if they came from a joint family (Surti & Sarupia, 1983). The level of family liability can also attribute to this.

The age of the rural women is another factor that affects the development of rural female entrepreneurship. Studies show that the majority of rural female entrepreneurs start a business at the age of 20-29 years (Punitha, Sangeeta & Padmavathi, 1999). At this age, they no longer have many family bindings, and they can work freely in their business projects. There are many places in Bangladesh where there is no real economic development, but because of the presence of the rural microcredit programs in those areas, rural women are becoming more enthusiastic about initiating new economic projects. Therefore, properly supervised microcredit can help to improve socioeconomic conditions of these women in Bangladesh (Begum et al., 2005).

However, a lack of family and community support, an ignorance of available opportunities, the lack of motivation in initiating new projects, shyness and apprehensiveness to get involved with economic activities, and a preference for traditional occupations are all factors that inhibit the promotion of grassroots entrepreneurship development among rural women (Rao, 1991).

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Methodology The Bangladesh Rural Development Board (BRDB) is the largest service-oriented government institution and is directly engaged in rural development and poverty alleviation activities in Bangladesh. The ASA was developed in an atempt to gradually eradicate poverty from society in Bangladesh. BRDB started its credit activities in the study area in 1993, while the inception of the ASA was in 1996. The target people of BRDB for credit programs are poor farmers and rural women who have at least some productive assets. On the other hand, the focus of the ASA is to give credit to the poor women who have no productive assets. ASA provided microcredit to 1,200 women and 295 for the BRDB study area. BRDB gave loans for the purpose of poverty alleviation primarily in the projects of agriculture, fish culture, poultry raising, and petty trading. ASA gave credits for poverty alleviation in the areas of paddy husking, rice frying, running small hotels, petty trading (i.e., vegetables trading, molasses trading, etc.), transportation, purchasing cows, fish culture, and raising poultry. The minimum amount of credit given by BRDB is Tk. 2,500 and the maximum is Tk. 7,000. The ASA ranged from Tk. 3,000 to Tk. 12,000. Along with microcredit, the ASA also has microinsurance services. BRDB does not offer an insurance policy.

However, BRDB does provide advice in family planning along with microcredit, but the ASA does not. The ASA is significantly more strict about installments that are supposed to be given every week. BRDB’s loanees repay monthly installments, which is less strict in comparison to the ASA. Characteristics of the Respondents The respondents of this study are rural female borrowers of two leading NGOs, the ASA in the private sector and the BRDB in the public sector. All the borrowers of BRDB are Hindu, while the borrowers of the ASA are comprised of 77.60% Muslims and 22.40% Hindus. The age distribution of the borrowers of the ASA and BRDB is different. About 29% of ASA’s borrowers are between the ages of 20 and 25, followed by 30 to 35 years (24.10%), 35 to 40 years (22.40%), 25 to 30 years (18.40%), and 15 to 20 years old (6.10%). On the contrary, 49% of the borrowers of BRDB are between 35 and 40 years old. About 21% of this group is between the age of 25 and 30 years followed by 20 to 25 years (15.00%), and 30 to 35 years (15.00%) (Table 1). The average age for the borrowers of the ASA is 29 years and for the BRDB is 32 years.

Table 1: Age Distribution of the Microcredit Borrowers

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About 88% of the borrowers of the BRDB and 98% of ASA are married. The difference between the educational qualifications of the borrowers of the ASA and BRDB has been observed. About 33% of the ASA’s borrowers are self-literate. They become literate after joining microcredit programs to manage financial matters. About 29% of them are primary educated, followed by illiterate (22.00%), and secondarily educated (16.00%).

About 36% of the borrowers of BRDB are secondarily educated. Those who are illiterate are also similar (36.40%). The self-literate borrowers in BRDB are 15.20%, and primary educated borrowers are 12.10% (Table 2). This educational status indicates that the female borrowers were self-literate after their involvement with credit programs.

Table 2: Educational Qualifications of the Microcredit Borrowers

The training status of the rural female borrowers shows that the majority of the respondents have no training in technology or marketing. More than 75% of the borrowers in both the groups did not receive any formal training from the credit providers. Only 18% of the borrowers of ASA and 12% of BRDB have received technical training from anything other than loan providers. Only 8.20% of ASA’s borrowers and 12.10% of BRDB’s borrowers obtained nontechnical training from the credit providers. The nature of this training is only to give ideas about technology and other aspects of the business (Table 3). This study noted that ASA and BRDB have no arrangement for organized training in the study area.

Table 3: Training Status of the Microcredit Borrowers

Sample Design and Determination Bangladesh is divided into six divisions. To select the sample respondents, the second level administrative unit of Bangladesh, the Khulna division, was selected.

Under this division, Khulna is an important district (a district refers to the third administrative unit of Bangladesh). A group of Thanas constitutes a district. Under this district, there are 10 Thanas: Khulna Sadar, Batiaghata, Dacope, Daulatpur, Dumuria, Koyra, Paikgacha, Phultala, Rupsa, and Terokhada. A Thana is also called Upa-Zila. It is the fourth level administrative unit of Bangladesh. It consists of a group of Unions,

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and every Union is formed with a group of villages. The reason for selecting the Khulna district is that the most densely populated district is the Khulna Division.

There are about 2.38 million people living in this district with approximately 375,000 households (BBS, 2005). About 50% of population in this district is female.

Batiaghata Thana was selected as the sampling area which is located adjacent to Khulna City. This Thana consists of 7 Unions, with 159 villages. The population of this Thana is 128,184, with 516 persons per sq. km. The land is about 1,468.38 acres. Only 37.70% of the population is literate. There are 23,698 families in this Thana. The total number of dairy and poultry farms is 12 and 57 respectively. There are 12,088 sanitary latrines and 1,024 tube wells in the Thana. The numbers of deep tube wells are 896.

Most of the families are involved in agricultural farming followed by petty trading, fishing, pottering, paddy husking, gold-making business, kamar, and spinning. There are 26 village hat/bazaars in the Thana.

Borrowers who are already engaged in 3-10 years or more with the credit programs are used as respondents. Sample respondents were selected by using two sampling methods: the purposive sampling method and the random sampling method.

Purposive Sampling Method This method was used to select the types of activities of rural female borrowers including fish culture, paddy husking, poultry farming, petty trading, grocery, animal husbandry, weaving, handicrafts, dairy farming, and plant nursery. All the female borrowers of BRDB were selected from the Rajbadh village, and 25% of the borrowers from the ASA were selected purposively from Hatbati, Wazed Akundi Nagar, Sachibunia villages who have been involved in microcredit programs. The individual selection was on a random basis to reduce the biases of the sample selection in this study.

Three criteria were used to select two Unions of Batiaghata Thana for this survey: (1) the intensity of credit programs, (2) the density of population, and (3) the intensity of poverty. Under each Union there are about 14 to 17 villages. One village named Rajbadh was selected for interviewing the borrowers of BRDB. Sachibunia have been selected for interviewing the borrowers of ASA. ASA and BRDB are intense microcredit programs in these selected villages because of large population size and high poverty.

The sample size was determined by using a formula suggested by Yamane (1967).

The following formula was used to determine the sample size of the study: n = N/1+N(e)2 where, n = sample size, N = population, e = precision Levels, and where Confidence Level is 93%, and P = .50 (degree of Variability).

The degree of variability in the attributes being measured refers to the distribution of attributes in the population. The more heterogeneous a population, the larger the sample size required to obtain a given level of precision. The less varied (more

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homogeneous) a population is, the smaller the sample size. Note that a proportion of 50% indicates a greater level of variability than either 20% or 80%. This is because 20% and 80% indicate that a large majority do not or do, respectively, have the attribute of interest. Because a proportion of .5 indicates the maximum variability in a population, it is often used in determining a more conservative sample size. The sample size may be larger than if the true variability of the population attribute were used. The total number of female borrowers interviewed was 246, 198 of which were from the ASA and 48 from BRDB. Designing Measurement Instruments This study was based on primary data collected from the survey of rural women. A survey was conducted among the rural female borrowers of BRDB and ASA to collect information about the development of rural women entrepreneurship through microcredit programs, with the help of a structured questionnaire. A structured questionnaire in a 5-point scale was developed for the variables relating to the development of rural women entrepreneurship. A five-point scale ranging from 1 to 5, with 1 indicating strongly disagree and 5 indicating strongly agree, was used in this regard. This study used 40 entrepreneurship-related variables to explain the chance of rural women for being entrepreneurial-identified from the literature. The dependent variable is explained by four variables: independence, ability to make complex decisions, ability to seek and grasp opportunity, and ability to take risk and initiative.

The survey has been conducted with the assistance of MBA students from Khulna University, who explained the questions to the borrowers in detail. The interviewers were trained on the variables representing the questionnaire for data collection before starting the interview. Borrowers were surveyed from January 2006 to March 2007.

Data Analysis Along with descriptive statistics, multivariate analysis techniques including factor analysis and Structural Equation Modeling (SEM) were used to analyze the relationships of the variables relating to the development of rural female entrepreneurship. A principal factor analysis with an orthogonal Varimax rotation, using the SPSS statistical package, was performed on the survey data and was used to separate the factors for developing entrepreneurship. The relationship of entrepreneurial factors with the overall entrepreneurship development is assessed through the Analysis of Structural Equation Modeling by using Amos version 4.

It was the ultimate intention of this study to test the conceptual model developed from the theoretical analysis and to estimate the parameters for the structural equation model. Hence, data were analyzed through the SEM using Analysis of Moment Structures (AMOS) to perform path analysis. Amos’s method of computing parameter estimates is called maximum likelihood. Hypothesis testing procedures, confidence intervals, and claims for efficiency in maximum likelihood or generalized least squares estimation by Amos depend on certain statistical distribution assumptions. First, observations must be independent. Second, the observed variables must meet certain distributional requirements. For instance, it will suffice if the observed variables have a multivariate normal distribution. Amos implements this general approach to the

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SEM data analysis, also known as analysis of covariance structures, or causal modeling.

SEM is a computer program for estimating the unknown coefficients within a system of structural equations, and is one of several computer-based covariance structure models for conducting such analysis. LISERAL or Lineral Structural Relations, is a special purpose statistical software package that estimates structural equation models for manifest and latent variables. AMOS, like LISREL, is useful when the researcher desires to explore the causal relationships among a set of variables. The method is called covariance structure analysis because the implications of the simultaneous regressions are studied primarily at the level of correlations or covariances. Typically, a covariance structure model is specified through a simultaneous set of structural linear regressions of particular variables on other variables. The field of covariance structure analysis actually covers a wide range of topics, including confirmatory factor analysis, path analysis, and simultaneous equation and structural equation modeling.

Much research in the social sciences including business involves the measurement of latent constructs. The method is useful for analysis of structural equations involving experimental data. In business applications, theoretical constructs are typically difficult to operationalize in terms of a single measure, and the measurement error is often unavoidable. As a result, given an appropriate statistical testing method, the structural equation models are likely to become indispensable for theory evaluation in business research. The approach provides a means for examining causal relationships among multiple variables, the magnitude of hypothesized relationships, and the extent of measurement error of constructs in application of experimental designs (Bagozzi, 1977). When researchers attempt to measure constructs such as perceptions to something, they are attempting to gauge unobservable cognitive processes with measurement devices that can only approximate the latent constructs of interest. This process is typically fraught with measurement error. Because of their ability to control or allow for such measurement error when estimating the relationships between variables, covariance structure models have been gaining in popularity in business studies (Bagozzi, 1980, 1981). Howard (1977) suggests in this regard that structural modeling sharply highlights the intimate, powerful, mutually reinforcing relationship between theory and measurement. In this study, it was perceived that structural equation modeling would be the best approach to understand the relationships between the constructs.

In this study, covariance and structural modeling was performed in two distinct stages. First, observed variables are linked to unobserved variables through a Confirmatory Factor Analytic (CFA) model. CFA is a means of discovering an underlying structure in one’s data, given some prior theoretical or empirical information. The set of connections between the observed and unobserved variables is often called the measurement model. The measurement model specifies how the latent variables are measured in terms of observed indicators and explicitly introduces measurement error. Second, the causal relationships between the resulting latent variables are examined in a structural equation model. The model component connecting the unobserved variables to each other is often called the structural model.

The structural equation model specifies the causal relationships among the latent and unobserved variables.

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Results of Factor Analysis A Multivariate Analysis technique, factor analysis, was used to identify the factors responsible to development women entrepreneurship in the rural areas of Bangladesh with the support of microcredit. A principal factor analysis with an orthogonal rotation using the SPSS statistical package was performed on the survey data and was used to separate the factors. Factor analysis of 40 variables in the rural women entrepreneurship survey identified 13 main factors that account for 75.74% of the variance in the data (Table 4). The initial factor structure derived from varimax rotation extracted thirteen factors. Scrutiny shows that some of the factors were unclear, particularly when several items loaded simultaneously on more than one factor. All of these factors are reflected in Table 4.

Table 4: Women Entrepreneurship Development Factors

The first factor, financial management skill and group identity, accounts for 18.16% of the variance in the data. The development of financial skill and the creation of group identity by the microcredit is the most important factor for the development of rural women’s entrepreneurship in Bangladesh. The eigenvalue of this factor is 7.26.

Financial management skill and group identity are related to six variables, including increased family relationships and cohesiveness (0.536), involved rural women-folk (0.822), development of financial management skills (0.866), realized self and collective identity (0.880), getting adult education (0.621), and developing awareness of health and women’s rights (0.696). A relatively higher level of factor loading of almost all the variables indicates that these variables are very important to constitute the rural women entrepreneurship development factor. The communality values for these variables are 0.705, 0.818, 0.835, 0.901, 0.742, and 0.630 respectively. The higher level of communality of the variables associated with financial management

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skill and group identity indicates that each variable is very much related to the factor.

The next important factor is creative urge and self interest with an eigenvalue of 3.57. The variance of this factor is 9.73%. It indicates that creative urge and self interest is an important factor for the development of rural female entrepreneurship.

Seven variables constituted this factor. The variables are creative urge (0.843), selfinterest and self dependent (0.815), inadequacy of family supplement income (0.538), family support is required (0.534), attractive source of income (-0.441), competent to take and use loan (-0.426), and getting educated (0.416). These variables are highly important for determining the entrepreneurial status of the rural women borrowers.

The communality of the variables is also higher.

Family funds and female involvement is the third important factor for the rural female entrepreneurship development with an eigenvalue of 2.76. This factor explains 6.10% of the variance. The women borrowers are concerned with self-independence (0.852), family peace (0.787), gaining social prestige (0.664), ability to accumulate family fund (0.525), and alleviation of gender discrepancies (0.488). Another entrepreneurship factor is employment of family members and the creation of new jobs with eigenvalue of 2.75 and variance of 6.87%. This factor is constituted by four variables: can employ others (0.827), new work and work environment (0.761), training (0.758), and scope to utilize own skills and talents (0.549). Independence and keeping oneself busy is the fifth factor for the development of rural women entrepreneurship in Bangladesh. The eigenvalue and the variance of this factor are 2.205 and 6.51% respectively. The variables forming this factor includes doing something independently (0.920), can keep myself busy (0.825) and career and family security (-0.447). Family experience and option limitation is the next important factor for the development of rural women entrepreneurship in Bangladesh. Two variables constituted this factor such as, experience and competencies (0.835) and no other option available (0.764).

Other factors like knowledge of business, economic necessity of the family, self confidence, technical knowledge of business, money earning, unable to find suitable work or job, and contribute to the economic growth were found not significant to build the model.

Results of Structural Equation Modeling (SEM) Analysis The data of this study were analyzed in two stages. First, the measurement model was assessed to confirm that the scales were reliable. Second, when the reliability of the measures had been established, the structural model was tested. This testing determined the strength of individual relationships, goodness of fit of the model, and the various hypothesized paths.

The first step of the analysis was a test of the measurement model. Objectives of this test were: (1) to contain the validity and reliability of measures, and (2) to select the best subset of observed measures for use in testing the structural model. The data depicted a normal distribution with acceptable skewness and kurtosis values. Coefficient alpha was computed for each set of observed measures associated with a given latent variable, and a Confirmatory Factor Analysis (CFA). Alpha values of each item in each dimension

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were performed separately and were found acceptable. Estimation of Measurement model for the six constructs (factors) of interest was performed using AMOS 4.01.

The results of overall structural model fit as indicated by the chi-square statistic, was significant chi-square = 707.80; df = 168; p = 0.000 (Table 5). The overall fit of the confirmatory factor analysis model to the sample variance/covariance matrix, as measured by chi-square, provides a test of the overall reliability of observed measure (Bagozzi, 1980). The statistic is computed under the null hypothesis that the observed covariances among the answers came from a population that fits the model. A statistically significant value in the goodness of fit test would suggest that the data do not fit the proposed model, i.e., that the observed covariance matrix is statistically different than the hypothesized matrix. The assumptions required to employ chi-square as a significance test (in support of the hypothesis that the predicted covariance matrix does not differ from the sample covariance matrix) are typically violated in most covariance structure analysis. Accordingly, when the results of chi-square analysis are favorable, it is best to say that the fit between predicted and observed covariance matrices is “acceptable” rather than “significant” (Joreskog & Sorbom, 1986). In this study, however, both terms are used interchangeably to mean “acceptable”.

Table 5: Fit Indices of the Model

The fit of the structural model was estimated by various indices and the results demonstrated good fit. For models with good fit, most empirical analyses suggest that the ratio of chi-square normalized to degree of freedom (chi-square/df) should not exceed 3.0 (Carmines & Mclver, 1981). In addition, the obtained goodness-of-fit (GFI) measure was 0.809 and the adjusted goodness-of-fit (AGFI) measure was 0.737 respectively, which are both higher than the suggested values. The other two indices of goodness-offit (GFI), the normalized fit index (NFI), and the comparative fit index (CFI) are recommended to exceed 0.90. The results also meet these requirements. Finally, the discrepancies between the proposed model and population covariance matrix, as measured by the root mean square error of approximation (RMSEA), are in line with the suggested cutoff of 0.08 for good fit (Byrne, 1998). The complete model of microcredit program and the development of rural women entrepreneurship is shown in Figure 1.

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Figure 1: A Model for the Development of Rural Women Enterpreneurship through Micro Credit Program

Table 6 shows that the relationships of the factors that built the model for the women entrepreneurship development in Bangladesh through microcredit programs.

After identifying the female entrepreneurial development factors, a hypothesis was developed for each construct and the important factors that were significantly associated with the rural female entrepreneurship development.

Table 6: Standardized Regression Weights

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Financial Management Skill and Group Identity In Hypothesis 1 (H1), it was predicted that the financial management skill and the group identity have a direct and positive relationship with the female entrepreneurial development (WED) in rural areas of Bangladesh. It was presumed that higher financial management skill and group identity will lead to higher level of encouragement among the rural borrowers for taking new initiative of business. The results show that the direct effect of financial management skill and group identity on the development of women entrepreneurship is positive and significant (β = 0.24, p < 0.008). This result indicates that the higher the financial management skill and better the group involvement, the higher the chance of being entrepreneurial. In Bangladesh, many people who live in rural areas are illiterate, including the female borrowers.

Therefore, they face the problems of financial planning, financial record keeping, financial calculations, and the identification of profits, etc. In addition, there is also a group effect on the development of women entrepreneurship in Bangladesh.

Family Experience and Option Limitation Hypothesis 6 (H6) states that family experience and option limitation has a direct positive effect on the development of rural female entrepreneurship in Bangladesh.

This means that if the rural woman has a business orientation from her parent’s family and if she has some fund from the microcredit providers, she will take initiative to do business or she will initiate economic projects which will help her to earn money and obtain social status. This hypothesis was supported by the analysis that provides positive and significant values (β = 0.13, p < 0.11). Although this factor is significant at 11%, it’s an important factor to be entrepreneurial for the rural women through microcredit programs. Since this study is the first of its kind, this result is acceptable.

Independence of the Women and the Urge to Keep Busy In Hypothesis 5 (H5), we hypothesized that the independence of the rural women and the urge to be kept busy can make them entrepreneurial which has a positive and significant effect on female entrepreneurial development in the rural areas of Bangladesh. This indicates that more independence and more enterprising by a rural women will lead to a higher level of entrepreneurship. The results support this hypothesis and positive and significant (β = 0.08, p < 0.13). We also accept this result on the grounds that the significant level is 13%.

Other factors In Hypothesis 2 (H2), we predicted that the relationship between creative urge and self-interest and the rural female entrepreneurship is positive and significant. But the results show that the relationship between these constructs are negative and not significant (β = -0.063, p > 0.38). This indicates that if there is a change in the creative urge and self-interest factor, it will not lead to the development of rural women entrepreneurship through microcredit programs in Bangladesh. That means through microcredit programs, the creative urge and self-interest is not developed among the rural female borrowers, as it depends on environmental factors which are unfavorable for the rural women in Bangladesh.

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In Hypothesis 3 (H3), it was predicted that the relationship between family funds and involvement in business and rural female entrepreneurship is positive and significant. However, the results show the opposite situation in this regard (β = -0.120, p > 0.21). This indicates that the change in financial status and female involvement with money matters will not change in the entrepreneurship development characteristics among the rural women in Bangladesh. If the rural families are financially solvent, they will not lean towards doing business in Bangladesh where it is culturally discouraged.

In Hypothesis 4 (H4), it was perceived that there is a positive and significant relationship between a new job and the employment of family members with rural female entrepreneurship development. But the results show that there is no significant relationship between the two constructs (β = 0.035, p > 0.67). This indicates that employment of family members and the new job will not develop any entrepreneurial characteristics among the rural female borrowers through microcredit programs.

Conclusions and Recommendations It is generally perceived that the microcredit program helps to develop socioeconomic status of the rural women in Bangladesh. In addition, it is perceived that microcredit is helping not only to bring socioeconomic changes, but also to make the borrowers entrepreneurial. This study tried to resolve these questions by constructing a model which was supported by the results of multivariate analysis.

This study identified that factors like the financial management skill of the borrowers and group identity, experience from the fathers’ family and option limitation, independence of the rural women, and the urge to make them entrepreneurs have a significant relationship with the rural female entrepreneurship development in Bangladesh. On the other hand, factors such as creative urge and self interest, family fund and previous involvement in business, and job and employment of the family members are not significantly related to the rural women entrepreneurship development. SEM analysis shows that among seven hypotheses, only three hypotheses are supported by the analysis. This indicates that other factors are not appropriate for the development of rural women entrepreneurship in Bangladesh.

The most important finding of this study is that the financial management skill and the group identity of the borrowers have a direct and significant relationship with the development of rural women entrepreneurship (WED) through microcredit programs.

When rural women receive financial support from the microcredit providers, they feel encouraged to involve themselves in the financial projects that subsequently increase the financial management skills of the borrowers. Microcredit also provides group identity to the rural women. When women acquire knowledge of financial management and get group identity, they become more enthusiastic to initiate new business projects. These significant relationships indicate that if the microcredit borrowers can enhance this skill among the rural female borrowers, it would lead them towards the development of entrepreneurship. As a result, the borrowers will be able to stand on their own feet.

The second important finding of this study is that the experience from the parent’s

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family of the borrower and option limitation have a direct positive impact on the development of rural women entrepreneurship in the rural areas of Bangladesh. This means that if a rural female has a business orientation from her parent’s family and at the same time, has some funds at her disposal, she will initiate new business or economic projects which will help her to earn a profit and obtain social status as well.

The third important finding is that the rural women who are independent by nature and would like to keep busy with economic activities could be identified by the borrowers. This section of rural female has the highest potential to be entrepreneurial.

This study supports this observation for the rural women borrowers in Bangladesh.

The main problem of any small business in Bangladesh is the management skills related to financial affairs of the business. The businessmen or entrepreneurs are unable to make financial plans and maintain financial accounts of the business because of their illiteracy. Most of the people in rural areas are illiterate in Bangladesh and women are in a more disadvantageous position in this regard. Hence, microcredit providers should give importance to the development of the financial management skills of the borrowers and create group identity of the borrowers. They also should identify the rural women who have their family experience and no other options but to do business or get involved with loan providers. Loan providers should also be mindful of the fact that the rural women of Bangladesh have an independent mentality and they would like to take on the challenge of being entrepreneurs. Therefore, to design and implement a loan program, microcredit providers should keep this independence in mind. If these aspects are properly addressed by the loan providers, rural female borrowers will be more entrepreneurial and as a result, the borrowers will be able to stand on their own feet and rural women entrepreneurship will be developed in Bangladesh.

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Ackerly, B. (1995). Testing Tools of Development: Credit Programs, Loan Involvement and Women’s Empowerment. IDS Bulletin, 26(3): 56-68.

Ahmed, S. M., Adams, A., Chowdhury, A. M. R. & Bhuiya, A. (1997). Income-Earning Women from Rural Bangladesh: Changes in Attitudes and Knowledge.

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Ahmed, A. & McQuaid, R. W. (2005). Entrepreneurship, Management, and Sustainable Development. World Review of Entrepreneurship, Management and Sustainable Development, 1(1): 6-30.

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Heterogeneity in Consumer Sensory Evaluation as a Base for Identifying Drivers of Product Choice Oded Lowengart Ben Gurion University

In this paper we propose a multiattribute choice modeling approach to explore the heterogeneity in the saliency of product attributes in the process of a product choice that is based on sensory evaluations. We demonstrate this idea by using data about consumersâ&#x20AC;&#x2122; red wine evaluation. Such an approach enables managers to add knowledge about consumers' needs and wants beyond traditional art and the experience of wine makers into the process of designing a product. We utilized a choice model that enables us to identify such attributes and, simultaneously, to estimate the choice probabilities for each different wine. Our results, based on four different red wines, indicate that based on their sensory evaluation, consumers tend to utilize several wine attributes in their choice process. The saliency of these attributes varies in different consumer segments such as gender and frequency of wine drinking.

Choosing among products characterized by many different types of attributes is difficult for consumers, as it requires a considerable cognitive effort. This is particularly true when the product category offers many different alternatives with various tastes. In such cases, consumers can rely on extrinsic (i.e., signals of quality such as brand name or package) or intrinsic (i.e., taste of the product) product characteristics to choose among alternative products. The latter might be more reliable than the former, as consumers can develop their own direct evaluation criteria (their own taste) and test that product. Wines, for example, provide consumers with a wide variety of products with different tastes, qualities, prices, and other related attributes.

Choosing a specific wine, therefore, is a complex task for consumers. Furthermore,

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verifying the qualities of such products is usually possible only after actually using the product. Moreover, due to the wide selection of possible alternative products, consumers cannot be sure they made the right decision even after consuming the product. This makes wine a typical credence product – products that are difficult to evaluate before as well as after consumption (Darby & Karni, 1973), as opposed to search products (that can be evaluated prior to consumption) and experience products (that can be evaluated after consumption) (Nelson, 1974). It is logical to expect that consumers cannot solely rely on their own taste test for wine choice, since, in many purchasing situations, this option is not easily available. As a result, other methods of reducing uncertainty can be used by consumers. For example, Lynch and Ariely (2000) found that electronic shopping can reduce search costs and price sensitivity, while maximizing the transparency of quality information specifically for a differentiated product such as wine. Nevertheless, a taste test is still the more reliable selection criteria for choosing such products when possible.

Consumers can use their own sensory evaluation to verify product qualities, when possible. Shepherd and Towler (1992), for example, argue that experience (and valuation) of consumers with food products is shaped by sensory attributes and particularly, by taste. Koivisto and Sjóden (1996) argue that taste is a good explanatory variable for food choices. As in many aspects of consumer products, there is heterogeneity among consumers when it comes to the exact combination of marketing mix variables that fit their needs. Heterogeneity stemming from personal differences (e.g., gender) geographical, behavioral (e.g., experience with the product) and other sources can have an effect on the desired product characteristics and preferences for it.

For example, Scarpa, Philippidis and Spalatro (2005) found a variation in choice that is associated with socioeconomic variables in several food products. Hu et al. (2004) found gender differences in a latent class model analysis of choice of genetically modified ingredients of food products. The same type of difference was also found in wine (Goodman, Lockshin & Cohen, 2008).

The current study explores how the effect of consumer sensory evaluations on the choice among different products can provide diagnostic information about product modification, or new product development. In order to demonstrate this approach, we analyze red wines, where sensory evaluation plays a significant role, as this product is characterized by a variety of attributes that are evaluated by different sensors (e.g., taste, smell). To expand our understanding about the potential difference among consumer segments with respect to such product modifications, we explore two different sources of potential heterogeneity in consumers’ evaluation: gender differences (personal source) and frequency of drinking wine (behavioral source).

To address the objective of this paper, a probabilistic choice model formulation is used to identify the salient product attributes in choice formation. The results of the analysis reveal that such attributes can be identified and consumers' heterogeneity in sensory evaluation that is reflected in the saliency of the wine attributes exist across different consumer segments. That is, a segment-to-segment difference is revealed. Better understanding of such a pattern of results can provide a better understanding of sensorybased evaluation methods and scenarios and, at the same time, provide insight into the type of product (wine) managers should develop to better cater to their target markets.

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The rest of the paper is organized as follows. In Section 2, we present the background for this study and present the conceptualization of the research at hand.

In Section 3, the methodology used in this study is presented. This is followed by Section 4, where the results of the analysis are laid out. Section 5 provides the discussion, conclusion, and summarizes the study.

Problem Conceptualization Traditionally, winemakers make wines that preserve the qualities of the different wine varieties and at the same time, attempt to create a wine that will appeal to the palettes of wine consumers. In a sense, it is an art of blending two aspects of product creation into the resulting outcome: wine taste. Research aimed at improving grape quality in the agricultural area is grounded in extensive accumulated knowledge that can provide wine growers with better agro-technical methods to improve the cultivation of their vineyards (Weaver, 1976; Seguin, 1986) or improve the technology of wine making (Pretorius & Bauer, 2002) and bottling (Prescott et al., 2002).

Substantial research has also been conducted on the other domain of importance to winemakers; consumer preferences. Such research is mainly concerned with taste tests and the development of information cues that try to assist consumers in identifying and selecting wines (Johnson et al., 2001). The latter includes the effect of countries and regions within a country on the evaluation of wine (Orth, Wolf & Dodd, 2005; Skuras, 2002) and branding (Thode & Maskulka, 1998; Walker, 2003). Another type of research has focused on consumer heterogeneity with respect to wine preference. This has taken the form of appropriate methodology for heterogeneity detection (Mueller, Francis & Lockshin, 2009; Cordelle, Lange & Schlich, 2004) or consumer demographic effects (Scarpa et al., 2005; Hu et al., 2004), among others. As noted earlier, tasting the wine is probably the best method consumers can use in selecting a wine, as it is probably more reliable in examining wine qualities. Indeed, winemakers frequently use taste tests to persuade consumers to test different wine blends for qualities such as aroma, bouquet, after taste, and other characteristics.

Sensory Evaluation and Preference Since wine can be considered as a credence quality type of good, consumers use a variety of direct and indirect product attributes to evaluate the product since consumers cannot be sure they made the right decision. To address these difficulties, wine producers, for example, try to influence potential consumers by reducing some of the uncertainty concerning their wines. To this end, producers create several wine brands for the same varieties based on the quality of the grape juice, which could be a signal or self-declaration of quality. Other indicators are vintage, winery and reputation, geographical location, and other external characteristics that may classify the wine. All these indicators serve as a proxy to the product quality. Since wine quality is marked by relatively high heterogeneity, even when dealing with the same variety and the same production year, the best tool for consumers to evaluate the quality of the wine is still their own tasting experiment. It is very difficult for consumers to taste all wines they might like to buy before an actual purchasing has

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taken place. Wine marketers usually provide sampling procedures to foster such testing. This procedure provides marketers with primarily two types of information from consumers: 1) the opportunity to gain insight into the overall preference for a certain wine, and 2) evaluation of the different wine qualities based on consumers' sensory evaluation (Lesschaeve, 2007). Relating the information from the second source to the first (attributes to preference) can reveal more insight about the formation of consumer preferences. This is particularly important to winemakers, as it will allow them to lower the number of blends they create to better target the desired preferred wine. In other words, identifying attributes that drive consumer preferences can indicate to winemakers what aspects of the wine need to be changed to increase its preference among consumers. Wine testing and a short follow-up questionnaire completed by consumers after tasting the wine regarding product attributes that are evaluated by their taste and smell sensors can indicate what kind of product attributes create a preferred product.

We frame the consumer decision of whether to buy a certain wine in this study to the sensory evaluation case. The decision about such a purchase, therefore, depends on consumer perceptions of these sensory-based product attributes. On regular purchasing occasions, consumers are faced with more than a single alternative of wine from which they can choose. The purchasing decision in such real-world scenarios becomes even more complex to analyze as there are common attributes across products and one choice decision that, in a sense, captures a competitive scenario between alternative products. Since this case is probably more important to winemakers than the single (i.e., monopoly type) case where only one wine is considered, rather limited work aimed at modeling this purchasing decision process in wines has been done. More specifically, no complete understanding exists of the competitive intensity between various wines available to the consumer that is based on sensory-type attribute evaluations. Furthermore, the effect of the wine attributes on the purchase decision has not been adequately addressed in the literature. To fill this void, we propose a probabilistic modeling approach that will address these issues. In particular, we employ a multinomial Logit choice model to examine the choice probabilities of different red wines as a function of the wine sensory-based attributes.

Researchers have tried to define wine quality according to objective characteristics based on chemical and instrumental analyses of wine attributes. Such characteristics include acidity, color, volatile components, and other aroma-related and measurable attributes. Wineâ&#x20AC;&#x2122;s compositional and sensory profiles are widely documented, and several models have been proposed to identify and classify wine quality and origin, based on these profiles (Cliff & Dever, 1996; Vanier, Brun & Feinberg, 1999). These measures, however, are not fully appreciated by consumers, who generally rely on their own perception of product qualities.

Some characteristics are not easily measurable either. For example, the aroma and sensory attributes of wine are complex and difficult to measure and describe. Hence, a sensory evaluation of wine is generally performed by wine experts, who evaluate the wine and describe its attributes to potential consumers. However, consumers will frequently rely on their own judgments about these qualities. Since consumers make the purchasing decision, it would be prudent for winemakers to use a consumer

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sample to evaluate such wine qualities and preferences to better identify the preferred wine taste.

Consumer Heterogeneity As noted above, wine tasting is a common method for selecting a wine in wineries or wine stores, as it reduces uncertainty about the product qualities. What are the attributes that most affect consumers in such a choice process? Do these attributes differ across different consumer segments? In other words, does heterogeneity among consumers have an effect on the saliency of the wine attributes in a choice context?

From the marketers' perspective, the answer to these questions might indicate a potential for constructing a marketing strategy based on those important attributes.

Such a strategy might be more effective and efficient than others, because it would focus on the potential drivers of consumer preferences and choice. That said, a lack of understanding continues to exist with respect to the salient attributes of red wines which differ from white wines in their complex characteristics and the variation in different consumer segments.

The aforementioned discussion about winemaking that is primarily based on the winemakersâ&#x20AC;&#x2122; experience and consumer evaluations primarily based on their sensory evaluation, yields some inconsistencies regarding the issue of how to develop a wine with the highest consumer preference. The art of winemaking, as exhibited by the knowledge of the winemakers, was eventually tested by consumer sensors. Such wine taste tests evaluate the overall quality of the product and give winemakers an indication as to whether they are on the right track. This type of test has one shortcoming since it involves a sequential evaluation of each wine, one at a time, with an evaluation of that wine on its own. That is, there is no provision for the relative effect of the one wine characteristic on the relative preference of this wine compared to other wines in the choice set. This issue becomes even more complex as pooling consumers evaluation might lean to an â&#x20AC;&#x153;averageâ&#x20AC;? wine taste that will not necessarily fit the desired preference of a certain segment. It is therefore essential to identify heterogeneity among consumers in terms of preference formation to reveal the drivers of this preference formation.

Heterogeneity in consumer sensory evaluation is well documented in the literature (Tomlins et al., 2007). In a study conducted by Weaver (2001), heterogeneity in food preference based on sensory evaluation was observed, to a certain degree, between men and women. In addition, preference and frequency of consumption were also correlated. Differences between consumers based on gender behavior of alcohol consumption have been widely documented (Ricciardelli et al., 2001). Since heavy alcohol drinkers may be more experienced in wine styles, segmenting the market based on the frequency of drinking wine might be valuable in gaining more insight into different consumer needs.

Figure 1 summarizes the proposed framework of analysis of this study.

In short, this study is aimed at filling the void in the literature on gaining additional insight into sensory-based attributes and their effect on consumer choice in a heterogeneous consumer group. Winemaking is considered by many as a combination of art and science, so we worked to increase knowledge of the exact

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wine attributes that drive consumer choice and, therefore, provide managers with more â&#x20AC;&#x153;knowledge to improve art,â&#x20AC;? while capturing the competitive intensity that prevails in such product category.

Figure 1: Sensory-based Evaluation Analysis

Methodology In terms of methodology, we used a descriptive research approach that was based on two stages. In the first, we identified the relevant red wine attributes that consumers consider when purchasing red wines. In the second stage, we conducted a blind taste test experimental design to capture the effect of the wine qualities only (i.e., not the brand effect or other external cues). We used the following list of characteristics as representative of the wine attributes: color intensity, aroma, bouquet, taste, tannic, harmony, and after-taste sensation. This set of wine attributes conforms to the generally accepted rules of wine tasting (Kolpan, Smith & Weiss, 1996).

Procedure and Data collection The subjects used for this study were students, visitors and staff members at a large university. The taste tests were conducted during a time period of two days that lasted from late morning to late afternoon. One hundred and thirty-five respondents participated in the study. The tasting experiment was performed in the lobby of a large building complex to attract potential participants. The researchers suggested wine tasting to the visitors who walked through the building. They presented four bottles of wine wrapped in brown paper. All of the wines tested were presented to the subjects simultaneously, without any information about the wine. Furthermore, random mixing of the alternatives across participants was carried out to avoid potential primary or recency effects. Overall, four red generic wines of different brands were tested (i.e., an unknown producer with a private label, a well-known brand, a wine from a boutique winery, and a very well-known brand).

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Overall, 135 participants took part in the wine tasting procedure and answered the questions pertaining to this test. The sample was formed by 88 males and 47 females.

The participants were mostly young adults, 41 of which were between the ages of 18 and 24 (since the legal drinking age is 18 in the area where the study was performed), 89 between 25 and 40, and 5 over 40. It is acknowledged that this sample might be skewed toward younger male customers. Further exploration of other demographic variables can be carried out in future research. With respect to income level, 81 of the participants earned less than the average salary, 46 at about the average, and 14 above the average income. The level of employment ranged from full-time, 64, to part-time, 8, and full-time students (unemployed), 62. Subjects were asked to taste the wine and to rate each of the following wine attributes described earlier: color intensity, aroma, bouquet, taste, tannic, harmony, and aftertaste. Respondents were asked to rate their responses on an interval scale of 1 (very low level) to 10 (very high level). For instance, a respondent would be asked: â&#x20AC;&#x153;On a scale of 1 to 10, where 1 is very light and 10 is very dark, how would you rate the color intensity of this brand?â&#x20AC;?

Descriptions for the scales used for the other attributes are also given in Table 1.

Table 1: Attributes Involved in Product Evaluation

Respondents were informed about the characteristics of the different product attributes. For example, aromas are the smell stemming from the grape, bouquet is the smell coming from the production process (e.g., aging in oak barrels) of the wine and not the grape itself. Harmony is the balance between the wine components, while tannic is the dry feeling in the mouth after drinking the wine, and so on. Similar measures were used in other studies (Nerlove, 1995; Hughson & Boakes, 2001).

In addition, respondents were asked to rate their overall evaluation of each wine and to rate their overall preference for each of the four wines they tasted (Cohen & Lowengart, 2003).

Choice Model The main objectives of this study, as noted above, are twofold: 1) estimating the probability that a consumer would choose a specific wine from a set of alternative wines, and 2) identifying the red wine attributes that most affect customers in their purchasing decision. The latter will assist managers and winemakers in deciding which wine attribute they need to modify to improve the choice probability of their wine.

We employed a probabilistic multinomial Logit choice model (McFadden, 1974) to

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analyze the data. The MNL model is a simultaneous compensatory attribute choice model that incorporates the concepts of thresholds, diminishing returns to scale and saturation levels (McFadden, 1974). Furthermore, the MNL is based on the assumption that the overall preference of a consumer for a choice alternative (i.e., the preferred wine) is a function of the perceived relative utility that the alternative (wine) holds for the consumer.

Let Uij be the utility of alternative product j for customer i, and m the number of alternative products. The utility function can be separated into a deterministic component Vij (measured in terms of perceived value associated with the characteristics of the products), and an unobserved random component, eij, which is assumed to be drawn from independent and identically distributed such that: Uij = Vij + eij

(1)

The distribution of eij is assumed to be exponential (Gumbel type II extreme value) and thus the probability that alternative product j will be chosen by customer i is represented by: Pij =

exp(Vij )

S

j =m j=1

(2)

exp(Vij )

Utility Specification The deterministic component of the utility function is a product of the weighted sum of the product attributes identified earlier and has the following form: Vij = a1COLORij + a2AROMAij + a3BOUQUETij + a4TASTEij + a5TANNICij + a6HARMONYij + a7AFTERTASTEij

(3)

where, COLORij – consumer i' perceptions of the color intensity of wine alternative j AROMAij – consumer i' perceptions of the aroma of wine alternative j BOUQUETij – consumer i' perceptions of the bouquet of wine alternative j TASTEij – consumer i' perceptions of the bouquet of wine alternative j TANNICij – consumer i' perceptions of the tannic of wine alternative j HARMONYij – consumer i' perceptions of the harmony of wine alternative j AFTERTASTEij – consumer i' perceptions of the aftertaste of wine alternative j for j=1,2,3,4. a1a2a3a4a5a6a7 – parameters to estimate.

Results and Discussion The estimated parameters a1,…, a7 for all subjects tasting red wine are presented in Table 2. The data indicate that four wine attributes are salient in the choice process – namely, taste and harmony and to a lesser degree, bouquet and aftertaste. Thus, wine

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producers and marketers should focus on these wine attributes, while targeting wine consumers similar to those in our study.

Table 2: Multinomial Logit Coefficients â&#x20AC;&#x201C; Aggregate Level

Understanding consumer preferences and what drives their choice is essential is developing marketing strategies. Based on the results in Table 1, it can be concluded that changing the wine taste and harmony will have a significant effect on the choice probability of red wines, and a marginal effect when improving the bouquet and aftertaste of the wine at the aggregate level. The exact attribute level can be determined in a different study when several categories, or values, of each variable are considered to find the optimal level of the specific attribute. The choice-based model was able to identify those attributes that drive wine choice among four alternative red wines.

As a next step in identifying drivers of wine choice in a heterogeneous consumer market, we employed the same multinomial logit analysis for different segments based on gender, frequency of wine drinking (less than once a week and twice a week or more, for low and high frequency wine drinking), and wine involvement.

With respect to male/female segmentation scheme, our results, presented in Table 3, show that taste is a salient attribute for both males and females. These two segments, however, are different with respect to other wine attributes. Harmony plays an important role in the male segment (harmony is recognized as the balance among all wine attributes) and, to a lesser degree, aftertaste. Bouquet is also significant in the female segment. A possible justification for this finding might be that bouquet is considered as the feeling in the mouth while drinking the wine, and not the actual meaning of bouquet, which is the combination of aromas and odors developed in the wine during fermentation and aging.

In sum, the gender segmentation variables revealed interesting dissimilarities between segments such that the male segment was concerned with intrinsic product characteristics that are taste sense-based evaluated. The preference for red wine in the female consumer segment, in contrast, was also driven by external product characteristics that are other sensor-based evaluated (smell). Based on these results, it can be seen that personal differences in consumers, such as gender, have an effect of the formation of preferences and choice.

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Table 3: Multinomial Logit Coefficients - Male and Female Segments

The next step of the analysis is exploring heterogeneity in consumers’ frequency of drinking alcohol beverages that is a proxy to their experience with the product.

Analyzing the results of this analysis (Table 4), it can be seen that bouquet is a salient attribute in the low frequency wine drinkers’ segment (Table 4). Both segments appreciate taste and harmony. The high frequency segment is also affected, to a certain degree, by the aftertaste and color of the wine. It comes as no surprise that less experienced and knowledgeable consumers tend to evaluate products with a smaller set of attributes (Sujan, 1985).

Table 4: Multinomial Logit Coefficients - Low and High Drinking Frequency Segment

To verify whether our segmentation schemes are meaningful (i.e., whether separating the sample into two segments should result in better data fitting than in an aggregate sample), we conducted log-likelihood tests, –2 log l, where l = (LLsegments – LLaggregate), (Gensch, 1985) on the different segmentation schemes. The results of this analysis are presented in Table 5.

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Table 5: Segmentation Scheme Log Likelihood Tests

All of these tests are significant at least at the 0.05 level, thus indicating that our segmentation schemes are meaningful and such consumer groups do behave differently in their choice decisions.

Discussion and Conclusions The purpose of this study was aimed at exploring the effect of sensory-based product attributes on consumer choice, and in a heterogeneous consumer market in particular. It therefore, presented a general approach for obtaining diagnostic information about the saliency of product attributes in a choice context. In order to demonstrate this framework, the paper focused on seven sensory based wine attributes that were identified as part of consumer considerations. We employed a probabilistic choice model to address this issue and were able to identify those wine attributes. In addition, we estimated the effect of a change in these attributes on the probability of choosing a wine. This methodological approach enabled us to gain insight into consumer preferences that are driven by attributes that can be managed scientifically, as well as practically, by winemakers. That is, the proposed method added science into art in the sense that part of the winemaking decision can be based on consumer research preferences and perceptions, and not just on expert opinion or trend guessing. However, this is not to say that the other methods supporting product design decisions are not important in consumers decision of wine purchase. This is also not to say that other product attributes (i.e., price, image, etc.) are of less importance. For example, mapping techniques that combine consumer perceptions and preferences can provide insight into the desired (ideal) product and the proximity of alternative products in the category to this ideal point (Ghose & Lowengart, 2001). There are other quantitative methods that utilize consumersâ&#x20AC;&#x2122; sensory evaluation to examine product preference that can be found in the literature (Saguy & Moskowitz, 1999; Lesschaeve, 2007). The approach proposed in this study provides a different tool to get better accuracy in understanding consumer needs and wants through choice process formation and relevant diagnostic information.

When constructing a marketing strategy for a red wine and utilizing the results of this study, marketers can increase the choice probability of their wines by improving the taste and emphasizing the wineâ&#x20AC;&#x2122;s harmony. This can be done either by technological improvements or by blends with other varieties of grapes. Naturally, it is

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not easy to delineate what is the exact taste and harmony for a preferred wine; rather, this study can indicate which sensory wine attributes are those that influence the choice process. Wine marketers, therefore, need to construct further sensory evaluations tests to identify the most preferred tastes and flavors for their wines.

Namely, we can indicate “what” should be improved and the question "how much" can be answered in another study.

Our results also indicated variation in the saliency of the wine attributes across different consumer segments that can be incorporated into a better understanding of customer preferences. This market-to-market variation in the male segment, for example, can be translated into offering a wine that is a bit more complex in that it will include indications of its harmony and aftertaste. A different approach, one that offers a wine that indicates the bouquet of the wine, can be targeted for the female segment. Such diagnostic information can aid wine marketers in constructing more effective marketing strategies to increase their market share. This can be done by introducing two different wines with different marketing communication strategies that will fit each segment. Such marketing responses will be more effective than marketing the same wine to both male and female segments. Similarly, the consumer segment that purchases wines at low frequency can be educated about the bouquet of the wine with appropriate communication schemes to increase the choice probability of purchasing specific red wine.

It should be noted that the proposed framework provides diagnostic information about which attribute is salient in the choice process that allows managers to design marketing strategies for product modifications, or new product development. It does not, however, provide insight about the exact level of such (salient) an attribute and the exact tactic to obtain it. This can be obtained in different research that can examine the different levels of this attribute.

Overall, this study presented a choice model-based approach for gaining knowledge about current product modifications, as well as developing new products in categories that are characterized by the high importance of consumer sensory evaluation in forming preferences toward brands. This is particularly valid in categories where product design decisions are based on experience and art. Identifying the salient product attributes for the aggregate and disaggregate markets provide managers and winemakers with information about the exact product attributes that need to be modified. Improving the relevant product attributes will increase consumer choice probabilities for the specific product (wine) alternative.

The current study introduced a framework for future studies that can focus on the effect of other consumer characteristics, demographics and others, on wine selection, as well as the manufacturer’s (i.e., winery) effect on the choice of such a product. That is, exploring whether consumer heterogeneity in responsiveness to various wine attributes might aid marketers in tailoring marketing strategies that are more targeted and therefore more efficient.

Many different factors can affect the choice decision of a product in a product category. These include tangible (e.g., price, quality, packaging, taste, etc.) and nontangible aspects (e.g., reputation, image). For complex products, those that have many different types of product characteristics, or that have experience or credence in nature, the choice task of consumers is even more difficult.

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Group Attributional Style: A Predictor of Individual Turnover Behavior in a Manufacturing Setting Laura Riolli California State University â&#x20AC;&#x201C; Sacramento Steven M. Sommer Pepperdine University, Irvine Graduate Campus

Separate research streams have examined (1) teamwork and (2) turnover.

We examined the interaction of group beliefs on team member turnover behavior. We hypothesized that groups with more pessimistic attributional styles would experience greater turnover than optimistic attributional style groups. This effect would be independent of influences of group potency and social identity. A study of fifty intact work teams in a manufacturing facility was conducted, with special attention devoted to recommendations for enhancing the validity of multilevel research. The results supported the hypotheses. Implications for attributional processes, shared team mental models, and social capital are discussed.

Work teams and groups continue to receive increasing attention in management theory, research and practice (Goodman, Ravlin & Schminke, 1990; Guzzo & Dickson, 1996; Hackman, 1990; Jackson, Stone & Alvarez, 1993; Labianca, Brass & Gray, 1998; Liden, Wayne & Bradway, 1997). To date, this research has identified antecedents of group effectiveness (Earley & Mosakowski, 2000; Gibson, Randell & Earley, 2000; Lau & Murninghan, 1998; Stevens & Campion, 1999) and more often has examined resultant performance (Hollenbeck et al., 1998; Jung & Avolio, 1999; Sparrowe et al., 2001). However, groups are â&#x20AC;&#x153;a collection of individuals with a definite

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sense of membership and shared beliefs” (Bar-Tal, 1990, p. 41). Those beliefs, in turn, guide group behaviors that relate to collective issues. Only recently has attention specifically focused on the formation process of group beliefs (Eby et al., 1999; Gibson, 1999; Guzzo et al., 1993) and their implications for organizational outcomes (Kirkman & Shapiro, 2001).

Group beliefs are neither absolutely an aggregation of individual characteristics nor a set of wholly group-level characteristics (Crocker & Luhtanen, 1990; Guzzo et al., 1993; Sayles, 1958). Rather, as members interact, they develop a collective sense of their role requirements, behavior patterns, and the connectedness of their actions (Weick & Roberts, 1993). Thus, individual attributes and the group’s contextual characteristics meld together to create a team mental model (Eby et al., 1999; Klimoski & Mohammed, 1994) of shared expectations and rules (Hackman, 1990) that guide future action. It is important to note these “shared cognitions” need not be identical among group members. However, there must be a significant overlap of understanding among members (Earley & Mosakowski, 2000; Waller et al., 2001) so that they “hold compatible models that lead to common expectations” (Klimoski & Mohammed, 1994, p. 421). In particular, while there is much debate about the nature of shared mental models (Klimoski & Mohammed, 1994), there is consensus that they are “shared understandings of task demands, environmental contingencies, and appropriate behavior” (Eby et al., p. 367). Thus, they are cognitive frameworks with motivational potential.

While studies of group processes have examined a variety of topics (e.g., teamwork expectations, group efficacy, social identity, communication), we propose that one area of shared cognition thus far overlooked that impacts subsequent behavior is attributional style—the manner in which groups collectively interpret good and bad events relevant to the group. Numerous studies in the behavioral sciences have examined the possibility that certain individuals favor some explanations over others for different events (Peterson, Buchanan & Seligman, 1995). That is, rather than independently evaluating the cause of each experience, over time they develop a consistent cognitive orientation and interpretive framework. Furthermore, an individual’s future expectations (e.g., self-efficacy) and behavior (e.g., expended effort) are significantly influenced by their perceptions and explanation of past events (Luthans, 2002a; Stajkovic & Sommer, 2000; Weiner, 1986). Although the major interest in attributional style has been at the individual level of analysis (Martinko, 1995), some work has been done to extend attribution theory to group settings, especially in sports (Zaccaro et al., 1987; Rettew & Reivich, 1995). This stream suggests that when individuals work in groups, they also generate shared understandings of the relationship between group attributes and group outcomes (Hewstone, Jaspair & Lalljee, 1982).

It should be noted that attributional style is specifically a cognitive process (Seligman & Csikszentmihalya, 2000) rather than an affective disposition like positive and negative affectivity (George, 1990; Judge, Locke & Durham, 1997). Dispositions may become more state-like upon accumulation of experience (Chen, Gully & Eden, 2001), but do not always influence actual behavior (Fishbein & Ajzen, 1975). For example, team member negative affectivity does not influence teamwork expectations

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(Eby et al., 1999). Attributional style, however, is a cognitive process shown to be motivational (thus impacting behavior) even when manifesting different emotions (Weiner, 1986). Similar to the distinction Chen et al. (2001) makes between selfefficacy and self-esteem, attributional processes involve motivational evaluations of self in the context of internal and external criteria, whereas self-esteem is an evaluation resulting in an affective orientation towards self based on external characteristics.

These unique implications of attributional style for group behavior relative to other group dynamics in the extant research further suggest the need for study.

We investigated the implications of attributional style in the context of turnover behavior. Employee turnover is frequently cited as the most prominently studied and among the most practically relevant topics in organizational behavior research (Luthans, 2002b; Robbins, 2003). Recent discussions have gone beyond indirect cost considerations and have demonstrated the significance of turnover on firm performance (Guthrie, 2001). These efforts seem equally divided between examinations of the process of an individual voluntarily leaving an organization (Lee et al., 1999) and the impact of involuntary turnover events like downsizing (Brockner et al., 1987; Mishra & Spreitzer, 1998). While some suggest teams should strengthen an individual’s attachment to an organization (Kirkman & Shapiro, 2001), recent work on social networks and social capital indicate that an individual’s turnover behavior may be greatly (and negatively) influenced by the turnover behavior of relevant others (Dees & Shaw, 2001; Mollica & DeWitt, 2000; Shah, 2000). Thus, we suggest that an individual’s turnover behavior is influenced by the collective explanation of the group’s experiences, and that a shared mental model of pessimistic explanations will create a snowball effect (Krackhardt & Porter, 1985) on turnover.

Attributional Style Attribution theory is concerned with how individuals perceive causes of events and the consequences of those perceptions. There is no single theory of “attribution” (Kelley & Michella, 1980; Martinko, 1995). However, research performed by Heider (1958) on how people explain their own actions and those of others’ is widely considered the birth of attribution theory. Subsequent research shows beliefs about causation affects mood, expectations, and subsequent behavior (Stajkovic & Sommer, 2000; Weiner, 1986). Weiner’s (1986) theory of achievement motivation deals with how individuals explain their successes and failures and how this impacts subsequent mood and behavior (self perspective). Kelley (1967) and Green and Mitchell’s (1979) models are concerned primarily with how observers assign responsibility for the outcomes of others. The application of attribution theory to group settings would suggest members of the group also generate a naive theory of the relationship between group characteristics and group outcomes. The key to understanding the group explanation of good and bad events is to be found in the ongoing interaction process among the group members. While these group level effects have been postulated (Brown, 1984), they have rarely been empirically examined.

One specific stream within the attribution literature—“explanatory style”— examines “one’s tendency to offer similar sorts of explanations for different events”

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(Peterson, Buchanan & Seligman 1995, p. 4) or, simply put, the habitual way in which people explain the favorable and unfavorable events that happen to them (Peterson & Seligman, 1984). For example, an individual who habitually explains bad events as “I caused it,” “It’s an ongoing thing and everything else will go wrong” (i.e., internal, stable, and global), is labeled as having a “pessimistic” explanatory or attributional style. In contrast, one who attributes failure to external, unstable, and specific causes is labeled as having an “optimistic” explanatory style. Research on learned helplessness shows an individual with a “pessimistic” style is more likely to exhibit helplessness deficits when confronted with bad events than individuals with an optimistic style (Seligman et al., 1979; Peterson, 2000; Seligman & Schulman, 1986), which will likely lead to dysfunctional consequences in terms of future behavior and performance (Luthans, 2002a). At this point, we should mention that what has been called “explanatory style” in the psychology research has been referred to as “attributional style” in the sparse management literature (Furnham, Sadka & Brewin, 1992; Martinko, 1995) on the topic. From here on, we will use the latter term.

There is a prolific body of literature showing that an individual’s attributional style has significant effects on their mental and physical well-being, task persistence, and performance success (Peterson, 2000), and helped to launch the growth of the research stream called ‘positive psychology’ (Seligman & Csikszentmihalya, 2000). To date, attributional style has been related to such diverse outcomes as physical illnesses (Peterson, Seligman & Vaillant, 1988), anxiety (Seligman et al., 1979), academic performance (Peterson & Barret, 1987), burnout (Wade, Cooley & Savicki, 1986), work exhaustion (Moore, 2000), low self-esteem (Kao, Nagata & Peterson, 1997), hardiness (Hull, Van Treuren & Propson, 1988), and workplace aggression (Douglas & Martinko, 2001). Indeed, a meta-analysis of over 100 studies supported the proposition that depression is positively related to internal, stable, and global attributions for failure and external, unstable, and specific attributions for success (Sweeney, Anderson & Bailey, 1986). Findings from two decades of research have shown that an individual’s conclusions that outcomes were uncontrollable were associated with cognitive, motivational, and emotional deficits (Abramson, Seligman & Teasdale, 1978; Seligman & Csikszentmihalya, 2000). The motivational deficits are a result of the expectation that responses are in vain (Peterson, 2000). The cognitive deficit is comprised of difficulties in learning, given that one’s responses are not seen as producing outcomes (Hjelle, Busch & Warren, 1996). Finally, the depressed affect (e.g., frustration or sadness) is a consequence of believing outcomes are independent of responses (Garber, Miller & Abramson, 1980).

Attribution theory has long received significant attention in both the clinical and organizational research (Knowlton & Ilgen, 1980; Liden & Mitchell, 1985; Heneman, Greeberger & Anonyou, 1989; Ployhart & Ryan, 1997). Attributional style, however, has only recently garnered the level of attention in organizational behavior that approaches the interest shown in the clinical and social arenas (Furnham et al., 1992; Judge & Martocchio, 1996; Moss & Martinko, 1998; Wunderley, Reddy & Dember, 1998). A few relevant studies have examined productivity and turnover among insurance sales staff. For example, Seligman and Schulman (1986), using a sample of 94 experienced life insurance sales agents, found that individuals who interpreted

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failure as internal, stable, and global were less persistent, produced less, initiated fewer sales attempts, and quit more frequently. Corr and Gray (1996) replicated these findings in a study of an insurance sales staff in the U.K. The focus of attributional style research has thus far examined individual processes and implications. We reiterate the importance of the work team and repeated calls for research to examine group-level influences on theories traditionally examined at the individual level of analysis (Eby et al., 1999; Pelled & Xin, 1999; Yammarino & Dubinsky, 1990). Prior research has demonstrated the ease with which an individual identifies with a group. For example, a nominal cue like wearing similar clothing can cue a significant in-group categorization effect (Dovidlio et al., 1995). Once perceiving himself or herself as a member, the individual is prone to adopt similar attitudes (George, 1990; Salancik & Pfeffer, 1978) and personalize the groupâ&#x20AC;&#x2122;s success and failures (Ashforth & Mael, 1989). Given the importance of the individual being in sync with the group on key coordination and perception issues, if the group is to be effective (Waller et al., 2001), it seems reasonable to determine if psychological withdrawal (e.g., being in a bad mood) effects found for individuals (Judge & Martocchio, 1996; Pelled & Xin, 1999) would also occur at the group level.

While some attribution work has looked at athletic performance in sports teams (Rettew & Reivich, 1995), attributional style research at the group level is scant. This study seeks to determine if a construct of group attributional style exists. We define group attributional style (GAS) as the groupâ&#x20AC;&#x2122;s habitual and collective manner of explaining the causes of bad and good events happening to them. As discussed, groups create shared cognitions and collective mental models through their interactions (Earley & Mosakowski, 2000). We again propose that one set of beliefs involves the collective sense, making governing the explanation of good and bad events happening to the group. Extrapolating from the existing research, members engaged in pessimistic attributional style discussions may share feelings of helplessness. This group will experience and collectively amplify/reinforce debilitating deficits that will hinder efforts to correct or improve their activities. Consequently, members of pessimistic groups will be more inclined to withdrawal, express thoughts of quitting, and intentions to search for alternative employment. When one member quits to take a job elsewhere, others may reevaluate their job status (Dess & Shaw, 2001; Mowday, 1981) and likely quit as well. Studies (Lee et al., 1999; Mollica & DeWitt, 2000; Sheehan, 1995) empirically show this shock may lead to potential turnover even if the individual is satisfied with their current position. We propose this effect will be more pronounced in teams given the more active discussions and higher sense of collective expectations. Therefore: Hypothesis 1: A pessimistic group attributional style will lead to higher turnover than turnover in groups characterized by an optimistic attributional style.

Accounting for the Effects of Other Group Beliefs Group attributional style (GAS) will not occur in a vacuum (Klein & Kozlowski, 2000). In particular, we expect the role of GAS to operate in conjunction with other

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group beliefs that have been demonstrated to influence shared mental models (Eby et al., 1999). In particular, research has demonstrated that group potency and social identity produce significant impacts on group dynamics. For example, while attributional style will result in the collective assessment of an event’s causality, future behavior will also be influenced by the confidence the group has that they can successfully mobilize the necessary subsequent resources and tactics. So, whereas, atributional style concerns “Why did it happen?” and “Do we want to do something about it?” we present group potency as the “Can we do something about it?” dynamic.

Furthermore, the extent to which the individual is emotionally attached to the particular group will also add to the desire to remain or turnover. This we consider the “do I care?” or social identity dynamic.

Group Potency The efficacy literature has extensively focused on individual self-regulating behaviors (Bandura, 1997). However, since the early 1980’s, attention has also been given to team-and-group performance beliefs. This trend started with the concept of team-potency, which was defined as “a shared conception of group ability across situations” by Guzzo et al. (1993, p. 87). This definition shares elements of and in fact, is often cited as a precursor to the term shared mental models (Eby et al., 1999). There is empirical evidence of the relation of potency to performance related criteria (Gibson, 1999). This research stream demonstrates that in different settings, group beliefs have a significant effect on different group outcomes. Subsequent work on group potency more often uses the term “group efficacy,” defined as the collective belief of a group that it can successfully perform a specific task (Gibson et al., 2000; Lindsley, Brass & Thomas, 1995).

Similar to discussions of shared mental models described above, this belief is not the simple sum of group members’ efficacy beliefs but an “emergent” expectation generated through collective sense making (Bandura, 1997). By emergent, we mean the process of member interactions and accumulated experiences that lead to a framework of shared cognition. The empirical evidence shows this collective (often called the group discussion) approach best predicts group outcomes (Gibson et al., 2000; Little & Madigan, 1997). By suggesting that collective efficacy is deeply grounded in self-efficacy, Bandura (1997) was among the first researchers to see the connection between performance beliefs across the two levels of analysis. In an attempt to discriminate between efficacy at the individual and collective levels, Bandura stated: Linking efficacy assessed at the individual level to performance at the group level does not necessarily represent a cross-level relation. An assessment focus at the individual level is steeped in processes operating within the group. Nor does a focus at the group level remove all thought about the individuals who contribute to the collective effort (1997, p. 478).

Consistent with past research (Silver, Mitchell & Gist, 1995), we expect turnover behavior to be further influenced by the group’s collective feeling of potency. Teams

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that have a strong sense of potency are less likely to experience the sense of helplessness that would lead to turnover. Teams with low potency are likely to create an environment that amplifies feelings of helplessness, especially related to assembling the skills necessary to succeed. As a result, the effect of potency can potentially mask attributional dynamics such as members relieving their dissonance by leaving the organization (Abraham, 1999).

Hypothesis 2: High potency groups will experience a lower rate of turnover than low potency groups.

Social Identity Another important group characteristic that may detract from the effect of group attributional style is the level of member identification with the group. Individuals define themselves and others not simply in interpersonal terms, but also in terms of their various category memberships (Hewstone et al., 1982) and group or organizational affiliations (Tajfel & Turner, 1979). As defined by Tajfel (1982), social identity is “that part of an individual’s self-concept that derives from their knowledge of their membership in a social group (or groups) together with the value and emotional significance attached to that membership” (1982, p. 2). Turner (1982) stressed that cohesiveness is the concept of belonging based on affection, whereas social identity is related to the member’s cognition regarding criteria describing the group’s characteristics. Social groups possess specific behavioral expectations, and this shared understanding of group characteristics and expectations is again relevant to the definition of shared mental models.

Individuals with strong identification tend to exert more effort towards group objectives and engage in more prosocial behavior (Mael & Ashforth, 1992; Kirkman & Shapiro, 2001). Indeed, one can extrapolate from the social capital literature that individuals with strong referent identity tend to build more supportive networks, cooperative work relationships, and higher levels of trust, all of which would reduce one’s motivation to leave the organization (Nahapiet & Ghoshal, 1998).

Contemporary work on diversity has emphasized the need to focus on cognitions of salient work-related characteristics rather than or in addition to emotional reactions to demographic attributes. Other work on social identity theory (Ashforth & Mael, 1989; Tajfel & Turner, 1986) describes the process by which members will seek to distinguish the group, and by which members’ strength of identification with and egoenhancement from the group influence intentions to remain.

Thus, in spite of a pessimistic interpretation of group experiences, a high identification with the group itself may counter turnover. That is, the group itself provides a source of support that outweighs experience generated image concerns.

Hypothesis 3: Groups with strong social identity will experience lower turnover than low identity groups.

At this point it might be prudent to restate Hypothesis 1 as follows:

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Hypothesis 1: A pessimistic group attributional style will lead to higher turnover than turnover in groups characterized by an optimistic attributional style, independent of the effects of group potency and social identity.

Methods Sample The sample consisted of teams drawn from a major division of a large manufacturing operation located in the Midwest. This division was responsible for mail inserting for large financial institutions. The teams worked around mail inserting machines and had to collectively coordinate several activities in order to minimize performance defects; for example, loading and unloading of different materials (e.g., envelopes, ink, paper statements), synchronization with machine speed, maintenance and repair, general work area cleanliness while maintaining or repairing the machines.

Each team had 3 or 4 members, a size determined by industrial engineering to be most effective for the technology. No difference due to group size was observed for any of the study variables. These were permanent groups that spent a considerable amount of time working in close proximity and socializing with each other during breaks and lunch. Pooling of effort from the group members was an important factor for the team performance. Thus, the sample meets the proximity, similarity, interdependence, and interaction criteria to be considered a team.

Data were retained only for employees where a team’s complete membership completed surveys. This resulted in 180 employees comprising 50 wholly intact teams used in the actual analyses. Age was measured categorically. The median and most frequent response was “3” representing the individual as being in the 26-30 year old age range. Average organizational tenure was 22.7 months, and 50% of the respondents were female. A comparison of the demographic composition of the whole company and the demographics of the investigated division showed the sample that completed the questionnaires was representative of the plant population. Therefore, we are confident there was no sampling bias.

This organization was selected for several expected contributions to validity. For one, performance was dependent on both human and technological inputs. The measure of performance was the number of envelopes zip-sorted in a month. Counters attached to the employee machines tabulated completed envelopes. Managers recorded group membership and attendance. While a Within-and-Between Analysis (WABA) analysis determined that performance was more variable across groups than within (E = 2.06, significant at 150 [a < .01]), the variance in performance was quite restricted (3%). Indeed, an ANOVA showed no difference across teams, and a regression of the study variables on performance was also not significant. This adds to the power of the design as group beliefs and subsequent behavior will be more a function of differences in collective interaction and perceptions than a product of actual productivity differences.

Finally, the group size provided reasonable opportunity to satisfy Bar-Tal’s (1990) four requirements for effectively measuring group beliefs. The ability to survey the

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entire group membership addressed the first two requirements that the constructs reflect the group as a whole, and that members agree with regard to the construct (typically addressed by scale construction). The use of WABA techniques would help address the other two requirements concerning group differentiation and within-group processes. Finally, group beliefs are more predictive of group outcomes when based on group interaction processes (Gibson, 1999; Gibson et al., 2000); in this case group discussions of many topics were promoted by the fact that the groups worked together around the machines.

Procedure Respondents were invited to participate in the study as part of an ongoing project within the context of reducing turnover at the organization level. The survey instrument was directly distributed and collected by the senior author. Completion of the survey required approximately 30 minutes and was done on company time.

Respondents were given the choice of completing the survey at the start or end of their shift. All participants were provided with explanations of the general purpose and nature of the study prior to responding. Confidentiality of individual responses was emphasized in the instructions, and it was stated that only the summaries of the research would be provided to management.

Measures Independent Variables Group Attributional Style. Several instruments have been developed to assess attributional style at the individual level: the Organizational Attribution Scale Questionnaire (OASQ) developed by Kent and Martinko (1995), the Attribution Style Questionnaire (ASQ) adapted by Peterson et al. (1988), and the Occupational Attributional Style Questionnaire developed by Furnham et al. (1992). Each is worded specific to a certain population or application. The Group Attributional Style measure (GASQ) used here was based on the Kent and Martinko measure, as it has shown strong psychometric properties and is specifically worded to tap work-related events. The items were modified to reference group-level opinions in order to follow recommendations for considering issues related to research crossing multiple levels of analysis (Eby et al., 1999; House, Rousseau & Hunt, 1995; Klein, Dansereau & Hall, 1994). In particular, shifting the item referent from the individual to the group tends to enhance the level of within group agreement and between group variance required to alleviate construct validity threats due to level of analysis (Klein & Kozlowski, 2000).

The measure was presented to the respondents with the following directions: Read each of the situations and imagine a time it happened to you and to your group. Even if it is unlikely that the situation will actually occur, still imagine it is happening and respond to the questions. Based on what you know about yourself, your group, and the organization in which you are employed, write down what you think is the one major cause of the event in the space provided

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(e.g., bad luck). Respond to each of the items that follow the event by circling the number on the scale which best describes the cause you identified.

Following the instructions were the 12 modified events from the Kent and Martinko instrument: 6 good and 6 bad outcomes. A sample reworded negative event was “Members of your group have great difficulty in getting along with each other.” A sample reworded positive event was “All the feedback your group has received from your supervisor lately concerning the group’s performance has been positive.”

Consistent with recommendations for usage, following each event was a space to provide their narrative cause then parallel questions along the three dimensions of internality, stability, and globality. Response anchors used a 7-point Likert format (e.g., 1 = completely external to the group; 7 = completely internal to the group).

Given the high intercorrelations among the three attributional dimensions in past research, Reivich (1995) recommends the use of composite scores for determining attributional style (Corr & Gray, 1996; Seligman & Schulman, 1986). Again following Kent and Martinko’s recommendations, the composite negative and the composite positive attributional style scores were calculated first. These scores represent the combined mean responses across the three dimensions for the 6 events in each category. Next, the total score (CPCN) is obtained by calculating the composite positive minus the composite negative scores. Higher scores reflect a more optimistic attributional style. Past research (Peterson & Seligman, 1984; Seligman & Schulman, 1986; Reivich, 1995; Corr & Gray, 1996) indicates the CPCN is the most valid empirical predictor of attributional style at the individual level of analysis. Thus, we expect the Group-CPCN to be a valid extrapolation to the group level of analysis.The Cronbach alpha for the measure was .76.

Group Potency. We assessed collective self-efficacy with the eight-item scale developed by Guzzo et al. (1993). Other researchers have used different methods to measure group-efficacy. For example, Gibson (1999) employed a method called “group discussion procedure,” where a group is presented with a rating scale to use in forming a single consensus response to a question about its sense of efficacy with regard to a given task. Limitations of this method include the inability to calculate statistical indicators of agreement (Gibson, 1999), and that group interaction during the process of arriving at an efficacy estimate may change a group’s efficacy to the point that it is unrealistic (Bandura, 1997). Even so, Gibson et al. (2000) has shown potency measures like Guzzo’s to be unidimensional and sound and furthermore, that the two measures are equally sound. While the potency measure was shown to have lower predictive validity (Gibson et al., 2000), we propose the nature of the workplace mentioned above might exploit both approaches. These individuals work side-by-side and discuss several topics (including work), so it is highly probable that each individual’s response reflected the group’s attitude, thus providing the “referent shift consensus” necessary for crossing levels of analysis (Chan, 1998).

Scale items included “My group has confidence in itself,” “My group expects to have power around here,” and “My group believes it can become unusually good at producing high-quality work.” Group members completed the eight items using a tenpoint format (1 = To no extent, 3 = To a limited extent, 5 = To some extent, 7 = To a

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considerable extent, and 10 = To a great extent). The Cronbach alpha was .91.

Social identity was measured using a modified version of the Mael and Ashforth (1992) six-item organizational identification scale. Items included “If someone were to criticize this group, it would feel like a personal insult,” “When I talk about this group, I say ‘we’ rather than ‘they,’” and “If someone were to praise this group, it would feel like a personal compliment.” Participants were asked to indicate their agreement with each statement on a five-point scale (5 = To a very great extent; 1 = To no extent).

Mael and Ashforth (1992) reported a reliability of .79 and the reliability for this study was an acceptable .70.

Dependent Variable Turnover Behavior. Organizational records were examined for the 6-month period following the study to identify study participants who terminated their employment.

Unfortunately, we did not obtain sufficient information on date of turnover and thus, could not perform a stronger test using survival analysis.

Significant discussion has recently focused on methods for measuring turnover, as well as potential flaws in past data collection efforts. For example, turnover is one form of role transition (Ashforth, 2001) and some transitions can mask withdrawal behaviors. Transferring jobs or geographic relocation within an organization is a more acceptable way to leave an unsatisfactory position than quitting, especially if opportunities in other organizations are limited. Similarly, turnover is defined as “the entire cycle in organizations of entries and leaving” (Bluedorn, 1982, p. 78-79) and again includes transfers, promotions, and relocations. Given this, and that the constructs examined here classify as “shared team properties” that are influenced by the members (Klein & Kozlowski, 2000, p. 215), we utilized a more elaborate operationalization of turnover.

Exit interview data listed the reason for turnover. For example, some left to return to school, some were fired, some had immigration problems, and some were transferred or requested transfers to other departments. Seven categories, including one for still employed, were created. Consistent with the literature on voluntary turnover (Lee et al., 1999), these causes were coded into three categories: (1) still employed, (2) voluntary turnover or school, requested transfer, and (3) involuntary turnover—fired, immigration. As described later, since the dependent variable is categorical, logit regression techniques were used to analyze the data. One analysis was conducted to examine those who turned over (coded as 0) versus those who stayed (coded as 1), while a second analysis was performed to examine those who left voluntarily (coded as 0) versus those who left involuntarily (coded as 1).

Results Test of Group-Level Effects (WABA) Discussions of multilevel research provide cautions for examining the impact of group level constructs on individual level variables (House et al., 1995; Klein & Kozlowski, 2000).

In this study, we examined how group effects may influence actual turnover behavior at the individual level. Beyond the climate and intent created by discussion, the mere act of a friend

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leaving is a significant predictor that the individual may also leave (Krackhardt & Porter, 1985) since closeness (e.g., social identity) is a form of friendship.

In order to justify the group level of analysis, it is important to demonstrate homogeneity within the group and further, that two people within the same group are more similar than two people who are members of different groups (Bar-Tal, 1990; Florin et al., 1990). WABA was conducted in order to verify the existence of group level effects. This technique uses the within-group correlation and the between-group correlation (called “eta”) to determine if there is more variance among members within a group than variance accounted for by differences across groups. The squared eta’s (η2’s, similar to R2) are tested relative to one another with F-tests of statistical significance and an E-test of practical significance, making it a more robust measure of group level effects (Klein & Kozlowski, 2000). The cutoff value to conclude construct validity at the group level is E larger than 1.3 for the 15° angle test, comparable to a = .01 (Dansereau, Alutto & Yammarino, 1984). Values less than .77 indicate individual differences are greater than group effects, and that individual-level data cannot be aggregated. Cutoff scores for the F-tests are obtained from critical value tables and determined by the degrees of freedom (n – J and J – 1). WABA is a useful and one of the more rigorous tools for determining appropriate levels of analysis in multilevel research (Klein & Kozlowski, 2000). However, George (1990) points out one should not expect to find extremely large differences across groups when all members of a group belong to the same organization and are performing the same task.

Even so, the results of the WABA indicated group level effects for these data. The E scores for the three independent variables (1.42 to 1.60) all surpassed the 15° value for practical significance. Furthermore, the F (df = 130, 49) scores (2.05 to 2.09) surpassed the bracketed critical values of 1.84 (df = 100, 48) and 1.76 (df = 200,50) listed in Appendix A for a = .01 (Dansereau et al., 1984). Thus, variation between groups was significantly greater than the variations within groups for the variables of interest, so one can conclude that there is an effect of group membership on the measures.

Therefore, we can conclude the existence of collectively shared mental models within these groups. Given this, the remaining analyses and testing of hypotheses were performed using data aggregated to the group-level. Finally, examination of the data suggested no violations of normality.

Descriptive Statistics Inspection of the correlation matrix reveals this organization experiences a moderately high rate of turnover (annualized 60%) that varies across the groups. Older employees had lower education levels, and higher levels of turnover which is likely a result of the physical demands of the job. While there was great variance in the attributional style across the manufacturing teams, the mean was modestly optimistic.

Additionally, the groups reported relatively strong levels of potency and identity. Given none of the demographic measures were related to the dependent variable, they were excluded from further analyses. Thus, Hypothesis 1 could be initially tested by examining the correlation between attributional style and turnover behavior. Given the coding scheme (lower CPCN = more pessimistic; turned over = 0), the positive correlation shows that pessimistic groups were likely to produce higher individual turnover.

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Hypothesis Testing Moderated regression is typically used to study the unique and relative effects of independent variables on a dependent variable when controlling for each other (Pedhazur, 1982). However, when analyzing a categorical dependent variable with categorical and continuous independent variables, logistic regression analysis is recommended (Goodman & Blum, 1996; Tansey et al., 1996).

Hypothesis 1 predicting group explanatory style would relate to group member turnover was supported. Groups with a more pessimistic explanatory style led to higher turnover among members than groups with an optimistic explanatory style (b = .23, t < .01), even after accounting for cross-level effects and holding constant the effects of group potency and social identity (see Table 2). In addition, group members who left for voluntary reasons were likely to have left more pessimistic groups (b = .21, t < .05) than individuals who turned over for involuntary reasons (see Table 3).

Table 1: Descriptive Statisticsa

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Table 2: Logistic Regression (Employees That Left Versus These That Stayed With The Company)

Table 3: Logistic Regression (Employees That Left For Voluntary Versus Involuntary Reasons)

Hypothesis 2 proposed an inverse relationship between group potency and turnover. Again, Tables 2 and 3 present the results of the analysis. Groups with higher potency experienced lower turnover (b = .67, t < .05). However, there was no difference for high versus low potency groups in terms of voluntary versus involuntary reasons for leaving.

Hypothesis 3 was supported in that higher social identity groups experienced lower turnover in general (b = 2.41, t < .01), and a lower turnover rate due to voluntary reasons (b = 2.41, t < .01).

Discussion This study proposed and tested the idea that the attributional style construct existed at the group level of analysis. WABA analysis of responses from 50 work teams illustrated the attributional style measures (in fact all the measures of interest) did display group level characteristics in accordance with existing recommendations for

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conducting multilevel research (House et al., 1995; Klein et al., 1994; Klein & Kozlowski, 2000). It was further proposed that this group level phenomenon would influence turnover behavior. This proposition was also supported. This impact was established while simultaneously accounting for the influences of group potency and social identity firmly established in shared group cognition research. The following discussion will outline theoretical and practical implications.

Consistent with expectations, group attributional style had a significant impact on individual turnover. This finding adds to the rich body of previous research on factors that contribute to employee withdrawal behavior. Previous related research examined the increasing effect on individual turnover of communication networks (Krackhard & Porter, 1985), the centrality of network position (Eisenberg, Monge & Miller, 1983), and social influence (Kincaid, 1993). More recent studies have shown that others can indirectly influence an individual’s turnover behavior—a friend’s turnover will increase the chance of an individual’s departure (Mollica & DeWitt, 2000; Shah, 2000). Findings from this study showed that a group member’s decision to stay at or to leave a particular job is a function of the quality and pattern of interaction with other group members (Feeley & Barnet, 1997). For example, Krackhardt and Porter (1985) argued that the ‘closer’ the friends who leave the organization are to the person who stays, the stronger the effect will be on the latter’s turnover considerations. Again, recent research on voluntary turnover shows ‘such a shock’ may induce an individual to leave a job, even if they are not personally dissatisfied (Dess & Shaw, 2001; Lee et al., 1999).

As shown here, and consistent with prior work of shared team mental models, team member interactions likely developed a group level schema regarding the nature and causes of their experiences. In particular, the negative attributional style likely reflected interactions among group members that created collectively agreed upon, ego-protecting explanations for perceived failure. Furthermore, a normative belief in the immutability of the situation collectively indicated such experiences were not necessarily indicative of personal failure on any one member’s part. Consequently, this process created mutual social support for leaving. This effect was compounded by the degree to which the group collectively felt incapable of mobilizing an effective improvement response (potency).

The significant finding for social identity speaks to the issue of who group members may see as “friends” in making cognitions. Similarity across group members may enhance social integration (i.e., the degree to which an individual is psychologically linked to others in the group) and in turn lead to a lower likelihood of leaving (O’Reilly, Caldwell & Barnett, 1989). Historically, research has shown that demographically similar people create a supportive identity group that can reduce pressures to leave. Prior research has found higher turnover rates in demographically diverse work groups (Jackson et al., 1993), and O’Reilly et al. (1989) discovered that disagreement within heterogeneous groups accelerates the departure of members. The traditional perspective holds that the presence of demographically or socially “different” members of an otherwise homogeneous group may make the other members of the group uncomfortable.

Our findings were to the contrary. The teams in this study were ethnically diverse

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(Caucasian, Hispanic, Vietnamese, Native American) and displayed no significant differences related to the measures. One explanation may be that communication is easier and support stronger between individuals with shared social experiences (Zenger & Lawrence, 1989) and the physical arrangement of the workplace created close proximity and prolific social interaction as well. The groups in our sample were heterogeneous, yet the results show the existence of high within-group consensus which is consistent with recent research (Earley & Mosakowski, 2000; Lau & Murninghan, 1998; Pfeffer, 1998) showing the “right balance” of diversity in groups is necessary for greater organizational effectiveness. This study thus shows people in the workplace can be attracted to each other and create a shared identity because of the work even when they are not similar demographically. This would be consistent with the growing body of literature claiming social identity can be created around taskrelated as well as demographic criteria.

As with all research, this study had its strengths and limitations. First, this was the first known attempt to specifically examine attributional style as a group level construct, and one of the few to examine how attributional style affects turnover behavior. This issue is especially important given the increase in the use of groups and teams in today’s organizations and how little we understand group versus individual motivation (Sundrom, DeMeuse & Futrell, 1990). Second, as a field study in a manufacturing organization it adds to the generalizability of attributional style beyond the traditional insurance and sales domains. Third, this research studied groups in their natural settings with hard outcome measures, thus responding to the need for research studies on groups in real organizations (Langfred, 1998), as well as avoiding common threats like common method variance. Fourth, this study sought to reduce internal validity threats by examining teams of a fixed size doing the same task. Finally, our analysis sought to control for multilevel issues that would potentially contaminate our variable of interest.

One important limitation of this study was the sample size. Survey responses were only obtained from 50 intact teams. While this number minimally met the threshold for sufficient power (.8 at α = .05), small sample sizes tend to be problematic when investigating complex phenomena (Cohen & Cohen, 1983). Thus, interpretations of these results must be made cautiously. Even so, our sample of identical and fully intact teams meets or exceeds the sample size typically obtained in research in this stream, and we hope future studies with larger samples will provide support for the moderating effects we proposed.

While this study was longitudinal in terms of the independent variables preceding the dependent variable, further research using repeated measures designs might result in a more robust and comprehensive understanding of how the group beliefs identified here influence group outcomes. As mentioned before, we were not able to obtain dates of turnover though such an analysis would have provided a robust insight into possible relationships between degrees of attributional style and time to turnover. It has been noted that many findings concerning groups may not equally apply to “newly born” groups (Jackson et al., 1993). The distinguishing characteristic of “newly born” groups is that prior to the formation of the group none of the group’s members have any formal experience working with one another. In this organization there is a

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practice of occasionally collapsing remnants of former groups into a new group. Thus, it would be interesting to look at the antecedents and the formation process of optimistic and pessimistic group norms in newly-created groups versus groups where members are carried over from prior (optimistic or pessimistic) groups. Indeed, such an approach would be extremely valuable given the rotation practice commonly used in professional services firms.

The results of this research suggest that organizations should pay close attention to the habitual explanations of work groups. The findings of this study indicated group attributional styles (optimistic or pessimistic) did impact turnover. Companies that require persistence and initiative due to frequent frustration, rejection, and even defeat should focus on more training that might instill optimism in their employees (Luthans, 2002b). More importantly, these effects were found even though performance was not highly variable. Thus, turnover behavior here was almost exclusively due to differences in collective sense making than to differences in actual outcomes. Therefore, efforts to help frame experiences optimistically might benefit the organization even when no actual change in objective circumstances may be needed (Luthans, 2002a).

Furthermore, our findings relate to work on social networks and social capital. A person’s position in a social network can greatly influence their impact on an organization (Sparrowe et al., 2001), including being a key player that instigates a cohort to turnover (Dess & Shaw, 2001). Professionals commonly demonstrate greater loyalty to their network than the organization (Cappelli, 2000) and thus, a snowball of turnover is common (Dess & Shaw, 2001). However, we, like Krackhardt and Porter (1985), found similar behavior among low-level production employees and this, in and of itself, deserves recognition beyond the implication it suggests for less organizationally committed cohorts. It takes considerable time for an organization to develop the unique knowledge, memory, and interpersonal connections that result in increased efficiency (Nahapiet & Ghoshal, 1998). Yet, collective turnover of a social cohort can impede the development of or even eliminate valuable human and social capital. While recent recommendations emphasize investing in developing people (Pfeffer, 1998), organizations would be foolhardy to do so, knowing they will leave before any return is realized (Guthrie, 2001).

Seligman (1991) explains “learned optimism gets people over the wall—and not just as individuals but the whole team” (p.256). Workshops for optimism training could teach members of the group how to cope with adversity. How to utilize workrelated team attitudes instead of individual differences related to demographics could be the source of social support that makes the difference between renewed commitments to the organization versus pessimistic abandonment through leaving.

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Business Failure Prediction for Publicly Listed Companies in China Ying Wang Montana State University-Billings Michael Campbell Montana State University-Billings

This study uses data from Chinese publicly listed companies for the period of September 2000-September 2008 to test the accuracy of Altman’s Z-score model in predicting failure of Chinese companies. Prediction accuracy was tested for three Z-score variations: Altman’s original model, a reestimated model for which the coefficients in Altman’s model were recalculated, and a revised model which used different variables. All three models were found to have significant predictive ability. The reestimated model has higher prediction accuracy for predicting nonfailed firms, but Altman’s model has higher prediction accuracy for predicting failed firms. The revised Z-score model has a higher prediction accuracy compared with both the reestimated model and Altman’s original model. This study indicates that the Z-score model is a helpful tool in predicting failure of a publicly listed firm in China.

Developing countries are attracting more foreign investment than ever before. Since 2000, foreign direct investment inflows have rocketed from $165.5 billion to an estimated $470.8 billion in 2007. According to the World Bank, China draws the most foreign investments, attracting $84 billion of investment in 2007 and representing 18% of the total. Although China is an attractive place for investment, publicly listed Chinese companies suffer credibility issues. All three stock exchange markets – Shanghai, Shenzhen, and Hong Kong – are to varying degrees, known for government intervention and a club type atmosphere. Investors need guidelines to distinguish lowrisk investments from higher-risk ones. The objective of this study is to determine if the

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information available in the annual reports of Chinese publicly listed companies is useful to predict which companies are likely to fail.

The following research questions are considered in this paper: Is Altman’s Z-score model effective for predicting company failure in China during the period of 20002008? Is the model effective for predicting company failure for many different types of firms, not solely for manufacturing companies? Will recalculation of the coefficients of Altman’s variables result in more accurate failure prediction? Can other variables be substituted in the basic Z-score model to create a more accurate model?

Previous Studies The prediction of company failure has been well-researched using developed country data (Beaver, 1966; Altman, 1968; Wilcox, 1973; Deakin, 1972; Ohlson, 1980; Taffler, 1983; Boritz, Kennedy & Sun, 2007). A variety of models have been developed in the academic literature using techniques such as Multiple Discriminant Analysis (MDA), logit, probit, recursive partitioning, hazard models, and neural networks.

Summaries of the literature are provided in Zavgren (1983), Jones (1987), O’Leary (1998), Boritz et al. (2007) and Agarwal and Taffler (2007). Despite the variety of models available, both the business community and researchers often rely on the models developed by Altman (1968) and Ohlson (1980) (Boritz et al., 2007). A survey of the literature shows that the majority of international failure prediction studies employ MDA (Altman, 1984; Charitou, Neophytou & Charalambous, 2004).

Beaver (1966) presented empirical evidence that certain financial ratios, most notably cash flow/total debt, gave statistically significant signals well before actual business failure. Altman (1968) extended Beaver’s (1966) analysis by developing a discriminant function which combines ratios in a multivariate analysis. Altman (1968) found that his five ratios outperformed Beaver’s (1966) cash flow to total debt ratio and created the final discriminant function: Z=1.2X1+1.4X2+3.3X3+0.6X4+0.999X5 where, X1 = working capital/total assets X2 = retained earnings/total assets X3 = earnings before interest and taxes/total assets X4 = market value of equity/book value of total liabilities X5 = sales/total assets Firms with Z-scores less than 2.675 are predicted to be bankrupt, and firms with Z-scores greater than 2.675 are predicted to not be bankrupt.

Boritz et al. (2007) reestimated the model using Canadian company data and obtained the following: Z=2.149X1-0.624X2+1.354X3-0.018X4+0.463X5

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The cutoff point is 0.27.

Taffler (1983) developed a UK-based Z-score model as follows: Z=3.20+12.18X1+2.50X2-10.68X3+0.029X4 where, X1 = profit before tax/current liabilities X2 = current assets/total liabilities X3 = current liabilities/total assets X4 = (quick assets-current liabilities)/daily operating expenses with the denominator proxied by (sales-PBT-depreciation)/365 Sandin and Porporato (2007) use data from a developing country, Argentina, and retain 2 out of 13 ratios after stepwise selection and come up with the final model: As=15.06R5+16.11S3-4.14 where, R5 = operative income/net sales S3 = shareholderâ&#x20AC;&#x2122;s equity/total assets Despite the popularity of the MDA technique in constructing failure classification models, questions were raised regarding the restrictive statistical requirements imposed by the models (Ohlson, 1980). To overcome the limitations, Ohlson (1980) employed logistic regression to predict company failure, but the model was suggested to be insensitive to financial distress situations (Grice & Dugan, 2001).

Boritz et al. (2007) question the suitability of using the Altman (1968) and Ohlson (1980) models for Canadian companies since the Altman-Ohlson models were developed using data from U.S. firms. They contend that new models must be developed and validated for use with Canadian firms because of various differences in the environments in which firms of the two countries operate. This argument applies equally well to the need to develop and validate new models for evaluating Chinese firms. Along these same lines, Grice and Ingram (2001) argue that original Z-score coefficients should be reestimated when examining firms of different time periods or in different industries.

Methodology As mentioned earlier, the majority of international failure prediction studies employ MDA (Altman, 1984; Charitou et al., 2004). This study employs MDA to allow better comparison with other international studies. This research plan avoids one previous criticism of MDA analysis. Ohlson (1980) is concerned about using predictors of failure

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that are derived from information published after bankruptcy has occurred. In this study, all information is from reports published at least three months before a company was delisted. Agarwal and Taffler (2007) emphasize the importance of testing the predictive ability of models against an entire population instead of using only a relatively small sample. The authors plan to address this issue in a subsequent study. The current research plan is to test the predictive ability of three Z-score based models using the matched pair technique. Two of the models are actually developed in this study.

Selection of Failed Firms In order to select failed firms, we must define “failure” first. “Failure” is defined as the inability of a firm to pay its financial obligations as they mature (Beaver, 1966).

In another words, insolvency. In the analysis in this paper, we work exclusively with firm insolvencies on the basis that these are clean measures. Because firm insolvency is such a stringent criterion, this approach potentially weakens the predictive ability of the Z-score model, in particular in terms of increasing the type II error rate – misclassification of nondelisted firms as delisted.

The failed firms in this sample are firms that were publicly listed in Shanghai Stock Exchange Market (SHSE) or Shenzhen Stock Exchange Market (SZSE) for at least two consecutive years and then were delisted during 2000-2008 due to financial problems.

According to the “Public Listing Regulation” published in 2000 by the China Securities Regulatory Committee, four situations will lead to the delisting of a publicly listed company. The first situation is privatization or other changes of shareholders composition. The second situation is failing to disclose financial information or financial fraud. The third situation is illegal activities by the listing firm. The fourth is being unprofitable for three consecutive years. This study selected only those firms that were delisted for either situation two or four. For firms delisted because of situation one, the company is not considered failed, only that the shareholders have decided to privatize the company or the company is merged into another company. For situation three, this study believes that firms delisted because of illegal activities are different from firms delisted because of financial problems. Firms delisted because of illegal activities might still be financially sound and thus cannot be predicted with financial ratios. We treat the event of being delisted as a clear signal of firm failure. We look at firm failure from the investors’ standpoint. Once the firm is delisted, its stocks become worthless since there is no platform for exchange of the stocks any more. The delisted firm in general will continue operating for a period of time, but shareholders have essentially lost their investment. Although there have been continuous demands for establishing platforms for exchanges of stocks of delisted firms, no such platform has been created.

Selection of matching firms The selection process was based upon a paired-sample design. For each delisted firm in the sample, a nondelisted firm of the same industry and asset size was selected.

If the exact match of asset size could not be found, the firm which had the closest asset size was chosen. The asset size was based upon the asset size reported on the last financial statement of the delisted firm and the asset size of the matching firm reported for that same year.

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Data Collection For every delisted and matching nondelisted firm, the financial data were manually collected for up to two years prior to delisting from www.sina.com.cn.

According to Altman (1968), the bankruptcy prediction model is an accurate forecaster of failure for up to two years prior to bankruptcy. Accuracy diminishes substantially as the lead time is increased. A total of 42 delisted firms (16 manufacturing companies) were collected along with 42 (16 manufacturing companies) matching nondelisted firms. We then randomly selected 12 out of the 42 delisted firms along with their matching nondelisted firms as the prediction or hold out sample to test the validity of our Z-score model. The final sample was divided into two subsamples: the estimation sample which includes 30 delisted firms and 30 matching nondelisted firms, and the prediction sample which includes 12 delisted firms and 12 matching nondelisted firms.

Results Descriptive statistics The average time between the actual delisting date and submission of the last financial report prior to the delisting for the 30 failed firms was eight months, ranging from three months to 23 months. The average asset size for the delisted firms was 466,629,673 Chinese dollars versus 747,952,379 for the nondelisted firms for the first year prior to failure. The respective numbers are 882,387,177 and 693,322,301 for the second year prior to delisting. There was a sharp decrease of mean total asset size of the delisted firms between the two financial reporting periods prior to delisting, while the total assets of the nondelisted firms increased.

The sizes of the firms vary. The total assets of the delisted companies range from RMB 21,514,900 to RMB 1,211,942,318 the first year before delisting. The sales of the delisted firms range from 0 to RMB 433,961,140 in the first year before delisting. The total assets of the nondelisted firms range from RMB 208,295,652 to RMB 2,394,944,689 for the corresponding year. The sales of the nondelisted firms range from RMB 5,918,570 to RMB 986,715,195 for the corresponding year.

The means of the financial ratios using the financial reports one and two years prior to delisting are summarized in Tables 1 and 2, respectively. The results are consistent between the estimation and prediction groups for both years. A comparison of the delisted and nondelisted variable means indicates that working capital/total assets (X1), retained earnings/total assets (X2), earnings before interest and taxes/total assets (X3), market value of equity/book value of total debt (X4) and sales/total assets (X5) are lower in the delisted than in the nondelisted group. The p-values for the test of mean differences between delisted and nondelisted companies are significant for each of these variables. The results are similar to those reported by Altman (1968) for his estimation sample except for the sales/total assets variable (X5), which is not significantly different between his bankrupt and non-bankrupt groups. The results reported by Grice and Ingram (2001) do not find significant differences between the distressed and nondistressed groups for variables X4 and X5.

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Table 1: Descriptive statistics for estimation subsample and prediction subsamples using the annual financial report one year prior to delisting

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Table 2: Descriptive statistics for estimation subsample and prediction subsamples using the annual financial report two years prior to delisting

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Classification accuracy of Altman’s (1968) Z-score model We evaluated the classification accuracy of Altman’s (1968) Z-score model using the estimation sample and prediction sample respectively. The Z-scores are derived for both samples using two years of financial data. The accuracy of the Z-score model is calculated by dividing the number of firms correctly predicted by the total number of firms in the sample.

Table 3 reports results of tests of Altman’s (1968) model. The model does fairly well for predicting the delisting of a firm, with accuracy ranging from 91.67% to 100%. The model tends to misclassify a nondelisted firm into the delisted group with Type II error ranging from 16.67% to 43.33%. The model does well using financial data 2 years prior to delisting, with an overall accuracy of 85% for the estimation sample and 87.5% for the prediction sample. The tendency to misclassify a nondelisted firm into the delisted group persists.

Table 3: Comparisons of classification accuracies using coefficients from Altman’s (1968) model

Classification accuracy of the reestimated model (one year prior to delisting) Additional evidence of the stationary nature of the Z-score model is obtained by reestimating the model’s coefficients using our estimation sample, then testing the prediction accuracy of our model using the prediction sample. Table 4 reports results for the reestimated model.

The sample of 30 delisted firms and the 30 corresponding nondelisted firms is examined using MDA. Since the discriminant coefficients and the group distributions are both derived from this sample, a high degree of successful classification is expected.

The Z-score model derived is: Z=0.8059X1-0.2898X2+0.0440X3+0.1971X4+6.3327X5 Firms with Z-scores less than 2.2373 are predicted to be delisted and Z-scores greater than 2.2373 are predicted to be nondelisted.

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Table 4: Comparisons of classification accuracies using newly derived coefficients

The model correctly predicted 90% of firms (54 out of 60), with both type I and type II error at 10%. This is higher than 76.67% overall accuracy for the estimation sample using Altman’s (1968) model. However, the Type I error is lower using Altman’s (1968) model (3.33%) compared with our model. Altman’s (1968) model misclassified 13 out of 30 nondelisted firms into delisted group while it only misclassified 1 out of 30 delisted firms into nondelisted group. The model this study derives misclassified 3 out of 30 nondelisted firms into the delisted group and 3 out of 30 delisted firms into the nondelisted group.

Classification accuracy of the reestimated model (two years prior to delisting) The second test is made to observe the discriminating ability of the model for firms, using data from two years prior to delisting. Fifty two out of 60 firms are properly classified (86.67%), with a Type I error of 10% and a Type II error of 16.67%. The prediction power of the model is quite constant across the two years. The prediction accuracy is 85% using Altman’s (1968) model with a Type I error of 0 percent and Type II error of 30%. Our model correctly classified 27 out of 30 delisted companies and 25 out of 30 nondelisted firms, while Altman’s (1968) model correctly classified all the 30 delisted firms and misclassified 9 out of the 30 nondelisted firms.

Cross-validation It is important to cross-validate the result using hold out data. Using data one year prior to delisting, 21 out of 24 of the prediction group firms (87.5%) are correctly classified using the derived Z-score model, with a Type I error of 16.67% and a Type II error of 8.33%. The model misclassified 2 out of the 12 delisted firms and 1 out of the 12 nondelisted firms. Altman’s (1968) model has an overall accuracy of 83.33%. It correctly classified all the delisted firms and misclassified 4 out of the 8 nondelisted firms.

Using two years prior to delisting data, our model arrives at exactly the same results as using one year prior to delisting data. Altman’s (1968) model has an overall

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accuracy of 87.5% and it misclassified 1 out of the 12 delisted firms and 2 out of the 12 nondelisted firms.

Analysis During the process of data collection, we noticed that the delisted firms’ total assets decreased over the two year period, while the nondelisted firms’ total assets increased.

Although no previous research has taken this into consideration, we believe it worth further exploration. We thus added another variable into the discriminant function.

The sixth variable is defined as follows: (Total assets one year prior to delisting – Total assets two years prior to delisting)/Total assets two years prior to delisting. We then applied a backward elimination procedure. Three variables remained after the procedure with a significance level of p<0.05. The specific p values are shown in Table 5. The three variables are: X4, X5 and X6. Using these three variables, we created another Z-score model, the revised Z-score model.

Z=0.2086X4+4.3465X5+4.9601X6 Table 5: Variables retained after backward elimination procedure

Firms with a Z-score smaller than 1.5408 are predicted to be delisted, while firms with a Z-score larger than 1.5408 are predicted to be nondelisted. The prediction results using the revised Z-score model are reported in Table 6. The revised model correctly classified 95% of the firms in the estimation sample. It misclassified 3 out of the 30 delisted firms and correctly classified all the nondelisted firms. The estimation sample overall accuracy rates of the revised model are 95% and 91.67% respectively for one year and two years prior to delisting. These rates were comparatively more accurate than those of Altman’s model at 76.67% and 85% and the re-estimated model at 90% and 86.67%. The cross validation results are also reported in Table 6. Using the prediction sample, the revised model yields superior results one year prior to delisting, and all 3 models yield the same overall accuracy for two years prior to delisting.

Relatively few studies of this type have been done in emerging countries. Sandin and Porporato (2007) did a study for Argentine companies. New Z-score variables specific to Argentine companies were developed. Then, both the new model and Altman’s original Z-score model were tested. Both models were found to have predictive ability, with the new model enjoying enhanced predictive power. Thus, both the current study on Chinese companies and Sandin and Porporato (2007) support the contention that the Z-score model is an effective predictor of company

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failure in emerging countries, especially when the model is revised based on data from the specific country being studied.

Table 6: Comparisons of classification accuracies using revised Z-score model

Because of the relatively small number of failed firms during the period under study, all failed firms were included regardless of their industry. As mentioned earlier, of the 84 firms used in this study, only 32 (16 failed and 16 healthy) were manufacturing firms. It is interesting to note that even though Altmanâ&#x20AC;&#x2122;s original Zscore model was developed based only on manufacturing firms, it performed well on this cross section of Chinese firms.

Our study shows that the revised model with three variables has a comparatively more accurate prediction than both the Altmanâ&#x20AC;&#x2122;s model and the reestimated model using one year prior to delisting data for both the estimation and the prediction sample. The revised model also has comparatively more accurate prediction than both the Altmanâ&#x20AC;&#x2122;s model and the reestimated model using two years prior to delisting data for the estimation sample. The three models perform the same using two years prior to delisting data for the prediction sample. Table 7 summarizes the results.

Table 8 shows the results in separate charts to facilitate a comparison.

Table 7: Comparison of classification accuracies of different models

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Table 8: Comparison of classification accuracies of different models (Chart presentation)

Conclusion Our study supports the effectiveness of the Z-score methodology for predicting company failure in China. Overall, the re-estimated model with recalculated coefficients but the same five financial ratios as Altman’s model has a higher prediction accuracy for the nondelisted group, while Altman’s (1968) model has higher prediction accuracy for the delisted group. Our revised model with three financial ratios has higher overall prediction accuracy than both the re-estimated model and Altman’s model. The revised model includes a financial ratio that is not considered in the other two models. It is defined as follows: X6 = (Total assets one year prior to

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delisting – Total assets two years prior to delisting)/Total assets two years prior to delisting. This variable indicates the extent of asset decrease from two years to one year prior to delisting.

Our models use companies from various industries. The models developed should apply to a wide variety of firms. Due to the limitations of data access and the matched sample method when estimating Z-score model, this study uses a relatively small sample. One of the criticisms of failure prediction models in general, is that they have not been tested on an entire underlying population (Agarwal & Taffler, 2007). Future research is planned to test the 3 models in this paper against the entire population of Chinese listed companies for a longer period. Future research also is planned to employ Ohlson’s (1980) logit model with a large sample or whole population. It then may be possible to compare the efficacy of MDA versus logit for Chinese listed companies.

References Agarwal, V. & Taffler, R.J. (2007). Twenty-five Years of the Taffler Z-score Model: Does It Really Have Predictive Ability? Accounting and Business Research, 37(4): 285-300.

Altman, E.I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporation Bankruptcy. The Journal of Finance, 23: 589-609.

Altman, E.I. (1984). The Success of Business Failure Prediction Models: An International Survey. Journal of Banking & Finance, 8: 171-98.

Beaver, W.H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4: 71-111.

Boritz, J., Kennedy, B. & Sun, J.Y. (2007). Predicting Business Failure in Canada.

Accounting Perspectives, 6(2): 141-65.

Charitou, A., Neophytou, E. & Charalambous, C. (2004). Predicting corporate failure: Empirical Evidence for the UK. European Accounting Review, 13(3): 465-97.

Deakin, E.B. (1972). A Discriminant analysis of Predictors of Business Failure. Journal of Accounting Research, 10: 167-79.

Grice, J.S. & Dugan, M.T. (2001). The Limitations of Bankruptcy Models: Some Cautions for the Researcher. Review of Quantitative Finance & Accounting, 17(2): 151-166.

Grice, J.S. & Ingram, R.W. (2001). Tests of the Generalizability of Altman’s Bankruptcy Prediction Model. Journal of Business Research, 54(1): 53-61.

Jones, F. (1987). Current Techniques in Bankruptcy Prediction. Journal of Accounting Literature, 6: 131-64.

Ohlson, J.A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy.

Journal of Accounting Research, 18(1): 109-31.

O’Leary, D. (1998). Using Neural Networks to Predict Corporate Failure. International Journal of Intelligent Systems in Accounting, Finance and Management, 7(3): 187-97.

Sandin, A. & Porporato, M. (2007). Corporate Bankruptcy Prediction Models Applied to emerging Economies: Evidence from Argentina in the Years 1991-1998.

International Journal of Commerce and Management, 17(4): 295-311.

Taffler, R.J. (1983). The Assessment of Company Solvency and Performance Using a

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Statistical Model. Accounting and Business Research, 15(52): 295-308.

Wilcox, J.W. (1973). A Prediction of Business Failure Using Accounting Data. Journal of Accounting Research, 11: 163-79.

Zavgren, C. (1983). The Prediction of Corporate Failure: The State of the Art. Journal of Accounting Literature, 2: 1-38.

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Executive Compensation as a Moderator of the Innovation â&#x20AC;&#x201C; Performance Relationship1 Kathleen K. Wheatley University of Tennessee at Chattanooga D. Harold Doty University of Texas at Tyler

Little research has been done to try and connect type of compensation with the use of a specific competitive strategy. We propose that compensation (percentage of base, bonus, options-granted, and stock for the top management team) will moderate the innovation strategy to performance relationship based on risk and time horizon. Analyses of panel data from 1994 to 1998 for 380 firms show that the innovation strategy to performance relationship is moderated by bonus and options-granted compensation.

These findings suggest that implementing an innovation strategy and using a high percentage of bonus compensation will lead to greater performance.

Alternately, implementing an innovation strategy and using a low percentage of options granted will create the best outcome. Our findings help shed light on the firm-specific mechanisms that enable strategy implementation.

Recent global and economic conditions have reduced the slack available to organizations and have also heightened the need for effective strategy implementation.

Given global economic realities, it is critical that firms focus on all aspects of the organization necessary to implement their chosen strategy. Previous research has demonstrated that a variety of organizational attributes are critical to implementation efforts. These include supply chain coordination, organizational design, workforce configuration, and human resource management policies (Shaw, Gupta & Delery, 1 This study has been partially funded by the Snyder Innovation Research Center at Whitman School of

Management, Syracuse University.

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2001; Slater & Olson, 2001). Firms that establish a better fit between organizational attributes and their strategy are better able to implement the strategy and have performance advantages as well (Allen & Helms, 2002; Gomez-Mejia, 1992; Lerner & Wulf, 2007; Slater & Olson, 2001; Yanadori & Marler; 2006; Xue, 2007).

A second area of popular concern, particularly after highly visible corporate collapses, bankruptcies, and accounting scandals, is the role of executive compensation in firm performance. Much of the current compensation research has been framed using agency theory (Fama & Jensen, 1983; Jensen & Meckling, 1976) and has provided inconsistent findings (Barkema & Gomez-Mejia, 1998). As a result of these divergent findings, some researchers have suggested looking outside of the agency framework (Garen, 1994; Jensen & Murphy, 1990). We agree that limiting our viewpoint to agency theory and considering only the direct relationship between compensation and performance is too restrictive. This restriction is not only responsible for some of the divergent compensation results, but has also delayed the integration of compensation research in the area of strategy implementation.

An issue at the intersection of the implementation and compensation research is the role of executive compensation in strategy implementation. As noted by Barkema and Gomez-Mejia: An unresolved issue that remains to be explored is the extent to which the design of a CEO compensation package supports the implementation of a given strategy or instead, helps determine a firmâ&#x20AC;&#x2122;s strategic choices (1998, p. 139).

While Barkema and Gomez-Mejia do not focus explicitly on strategy implementation, they do provide a general framework for understanding executive compensation based on criteria, governance, and contingencies.

Our research contributes to the literature by examining the importance of executive compensation for firms implementing an innovation strategy. We chose to investigate innovation strategies since such strategies incorporate two constructs relevant to compensation research: time horizon and risk. Time horizon, as used in compensation research, typically is defined as either short-term or long-term. Time horizon is especially important to innovation strategy since innovation itself is generally considered a long-term commitment. There is a great deal of up-front research and development (R&D) expenditure that must be undertaken before receiving any future benefit. In addition, innovation strategies incorporate greater strategic risk. As strategy risk increases, executives will attempt to reduce their exposure to this risk (Harrison & March, 1984; Miller & Friesen, 1982) even though risk-taking has been shown to have a positive effect on firm performance (Aaker & Jacobsen, 1987; Gilley, Walters & Olson, 2002).

In summary, this study is intended to extend the compensation and innovation literatures in three ways. First, we attempt to understand the role compensation plays in enabling the implementation of an innovation strategy. Second, we base our moderating arguments on the role of risk and time-horizon in combination with compensation and strategy. Finally, we employ a panel data methodology (380 firms over a 5 year time period) in order to benefit from both cross-sectional and time series data.

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Innovation Strategy and Executive Compensation The major thrust of our argument is that the appropriate executive compensation policy will facilitate the implementation of an innovation strategy. Thus, we expect executive compensation to moderate the relationship between innovation strategy and firm performance. To develop our argument, we begin by briefly discussing innovation and then exploring four elements of executive compensation as a function of time horizon and risk.

Innovation Strategy One way in which firms try to compete within (and buffer against) the competitive landscape and environmental uncertainty is through the increased use of innovation, either for preemptive reasons or in response to internal or external environmental change (Damanpour, 1991; Hage, 1980; Thompson, 1967). A defining component of an innovation strategy is the firmâ&#x20AC;&#x2122;s spending on R&D. The operationalization of innovation as R&D spending is well-suited for the purposes of this study for three reasons. First, R&D decisions are directly related to the implementation of an innovation strategy. Second, R&D spending is under the direct control of the CEO and top management team (TMT). Thus, executive compensation policies are likely to have a greater effect on the firmâ&#x20AC;&#x2122;s R&D spending. Third, decisions about R&D spending incorporate (either explicitly or implicitly) statements about risk preferences and organizational time horizons. Each of these two constructs is used below to characterize important elements of executive compensation.

Executive Compensation Many of the important differences between the various forms of compensation can be represented by two interdependent constructs: risk and time-horizon (Table 1). We suggest that risk is a crucial factor in the compensation-performance relationship.

Risk reduction is dependent on the type of compensation provided. If executives are not in fear of losing compensation based on performance, they may be more likely to take on the additional strategy risk. If their compensation is tied directly to firm performance and a loss of compensation is possible, the need to reduce their risk would be more likely, resulting in the desire to implement a less risky strategy.

Table 1: Compensation Time Horizon and Risk Relationship

Base compensation. Quadrant 1 (Table 1) shows slow risk and short-term and is defined as basic cash compensation that an employer provides in exchange for work performed. Because of this low compensation risk, executives would feel more at liberty to attempt implementation of a higher-risk strategy (i.e. base would be

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considered over bonus because of the lower risk). Innovation strategy is defined as high risk/high return (Hansen & Hill, 1991; Hitt, Hoskisson & Ireland, 1990).

Therefore, if executive compensation is not contingent upon implementation success, as in base compensation, the strategic leadership would enjoy more freedom to attempt to implement a higher risk strategy. Firm executives would be motivated to implement an innovation strategy because of the transparent potential for payout.

As the proportion of base pay increases, the strategic leadershipsâ&#x20AC;&#x2122; comfort with risk taking would also increase (especially when compared to bonus). An alternate perspective on base compensation is that if no compensation risk were involved, executives would be less likely to implement a more risky strategy due to their desire to follow the status quo. However, as defined, innovation strategy is high risk/high return. Anticipation of high return may be one factor that drives risk-taking and enables the implementation of an innovation strategy.

Therefore we hypothesize, Hypothesis 1: The percentage of base compensation moderates the relationship between innovation strategy and firm performance.

Bonus compensation. Quadrant 2 (Table 1) reflects high-risk and short-term bonus compensation and ties compensation to short-term success or performance measures. Bonus is considered high-risk because of the short-term nature and the contingency on performance (especially when compared to base). Bonus pay is often predicated on specific performance standards, thus the TMT is aware of what needs to be accomplished in order to capitalize on the bonus pay component. The risk of not being granted a bonus is an important factor to consider. However, bonus has a shortterm time frame which provides the TMT with less ambiguity and better forecasting techniques. It is easier to forecast the result of a decision in the short-term versus considering the long-range implications of decisions as in the case of options compensation. Compared with strictly base compensation, bonus compensation has greater risk in implementing a high-risk innovation strategy.

A short-term, results-based bonus, especially if it constitutes a large portion of the compensation package, will discourage executives from taking the long-term risk involved with innovation strategy because of the lack of predictable compensation.

Implementing an innovation strategy is a long-term endeavor. A firm needs to make a conscious decision to pursue innovation and needs to provide ample resources. If a firm were to provide short-term compensation in the form of bonus, this would not support the long-term orientation of the innovation strategy. Thus, no relationship would be present to tie bonus and firm performance together. If executives are presented with specific performance criteria for bonus compensation, they will most likely do whatever is necessary to gain that bonus, instead of focusing on the longterm implications. Another aspect of bonus compensation is the difference between a bonus being available (which motivates future performance) and the actual awarding of a bonus which rewards prior performance. Stock and options are similar in that they reward future performance with anticipation as the motivator and realization (or nonrealization) as the reward.

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Hypothesis 2: The percentage of bonus compensation moderates the relationship between innovation strategy and firm performance.

Options compensation. Options compensation which is low-risk and long-term (see Quadrant 3, Table 1), provides the most flexibility for executives. The individual executive has the most control over options, as individuals choose whether or not to exercise them. This compensation method provides the strategic leadership with the ability to hedge against a negative outcome using their incentive compensation. In the event that their projects/innovations are unsuccessful, the strategic leadership could choose not to exercise their options and instead wait until the firm moves into a more favorable position. This flexibility promotes risk-taking by the TMT and mitigates the inherent risk of an innovation strategy.

To better understand options compensation, we contrast it with stock compensation based on three key differences: 1) amount of control and flexibility, 2) downside risk, and 3) ability to buffer. Ultimately, stocks and options are the same piece of company ownership. However, the options alternative gives individuals the choice of whether or not they want that piece of ownership at a specific point in time, with a specific price and value. Options must be exercised to become shares of stock, with the decision of timing being made somewhat by the individual. The second major difference is downside risk. With stock compensation, downside risk is always present. If the firm's stock begins to fall, the strategic leadership has no way to change their compensation.

However, with options, if the stock begins to fall, the strategic leadership could choose not to exercise their options and thus, endure no downside risk. Although the risk of options is much lower, and the downside risk is minimal, there are some who would argue that options do carry with them an opportunity cost, which should be figured into downside risk. Finally, because the environment is constantly changing, the use of options provides executives with the opportunity to buffer against poor performance and fluctuations in internal and external environments.

Options carry with them no downside risk essentially, whereas stock compensation does carry some of that risk. It is this lack of risk that promotes more risk-taking in strategy implementation. The risk literature provides support for the distinction between stock and options compensation by suggesting that as contingent compensation increases, managersâ&#x20AC;&#x2122; risk-taking propensity decreases (Finkelstein & Hambrick, 1988; Zajac, 1992). Presumably, options are given in lieu of a higher level of base compensation, with the thought that executives will be positively motivated to look for long-run increases in the stockâ&#x20AC;&#x2122;s value. The lack of downside risk aligns options compensation with innovation strategy and should improve firm performance.

From the dynamic perspective (as opposed to a static one), options do carry risk. This is especially apparent in today's economic environment where executives and directors have lost substantial amounts of money because of the increased use of options compensation. As the firm's stock price falls below the options purchase price, the value of the compensation becomes worthless.

So with innovation strategy (high-risk), options will provide less compensation risk than that of stock compensation. Therefore,

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Hypothesis 3: The percentage of options compensation moderates the relationship between innovation strategy and firm performance.

Stock. The final quadrant, Quadrant 4 (Table 1), is stock compensation (high-risk and long-term). As compensation risk increases, so does the strategic leadershipsâ&#x20AC;&#x2122; risk aversion, making it less likely that they will attempt to implement a risky endeavor such as an innovation strategy (Beatty & Zajac, 1994; Gomez-Mejia, 1994; Gray & Cannella, 1997; Hill & Phan, 1991; Wiseman & Gomez-Mejia, 1988). Stock compensation is considered pay for performance and the strategic leadership does not have discretionary control over this type of compensation. Restricted and common stock is awarded to executives without their making the decision to exercise (unlike options compensation). Similar to bonus type compensation, a specified level of performance is defined, and if the strategic leadership meets or exceeds this target, they are rewarded (i.e. bonus is also high-risk on the short-term continuum). Because of this lack of exercise choice and long-term characteristic, stock carries the most risk for executives. Stock compensation is used to align the interests of the TMT with the shareholders by providing rewards for increasing shareholder value (Jensen & Murphy, 1990). The TMT's fear of adversely affecting present shareholder value would deter the TMT from taking what they perceive to be high-risk actions. In the case of high innovation strategy (high-risk), a low-risk compensation type would be preferred (i.e. base or options). Thus, Hypothesis 4: The percentage of stock compensation moderates the relationship between innovation strategy and firm performance.

Methods Sampling and Data Collection Publicly traded firms were selected from ten industries that varied based on R&D intensity. Only publicly traded firms were used because of the sensitive nature of compensation data. A two-stage process was employed during sample identification.

First, compensation data were collected by industry from the Execucomp database which contains data on companies in the Standard & Poor (S&P) 1500. Next, these data were matched to data from Compustat, removing companies with missing R&D data. We selected the final sample based on industries with the greatest number of matches and varying levels of R&D intensity (measured by R&D expenditure/number of employees) (Hill & Snell, 1988; Scherer, 1984).

Compensation data covered a 5-year time span (1994-1998). Performance data were lagged to cover 1995-1999 in order to better estimate the effect of compensation on future performance (Finkelstein & Boyd, 1998). Our final sample consisted of 1900 observations and included data on 380 firms. All dollar values were adjusted for inflation and all data were archival. In addition, outliers were removed from the sample and normality was checked for each variable. Variables that were not normal were transformed when possible by using the natural log.

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Independent and Moderator Variables Innovation strategy. Innovation strategy was measured using R&D expenditure per sales as an indicator of what is being accomplished from R&D money spent, controlling for firm size. This strategy also provides a richer variable than using R&D expenditure alone (Hansen & Hill, 1991; Hay & Morris, 1979; Meyer-Krahmer & Reger, 1999; Scherer, 1984). This is an important indicator of an innovation strategy since the focus is on how companies transform R&D money into a successful outcome.

Compensation. Compensation data came from the S&Pâ&#x20AC;&#x2122;s Execucomp database, which is compiled from SEC Filings requiring compensation information for the CEO and the 4 highest paid executives. Compensation was divided into base, bonus, options granted, and stock representing both short- and long-term compensation. All compensation was reported in dollars. The value of options granted was estimated using a Black-Scholes based (1973) option valuation model, which incorporates the exercise price of the option, the option term until exercise, an interest rate factor, a volatility factor, and dividend rate.

To calculate percent compensation, we summed each compensation type over all executives listed. A grand total of all compensation (base, bonus, stock, options granted) for each TMT was then calculated for use in generating the percentage compensation figure. These percentages were used for hypothesis testing, trying to tease out the role each compensation type plays in enabling the implementation of an innovation strategy.

Dependent and Control Variables Financial performance. Return On Assets, Return On Equity, and Earnings Per Share data were collected from the Compustat database maintained by the S&P. After preliminary analysis provided similar results for all three financial measures, we performed a factor analysis to assess the number of factors present (Gomez-Mejia, Tosi & Hinkin, 1987; Tosi & Gomez-Mejia, 1994). This analysis suggested the presence of only one factor with all component loadings greater than 0.5. The loadings were as follows: EPS (.781); ROA (.882); ROE (.862); Eigenvalues (2.132); Percent of Variance=71.057. In order to create one aggregate measure, we multiplied the variableâ&#x20AC;&#x2122;s z-score by the factor loading, then summed the three weighted scores to create the final variable called financial performance (Gomez-Mejia et al., 1987; Tosi & Gomez-Mejia, 1994).

Control variables. We controlled for industry using dummy variables based on a 2-digit SIC code. Company and year were also controlled through dummy variables from our use of the least squares dummy variable (LSDV) analysis, which categorizes data into groups.

Analysis We employed a panel data methodology using LSDV because of our use of crosssectional (380 firms) as well as time series data (5-years). Two of the key problems with panel data methodology are heteroscedasticity and auto-correlation (Hannan & Young, 1977). In this case, ordinary least squares (OLS) are ineffective in determining the regression estimates.

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To interpret the direction of the moderating term, a graphing procedure was used whereby the independent variable (innovation strategy) was categorized as high or low, as was the moderator variable (i.e. high percent base compensation and low percent base compensation) (Cohen & Cohen, 1983; Dwyer & Fox, 2000; Hitt et al., 2001; McFarlin & Sweeney, 1992; Welsh & Dehler, 1988). This information was then graphed, resulting in two representative lines plotted against the independent variable (x-axis) and the dependent variable (y-axis). For example if the moderator of interest was percent base compensation the resulting lines would be high percent base compensation and low percent base compensation. The lines were then interpreted for the direction of slope, as well as interception of the two lines.

Results The correlations, means, and standard deviations of all the study variables are presented in Table 2. Innovation strategy (measured by R&D/Sales) is positively and significantly correlated with percent base compensation and percent options compensation. Alternatively, innovation strategy is negatively and significantly correlated with percent bonus compensation and percent stock compensation.

Financial performance is significantly correlated with all independent and moderator variables.

Table 2: Descriptive Statistics and Zero-Order Correlation Coefficients

Table 3 presents the results of hypothesis testing. The results are presented in hierarchical fashion to better represent the effect of the interaction between innovation strategy and compensation. Model 1 includes dummy variables for company, year, and industry (coefficients not shown), innovation strategy, and compensation. Model 2 expands on Model 1 by adding the interaction between innovation strategy and compensation.

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Hypothesis 1, which predicted a significant moderating effect of base compensation on the innovation strategy-performance relationship, was not supported. The coefficient for base compensation was not significant with financial performance as the dependent variable.

Table 3: Results of Generalized Least Squares Regression Analysis of Innovation Strategy and Base Compensation Effects on Firm Financial Performance

Hypothesis 2, which predicted a significant moderating effect of bonus compensation on the innovation strategy-performance relationship, was supported.

All of the eight models with the interaction term entered were significant. The coefficients for percent bonus compensation were both positive and significant with financial performance as the dependent variable (β=.37, p<.001; F=32.92, p<.001).

Hypothesis 3, which predicted a significant moderating effect of options compensation on the innovation strategy-performance relationship, was also supported. The coefficient for percent options granted compensation was both negative and significant with financial performance as the dependent variable (β=-.20, p<.001; F=20.45, p<.001).

Hypothesis 4, which predicted a significant moderating effect of stock compensation on the innovation strategy-performance relationship, was not supported for financial performance.

The models with significant interaction effects were further analyzed to correctly interpret the interaction effects. We followed Dwyer and Fox (2000) and graphically represented the moderating effect of compensation on innovation strategy and performance. Figure 1 illustrates the bonus compensation interaction for financial performance. The interaction graph for bonus compensation suggests that for both low and high innovation strategy (measured as R&D/Sales), the use of high bonus compensation is most beneficial. We interpret the results in this manner because the low and high base compensation lines do not intersect (nor are they parallel).

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Figure 1: Interaction Between R&D/Sales and TMT Percent Bonus Compensation

The options compensation graph (Figure 2) has the most interesting interpretation because the high and low compensation lines intersect. This suggests that for high innovation strategy (measured as R&D/Sales), the use of low-percent options granted compensation is most beneficial. Alternatively, for low innovation strategy, the use of high-percent options granted appears to provide improved financial performance.

Figure 2: Interaction Between R&D/Sales and TMT Percent Options Granted Compensation

Discussion and Conclusion In this paper, we investigated the relationship between innovation strategy and firm performance, especially under various conditions of short- and long-term compensation. Our findings provided a road map for companies that are pursuing an innovation strategy and need to design the most beneficial compensation package for

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their top management team. Companies pursing an innovation strategy should design their compensation packages in such a way as to be heavy on bonus and light on option type pay. For companies not focused on innovation, compensation packages should still be heavy on bonus type pay, but also heavy on option pay.

We drew on agency theory as well as the risk and time horizon relationship in order to frame our ideas and explain this relationship. Analyses of data from 380 firms over 5 years support some of our assertions. Results indicated that compensation does moderate the innovation strategy to the firm performance relationship when considering bonus and options compensation. More specifically, we found that shortand long-term compensation have different driving mechanisms in organization decision-making when regarding strategy implementation.

We used a two-by-two matrix to model our arguments and show the distinction between types of compensation. These arguments were also framed using risk to try and understand what is driving managersâ&#x20AC;&#x2122; decision-making. Our results suggest that all strategies, whether they be low- or high-risk require short-term compensation.

This provides additional support for the focus of compensation being placed on the time component of compensation, as opposed to the risk component. Our findings defined this difference by showing that high-percent bonus compensation is related to greater performance levels, no matter the strategy risk involved. We believe these findings emphasize the pay-for-performance relationship (one that is especially prevalent in todayâ&#x20AC;&#x2122;s organizations) and highlight the positive benefits of bonus compensation. Bonus compensation has the added benefit of being a clearer, more predictable form of compensation since bonus pay occurs in the short-term. It is easier for managers to forecast and predict short-term effects of strategy implementation than long-term effects.

Alternatively, long-term compensation and level of risk provide different findings.

Our findings suggest that if low-risk strategies are being implemented, compensation can be tied directly to performance in the form of long-term compensation without any reduction in firm performance. In contrast, when high-risk strategies are being implemented, long-term compensation must not be tied directly to performance in order to foster better firm results. This result is an important finding and should be considered when determining compensation packages.

Contributions, Limitations, and Future Directions for Research We made three significant contributions to the strategic management literature.

First, we tried to address and translate Barkema and Gomez-Mejiaâ&#x20AC;&#x2122;s (1998) call for research into how compensation is related to strategy implementation. This paper is one of the first to treat compensation as a moderating factor and suggests that compensation enables the implementation of a specific strategy. Secondly, we extended the compensation literature by basing this moderating relationship not only on compensation time-horizon, but the risk relationship as well. Finally, we utilized panel data methodology, which maintains the richness of cross-sectional and time-series data.

In spite of the above contributions, there are some important limitations to this research. One such limitation was the use of completely archival data. Although some would argue that archival data are more accurate than informant data, archival also has

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limited richness. The main limitation for this study arises when measuring innovation strategy. We were looking to capture the broadest possible conceptualization of innovation strategy. However, using archival sources limited our measuring capability.

Although we set out to cast a broad net, the R&D measure used is skewed toward product innovation.

Sample selection was also a problem. In the original design of the study, we attempted to sample from 6 industries (2 low R&D intensity, 2 medium R&D intensity, 2 high R&D intensity) providing a "balanced" sample. In addition, we hoped to stratify the sample by size to focus on business level decisions, as opposed to corporate level ones. The available data did not allow for this split. Of the 380 companies in the final sample, 348 fell in the greater-than $100,000,000 sales category. Our final sample selection consisted of 10 industries. This change in the design was necessary due to limited compensation data. The final sample was also somewhat unbalanced. A single industry, Chemical and Allied Products (SIC 28), considered high R&D intensity had 86 companies. At the next level of R&D intensity, 4 industries were represented with 202 companies. At the low R&D intensity end, 5 industries were represented with 92 companies.

This study moved research a step closer to understanding the intricacies of strategy implementation. Although this study did not open the “black box” of implementation, it did shed some light on mechanisms that enable implementation. Future studies might look to broaden the sample with additional industries and a more balanced design to enhance the generalizability. Investigating other strategies and the role of the enabling mechanism holds many possibilities as well.

There are also additional opportunities in considering other enabling mechanisms.

For instance, options research is becoming much more popular and useful in examining the incentive relationship. We merely scratched the surface looking at options granted as representative of long-term compensation in this study. A much more in-depth investigation of options may help to shed more light on this “special” compensation type, especially as ethical and legal issues surround this form of compensation. Options have many more components to consider such as type of options granted, time period for vesting, and awards schedule, all of which may prove to be a driving factor for the interaction between strategy and compensation.

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