International Bulletin of Business Administration - EuroJournals

International Bulletin of Business Administration
ISSN: 1451-243X
Issue I
June, 2006

INTERNATIONAL BULLETIN OF BUSINESS ADMINISTRATION
http://www.eurojournals.com/BUSINESS.htm
Editor-In-Chief
Adrian M. Steinberg, Wissenschaftlicher Forscher
Editorial Advisory Board
M. Femi Ayadi, University of Houston-Clear Lake
Jwyang Jiawen Yang, The George Washington University
Emanuele Bajo, University of Bologna
Constantinos Vorlow, University of Durham
Zhihong Shi, State University of New York
Jan Dutta, Rutgers University
Maria Elena Garcia-Ruiz, University of Cantabria
Christos Giannikos, Columbia University
Emmanuel Anoruo, Coppin State University
H. Young Baek, Nova Southeastern University
Wen-jen Hsieh, National Cheng Kung University
George Skoulas, University of Macedonia
Mahdi Hadi, Kuwait University
David Wang, Hsuan Chuang University
Stylianos Karagiannis, Center of Planning and Economic Research
John Mylonakis, Tutor - Hellenic Open University
Athanasios Koulakiotis, University of the Aegean
Basel M. Al-Eideh, Kuwait University
Gregorio Giménez Esteban, Universidad de Zaragoza
Mukhopadhyay Bappaditya, Management Development Institute
Katerina Lyroudi, University of Macedonia
Narender L. Ahuja, Institute for Integrated Learning in Management
Amita Mital, Xavier Labour Relations Institute
Wassim Shahin, Lebanese American University
M. Carmen Guisan, University of Santiago de Compostela
Zulkarnain Muhamad Sori, University Putra Malaysia
Syrous Kooros, Nicholls State University
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International Bulletin of Business Administration
Issue I
June, 2006
Contents
Social Identity Enhancement Strategies Used in the Workplace 6-14
Craig R. Erwin
Keeping the Wheels of the South African Automotive Industry Turning: Challenges
Facing Exporters within the Automotive Component Manufacturing Industry 15-24
Micheline Naude
Size and Book-to-Market Risk Factors in Earnings and Returns for the Greek Stock Market 25-41
Grigoris Michailidis, Stavros Tsopoglou and Demetrios Papanastasiou
Business Performance Measurement Frameworks and SMEs 42-47
George Ntalakas, Athanassios Mihiotis and John Mylonakis
The Effects of Emotional Intelligence On Leader Impression Management and Group
Satisfaction 48-64
David E. Gundersen and Elizabeth J. Rozell
Managing Mobile Commerce Quality: A Long Way to Run 65-74
Emmanouil Stiakakis
Description of Growth Price Model in a Random Environment 75-81
Basel M. Al-Eideh
Multicultural analysis on social influence and purchasing decision: East vs. West 82-91
Kritika Kongsompong
Acquisition Announcements, Firm Value and Volatility: The Case of Greek Financial Firms 92-102
Athanasios Koulakiotis, Nicholas Papasyriopoulos and Apostolos Dasilas
Profile of EMBA Students in AACSB Accredited Public and Private Institutions in the
United States 103-108
John Coleman, Stan Bazan, and Fred Tesch
The Import Problem and How Companies Operating in the United States Should Address
the Challenge 109-114
Scott D. Goldberg
Applying Benchmarking Practices in Small Companies: An Empirical Approach 115-126
Emmanouil Stiakakis and Ioannis Kechagioglou
An Empirical Analysis of The Federal Emergency Management Agency 127-134
G.S. Osho, M.O. Adams, R.N. Jones and I.D. Onwudiwe

International Bulletin of Business Administration
ISSN: 1451-243X Issue 1 (2006)
© EuroJournals, Inc. 2006
http://www.eurojournalsn.com
Social Identity Enhancement Strategies
Used in the Workplace
Craig R. Erwin
Department of Business Administration
Eastern Connecticut State University, 83 Windham Street, Willimantic, CT 06226
E-mail: erwinc@easternct.edu
Abstract
Social identity theory (Tajfel and Turner, 1986) contends that higher-status employees
(e.g., nonminorities) use a status maintenance strategy to enhance their social identities
whereas lower-status employees such as women and minorities use various strategies (e.g.,
social creativity, social mobility and social competition) to enhance their social identities. I
propose to test part of a model that Chattopadhyay, Tluchowska, and George (2004)
developed based on social identity theory and a related theory, self-categorization theory
(Turner, 1987). I provide a brief review of the literature on social identity and relational
demography and propose testable hypotheses to determine the extent to which social
identity enhancement strategies are used in the workplace and whether women and
minorities tend to identify more with their workgroups, with nonminorities, or with
demographically similar people. The study seeks to determine the extent to which women,
minorities and nonminorities attempt to enhance their social identities in the workplace. I
outline methods that I will use to test the hypotheses and discuss possible implications of
the study.
Key words: Diversity, workgroup, team, social identity, minority, gender
I. Introduction
Organizations and the groups within them are becoming increasingly diverse as they mirror society and
as organizations strive to provide employment and advancement opportunities for women and
minorities. As a result, organizations and groups must consider how best to draw on the various skills
and capabilities of a diverse workforce and minimize friction and miscommunication. Social identity
theory (Tajfel and Turner, 1986) holds that individuals use a strategy called social categorization to
cope with a complex environment. Social categorization is the process by which people categorize
themselves and others based on salient demographic characteristics such as sex and race. Social
categorization enables individuals to identify with others like themselves, satisfying a need for
affiliation. It also allows them to develop and maintain a positive social identity (Tajfel and Turner,
1986). This study tackles the following research question: To what extent do women, minorities and
nonminorities attempt to enhance their social identities in the workplace?
II. Literature Review
Although diverse teams are believed to have some advantages over homogeneous teams, some have
observed that too often diverse teams tend to underperform (Elsass and Graves, 1997). Social

categorization processes, which consist of group members evaluating themselves and other members
based on salient demographic characteristics, may adversely affect group processes, communication,
and outcomes as well as relationships in the group. Tajfel and Turner (1986) developed social identity
theory to describe the mechanism of social categorization. Turner (1987) extended the theory to
produce self-categorization theory. With these two theories Tajfel and Turner attempt to describe the
process by which women and minorities label themselves and are labeled by non-minority group
members based on salient demographic characteristics. Social identity theory posits that individuals
possess a social identity based on their membership in socially distinct groups (e.g., gender, ethnicity,
and profession). Social- categorization theory suggests that individuals use visible and salient features
(e.g., skin color) associated with socially distinct groups to categorize each other. The labels or
categories affect group communication and group member behavior and interactions. Tajfel and
Turner suggest that self-categorization is used by individuals to help them maintain and improve their
self-images. Chattopadhyay, Tluchchowska, and George (2004) developed a model which describes
how individuals use various strategies to help them achieve their personal goals in the workplace.
Chattopadhyay et al. contend that social categorization is likely to be conducted based on race and sex
because Bargh’s (1999) literature review revealed that race and gender-based stereotypes are
widespread and greatly affect individual thoughts, views and actions toward others.
By and large, in the past, white and male employees have enjoyed higher status, better job.s, higher
pay, and better employment and advancement opportunities than women and minorities (e.g., Amott &
Matthaei, 1991). Although this has begun to change in recent decades, such patterns are unlikely to
reverse themselves quickly and easily. However, the demographic diversity of workers in
organizations is increasing (e.g. Williams and O'Reilly, III, 1998). Understandably, women and
minorities are anxious to see systems and norms revoked that have frustrated the achievement of their
vocational goals. In contrast, white and male employees are reluctant to give up their preferred status,
let alone to accept a role-reversal in which women and minorities are given preferential treatment.
Although workgroups are likely to become increasingly diverse as workplaces become more
diverse, this trend will not be embraced by all employees. White males will be threatened by increasing
workplace and workgroup diversity as they become outnumbered by women and minorities. White
males will perceive that the status and benefits that they have enjoyed are likely to be taken away and
conferred on others. As a result, white males are likely to develop coping strategies to help them deal
with changes in workplace and workgroup diversity. One strategy likely to be used is to attempt to
maintain the status quo, keeping the prestige and benefits that white and male employees have
traditionally had.
Hypothesis 1. White and male employees are more likely than women and minority employees to prefer
the status quo.
In general, such comparisons favoring the in-group over the outgroup are easier to make for white
and male employees, because they have traditionally been accorded higher status in organizations than
minority and female employees--a practice that continues in organizations today (e.g., Amott &
Matthaei, 1991). The categories "white" and "male" thus may be associated with higher value in
organizations than the categories "minority" and "female."
White and male employees will then tend to identify with their own demographic categories,
because that will facilitate the enhancement of their social identity. This is referred to as a status
maintenance strategy, since it is associated with higher-status employees holding on to their status. I
note that this tendency to use a status maintenance strategy may, in turn, further reinforce sex and race
as salient bases of categorization in an organizational context.
Given that white and male employees have traditionally received better treatment and greater
resources and rewards than women and minorities, they have an incentive to stick together and to keep
from being lost in the crowd. If white and male employees band together, they may be able to stem the

tide, continuing to maintain the status quo. If, however, white and male employees are divided and
conquered, they will have no more power or prestige than any other category.
It is highly likely that white and male employees will identify with other white and male employees.
They share a number of bonds. It is they that have worked, socialized and made up the in-group for
centuries. They also have highly visible characteristics in common. Hogg and Terry (2000) claim that
employees in the in-group (e.g., white males) may identify with others like themselves if they tend to
have qualities thought to be more desireable. Ellemers, Kortekaas, & Ouwerkerk (1999) claim that
white and male employees who identify with others of the same gender and sex are likely to be
committed and emotionally involved with their demographic category. They are also likely to believe
that members of their demographic category are all in the same boat and destined to end up in the same
way (Ashforth & Mael, 1989).
Hypothesis 2. The more diverse the workgroup, the less that white and male employees will identify
with their workgroup.
Hypothesis 3. The more diverse a workgroup becomes, the more that male employees with identify with
other male employees.
Hypothesis 4. The more diverse a workgroup becomes, the more non-minorities will identify with other
non-minorities.
Male employees are used to having a variety of advantages in the workplace. They have
traditionally been in charge, had better access to resources, had better opportunities for advancement
and been viewed as having higher status than female employees. Male employees have nothing to gain
and much to lose if they lose their place in the sun. Understandably, they tend to stick together and
have similar views and ways of doing things. Male employees have difficulty empathizing with female
and minority employees because they have never been in their situation. Male employees embrace
tradition and resist change because they have nothing to gain from change. As such, they prefer
homogeneous groups to diverse groups, prefer to work with others like themselves, and prefer to
maintain the status quo. Male employees tend to be uncomfortable in diverse workgroups, avoiding
them if possible and clinging to other men. As such, male employees are unlikely to feel at home in
diverse workgroups or to embrace such groups and their goals.
It is likely that males will attempt to protect current systems and practices, viewing any efforts to
change the status quo as threatening (e.g., Brown, 1984). Researchers have found that members of a
group in power may take extraordinary steps to maintain power and are likely to hold negative views
of those attempting to make changes (e.g., Kanter, 1993).
Women and minority employees are likely to be more open-minded than male employees whether
for pragmatic or other reasons. In order to make their way into workplaces dominated by white males,
women and minorities have had to be flexible, creative, persistent and collaborative. As a result,
women and minority employees tend to be better team players, more adaptable and more accepting
than male employees. In order to gain entry to organizations and enjoy rewarding careers, women and
minority employees have had to accept treatment and outcomes that male employees would never have
tolerated. In many cases, female and minority employees had to adopt views, styles and manners of
those in charge (e.g., white males) if they wanted to get and keep jobs and advance. As a result, female
and minority employees are capable of understanding and accepting the views and ways of male
employees. They are also better team players, tending to put the team and its goals ahead of their own
agendas, goals and egos.
Hypothesis 5. Female employees are more likely to ID with male employees than male employees are
to identify with female employees.

Hypothesis 6. In diverse workgroups female and minority employees are more likely than white and
male employees to identify with their workgroups.
III. Methods
I developed a questionnaire (see Appendix 1) by making slight modifications to existing scales. The
questionnaire combines several measures which have been tested previously for validity and reliability.
I modified a social dominance scale from Sidanius & Pratto (1999), a pro-male bias scale from
Tougas, Brown, Beaton, and St. Pierre (1999), a group identification scale developed by Brown,
Condor, Mathews, Wade and Williams (1986), and Luhtanen’s (1990) collective self-esteem scale. In
addition, I developed a workgroup diversity scale. I also included additional items as necessary to
measure demographic characteristics of the respondent.
The published scales measure individuals’ beliefs or tendencies. The social dominance scale
measures whether individuals prefer to maintain or change the existing social order. The pro-male bias
scale measures whether individuals believe that one gender tends to be more competent or qualified
than the other. The group identification scale seeks to determine the extent to which an individual
identifies with his or her workgroup. The collective self-esteem scale was used to measure both the
extent to which a respondent identifies with his or her own ethnic group and with his or her own
gender. Finally, the workgroup diversity scale measures how diverse a respondent’s workgroup is
along the dimensions of age, tenure with the workgroup, gender, ethnicity, and education.
I tested the questionnaire on colleagues and students and then revised it to minimize confusion and
address various shortcomings. I used a snowball sample by having my students ask colleagues in their
workplaces or employed friends to complete questionnaires. Ninety-nine questionnaires were
completed and returned. The data from the completed questionnaires is to be entered into a
spreadsheet so that it can be analyzed using SPSS. ANOVA’s are to be conducted to compare means
and linear regressions will be run to test the strength of hypothesized relationships.
IV. Results
Scholars, managers, Human Resource professionals, and executives in multinational corporations can
best benefit from this study. They can gain a better understanding of strategies used by male, female
and minority employees to enhance their social identities. The results of this study should help
advance knowledge of diversity in organizations and of strategies used in the workplace to enhance
social identity.
Although much research has been conducted on diversity in organizations, the focus has been on the
behavior of women and minorities. This study also examines the behavior of white males. The study
can help determine if there are important differences in strategies used by various individuals in the
workplace. If employees are devoting time and effort to strategies intended to enhance their social
identities, productivity may suffer.
Managers with a need to know what they must consider as they expand, hire employees, and form
teams can benefit from this study. They can learn how to strengthen their teams and organizations and
increase employee morale, and team effectiveness and cohesiveness.
V. Discussion
It is likely to be difficult for a group to reach its potential if some members fail to identify with the
group. If an individual identifies with her workgroup, she is more likely to exhibit positive attitudes
and behaviors toward the group (Tsui, Egan, & O'Reilly, 1992) and to be a productive member. The
extent to which employees perceive that they are different from colleagues may affect organizational
outcomes such as absenteeism and turnover (Tsui, Egan, & O'Reilly, 1992). Identification with a

group influences group outcomes, such as cooperation, altruism, cohesion, and evaluation of the group
(Turner, 1982), as well as one’s loyalty to and pride in the group (Ashforth & Mael, 1989). Although
diverse teams are believed to have some advantages over homogeneous teams, they often
underperform (Elsass and Graves, 1997). At the same time that workplace diversity and diversity in
society at large are increasing, political pressure is increasing to reduce societal diversity through
tougher border controls and immigration law reforms. Decades after passage of the Civil Rights Act,
even though workplace diversity has increased sharply, women and minorities are still
underrepresented in areas such as upper management. Although we know that many women and
minorities successfully join organizations and advance, we know little about strategies that they and
nonminorities employ to maintain or change the status quo and if such strategies are successful.
Whether or not women and minorities are treated fairly and given equal opportunities and rewards,
it is their perception that will drive their behaviors. If they perceive that all employees receive equal
treatment and opportunities, their behavior will reflect it. They will feel less need to pursue strategies
to enhance their social identities. This will result in more productive teams and individuals better able
to cooperate and share information and less in need of devoting time to personal needs. If employers
better understand their employees and workgroups, including strategies used to change or maintain the
status quo, they can develop better hiring and promotion practices, provide more effective training, and
form and nurture more effective and efficient groups.
It is important to understand the extent to which an employee identifies with various individuals and
groups. Scholars claim that employee attitudes and behaviors are affected by the degree to which
employees identify with their workgroups (e.g., Tsui, Egan, & O'Reilly, 1992). Researchers have found
that identification with a workgroup is related to positive attitudes and behaviors toward the group
(e.g., Tsui et al., 1992). It seems reasonable that individuals who are emotionally attached to their
workgroup, feel that they belong in the group, are concerned about the group’s goals and outcomes are
more likely to exhibit positive behaviors and attitudes toward the group (Tsui et al., 1992).
This study is important because, decades after passage of the Civil Rights Act, women and
minorities are still underrepresented in upper management. This study will investigate strategies that
women and minorities employ to deal with workplace barriers to entry and advancement and the
strategies that nonminorities use to maintain the status quo. As the 21 st century marches on, it is
important for employers to understand how their employees see themselves, adopt values and attempt
to fit in and advance. Scholars believe that the extent to which employees perceive that they are
different from colleagues may affect organizational outcomes such as absenteeism and turnover
intentions (Tsui, Egan, & O'Reilly, 1992). This study will shed light on methods used by minorities
and nonminorities to deal with workplace diversity and disparities.
VI. References
[1] Amott, T., & Matthaei, J. 1991. Race, gender and work. Boston: South End.
[2] Ashforth, B. E., & Mael, F. 1989. Social identity theory and the organization. Academy of
Management Review, 14: 20-39.
[3] Bargh, J. A. 1999. The cognitive monster: The case against the controllability of automatic
stereotype effects. In S. Chaiken & Y. Trope (Eds.), Dual-process theories in social
psychology: 361-382. New York: Guilford Press.
[4] Brown, R. 1984. The effects of intergroup similarity and cooperative vs. competitive
orientation on intergroup discrimination. British Journal of Social Psychology, 23: 21-33.
[5] Chattopadhyay, P., Tluchowska, M. & George, E. 2004. Identifying the ingroup: A closer look
at the influence of demographic dissimilarity on employee social identity. Academy of
Management Review, 29(2): 180-202.
[6] Ellemers, N., Kortekaas, P., & Ouwerkerk, J. W. 1999. Self-categorisation, commitment to the
group and group self-esteem as related but distinct aspects of social identity. European Journal
of Social Psychology, 29: 371-389.

[7] Elsass, P.M., & Graves, L.M. (1997). Demographic diversity in decision-making groups: The
experiences of women and people of color. Academy of Management Review, 22: 946-973.
[8] Hogg, M. A., & Terry, D. J. 2000. Social identity and self-categorization processes in
organizational contexts. Academy of Management Review, 25: 121-140.
[9] Kanter, R. M. 1993. Men and women of the corporation. New York: Basic Books.
[10] Tajfel, H., & Turner, J.C. (1986). The social identity theory of intergroup behavior. In S.
Worchel and W.G. Austin (Eds.), Psychology of Intergroup Relations. Chicago, IL: Nelson-
Hall.
[11] Tsui, A., Egan, T., & O'Reilly, C. A., III. 1992. Being different: Relational demography and
organizational attachment. Administrative Science Quarterly, 37: 549-579.
[12] Turner, J. C. 1987. A self-categorization theory. In M. Hogg, P. Oakes, S. Reicher, & M. S.
Wetherell (Eds.), Rediscovering the social group: A self-categorization theory: 42 67. Oxford:
Blackwell.
[13] Williams, K., & O'Reilly, III, C. (1998). Demography and diversity in organizations: A review
of 40 years of research. Research in Organizational Behavior, 20, 77-140.
Appendix 1 - Questionnaire
Strongly Mostly Unsure Mostly Strongly
Disagree Disagree Agree Agree
1. This country would be better off if we cared 1 2 3 4 5
less about how equal all people were.
2. If people were treated more equally we would 1 2 3 4 5
have fewer problems in the country.
3. It really is not a problem if some people have 1 2 3 4 5
more of a chance in life.
4. This country would be better off if inferior 1 2 3 4 5
groups stayed in their place.
5. We have gone too far in pushing equal rights 1 2 3 4 5
in this country.
6. We should strive for increased social equality 1 2 3 4 5
between groups.
______________________________________________________________________________
B. The following statements deal with how you view people in your workplace. Please circle the
appropriate number from 1 to 5.
Strongly Mostly Unsure Mostly Strongly
Disagree Disagree Agree Agree
7. In my organization, women in management 1 2 3 4 5
have the necessary qualifications to do their work.
8. In my organization, men in management have 1 2 3 4 5
the necessary qualifications to do their work.
9. In my organization, women are competent in 1 2 3 4 5
general.
10. In my organization, men are competent in general. 1 2 3 4 5
______________________________________________________________________________

C. The following statements deal with how you feel about your workgroup. Your workgroup is
the group at your workplace which you most feel a part of. Your workgroup might be your shift,
department, assembly line, crew, or project team. Or it might be a supervisory, management, or
executive team if you are a supervisor, manager, or executive. Typically you would interact with
members of your workgroup frequently and share common resources and goals. In a very small
organization, your workgroup might consist of everyone in the organization. If you are a
member of more than one workgroup, focus only on the group you feel you belong to most.
Please circle the appropriate number from 1 to 5.
Strongly Mostly Unsure Mostly Strongly
Disagree Disagree Agree Agree
11. I am a person who considers my workgroup 1 2 3 4 5
important.
12. I am a person who identifies with my workgroup. 1 2 3 4 5
13. I am a person who feels strong ties with my 1 2 3 4 5
workgroup.
14. I am a person who is glad to belong to my 1 2 3 4 5
workgroup.
15. I am a person who sees myself as belonging to 1 2 3 4 5
my workgroup.
16. I am a person who makes excuses for belonging 1 2 3 4 5
to my workgroup.
17. I am a person who tr ies to hide belonging to my 1 2 3 4 5
workgroup.
18. I am a person who feels held back by my 1 2 3 4 5
workgroup.
19. I am a person who is annoyed to say I’m a 1 2 3 4 5
member of my workgroup.
20. I am a person who criticizes my workgroup. 1 2 3 4 5
21. All of the people in my workgroup are close to 1 2 3 4 5
the same age.
22. The ages of the people in my workgroup vary 1 2 3 4 5
greatly.
23. Everyone in my workgroup has been in the 1 2 3 4 5
group for about the same number of years.
24. Some of the people in my workgroup have been in 1 2 3 4 5
the group for many more years than others.
25. All of the people in my workgroup have the 1 2 3 4 5
same gender.
26. My workgroup is about 50% men and 50% women. 1 2 3 4 5
27. All of the people in my workgroup have the 1 2 3 4 5
same ethnic background.
28. My workgroup has people with many different 1 2 3 4 5
ethnic backgrounds.
29. All of the people in my workgroup have about 1 2 3 4 5
the same educational background.
30. The educational backgrounds of the people in my 1 2 3 4 5
workgroup vary a great deal.

D. Please answer the following questions about your workgroup by circling the appropriate
response.
31. How long have you been a member of your workgroup (circle one)?
Under 1 year 1-2 years 3-5 years 6-10 years Over 10 years
32. How many people are in your workgroup (circle one)?
1-2 people 3-5 people 6-10 people 11-20 people Over 20 people
______________________________________________________________________________
E. The following statements deal with your ethnicity. Please circle the appropriate number from
1 to 5.
Strongly Mostly Unsure Mostly Strongly
Disagree Disagree Agree Agree
33. Overall, my ethnicity has very little to do with 1 2 3 4 5
how I feel about myself.
34. My ethnicity is an important reflection of who 1 2 3 4 5
I am.
35. My ethnicity is unimportant to my sense of 1 2 3 4 5
what kind of a person I am.
36. In general, my ethnicity is an important part 1 2 3 4 5
of my self image.
______________________________________________________________________________
F. The following statements deal with your gender. Please circle the appropriate number from 1
to 5.
Strongly Mostly Unsure Mostly Strongly
Disagree Disagree Agree Agree
37. Overall, my gender has very little to do with 1 2 3 4 5
how I feel about myself.
38. My gender is an important reflection of who 1 2 3 4 5
I am.
39. My gender is unimportant to my sense of 1 2 3 4 5
what kind of a person I am.
40. In general, my gender is an important part 1 2 3 4 5
of my self image.
______________________________________________________________________________

G. Almost finished. This section asks you to provide some information about yourself. This
questionnaire never asks your name. Please provide the information requested below.
41. Your age: under 18_____ 18-25 _____ 26-40_____
41-55 _____ Over 55 _____
42. Your gender: Male_________ Female________
43. Your ethnicity: Hispanic________ African American ________ `
Asian/Pacific Island ________ Native American ________
Caucasian_________ Other (Specify)____________________
44. Marital Status: Never married _____ Married ______ Widowed_______
Divorced ______ Separated ______
45. Highest Educational Level Completed:
Didn’t finish high school__________ Finished high school _______
Associates_________
Bachelors __________ Masters _________ Doctoral_________
46. Where were you born? In the U.S._______ Outside the U.S.________
47. Was one or more of your parents born outside the U.S.? Yes____ No____
Unsure____
48. Do you consider yourself to be: Religious?____ Spiritual?____ Unsure?_____
49. Your position with your primary employer (check all that apply):
Employee_______ Supervisor_______ Manager________
Owner_______ Executive_______ Other (specify)___________________________

International Bulletin of Business Administration
ISSN: 1451-243X Issue 1 (2006)
© EuroJournals, Inc. 2006
http://www.eurojournalsn.com
Keeping the Wheels of the South African Automotive Industry
Turning: Challenges Facing Exporters within the Automotive
Component Manufacturing Industry
Micheline Naude
School of Management
University of KwaZulu-Natal
Private Bag X01
Scottsville 3207, Pietermaritzburg
e-mail: naudem@ukzn.ac.za
naudem@ukzn.ac.za
Abstract
The South African automotive component industry faces huge challenges in a very
competitive global market. There are, however, also significant opportunities, which for
the purpose of this research paper are briefly highlighted. However, the primary focus is
the challenges facing South African exporters with special reference to selected subsectors.
The challenges have been approached from a supply chain perspective only.
This research paper includes a combination of literature review, interviews with
managers in the selected sub-groups and questionnaires that have been sent out to
determine the challenges facing automotive component exporters. The outcome of the
empirical research to establish these challenges will then be dealt with, together with the
findings with regard to the identified challenges. Recommendations on how to address
these in order to establish a competitive advantage as well as the limitations of the research
and suggestions for further research will be discussed.
Key words: OEMs, automotive component industry, competitive advantage, globilisation,
South African motor industry, MIDP, IRCCs, exports, challenges, continuous
improvement.
I. Introduction
A major transformation in the global economy has been taking place. Globalisation has increased the
scope of opportunities for well-established industries such as among others; the textile,
communications, automotive, computers and semi-conductors industry. In the past, national economies
were comparatively isolated from each other. Distance, time zones, language, national differences in
government regulations, culture and business systems also isolated national economies. Today, there is
a movement towards a world in which national economies are merging into a mutually dependent
global economic system, commonly referred to as globalisation (Hill, 2001:4).
Because of globalisation, international competition has increased performance standards in many
facets, for example cost, quality, service, dependability, flexibility, productivity and time compression.
Consequently, companies are finding that they must develop global management expertise in order to
compete successfully in global markets. In the 21 st century, competitiveness will be achieved only by
those capable of not just meeting but exceeding global standards. Furthermore, it must be noted that

global standards are not fixed but require continuous improvement from a company and its employees
(Hitt, Ireland & Hoskisson, 2001:16). This also applies to the automotive industry as it has undergone
considerable turmoil in recent times - not just globally but also in South Africa. Indeed, during the past
decade, the industry has experienced some of the greatest changes in history.
For example, during the 20 th Century, the automotive industry was dominated by three original
equipment manufacturers (OEMs), namely, Ford Motor Company, General Motors and Chrysler which
became known as the “the Big 3”. In 1998, Chrysler merged with Daimler Benz to become Daimler-
Chrysler. Traditionally, in the automotive industry, the chain of command has been one whereby the
OEMs purchased components and supplies from large Tier 1 suppliers such as, for example Johnson
Controls and Delphi, who in turn procured components and supplies from smaller Tier 2 suppliers,
who in turn procured from yet smaller Tier 3 suppliers. This hierarchical structure resulted in
ineffectual processes, inefficiencies and a strong culture of distrust among industry participants
(Applegate & Collins, 2005:1).
During the 1970s, the American OEMs gradually began losing market share to foreign competitors,
particularly to the Japanese competitors, who followed lean and flexible manufacturing principles, as
well as using Keiretsu-style supply chains, and were thus able to build and deliver cars faster, and at a
lower cost than their American counterparts (Applegate & Collins, 2005:1). Far-reaching changes in
the competitive business world, including the presence of foreign competitors in domestic markets, are
forcing businesses to rethink their strategies in order to retain current customers and maintain a
competitive advantage (Hodgetts et al 2000:5). This can be achieved by way of competing on a global
basis, working harder to protect local market shares, and by suppliers offering higher quality products
at competitive prices.
Literature Review
South Africa has a number of OEMs namely, BMW, Ford, Volkswagen, Daimler-Chrysler and
Toyota and they all have production facilities in various locations in the country. Vehicles are
produced for the local and international market. This motor industry has been well supported via a
vibrant automotive component industry which supplies the OEMs, the South African aftermarket and a
spread of 249 export markets, mostly in the European Union (EU).
During the apartheid years the South African motor industry was aided by the government and
protected via high import duties. Since the advent of democracy in 1994, and the subsequent
liberalisation of the economy, the government has had to reduce its import tariffs on an ongoing basis,
in line with World Trade Organisation requirements. At the same time, to assist the industry to be
economically viable in the global arena, the South African government devised the Motor Industry
Development Programme (MIDP). MIDP is a programme that was launched in September 1995 to readjust
the structure of the automotive industry so that global competitiveness could be achieved (TISA
2003:8). The aim of MIDP was to slowly reintegrate South Africa into the global automotive industry.
Import tariffs were slowly reduced to give the local industry an opportunity to adapt (Williams,
2004:1). This well designed and well managed programme has given the industry much needed time to
improve its efficiencies and to become more cost effective.
II. Brief History of MIDP
TISA (2003:8) specified that government support to the Motor industry in South Africa began in the
1920’s. The preliminary phase – classical import substitution which supported simple assembly for the
domestic market - lasted until 1961. During this time, heavy import duties on imported motor cars
promoted the development of an industry of small assembly plants producing a relatively wide range of
models in small volumes at high cost. Barnes (2000:2) pointed out that during the 1920’s, as a direct
result of tariff protection being afforded to automotive assemblers, some basic components such as
batteries, glass and tyres were being procured locally.

During 1961 and 1995 five new distinct phases of government support for the industry were identified.
These phases included ongoing market protection and an array of incentives and requirements for
increased local content. Local content requirements were supported by punitive tariffs on imported
components. These phases involved a combination of tariffs and import permits, with each phase
intending to increase the amount of local content and further promote OEM-component relationships in
South Africa (TISA 2003:8).
MIDP was launched in September 1995, and aimed to develop an internationally competitive and
growing automotive industry. MIDP has five main objectives: (1) improve the global competitiveness
of OEMs and automotive component firms; (2) provide superior quality; (3) produce affordable
vehicles and components to the domestic and international markets; (4) enhance growth of the
assembly and automotive components firms; and (5) stabilise employment levels, thereby improving
the economic growth of the country by increasing production and thus improve the industry’s trade
balance.
The major policy instruments to achieve these objectives have been a gradual and ongoing
reduction in tariff protection in order to expose the industry to greater global competition; and the
support of greater volumes and specialisation by allowing exporting automotive component firms to
earn rebates on import duties (TISA 2003:8). For example (Haynes 2004:1) under the MIDP export
scheme, when a firm exports automotive components they qualify for Import Rebate Credit Certificates
(IRCCs). These IRCCs can be used to offset customs duty on automotive imports. Exporters unable to
use the IRCCs can use the option to sell these negotiable instruments to an importer.
Despite various incentives the automotive component industry receives only insignificant
government protections and is currently faced with increased competition. This competition is a
challenge from two points, (a) foreign imports into the domestic market, hence the firm needs to
improve competitiveness; and (b) firms need to reposition themselves in new value chains in order to
combine relationships with OEMs and thus facilitate exports (Barnes 2000:5).
MIDP has now been in operation for ten years and has successfully helped guide the automotive
industry’s integrated emergence from isolation to becoming a global supplier that exports high
technology and quality automotive components to demanding world markets. MIDP has been extended
until 2012 in order to maintain and boost the South African industry’s attractiveness as a foreign
investment destination and production base for exporting completely built-up vehicles and
components; to maintain the impetus of exports; and to secure the continued feasibility of domestic
vehicle and component manufacture (TISA 2003:9).
III. The Automotive Component Industry
South Africa has an abundance of raw materials and produces in excess of 60% of the world’s
platinum, rhodium and palladium, which are essential catalysts in catalytic converters (TISA 2003:41).
Hence, where a single production facility is concerned, the South African automotive component
industry is able to manufacture a range of quality products at competitive prices because of access to
these raw materials and therefore lower input costs. South African automotive component
manufacturers have also had a competitive advantage from a flexibility point of view, as local
automotive manufacturers are able to produce lower volumes on relatively short notice compared to
other countries where production is set up for long high-production runs (TISA 2003:41).
Graph 1 provides a picture of how South African component exports have increased since 1994 and
peaked in 2002. In 2003 automotive component exports declined from R22.9 billion to R21.2 billion,
but increased in 2004 to R21.7 billion. This decline can be attributed to the strengthening and
stabilisation of the value of the Rand versus the US Dollar and the Euro.
Graph 1: SA Component Exports: 1995 to 2004

Rands (Millions)
26,000
24,000
22,000
20,000
18,000
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
3,318
4,051
SA component exports: 1995 to 2004
5,115
7,895
9,674
12,640
Source: Trade and Investment South Africa (TISA): Department of Trade and Industry (DTI)
Catalytic converters, leather seat covers and aluminium based products make up the bulk of automotive
component exports. The main destinations continue to be first-world markets with Germany
comprising 35.9%, in rand terms, of the total component exports. The EU was South Africa’s main
export destination in 2003 with 69,9% of the automotive component industry compared to 71,3% in
2004 (Lamprecht, 2006:1).
IV. Trends in the Global Automotive Market
During the 1990s most industries globally were changed from closed to open industries resulting in an
increase in global trade and the integration of business. Through this integration progression, national
economic borders and trade barriers have almost totally disappeared. People, materials, products and
information now move more freely between countries than before the integration process. As firms
become more global, supply chains will become more global. Global supply chains face different
challenges to those of domestic chains (Hugo, Badenhorst-Weiss & Van Biljon, 2004:325). For
example, global markets will not accept poor customer service, inconsistent quality, higher pricing and
stock-out situations to the same degree as local markets.
There is a movement towards the globalisation of production, as goods and services are purchased
from different parts of the word in order to take advantage of national differences in the cost and
quality of factors of production (Hill, 2001:7). By doing this, businesses hope to reduce their overall
cost structure and improve the quality of their products, thus enabling them to sell their products at a
lower price and ultimately sustain a competitive advantage.
The strengths in the South African market could be used as a source for competitive advantage.
South Africa’s automotive component manufacturing industry is internationally renowned for its
technological sophistication, expertise and flexibility. This enables local automotive component
manufacturers to manufacture a wide variety of products quickly and economically in small volumes
whilst at the same time meeting high international quality and supply reliability standards (TISA,
2003:41).
18,586
22,883
21,269
21732.6
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Year

V. Data and Methodology
Research Problem
South African automotive component manufacturers face unique challenges in a very competitive
market that could be approached from various angles. The research problem of this study was to
identify these unique challenges and ascertain whether the implementation of a `philosophy of
continuous improvement` can be used as a strategic tool to address the challenges they face in the
market.
Research Objectives
The objectives of this research were to:
1. Identify the challenges facing South African automotive component manufacturers.
2. Determine whether a ‘philosophy of continuous improvement’ can be used as a strategic tool to
help the South African automotive manufacturers address the challenges they face.
Sampling
As the automotive component industry consists of numerous sub-sectors, many of whom contribute
only minute portions to the total exports, initial research and practical considerations suggested that it
would make more sense to concentrate the empirical research on the larger sub-sectors that represent
more than 60% of the total value of exports. The non-probability quota sample technique was used to
select the sample. The sample consisted of selected sub-sectors: catalytic converters, stitched leather
components, tyres and road wheels/parts – which contribute 64,1% of the total value of automotive
component exports in South Africa.
Questionnaire Design
The questionnaire was based on the literature survey and was divided into three sections, namely
section A, B and C. Section A primarily dealt with the company profile. The aim of Section B was to
investigate challenges faced in global markets from a supply chain management perspective. These
challenges were identified in the literature review and used as a basis for the questionnaire. Section C
included a series of questions designed to determine opinions on various issues which are relevant in
the South African business environment.
Pilot Study
To test the content validity and the reliability of the questionnaire, the initial questionnaire was used to
conduct a pilot study at two companies who are the main suppliers of automotive filters for passenger
vehicles. The results of this questionnaire were used in the main research.
Data Collection
A list of all automotive component manufacturers, together with contact details was obtained from The
National Association of Automotive Component and Allied Manufacturers, The Department of Trade
and Industry and the Chamber of Commerce. Because of the small number of manufacturers within the
selected sub-sectors, it was feasible to contact all respondents via telephone prior to sending out the
questionnaire. This was found to be most useful as in each instance, the questionnaire was sent to a
senior manager who was dealing with exports. The majority of completed questionnaires were returned
within one week.
Empirical Findings
Response
The number of manufacturers within the four selected sub-sectors and the filter industry amounted to
thirty-one. Of these companies, two declined to partake in this research study owing to time constraints
and two others were no longer exporting. Twenty-seven questionnaires were sent out and a total of
twenty (74% response rate) were duly completed by the respondents and returned to the researcher.

Findings
South Africa faces unique challenges and these are listed and ranked according to priority from most to
least important. During analysis, the non-standardised and complex nature of the data was classified
into categories before they were discussed. Rating or scale questions were used to collect opinion data,
and the Likert-style rating scale approach was used. The objective of this study was to provide policy
makers with some insight into real problems with regard to supply chain management in the
automotive component industry, although the stance has in no way been taken that all the problems in
the automotive component industry are supply chain related.
Questionnaires were analysed using both Microsoft Excel and Word. Eleven major challenges facing
exporters within this industry were identified. These are as follows:-
1. The reduction of production costs
2. R/US$ exchange rate effect on respondent’s export sales and profit margins
3. Exchange rate fluctuations
4. China – a threat to the local automotive component market
5. Increased competition by way of manufactured imports being sold in the South African Market
6. Emergence of the Chinese automotive component industry from a global perspective
7. Supplier relationships
8. HIV/Aids pandemic
9. A common approach to environmental management
10. The industry is not improving its competitiveness quickly enough to keep up with continuously
improving international competitors
11. The incorporation of ten additional countries in the EU is a threat to the local component
market
Recommendations on how to deal with these challenges are dealt with in the next section
Major challenges categorised together with some proposed recommendations:-
As shown in table 1, all challenges relating to increased competition are grouped together and ranked
according to priority from most to least important, together with some proposed recommendations on
how these challenges may be addressed in order to enhance competitive advantage.
Table 1: Increased Competition
Challenges
1. The reduction of
production costs.
2. Increased competition by
way of manufactured
imports being sold in the
South African market.
Recommendations
1. Become more globally competitive
through lean production or Just-in-time
production programmes.
2. Reduce supplier database and develop
partnership relationships with suppliers.
3. Automate production processes, thereby
reducing costs of labour.
1. Focus on the poor quality aspect of
imported components in that they do not
meet OEM specifications.
2. Initiate distributor loyalty incentives.
3. Reduce costs, thereby leaving selling
prices unchanged.
4. Increase production efficiency.

3. The industry is not
improving its
competitiveness fast
enough to keep up with
continuously improving
international competitors.
1. Competitive benchmarking - eliminating
gaps.
2. Identify the gap between industry
standards and an organisation’s standards.
3. Instill a philosophy of continuous
improvement.
Table 2 lists and ranks all challenges relating to shared or reduced risk according to priority from most
to least important, together with some proposed recommendations on how these challenges may be
addressed from a supply chain perspective.
Table 2: Shared or Reduced Risk
Challenges Recommendations
1. R/US$ exchange
rate effect on
respondent’s export
sales and profit
margins.
2. Exchange
rate
fluctuations.
1. Adjust trade balances by increasing imports
(inputs)
2. Purchase forward cover.
3. Improve negotiation strategies in order to reduce
the cost of raw materials.
4. Internal restructuring – eliminate non-value
adding processes.
1. Purchase forward cover when
importing and when exporting, change sell rate to
SA Rands.
2. Where the firm is involved in a longterm
supply relationship, maintain a stable price
in US$ and absorb the shocks caused by
temporary fluctuations.
3. Reduce current profit margins rather
than permanently lose market share.
4. Export to Sub-Saharan African
countries that have currencies that are weaker than
the Rand to offset losses incurred on overseas
exports (USD based).
Table 3 lists and ranks all challenges relating to global competitors ranked from most to least
important, together with proposed recommendations on how these challenges may be managed from a
supply chain perspective.

Table 3: International Competitors: China and EU
Challenges Recommendations
1. China, a threat to
the local automotive
component market.
2. Emergence of the
Chinese automotive
component industry
from a global
viewpoint.
3. Absorption
of ten new countries
into the EU – a threat
to the local component
market.
1. Benchmark against Chinese automotive
component manufacturers and eliminate any gaps.
1. Provide a superior service.
2. Become more flexible in terms of management
and production processes.
3. Offer branding warranties
1. Instill a company culture of continuous
improvement.
2. Monitor the competitive environment and
research the market
4. Search for new investment areas in this region,
as competitors have done.
5. Offer better quality and lower prices than
competitors
Table 4 records and ranks all challenges relating to supplier relationships according to priority from
most to least important, together with some proposed recommendations on how these challenges may
be dealt with from a supply chain perspective.
Table 4: Supplier Relationships
1. Supplier relationships. 1. Suppliers to ensure that they
continuously meet contractual
quality requirements and delivery
deadlines
2. Remain cost competitive.
3. Offer exceptional customer
service.
Lastly, table 5 records and ranks all challenges relating to general challenges facing exporters
according to priority from most to least important, together with some proposed recommendations on
how these challenges may be dealt with from a supply chain perspective

Table 5: General Challenges
1. HIV/Aids pandemic. 1. Implement an HIV/Aids policy.
2. Run training programmes for HIV
counsellors.
3. Offer voluntary HIV testing and antiretrovirals
2. Approach to
environmental
management.
3. Shorter product life
cycles
1. Reduce the amount of waste.
2. Reuse materials wherever possible.
3. Recycle / remanufacture products.
4. Substitute raw materials with synthetic
materials wherever possible.
5. Adhere to ISO14001 accreditation
requirements.
1. Reduce production time.
2. Continuous research combined with the
utilisation of better and improved components.
South African suppliers are facing enormous challenges from competitors in China, Eastern Europe
and South America, with perhaps the emergence of the Chinese market providing the largest challenge.
In order to survive, it is vital that exporters within this industry improve production costs and
efficiencies in order to remain competitive in this global environment.
Most respondents were confident that South African automotive component manufacturers achieved
and sustained world-class performance standards.
Lastly, respondents noted that the strengthening and stabilisation of the value of the Rand versus the
US Dollar and the Euro, and the increase in producer price index costs above the level of their
international competitors, are eroding the improvements achieved by the South African manufacturers.
This issue, together with the demise of MIDP in 2012 might see the collapse of the export industry.
The viability of automotive component exports from SA is highly dependent on the MIDP export
benefits and will recede once these support measures are removed, unless manufacturers are able to
further reduce production costs and increase productivity.
Limitations to the study
This research study has focused on four selected sub-sectors of the automotive component industry,
including the filter industry which formed part of the pilot study. These sub-sectors make up 64.1% of
the total component exporters in South Africa. The limitation of the research was that as the nonprobability
quota sample technique was used not all sub-sectors within the automotive component
industry were included in this study.
Areas for further research
This study would have been more accurate and meaningful if all South African exporters of all
component categories in the automotive component industry had been included in this study. Further
research involving all exporters within this industry could be undertaken at a later stage, based on
random sampling while the possibility of a comparative study with another country could be explored.

Conclusion
In order to survive, South African automotive component manufacturers/exporters need to remain
globally competitive. The industry is facing enormous challenges, particularly from China and other
foreign imports entering the domestic market. This competition is impacting not only on selling prices
but also on profit margins, and ultimately will affect South Africa’s employment market via job losses.
The fact that South African automotive component manufacturers succeeded in identifying and
supplying profitable market niches in a very competitive global market is proof that these companies
possess the required entrepreneurial acumen to survive the anticipated onslaught from the Chinese and
East European markets. In this regard, it is important that component manufacturers benchmark against
all major competitors, and aggressively work at eliminating any gaps. A vital aspect of this will be the
reduction of production costs and the improvement in efficiencies in order to maintain and improve
their competitiveness. This goal can only be achieved through the striving for a culture of continuous
improvement in all aspects of their businesses.
Tools that can be used to achieve this end will be the implementation of world class manufacturing
programmes and adhering to lean production practices.
VI. References
[1] Applegate, L.M. & Collins, E.L. (2005). “Covisint (A): The Evolution of a B2B Marketplace”.
Harvard Business School, June 29 2005. (9-805-110).
[2] Barnes, J.R. (2000). “Changing Lanes: The Political Economy of the South African Automotive
Value Chain”. Development of Southern Africa, 0376835X, September 2000, Vol. 17, Issue 3,
pp1-12.
[3] Haynes, C. (2004). “Overview of the South African Motor Industry”. May 2004 [Online]
Available: http://www.autocluster.co.za/ id316_m.htm.
[4] Hill, C.W. (2001). International Business. “Competing in the Global Marketplace”. 3 rd Edition.
New York: McGraw-Hill.
[5] Hill, C.W.L & Jones, G.R. (2004). Strategic Management. An Integrated Approach. Sixth
Edition. Boston: Houghton Mifflin Company.
[6] Hitt M.A., Ireland, R.D & Hoskisson, R.E. (2001) Strategic Management. Competitiveness and
Globalisation. Concepts and Cases. Fourth Edition. Australia: South Western College
Publishing. Thomson Learning.
[7] Hodgetts, R.M. & Luthans F. (2000). “International Management: Culture, Strategy and
Behaviour”. International Edition. Boston: McGraw-Hill.
[8] Hugo, W.M.J., Badenhorst-Weiss, J.A. & van Biljoen, E.H.B. (2004). “Supply Chain
Management. Logistics in Perspective”. Pretoria: Van Schaik Publishers
[9] Lamprecht, N. (2006) “Main Export Destinations for all Components”. South African
Automotive Yearbook and Industry Databases. 9 th Edition. Section 2, Component
Manufacturing, Table 2. KwaZulu-Natal: Balgair Publications.
[10] Trade and Investment South Africa (TISA). “Current developments in the Automotive
Industry”. (2003). 7 th Report. The Department of Trade and Industry: Pretoria.
[11] Williams, C. “National Association of Automotive Component & Allied Manufacturers”.
February 2004 [Online] Available: http://www.naacam.co.za/sami. htm.

International Bulletin of Business Administration
ISSN: 1451-243X Issue 1 (2006)
© EuroJournals, Inc. 2006
http://www.eurojournalsn.com
Size and Book-to-Market Risk Factors in Earnings and Returns
for the Greek Stock Market
Grigoris Michailidis
University of Macedonia, Economic and Social Sciences,
Department of Applied Informatics,
Thessaloniki, Greece
E-mail: mgrigori@uom.gr
Stavros Tsopoglou
University of Macedonia, Economic and Social Sciences,
Department of Applied Informatics,
Thessaloniki, Greece
E-mail: tsopstav@uom.gr
Demetrios Papanastasiou
University of Macedonia, Economic and Social Sciences,
Department of Applied Informatics,
Thessaloniki, Greece
E-mail: papanast@uom.gr
Abstract
The main prediction of the CAPM, a linear cross-sectional relationship between mean
excess returns and exposures to the market factor, is violated for the Athens stock market.
Exposures to two other factors, a size-based factor and a book-to-market-based factor, often
called a "value" factor, explain a significant part of the cross-sectional dispersion in mean
returns. Portfolios constructed to mimic risk factors related to market, size, and value all
help to explain the random returns to well-diversified stock portfolios. There is evidence
that past sales growth of a firm is associated with market, size and value factors in returns
and can be inferred that help in capturing the cross-section of average stock returns. Size
and BE/ME remain indicator variables that, for unexplained economic reasons, are
related to risk factors in returns. Stock prices properly reflect differences in the
evolution of profitability when stocks are grouped on size and BE/ME (JEL G11, G12,
and G15).
Key words: CAPM, Fama and French model, size, BE/ME, profitability, sales of firms,
portfolio returns, Athens Stock Exchange.
I. Introduction
Fama and French [1992] find that the main prediction of the CAPM, a linear cross-sectional
relationship between mean excess returns and exposures to the market factor, is violated for the US
stock market. Exposures to two other factors, a size-based factor and a book-to-market-based factor,

often called a "value" factor, explain a significant part of the cross-sectional dispersion in mean returns.
If stocks are priced rationally, then systematic differences in average returns should be due to
differences in risk. Thus, given rational pricing, the market, size and value exposures must proxy for
sensitivity to pervasive risk factors in returns.
Fama and French [1993] confirm that portfolios constructed to mimic risk factors related to market,
size, and value all help to explain the random returns to well-diversified stock portfolios. Fama and
French [1995] attempt to provide a deeper economic foundation for their three-factor pricing model by
relating the random return factors to earnings shocks. They claim that the behavior of stock returns in
relation to market, size and value factors is consistent with the behavior of earnings. They admit that
their findings are weak, especially relating to the value factor, but attribute this to the measurement
error problems in earnings data.
The paper continues arguing that some of the CAPM average-return anomalies are related and is
possible to be captured by the three-factor model of Fama and French [1993]. The validity of the model
is examined for the Athens stock exchange. We analyze whether the market, size and value factors are
pervasive in the cross-section of random stock returns.
The study continues by giving answer to an important question about the way that size and bookto-market
factors in returns are driven by the behavior of sales growth and how profitability produce
common variation in returns associated with size and BE/ME that is not identified by the market
return. The purpose is to provide evidence of how size and BE/ME are related to firms’ fundamental
characteristics.
Tests are conducted for a period of seven years (1997-2003), which is characterized by intense
return volatility (covering historically high returns for the Greek Stock market as well as significant
decrease in asset returns over the examined period). These market return characteristics make it
possible to have an empirical investigation of the pricing model on differing financial conditions thus
obtaining conclusions under varying stock return volatility.
Existing financial literature on the Athens stock exchange is rather scanty and it is the purpose of
this study to widen the theoretical analysis of this market by using modern finance theory and to
provide useful insights for future analysis of this market.
II. Empirical Appraisal of the model
Recent Tests
One of the most important developments in modern capital theory is the capital asset pricing model
(CAPM). The capital asset pricing model (CAPM) of William Sharpe [1964], John Lintner [1965] and
Fischer Black [1972] marks the birth of asset pricing theory. Four decades later, the CAPM is still
widely used in applications, such as estimating the cost of capital for firms and evaluating the
performance of managed portfolios. The attraction of the CAPM is that it offers powerful and
intuitively pleasing predictions about how to measure risk and the relation between expected risk and
return. Unfortunately, the empirical record of the model is poor – poor enough to invalidate the way it
is used in applications.
Starting in the late 1970s, empirical work appears to challenge CAPM. Specifically, evidence
mounts that much of the variation in expected return is unrelated to market beta.
The first contradiction is Basu’s [1977] evidence that when common stocks are sorted on earningsprice
ratios, future returns on high E/P stocks are higher than predicted by the CAPM. Banz [1981]
documents a size effect; when stocks are sorted on market capitalization (price times shares
outstanding), average returns on small stocks are higher than predicted by the CAPM. Bhandari [1988]
finds that high debt-equity ratios (book value of debt over the market value of equity, a measure of
leverage) are associated with returns that are too high relative to their market betas. Finally, Statman
[1980] and Rosenberg, Reid, and Lanstein [1985] document that stocks with high book-to-market
equity ratios (BE/ME, the ratio of the book value of a common stock to its market value) have high
average returns that are not captured by their betas.

There is a theme in the contradictions of the CAPM summarized above. Ratios involving stock
prices have information about expected returns missed by market betas. Fama and French [1992]
update and synthesize the evidence on the empirical failures of the CAPM. Using the cross-section
regression approach, they confirm that size, earnings-price, debt-equity, and book-to-market ratios add
to the explanation of expected stock returns provided by market beta. Fama and French [1996] reach
the same conclusion using the time-series regression approach applied to portfolios of stocks sorted on
price ratios. They also find that different price ratios have much the same information about expected
returns.
Kothari, Shanken, and Sloan [1995] try to save the CAPM by arguing that the weak relation
between average return and beta is just a chance result. But the strong evidence that other variables
capture variation in expected return missed by beta possibly makes this argument irrelevant.
Explanations on the model’s failures
The evidence on the empirical problems of the CAPM provided by Fama and French [1992] serves as a
catalyst, implying that the CAPM may have fatal problems. Research then turns to explanations.
One possibility is that the CAPM’s problems are not authentic, meaning that researchers use data
and discover contradictions that occur in specific samples as a result of chance. A standard response to
this concern is to test for similar findings in other samples. Chan, Hamao, and Lakonishok [1991] find
a strong relation between book-to-market equity (BE/ME) and average return for Japanese stocks.
Capaul, Rowley, and Sharpe [1993] observe a similar BE/ME effect in four European stock markets
and in Japan. Fama and French [1998] find that the price ratios that produce problems for the CAPM in
U.S. data show up in the same way in the stock returns of twelve non-U.S. major markets, and they are
present in emerging market returns. This evidence suggests that the contradictions of the CAPM
associated with price ratios are not sample specific.
Among those who conclude that the empirical failures of the CAPM are fatal, two views emerge.
The firsts view is based on evidence that stocks with high ratios of book value to price are typically
firms that have fallen on bad times, while low BE/ME is associated with growth firms (Lakonishok,
Shleifer and Vishny, [1994]; Fama and French, [1995]). They argue that sorting firms on book-tomarket
ratios exposes investor overreaction to good and bad times. Investors over-extrapolate past
performance, resulting in stock prices that are too high for growth (low BE/ME) firms and too low for
distressed (high BE/ME, so-called value) firms. When the overreaction is eventually corrected, the
result is high returns for value stocks and low returns for growth stocks. Proponents of this view
include DeBondt and Thaler [1987], Lakonishok, Shleifer, and Vishny [1994], and Haugen [1995].
The second view for the empirical contradictions of the CAPM is based on the need for a more
complicated asset pricing model. The CAPM is based on many unrealistic assumptions. For example,
the assumption that investors care only about the mean and variance of distributions of one-period
portfolio returns is extreme. It is reasonable that investors also care about how their portfolio return
covaries with labor income and future investment opportunities, so a portfolio’s return variance misses
important dimensions of risk. If so, market beta is not a complete description of an asset’s risk, and we
should not be surprised to find that differences in expected return are not completely explained by
differences in beta. In this view, the search should turn to asset pricing models that do a better job
explaining average return.
III. Sample Selection and Data
The sample securities
The study covers the period from January 1997 to December 2003. This time period was chosen
because it is characterized by intense return volatility with historically high and low returns for the
Greek stock market incorporating changes in fundamental variables of the enterprises, giving us the
opportunity to test the model on differing financial conditions thus obtaining conclusions under
varying stock return volatility.

The selected sample consists of the majority of the stocks that were trading on the Athens Stock
Exchange over the examined period. We excluded financial firms because the high leverage that is
normal for these firms probably does not have the same meaning as for non financial firms, where high
leverage more likely indicates distress. The sample companies account for a major portion of market
capitalization as well as average trading volume for the Greek stock market. Shares not included in the
sample are either thinly traded or do not have accounting and financial information on a continuous
basis.
The share data has been obtained form the MetaStock and the Athens stock exchange, financial
databases widely used in Greece by practitioners and researchers. The price data has been adjusted for
capitalization changes such as bonus rights and stock splits. All the selected securities are traded on the
ASE on a continuous basis throughout the full Athens stock exchange trading day, and are chosen
according to prespecified liquidity criteria set by the ASE Advisory Committee. 1 The selection was
made on the basis of the trading volume and excludes stocks that were traded irregularly or had small
trading volumes.
All the selected stocks are included in the formation of the FTSE/ASE 20, FTSE/ASE Mid 40 and
FTSE/ASE Small Cap index. These indices are designed to provide real-time measures of the Athens
Stock Exchange (ASE).
The above indices are formed subject to the following criteria:
(i) The FTSE/ASE 20 index is the large cap index, containing the 20 largest blue chip
companies listed in the ASE.
(ii) The FTSE/ASE Mid 40 index is the mid cap index and captures the performance of the next
40 companies in size.
(iii) The FTSE/ASE Small Cap index is the small cap index and captures the performance of the
next 80 companies.
Data
The study uses weekly stock returns for the selected companies listed on the Athens stock exchange for
the period of January 1997 to December 2003. The data are obtained from MetaStock (Greek) Data
Base.
Most firms in Greece have their fiscal year ends on December. So tests did not have to deal with
matching the accounting data for all fiscal year ends in every calendar year. We use a firm’s market
equity at the end of December of each year to compute its book to market, leverage and earnings price
ratios and we use its market equity of June of each year to compute its size. The accounting
information combined with share price data has been used to construct measures of size and value
employed in the study, as discussed in the next section.
Additionally annual profit information measured as Profit before Depreciation and Taxes (PBDT)
has been collected for the sample companies from 1997 to 2003. The choice of profit figure has been
guided by the fact that PBDT figures are seldom negative, making them appropriate for growth rate
calculations.
In order to obtain better estimates of the value of the beta coefficient, the study utilizes weekly
stock returns. Returns calculated using a longer time period (e.g. monthly) might result in changes of
beta over the examined period introducing biases in beta estimates. On the other hand, high frequency
data such as daily observations covering a relatively short and stable time span can result in the use of
very noisy data and thus yield inefficient estimates.
All stock returns used in the study are adjusted for dividends as required by the CAPM. The ASE
Composite Share index is used as a proxy for the market portfolio. This index is a market value
weighted index, is comprised of the 60 most highly capitalized shares of the main market, and reflects
general trends of the Greek stock market.
1 www.ase.gr

The 3-month Greek Treasury Bill is used as the proxy for the risk-free asset. The yields were
obtained from the Treasury Bonds and Bill Department of the National Bank of Greece. The yield on
the 3-month Treasury-bill is specifically chosen as the benchmark that better reflects the short-term
changes in the Greek financial markets.
IV. Tests of the CAPM, Fama-French Model
Size and market factors
Fama and French find that the main prediction of the CAPM, a linear cross-sectional relationship
between mean excess returns and exposures to the market factor, is violated for the US stock market.
Exposures to a size based factor and a book to market-based factor explain a significant part of the
cross-sectional dispersion in mean returns. They confirm that portfolios constructed to mimic risk
factors related to market, size, and value all help to explain the random returns to well-diversified stock
portfolios.
The model says that the expected return on a portfolio in excess of the risk free rate is explained by
the sensitivity of its return to three factors: i) the excess return on a broad market portfolio, ( RΜ - R f ),
ii) the difference between the return on a portfolio of small stocks and the return on a portfolio of large
stocks and iii) the difference between the return on a portfolio of high-book-to-market stocks and the
return on a portfolio of low-book-to-market stocks.
This part of the paper continues a previous study on the ASE about the joint roles of market beta,
size, E/P and book-to-market equity in the cross-section of average stock returns. The results of
the previous study indicate that the above factors either used alone or in combination, beta (the
slope in the regression of a stock's return on a market return) has little information about average
returns. This paper extends the previous asset pricing tests on the ASE in the way of creating
portfolios mimicking for size and value, book-to-market equity, and then regressed them on the
returns to a market portfolio.
The purpose of the study is to empirically examine the Fama-French three-factor model for the
Athens stock exchange. The time-series regressions are convenient for studying the specific asset
pricing issue. One of the central themes is that if assets are priced rationally, variables that are related
to average returns, such as size and book-to-market equity, must proxy for sensitivity to common
(shared and thus undiversifiable) risk factors in returns. The time-series regressions give direct
evidence on this issue. In particular, the slopes and R 2 values show whether mimicking portfolios for
risk factors related to size and BE/ME capture shared variation in stock returns not explained by
other factors.
Size and value sorted portfolios
In June of each year t from 1997 to 2003, all the sample stocks are ranked on the basis of their size
(share price times number of shares). The median sample size is then used to split the sample
companies into two groups: small (S) and big (B). Book equity to market equity (BE/ME) for year t is
calculated by dividing book equity at the end of financial year t by market equity at the end of financial
year t. The sample stocks are broken into three BE/ME groups based on the breakpoints for the bottom
30% (low), middle 40% (medium) and top 30% (high) of the ranked values of BE/ME for the sample
stocks.
We construct six portfolios (S/L, S/M, S/H, B/L, B/M, and B/H) from the intersection of the two
sizes and the three BE /ME groups. For example S/L portfolio contains stocks that are in the small size
group and also in the low BE/ME group while B/H consists of big size stocks that also have high
BE/ME ratios. The average portfolio returns are calculated from the individual stock returns that the
above portfolios are comprised.

The factor portfolios
The Fama-French model involves the use of three factors for explaining common stock returns: the
market factor (market index return minus risk-free return) proposed by the CAPM, and factors relating
to size and value. For the market index the ASE Composite Share index is used as a proxy.
SMB (Small minus Big) is meant to mimic the risk factor in returns related to size. SMB is the
difference between the simple average of the returns of the three small stock portfolios (S/L, S/M and
S/H) minus the average of the returns on the three big stock portfolios (B/L, B/M, B/H). It is the
difference between the returns on small and big stock portfolios with about the same weighted-average
BE/ME. Hence SMB is largely clear of BE/ME effects, focused on the different behavior of small and
big stocks.
HML (High minus Low) is meant to mimic the risk factor in returns related to value (that is bookto-market
ratios). HML is the difference between the simple average of the returns on two high BE/ME
portfolios (S/H and B/H) minus the average returns on two low BE/ME portfolios (S/L and B/L); it is
constructed to be relatively free of the size effect.
SMB is the average of the returns on the small stock portfolios minus the average of the returns on
the big stock portfolios as follows:
SMB = ((S/L – B/L) + (S/N – B/N) + (S/H – B/H))/3,
Similarly, HML (High minus Low) is the average of the returns on the value stock portfolios minus the
average of the returns on the growth stock portfolios as follows:
HML = ((S/H – S/L) + (B/H – B/L))/2.
The inputs to the time series regressions
The Fama and French model says that the expected return on a portfolio in excess of the risk free rate
is explained by the sensitivity of its return to three factors: i) the excess return on a broad market
portfolio, ( RΜ - R f ), ii) the difference between the return on a portfolio of small stocks and the return
on a portfolio of large stocks and iii) the difference between the return on a portfolio of high-book-tomarket
stocks and the return on a portfolio of low-book-to-market stocks. Specifically, the excess
return on a portfolio i is,
Ri − Rf = ai + bi( Rm − Rf) + sSMB i + hHML i + ei
(1)
where bi, si, and hi are the slopes in the time-series regression.
The explanatory variables in the time series regressions include the returns on a market portfolio of
stocks mimicking portfolios for the size and book-to-market equity in returns.
Although size and book-to-market equity are fundamental variables for explaining average stock
returns, it is believed that they proxy for common risk factors in returns. In Fama and French [1992] it
is documented that size and book-to-market equity are related to economic fundamentals. Not
surprisingly, firms that have high BE/ME (a low stock price relative to book value) tend to have low
earnings on assets while low BE/ME (a high stock price relative to book value) is associated with
persistently high earnings.
Size is also related to profitability. Controlling for book-to-market equity, small firms tend to have
lower earnings on assets than big firms. The fact that small firms can suffer a long earnings depression
that bypasses big firms suggests that size is associated with a common risk factor that might explain
the negative relation between size and average return. Similarly, the relation between book-to-market
equity and earnings suggests that relative profitability is the source of a common risk factor in returns
that might explain the positive relation between BE/ME and average return. Measuring the common
variation in returns associated with size and BE/ME is a major task of this paper.
Explaining common variation in returns with the factor portfolios
The role of stock-market factors in returns is developed in three steps. We examine (a) regressions that
use the market return (MKT), to explain excess stock returns, (b) regressions that use SMB and

HML, the mimicking returns for the size and book-to-market factors, as explanatory variables,
and (c) regressions that use MKT, SMB and HML.
Given rational pricing, in order to justify their use in the asset pricing model the factors must
contribute substantially to the risk of well-diversified portfolios. Table 1 show that the market factor
explains by far the largest fraction of common variation in stock returns for the six sizes and value
sorted portfolios. Used alone, the market factor produces an R 2 of above 60 to 80% while the Rsquared
estimates increase when the other two factors, of size and value, are used without the market
factor. The inclusions of these two factors contribute substantially in explaining portfolio returns.
The combination of the market and size factor produce significantly high R 2 values indicating that
there is a strong relation between the size of stocks and the market returns in explaining variation in
stock returns. Adding HML to the market model, regression also produces high R 2 results although
lower than the R 2 values produced by the inclusion of SMB. As in the SMB case the inclusion of the
value factor also contributes in explaining portfolio returns.
Given the strong slopes on SMB and HML for stocks, it is not surprising that adding the two
returns to the regressions results in large increases in R-squared values. As expected, the estimated
size exposures increase monotonically with size ranking, and analogously for the estimated value
exposures and value ranking. The market exposures of the portfolios are mostly in the range 0.9 to 1.
The results of the study, as presented in Table 1, indicate that the variants considered here, the
three-factor model provides the most suitable description of pervasive risk in these size and valuesorted
portfolios. The findings of the study provide supportive evidence of the Fama and French model
applied to Greek equities.
Table 1: Regressions of size and book-to-market portfolio returns on combinations of the market
(MKT), size (SMB) and value (HML) factors.
Coefficient estimates, (t) statistics of the estimated coefficients, standard errors and R-squared values
Explanatory Dependent
Variables Variables
Market
SMB and
HML
S/L
S/N
S/H
B/L
B/N
B/H
S/L
Coefficients, (t) statistics, standard errors and R-squared values
a 0.1210 b 1.5405 s - h - R 2
Std.Error 0.1991 Std.Error 0.4436 Std.Error - Std.Error - 0.7069
t(a) 0.6077 t(b) 3.4727 T(s) - T(h) -
a -0.0835 b 1.8367 s - h - R 2
Std.Error 0.2453 Std.Error 0.5466 Std.Error - Std.Error - 0.6931
t(a) -0.3404 t(b) 3.3601 T(s) - T(h) -
a -0.2964 b 1.8425 s - h - R 2
Std.Error 0.2809 Std.Error 0.6259 Std.Error - Std.Error - 0.6341
t(a) -1.0553 t(b) 2.9437 T(s) - T(h) -
a 0.0416 b 1.4042 s - h - R 2
Std.Error 0.1432 Std.Error 0.3191 Std.Error - Std.Error - 0.7947
t(a) 0.2908 t(b) 4.3997 T(s) - T(h) -
a -0.1594 b 1.5222 s - h - R 2
Std.Error 0.1491 Std.Error 0.3323 Std.Error - Std.Error - 0.8076
t(a) -1.0688 t(b) 4.5811 T(s) - T(h) -
a -0.3360 b 1.4838 s - h - R 2
Std.Error 0.1858 Std.Error 0.4140 Std.Error - Std.Error - 0.7198
t(a) -1.8090 t(b) 3.5843 T(s) - T(h) -
a 0.2416 b - s 2.7706 h 0.6347 R 2
Std.Error 0.4133 Std.Error - Std.Error 0.9328 Std.Error 0.9511 0.7882

Mkt and
SMB
Mkt and
HML
S/N
S/H
B/L
B/N
B/H
S/L
S/N
S/H
B/L
B/N
B/H
S/L
S/N
S/H
B/L
t(a) 0.5845 t(b) - T(s) 2.9703 T(h) 0.6673
a 0.1971 b - s 3.1985 h 1.1357 R 2
Std.Error 0.4399 Std.Error - Std.Error 0.9929 Std.Error 1.0125 0.8345
t(a) 0.4481 t(b) - T(s) 3.2213 T(h) 1.1218
a -0.0027 b - s 3.4513 h 1.2446 R 2
Std.Error 0.3779 Std.Error - Std.Error 0.8529 Std.Error 0.8697 0.8889
t(a) -0.0071 t(b) - T(s) 4.0464 T(h) 1.4310
a 0.2247 b - s 2.1932 h 0.7029 R 2
Std.Error 0.3933 Std.Error - Std.Error 0.8878 Std.Error 0.9053 0.7404
t(a) 0.5714 t(b) - T(s) 2.4703 T(h) 0.7764
a 0.1441 b - s 2.0920 h 0.9945 R 2
Std.Error 0.4595 Std.Error - Std.Error 1.0371 Std.Error 1.0575 0.6936
t(a) 0.3135 t(b) - T(s) 2.0171 T(h) 0.9404
a 0.0672 b - s 2.1353 h 1.3176 R 2
Std.Error 0.3894 Std.Error - Std.Error 0.8789 Std.Error 0.8963 0.7936
t(a) 0.1725 t(b) - T(s) 2.4294 T(h) 1.4701
a -0.0162 b 0.9707 s 2.1115 h - R 2
Std.Error 0.0752 Std.Error 0.1863 Std.Error 0.3580 Std.Error - 0.9698
t(a) -0.2149 t(b) 5.2107 t(s) 5.8984 T(h) -
a -0.2540 b 1.1280 s 2.6261 h - R 2
Std.Error 0.0848 Std.Error 0.2101 Std.Error 0.4037 Std.Error - 0.9735
t(a) -2.9974 t(b) 5.3696 t(s) 6.5050 T(h) -
a -0.4936 b 1.0230 s 3.0364 h - R 2
Std.Error 0.0863 Std.Error 0.2140 Std.Error 0.4112 Std.Error - 0.9750
t(a) -5.7170 t(b) 4.7805 t(s) 7.3836 T(h) -
a -0.0592 b 0.9850 s 1.5529 h - R 2
Std.Error 0.0422 Std.Error 0.1045 Std.Error 0.2008 Std.Error - 0.9871
t(a) -1.4042 t(b) 9.4262 t(s) 7.7327 t(h) -
a -0.2534 b 1.1316 s 1.4474 h - R 2
Std.Error 0.0875 Std.Error 0.2168 Std.Error 0.4165 Std.Error - 0.9521
t(a) -2.8972 t(b) 5.2206 t(s) 3.4748 t(h) -
a -0.4512 b 1.0051 s 1.7737 h - R 2
Std.Error 0.1142 Std.Error 0.2829 Std.Error 0.5437 Std.Error - 0.9235
t(a) -3.9529 t(b) 3.5523 t(s) 3.2623 t(h) -
a 0.3657 b 1.3658 s - h 0.6731 R 2
Std.Error 0.4457 Std.Error 0.5499 Std.Error - Std.Error 1.0775 0.7330
t(a) 0.8206 t(b) 2.4840 t(s) - t(h) 0.6247
a 0.3683 b 1.5143 s - h 1.2425 R 2
Std.Error 0.5148 Std.Error 0.6351 Std.Error - Std.Error 1.2445 0.7543
t(a) 0.7154 t(b) 2.3842 t(s) - t(h) 0.9983
a 0.2686 b 1.4392 s - h 1.5539 R 2
Std.Error 0.5751 Std.Error 0.7095 Std.Error - Std.Error 1.3904 0.7212
t(a) 0.4671 t(b) 2.0284 t(s) - t(h) 1.1176
a 0.2435 b 1.2601 s - h 0.5550 R 2
Std.Error 0.3157 Std.Error 0.3895 Std.Error - Std.Error 0.7632 0.8187
t(a) 0.7712 t(b) 3.2354 t(s) - t(h) 0.7272

Mkt, SMB
and HML
B/N
B/H
S/L
S/N
S/H
B/L
B/N
B/H
a 0.1027 b 1.3351 s - h 0.7207 R 2
Std.Error 0.3164 Std.Error 0.3903 Std.Error - Std.Error 0.7649 0.8425
t(a) 0.3246 t(b) 3.4206 t(s) - t(h) 0.9423
a 0.1140 b 1.1625 s - h 1.2377 R 2
Std.Error 0.3526 Std.Error 0.4350 Std.Error - Std.Error 0.8524 0.8165
t(a) 0.3234 t(b) 2.6725 t(s) - t(h) 1.4521
a -0.0160 b 0.9706 s 2.1114 h 0.0003 R 2
Std.Error 0.1902 Std.Error 0.2286 Std.Error 0.4355 Std.Error 0.4410 0.9698
t(a) -0.0843 t(b) 4.2454 t(s) 4.8484 t(h) 0.0007
a -0.0813 b 1.0489 s 2.4861 h 0.4502 R 2
Std.Error 0.1829 Std.Error 0.2198 Std.Error 0.4187 Std.Error 0.4239 0.9807
t(a) -0.4444 t(b) 4.7721 t(s) 5.9381 t(h) 1.0620
a -0.2439 b 0.9087 s 2.8342 h 0.6507 R 2
Std.Error 0.1466 Std.Error 0.1762 Std.Error 0.3356 Std.Error 0.3398 0.9887
t(a) -1.6638 t(b) 5.1576 t(s) 8.4452 t(h) 1.9149
a -0.0336 b 0.9733 s 1.5322 h 0.0668 R 2
Std.Error 0.1054 Std.Error 0.1267 Std.Error 0.2413 Std.Error 0.2444 0.9874
t(a) -0.3186 t(b) 7.6828 t(s) 6.3491 t(h) 0.2733
a -0.1428 b 1.0810 s 1.3578 h 0.2880 R 2
Std.Error 0.2094 Std.Error 0.2517 Std.Error 0.4794 Std.Error 0.4854 0.9571
t(a) -0.6821 t(b) 4.2953 t(s) 2.8326 t(h) 0.5934
a -0.1648 b 0.8739 s 1.5418 h 0.7464 R 2
Std.Error 0.2211 Std.Error 0.2658 Std.Error 0.5063 Std.Error 0.5127 0.9552
t(a) -0.7451 t(b) 3.2878 t(s) 3.0450 t(h) 1.4559
Greece is characterized as an emerging market with a growing and fast maturing equity market.
A better understanding of the risk and return characteristics of this market is an important research
problem. The tests here show that there are common return factors related to size and book-tomarket
equity that help capture the cross-section of average stock returns in a way that is consistent
with multifactor asset-pricing models. However one important question has not been answered and
is related to the way that size and book-to-market factors in returns are driven by the behavior of
earnings and how profitability produce common variation in returns associated with size and BE/ME
that is not identified by the market return.
V. Common risk factors in Sales
There is evidence that market, size and value equity factors are pervasive risk factors in portfolio
returns and this is consistent with the rational asset pricing explanation for the role of their factor
exposures in the cross-section of returns. This part of the paper attempts to expand a previous analysis
on the ASE where it was shown that there are common return factors related to size and book-tomarket
equity that help capture the cross-section of average stock returns. The study is expanding by
investigating how sales growth, a fundamental firm’s variable, is associated with size and BE/ME that
is not identified by the market return. We continue the examination in regard to the Athens stock
exchange.
For many years, scholars and investment professionals have argued that one of the rules to
outperform the market is to buy stocks that have low prices relative to earnings, sales growth, book
assets, or other fundamental analysis firms’ criterion. The purpose is to try to shed further explanation
on how sales growth of a firm is associated with market, size and value factors in returns.

Size and value portfolios
The common factors in sales growth are constructed like those in stock returns. ΔSalesSMB, the size
factor in sales growth, is the simple average of the change in sales for the three small stock portfolios
(S/L, S/M and S/H) minus the average of the change in sales for the three big stock portfolios (B/L,
B/M, and B/H). The value factor in sales growth, ΔSalesHML, is the simple average of the change in
sales for the two high BE/ME portfolios (S/H and B/H) minus the average of the two low BE/ME
portfolios (S/L and B/L). The market factor in sales growth, ΔSalesMKT, is the average of the change
in sales for all firms.
Tests have been conducted from 1998 to 2003 using a selected number of one hundred and
twenty stocks that are traded on the ASE. These selected stocks are included in the formation of the
FTSE/ASE 20, FTSE/ASE Mid 40 and FTSE/ASE Small Cap index that have designed to provide realtime
measures of the Athens Stock Exchange. The purpose of this part of the paper is to examine the
interaction between market, size and value factors in returns with firms’ sales growth.
The time-series regression that has been used for examining the common risk factors that are
associated to sales is the following and the results are shown in Table 2.
Δ PSalest = ai + biΔ SalesMKTt + siΔ SalesSMBt + hiΔ SalesHMLt + et
(2)
Explaining common variation
This alternative way to examine how other fundamental variables are associated with market, size and
value factors in stock returns is based on past sales growth. This measure, of past sales growth, is less
volatile than either cash flow or earnings particularly for the portfolios that include high BE/ME
stocks.
In Table 3, the returns of the six constructed portfolios are presented, from where it can be inferred
that the created portfolios from the intersection of the small size stocks and the stocks with low book
equity over market equity (S/L) produces the highest returns. The average SL portfolio return for the
examined period from 1998 to 2003 is almost 65% while the lowest portfolio return is produced from
the S/H portfolio, the portfolio constructed from small stocks in size and stocks with high book equity
over market equity that produces 9%. However, it should be noted the high return portfolio is the most
risky of all the constructed portfolios with the highest value of variance.
Table 3: Returns of the six constructed portfolios based on sales growth
Portfolio BH Portfolio BL Portfolio BN Portfolio SH Portfolio SL Portfolio SN
1998 0.605163 0.199936 0.229623 0.028105 0.168382 0.099329
1999 0.427423 0.243117 0.164014 0.271542 1.314729 0.396542
2000 0.481105 0.528147 0.434735 0.125458 0.235116 0.565849
2001 0.033123 1.150248 0.240940 -0.013625 1.791478 -0.115205
2002 0.973386 0.058699 0.120516 0.067413 0.269925 0.090639
2003 1.342045 0.015739 0.449824 0.067586 0.101941 0.333595
AVERAGE 0.643707 0.365981 0.273275 0.091080 0.646928 0.228458
AVERAGE % 64.37% 36.60% 27.33% 9.11% 64.69% 22.85%
VARIANCE 0.2088 0.1802 0.0191 0.0100 0.5187 0.0614
The results from the regression analysis as presented in Table 2 provide supportive evidence that the
variants considered here, the three-factor model provides a suitable description of pervasive risk in
these size and value-sorted portfolios. All the calculated intercepts are statistically different from zero
with values of t-statistics not greater than 2 and large R-squared values. Only in the S/H portfolio the tstatistics
values are greater than 2 but with high R-squared values. In addition, the estimated Durbin-
Watson values for the constructed portfolios are not greater than 2 providing with no evidence for
autocorrelation in stock returns.

Table 2: Regressions of sales growth for the six size and value sorted portfolios (ΔSales) regressed on
market (ΔSalesMKT), size (ΔSalesSMB) and value factors (ΔSalesHML) in sales
Δsales = α + bΔSalesMKT + sΔSalesSMB + hΔSalesHML
Portfolio a b s h R 2
S/L 0.7019 -0.1802 0.8828 -0.6749 0.9170
t-Statistic 1.2704 -0.1048 1.2259 -2.1164
Std. Error 0.5525 1.7200 0.7201 0.3189
Probability 0.3317 0.9261 0.3450 0.1685
Durbin-Watson stat 1.8562
S/N -0.3227 2.1027 0.3009 0.4798 0.8931
t-Statistic -1.4964 3.1320 1.0706 3.8545
Std. Error 0.2156 0.6714 0.2811 0.1245
Probability 0.2732 0.0886 0.3964 0.0612
Durbin-Watson stat 1.5096
S/H 0.0377 0.3905 0.4094 0.1729 0.9956
t-Statistic 2.1296 7.0873 17.7496 16.9305
Std. Error 0.0177 0.0551 0.0231 0.0102
Probability 0.1670 0.0193 0.0032 0.0035
Durbin-Watson stat 1.9018
B/L 0.0040 0.0183 -0.0280 -0.0802 0.9085
t-Statistic 0.0808 0.1170 -0.4251 -2.0822
Std. Error 0.0501 0.1561 0.0659 0.0385
Probability 0.9487 0.9258 0.7441 0.2850
Durbin-Watson stat 1.2023
B/N -0.1484 1.2633 -0.4130 0.0876 0.8772
t-Statistic -1.1514 3.1481 -2.4582 1.1769
Std. Error 0.1289 0.4013 0.1680 0.0744
Probability 0.3686 0.0878 0.1332 0.3603
Durbin-Watson stat 1.6966
B/H 0.6148 0.2395 -0.2602 0.5212 0.8564
t-Statistic 1.3337 0.1669 -0.4331 1.9589
Std. Error 0.4610 1.4351 0.6008 0.2661
Probability 0.3139 0.8828 0.7072 0.1892
Durbin-Watson stat 1.7335
The findings of the study provide supportive evidence of the Fama and French model applied to
Greek equities. There is evidence that past sales growth of a firm is associated with market, size and
value factors in returns so it can be inferred that this fundamental variable is related to size and bookto-market
equity that help capture the cross-section of average stock returns in regard to the Athens
stock exchange.
VI. Size and Value factors in earnings and returns
Fama and French [1992] find that two variables, market equity (ME) and the ratio of book to market
equity (BE/ME) capture much of the cross section of average stock returns. If stocks are priced
rationally, systematic differences in average returns are due to differences in risk. Thus, with rational
pricing, size and BE/ME must proxy for sensitivity to common risk factors in returns. Fama and French
[1993] confirm that portfolios constructed to mimic factors related to size and BE/ME add substantially
to the variation in stock returns explained by the market portfolio. The evidence that size and book to
market equity proxy for sensitivity to risk factors in returns is consistent with a rational pricing story

for the role of size and BE/ME in average returns. But return tests cannot tell the complete economic
story.
Size and BE/ME remain indicator variables that, for unexplained economic reasons, are
related to risk factors in returns. The purpose of the study is to examine whether stock prices
properly reflect differences in the evolution of profitability when stocks are grouped on size and
BE/ME.
Tests have been conducted from 1998 to 2003 using a selected number of one hundred and
twenty stocks that are traded on the ASE. These selected stocks are included in the formation of the
FTSE/ASE 20, FTSE/ASE Mid 40 and FTSE/ASE Small Cap index that have designed to provide realtime
measures of the Athens Stock Exchange.
The size-BE/ME portfolios
We focus on six portfolios, formed yearly from a simple sort of firms into two groups on ME and
another simple sort into three groups on BE/ME.
In June of each year t from 1998 to 2003, all the sample stocks are ranked on the basis of size (stock
price times shares outstanding). The median sample size is then used to split the sample companies into
two groups: small (S) and big (B). Book equity to market equity (BE/ME) for year t is calculated by
dividing book equity at the end of financial year t by market equity at the end of financial year t. The
sample stocks are broken into three BE/ME groups based on the breakpoints for the bottom 30% (low),
middle 40% (medium) and top 30% (high) of the ranked values of BE/ME for the sample stocks. We
construct six portfolios (S/L, S/M, S/M, B/L, B/M, and B/H) from the intersection of the two sizes and
three BE/ME groups. For example the S/L portfolio contains stocks that are in the small size group and
also in the low BE/ME group while B/H consists of big size stocks that also have high BE/ME ratios.
The equally weighted returns on the portfolios are calculated.
Profitability: Earnings on Book Equity
Our measure of profitability is EI(t)/ BE(t),is the ratio of common equity income for the fiscal year
ending in calendar year t to the book value of common equity for the same year. EI(t) is the earnings
before extraordinary items but after depreciation and taxes, interest and dividends. An inflationadjusted
measure of book common equity in EI(t)/BE (t) would be more preferable, but it is not
generally available. For our purposes, this is not a problem if the effect of inflation on
EI(t)/BE(t) does not differ systematically across the six size-BE/ME portfolios.
The question is how do earnings behave after firms are classified as small or big on ME and low or
high on BE/ME.

EI/BE
The evolution of earnings on book equity for size-BE/ME
portfolios
1.200000
1.000000
0.800000
0.600000
0.400000
0.200000
0.000000
1998 1999 2000 2001 2002 2003
Year
Figure 1 shows that book-to-market-equity is associated with persistent differences in profitability,
measured by EI/BE. Low-BE/ME stocks are on average more profitable than high-BE/ME stocks.
Specifically, the B/L and S/L prove to be the most profitable portfolios from their formation date until
the year of 2001. This is a very important point of the study because this period is characterised by
intense return volatility where the ASE reached its highest level of returns but also suffered from a
sharp and sudden decrease in stock returns. Thus, in high volatile times, the typical big low-book-tomarket
firm is more profitable than the typical big high-BE/ME firm. For small stocks, the S/L portfolio
has higher earnings on book equity than the S/H portfolio in every year, so again low-BE/ME is
associated with higher profitability.
Common factors in Returns and Earnings
The Fama-French model involves the use of three factors for explaining common stock returns: the
market factor proposed by the CAPM, and factors relating to size and value.
SMB (small minus big) is meant to mimic the risk factor in returns related to size. SMB is the
difference between the simple average of the returns of the three small stock portfolios (S/L, S/M and
S/H) and the average of the returns on the three big portfolios (B/L, B/M, B/H).
HML (high minus low) is meant to mimic the risk factor in returns related to value (that is book-tomarket
ratios). HML is the difference between the simple average of the returns on two high BE/ME
portfolios (S/H and B/H) and the average returns on two low BE/ME portfolios (S/L and B/L).
The next step is to test for links between the risk factors in returns and earnings. To provide a
reference point, time series regressions are run to examine the relation of risk factors in stock returns to
size and BE/ME. The dependent variables in the regressions are the returns on the six size BE/ME
portfolios. The explanatory variables are the return on the market portfolio and the returns SMB (small
minus big) and HML (high minus low) on the created portfolios to mimic the risk factors in returns
related to size and BE/ME.
In a standard valuation model, a stock price is the present value of expected future net cash flows to
stockholders. Unexpected changes in prices are caused by shocks to expected cash flows and discount
rates. Thus, to measure the relation between returns and common factors in net cash flows, we must
measure i) shocks to expected cash flows and ii) the common factors in the shocks. As a crude proxy
for shocks to expected net cash flows, we use changes in earnings yield EI/BE. We use changes in
B/H
B/L
S/H
S/L

EI/BE, rather than growth rates of EI, because equity income is sometimes negative for the small-stock
portfolios. In addition, we use changes in EI/BE, rather than the residuals from a times series model
because earnings yields are highly auto correlated and because we would have a very limited number
of observations on EI/BE to estimate a richer time series model. The time series regression that is used
is the following,
Δ ( EI / BE) t = a + biΔ ( EI / BE) tMKT + siΔ ( EI / BE) tSMB + hiΔ ( EI / BE) " tHML + et
(3)
Where Δ (EI/BE) MKT is the change of the fundamental variable EI/BE and comes from the value of
the quotient of the division with numerator the sum of earnings of all stocks of the current year over
the book equity of all stocks of the same year and denominator the sum of earnings of all stocks of the
previous year over the book equity of all stocks of the same previous year. Δ (EI/BE) is the change of
the fundamental variable EI/BE from year t for the constructed portfolios.
We test for common factors in the year-to-year changes in earnings yields. Table 4 shows
time-series regressions in which changes in EI/BE for the six size-BE/ME portfolios are regressed on
market, size, and book-to-market factors in yield changes.
Table 4: Changes in EI/BE for the six size and value sorted portfolios Δ(EI/BE) regressed on market
Δ(MKT_EI/BE), size Δ(SMB_EI/BE) and value factors Δ(HML_EI/BE) in profitability
Δ(EI/BE)=α+bΔ(EI/BE)MKT+sΔ(EI/BE)SMB+hΔ(EI/BE)HML
Portfolio a B s h R 2
S/L -0.0877 1.8683 1.0302 -0.2780 0.9989
Std. Error 0.0156 0.1244 0.0648 0.0893
t-Statistic -5.6186 15.0134 15.8901 -3.1128
Probability 0.0302 0.0044 0.0039 0.0896
S/N 0.0506 0.5767 0.3222 0.0035 0.9783
Std. Error 0.0186 0.1481 0.0771 0.1063
t-Statistic 2.7263 3.8955 4.1768 0.0330
Probability 0.1123 0.0600 0.0528 0.9767
S/H -0.0035 1.1119 0.5258 0.5058 0.9687
Std. Error 0.0211 0.1679 0.0875 0.1205
t-Statistic -0.1654 6.6229 6.0112 4.1980
Probability 0.8839 0.0220 0.0266 0.0523
B/L 0.0420 0.7988 -0.0216 -0.4869 0.9996
Std. Error 0.0093 0.0744 0.0388 0.0534
t-Statistic 4.5044 10.7346 -0.5581 -9.1184
Probability 0.0459 0.0086 0.6329 0.0118
B/N -0.0404 1.2030 -1.5830 -0.0110 0.9997
Std. Error 0.0382 0.3050 0.1589 0.2189
t-Statistic -1.0565 3.9444 -9.9629 -0.0502
Probability 0.4015 0.0587 0.0099 0.9645
B/H -0.0422 1.5552 0.4828 0.7292 0.9860
Std. Error 0.0404 0.3222 0.1679 0.2312
t-Statistic -1.0436 4.8268 2.8762 3.1538
Probability 0.4062 0.0403 0.1026 0.0875
In the earnings-yield regressions that use all stocks to form the explanatory variables,
measurement error has two effects that tend to offset, (i) Since measurement error in the earnings
yield for a given size-BE/ME portfolio is also in the explanatory yields, it can spuriously increase
the explanatory power of the regression for that portfolio, (ii) Measurement error in the explanatory

yields that is not shared with the dependent yield spuriously decreases the estimated role of the
common yield factors. It is difficult to judge whether, on balance, measurement error increases
or decreases the explanatory power of the regressions that use all stocks in the independent
variables.
Despite these measurement-error problems, the results from the earnings-yield regressions are
positive. The regressions identify market, size, and book-to-market factors in earnings. All the
regressions produce strong evidence of a market factor in earnings. The t-statistics for the
slopes on the market factor are all greater than 3.0. The earnings-yield regressions say that the
size factor is important in distinguishing the earnings variation of small stocks and big stocks.
The goal of the study is to provide an economic foundation for the empirical relations between
average stock return and size, and average return and book-to-market-equity. This is guided by
two hypotheses. If the average-return relations are due to rational pricing, then (i) there must be
common risk factors in returns associated with size and BE/ME, and (ii) the size and bookto-market
patterns in returns must be explained by the behavior of earnings. In a rational
market, short-term variation in profitability should have little effect on stock price and book-tomarket-equity;
BE/ME should be associated with long-term differences in profitability. The
evidence presented here shows that size and BE/ME are related to profitability.
Finally, our work on stock returns and profitability leaves important open questions. Several
unanswered questions arise from this study like (i) what are the underlying economic state
variables that produce variation in earnings and returns related to size and BE/ME? (ii) do
these unnamed state variables produce variation in consumption and wealth that is not captured
by an overall market factor and so can explain the risk premiums in returns associated with size
and BE/ME?.
A number of variables, like gross national product, consumption, employment, inflation, level of
interest rates and others, can be named that may affect the level of earnings-profitability and stock
returns. This point of examining the underlying factors that drive earnings and returns is left for future
work.
Adler, N. J.(1983). Cross-Cultural Management Research: The Ostrich and the Trend. Academy of
Management Review, 8, 226-232.
VII. References
[1] Bailey J.W, Alexander J.G, Sharpe W. (1998). Investments. 6th edition, London: Prentice-Hall.
[2] Banz, Rolf W. (1981). The Relationship Between Return and Market Value of Common
Stocks: Journal of Financial Economics. 9:1, pp. 3-18.
[3] Basu, Sanjay. (1977). Investment Performance of Common Stocks in Relation to Their Price-
Earnings Ratios: A Test of the Efficient Market Hypothesis: Journal of Finance. 12:3,pp. 129-
56.
[4] Bekaert, G., Harvey, C. (1997). Emerging equity market volatility: Journal of Financial
Economics 43: 29-78.
[5] Bhandari, Laxmi Chand. (1988). Debt/Equity Ratio and Expected Common Stock Returns:
Empirical Evidence. Journal of Finance. 43:2, pp. 507-28.
[6] Black, Fischer, Michael C. Jensen and Myron Scholes. (1972). The Capital Asset Pricing
Model: Some Empirical Tests, in Studies in the Theory of Capital Markets. Michael C.
Jensen,ed. New York: Praeger, pp. 79-121.
[7] Black, Fischer. (1972). Capital Market Equilibrium with Restricted Borrowing: Journal of
Business. 45:3, pp. 444-454.
[8] Blume, Marshall and Irwin Friend. (1973). A New Look at the Capital Asset Pricing Model:
Journal of Finance. 28:1, pp. 19-33.

[9] Blume, Marshall. (1970). Portfolio Theory: A Step Towards Its Practical Application: Journal
of Business. 43:2, pp. 152-174.
[10] Bodie, Z., Kane, A. and Marcus, A. J. (1999). Investments. 4 th edition, New York New York:
McGraw- Hill.
[11] Brealey, R.A., and S.C. Meyers. (2002). Principles of Corporate Finance. New York: McGraw-
Hill.
[12] Campbell, J. Y., Lo, A. W. and MacKinlay, A. C. (1997). The Econometrics of Financial
Markets. Princeton, N. J.: Princeton University Press.
[13] Capaul, Carlo, Ian Rowley, and William F. Sharpe, (1993), International Value and Growth
Stock Returns: Financial Analysts Journal. January/February, pp. 27-36.
[14] Carhart, Mark M. (1997). On Persistence in Mutual Fund Performance: Journal of Finance.
52:1, pp. 57-82.
[15] Chan, Louis K.C., Yasushi Hamao and Josef Lakonishok. (1991). Fundamentals and Stock
Returns in Japan: Journal of Finance. 46:5, pp. 1739-1789.
[16] Chen, N., R. Roll, and S. A. Ross (1986). Economic forces and the stock market: Journal of
Business 59: 383-403.
[17] Cochrane, J. H. (1991). Volatility tests and efficient markets: A Review Essay: Journal of
Monetary Economics 127: 463-485.
[18] Cochrane, John H. (2001). Asset Pricing. Princeton, N. J.: Princeton University Press.
Conditions of Risk: Journal of Finance. 19:3, pp. 425-442.
[19] DeBondt, Werner F.M. and Richard H. Thaler, (1985), Does the stock market overreact?:
Journal of Finance 40, 793-805.
[20] Douglas, George W. (1968). Risk in the Equity Markets: An Empirical Appraisal of Market
Efficiency. Ann Arbor, Michigan: University Microfilms, Inc.
[21] Elton, E. J. and Gruber, M. J. (1995). Modern Portfolio Theory and Investment Analysis. 5th
edition, New York: John: Wiley & Sons, Inc.
[22] Fama, Eugene F. (1996). Multifactor Portfolio Efficiency and Multifactor Asset Pricing:
Journal of Financial and Quantitative Analysis. 31:4, pp. 441-465.
[23] Fama, Eugene F. and James D. MacBeth. (1973). Risk, Return, and Equilibrium: Empirical
Tests: Journal of Political Economy. 81:3, pp. 607-636.
[24] Fama, Eugene F. and Kenneth R. French. (1992). The Cross-Section of Expected Stock
Returns: Journal of Finance. 47:2, pp. 427-465.
[25] Fama, Eugene F. and Kenneth R. French. (1993). Common Risk Factors in the Returns on
Stocks and Bonds: .Journal of Financial Economics. 33:1, pp. 3-56.
[26] Fama, Eugene F. and Kenneth R. French. (1995). Size and Book-to-Market Factors in Earnings
and Returns: Journal of Finance. 50:1, pp. 131-155.
[27] Fama, Eugene F. and Kenneth R. French. (1996). Multifactor Explanations of Asset Pricing
Anomalies: Journal of Finance. 51:1, pp. 55-84.
[28] Fama, Eugene F. and Kenneth R. French. (1997). Industry Costs of Equity: Journal of Financial
Economics. 43:2 pp. 153-193.
[29] Fama, Eugene F. and Kenneth R. French. (1998). Value Versus Growth: The International
Evidence.” Journal of Finance. 53:6, pp. 1975-1999.
[30] Friend, Irwin and Marshall Blume. (1970). Measurement of Portfolio Performance under
Uncertainty: American Economic Review. 60:4, pp. 607-636.
[31] Gibbons, M. R., S. A. Ross, and J. Shanken. (1989). A test of the efficiency of a given
portfolio: Econometrica 57: 1121-1152.
[32] Hamilton, James D. (1994). Time Series Analysis: Princeton University Press, Princeton
[33] Haugen, Robert. (1995). The New Finance: The Case against Efficient Markets. Englewood
Cliffs, New Jersey: Prentice Hall.
[34] Jagannathan, R. and McGratten, E. R. (1995). The CAPM Debate: Quarterly Review of the
Federal Reserve Bank of Minneapolis 19: 2-17.

[35] Jagannathan, R. and Wang, Z. (1996). The conditional CAPM and the cross-section of expected
returns: Journal of Finance 51: 3-53.
[36] Jegadeesh, Narasimhan and Sheridan Titman. (1993). Returns to Buying Winners and Selling
Losers: Implications for Stock Market Efficiency: Journal of Finance. 48:1, pp. 65-91.
[37] Jensen, Michael C. (1968). The Performance of Mutual Funds in the Period 1945-1964: Journal
of Finance. 23:2, pp. 389-416.
[38] Kothari, S. P., Jay Shanken, and Richard G. Sloan. (1995). Another Look at the Cross-Section
of Expected Stock Returns: Journal of Finance. 50:1, pp. 185-224.
[39] Lakonishok, Josef, Andrei Shleifer and Robert W. Vishny. (1994). Contrarian Investment,
Extrapolation, and Risk: Journal of Finance. 49:5, pp. 1541-1578.
[40] Lintner, John. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in
Stock Portfolios and Capital Budgets: Review of Economics and Statistics. 47:1, 13-37.
[41] Loughran, Tim and Jay. R. Ritter. (1995). The New Issues Puzzle: Journal of Finance. 50:1,
pp.23-51.
[42] Loughran T., (1997), Book-to-market across firm size, exchange, and seasonality: is there an
effect?: Journal of Financial and Quantitative Analysis, Vol.30, 1997, 607-618.
[43] Markowitz, Harry. (1952). Portfolio Selection: Journal of Finance. 7:1, pp. 77-99.
[44] Markowitz, Harry. (1959). Portfolio Selection: Efficient Diversification of Investments. Cowles
Foundation Monograph No. 16. New York: John Wiley & Sons, Inc.
[45] Merton, Robert C. (1973). An Intertemporal Capital Asset Pricing Model: Econometrica. 41:5,
pp. 867-887.
[46] Michael C.Jensen, ed. New York: Praeger, pp. 47-78.
[47] Miller, Merton, and Myron Scholes. (1972). Rate of Return in Relation to Risk: A
Reexamination of Some Recent Findings: Studies in the Theory of Capital Markets.
[48] Mitchell, Mark L. and Erik Stafford. (2000). Managerial Decisions and Long-Term Stock Price
Performance: Journal of Business. 73:3, pp. 287-329.
[49] Mossin, J. (1966). Equilibrium in a capital asset market: Econometrica 34: 768-783.
[50] Reinganum, Marc R. (1981). A New Empirical Perspective on the CAPM: Journal of Financial
Quantitative Analysis. 16:4, pp. 439-462.
[51] Rosenberg, Barr, Kenneth Reid, and Ronald Lanstein. (1985). Persuasive Evidence of Market
Inefficiency: Journal of Portfolio Management. 11, pp. 9-17.
[52] Sharpe, William F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under
Stambaugh, Robert F. (1982). On The Exclusion of Assets from Tests of the Two-Parameter
Model: A Sensitivity Analysis: Journal of Financial Economics. 10:3, pp. 237-268.
[53] Stattman, Dennis. (1980). Book Values and Stock Returns: The Chicago MBA: A Journal of
Selected Papers. 4, pp.25-45.
[54] Stein, Jeremy. (1996). Rational Capital Budgeting in an Irrational World: Journal of Business.
69:4, pp. 429-455.
[55] Tobin, James. (1958). Liquidity Preference as Behavior Toward Risk: Review of Economic
Studies. 25:2, pp. 65-86.

International Bulletin of Business Administration
ISSN: 1451-243X Issue 1 (2006)
© EuroJournals, Inc. 2006
http://www.eurojournalsn.com
Business Performance Measurement Frameworks and SMEs
George Ntalakas
Hellenic Open University
School of Social Sciences, Faculty of Business Administration
16 Sachtouri str., 26222 Patra, Greece
E-mail: G.Ntalakas@sandb.com
Athanassios Mihiotis
Hellenic Open University
School of Social Sciences, Faculty of Business Administration
16 Sachtouri str., 26222 Patra, Greece
E-mail: mihiotis@eap.gr
John Mylonakis
10 Nikiforou str., Glyfada
166 75, Athens, Greece
E-mail: imylonakis@panafonet.gr
Abstract
This paper presents the major Performance Measurement frameworks and models
developed during the last 15 years mainly for large enterprises. It describes the basic
characteristics of these models, the needs of large companies met by these developments
and also the weaknesses of these frameworks. Also the peculiarities of SME sector are
presented, as they are reported in the literature, and the difficulties of applying these
models in this sector as well. The paper concludes by identifying the challenges these
frameworks and models have to cope with in order to become more effective in meeting
current needs of large and small to medium companies, namely what performance
measurement systems and how should be developed in dynamic and changing business
environments.
Keywords: Business Performance Measurement, SME, Balanced Scorecard,
Intellectual Capital, Skandia Navigator, Performance Prism Model
JEL Classification: M21, M20.
I. Introduction
During recent years much research has been carried out in order to develop and apply frameworks and
models concerning Business Performance Measurement Systems. These measurement frameworks
have been, also, used as management systems. The major break through these frameworks brought into
business and academic community was that they integrated financial and non financial metrics. They

also showed how the value creation process could be improved if quantifiable linkages between
strategic objectives and operational drivers were used. The capabilities needed for each organization to
handle competition have been put in a rational perspective by showing and quantifying how they relate
to value creation. Some of these frameworks take, also, into account the intellectual capital as source of
value creation.
However these developments are, mainly, focused on large companies. The distinct characteristics of
SME’s demand that these frameworks should be adapted and probably develop new ones, in order to
cope with the special needs of Small and Medium Enterprises (SME).
The purpose of this paper is to present the major frameworks and models developed during the last 15
years and, also, to describe the difficulties and potential benefits of applying these frameworks to
SME’s.
II. Major Frameworks developed on Business Performance Measurement since 1990’s
Companies realize that the behavior of their organization is strongly affected by the measurement
system used. It is repeatedly mentioned that ‘what you measure is what you get’. They, also,
understand that financial accounting measures (Net Income, Return On Equity, Return on Capital
Employed, Earnings per share, etc) could provide misleading information about future competitive
position of the company. Since these measures refer to the past, they do not give informative signals on
activities/processes/capabilities needed in competitive environment, such as continuous improvement,
innovation etc.
Conceptually, two extreme trends emerged by managers and academics, in order to handle the
inadequacies of performance measurement. The first one is focused, mainly, on financial, while the
second one is focused, mainly, on operational measures. The argument of the later was that achieving
operational results the financials will follow. Some tried to improve methods of measuring financial
performance by developing concepts such as Economic Profit, EVA, Free Cash Flow analysis. Others
tried to improve operational efficiency by developing concepts and methods, like Activity Based
Costing, Activity Based Management, Quality Management, JIT systems etc.
However managers do not have to rely on one measure at the expense of the other. In real life of
corporations, managers look at financial, operational, business and other measures in order to have a
more comprehensive view of the company. This was, also, confirmed by the research undertaken by
Robert Kaplan and David Norton in 12 companies at the leading edge of performance measurement.
The result of this research was the introduction of the Balanced Scorecard performance framework,
which the authors presented in Harvard Business Review in 1992 (Kaplan, Norton, 1998a).
The Balanced Scorecard provides a comprehensive view of the business as it includes financial
measures (i.e. lag drivers) and operational, customer expectations and Innovation/Learning measures
that are drivers of future financial performance (i.e. leading drivers). It provides a comprehensive
framework to translate company’s strategic objectives into an integrated set of performance
measurements. It also allows top management to look at the business from four important perspectives
answering essentially four basic questions:
a. Customer perspective. How customers perceive the company?
b. Financial perspective. How shareholders perceive the company?
c. Business Internal Perspective. What processes and how should the company improve?
d. Innovation and learning perspective. What and how can the company continue to improve and
create value?
According to Kaplan and Norton, companies adopting the Balanced Scorecard (BSC) as performance
measurement system have satisfied the following managerial needs:
� The BSC brings in one report many seemingly disparate functions (e.g. finance, operations,
marketing, business strategy etc)

� Avoids sub-optimization since the various functions are viewed from and integrated and
comprehensive point of view
� Focuses on critical to success processes, which add value and thus avoiding all the secondary
ones that can create huge amount of information without adding any value to the business
performance. Simplicity in the reporting is achieved but no critical information is lost.
However, the implementation of BSC is not that simple as it appears. Managers have to think
thoroughly and carefully in order to identify the proper Strategic Objectives, the Value Creation
processes, core capabilities needed to excel and, also, find the cause effect relation of these processes
to Strategic Objectives and Value Creation. If the Goals, measures and linkages selected do not reflect
the Value Creation in the business then the BSC would not be sufficient to drive successfully the
company. The driving principle should be value creation, not just developing numerous metrics
without significant relevance to value creation process. Kaplan and Norton proposed, presenting
numerous examples (Kaplan, Norton, 1998b) from companies, that the BSC should be used not as a
measurement exercise but as a management system (Kaplan, Norton 1998c) and that is most successful
when used to drive the process of change.
Intellectual Capital supporters criticized the Balance Scorecard framework as being too rigid and its
categories too limiting. Sveiby developer of “the Intangible Asset Monitor” considers the scorecard as
a tool rooted in industrial age thinking (Skyrne, 1998). Sveiby considers that the scorecard method
does not consider employees to be the primary drivers of corporate profits. Intellectual Capital Metric
Systems do better job in defining activities driving competitive advantage.
Intellectual Capital Skandia Navigator model (inventor: Leif Edvinsson, Skandia, 1995), developed
by the Swedish Insurance Company Skandia, replaced the fourth perspective of Balanced Scorecard
with three sub categories:
a. Human capital (the knowledge and skills of individuals),
b. Structural capital (organizational processes and databases), and
c. Customer capital (customer relationships and brands).
The objective was to use these three categories as measurement for the investing community, not
for a company’s internal strategies. The Skandia Navigator approach takes into account the same set of
financial, operational and customer perspectives as the scorecard (Shand, 1999). The distinguishing
issue is that it emphasizes more strongly the need to consider these issues (organization and its
structure and processes) for developing employees.
Although such metrics could contribute in signaling general direction, they cannot provide the
investment trade offs that traditional financial analysis does (Shand, 1999). It is very difficult for the
Knowledge Manager to use feedback procedures to evaluate current initiatives, modify decisions and
to make decisions on how to implement concepts and tools.
The Performance Prism Model (Neely) offers a different perspective on performance. While the BSC
focuses on financials (shareholders), customers, internal processes, innovation and learning, it does not
highlight the importance of other stakeholders.
This model specifically addresses the organization’s relationship with all its key stakeholders and links
to its strategies, processes and capabilities. It starts by setting five critical questions (Adams), which
also define the five facets of the model:
a. Who are the Key stakeholders and what do they want in order to be satisfied? Stakeholder
Satisfaction
b. What does the company wants and needs from its stakeholders? Stakeholder contribution
c. What strategies does the company need to have in place to meet stakeholder satisfaction and
contribution? Strategies
d. What processes has the company to put in place to enable to execute the strategies? Processes
e. What capabilities does the company need to put in place in order to operate and improve
processes? Capabilities

The developers suggest that by answering these questions the organization could build a structured
business performance model and, also, having an integrated framework for managing organizational
performance. This framework starts from what satisfy stakeholders while BSC derives its performance
measures from strategy. The authors argue that, understanding, the operating environment must be the
starting point. The strategy is not an objective but a mean to achieve objectives. Understanding the
changing needs and wants of stakeholders’ satisfaction should be the starting point.
In Performance Prism framework key stakeholders could be a combination of investors, other
capital providers, customers, employees, labor unions, suppliers, regulators, state, pressure groups and
local communities. Their importance is likely to vary from company to company. It is obvious that a
nonprofit organization is likely to have greater social impact responsibilities. This broader view the
Performance Prism adopts is aligned with the latest developments in Social Responsibility of an
organization.
Stakeholder contribution is perceived to mean invested capital to the company, employees’ work,
customer profitability and loyalty, grants from the state, infrastructure, local community’s acceptance
of the company’s operations etc.
This broader perspective provides a better paradigm for the complex and dynamic world of
organizations, but at the same time, it poses an increasingly complex problem of how to link and
integrate all of the individual measures (Neely, 2003).
In order to handle with this problem, Kaplan and Norton introduced Strategy maps (Kaplan, Norton,
2005), Neely and colleagues introduced success (Marr et.al, 2002) and risk maps (Neely et.al., 2002),
while Goran Roos and colleagues introduced the IC Navigator model (Pike et.al., 2002). These
frameworks coped with the dynamics of value creation by investigating transformation of resources
as well the stocks of these resources. The emphasis is now placed on the transformation rather than the
individual measures. The objective of these methodologies was to show the most important pathways
for value creation and also visualize the linkage between intangible assets and business value (Neely
et.al., 2003).
However, Neely and colleagues, state that, also, these frameworks have a fundamental weakness
(Neely et.al., 2003). Namely, there is no ability to link the business-oriented methodology to Cash
Flows and their discounting, which is placed at the center of current valuation approaches and market
valuation. Another factor that complicates further the problem, is that firms operate in dynamic
environments and are subject to constant changes. They propose that this gap should be coped the third
generation frameworks which is the agenda for future research efforts.
III. Business Performance Measurement in SME
The frameworks presented above have been designed primarily for use in large companies. The Small
and Medium Enterprises have characteristics (Storey, 1994, Mintzberg et.al., 1998) that make them
discrete from the large companies. In general, these characteristics could be summarized as
(Mintzberg et.al., 1998):
� Personalized management with little delegation of management
� Flat, flexible organizational structures
� Reactive, fire-fighting mentality
� Informal, dynamic strategies
� Resource scarcity in management, finance
� Serve small number of customers and operate in small markets (i.e. niches)
As much as it concerns strategic planning in SME’s it is referred in the literature that there is a
distinct scarcity of strategic planning (Mintzberg et.al., 1998, Cagliano et.al., 1998). In order to be used
the existing Business performance Measurements on SME’s there is need to identify the relevance of

existing approaches for SME’s and, also, identify an appropriate process for the design and
implementation of these frameworks.
Hudson et al (Hudson et.al., 2001) specified a set of requirements for a SME Business Performance
development process. They present a typology that synthesizes the characteristics of performance
measurement development process and, also, identify the characteristics of well-designed performance
measures for SMEs. According to their findings among the managers of the SMEs studied, none had
taken steps to redesign or update their current performance measurement systems. The managers of the
SME’s researched, reported that the development process was too resource intensive and too
strategically (i.e. Long term) oriented. The authors conclude that in order the Business Performance
Measurement development process to be successful, it must be very resource effective, produce short
term (i.e. in order to maintain the enthusiasm of development team) and long-term benefits, and be
dynamic and flexible due to the operational environment of SME’s.
Globerson (1985) as well as Lynch and Cross (1991) suggest that the staff that will be the key users
of the performance measurement system should be also included in the process. However, research
indicates that SME’s which link operations to business strategies outperform the competition
(Argument et.al., 1997). This shows that aligning measures to business strategy and use those in
decision making could provide valuable contribution to achieving competitive advantage.
In attempting to identify whether the right things are being measured, Dixon et al (1990) proposed
the performance measurement questionnaire, which is specifically designed to help an organization to
identify the appropriateness of its measurement system.
Kennerley and Neely (2003) suggest that the evolution of performance measurement system should,
also, be managed. The business environment and strategy of the companies’ change, as well as the
performance measurement system should be aligned. In companies where performance systems exist,
they identify factors, categorized as people, process, systems and culture that can enable the company
to overcome the barriers to evolve the performance measurement system. They, also, suggest that
there are distinct stages (i.e. reflection, modification, deployment) in the evolution of performance
measurement systems, where each stage poses different issues. In the reflection stage the issue is to
identify where the existing performance system is no longer appropriate and what improvements
should take place. In the modification stage, the issue is to ensure alignment of the performance system
to the company’s new environment. . In the deployment stage the new system can be used to manage
the organization.
IV. Conclusions
The development of Business Performance Measurement frameworks followed the need of the
companies to have a management tool for measuring and establishing strategic directions (Balanced
Scorecard). When the companies were forced to realize that they operate in a business world where
multiple stakeholders contribute and demand rewards from them, then Performance Prism type models
were introduced. As the competitive environment valued significantly the Intellectual Capital, Skandia
Navigator type models started to develop.
These efforts were primarily focused on large organizations. A consequence of that was that the
SME sector lagged progress. In recent years, the peculiarities of SME sector have started to be taken
into account in order such frameworks to be effectively applied also in SME’s. However, since the
business environment for both large and SME enterprises is dynamic and constantly changing these
frameworks and their application should cope with that. The linkage and modeling of value creation
process with performance objectives, using both tangible and intangible, financials and non-financials
drivers should take also into account the dynamic and changing business environment.

References
[1] Adams Chris, Andy Neely (2003) “The new spectrum: How the performance prism framework
helps”
[2] www. Bpmmag.net/magazine/article.html?articleID=14101&pg=1
[3] Argument L., Harrison D., Wainwright C. (1997), “Manufacturing strategy within the SME
sector”, 13 th National Conference of Manufacturing (Proceedings), Glasgow Caledonian
University, Glasgow, pp. 6-10
[4] Cagliano R., Spima G., Verganti R., Zotteri G. (1998), “Designing BPR support services for
small firms”, International Journal of Operations & Production Management, Vol. 18, No 9/10,
pp. 865-885
[5] Dixon J.R., Nanni A.J., Vollmann T.E. (1990), “The new performance challenge – Measuring
operations for world class competition”, Dow Jones – Irwin, Homewood, IL
[6] Globerson S. (1985), “Issues in developing a performance criteria system for an organization”,
International Journal of Production Research, Vol. 23, No 4, pp. 639-646
[7] Hudson Mel, Smart Andi, Bourne Mike (2001), “Theory and practice in SME performance
measurement systems”, International Journal of Operations & Production Management, Vol.
21, No 8, pp. 1096-1115
[8] Kaplan Robert S., David P. Norton (1998a), “The Balanced Scorecard – Measures that drive
Performance”, Harvard Business Review on Measuring Corporate Performance, Harvard
Business School Press, pp. 123- 145
[9] Kaplan Robert S., David P. Norton (1998b), “Putting the Balanced Scorecard to work”.
Harvard Business Review on Measuring Corporate Performance, Harvard Business School
Press, pp. 147-181
[10] Kaplan Robert S., David P. Norton (1998c), “Using the Balanced Scorecard as a strategic
Management system”, Harvard Business Review on Measuring Corporate Performance,
Harvard Business School Press, pp. 183-211
[11] Kaplan R. S., Norton D. P. (2005), “The strategy focused organization: How Balanced
scorecard companies thrive in the new business environment”, Boston
[12] Kennerley Mike, Neely Andy (2003), “Measuring performance in a changing business
environment”, International Journal of Operations & Production Management, Vol. 23, No 2,
pp. 213-229
[13] Lynch R., Cross K. (1991), “Measure Up! Yardsticks for continuous improvement”, Blackwell,
Oxford
[14] Marr B., Neely A., Thomas G. (2002), “Balanced scorecard and strategy maps: How
intangibles drive corporate performance at Shell International”, Research and Action, Boston
[15] Mintzberg H., Quinn J., Ghoshal S. (1998), “The strategy process”, The Strategy process,
Prentice Hall, London
[16] Neely A., Adams C., Kennerley M. (2002), “The performance prism: The scorecard for
measuring and managing business success¨, London
[17] Neely Andy, “Perspectives on Performance: The Performance Prism”, Cranfield School of
Management, Chris Adams, Andersen Consulting
[18] Neely Andy, Bernard Marr, Goran Roos, Stephen Pike and Oliver Gupta (2003), “Towards the
third generation of performance measurement”, Controlling, Heft ¾, March/April
[19] Pike S., Rylander A., Roos G. (2002), “Intellectual capital management and disclosure” in
“The strategic management of intellectual capital and organizational knowledge”, A selection
of readings”, Bontis N, Choo C, Willey edition, New York
[20] Shand Dawne (1999), “Return on Knowledge”, Knowledge Management Magazine, April
[21] Skyrne David (1998), “Measuring the Value of Knowledge”, Business Intelligence LTD
[22] Storey D. (1994), “Understanding the Small Business Sector”, International Thompson
Business Press, London

The Effects of Emotional Intelligence On Leader Impression
Management and Group Satisfaction
David E. Gundersen
Professor of Management
Department of Management and Marketing
School of Business
Stephen F. Austin State University
Nacogdoches, TX 75962
Elizabeth J. Rozell
Professor of Management
Department of Management
College of Business Administration
Southwest Missouri State University
Springfield, MO 65804
Abstract
The primary purpose of the current study was to extend previous research by examining
actual leaders in a small group setting to empirically assess the impact of emotional
intelligence (EI) on leader impression management (LIM) and group satisfaction (GS) of
undergraduate business students. The current study explored EI items as predictors of LIM
and GS using a sample of 105 undergraduate business students from a mid-western
university. Results indicated that specific components of EI significantly predicted (LIM)
tactics and was predictive of GS including its components of group cohesion, feelings
regarding group member relationships and decision processes, feelings regarding group
decision outcomes, and feelings regarding individual effectiveness. Implications for the
research findings as they relate to management training and selection practices are
discussed.
Key Words: emotional intelligence, leader impression management, group satisfaction
Emotion is a relatively untapped field in management literature. However, there exists much interest
in the area of EI on the part of both academic scholars and practitioners (Montemayor & Spee, 2004;
Ashkanasy & Daus, 2002; Fox, 2000). Mayer and Salovey (1995) define EI as “the capacity to process
emotional information accurately and efficiently, including that information relevant to the recognition,
construction, and regulation of emotion in oneself and others” (p. 197). Individuals with high EI are
likely to be more aware of their own feelings as well as the feelings of others, better able to identify
their feelings, and better able to communicate them when appropriate (Mayer & Salovey, 1993).
Emotions often influence behavior choices in the workplace and can undermine rational selection of
optimal courses of action (Leith & Baumeister, 1996).
Others have used the term EI to describe the ability to know one’s own emotions and be skilled at
reading others’ emotions (Law, Chi-Sum, & Song, 2004; Goleman, 1995, 1998). This capability has
been suggested as a predictor of elevated performance on a variety of tasks (Goleman, 1995, 1998).
LIM is the process by which a leader attempts to control the impression others form of them (Gardner
& Cleavenger, 1998). Similar to the construct of EI, LIM has been linked to heightened performance

appraisals (Wayne & Kacmer, 1991). The primary purpose of the current study is to empirically assess
the efficacy of using EI as predictors of LIM and GS of undergraduate business students. The
determination of the viability of using EI predictors of LIM and GS may then lead to the more efficient
use of EI and LIM in small group interactions as well as the use of these constructs in the development
of core management curricula. The current study is intended to serve as an initial step toward filling
this knowledge void. Specifically, these previously described potential relationships will be explored
via regression analysis of data collected from a sample of 105 students from a university setting.
I. Theoretical Foundations
Emotional Intelligence (EI)
Much of the recent work on EI is based on the foundation provided by Gardner (1983). Although he
did not use the term “emotional intelligence”, his reference to intrapersonal and interpersonal
intelligence has been used as a foundation in more recent models on this topic. Gardner’s (1983)
concept refers to having the ability to know and understand one’s own emotions and other individuals’
emotions and intentions. This understanding, in turn, is presumed to guide one’s behavior.
Salovey and Mayer (1990) were the first to formally conceptualize and use the term “emotional
intelligence”. Their conceptualization included three mental processes: a) the appraisal and expression
of emotions in oneself and others, b) the regulation of emotion in oneself and others, and c) the
utilization of emotions to facilitate thought. These three processes are further divided into
subcomponents within the model. Although the model is general in nature, it also addresses individual
differences in mental processes and abilities (Law et al. 2004; Lubit, 2004; David & Jackson, 2003;
Mayer & Geher, 1996; Mayer & Salovey, 1993, 1995; Salovey & Mayer, 1990).
In a reformulation of their original model, Mayer and Salovey (1997) developed a revised
framework within which to study EI. Their model presents EI as having four branches ranging from
the most basic psychological processes to those that are more advanced. The most basic level of
processing involves the perception, appraisal and expression of emotion. As these skills are mastered,
one would advance to the emotional facilitation of thinking and then on to the understanding and
analysis of emotions and the utilization of emotional knowledge. The most integrated and highest level
of processing involves the reflective regulation of emotions to further emotional and intellectual
growth. Within each level, there exist representative abilities ranging from those that emerge early in
development to those appearing later, usually in a more integrated adult personality. Individuals high
in EI are expected to progress more quickly through the branches and master each ability to its fullest
(Mayer & Salovey, 1997).
Goleman’s (1995) book “Emotional Intelligence” builds on many of the foundations laid by both
Gardner (1983) and Salovey and Mayer (1990). Five competencies are examined in this approach;
self-awareness, self-regulation, self-motivation, social awareness (empathy), and social skills
(relationship management). Goleman provides much anecdotal evidence of these competencies and
their relationship to individual and organizational success. Since Goleman’s original work, other
authors have followed suit, including Cooper and Sawaf (1997).
Traditional leadership theories contend that leaders must rationally plan, organize, motivate, and
control, while contemporary theories assert that leaders must also emotionally engage followers to
perform above expected standard levels (Bass & Avolio, 1994). Megerian & Sosik (1996) contend that
the exhibition of emotions by charismatic and transformational leaders can have important effects on
follower motivation. Although management theorists make this contention, few empirical studies exist
that substantiate this assertion. Indeed, Gardner & Avolio (1998) stated that much work is needed to
clarify emotional attributes of charismatic leaders and affective processes that produce attributions of
charisma. Montmayor & Spee (2004) concur that more study is needed in articulating relationships
involving and defining EI. Perhaps EI is the missing link in understanding group behavior and the
effectiveness of charismatic leaders.

Leader Impression Management (LIM)
According to Schlenker (1980), impression management is “a conscious or unconscious attempt to
control images projected in real or imagined social interactions.” Impression management can be
likened to an advertising campaign that individuals conduct on their own behalf by highlighting their
virtues and minimizing their deficiencies (Ralston & Kirkwood, 1999). Performance appraisal settings
are a likely context for impression management use by lower-level individuals who attempt to
influence superiors.
Gardner & Martinko (1988) conceptualize impression management as the behaviors people direct
toward others to create and maintain desired perceptions of themselves. In an early article, Jones and
Pittman (1982) contended that actors use the impression management strategies of ingratiation, selfpromotion,
intimidation, exemplification, and supplication to appear likable, competent, dangerous,
morally worthy, and pitiful, respectively. Rozell and Gundersen (2003) confirm that these LIM
strategies do influence group cohesion, group member relationships, and feelings regarding group
outcomes. Definitions and descriptions of LIM strategies can be found in Table 1. These strategies
have been postulated as attributes displayed by charismatic leaders (Gardner & Avolio, 1998).
Table 1
Leader Impression Management Strategies (LIM)
________________________________________________________________________
Behavior Definition/Description
________________________________________________________________________
Ingratiation Behaviors that actors use to make themselves appear more
attractive and likable to others.
Self-Promotion Behavior that presents the actor as highly competent with regards
to certain skills or abilities.
Exemplification Behavior that presents the actor as morally worthy; it may also be
designed to elicit follower emulation.
Intimidation Behaviors that present the actor as a dangerous person who is able
and willing to inflict pain on the audience.
Supplication Behaviors that present the actor as helpless to solicit aid from
others.
Indeed, leadership research over the last decade has been rejuvenated by several new perspectives,
including charismatic, transformational, and inspirational leadership (Bass, 1985, 1990; Conger &
Kanungo, 1987, 1988; House 1977; Shamir, House, & Arthur, 1993). The primary focus of these new
approaches is that leadership is a dynamic and interactive process, whereby leaders inspire and
energize followers to put forth heightened levels of effort in pursuit of a vision (Gardner & Cleavenger,
1998). Importantly, each new approach recognizes the importance of LIM as part of the process as
leaders create charismatic and inspirational images. For example, Bass (1985) contended that
“charismatic leaders engage in impression management techniques to bolster their image of
competence, increasing subordinate compliance and faith in practices to inspire followers in pursuit of
the vision”(p. 40). Further, Conger & Kanungo (1988) asserted that the use of impression management
was a fundamental element of their operational definition of charismatic leadership.
The past decade has viewed impression management as an important variable in which to study and
understand a wide variety of organizational behavior topics. Gardner & Avolio (1998) recently
advanced a dramaturgical perspective of the leadership process whereby leaders and followers jointly
construct a charismatic relationship. The theory posits that the leader’s impression management
behaviors combine with situational cues and follower attributes to elicit attributions of leader charisma

from followers. Despite the importance of impression management to the understanding of leadership
effectiveness, little empirical research has focused on such behaviors (Gardner & Cleavenger, 1998).
Group Cohesion, Consensus, and Communication
There have been challenges to small group researchers to conduct studies that explore interesting
questions that would be useful to managers of all types (Anderson & Martin, 1995; Frey, 1999; Poole,
1999). Of particular interest would be the impact of the impressions the leader makes on the
satisfaction of the followers. The variable of GS has often been captured in terms of group cohesion,
consensus, and satisfaction with group communication (Anderson & Martin, 1995, 1999; Cragan &
Wright, 1990).
One aspect of GS, group cohesion, has received considerable attention in small group research
(Anderson & Martin, 1995, 1999; Carron & Brawley, 2000). Mullen et al. (1994) describe group
cohesiveness as “one of the most interesting, and most elusive, constructs in the study of small group
behavior” (p. 189). Group cohesion has been defined in numerous ways. Early researchers defined the
concept as the degree of group cooperation toward a goal (Weinberg, 1979). Subsequent researchers
defined cohesion as a trait variable (Bormann, 1990) and others have likened it to group attraction
(Cragan & Wright, 1995). Carron, Brawley, and Widmeyer (1998) defined cohesion as “a dynamic
process which is reflected in the tendency for a group to stick together and remain united in the pursuit
of its instrumental objectives and/or for the satisfaction of member affective needs” (p. 213).
Additionally, Carron, Widmeyer, and Brawley (1985) have described cohesion as a multidimensional
construct that can be categorized into two major groups: a) group integration (GI) “a member’s
perceptions of the group as a totality”; and b) individual attraction to the group (ATG), “a member’s
personal attraction to the group” (p. 248). Further, Carron and Brawley (2000) asserted that GI and
ATG could be concentrated on the task or social aspects of the group. Indeed, Chang and Bordin
(2001) found support for the multidimensionality of cohesion. The definition of group cohesiveness
for this study incorporates aspects from both task and social cohesion. Importantly, researchers have
linked group cohesion to heightened performance (Evans & Dion, 1991; Langfred, 1998; Michalisin,
Karau & Tangpong, 2004) and effective group communication (Weinberg, 1979, Rosenfeld & Gilbert,
1989).
The concept of GS has also been conceptualized as consensus. Researchers have repeatedly found
that consensus results in more advantageous outcomes than majority rule (Hare, 1980). It is also
thought to be more appropriate than compromise as a group achieving consensus oftentimes
experiences increased cohesion and commitment. Other researchers contend that in order for
consensus to exist, members must be satisfied with the decision process utilized by the group
(DeStephens & Hirokawa, 1988). Most academic research studies have supported the notion that
greater cohesiveness results from greater consensus. It has been theorized that when team members are
given adequate time to discuss problem-solving strategies and solutions, a consensus approach usually
results in greater satisfaction with the group’s solution (Hare, 1980, Anderson & Martin, 1999).
GS has also been defined from a communicative perspective. Similar to group cohesion, group
communication has also been thought to be comprised of both task and social (relational) components
(Frey, 1999). Keyton (1999) has described relational communication in groups as the “verbal and
nonverbal messages that create the social fabric of a group by promoting relationships between and
among group members” (p. 192). This is in contrast to the instrumental or task-oriented dimension of
group communication which is largely focused on task-group communication and group decisionmaking
performance (Hirokawa & Salazar, 1999). Indeed, Anderson and Martin (1995) view
satisfaction from a communication perspective where members’ group experiences result in the
affective state of fulfillment. Their research supports the notion that members who believe their inputs
are valued are more satisfied (Anderson & Martin, 1995). Further, Anderson and Martin (1999)
contend that satisfaction with the communication among group members is a key ingredient for
successful group interaction and has been defined as a function of pride in membership (Cragin &
Wright, 1995) and as an individual’s self-evaluation of rewards received (Jurma, 1978). The definition

of group cohesiveness for this study incorporates aspects from both task and social cohesion. This is in
keeping with recent literature that supports the notion that the task and social dimensions of groups
should not be in competition with one another (Frey, 1999).
In light of these varying ways in which GS has been defined, we have incorporated perceptions of
cohesion, consensus, and communication into our operational definition of the construct.
Research Questions
The purpose of this study was to contribute to a relatively small and inconsistent body of literature
pertaining to EI, LIM, and group perceptions of cohesion, consensus, and communication. An overall
assertion made by the authors is that group member’s EI will influence their perceptions of both
impression management tactics used by group leaders and may heighten specific perceived aspects of
GS. Literature has noted that charismatic leaders attempt to portray themselves as morally worthy to
gain follower acceptance. Likewise, ingratiation has been noted as affecting group leadership and
follower perceptions (Rozell & Gundersen, 2003; Gardner & Avolio, 1998; Gardner & Cleavenger,
1998).
III. METHOD
Participants
The participants were 105 undergraduate students enrolled in an upper-divisional management course
at a large Midwestern university. Of the 105 participants, 49 were male and 56 were female. The
median age was just under 22 years.
Emotional Intelligence (EI)
A measure of EI developed by Schutte et al. (1998) was employed in the current study. Development
of the scale was based on the model of EI proposed by Salovey and Mayer (1990). The scale is a 33item
self-report measure that includes items such as “By looking at their facial expression, I recognize
the emotions people are experiencing,” and “I easily recognize my emotions as I experience them.”
Respondents use a 5-point scale, on which a “1” represents “strongly disagree” and a “5” represents
“strongly agree,” to indicate to what extent each item describes them. Schutte et al. (1998) provide
extensive validation and reliability evidence and report coefficient alphas in the range of .90. The
coefficient alpha for the current study was .92. Table 2 contains a list of all EI items that were used in
the study and used in further analyses.
Impression Management
A 29-item instrument was created to measure how subordinates view leaders in terms of impression
management behaviors exhibited by the leader. Responses were recorded on a 5-point Likert-type scale
indicating the frequency of observation of the particular behavior (from not at all to frequently). A
principal components analysis was conducted to identify underlying dimensions of impression
behaviors across items. This procedure provides for data reduction and summarization helping simplify
further analyses (Hair, Anderson and Tatham, 1987).
To assess the appropriateness of the data for factor analysis, several key statistics were
examined. First, a review of the communalities derived from the factor analysis was conducted. These
were all relatively large, with none below the .6 level, suggesting that the data set is appropriate
(Stewart, 1981). Next, the Kaiser-Meyer-Olkin measure of sampling adequacy was computed. Based
on Kaiser and Rice’s (1974) evaluative criteria, the result of .885 is considered “meritorious.” Finally,
the statistic for Bartlett’s (1950) sphericity test was 2218.9 (p < .000), providing further evidence that
the population of variables are independent and appropriate for factor analysis. Two items had low
loadings across all factors indicating lack of fit with the established factors and were systematically

emoved according to a procedure prescribed by Comrey (1973). The resulting factor structure
provided five (5) factors of impression management behavior as seen in Table 3.
The reliability of the factors was checked to support any measures of validity that might be employed.
All factors were checked for internal consistency using Cronbach alphas. According to Nunnally
(1978), the Cronbach alpha procedure is an estimate of reliability based on the average correlations
between items within each factor where 0.6 is sufficient. No values of coefficient alpha were lower
than 0.80 with two beyond the 0.90 level.
Table 2. Emotional Intelligence Items (EI)
________________________________________________________________________
1. I know when to speak about my personal problems to others.
2. When I am faced with obstacles, I remember times I faced and overcame similar obstacles
3. I expect that I will do well on most things I try.
4. Other people find it easy to confide in me.
5. I find it hard to understand the non-verbal messages of other people.
6. Some of the major events of my life have led me to re-evaluate what is important and not important.
7. When my mood changes, I see new possibilities.
8. Emotions are one of the things that make my life worth living.
9. I am aware of my emotions as I experience them.
10. I expect good things to happen.
11. I like to share my emotions with others.
12. When I experience a positive emotion, I know how to make it last.
13. I arrange events others enjoy.
14. I seek out activities that make me happy.
15. I am aware of the non-verbal messages I send to others.
16. I present myself in a way that makes a good impression on others.
17. When I am in a positive mood, solving problems is easy for me.
18. By looking at their facial expressions, I recognize the emotions people are experiencing.
19. I know why my emotions change.
20. When I am in a positive mood, I am able to come up with new ideas.
21. I have control over my emotions.
22. I easily recognize my emotions as I experience them.
23. I motivate myself by imagining a good outcome to tasks I take on.
24. I compliment others when they have done something well.
25. I am aware of the non-verbal messages other people send.
26. When another person tells me about an important event in his or her life,
I almost feel as though I have experienced this event myself.
27. When I feel a change in emotions, I tend to come up with new ideas.
28. When I am faced with a challenge, I give up because I believe I will fail.
29. I know what other people are feeling just by looking at them.
30. I help other people feel better when they are down.
31. I use good moods to help myself keep trying in the face of obstacles.
32. I can tell how people are feeling by listening to the tone of their voice.
33. It is difficult for me to understand why people feel the way they do.
Several assessments were also made to determine the construct validity of the impression management
factors. An individual principle components analysis was conducted on each factor to determine if its
set of variables would form a single factor independent of other variables (Nunnally, 1978). All five
factors were shown to be unifactorial, suggesting each was a valid construct. The KMO measure of
sampling adequacy was also used to provide empirical evidence supporting the appropriateness of the
data for each unifactorial determination. The KMO values were acceptable especially in relation to the
“meritorious” KMO statistic (Kaiser and Rice, 1974) for the original structure that derived 5 factors.
Table 4 shows results from the unifactorial tests supporting construct validation of the impression
management factors.

Table 3. Factor Analysis of Impression Management Items Across All Leaders
___________________________________________________________
Items Loadings Alpha
Factor 1: Exemplification .9006
Willingness to make personal sacrifices to benefit others .853
Is generous with time and energy in helping others .826
Holds personal performance to high standards .760
Behaves in ways that are consistent with others’ expectations .736
Leads by example whenever possible .706
Does personal favors for others .613
Demonstrates a high level of personal injury .600
Presents oneself as warm and charming to others .578
Praises others’ ideas or work .566
Factor 2: Self-Promotion .9097
Readily takes credit for past and current successes .829
Boasts about achievements to others .819
Points out accomplishments to others .803
Takes advantage of opportunities to demonstrate skills/abilities .757
Uses status symbols to communicate position and power .748
Factor 3: Ingratiation .8568
Flatters others regarding appearance and conduct .797
Makes non-work related compliments to others .783
Inquires about non-work life with subordinates .743
Publicly expresses agreement while personally disagreeing .692
Factor 4: Helplessness .8332
Stresses dependence on others for assistance .813
Emphasizes personal shortcomings while appealing for help .791
Downplays personal abilities to secure help from others .576
“Plays dumb” to secure aid from others .560
Factor 5: Coerciveness .8883
Makes it clear that his or her decisions are to be followed .779
Threatens severe sanctions for subordinates who defy directions .581
Demands respect from subordinates .566
Makes threats to persons who do not meet leader expectations .554
Publicly ridicules persons who oppose him or her .534
_______________________________________________________________________
Results indicate that established factors explain at least 60 percent of the total amount of variance of
their respective data sets. A relatively small amount of variance is associated with other causes. The
unifactorial tests provide adequate support for the construct validity of each factor (Black and Porter,
1996). Further construct validation would require new data to confirm the existing factors (Cattell), and
can be the basis of future studies.
Group Satisfaction (GS)
The Classroom Cohesion Scale by Rosenfeld and Gilbert (1989) assessed GS in the current study.
Responses were recorded on a 5-point Likert-type scale ranging from disagreement to agreement with

the statements regarding GS. A principal components factor analysis was again used as a data
reduction and summarization method (Hair, Anderson and Tatham, 1987).
Table 4
Unifactorial Tests for Impression Management
________________________________________________________________________
Factor KMO Variance
Explained (%)
________________________________________________________________________
Factor 1: Exemplification 0.917 60.348
Factor 2: Self-Promotion 0.841 74.265
Factor 3: Ingratiation 0.790 70.388
Factor 4: Helplessness 0.779 67.096
Factor 5: Coerciveness 0.865 72.766
________________________________________________________________________
Table 5. Factor Analysis of Group Satisfaction Items
___________________________________________________________
Items Loadings Alpha
Factor 1: Group Cohesion .9755
I believe other group members liked me .796
I felt that I was a genuine member of the group .778
During group meetings, I got to participate whenever I wanted to .767
Other members of the group really listened to what I had to say .720
I liked the group I was in .718
I enjoyed interacting with this group very much .708
I wanted to remain a member of this group .703
I trusted group members .700
Many members of the group have ideal member qualities .697
I felt attracted to the group .692
The group was composed of people who fit together .689
There was a feeling of unity and cohesion in the group .665
I would like future group members with the same qualities .657
Compared to other groups, this group worked well together .626
Factor 2: Feelings regarding Group Member Relationships
and Decision Processes .9367
I would like to work with current members on a similar project .830
We were a closely knit group .755
I like the members of the group .701
Our group worked well together .659
This group used effective decision-making techniques .621
This group provided for comfortable expression for members .620
I believe we approached our task in an organized manner .573
This group accomplished what it set out to do .509

Factor 3: Feelings Regarding Group Decision Outcomes .8883
I believe our group’s decision/solution is appropriate .855
The group reached the right decision .822
I believe we selected the best alternative .784
I support the final group decision .749
I willingly give my best effort implementing the group decision .619
Factor 4: Feelings Regarding Individual Effectiveness .8573
I believe I contributed important ideas for the group decision .865
I believe I had a lot of influence on group decisions .846
I contributed important information during the decision process .832
_______________________________________________________________________
Key statistics were analyzed as done previously with the impression management data. First, the
communalities were analyzed and all were relatively large with none below .7 showing the data is
appropriate for the analysis (Stewart, 1981). ). Next, the Kaiser-Meyer-Olkin measure of sampling
adequacy was computed. Based on Kaiser and Rice’s (1974) evaluative criteria, the result of .915 is
considered “meritorious to marvelous.” Finally, the statistic for Bartlett’s (1950) sphericity test was
3697.8, (p < .000) providing further evidence that the population of variables were independent and
appropriate for factor analysis. The resulting factor analysis provided four (4) factors comprising the
GS items as seen in Table 5.
As done previously with the impression management factor structure, the reliability of the GS
factors was checked to support any measures of validity that might be employed. All factors were
checked for internal consistency using Cronbach alphas as discussed previously. No values of
coefficient alpha were lower than 0.85 with two beyond the 0.90 level.
Assessments were also made to determine the construct validity of the GS factors. As done with
impression management, an individual principle components analysis was conducted on each factor to
determine if its set of variables would form a single factor independent of other variables (Nunnally,
1978). All four factors were shown to be unifactorial, suggesting each was a valid construct. The KMO
measure of sampling adequacy was also used to provide empirical evidence supporting the
appropriateness of the data for each unifactorial determination. The KMO values were acceptable
especially in relation to the “meritorious to marvelous” KMO statistic (Kaiser and Rice, 1974) for the
original structure that derived four factors.
Table 6 presents the unifactorial tests supporting construct validation of the GS factors. Results
indicate that established factors explain at least 70 percent of the total amount of variance of their
respective data sets. A relatively small amount of variance is associated with other causes. The
unifactorial tests provide adequate support for the construct validity of each factor (Black and Porter,
1996). Further construct validation would require new data to confirm the existing factors (Cattell,
1978), and can be the basis of future studies.
IV. Procedure
Work groups. As part of the classroom structure, student groups were formed. The work teams
consisted of 4 to 6 members, including team managers. Subordinate members of the team were
responsible for completing all tasks assigned to them by their managers. Tasks assigned included
reading relevant materials, completing group exercises, contributing and critiquing ideas, writing case
sections, preparing audio-visual aids, making presentations, typing, proofreading, and editing.

Subordinate members were also responsible for providing written and numerical evaluations of their
team manager.
Managers. The managers were responsible for the performance of their groups on all assigned cases,
exercises and projects. As such, the managers were provided with the authority to assign tasks to
group members and direct the group toward task accomplishment. After the completion of each
project, the manager was required to provide a written and numerical evaluation of the contributions of
individual group members.
Group interactions. Groups were generally composed of between 4 to 6 members and met during and
outside of class. The days and number of meetings varied. The groups remained the same and worked
together over the 16-week semester for an average of 2.53 hours per week outside of the classroom.
The groups had complete discretion over the division of labor and coordination of effort over group
tasks. The first group task required group members to research an organizational behavior concept and
write a report reviewing and critiquing three organizational examples that utilized the concept. The
second group project required members to analyze their own group’s development over the course of
the semester. Both projects necessitated team interdependence, and, because of the peer evaluation
system, active participation was more likely to occur. These same groups also engaged in in-class
assignments frequently throughout the semester.
At the time of team formation, each student group was asked to identify a “secret number” sequence
that was used on all questionnaires and was only known by immediate team members. This procedure
was used to insure anonymity for the students. LIM data was collected near the end of the semester
after all projects and in-class assignments were completed. Each team member rated their respective
team leader on the frequency of impression management tactics used. Following this evaluation, all
team members were asked to rate their degree of satisfaction with their team experience. At the end of
the semester, students were debriefed about the study. Participants received extra points over and
above individual and group grades.
Analyses
Stepwise regression analyses were used to investigate the predictive relationships of EI items on both
impression management factors and GS factors. Additionally, stepwise regression analysis was used to
show the relationship between impression management factors and GS factors with impression
management factors used as the predicting variables. The results of all analyses are presented in the
results section.
V. Results and Discussion
Emotional Intelligence (EI) Predicting Leader Impression Management (LIM)
EI items were used to predict perceived factors representing impression management. To address
multicollinearity problems, stepwise regression analyses were used that provided for the removal of
highly intercorrelated EI items as suggested by Tabachnick and Fidell (1983). The p-value required for
entry into the stepwise regression was set at .05; all variables not meeting this criterion were excluded
from the equations. The results of this analysis can be seen in Table 7 where only statistics from the
last step in the regression are reported.
The full model for exemplification (Factor 1) considering all significant EI items provided an
adjusted R 2 of .150 and an F = 5.548 (p < .000). From the total EI item list, three (3) items were
significant at the p < .05 level. Another item “I present myself in a way that makes a good impression
on others” was more influential with a t = 2.714 (p < .01) and a b = .257. Factor 1 was predicted by EI
items relating to confidence in effective impression management and being a confidante to others and
awareness and understanding of others feelings. The findings suggest that one must have self-

awareness and an understanding of others emotions in order to be successful at presenting oneself as
morally worthy.
Similar findings occurred for both Factor 2 self-promotion (F = 6.545, Adjusted R 2 = .177; p < .000)
and Factor 4 helplessness (F = 11.039, Adjusted R 2 = .281; p

Factor 2, feelings regarding group member relationships and decision processes, was the most
highly predicted by EI items (F = 5.389, Adjusted R 2 = .204; p < .000). Six (6) individual items were
found to significantly influence Factor 2 with “I motivate myself by imagining a good outcome to tasks
I take on” (t = 3.319, b = .318; p < .000) being the most influential. An inverse relationship was found
with the EI item “When I am faced with a challenge, I give up because I know I will fail” (t = -2.319, b
= -.211; p < .05). This factor was predicted by EI items that related to self-awareness, optimism, selfmotivation
and a desire to make a good impression on others. Similar to our other findings, these EI
items are consistent with this GS factor which is characterized by confidence in the group and a general
likeability of the members of the group. The inversely related item regarding giving up when
challenged is at odds with the other EI items supporting Factor 2 and logically supports the results.
Table 7
Regression Results for Emotional Intelligence Predicting LIM
EI Predictor Impression
Variables Management
F1: Exemplification Adj. R 2 F b t____
.150 5.548***
I present myself in a way that makes a good impression on others .257 2.714**
It is difficult for me to understand why people feel as they do -.202 -1.992*
Other people find it easy to confide in me .260 2.577*
I can tell how people are feeling by listening to the tone of their voice .204 2.030*
F2: Self-Promotion Adj. R 2 F b t____
.177 6.545***
I help other people feel better when they are down .240 2.497*
I am aware of my emotions as I experience them -.317 -3.397**
I find it hard to understand non-verbal messages of other people -.221 -2.461*
I arrange events others enjoy .194 2.061*
F3: Ingratiation Adj. R 2 F b t____
.060 7.570**
I arrange events others enjoy .263 2.751**
F4: Helplessness Adj. R 2 F b t____
.281 11.039***
I use good moods to motivate myself in facing obstacles -.391 -4.614***
I like to share my emotions with others .265 3.137**
It is difficult for me to understand why people feel as they do .281 3.334**
When challenged I give up because I feel I might fail .224 2.652**
F5: Coerciveness Adj. R 2 F b t____
No Significance
________________________________________________________________________
*p

arranging enjoyable events, helping others feel better, and confidence in making good impressions on
others. These 3 EI items are consistent with the cohesion factor which is characteristic of enjoying the
group and a sense of attraction and unity within the group.
Factor 4, feelings regarding individual effectiveness (F = 6.472, Adjusted R 2 = .096; p < .01), had
two (2) individual EI items as significant predictors. These items were “I am aware of my emotions as
I experience them (t = 3.035, b = .219; p < .01) and “I easily recognize my emotions as I experience
them” (t = 2.575, b = .248; p < .05). Interestingly, both EI items related to self-awareness. The GS
factor is characterized by individual feelings of contribution and influence on group decisions, the
effectiveness of which would be dependent on self-awareness.
Table 8
Regression Results for Emotional Intelligence Predicting GS
EI Predictor GS
Variables Factors
Factor 1 Adj. R 2 F b t_____
.123 5.822***
I arrange events others enjoy .257 2.628**
I help other people feel better when they are down .289 2.924**
I present myself in a way that makes a good impression on others .227 2.356*
Factor 2 Adj. R 2 F b t_____
.204 5.389***
I am aware of the non-verbal messages I send to others .190 1.898*
When my mood changes, I see new possibilities .283 3.006**
I motivate myself by imagining a good outcome to tasks I take on .318 3.319***
When I am faced with a challenge, I give up because I know I will fail -.211 -2.319*
Other people find it easy to confide in me .217 2.260*
I present myself in a way that makes a good impression on others .211 2.212*
Factor 3 Adj. R 2 F b t_____
.039 5.146*
I know when to speak about my personal problems with others .219 2.269*
Factor 4 Adj. R 2 F b t_____
.096 6.472**
I am aware of my emotions as I experience them .219 3.035**
I easily recognize my emotions as I experience them .248 2.575*
________________________________________________________________________
*p

VI. Implications
Our finding that EI is an influential variable on impression management and GS is both noteworthy
and compelling. Further, this study is the first of its kind to investigate the impact of EI on LIM and
GS. Although a multitude of studies have been conducted on EI, this study provides additional insight
into the complexities of this construct with regard to impression management and GS. Our results
consistently show the impact of EI on these two variables through regression analysis.
Our research confirms that, as expected, EI is a predictor of IM and GS. Because emotionally
intelligent individuals tend to be more effective at applying and perceiving impression management it
has implications for enhanced GS. Consequently, organizations should seek to hire and retain
employees with high EI. Incorporating EI into daily organizational life will include training for EI,
revising selection and placement practices, counseling, and encouraging constructive behaviors
through the organization’s performance appraisal and reward structure.
Although EI may be somewhat influenced by genetic predisposition, research supports the idea that
EI may be learned (Goleman, 1995; Goleman, 1998; Mayer & Salovey, 1997). Training might be
conducted in a group setting to familiarize employees with the concepts of EI. EI training involves
assessment of the job, assessment of the individual, motivation, self-directed change, performance
feedback, practice, support, encouragement, reinforcement, and evaluation (Goleman, 1995). This
training should focus on improving employee skills through participation in a non-threatening
environment. Once employees gain support by learning in a group setting, they can practice what they
have learned and apply EI to specific situations in the workplace.
The current study has highlighted the impact EI has on member satisfaction. Based on our findings,
we would suggest that selection is an important consideration in choosing group leaders and members.
Proper selection could greatly enhance satisfaction and subsequent performance. Organizations should
adapt their selection practices to recruit and hire people who will engage in constructive behaviors at
work. Regardless of labor market concerns, organizations must be careful when considering applicants
for select managerial positions. Andersson and Pearson (1999) suggest hiring people whose
characteristics are expected to facilitate courteous interaction. Selecting for EI has received recent
attention especially among life insurance companies. Seligman and Schulman (1986) showed that
optimism, one component of EI, impacts the productivity of life insurance agents. Therefore,
companies such as Metropolitan Life have begun testing applicants for optimism as a selection tool for
life insurance agents (Seligman, 1990). Productivity may increase if organizations are aware of the
aspects of EI when selecting employees.
In some cases, employees may require in-depth counseling for EI. This counseling would apply to
individuals possessing very weak emotional skills, yet who are valued highly by the organization. Indepth
training would be conducted on an individual basis to help the employee acquire emotional skills
most effectively.
Organizations that train for EI should follow through by incorporating these important behaviors
into their performance appraisals and reward structures. Emotionally intelligent behaviors that
promote adaptive and constructive responses to conflict and stress should be rewarded. The
importance of EI should be communicated at all levels of the organization and employees should
receive feedback on their efforts to behave constructively in stressful situations.
Our study provides insight into the long researched issues of group cohesion, consensus, and
communication. The findings of the current research confirmed many of the contentions posited at the
outset of the study. Certain IM tactics used by team leaders were predicted by specific aspects of EI.
In addition, EI was found to significantly increase certain aspects of GS.

References
[1] Anderson, C.M. & Martin, M.M. (1995). The effects of communication motives, interaction
involvement, and loneliness on satisfaction: A model of small groups. Small Group Research,
26, 118-137.
[2] Anderson, C.M. & Martin, M.M. (1999). The relationship of argumentativeness and verbal
aggressiveness to cohesion, consensus, and satisfaction in small groups. Communication
Reports, 12, 21-31.
[3] Andersson & Pearson (1999). Tit for tat? The spiraling effect of incivility in the workplace.
Academy of Management Review, 24(3), 452-471.
[4] Ashkanasy, N.M. & Daus, C.S. (2002). Emotion in the workplace: The new challenge for
managers. Academy of management Executive, 16 (1) 76-86.
[5] Bartlett, M.S. (1950). Tests of significance in factor analysis. British Journal of Statistical
Psychology, 3, 77-85.
[6] Bass B.M. (1985). Leadership and Performance Beyond Expectations. New York: Free Press.
[7] Bass, B.M. (1990). Bass & Stogdill’s handbook of leadership : Theory, Research , &
Managerial Applications (3 rd ed.), New York: Free Press.
[8] Bass, B. M. & Avolio, B. J. (Eds.). (1994). Improving Organizational Effectiveness Through
Transformational Leadership. Thousand Oaks, CA: Sage.
[9] Black, S.A. & Porter, L.J. (1996). Identification of critical factors of TQM. Decision Sciences.
27(1), pp. 1-21.
[10] Bormann, E. (1990). Small group communication: Theory and Practice (3 rd ed.). New York:
Harper & Row.
[11] Carron, A.V. & Brawley, L.R. (2000). Cohesion: Conceptual and measurement issues. Small
Group Research, 31, 89-106.
[12] Carron, A.V., Brawley, L.R., & Widmeyer, W.N. (1998). The measurement of cohesiveness in
sport groups. In J.L. Duda (Ed.), Advances in sport and exercise psychology measurement (pp.
213-226). Mortgantown, WV: Fitness Information Technology.
[13] Carron, A.V., Widmeyer, W.N., & Brawley, L.R. (1985). The development of an instrument to
assess cohesion in sport teams: The group environment questionnaire. Journal of Sport
Psychology, 7, 244-266.
[14] Cattell, R.B. (1978). The Scientific Use of Factor Analysis in Behavioral and Life Sciences,
Plenum Press, New York, NY.
[15] Chang, A. & Borden, P. (2001). A multidimensional approach to the group cohesion-group
performance relationship. Small Group Research, 32, 379-405.
[16] Comrey, A.L. (1973), A first course in factor analysis, Academic Press , New York.
[17] Conger, J. A. & Kanungo, R.N. (1987). Toward a behavioral theory of charismatic leadership
in organizational settings. Academy of Management Review, 12, 637-647.
[18] Conger, J.A. & Kanungo, R. N. (1988). Behavioral dimensions of charismatic leadership. In J.
A. Conger, R.N. Kanungo, & Associates (Eds.), Charismatic Leadership: The Elusive Factor
in Organizational Effectiveness (pp.309-323). San Francisco: Jossey-Bass.
[19] Cooper & Sawaff (1997). Executive EQ: Emotional Intelligence in leadership and
organizations. New York: Grosset/Putname.
[20] Cragan, J.F. & Wright, D.W. (1995). Communication in small groups (4 th ed.). New York:
West.
[21] Cragan, J.F. & Wright, D.W. (1990). Small group communication research of the 1980s: A
synthesis and critique. Communication Studies, 45, 92-96.
[22] David, S.A. & Jackson, H.J. Relationship of a measure of perceived emotional intelligence to
personality and impression management. Australian Journal of Psychology; 2003 Supplement,
Vol55, p121, 1/5p.

[23] DeStephen, R.S., & Hirokawa, R. Y., (1988). Small group consensus: Stability of group
support of the decision, task process, and group relationships. Small Group Behavior, 19, 227-
239.
[24] Evans, C.R. & Dion, K.L. (1991). Group cohesion and performance: A meta-analysis. Small
Group Research, 22, 175-186.
[25] Fox (2000). Where's the intelligence in "emotional intelligence?." Paper presented at the
Academy of Manaement Annual Meeting, Toronto, Ontario.
[26] Frey, L.R. (1999). Introduction. In L.R. Frey, D.S. Gouran, & M.S. Poole (Eds.), The
handbook of group communication theory and research (pp.ix-xxi). Thousand Oaks, CA:
Sage.
[27] Gardner (1983). Frames of the mind. New York: Basic Books.
[28] Gardner, W.L. & Avolio, B.J. (1998). The charismatic relationship: A dramaturgical
perspective. Academy of Management Review, 23, 32-58.
[29] Gardner, W.L. & Cleavenger, D. (1998). The impression management strategies associated
with transformational leadership at the world-class level. Management Communication
Quarterly, 12, 3-42.
[30] Gardner, W.L. & Martinko, M.J. (1988). Impression management in organizations. Journal of
Management, 14, 321-338.
[31] Goleman, D. (1995). Emotional intelligence. New York: Bantam Books.
[32] Goleman, D. (1998). Working with emotional intelligence. New York: Bantam Books.
[33] Hair, J.F., Anderson, R.E., & Tatham, R.L. (1987). Multivariate Data Analysis. Macmillan
Publishing Company, New York.
[34] Hare, A.P. (1980). Consensus versus majority vote: A laboratory experiment. Small Group
Behavior, 11, 131-143.
[35] Hirokawa, R.Y. & Salazar, A.J. (1999). Task-group communication and decision-making
performance. In L.R. Frey, D.S. Gouran, & M.S. Poole (Eds.), The handbook of group
communication theory and research (pp.167-191). Thousand Oaks, CA: Sage.
[36] House, R. J. (1977). A 1976 theory of charismatic leadership. In J.G. Hunt & l.L. Larson
(Eds.), Leadership: The cutting edge (pp. 189-207). Carbondatle: Southern Illinois University
Press.
[37] Jones, E.E. & Pittman, T. S. (1982). Toward a theory of strategic self-presentation. In J. Suls
(Ed.), Psychological Perspectives on the Self (pp. 231-262). Hillsdale, NJ: Lawrence Erlbaum.
[38] Jurma, W.E. (1978). Leadership structuring style, task ambiguity, and group member
satisfaction. Small Group Behavior, 9, 124-134.
[39] Kaiser, H.F., & Rice J. (1974). Little jiffy mark IV. Educational and Psychological
Measurement, 34, 111-117.
[40] Keyton, J. (1999). Relational communication in groups. In L.R. Frey, D.S. Gouran, & M.S.
Poole (Eds.), The handbook of group communication theory and research (pp.192-222).
Thousand Oaks, CA: Sage.
[41] Langfred, C.W. (1998). Is group cohesiveness a double-edged sword? An investigation of the
effects of cohesiveness on performance, Small Group Research, 29, 124-143.
[42] Law, K.S., Chi-Sum,W., Song, L.J. (2004). The construct and criterion validity of emotional
intelligence and its potential utility for management studies. Journal of applied Psychology,
Vol. 89 Issue 3 p483,14p.
[43] Leith & Baumeister (1996). Why do bad moods increase self-defeating behavior? Emotion,
risk taking, and self-regulation. Journal of Personality and Social Psychology, 71(6), 1250-
1267.
[44] Lubit, R. (2004). Emotional Intelligence. Consulting to Management, C2M; Jun2004, Vol. 15
Issue 2, p8, 6p, 1 chart.
[45] Mayer, J. , & Salovey, P. (1993). The intelligence of emotional intelligence. Intelligence, 17,
433-442.

[46] Mayer, J.D., & Salovey, P. (1997). What is emotional intelligence? In P. Salovey & D. Sluyter
(Eds.), “Emotional development, emotional literacy and emotional intelligence.” New York:
Basic Books.
[47] Mayer & Geher (1996). Emotional intelligence and the identification of emotion. Intelligence,
22, 89-113.
[48] McCall, M. W., & Lombardo, M. (1982). Using simulation for leadership and management
research: Through the looking glass. Management Science, 28, 533-549.
[49] Megerian, L.E. & Sosik, J.J. (1996). An affair of the heart: Emotional intelligence and
transformational leadership. The Journal of Leadership Studies, 3, 31-48.
[50] Michalisin, M.D., Karau, S.J., & Tangpong, C. (2004). Top management team cohesion and
superior industry returns. Group & Organization Management, 29, 125-140.
[51] Montemayor, E.F. & Spee, J. (2004). The dimensions of emotional intelligence construct
validation using manager and self ratings. Academy of Management Proceedings, pC1, 6p, 1
chart.
[52] Mullen, B., Anthony, T., Salas, E., & Driskell, J.E. (1994). Group cohesiveness and quality of
decision-making: An integration of tests of the group think hypothesis. Small Group Research,
25, 189-204.
[53] Nunnally, J.C. (1978), Psychometric Theory, McGraw-Hill, New York, NY.
[54] Poole, M.S. (1999). Group communication theory. In L.R. Frey, D.S. Gouran, & M.S. Poole
(Eds.), The handbook of group communication theory and research (pp.37-70). Thousand
Oaks, CA: Sage.
[55] Ralston, S. M., & Kirkwood, W. G. (1999). The trouble with applicant impression
management. Journal of Business & Technical Communication, 13, 190-208.
[56] Rosenfeld, L. B., & Gilbert, J. R. (1989). The measurement of cohesion and its relationship to
dimensions of self-disclosure in classroom settings. Small Group Behavior, 20, 291-301.
[57] Rozell, E.J. & Gundersen, D.E. (2003). The effects of leader impression management on group
perceptions of cohesion, consensus, and communication. Small Group Research, April 2003,
Vol. 34, No. 2.
[58] Salovey, P., & Mayer, J.D. (1990). Emotional intelligence. Imagination, Cognition, and
Personality, 9, 185-211.
[59] Schlenker, B. R. (1980). Impression Management. Monterey, CA: Brooks/Cole.
[60] Schutte, N. , Malouff, J. , Hall, L. , Haggerty, D. , Cooper, J. , Golden, C. , & Dornheim, L.
(1998). Development and validation of a measure of emotional intelligence. Personality and
Individual Differences, 25, 167-177.
[61] Seligman, M.E.P. & Schulman, P. (1986). Explanatory style as a predictor of productivity and
quitting among life insurance sales agents. Journal of Personality and Social Psychology,
50(4), 832-838.
[62] Seligman, M.E.P. (1990). Learned optimism. New York: Pocket Books.
[63] Shamir, B., House R. J., & Arthur, M. B. (1993). The motivational effects of charismatic
leadership: A self-concept based theory. Organization Science, 4, 577-594.
[64] Stewart, D.W. (1981). The application and misapplication of factor analysis in marketing
research. Journal of Marketing Research, 18, 51-62.
[65] Tabachnick, B.G., & Fidell, L.S. (1983). Using Multivariate Statistics, Harper and Row, New
York, NY.
[66] Wayne, S.J. & Kacmer, K.M. (1991). The effects of impression management on the
performance appraisal process. Organizational Behavior and Human Decision Processes, 48,
70-88.
[67] Weinberg, S.B. (1979). Measurement of communication aspects of group cohesion. Journal of
Applied Communication Research, 8, 55-60.

Managing Mobile Commerce Quality: A Long Way to Run
Emmanouil Stiakakis
Department of Marketing
Alexander Technological Educational Institution of Thessaloniki, Greece
E-mail: steiakakis@hol.gr
Tel: + 2310618777
Abstract
The development of the information and communication means has offered to the
companies a wide range of possibilities for evolution and penetration to new markets and
has simultaneously created the need for a competitive advantage. Mobile commerce (mcommerce)
has offered this possibility to the companies through flexible and integrated
solutions of positioning to the new electronic reality by intervening in a beneficial way to
all the stages of supply chain management. M-commerce provides basic advantages not
only to the companies but also to the consumers by making their daily commercial
transactions easier and more efficient. This paper aims at investigating and assessing the
qualitative factors that characterize electronic services provided through mobile phone. At
the same time the particularities of this specific type of electronic commerce are
emphasized in relation to other types which are more widely known.
Key words: m-commerce, mobile phoning network, electronic services, quality
management
I. E-Commerce through Mobile Phoning
E-commerce is the process of buying, selling and exchanging goods, services and information through
computer networks, Internet included (Themistocleus, Doukidis and Drakos, 1997). E-commerce
through mobile phoning (known as e-commerce of the next generation) was established in Japan. Mcommerce
allows the users to make any transaction they want at any time by having access to the
Internet, without having to find a site to be connected. The new technology behind m-commerce,
which is based on the wireless application protocol, has made the bigger steps in Europe, where mobile
appliances equipped with micro-computers are more common than in the rest of the continents (Saden,
2002). Companies like NOKIA, SONY, ERICSSON and MOTOROLA offer different kinds of
communication possibilities, such as fax, e-mail, Internet access etc., “all in one appliance”, in their
effort to make the possibility of transacting through mobile phones come true.
The services that m-commerce offers are various and they facilitate their users a lot (Tsalgatidou
and Pitoura, 2005). Through m-commerce we can draw financial information, e.g. about the
economical route of specific companies in specific time periods, as well as the current economical
developments. Moreover, we can find out about the course of the stock market, e.g. the buying and
selling prices of shares (of all the international stock markets). Furthermore, news concerning national
and international events is broadcasted. Another important offer of m-commerce is to the conduct of
banking transactions. As far as entertainment is concerned, m-commerce gives the possibility to its
users to book tickets for theatrical plays and films, as well as to make reservations in restaurants. In
addition to that, it informs immediately the users about the results of sports and gambling games. It
also gives information about the possibility of buying various products from e-shops, as well as
specific information about the precise location of the nearest to the user physical store. We can also

acquire information about the traffic conditions, the timetables of various means of transport and the
weather forecast.
The applications of electronic commerce through mobile phone exploit mobile phoning in an effort
to offer to the consumers and the companies more benefits in relation to the traditional transactions
through the Internet (Szymanski and Hise, 2001):
• Knowledge of the location
For m-commerce the knowledge of the physical location of the user is a matter of extremely
importance. The location of the mobile appliance is available to the operator of the mobile
network, but it can also be found through technologies, such as Global Positioning System
(GPS). GPS uses satellite stations to estimate with great precision the exact location of
appliances, which are equipped with GPS receivers. There are many examples of the
applications of electronic commerce, which are based on “location” (Chou, Yen, Lin and
Cheng, 1999), such as: geographically oriented advertisement (e.g. anyone near a fast food
store gets electronic coupons for free food), car tracing for security reasons, traffic control,
telemetry etc.
• Use conditions
The mobile phone user can travel or talk to other people at the same time and is not obliged to
sit in his office/room.
• Adaptability
The m-commerce applications are adapted to the customer’s environment. The adaptability
refers to various fields, like the type of the appliance that is used, the current available band of
frequencies, as well as the location and time.
• Continuous presence
The communication based on mobile phoning has the advantage against e-commerce that it
offers services and applications at any time and any place. Through mobile phones, users can
communicate whenever they want despite their location (Baldwin and Currie, 2000). The user
is informed about the various events at any time. Moreover, mobile phoning allows the prompt
delivery of that information which is based on time.
• Personalization
The Internet includes an enormous amount of information. However, the user should receive
the information which is related to his/her interests (Hoffman, Novak and Chatterjee, 1997).
Research has shown that an additional blow of a key can decrease the possibility of the
accomplishment of a transaction by 50 percent. Consequently, the applications of m-commerce
should be personalized, so that they present the information in a summarized and attractive
way, by making the path of interaction better, facilitating thus the user to come in contact with
the desired information by pressing the fewest keys possible.
II. M-Commerce Theoretical Models
The M-Commerce Main Phases Model
Various theoretical models have been developed for the accomplishment of electronic transactions
through mobile phone. Having reviewed the relative literature, three basic approaches are presented, by
which a high quality level of electronic mobile services could be assured. The M-Commerce Main
Phases Model (Buhan, Cheong and Lin Tan, 2000) is shown in Figure 1.

Customer
Intermediary
Figure 1: The M-Commerce Main Phases Model.
5. Transit – delivery of the product
1. Intention of purchase
6. Account
0. Registration
7. Payment 2. Transaction
requirements
3. Transaction principles
8. Profit distribution
Service
Provider
Transaction
Service
Provider
4. Transaction
principles
8. Profit
distribution
The M-Commerce Main Phases Model presents all the processing stages, so that the right product /
service is delivered to the right customer through a mobile phoning network. The eight phases of this
model (registration phase is omitted) are summarized below:
1. The customer expresses his/her intention to purchase a product / service through a mobile
phoning network. This intention is indicated by pressing the appropriate key on his/her mobile
phone or sending an SMS to the appropriate number.
2. The service provider (or network service provider), as long as the purchase is accepted, carries
the request to the transaction service provider.
3. The transaction service provider in his turn asks the intermediary for information about the
transaction principles that should be followed, as well as supplementary information concerning
the authenticity of the customer.
4. The transaction service provider, as long as he gets an approval, informs the service provider
that he could proceed to the accomplishment of the specific transaction.
5. The service provider is now ready to transfer and deliver the product to the customer.
6. At the time of delivery, the intermediary sends to the customer the account, including the value
of the product and the various expenses.
7. The customer should pay the intermediary.
8. The profit is distributed among the intermediary, the transaction service provider and the
service provider.
The Compass Interaction Model
This model has been proposed by Amberg, Figge and Wehrmann (2003). The service and information
relationships among the four main participants of a mobile phoning network are described in this
model. These participants are the service provider, the Internet operator, the Logistics service provider
and of course the customer (Figure 2).

Product
Service
Provider
Payment
Logistics
Service
Provider
Identity
Figure 2: The Compass Interaction Model.
Description of the situation
Access code
Product information
Access code
Payment
Product
Product information
Payment
Internet
Operator
Personal
data
Product
information
Customer
The service provider gives information to the Internet operator with the customer being the final
recipient. As far as the delivery of the product is concerned, the Logistics service provider plays the
significant role of the intermediary. From the customer’s perspective, the role of the Internet operator
has a great importance. The operator confirms the access to the Internet, makes personal arrangements
(e.g. security of personal data), responds promptly to the user’s requirements, provides all the
necessary information and finally the operator is responsible for accomplishing the payment process.
Factors, such as security and assurance of authenticity of the participating parts should be characterized
by a high level of quality (McDonald, Loew, Stengel and Bleimann, 1999). That is the reason that the
Internet operator is often called as Trusted Third Part. The severe laws concerning security of data in
combination with the cautiousness of the customer to notify his/her personal data force the Internet
operator to provide anonymous descriptions of the transactions (Chaffey, Mayer, Johnston and Ellis-
Chadwick, 2003). However, if the service package includes the transportation of the product, retaining
the customer’s anonymity is a difficult task.
The Marketspace Mobile Model
This theoretical model, shown in Figure 3, presents the process that is followed, so that the product /
service is delivered to the final customer (company or consumer) through a mobile phoning network
(Birch, 2002). To start with, there are the companies involved which offer their products or services
through the network. The customers demand specific prerequisites in order to accomplish a transaction
through the network. Based on this model, these prerequisites are: completeness, after sales service,
attractive price, a high quality product, security, consistency, ease of use and high access speed. Some
of them are indicatively examined as follows: Consistency means the reliability and accuracy that this
system offers to the company-seller, as well as to the customer. For example, the companies which sell
their products or services through mobile phone want to assure that they are going to get paid for what
they offer.

Completeness
Security
PRODUCT –
SERVICE
After sales
service
CUSTOMER
Consistency
Attractive
price
Ease
INTERNET
PORTAL
Figure 3: The Marketspace Mobile Model.
Quality
product
Access
speed
TECHNOLOGY
SERVICES
Attractive price is another significant factor to attract the customer in the process of electronic
commerce through mobile phone. The low cost of promotion is also an attractive means for many
companies despite their size and sales volume. Moreover, ease of use is desired from companies to
ensure the existence of customers, because an easy-to-access-network is a guarantee of use from
customers-consumers. Security of transactions and protection of personal data are also matters of great
concern. The concept of quality has a wide meaning for the transactions through mobile phone. Quality
consists of all the factors depicted in Figure 3. However, quality is also the way of promotion, as well
as the whole process of a transaction, from the order, the payment until the delivery of the product to
the final customer. One of the main factors of this theoretical model is technology. None of these
transactions would be possible without the aid of the state-of-the-art technology. Technology refers
mainly to the manufacturers of the appliances. They supply all the necessary equipment to the final

users, who can then have access to a mobile phoning network and run an application. Amongst the
many manufacturers of mobile phones, we find the most widely known Nokia, Sony, Ericsson,
Motorola, as well as the manufacturers of PDAs and electronic-magnetic cards.
II. Research Methodology – Findings
The research methodology of this study was founded on a survey which was conducted among 311
people (male: 53 percent, female: 47 percent) from January till March 2006. The sample distribution
concerning the age was as follows: 1) 18-25 years old: 49 percent, 2) 26-35 years old: 35 percent, and
3) 36-50 years old: 16 percent. Geographically the sample was located in the four biggest cities of
Greece (Athens, Thessaloniki, Patras and Heraklion). The basic prerequisite for participating in the
sample was the use of electronic services through mobile phone for a long period. As far as the
structure of the questionnaire is concerned, the Likert scale was used, meaning that the respondents
were asked to declare the degree of agreement or disagreement with the research proposition. The
research hypotheses that were initially tested were the following:
The access speed to electronic services of m-commerce is very high.
The information provided in electronic services of m-commerce is reliable.
The cost of provided electronic services of m-commerce is satisfactory.
The results are depicted in the pies of Figures 4, 5 and 6. According to the survey results, only 9
percent of the sample agree that the access speed is actually very high (Figure 4). On the contrary,
approximately 67 percent of the sample disagree (strongly: 19 percent, in general terms: 48 percent)
with this proposition. Apart from access speed, reliability of provided information through electronic
mobile services is another important quality factor. A 56 percent of the respondents agree (strongly: 10
percent, in general terms: 46 percent) that provided information is reliable, whilst only a 7 percent
dispute the reliability of provided information (Figure 5). A 37 percent neither agree nor disagree with
the above statement. Finally, the cost of electronic mobile services was another factor that was
assessed in the framework of our research. A 48 percent of the sample disagree (strongly: 13 percent,
in general terms: 35 percent) with the statement that the cost of provided services is satisfactory
(Figure 6). A 30 percent neither agree nor disagree with this statement. It is worth pointing out that
only a 4 percent of the respondents strongly agree that the cost of provided electronic services of mcommerce
is satisfactory.

Figure 4: The Access Speed to Electronic Services of M-Commerce is Very High.
19%
9%
13%
Strongly agree
Agree
48%
11%
Neither agree nor disagree
Disagree
Strongly disagree
Figure 5: The Information Provided in Electronic Services of M-Commerce is Reliable.
37%
6% 1%
10%
46%
Strongly agree
Agree
Neither agree nor disagree
Disagree
Strongly disagree

Figure 6: The Cost of Provided Electronic Services of M-Commerce is Satisfactory.
35%
13%
4%
18%
30%
Strongly agree
Agree
Neither agree nor disagree
Disagree
Strongly disagree
Following the above research hypotheses, the respondents were asked to assess the frequency of
problems encountered in m-commerce transactions. Based on the findings of this research, the most
frequent problem was the low access speed, since the mobile phone users need to wait for a long time
to accomplish an electronic transaction. Figure 7 shows that low access speed received a 27 percent out
of the whole of the mentioned problems. Complexity of the electronic services provided to mobile
phone users was assessed as the second most frequent problem (17 percent). It is generally accepted
that mobile phoning companies offer quite high charging prices in their electronic services, due to the
pricing police they usually adopt. According to this research, the problem of high charging prices was
classified in the third position, receiving a 14 percent of the respondents’ preferences. Inadequate
amount of information was about at the same level (13 percent out of the whole of the problems
encountered in m-commerce). The mobile phone users apparently pursue all the necessary information
before proceeding to an electronic transaction accomplishment. Low security of data is considered an
important obstacle in the extended use of electronic mobile services. However, in this research low
security of data gathered only a percentage of 9 percent. Problems, such as access difficulty (7
percent), inconvenience of mobile phone (7 percent) and low quality of electronic services (6 percent)
were classified in the sixth, seventh and eighth position respectively.

Figure 7: Frequency of Problems Encountered in M-Commerce.
17%
27%
7%
6%
14%
9%
7%
High charging prices Inconvenience of mobile phone
Inadequate amount of information Low security of data
Low quality of electronic services Low access speed
Complexity of electronic services Access difficulty
Another issue of concern was whether the innovative electronic services provided through a new
mobile phone could be a powerful motive for purchasing this phone. As it is shown in Figure 8, a 53
percent of the sample were positive with this statement, but the percentage of the respondents who had
a negative attitude is considered particularly high. This finding is not an encouraging element for
mobile phoning companies, which invest huge financial resources to incorporate innovative services to
their new products.
Figure 8: Are Innovative Electronic Services a Powerful Motive to Purchase a New Mobile Phone?
47%
53%
13%
YES
NO

III. Conclusion
This paper was based on the Marketspace Mobile Model, proposed by Dave Birch. Apart from
depicting the procedure followed in m-commerce, it examines the most critical quality factors of this
procedure. Taking into account our findings, there are serious problems concerning these quality
factors, which give reasons for the non-successful course of m-commerce in Greece. According to the
Marketspace Mobile Model, the customer demands completeness, after sales service, attractive price,
quality product, security, consistency, ease and high access speed, so that he/she is willing to
accomplish an electronic transaction through his/her mobile phone.
The respondents to this survey believe that the charging prices of electronic mobile services are
particularly high and that is a suspending factor to the evolution of m-commerce. Low access speed
and the complexity of electronic services, which is connected to the “ease” factor of the Marketspace
Mobile Model, also seem to be serious obstacles to the evolutionary course of m-commerce. It is
important that all the problems encountered in m-commerce (corresponding to the quality factors of the
model) received almost equivalent percentages, without any extremes, implying that attention should
be paid to all of them. Based on the survey results, the conclusion is that electronic services provided
through mobile phone are not broadly known and consequently not much used in Greece. M-commerce
is still at a primary stage and it has a long way to run. More specifically, the mobile phone users insist
on making use of the basic operations on their phones, such as calling, sending and receiving SMS or
MMS. They should realize that electronic services could facilitate their living and provide them with a
big variety of useful information (Online Publishers Association, 2006). It is worth emphasizing that
the sample was chosen from a large number of possible participants, since the population of the users
of electronic mobile services in our country is very limited.
IV. References
[1] Amberg M., Figge S. and Wehrmann J. (2003) A Cooperation Model for Personalized and
Situation Dependent Services in Mobile Networks:6-9.
[2] Baldwin L.P. and Currie W.L. (2000) Key Issues in Electronic Commerce in Today’s Global
Information Infrastructure. Cognition Technology & Work Vol. 2(Spring):57-66.
[3] Birch D. (2002) Strategies for Mobile Commerce. Workshop.
[4] Buhan D., Cheong C. and Lin Tan C. (2000) Mobile Payments in M-Commerce:10-11.
[5] Chaffey D., Mayer R., Johnston K. and Ellis-Chadwick F. (2003) Internet Marketing: Strategy,
Implementation and Practice 2 nd Ed Financial Times – Prentice Hall.
[6] Chou D.C., Yen D.C., Lin B. and Cheng P.H. (1999) Cyberspace Security Management.
Industrial Management & Data Systems Vol. 99(8):353-361.
[7] Hoffman D.L., Novak T.P. and Chatterjee P. (1997) Commercial Scenarios for the Web:
Opportunities and Challenges. Journal of Computer-Mediated Communication Vol. 1(3):51-53.
[8] McDonald A., Loew R., Stengel I. and Bleimann U. (1999) Security Aspects of an Enterprisewide
Network Architecture. Internet Research: Electronic Networking Application and Policy
Vol. 9(1):105-113.
[9] Online Publishers Association (2006) Detailed Analysis of Online Activities of Various
Demographic Groups www.online-publishers.org.
[10] Saden N. (2002) M-Commerce. Wiley Computing Publishing.
[11] Szymanski D.M. and Hise R.T. (2001) e-Satisfaction: An Initial Examination. Journal of
Retailing Vol. 76(3):309-322.
[12] Tsalgatidou A. and Pitoura E. (2005) Business Models and Transactions in Mobile Electronic
Commerce: Requirements and Properties. Dissertation Project:71-84.
[13] Themistocleus M., Doukidis G. and Drakos W. (1997) Evaluation Plan for the Pilot Operation
of an Integrated Electronic Commerce Environment. EOUG Conference, Wienna Austria.

International Bulletin of Business Administration
ISSN: 1451-243X Issue 1 (2006)
© EuroJournals, Inc. 2006
http://www.eurojournalsn.com
Description of Growth Price Model in a Random Environment *
Basel M. Al-Eideh
Department of Quantitative Methods & Information Systems
College of Business Administration, Kuwait University
P.O. Box 5486, Safat 13055, Kuwait.
E-mail: basel@cba.edu.kw
Abstract
There is a considerable interest in stochastic analogs of classical difference and differential
equations describing phenomena in theoretical models involving economic structure. In
this paper a description of growth price model in a random environment using a solution of
stochastic differential equation is considered. The mean and the variance as well as the
sample path of such a process are determined. Also, the necessary and the sufficient
conditions for the solution of the SDE to be covariance stationary are considered and then
the mean and the covariance functions are derived.
Keywords: Stochastic differential equations, covariance stationary, sample path, mean
and covariance functions.
JEL classification codes: C44, C32.
I. INTRODUCTION
This paper shows how decision makers’ concerns about model specification can affect prices and
quantities in a dynamic economy. We use this new approach of stochastic growth process in price
models for two reasons. The important one is this model is the continuous – time analog of an
autoregressive process of order 1, AR(1), which also has a geometrically decaying auto-covariance
function ACVF which is different from what we have already known from the discrete-type
autoregressive models ( cf. Brockwell and Davis (2002)). The second reason is this kind of models is
not widely used in various economic models.
David (1997) studies a model in which production is linear in the capital stocks with technology
shocks that have hidden growth rates. Veronesi (1999) studies a permanent income model with a
risckless linear technology. Dividends are modeled as an additional consumption endow cent. Hidden
information was introduced into asset pricing models by Detemple (1986), who considers a production
economy with Gaussian unobserved variables.
Hand (2001) have developed methods using statistical tools such as logisting regression and naïve
Bayes as well as neural networks for assessing performance of the models to the consumer credit risk.
During this past decade there has been increasing effort to describe various facts of dynamic
economic interactions with the help of stochastic differential processes. Thus stochastic differential
processes provide a mechanism to incorporate the influences associated with randomness,
uncertainties, and risk factors operating with respect to various economic units (stock prices, labor
force, technology variables, etc.)

Stochastic differential equation processes have been introduced in the study of three principal
categories of economic phenomena: (a) description of growth of certain factors under uncertainty (b)
the nature of option price variations presenting certain market conditions, and (c) stochastic dynamic
programming and control objectives.
Numerous researchers have worked on studying various economic units from different points of
view. For example, Aase and Guttrop (1987) studied the role of security prices allocative in capital
market, they present stochastic models for the relative security prices and show how to estimate these
random processes based on historical price data. The models they suggest may have continuous
components as well as discrete jumps at random time points. Also, two classical applications are
Metron (1971) and Black and Scholes (1973). New references include Harrison and Pliska (1981) and
Aase (1984). Whereas the first two works only study processes with continuous sample paths, the
other two allow for jumps in the paths as well. In other words, the processes have sample paths that
are continuous from the right and have left hand limits (in fact, these processes are semi-martingales;
for general theory of semi-martingales, see e.g. Kabanov et al., 1979 sec. 2).
Many other authors have studied this problem from different points of view, such as Stein and Stein
(1991), Tauchen and Pitts (1983), Schwert (1990), Duffie and Singleton (1993), McGrattan (1996),
Callen and Chang (1999), Karmeshu and Goswami (2001), etc.
In this paper, we present a new growth price model in a random environment. More specifically, we
consider the solution of the stochastic differential equation (SDE), and study the necessary and
sufficient condition for the solution of SDE to be covariance stationary and then the mean and the
covariance function are derived as well as the sample path of such model.
II. GROWTH PRICE MODEL
Assume the stock market prices be considered as a stochastic growth price process { ( t);
t ≥ 0}
which S (t)
can be regarded as the solution of the stochastic differential equation.
with S 0)
= x
α > 0 .
( and { B s);
s ≥ t}
Rewriting equation (1), we get
Now,
Assuming { ( t);
t ≥ 0}
since
Thus, we conclude
dS(
t)
dB(
t)
= rS(
t)
+ α + s
(1)
dt
dt
S in
( be a wiener processes independent with S ( 0)
. Note that r , s ε ℜ and
dS ( t)
− rS(
t)
dt = α dB(
t)
+ sdt
(2)
d
−rt
−rt
−rt
( S(
t)
e ) = e dS(
t)
− rS(
t)
e dt
S is an Ito process this implies that
S(
t)
e
d
t
−rt
−rt
s
− ( 0)
= ∫ ( ) + 1
S
0
−rt
[ − e ]
α e dB t
(3)
r
−ru
−ru
−ru
( e B(
u)
) = −re
B(
u)
du + e dB(
u)

S
s
r
−rt
[ − e ]
−rt
−rt
( t)
e − S(
0)
= e B(
t)
+ 1
(4)
III. STATIONARITY OF S (T)
In this section, we will study the properties of Stationarity for the solution of the SDE in equation (4),
by calculation its mean E (·) and its Variance V (·). And then study their behavior for large values
t t → ∞ .
of ( )
Now,
and
Therefore
E
V
V
−rt
s −rt
[ S t)
e − S(
0)
] = [ 1−
e ]
( (5)
r
t ⎡
⎤
−rt
−ru
[ S(
t)
e − S(
0)
] = V ⎢∫α
e dB(
u)
⎥
⎣ 0 ⎦
⎡
E⎢
⎣
t t
−ru
= ∫α e dB u)
∫
∫ e
− 2
= α
2
=
2r
α
t
0
( αe
0 0
ru
du
−2rt
[ 1−
e ]
2
−rt
α −2rt
[ S(
t)
e − S(
0)
] = [ 1−
e ]
Taking the limits of the results in equations (5) and (6), we get
and
t → ∞
−rt
Lim E[
S t)
e − S(
0)
]
2r
−rv
⎤
dB(
v)
⎥
⎦
s
( =
(7)
r
−rt
Lim E[
S(
t)
e − S(
0)
]
t → ∞
2
α
=
2r
Note that if r ≠ 0 , from equations (5) and (6), the mean and the variance function of S (t)
are given by
and
For these to be independent of t , we need
rt ⎡ s −rt ⎤
E( S(
t)
) = e ⎢E(
S(
0)
) + ( 1−
e )
⎣ r ⎥⎦
(9)
V
⎡
2rt
−2rt ( S(
t)
) = e ⎢V
( S(
0)
) + ( 1−
e ) ⎥⎦
⎣
2
α
2r
⎤
(6)
(8)
(10)

ut, if r = 0 , we get
s
E( S(
0)
) = −
r
(11)
2
α
V ( S(
0)
) = −
2r
(12)
( S t)
) = E(
S(
0)
) st
( S(
t)
) 2
V ( S(
0)
) + α t
E ( +
(13)
V
= (14)
From equations (9) and (10), we see that r < 0 is necessary for covariance Stationarity, as well as
conditions (11) and (12) above.
To check sufficiency we see that under conditions (11) and (12) with 0, E(
S(
t)
)
( S ( t + h),
S(
t)
) are independent oft .
Note that
is independent of t .
Now,
Therefore
is also independent of t .
s
E [ S(
t)
] = − ; ∀t
≥ 0
(15)
r
⎡
⎤
2
[ ( + ), ( ) ] = { ⎢ ( 0)
+ ∫ ( ) ⎥,
⎣
0 ⎦
+ t h
rt + h
−ru
Cov S t h S t e Cov S α e dB u
Cov
t ⎡
S(
0)
+ ⎢∫α
e
⎣ 0
−ru
t ⎡ 2rt
+ h
= e ⎢V
∫
⎣
2 2
2rt
rh ⎡ α α
= e ⎢−
+
⎣ 2r
2r
2
α rh
= − e
2r
⎤
dB(
u)
⎥
⎦
}
2 −2ru
( S(
0)
) + α e du⎥
0 ⎦
−2
⎤
( 1−
e ) ⎥⎦
+ rt
2
α
2r
rh
[ S(
t + h),
S(
t)
] = − e ; t ≥ 0
⎤
r < and Cov
There is a strictly stationary solution if S (0) is asymptotically normally distribution with mean
2
α
and variance − , i.
e
2r
⎟ 2 ⎛ s α ⎞
S(
0)
~ N ⎜
⎜−
, −
(17)
⎝ r 2r
⎠
and r < 0 .
(16)
s
−
r

IV. SAMPLE PATH OF THE GROWTH PRICE MODEL S (T)
In this section, we study the behavior of the sample path of the growth price process { ( t);
t ≥ 0}
Note that, the process S ( t,
w)
is given by
rt s rt
S ( t,
w)
= S(
0,
w)
e + e −1
r
almost surely since
Sup B(
t,
w)
o ≤ s ≤ t
t
[ ]
−rt
+ e α e
t
∫
0
−ru
dB(
u,
w)
⎛ s ⎞ rt s rt
= ⎜ S(
0,
w)
+ ⎟e
− + e o
⎝ r ⎠ r
→ 1
with probability 1.
( W ( t,
w)
+ ( 1)
)
2
α
Here W ( t,
w)
is asymptotically normally distributed with mean 0 and variance .
2r
⎟ 2 ⎛ α ⎞
W ~ N ⎜
⎜0,
⎝ 2r
⎠
Hence, the sample path of the growth price model S ( t,
w)
is given by
S .
rt ⎡ s ⎤ rt
S ( t,
w)
~ e ⎢S(
0,
w)
+ + W ( t,
w)
+ o(
e )
r
⎥
⎣
⎦
V. NUMERICAL EXAMPLE
(18)
Consider the following sample path of a growth price model S ( t,
w)
as an example which represents
the annual price of one component of a personal computer over a period of 23 years. Assume that the
model parameters are as follows: α = 0.
1,
r = 0.
2 and s = 4 we get:

Prices S(t,w)
70
60
50
40
Sample Path of a Growth Price Model S(t,w)
1.00
3.00
5.00
7.00
alpha=0.1, r=0.2 , s=4
9.00 13.00 17.00 21.00
11.00 15.00 19.00 23.00
Time (Years)
Note that there is a non-zero probability that S (t)
→ −∞ as t → ∞ since W is normally distributed.
This however should be very unlikely if the parameters are representative of a real price.
VI. CONCLUSIONS AND EXTENSIONS
This study provided a methodology for studying the behavior of the prices. More specifically, the study
departs from the traditional before - and – after regression techniques and the time series analysis and
developed a stochastic model that explicitly accounts for the variations in prices a random
environment. This model also is important since it is a continuous – time analog of the autoregressive
model, especially AR (1).
In terms of future research, this methodology could be applied not only in prices but on all aspects of
economics and operations research problems.
* This work was supported by Kuwait University, Research Grant No. [IQ 03/04].
References
[1] Aese, K.K. (1984). Optimum Portfolio diversification in a general continuous-time model.
Stoch. Proc. Applic., 18, 81-98.
[2] Aase, K.K. and Guttorp, P. (1987). Estimation in Models for security prices. Scand. Actuarial
J., 3–4, 211–224.
[3] Black, F. and Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of
Political Economy, 81, 637-659.
[4] Brockwell, P. J. and Davis, R. A. (2002). Introduction to time series and Forecasting. 2 nd
Edition, Springer – Verlag, New York, USA.
[5] Callen, T. and Chang, D. (1999). Modeling and Forecasting Inflation in India. IMF Working
Paper, WP/99/119.

[6] David, A. (1997). Fluctuating Confidence in Stock Markets: Implications for returns and
volatility. Journal of Finance and Quantitative Analysis, 32 (4), 457-462.
[7] Detemple, J. (1986). Asset pricing in a production economy with incomplete information.
Journal of Finance, 41, 383-390.
[8] Duffie, D. and Singleton, K.J. (1993). Simulated moments estimation of Markov models of
asset prices. Econometrica, 61, 929-952.
[9] Hand, D. J. (2001). Modeling Consumer Credit Risk. IMA Journal of Management
Mathematics, 12, 139-155.
[10] Harrison, J.M. and Pliska, S.R. (1981). Martingales and stochastic integrals in the theory of
continuous trading. Stoch, Proc. Appl., 11, 215-260.
[11] Kabanov, Ju. M., Lipster, R.S. and Shiryayev, A.N. (1979). Absolute continuity and singularity
of locally absolutely continuous probability distributions. I. Math. USSR Sbornik, 36, 31-58.
[12] Karmeshu and Goswami, D. (2001). Stochastic Evaluation of Innovation Diffusion in
Heterogeneous groups: Study of Life cycle Patterns. IMA Journal of Management
Mathematics, 12, 107-126.
[13] McGrattan, E. R. (1996). Solving the Stochastic Growth Model with a Finite Element Method.
Journal of Economic Dynamics and Control, 20, 19-42.
[14] Merton, R.C. (1971). Optimum Consumptions and Portfolio rules in a continuous-time model.
J. Econ. Theory, 3, 373-314.
[15] Schwert, G.W. (1990). Stock volatility and the cash of 87. Review of Financial Studies, 3, 77-
102.
[16] Stein, E.M. and Stein, J.C. (1991). Stock Price distributions with stochastic volatility: an
analytic approach. Review of Financial Studies, 4, 727-752.
[17] Tauchen, G.E. and Pitts, M. (1983). The Price Variability – volume relationship on speculative
markets. Econometrica, 51, 485-505.
[18] Veronesi, P. (1999). Stock Market Overreaction to Bad News in Good Times: A
rational expectations equilibrium model. Review of Financial Studies, 12, 976-1007.

International Bulletin of Business Administration
ISSN: 1451-243X Issue 1 (2006)
© EuroJournals, Inc. 2006
http://www.eurojournalsn.com
Multicultural Analysis on Social Influence and Purchasing
Decision: East vs. West
Kritika Kongsompong
Faculty of Marketing
Sasin Graduate Institute of Business Administration of Chulalongkorn University
Sasa Patasala Building
Payathai Road
Pathumwan, Bkk 10330
Thailand
Tel: + 66 (0) 2218-4071
Fax: + 66 (0) 2215-3797
Email: kritika.kongsompong@sasin.edu
Abstract
To understand the impact of cultures on why and how customers buy has dominated the
marketing challenge for the 21 st century. Both international and domestic marketers must
take a serious analysis on how their customers behave in order to make prudent strategic
moves. This study investigates the potential differences in purchasing decision in a
multicultural setting. Of particular interest, the research investigates the relationship
between the amount of social influence that occurs in consumer purchasing decisions and
consumer’s orientation toward consequences of their decision making. The construct
integrated into this study is called ‘Locus of Control’, which is a reflection of one’s belief
about the relationship between his/her behavior and the consequences of that behavior.
The results show that subjects from East Asian collectivist countries (namely Thailand
and Singapore) exhibit more external locus of control tendencies than the subjects from
typically individualist countries (Australia and America). Thus, the latter group is found to
exhibiting a greater degree of internal locus of control in buying situations. Further, this
study reports that East Asians are more responsive to social influence in a hypothetical
buying situation than that of their Western counterparts. Managerial implications are
discussed and direction of future research is also recommended.
Key words: Multicultural Marketing, Locus of Control, Consumer Behavior, Social
Influence.
I. Introduction
What is the impact of cultural difference on consumer behavior? This question has been the subject of
numerous pieces of research, but the answer can have profound implications for marketers who are
doing business across many cultures. It is important to understand the nature and impact of these
influences since they can affect virtually all aspects of the marketing mix. International marketers
must go further and understand the similarities and differences that exist across their markets with

egard to the relative impact of the various sources of influence and devise their marketing strategy and
tactics accordingly.
The study reported in this paper examines the relative importance of social influence in consumer
purchasing decisions across four countries: Two eastern/Asian countries: Singapore and Thailand;
and two Western/non-Asian countries: Australia and USA. The four main hypotheses developed
relate to: (1) East-West differences in level of social influence in purchasing decision; (2) East-West
differences in orientation to locus of control; (3) the nature of differences in the level of
external/internal locus of control to characterize the Eastern and Western countries and (4) differences
that exist in the level of social influence in the buying decision of the people across the two groups of
countries.
II. Background and Theoretical Framework
Eastern vs. Western Cultures: The concept of culture has been widely used in cross-cultural
psychology to explain the difference between cultural groups across the world. Countries of the non-
Western world are usually characterized as being collectivist in nature; whereas Westerners are often
regarded as being individualists (Hoftede 1983, 1984, 1991). Collectivists are those who either make
no distinction between personal and collective goals, or if they do make such distinctions, they
subordinate their personal goals to the collective goals (Triandis 1989). The reason behind
subordinating group goals to personal goals relates to the greater complexity in individualist cultures
than in collectivistic cultures. The more complex the culture, according to Triandis, the greater the
number of in-groups that one may have, thus the members within an in-group will be likely to have
influence over decisions made by other members in the group.
Social Influences. Social norms usually reflect what in-group members consider appropriate behavior.
Stronger social norms are likely to heighten people’s involvement in the situation and accentuate their
felt obligation to help (Schwartz 1994). For collectivist, conforming to the social norms may result in
the person’s feeling good about doing what norms require. Thus, collectivists are likely to have a high
tendency to conform to their social group and they often shift their behavior depending on context
more than do individualists (Hwang 1987).
According to the Fishbein behavioral intentions model (1969, 1975), a person forms intentions to
behave or not behave in a certain way, and these intentions are based on the person’s attitude toward
the behavior as well as his or her perception of the opinions of significant others. Congruent with this
notion, Lee and Green (1991) argue that although the basic framework of the Fishbein’s behavioral
intentions model has been generally accepted for Americans, there are questions concerning the
validity of the independence of attitudinal components and social influence components among people
in Asian cultures. Americans’ individualist nature is clearly manifested by their resentment of
conformity (Hui and Triandis 1986). Most Koreans, on the other hand, feel strong social pressure to
comply with group norms regardless of their own private view (Yau 1994; Lee and Green 1991).
Locus of Control. The Locus of Control (LOC) construct was first established in 1954 by Rotter, who
proposed that the probability of a behavior satisfying a need is dependent on the reinforcement control
that an individual has. According to Rotter (1966), people who believes that they are in control of their
own destiny have an internal locus of control, while those who believe what happens to them is the
result of uncontrollable factors are said to have an external locus of control.
LOC research in marketing context has primarily been concerned with predicting behavior
differences between the externals and internals in purchasing related situations. Consistent with
general LOC findings, externals exhibit a reluctance to make sound decisions after exposure to
environmental events. In the area of consumer credit, Tokunga (1993) found that internals are more
likely to use consumer credit successfully than externals. Lunt and Livington (1991, 1992) reported
that internals are more regular savers and have fewer problems with personal debts. Both Dessart and

Kuylen (1986), and Rundinick and Deni (1980) described internals as those who are less likely to
experience financial difficulties and to act impulsively, were more likely to plan ahead, to act
according to a plan and to be well informed.
Despite the widespread use of LOC as an explanatory tool, the construct and its measures have
raised certain concerns among researchers. LOC has been found most useful when tailored to predict
behavior in specific settings (Rotter 1990; Lefcourt 1982; Munro 1979; Furnham & Steele 1993; and
Marshall 1991). Specific context measures of LOC have been used successfully to predict behaviors
pertinent to health (Lau and Ware 1981; Wallston & Wallston 1981), work (Spector 1961, 1988),
management (Hodgkinson 1992), and consumer behavior (Busseri, Lefcourt & Ketton 1998). These
studies have supported the idea that the predictive powers of LOC measures are better enhanced when
the assessments of expectancies are tailored to particular social arenas. The present study therefore
employs a consumer behavior-focused measure of LOC developed by Busseri and Ketton (1997).
Social influences and locus of control. Social influences have been an integral part of both
individualism/collectivism in predicting social-related behavior. Because collectivists have a high
tendency to conform to their social group, they often shift their behavior depending on the nature of
people around them more than do individualist (Hwang 1987). In regards to the relationship between
social influences and LOC, internals often see themselves as the deciding element in what happens to
them; and that they have the power to change or influence the course of events. Externals, on the other
hand, see external factors, such as social influences, as the shaping factors of their lives and behavior,
and see themselves as having little power to change or influence the course of events (Parkes 1989).
While social influence in the buying decision is widely recognized to vary across cultures (Redding
1982; Fisher and Ackerman 1998), most LOC research has focused on behavioral attributions and
predictions relating to individuals’ perceptions of their control over the environment in which they
operate.
The relationship between locus of control and susceptibility to social influence has received
insufficient attention, but has been addressed conceptually in a few literature. Lefcourt (1982)
mentioned that in regards to purchasing decisions, internals are more resistant to social influences
while externals are more attentive and yielding to social cues. These observations are consistent with
findings that indicate internals pay more attention to information pertinent to purchasing outcomes,
exhibit more purposive decision-making, and have more confidence in their ability to succeed at
important tasks (Lefcourt and Davidson-Katz 1991).
General Hypotheses
Based on the above discussion, the present study is designed to test the following hypotheses: (1)
Overall, collectivist Asians are more subject to social influence than their western counterparts; (2)
collectivist Asians are expected to be characterized by more external LOC traits than the Westerners;
and (3) collectivist Asians are expected to be more responsive to social influence in their buying
decisions than are their counterparts in individualist nations.
IV. Method
Selection of Countries. Since there have not been prior studies that classify national cultures as being
characterized by internal or external LOC, the current study employed a surrogate indicator derived
from the literature. Hoftede (1980) classified countries according to the levels of
individualism/collectivism exhibited by their people. As noted above, individualism/collectivism has
been associated with LOC by several authors: individualists tend to have an internal LOC, collectivists
an external LOC. Based on Hofstede’s (1980) findings, two individualist countries (Australia and
USA) and two collectivist countries (Thailand and Singapore) were selected for this study. The
selected countries occupied extreme positions on Hoftede’s individualist/collectivist scale, with

Australia and USA being the two most individualist, and Thailand and Singapore among the most
collectivist.
Research Instrument and Sample. The instrument comprised a 14 item (5-point Likert scale) LOC
scale (Busseri and Kerton 1997), a buying scenario (“You need to buy some new sneakers. You are
considering two models, one that you like, and another that is liked by the person who is with you.
How likely would you be to purchase the sneakers that the other person likes if that person is”:
mother/father, close friend, boy/girlfriend, salesperson), and classification questions. “Sneakers” were
selected for this study because this product is equally available to the members of the four countries
being investigated. Furthermore, sneakers also have functional equivalence across the samples of the
four countries, and sneakers are a product that can be purchased regularly and conveniently in all four
countries. The LOC measure fits the requirements of the research in two major respects. First, it
focused specifically on consumer-related LOC issues. Secondly, the scale items addressed several
dimensions of LOC, one of which was social influence, the focus of the present study. After reading
the purchase situation scenario, respondents were asked the likelihood (5-point Likert scale from: 1 =
very likely to 5 = very unlikely) that they would be influenced by the other person’s opinion.
Questionnaires were pretested in each of the countries. English was employed in the Australian,
USA, and Singaporean questionnaires, since that is the first language of those nations. The Thai
questionnaire was translated and back-translated using two Thais proficient in English. To test the
psychometric equivalence of these measures, the author compared the reliability statistics between the
countries and checked the variances for floor or ceiling effects (Van de Vijver and Leung 1997).
Questionnaires were administered in classroom settings. The study employed samples of university
students from the four countries, thus controlling for age, occupational and social class factors. After
elimination of respondents for whom there was missing data, the sample consisted of 770 respondents:
243 Thais, 124 Singaporeans, 205 Australians, and 198 Americans.
Analysis. The data were first subjected to principal components factor analysis with varimax rotation
to determine validity and to potentially isolate the social influence component of LOC. Relevant
factors and total scale results were then subject to MANOVA and Scheffe tests across the four
countries to determined whether: (1) Australian and USA respondents reported to be more ‘internal’
than Thai and Singaporean respondents; and (2) Australian and USA respondents reported less social
influence in their buying decisions than Thai and Singaporean respondents.
V. Findings
Three clean factors emerged from the analysis of the LOC scale accounting for 62.5% of the total
variance. Cronbach’s alpha ranged from 0.59 to 0.70 meeting (or very close to) the reliability test for
exploratory/human behavior research (Nunnally and Bernstein 1994; Robinson et al. 1991). Of
particular interest to the present study is that one of the factors contained items that are related to the
level of social influence to which the person is susceptible in the buying decision. This factor, labeled
Susceptibility, also explained the greatest amount of variance. Given the study’s concentration on
social influence and LOC, the focus of the subsequent analysis is on results associated with the LOC
scale as a whole (LOC Total), and the results of the Susceptibility factor.
To test the hypothesis across nations, MANOVA was first employed to determine if differences
exist across the four nations and the three LOC factors, plus the sum of all LOC items. Significant
main effects were found (Wilk’s lambda, f = 15.7, sig. < .000) indicating the existence of pronounced
differences across the four countries and within individual countries. Table 1 shows the means for the
susceptibility to social influence and the level of the social influences in each country, as well as the
combined East/Asian and West/Non-Asian. The findings are consistent with hypotheses (1): Overall,
collectivist Asians are more subject to social influence than their western counterparts.

Table 1: Social influence: Comparison between East and West
Country Means of Social Influence (Std. Deviation)
Thailand 4.57 a (1.04)
Singapore 4.18 b (1.13)
Combined: Eastern/Asians 4.44 x (1.07)
Australia 4.05 c (1.07)
USA 3.56 d (1.20)
Combined: Western/Non-Asians 3.81 y (1.16)
Note: Using a Scheffe test, means for ‘a’ are significantly different from ‘b’, ‘c’, ‘d’, ‘x’, and ‘y’ , but
letters that are the same (e.g., ‘a’ and ‘a’) are not statistically different from one another.
The results in the second hypotheses confirm that collectivist Asians are expected to be characterized
by more external LOC traits than the Westerners According to this finding, both Thai and
Singaporean subjects have means that are significantly higher than those of Australia and USA,
indicating that the former are more externally oriented than the latter. Both the subjects in Thailand
and Singapore are not significantly different from each other on the Susceptibility dimension and
marginally different on the LOC total dimension. However, USA is significantly more internal than
Australia on both the susceptibility and LOC total.
Table 2: Locus of control – Comparison of means between East and West
Country Susceptibility All LOC
Mean St.Dev. Mean St.Dev.
Thailand 3.26 a 0.57 2.77 a
0.42
Singapore 3.12 a 0.57 2.63 a 0.44
Combined: East/Asians 3.21 a 0.57 2.72 a 0.43
Australia 2.86 b 0.67 2.49 b 0.48
USA 2.64 c 0.66 2.34 c 0.51
Combined: West/Non 2.75 b 0.68 2.41 b 0.50
Note: Using a Scheffe test, means for ‘a’ are significantly different from ‘b’ and ‘c’;
mean for ‘b’ are significantly different from ‘c’, but letters that are the same (e.g., ‘a’
and ‘a’) are not statistically different from one another.
The third hypothesis related to differences in the impact of social influence reported by
Eastern/Asians -- Thais and Singaporeans versus Western/Non-Asians -- Australians and Americans.
In this case, the dependent variables were the data related to levels of social influence that respondents
reported as subject to parents, friends, boy/girlfriends, and salespeople. The independent variables
were the four countries being investigated. MANOVA main effects were significant (F = 13.4, p <
.00) again indicating significance across the countries. The Scheffe results presented in Table 3
indicates a mixed pattern of results that indicate a qualified acceptance of the hypothesis. In all cases
(except boy/girlfriend influence), the Thai subjects report the highest level of influence and as
hypothesized, higher than the Australians and Americans. Similarly, in all cases except boy/girlfriend
influence, the American subjects participated in the survey had significantly lower mean influence than
the subjects of the other three countries. The Singaporean subjects reported less parental and

salesperson influence than the Thais. The combined values, however, fit the expected pattern and are
congruent to the hypothesis.
Table 3: Sources of social influence: Comparison between East and West
Country Parents Friends B/Gfriend Salesperson
Mean St.Dev. Mean St.Dev. Mean St.Dev Mean St.Dev
Thailand 4.66 a 1.54 4.96 a 1.24 4.98 1.43
3.67 a 1.57
Singapore 3.70 b 1.75 4.64 e 1.37 5.11 1.52 3.26 c 1.41
Combined: 4.34 x 1.67 4.85 x 1.29 5.02 1.46 3.53 x
1.53
East/Asians
Australia 3.50 c 1.71 4.57 c 1.29 5.01 1.39 3.12 c
1.53
USA 3.00 d 1.61 4.00 d 1.57 4.70 1.62 2.54 d
1.43
Combined: 3.25 y 1.68 4.28 y 1.46 4.86 1.51 2.83 y
1.51
West/Non-Asians
Note1: Employing a Scheffe test, means for ‘a’ are significantly different from ‘b’,
‘c’, and ‘d’; mean for ‘b’ are significantly different from ‘c’; means for ’c’ and ‘e’
significantly different from ‘d’
Note 2: ‘x’ and ‘y’ are compared with the combined values only, ‘x’ are significantly
different from ‘y’
Note 3: Social influence was measured by 7 point scale with: 1= very likely to
purchase & 7= very unlikely to purchase.
IV. Discussion
The findings provide evidence that largely confirms the hypotheses, as well as raising questions that
invite further research. In terms of LOC, the findings clearly indicated that subjects from the westernindividualist
nations of Australia and USA are more internally oriented, and those from the easterncollectivist
nations of Thailand and Singapore are more externally oriented.
In terms of differences in social influence across the nations, however, the findings are more
nuanced. One variable, boy/girlfriend influence showed no differences across the nations. This
finding could be explained by the likelihood that many sample members do not have a boy/girlfriend,
given their age of 19-23 years. Respondents were thus responded to a hypothetical situation with
regards to a person with whom they had a hypothetical relationship. Moreover, due to the
homogeneity nature of the samples from the selected countries, these young adults are more likely to
be influenced by others than any age group. With respect to the other sources of social influence,
Thailand and USA follow the expected patterns, but Singapore and Australia clearly do not. No
differences in influence levels were found between the latter two countries, despite the fact that the
findings associated with the second hypothesis clearly indicated that Singaporean subjects are
relatively more externally oriented and Australian subjects more internally oriented.
The social influence findings indicate that LOC may have some limitations when applied in an
international setting when trying to predict the levels of social influence to which consumers are
subjected. Other factors may intervene to counter the effects that LOC orientation has on social

influence in consumer decision making. One possibility may relate to level of economic development
(Table 4). Singapore and Australia are quite similar in terms of per capita GDP, suggesting that
consumers in the two countries have similar purchasing power, which could impact their perceived
‘independence’ in the buying decision. This explanation, as presented here, is strictly correlational and
cannot be taken as definitive. It appears, though, that the relationship between LOC and social
influence is a complex one in a cross-national context, and should be the subject of further research.
Table 4: Per Capita Gross Domestic Product between 1970 and 2000
Thailand Singapore Australia USA
Year GDP ($) GDP ($) GDP ($) GDP($)
1970 183.2 896.3 3,098.0 5,066.6
1975 349.2 2,608.7 6,885.6 7,567.8
1980 695.8 4,854.4 10,629.9 12,281.6
1985 752.7 6,466.1 10,569.5 17,670.8
1990 1,521.1 12,156.7 17,963.3 23,223.5
1995 2,816.0 23,962.3 19,956.9 28.138.0
2000 1,953.3 23,084.0 19,869.6 35,859.1
Source: World Economic Outlook 2004
Managerial Implications: The research has provided some marketing insights particularly for firms
that engage in international marketing. The findings show that large differences exist between people
across the nations in terms of the susceptibility of consumers to social influence in their buying
decisions. Thus the results of this study contain several implications for marketing practices:
• In developing and promoting a brand in East/Asian countries such as Thailand and Singapore,
marketers would have to promote as much with regards to reference groups as with regard to the
people who actually purchase the product.
• In terms of positioning a brand or a product, the importance of social influence in these
East/Asian countries may make positioning strategies more complicated and challenging than is the
case in the West/Non-Asian societies such as Australia and USA.
Limitation of the study. A major limitation of the study concerns the nature of the sample, which
consisted completely of college students. University students were used for the sampling mainly
because of the cross-national nature of the study. They represent closely matched samples that enabled
rigorous cross-national comparison. The limitation exists, however, because student samples are not
necessarily representative of the overall population. The external validity of findings, therefore, is
compromised. All of the conclusions and implications that are derived from the findings must be
qualified by this limitation.
Another limitation of this study relates to the fact that the results come from only four countries
rather than from a larger number of countries. These four countries are used to because two of the
countries had been identified as Eastern-collectivists, and the other two as Western-individualists in
previous research. However, this sample of nations does not necessarily reflect the findings of samples
from other nations. Thus, the presentation of the results and the implications should be interpreted in
the context of this limitation as well.

References
[1] Alwin, D.F. (1995) “Taking time seriously: Social change, social structure, and human lives.”
In P. Moen, G.H. Elder, Jr., and K. Luscher (Eds.), Examining lives in context: Perspectives on
the ecology of human development (pp. 211-262). Washington, DC: American Psychological
Association.
[2] Bagozzi, R.P., & Dabholkar, P.A. (1994). “Consumer recycling goods and their effect on
decisions to recycle: A means-end chain analysis.” Psychology and Marketing,
11(July/August), 313-40.
[3] Berger, I.E., & Corbin, R.M. (1992). “Perceived consumer effectiveness and faith in others as
moderators of environmentally responsible behaviours.” Journal of Public Policy and
Marketing, 11 (Fall), 79-89.
[4] Biswas, A., Licata, J.W., McKee, D., Pullig, C., and Daughtridge, C. (2000). “The recycling
cycle: An empirical examination of consumer waste recycling and recycling shopping
behaviours.” Journal of Public Policy and Marketing, 19 (Spring), 93-105.
[5] Burns, R.B. (1984) The self-concept: Theory, measurement, development and behaviors. New
York: Prentice-Hall.
[6] Busseri, M.A., & Kerton, R.R. (1997). “Beyond control? Understanding consumer behavior
using a measure for consumer locus of control.” Consumer Interests Annual, 43, 40-46.
[7] Busseri, M.A., Lefcourt, H.M., and Kerton, R.R. (1998). “Locus of control for consumer
outcomes: Predicting consumer behavior.” Journal of Applied Social Psychology, 28, 1067-
1087.
[8] Collins, P. H. (1997). The Meaning of Motherhood in Black Culture and Black
Mother/Daughter Relationships. In Toward a New Psychology of Gender. Eds. Mary M.
Gergen and Sara N. Davis. New York and London: Routledge, 325-340.
[9] Crawford, M. (1997). Agreeing to Differ: Feminist Epistomologies and Women's Ways of
Knowing. In Toward a New Psychology of Gender. Eds. Mary M. Gergen and Sara N. Davis.
New York and London: Routledge, 267-284.
[10] Cross and Hazel Rose Markus. (1993). Gender in Thought, Belief, and Action: A Cognitive
Approach. In The Psychology of Gender. Eds. Anne E. Beall and Robert J. Sternberg. New
York and London: Guilford Press, 55-98.
[11] Dessart, W.C.A.M., & Kuylen, A.A.A. (1986). The nature, extent, causes, and consequences of
problematic debt situations. Journal of Consumer Policy, 9, 311-334.
[12] Dietz, T., Stern, P.C., and Guagnano, G.A. (1998). Social structural and social psychological
bases of environmental concern. Environment and Behavior, 30 (July), 450-471.
[13] Fisher, R.J., & Ackerman, D. (1998). The effects of recognition and group need on
volunteerism: A social norm perspective. Journal of Consumer Research, 25 (December),
262-275.
[14] Furnham, A., & Steele, H. (1993). Measuring locus of control: A critique of general, children’s
health-, and work-related locus of control questionnaires. British Journal of Psychology, 84,
443-479.
[15] Halvari, H. (1996). Personality and educational choice of general versus vocational studies
among 16 – to 19- years-olds. Psychological Reports, 78, 1379-1388.
[16] Hodgkinson, G.P. (1992). Research notes and communications development and validation of
the strategic locus of control scale. Strategic Management Journal, 13, 311-317.
[17] Hofstede, G. (1980) Culture’s consequences: National differences in thinking and organizing.
Beverly Hills, California: Sage Press.
[18] Holland, J.H., Holyoak, K.J., Nisbett, R., & Thagard, P.R. (1986) Induction: Processes of
inference, learning and discovery. Cambridge, MA: MIT Press.
[19] Josephs, R. A., Markus, H. R. and Tafarodi, R. W. (1992). Gender and Self-Esteem. Journal
of Personality and Social Psychology, 63 (September), 391-402.

[20] Kren, L. (1992). The moderating effects of locus of control on performance incentives and
participation. Human Relations, 45(5), 991-1012.
[21] Lau, R.R., & Ware, J.F. (1981). Refinements in the measurement of health-specific locus of
control beliefs. Medical Care, 19, 1147-1158.
[22] Lefcourt, H. (1982) Locus of control: Current trends in theory and research, 2nd ed. Hillsdale,
NJ: Erlbaum.
[23] Lefcourt, H.M. (1991) Locus of control. In J. P. Robinson, P.R. Shaver, and L.S. Wrightsman,
(Eds.), Measures of personality and social psychological attitudes (pp. 413-499). San Diego:
Academic Press.
[24] Lefcourt, H.M., & Davidson, K. (1991) Locus of control and health. In C.R. Snyder, and D.R.
Forsyth (Eds.), Handbook of social and clinical psychology: The health perspective (pp. 246-
266). New York: Pergamon Press.
[25] Livingstone, S.M., & Lunt, P.K. (1992). Predicting personal debt repayment: Psychological,
social and economic determinants. Journal of Economic Psychology, 13, 111-134.
[26] Lunt, P. K., & Livingstone, S. M. (1991). Psychological, social and economic determinants of
saving: Comparing recurrent and total savings. Journal of Economic Psychology, 12, 621-641.
[27] Marks, L.I. (1998). Deconstructing locus of control: Implications for practitioners. Journal of
Counseling and Development, 76, 251-260.
[28] Markus, H.R. and Kitayama, S. (1991). Culture and the Self: Implications for Cognition,
Emotion and Motivation. Psychological Review, 98 (April), 224-253.
[29] Marshall, G. (1991). A multidimensional analysis of internal health locus of control beliefs:
Separating the wheat from the chaff. Journal of Personality and Social Psychology, 61, 346-
359.
[30] McCarthy, J.A., & Shrum, L.J. (2001). The influence of individualism, collectivism, and locus
of control on environmental beliefs and behavior. Journal of Public Policy & Marketing, 20
(1), 93-104
[31] Menon, T., Morris, M., Chiu, C.Y., and Hong, Y.Y. (1999). Culture and the construal of
agency: Attribution to individual versus group disposition. Journal of Personality and Social
Psychology, 76, 701-717.
[32] Meyers-Levy, J. (1988). The Influence of Sex Roles on Judgment. Journal of Consumer
Research, 14 (March), 522-530.
[33] Miller, J.G., (1984). Culture and the development of everyday social explanation. Journal of
Personality and Social Psychology, 46, 961-978.
[34] Mizerski, R.W., Golden, L.L., and Kernon, J.B. (1979). The attribution process in consumer
decision making. Journal of Consumer Research, 6 (September), 123-133.
[35] Morris, M.W., & Peng, K. (1994). Culture and cause: American and Chinese attributions for
social and physical events. Journal of Personality and Social Psychology, 67, 949-971.
[36] Munro, E. (1979). Locus-of-control attribution: Factors among Blacks and Whites in Africa.
Journal of Cross-Cultural Psychology, 10 (2), 157-172.
[37] Nunnally, J., and Bernstein, I. (1994) Psychometric theory, 3 rd ed. New York, McGraw-Hill.
[38] Parker, K.R. (1989) Personal control in an occupational context. In A. Steptoe and A. Appels
(Eds.), Stress, personal control, and health. Chicester, England: Wiley.
[39] Peng, K., & Nisbett, R.E. (1999). Culture, dialectics, and reasoning about contradiction.
American Psychologist, 54, 741-754.
[40] Redding, S.G. (1982). Cultural effects on the marketing process in Southeast Asia. Journal of
the Marketing Research Society, 24 (2), 98-114.
[41] Robbins, A. S., Spence, J. T., and Clark, H. (1991). Psychological determinants of health and
performance: The tangled web of desirable and undesirable characteristics. Journal of
Personality and Social Psychology, 61, 755-765.

[42] Robinson, J.P., Shaver, P.R., Wrightsman, L.S. (1991) Criteria for scale selection and
evaluation. In J.P. Robinson, P. R. Shanver, and L.S. Wrightsman (Eds.), Measures of
personality and social psychological attitudes. San Diego, California: Academic Press.
[43] Ross. L.D. (1977) The intuitive psychologist and his shortcomings. In L. Berkowitz (Ed.),
Advances in experimental social psychology, (Vol. 10, pp. 174-220). New York: Academic
Press.
[44] Rotter, J.B. (1966). Generalized expectancies for internal versus external control of
reinforcement. Psychological Monographs: General and Applied, 80, 1-28.
[45] Rotter, J. B. (1990). Internal versus external control of reinforcement: A case history of a
variable. American Psychologist, 45, 489-493.
[46] Rundnick, C., & Deni, R. (1980). Use of the internal-external control scale to predict
preferences of product. Psychological Reports, 47, 1193-1194.
[47] Shrum, L.J., McCarty, J.A., and Lowrey, T.M. (1995). Recycling as a marketing problem: a
framework for strategy development. Psychology and Marketing, 11 (4), 393-416.
[48] Shweder, R.A., & Bourne, E.J. (1984) Does the concept of the person vary cross-culturally? In
A.J. Marsella and G.M. White (Eds.), Cultural conceptions of mental health and therapy ( 97-
137). New York: Reidel.
[49] Spector, P.E. (1961). Development of the work locus of control scale. Journal of
Occupational Psychology, 61, 335-340.
[50] Spector, P.E. (1986). Perceived control by employees: A meta-analysis of studies concerning
autonomy and participation at work. Human Relations, 39, 1005-1016.
[51] Spector, P.E. (1988). Development of the work locus of control scale. Journal of
Occupational Psychology, 61, 335-340.
[52] Strickland, B. (1989). Internal-external control expectancies: From contingency to creativity.
American Psychologist, 44(1), 1-12.
[53] Tokunga, H. (1993). The use and abuse of consumer credit: Application of psychological
theory and research. Journal of Economic Psychology, 14, 285-316.
[54] Triandis, H.C. (1984). A theoretical framework for the more efficient construction of cultural
assimilators. International Journal of Intercultural Relations, 8(3), 301-30.
[55] Van de Vivjer, Fons and Leung, Kwok (1997), Methods and data analysis for cross-cultural
research. Thousand Oaks, California: Sage.
[56] Wallston, K.A., & Wallston, B.S. (1981). Health locus of control scales. In H.M. Lefcourt
(Ed.), Research with the locus of control construct, 1, 189-243, New York, NY: Academic.

International Bulletin of Business Administration
ISSN: 1451-243X Issue 1 (2006)
© EuroJournals, Inc. 2006
http://www.eurojournalsn.com
Acquisition Announcements, Firm Value and Volatility:
The Case of Greek Financial Firms
Athanasios Koulakiotis
University of the Aegean
Department of Financial & Management Engineering
31 Fostini Str., 82100, Chios, Greece
Tel: +30-22710-35464
Email: a_koulakiotis@aegean.gr
Nicholas Papasyriopoulos
Department of International & European Economic
& Political Studies
University of Macedonia
156 Egnatia Str., P.O. Box 1591
54006, Thessaloniki, Greece
Tel:+30-2310-891492
Email: papasur@uom.gr
Apostolos Dasilas
Department of Accounting and Finance
University of Macedonia
156 Egnatia Str., P.O. Box 1591
54006, Thessaloniki, Greece
Tel:+30-2310-891674
Email: tdasilas@hotmail.com
Abstract
In this paper, we follow and extend the approach of Ko et al. (1997) in order to examine
the effects that announcements of acquisitions by financial firms of the Athens Stock
Exchange (ASE) have on their stock prices and their abnormal return volatility. Apart from
the traditional event study methodology used to examine how the announcement of
acquisitions affects the value of 7 Greek financial firms listed on the ASE, the relationship
between announcement of acquisitions and abnormal return volatility is also analysed
using three models: the GARCH, the E-GARCH and the GJR-GARCH model in a simple
form. The empirical results indicate that for bidders the announcement of acquisitions
show positive but statistically insignificant average abnormal return (AAR) during the
event period. In addition, an abnormal return pattern reveals no significant movements
around the announcement day. The valuation effects of the three models with the simple
method indicate, in general, an insignificant impact on the average abnormal return
volatility at the 5% level of significance.
Keywords: Mergers and Acquisitions, Volatility, GARCH Models, Financial Firms.
JEL Classification: G34, G14.

I. Introduction
The recent integration of capital markets brought about the globalization of companies. Although there
are various ways in which a company globalises itself, one method is to acquire and trade its stocks on
foreign exchanges. There are potential benefits to be gained from domestic or foreign acquisitions. A
cross-border acquisition may enhance the establishment of an operating presence in a host country and
it may be a way to capture valuable technology rather than developing it internally. Regardless of
being the acquisition domestic or foreign, there exist economies of scale and scope that a firm can
exploit. On the other hand, cross-border or domestic acquisitions have some potential pitfalls. For
instance, the price paid by the acquirer may be too high and the method of financing too costly.
Furthermore, unfavourable host country political reactions may occur when a takeover involves a
foreign firm. Finally, contractual agreements, license fees, transfer prices, and other relationships
between the parties will be closely scrutinized than when they were independent.
The primary objective of this study is to determine the effect that the announcement of acquisitions
have on the value of stock prices and abnormal return volatility for Greek financial firms listed on the
Athens Stock Exchange (ASE). To the best of our knowledge, previous studies examining the impact
of the announcement of acquisitions on abnormal return volatility does not exist and this approach is
the first one that takes into account the impact of changes of stock prices and noise on abnormal return
volatility in the pre- and post- announcement period. In this respect, we follow and extend the approach
of Ko et al. (1997) who analyse the relationship between listing and abnormal return volatility using
the GARCH model. Their empirical results suggest that first, the Japanese firms’ listings on the U.S.
markets show positive, but statistically insignificant abnormal returns. Second, U.S. market listing has
no significant effects on stock prices. Third, an abnormal return pattern shows no significant movement
around the foreign listing date. Finally, the valuation effects of listing on the New York Stock
Exchange (NYSE) tent to be similar to those from the over-the-counter market.
Previous studies regarding the overall gains for bidders from bank mergers and acquisitions
(M&As) lack consistency. More specifically, various studies report positive, negative or insignificant
market reaction at the announcement day of M&As. Studies conducted mainly in the US market find
either insignificantly positive or negative abnormal returns on and around the announcement date [e.g.
Houston and Ryngaert (1994), Cornett and Tehranian (1992), Pilloff (1996), Siems (1996) DeLong
(2001)]. On the other hand, studies regarding the European market document positive market reaction
to the announcement of acquisitions in banking sector [e.g Beitel et al. (2002) and DeLong (2003)].
Vander Vennet (1998) observes that market reaction of European bank mergers is seldom studied
because many of the partners are not publicly traded. Our paper aims to fill this gap and contributes to
the existing literature by introducing a new approach which examines both wealth effects to bidders
and abnormal return volatility around the acquisition announcements. Our results are in line with those
of US studies that find insignificant market reaction around the acquisition announcements. Similar to
Ko et al. (1997), we find an insignificant impact on the average abnormal return volatility around the
acquisition announcements.
The remainder of the study is organized into four sections. Section 2 gives a short review over the
empirical research dealing with the factors that affect shareholder wealth creation with respect to bank
M&As. Section 3 presents the data sample and methodology. Section 4 summarizes the empirical
results of the three models and Section 5 provides the conclusions and implications.
II. Literature Review
Previous research focuses on the question whether a merger or acquisition (M&A) in the banking
sector is value enhancing or not, that is, it creates abnormal returns to both bidders (acquirers) and
targets (acquired firms), or to the combined entity (targets and bidders). The empirical evidence for
bidders, however, is mixed. Desai and Stover (1985), James and Weir (1987), Cornett and De (1991),
Cybo-Ottone and Murgia (2000) Beitel et al. (2002) and DeLong (2003) document positive stock price
93

eaction to bidding firms in banking acquisitions. On the other hand, Neely (1987), Hawawini and
Swary (1990), Houston and Ryngaert (1992, 1994), and Cornett and Tehranian (1992), Pilloff (1996),
Siems (1996) and DeLong (2001) report negative stock price reaction to bidding firms.
However, the empirical evidence for targets is almost unanimous. There are positive abnormal
returns at the announcement day of acquisition for the shareholders of targets. Houston and Ryngaert
(1992, 1994), Cybo-Ottone and Murgia (2000), DeLong (2001, 2003) and Beitel et al. (2002) are
among the researchers who confirm the positive stock price reaction at the announcement day of
acquisition for targets.
Some of the above studies try to examine the driving forces behind the abnormal behaviour of stock
prices at the announcement of acquisitions in banking sector. For example, James and Weir (1987) are
among the first who examine the effect of competition in the market for bank acquisitions on the
returns to bidders. They find positive abnormal returns to bidders, which are positively related to the
number of alternative target firms available and negatively related to the number of other potential
bidders in the market.
Houston and Ryngaert (1994) examine the stock market’s perception of bank mergers in the period
1985-1991. They find that the average total return to a completed bank merger is slightly greater than
zero at the merger’s announcement, though not significantly different from zero. Positive returns to
targets are essentially offset by negative returns to bidders. They also find that market responds most
favourably to acquisition announcements when the acquiring bank had a good past operating
performance.
Cybo-Ottone and Murgia 1 (2000) examine the stock market valuation of 54 M&As deals covering
13 European banking markets of the European Union plus the Swiss market from 1988 to 1997. Their
results document that there is a positive and significant increase in value for the average merger at the
time of the deal’s announcement. This finding contradicts the bulk of empirical studies conducted in
the US banking markets where no value creation effects are generally found. When Cybo-Ottone and
Murgia (2000) analyze the combined abnormal returns of bidder and target for different types of deals,
they provide some evidence that domestic combinations between banks and banking/insurance deals
tend to drive the results. They tentatively interpret the difference between their results and those of the
US literature as stemming from different regulatory regimes in Europe and the US.
DeLong (2001) examines the wealth effects of bank mergers by distinguishing between types of
mergers. Specifically, mergers are classified according to their focus or diversification along the
dimensions of activity and geography. The results show that bank mergers that focus both in a specific
geography and activity create value upon announcement, while those that diversify either in geography
or activity, or both, do not create value. Overall, mergers in the banking industry neither create nor
destroy shareholder wealth, but mergers that focus both in geography and activity earn a positive 3%
return. Bidders in this group do not destroy value, while bidders in the other groups do destroy value.
Targets that enter into a specific merger do not earn significantly more or less than targets in the other
groups. Finally, abnormal returns upon a merger announcement increase in relative size of the target
rather than the bidder, but decrease only in the pre-merger performance of the targets.
The paper of Beitel et al. (2002) empirically addresses the factors that influence announcement
effects of European bank mergers and acquisitions from 1985 to 2000. The results are in line with the
US evidence. Abnormal returns for targets are higher when the size of the target is smaller compared to
the bidder, when the target has a good cost-to-asset ratio relative to the bidder and when the target has
a poor past stock performance track record. Abnormal returns for bidders are higher when a transaction
is more focused and involves targets with higher growth rates, a high market-to-book ratio and when
targets are less profitable than bidders. Abnormal returns for the combined entity are the highest for
non-diversifying transactions, when the bidder is engaged in relatively few M&A transactions and
when the target exhibits a high market-to-book ratio and a poor past stock performance track record.
1
The study by Cybo-Ottone and Murgia (2000) is the first study that examines the consolidation of large listed banks and
financial institutions in the European markets.
94

DeLong (2003) investigates whether the market reaction to non-US bank mergers is comparable to the
market reaction of US banks. His results are similar to those of Cybo-Ottone and Murgia (2000).
Specifically, DeLong (2003) finds that announcements of non-US domestic bank mergers enhance the
value of combined entity, bidders do not lose, and targets increase their values. The value enhancement
for the combined entity of non-US merger announcements is not statistically greater than for US
mergers. However, non-US bidders earn about 2% more than US bidders and non-US targets earn
about 7% less than their US counterparts.
III. Data and Methodology
Data
Table 1 presents 7 Greek listing financial firms that have acquired shares on firms operating in the
Greek, Cypriot, Yugoslavian and FYROM markets. Five of the acquired firms were listed in the Greek
stock market, one in the FYROM stock market, one in the Yugoslavian stock market and one in the
Cypriot stock market.
In this study, all seven firms are selected as population. The names of bidders and targets as well as
the announcements dates of the acquisitions were extracted from the daily press releases of the Athens
Stock Exchange. Similar to Houston and Ryngaert (1994) and Cybo-Ottone and Murgia (2000), we are
dealing only with acquisitions for which the bidder announced that is going to acquire more than 50%
of the acquired firm (control of the target). Therefore, we refer only to big acquisitions in order to see
what happens around these announcement days. The stock prices of bidders 100 days before and 20
days after the announcement of acquisition are obtained from Dissemination Information Department
of the Athens Stock Exchange. The market reaction is proxied by the General Index of the Athens
Stock Exchange and the examined period is from 1998 to 2002.
Table 1. Greek Financial Firms that Announced Acquisitions of more than 50%.
Company Name Target Market Announcement date
Alpha Bank Cyprus 17/10/01
Piraeus Bank Greece 23/06/03
Piraeus Bank Greece 25/10/01
Commercial Bank Yugoslavia 20/06/02
EFG Eurobank Greece 18/02/02
Egnatia Bank Greece 31/07/98
National Bank of Greece SA FYROM 29/10/99
Marfin Investment Services SA Greece 11/10/00
Methodology
The focus of this study is to analyse the effects that Greek financial firms’ domestic and foreign
announcements of acquisitions have on their stock price and abnormal return volatility. The traditional
event study methodology combined with GARCH, E-GARCH and GJR-GARCH regression analysis is
used to determine the effects of acquisition announcements.
This study uses the event study methodology of Brown and Warner (1985). The event period begins
100 days prior to announcement and ends 20 days after the announcement of acquisition. To calculate
Abnormal Return (AR), the Market-Adjusted Model is used as follows:
ARit = Rit
− Rmt
(1)
where,
AR : the market-adjusted abnormal return i on day t,
it
95

R it : the rate of return for stock i on day t and
R mt : the market return on day t.
From Equation (1), the Average Abnormal Return (AAR) can be found as follows:
N ARit
AARt
= ∑ (2)
i=
1 N
where,
N is the number of stocks announcing an acquisition of more than 50%.
The calculation of the CAAR (Cumulative Average Abnormal Return) for the event period beginning
from the starting day, to day K is as follows:
K
∑
t=
−100
CAAR[
−100,
K]
= AAR
(3)
t
In order to model the impact of acquisition announcements on abnormal return volatility, the pattern
of time-varying abnormal return volatility is observed. Although there are many methods for
estimating the time-varying volatility of stock prices, we use three models taking into account two
dummy variables in the OLS equation in order to capture the effects in the pre-acquisition and postacquisition
period on average abnormal returns of the portfolio of equities. We do not include the
dummy variable that controls for the announcement day of the acquisition because the coefficient of
one of the three dummy variables in this case was given to be zero in the convergence process.
Therefore, we focus only on the above-mentioned two dummy variables and leave out the third dummy
of the announcement day of acquisition 1 in order to see the impact of the pre- and post- acquisition
period on the AAR of Greek financial firms’ equities.
The three methods employed in this study are the following:
A. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model which has got the
following form: 2
AAR t = β 0 + β 1 D 1 t + β 2 D 2 t + ε t
2
(5)
h t = Var ( ε t ) = α 0 + α 2ε
t − 1 + α 1h
t −1
where,
E( ε t ) = E(
rt
− μt
) for the simple method
where,
μ is the long-term drift coefficient for the constant,
t
AARt is the average abnormal return from Equation (2),
t ∈ [ −100,
+ 20]
, and
1 if t ∈ [ −100,
−1],
D1 t = 0 if not,
1 if t ∈ [ 1,
20],
D2 t = 0 if not,
The parameter restrictions α 0 > 0 , 1 0 ≥ α , 2 0 ≥ α and α 1 +α 2 < 1 ensure that the stochastic process
{ ε t } is well-defined (i.e., ht > 0 ∀t
) and the covariance is stationary with Ε( ε t ) = 0 , Var( ε t )=h, Cov
( ε t , ε s )=0 t ≠ s .
1
This is happened because in the process of modeling there was a problem of multicollinearity and the coefficient of one of
the three dummies was given to be zero. This does not hold when we add to the modeling process only two dummy
variables and we concluded that the one dummy variable should stay out of the process of modeling for statistical reasons.
2
Refer to Bollerslev (1986) and Bollerslev et al. (1992) for further information on the GARCH model. This study utilizes
the BHHH method presented by Berndt et al. (1974) for the model estimation.
96

B. The Exponential Generalized Autoregressive Conditional Heteroscedasticity
(E-GARCH) model, which has got the following form: 1
AAR β + β D + β D + ε
t = 0 1 1t
2 2t
q
log h t = Var( ε t ) = α 0 + ∑ 1 ht
−1
i=
1
log α + ) | (|
p
∑ α 3
j= 1
where,
E( ε t ) = E(
rt
− μt
) for the simple method
where,
ε t−1
ht
−1
−μ
μ is the long-term drift coefficient for the constant,
t
AARt is the average abnormal return from Equation (2),
t ∈ [ −100,
+ 20]
, and
1 if t ∈ [ −100,
−1],
D1 t = 0 if not,
1 if t ∈ [ 1,
20],
D2 t = 0 if not,
t
p
+∑
=
j 1
α ( ε h )
4
t−1
/ t−1
also μ=Ε(| ε t / h t |) (7)
The value of μ depends on the density function assumed for the standardized disturbances,
h . We have:μ=(2/π) 1/2 , if ut ≈ Ν(0,1).
u t = ε t / t
For estimation purposes the parameter α 3 is equal to one. This model is capable of capturing any
asymmetric impact of shocks on volatility. The EGARCH model allows good and bad news to affect
volatility in a different manner. A positive α 4 implies that large (small) price changes tend to follow a
large (small) price change. A negative α 4 captures asymmetry. If α 4

1 if t ∈ [ 1,
20],
D2 t = 0 if not,
εt,ut, ht are the error, the innovation and the conditional volatility, respectively.
Here, Ι t −1
= 1 if ε t−1
< 0 and Ι t −1
= 0 If ε t−1
≥ 0 .
We denote this model as asymmetric GARCH, or for short GJR-GARCH (1,1). The process is welldefined
if the conditionsα 0 > 0 , 1 0 ≥ α , 0 ) 1 ( α 5 −α 6 ≥ and α 6 ≥ 0 are satisfied.
The time-varying volatility of abnormal returns is estimated according to Equations (5), (6) and (8).
Moreover, from the dummy variables D1 t and D2 t , the averages of AAR for the 100 days before the
announcement of acquisition and 20 days after the announcement of acquisition can be determined.
β 0 is the AAR for the period [-100, +20] and β 1 and β 2 represent the average of AAR for the period
prior to and after the announcement of acquisition, respectively.
VI. Empirical Results
Table 2 presents the AAR and CAAR for the 7 Greek financial firms for the event period of 121 days
(100 days before announcement to 20 days after announcement). In general, there are positive AARs
prior to announcement, but negative AARs immediately thereafter. However, during this period the
AAR is not statistically significant. The first day after the announcement of acquisition displays an
AAR of –1.68%. This is a relatively sharp decline in stock price immediately after the announcement
of acquisition, which persists for the following five days (+1 to +5).
On the other hand, the CAAR is statistically insignificant (0.02%) in the pre-acquisition period
(days -100 to 0). The absolute value of CAAR for the 20-day period after the announcement increases.
There is an absolute increase in stock prices prior to announcement, but prices decline in the period
after the announcement.
However, since AR and CAR are not statistically significant, the announcement of acquisitions does
not seem to induce any significant effect on the value of bidders’ stocks. Therefore, we conclude that
the announcement of acquisitions by Greek financial firms does not render any value in stock prices.
These results are in line with those of US studies that find either insignificantly positive or negative
abnormal returns for bidders on and around the announcement date [Houston and Ryngaert (1994) and
DeLong (2001)].
The GARCH, E-GARCH and GJR-GARCH techniques in a simple form is conducted to estimate
the time-varying abnormal return volatility [Ko et al. (1997)]. The testing period is set for 20 days
around the announcement of an acquisition (-10, +10). Panel A and B of Table 3 present the results of
the GARCH, E-GARCH and GJR-GARCH estimations with a simple form. The abnormal returns for
the test period are estimated from β 1 and β 2 which are the coefficients of the dummy variables for the
pre- and post-announcement periods. Both β 1 (–0.001) and β 2 (–0.005) are statistically insignificant
when we use the GARCH, E-GARCH and GJR-GARCH in a simple form. These results suggest
insignificant stock price increase and decrease in the pre-and post-announcement period, respectively.
The t-value of α 1 is –0.448, 0.054, and –0.111 using GARCH, E-GARCH and GJR-GARCH
estimates of the simple form. These results indicate that the previous day volatility significantly affects
next day’s abnormal return volatility when the GARCH method with a simple form is considered in the
modelling process.
98

Table 2. AAR and CAAR for 7 Greek Financial Firms Announcing Acquisitions of more than 50%.
Day AAR t-value CAAR t-value
-100 -0.0162 -1.54 -0.0162 -1.54
-90 0.0080 1.15 -0.0007 -0.29
-80 -0.0142 -2.03 -0.0025 -1.52
-70 0.0089 0.72 -0.0012 -0.78
-60 -0.0051 -0.75 -0.0012 -0.98
-50 0.0217 1.30 -0.0010 -0.71
-40 -0.0207 -2.06 -0.0012 -0.97
-30 0.0002 0.05 -0.0006 -0.53
-20 0.0100 0.73 0.0003 0.28
-10 0.0114 1.08 0.0000 0.04
-5 0.0069 0.78 -0.0001 -0.06
-4 -0.0063 -2.97 -0.0001 -0.13
-3 -0.0155 -1.63 -0.0003 -0.30
-2 -0.0019 -0.16 -0.0003 -0.32
-1 0.0065 0.75 -0.0002 -0.25
0 0.0011 0.34 -0.0002 -0.23
1 -0.0168 -1.70 -0.0004 -0.41
2 -0.0169 -1.67 -0.0005 -0.58
3 -0.0083 -1.32 -0.0006 -0.66
4 -0.0069 -2.06 -0.0007 -0.73
5 -0.0079 -1.93 -0.0007 -0.81
10 0.0069 1.11 -0.0010 -1.10
20 -0.0039 -0.71 -0.0008 -0.95
The t-value of α 2 for the simple form of GARCH model is equal to 0.952. This means that the
impact of squared error insignificantly affects at the 5% level of significance the abnormal return
volatility when the simple method of GARCH modelling is considered. On the other hand, the t-values
of α 3 and α 4 of E-GARCH method are equal to 0.906 and –0.638 for the simple method. This
suggests that the impact of bad and good news of stock prices on the average abnormal return volatility
is not significant at the 5% level of significance. Therefore, any changes in the error term of stock
prices, that is, positive or negative stock prices changes, do not seem to affect in any direction the
value of the average abnormal return volatility.
Finally, the values of t-statistic for α 5 (1.06) and α 6 (1.407) coefficients of the simple GJR-
GARCH model indicate that the squared error and the bad news of error term are statistically
insignificant at the 5% level of significance. However, the value of bad news ( α 6 ) of GJR-GARCH
approach may affect the average abnormal return volatility at the 15% level of significance and the
squared error term ( α 5 ) is found to affect significantly the value of average abnormal return volatility
at the 30% significant level. These two findings may show that changes in squared error term and
negative values of the error term can have an impact on the average abnormal return volatility (changes
of stock prices). This is important if we want to understand the way in which the changes of error term
values, both positive and negative, affect the distribution of average abnormal return volatility of listed
Greek financial firms’ stocks before and after the announcement of acquisitions.
Panel B in Table 3 displays that the time varying abnormal return volatility for the 20 days period
around the announcement (-10,+10) hovers around the average value of 7.815306E-05, 7.53956E-05
99

and 7.60735E-05 for the GARCH, E-GARCH and GJR-GARCH simple models. Similarly, on the day
of announcement, the abnormal return volatility is 3.2488990E-05, 4.6379145E-05 and 4.2612767E-5,
respectively, indicating small volatility. On the day of announcement (t = 0), the abnormal return
volatility is very small in comparison to the value of the abnormal return average volatility.
Overall, the results suggest that the impact of squared error term, being positive or negative, and the
previous day’s volatility is statistically significant, however, in a significant level a little bit higher than
30%. In few cases, we found a level of significance smaller than 15%, indicating a stronger impact of
previous day’s volatility and squared error term on the value of average abnormal return volatility.
These results indicate that the impact of both changes in error and stock prices may affect the average
abnormal return volatility of Greek financial firms’ stocks after an announcement of acquisition (in a
higher level of significance from 5%, which is the critical level of significance in studies of volatility
of stock prices).
V. Conclusions
This study examines the wealth effects of acquisition announcements for 7 Greek listed financial firms
from 1998 to 2002. The empirical results indicate that first, the announcement of acquisitions of Greek
firms’ stocks creates positive, but statistically insignificant CAAR. The CAAR starts to decline right
after the announcement of acquisitions due to relatively negative AAR. However, these AARs are also
revealed to be insignificant. Overall, this result is in line with the majority of US studies that find
insignificant market reaction around the announcement of bank M&As for bidders. Second, the results
obtained from GARCH, E-GARCH and GJR-GARCH models for the simple form, in general, support
those from the market-adjusted model at the 5% significance level. Finally, the impact of bad news and
that of the squared error term on abnormal return volatility seem to be significant at the 15% and 30%
significance level, respectively.
Table 3. Simple GARCH, E-GARCH, and GJR-GARCH Estimates for 7 Greek Financial Firms
Listed on the Athens Stock Exchange.
Panel A: Model estimation
β
Model
0
1
2
GARCH 0.001
-0.001
-0.005
(0.107)
(-0.133)
(-0.491)
E-GARCH 0.001
-0.001
-0.005
(0.107)
(-0.133)
(-0.491)
GJR- 0.001
-0.001
-0.005
GARCH (0.107)
(-0.133)
(-0.491)
Panel B: Estimates of volatility
Day Volatility
GARCH E-GARCH GJR-GARCH
β
β
α
0
0.001
(1.60)
-8.965
(-1.132)
0.001
(1.042)
α
1
-0.299
(-0.448)
0.0455
(0.054)
-0.106
(-0.111)
α
2
0.148
(0.952)
α 3
0.205
(0.906)
Day Volatility
GARCH E-GARCH GJR-GARCH
α 4
-0.803
(-0.638)
-10 0.0001392983038 0.0001477095500 0.0001455469965 4 0.0000384093751 0.0000341806061 0.0000352327008
-9 0.0000102241966 0.0000081020967 0.0000086184167 5 0.0000518044346 0.0000468734425 0.0000481041288
-8 0.0000176192561 0.0000147949332 0.0000154898447 6 0.0001253846727 0.0001176447883 0.0001195898408
-7 0.0000048291370 0.0000034092603 0.0000037469887 7 0.0001741747917 0.0001650304612 0.0001673326969
-6 0.0000102241966 0.0000081020967 0.0000086184167 8 0.0000102241966 0.0000081020967 0.0000086184167
-5 0.0000608785419 0.0000664808958 0.0000650327086 9 0.0000518044346 0.0000468734425 0.0000481041288
-4 0.0000270143156 0.0000234877696 0.0000243612728 10 0.0000608785419 0.0000664808958 0.0000650327086
-3 0.0002309649108 0.0002204161341 0.0002230755529 Average 0,00007815306 7,53956E-05 7,60735E-05
-2 0.0000014340775 0.0000007164238 0.0000008755607
-1 0.0000462736014 0.0000511737322 0.0000499041366
0 0.0000032488990 0.0000046379145 0.0000042612767
1 0.0002623599703 0.0002511089706 0.0002539469809
2 0.0002623599703 0.0002511089706 0.0002539469809
3 0.0000518044346 0.0000468734425 0.0000481041288
Note: t-values are in parentheses.
In terms of implications of this study, the changes in stock prices and noise affect mostly the
previous day’s volatility and squared error term in the modelling process of abnormal return volatility
with a simple martingale method to be taken into account. This shows that policy makers should
α 5
0.217
(1.06)
α 6
0.992
(1.407)
100

concentrate on methodologies that bring alternative effects and combine more advanced methodologies
than previously used by researchers in order to study abnormal return volatility patterns in European
markets. Future research waits to see the impact of this research framework on abnormal returns and
perhaps volume volatility for the whole Greek and European market.
References
[1] Beitel, P., D. Schiereck, and M. Wahrenburg, (2002), “Explaining the M&A Success in
European Bank Mergers and Acquisitions“, Institute for Mergers and Acquisitions (IMA),
working paper.
[2] Berndt, E.K., H.B. Hall, R.E. Hall, and J.A. Hausman, (1974), “Estimation and Inference in
Nonlinear Structural Models”, Annals of Economic and Social Measurement, Vol. 4, pp. 653-
666.
[3] Bollerslev, T.P., (1987), “A Conditional Heteroscedastic Time Series Model for Speculative
Prices and Rates of Return”, Review of Economics and Statistics, Vol. 69, pp. 542-547.
[4] ------------, T., R.Y. Chou, and K.F. Kroner, (1992), “ARCH Modeling in Finance: A Review of
the Theory and Empirical Evidence”, Journal of Econometrics, Vol. 52, pp. 5-60.
[5] Brown, S.J., and J.B., Warner, (1985), “Using Daily Stock Returns: The case of Event Studies”,
Journal of Financial Economics, Vol. 14, pp. 3-31.
[6] Cornett, M. and S. De, (1991), “Medium of Payment in Corporate Acquisitions: Evidence from
Interstate Bank Mergers”, Journal of Money, Credit and Banking, Vol. 23, pp. 767-776.
[7] Cornett, M. and H. Tehranian, (1992), “Changes in Corporate Performance Associated with
Bank Acquisitions”, Journal of Financial Economics, Vol. 31, pp. 211-234.
[8] Cybo-Ottone, A amn M. Murgia, (2000), “Mergers and Shareholder Wealth in European
Banking”, Journal of Banking and Finance, Vol. 24, pp. 831-859.
[9] DeLong, G., (2001), “The Announcement Effects of US vs Non-US Bank Mergers: Do They
Differ?”, Journal of Financial Research, Vol. 26, pp 487-500.
[10] DeLong, G., (2001), “Stockholder Gains from Focusing vs Diversifying Bank Mergers”,
Journal of Financial Economics, Vol. 59, pp 221-252.
[11] Desai, S. and R. Stover, (1985), “Bank Holding Company Acquisitions, Stockholder Returns
and Regulatory Uncertainty”, Journal of Financial Research, Vol. 8, pp. 145-156.
[12] Glosten, L. R., R. Jagannathan, and D. E. Runkle, (1993), “On the Relation between the
Expected Value and the Volatility of the Nominal Excess Return on Stocks”, Journal of
Finance, Vol. 48, pp. 1779-1801.
[13] Hawawini, G. and I. Swary, (1990), “Mergers and Acquisitions in the US Banking Industry”,
Elsevier Science Publishers, New York.
[14] Higson, C., and J., Elliot, (1998), “Post-takeover Returns: The UK Evidence”, Journal of
Empirical Finance, Vol. 5, pp. 27-46.
[15] Houston, J. F. and M. Ryngaert, (1994), “The Overall Gains from Large Bank Mergers”,
Journal of Banking and Finance, Vol. 18. pp. 1115-1176.
[16] James, C. M. and P. Weir, (1987), “Returns to Acquirers and Competition in the Acquisition
Market: The Case of Banking”, Journal of political Economy, Vol. 95, pp. 355-370.
[17] Ko, K., I., Lee, and K. Yun, (1997), “Foreign Listings, Firm Value, and Volatility: The Case of
Japanese Firms’ Listings on the US Stock Markets”, Japan and the World Economy, Vol. 9, pp.
57-69.
[18] Madura, J., and K.J., Wiant, (1994), “Long-term Valuation Effects of Bank Acquisitions”,
Journal of Banking and Finance, Vol. 18, pp. 1135-1154.
[19] Neely, W., (1987), “Banking Acquisitions: Acquirer and Target Shareholder Returns”,
Financial Management, Vol. 16, pp. 66-73.
101

[20] Nelson, D.B., (1989), “Commentary: Price Volatility, International Market Links, and their
Implications for Regulatory Policies”, Journal of Financial Services Research, Vol. 3, pp. 247-
254.
[21] Pilloff, S. J., (1996), “Performance Changes and shareholder Wealth Creation Associated with
Mergers of Publicly Traded Banking Institutions”, Journal of Money, Credit and Banking, Vol.
28, pp. 294-310.
[22] Siems, T. F., (1996), “Bank Mergers and Shareholder Wealth: Evidence from 1995s
Megamerger Deals”, Financial industry Studies of the Federal Reserve Bank of Dallas, pp. 1-
12.
[23] Vander Vennet, R., (1998), “Causes and Consequences of EU Bank Takeovers”, in S.
Eijffinger, K. Koedijk, M. Pagano, and R. Portes, eds, The Changing European Landscape
(Centre for Economic Policy Research, Brussels, Belgium), pp. 45-61.
[24] Vander Vennet, R., (1996), “The Effect of Mergers and Acquisitions on the Efficiency and
Profitability of EC Credit Institutions”, Journal of Banking and Finance, Vol. 20, pp. 1531-
1558.
102

International Bulletin of Business Administration
ISSN: 1451-243X Issue 1 (2006)
© EuroJournals, Inc. 2006
http://www.eurojournalsn.com
Profile of EMBA Students in AACSB Accredited
Public and Private Institutions in the United States*
John Coleman, Stan Bazan, and Fred Tesch
Management Department
Ancell School of Business
Western Connecticut State University
181 White Street
Danbury, Connecticut 06810, USA
Contact information: teschf@wcsu.edu
(203) 837 - 8654
*An earlier version of this study was reported at the 2006 International Academy of Business and
Technology Conference, June 2006, Mystic, Connecticut.
Abstract
The study surveyed the 81 public and 65 private colleges of business administration in the
USA that are accredited by AACSB and offer an Executive Master of Business
Administration (EMBA) program. Based on the resulting sample of 24 public and 13
private EMBA programs, the survey generated profiles of these two student groups,
focusing on, for example, age, gender, diversity, and employment level. The survey found
several differences between the public and private EMBA student groups.
Key words: Executive Master of Business Administration, business administration,
graduate business education, executive education, training and development
I. Introduction
For many private and public schools of business, their Executive Master of Business Administration
(EMBA) degree serves as their flagship program and provides a significant source of revenue. The
EMBA student, typically in his late 30’s, makes a 20-25 month commitment in order to complete more
than 500 hours of instruction in a high intensity program. Many EMBA graduates receive a promotion
when they graduate to reward them for their sacrifice and hard work.
Where do these programs find their students? What characterizes these students? Is there a typical
student, a standard profile? In an exploratory effort to answers these questions, we invited 81 public
and 65 private AACSB-accredited schools of business to participate in our survey. Twenty-four of 81
(30%) public schools and 13 of 65 (20%) private schools responded. Copies of the full survey are
available from the authors. The data produced the public versus private profiles that follow.
EMBA program attributes
Are public and private EMBA programs at AACSB accredited schools structurally similar in terms of
their total enrollment (Table 1), the size of their student cohorts (Table 2), and the number of cohorts
they admit each year (Table 3)?

Table 1: Program Enrollments
Enrollment Public (n = 24) Private (n = 13)
0 – 25 3 0
26 – 50 7 6
51 – 75 7 2
76 – 100 4 3
101 – 125 1 0
126 – 150 1 0
151 – 175 1 0
176 – 200 0 1
200 + 0 1
The majority of both public and private EMA programs in our sample enroll 100 or fewer students.
Table 2: Students Per Cohort
Cohort size Public (n = 22) Private (n = 13)
20 or less 5 2
21 – 30 6 6
31 – 40 5 1
41 – 50 5 1
51 – 60 0 1
61 – 70 1 0
71 – 80 0 0
81 – 90 0 0
91 – 100 0 1
100 + 0 1
The typical cohort is less than 50 students in both types of programs.
Table 3: Number of Cohorts Per Year
Number/year Public (n = 23) Private (n = 13)
One 6 1
Two 14 11
Three 1 1
Four 1 0
Five 0 0
Six 0 0
Seven 1 0
Most public and private EMBA programs report admitting one or two cohorts each year.
In sum, our data portray public and private EMBA programs as highly similar in terms of their broad
structure.
104

Results by factors
Admissions requirements
High admissions standards help ensure the high quality of the EMBA program. Table 4 compares the
standards used by the public and private programs.
Table 4: Admission Requirements
Public (n = 24) Private (n = 13)
Years worked 24 13
References 23 13
Formal application 24 13
Personal interview 19 13
Undergraduate degree 20 6
Grade point average 20 9
Admission test 15 8
Nomination 7 3
Not surprisingly, a formal application, years of work experience, reference letters, undergraduate
degree and grade point average, and personal interview were widely used and are also typical of
regular MBA programs. Interestingly, admissions tests (e.g., Graduate Management Admissions Test)
were required by only 63% of the public and 62% of the private programs. Few schools required the
applicant be nominated by a superior, colleague, or program director. Public and private EMBA
programs have very similar admissions requirements.
Student age and sex
As revealed in Table 5, the average age of EMBA students in 27 of the 46 programs falls within the
31-35 years span. On the whole, the average private MBA program student was a bit older than her
public program counterpart.
Table 5: Age of Participant
Average Age Public (n = 23) Private (n = 13)
33– 35 22 5
36 – 40 1 7
41 – 45 0 1
This profile fits the EMBA concept: these students probably have at least 10 years of relevant work
experience and have climbed a rung or two on the management ladder before applying. The EMBA
Council survey (Shinn 2004) portrayed the average EMBA student as 36 years old, with 13 years of
work experience and eight years of management experience.
The EMBA Council survey (Shinn 2004) revealed that males comprised 74% of the total EMBA
students. In our survey, fifteen of the 24 public EMBA programs reported between 65 and 85 percent
male students and the remaining nine programs reported 55 to 65 percent male students. Private
programs ranged from 65-85 percent males. Nine of the private programs reported 65-85 percent
males and the other four private programs reported 55 to 65 percent male. EMBA students are still
predominantly males.
105

Average student income
Do public and private EMBA programs differ in terms of the average income of the students they
enroll (Table 6)?
Table 6: Average Income of Participant
Income Public (n = 20) Private (n = 11)
50 – 99.9K 13 3
100 – 149.9K 6 5
150 – 199.9K 1 2
200 – 249.9K 0 1
Assuming that the tuition for private EMBA programs is higher than for public ones, the results reflect
that differential. On the whole, students who entered private EMBA programs had higher average
incomes than their public program peers.
International students
Of the 24 public EMBA programs, 10 (42%) reported having no international students, seven (29%)
reported having 1% to 5% and the remaining seven (29%) reported 10% to 20% international students.
The private EMBA programs presented a different profile. Two private programs (15%) reported
having no international students, three private programs (23%) admitted up to 10%, and six private
programs (46%) reported admitting 10% to 20% international students. Private programs, not being
constrained to service their citizens first, appear to actively pursue international students.
Diversity of students
EMBA programs range widely in the diversity of their student bodies. For example, six of the 24
public programs had the following percentages of Asian students: 50% Asian students, one program;
60%, two programs; 80%, one program; and 100%, two programs. North American students were
20% of five programs and 80% of one program. One program had 80% Western European students,
and another program had 100% Caribbean students.
The private programs reported a different diversity profile. Four programs had Asian students: 9%,
15%, 30%, and 67%. Western European students in three programs represented 15%, 33%, and 55%
of those student bodies. One program had only Central American students, another had 81% North
American students, and a third had 30% South American students.
Diversity, at least at some level, is apparently important to many EMBA programs. In fact, a few
use their diversity as a recruiting point. Eight of the 24 public programs and two of the 13 private ones
did not seek to have an international focus but still had diversity of students.
Catchment area
Do public and private programs draw their students from different areas or ranges in terms of
geographic accessibility? To measure this we asked the schools to report the typical commuting
distance for its EMBA students.
In 16 of the 23 public EMBA programs, their students typically traveled more than 50 miles to their
classrooms. In the other seven programs, the typical commute was less than 50 miles one way.
In six of the 12 private programs, their students commuted less than 50 miles. In the other six
private programs most students traveled over 100 miles one way to attend classes. An unexpected
finding was six of these 12 private programs had approximately 10% of their students traveling more
than 500 miles. This may well reflect programs having brief, intense residency sessions in their
program design.
106

Levels of management
Eleven private programs reported having middle and senior managers as students. In 10 of these
programs, at least 40% of their students were middle managers, and in the other program they were
30%. These same 11 programs reported senior level manager percentages of 10% (two programs),
30% (five programs), and over 40% (four programs). Only 4 private programs had technical and
professional personnel as students, ranging from 10% to 40% of those student bodies.
Non-managers represented 10% of the public EMBA students in 70% (16 of 23) of these programs.
Thirty to 40% of the public EMBA students were technical or professional personnel. First-line
supervisors accounted for 10% to 20% of the students in 18 of the 23 (78%) programs. Middle
managers accounted for at least 40% of students in 16 of the 23 reporting programs. Nineteen public
programs (83%) had less than 40% of their students who were senior managers.
On this factor, the public and private programs are very similar. Both types primarily service the
middle management levels rather than senior or upper level managers.
Industries represented
In the 23 public EMBA programs, the majority of the students came from heavy manufacturing and
consumer goods employers. High technology and financial institutions provided the next largest bloc
of students. The next smaller block of students was employed by health and nonprofit organizations.
Finally, government and armed services organizations provided the fewest students.
Of the eight private EMBA programs that responded, one program’s students came only from health
care and medical organizations. In the remaining seven private programs, most of their students came
from heavy manufacturing and high technology companies. The next largest bloc of students was from
consumer goods and financial organizations. Students from health and medical organizations
constituted the third largest grouping. The remaining students were from government, nonprofit, and
armed services organizations.
It appears the public and private EMBA programs obtain their students from essentially the same
mix of industries and employers.
Size of employer organizations
One stereotype we have encountered is the notion that private EMBA programs draw students from
larger, national corporations and organizations. The converse of this stereotype is that public programs
draw their students from smaller, entrepreneurial organizations and local and regional companies. We
attempted to address this by determining organizational size based on the number of employees (Table
6).
Table 7: Typical Employer Size
Employer Size Public (n = 21) Private (n = 10)
0 – 99 1 0
100 – 499 5 1
500 – 1000 10 7
1000 + 5 2
Within our limited samples, the stereotypes were not supported. The typical EMBA student works for
an organization having 500 to 1,000 employees, most likely national or regional in scope.
Who pays?
Few students are receiving employer support in earning their degrees. From 2001 to 2003, the number
of students fully funded by their employers dropped from 44 percent to 38 percent (Shinn 2004).
107

Table 8: Who Pays For the Program?
Public (n = 22) Private (n = 13)
Employer direct 6 2
Student 1 0
Employer & student 14 11
Tuition reimbursement 1 0
A sharing of the educational expenses (tuition, fees, and books) appears to be the most frequent
arrangement (25 of 35 total programs). We were surprised how few programs billed the employer
directly.
Summary and discussion
Our survey explored some possible differences between students in AACSB-accredited public and
private EMBA programs in the USA and sought to create a profile for each group. Although the two
types of programs use similar admissions criteria, their student bodies are not indistinguishable.
Compared to students in public programs, the typical private EMBA student
• is a little older,
• earns a bit more,
• is more likely to be an international student,
• commutes farther to attend classes, and
• is more likely to work in a high technology firm.
The typical EMBA student, whether in a private or a public program,
• is likely to be male,
• participates in a relatively diverse student body,
• is a middle level manager,
• works for a firm in heavy manufacturing, consumer goods, high technology, or finance,
• works for a firm having 500 to 1,000 employees, and
• shares the education costs with his employer.
Employers can learn from our data as well. Human resource managers and EMBA program
directors may find the profiles useful in matching employees and applicants with educational
programs. This fit may be critical for international students and for organizations seeking to
“globalize” their employees. Is sharing of the education expenses with the employee the preferred
arrangement, or would organizations generate more loyalty by fully reimbursing or directly paying
them?
The survey and results are limited by the small sample sizes and possible systematic factors
determining which programs participated and which did not. Obtaining longitudinal data on these
factors would also illuminate the reliability of our findings. Perhaps the raising of questions and
challenges is the ultimate point of any exploratory effort like this one.
References
[1] Shinn, S. (2004) The executive’s degree. BizEd, July/August, 31-35.
108

International Bulletin of Business Administration
ISSN: 1451-243X Issue 1 (2006)
© EuroJournals, Inc. 2006
http://www.eurojournalsn.com
The Import Problem and How Companies Operating in the
United States Should Address the Challenge
Scott D. Goldberg
Adjunct Professor of Marketing
University of Phoenix
Graduate School of Business
Doctoral Candidate-Argosy University
E-mail: sktfree@aol.com
Tel: (818) 545-1515 ext 47
Abstract
The increase in imports entering the United States is forcing business to examine its
position on whether to manufacture products domestically or join those companies who are
offshoring. The new industry buzzword is offshoring (Plunkett Research Ltd, 2005) that
describes the process of finished consumer goods such as clothing, toys or electronics,
made in a foreign country being imported into the United States. Additionally, components
used to make products such as cars or airplanes (Plunkett Research Ltd, 2005) are made
offshore, brought back and assembled in the United States. Outsourcing, on the other hand,
occurs when an organization hires an overseas firm to perform work normally done
domestically by the company. Both practices are occurring across all industries today. The
increased cost of labor and materials in the United States has forced many companies to
look at alternative locations where production and labor costs are less. Today, the decision
to outsource or offshore can represent the difference between a company staying in
business or closing the doors. This paper addresses many key issues associated with this
new challenge. The literature review supports economic, social and marketing points of
view. The paper presents two research questions developed from the literature review, as
well as a null and alternative hypothesis. A proposed research design and the method used
to gather the information are presented. A statistical test is discussed. Further topics for
future discussions are presented.
Key words: imports, globalization, outsourcing, offshoring, job loss, domestic
employment
I. Introduction
Companies operating in the United States must examine their long-term strategic position about
offshoring and outsourcing as business becomes more complex. If competition is gaining a cost
advantage buying overseas, the decision to offshore may become necessary for survival of the
business. The cost savings associated with offshoring have left manufacturers faced with a difficult
decision. In the ideal business world, most companies want to keep their employees and manufacture
their products domestically. Producing domestically in a company plant provides more control.
However, business today is often not that simple. Trade publications such as Footwear News (Clark,
2005) report that China and other Asian countries are exporting more footwear to the United States
each year. The American Apparel and Footwear Association reports that for the year 2002, nearly 80%

of all shoes bought by consumers in the United States were made in China. (Plunkett Research Ltd,
2005) The import problem has hit the United States economy with a powerful punch and many
workers are facing the affects. The problem continues to grow as companies face a loss of business
coupled with increased unemployment because of cutbacks from increased imports.
The Importance of Studying the Problem
Economic highs and lows have faced the United States over the past several years. Periods of growth,
recessions, and high inflation have blessed and plagued the economy over the last several decades.
Companies today are looking for inexpensive products to fill the needs of the changing demographics
of the United States population. They look to both the Far East and Latin America to fill their
demands. This practice has occurred since the early 1960’s when many companies started to offshore.
Business continues to increase the volume of outsourcing and offshoring and as a result, many millions
of American workers have lost jobs. More than 318,000 jobs were lost in the textile industry since
2001. (Plunkett Research, Ltd, 2005) Tariffs are suggested, however long-term they will drive up
prices. (Plunkett Research Ltd, 2005) Tariffs have been in use for thousands of years.
(www.wikipedia.org, 2005). The United States must find an equitable solution if they are to remain
competitive in the new global economy.
Literature Review
There has been a great deal written on this subject over the last several years. There are many diverse
theories about why the problem has occurred. Most of the literature suggests that increases in imports
negatively impact the United States economy and job market.
Today’s problems with imports were the result of poor economic decisions made in the past by the
government and politicians. (Bivins, Scott and Weller, 2003) The manufacturing problems in the
United States were the result of the overvalued dollar. From 1998 to 2003, manufacturing employment
decreased by at least three million jobs and the Gross Domestic Product fell at the same time while the
dollar was overvalued. Further, the rise in the dollar led to large increases in the trade deficit that led
to many millions without jobs. (Bivens, et al., 2003) This was the result of poor economic policies set
by the United States government. Creating more effective long-term economic policies could have
prevented the current import challenges.
Kirkegaard also discussed the problem. He recognized the issue of manufacturing taking place
overseas. He also addressed outsourcing white-collar jobs. He reported a shift in manufacturing
overseas coinciding with the outsourcing of white color jobs. (Kirkegaard, 2002) An example of this is
an MRI taken at a hospital in the United States sent by e-mail read by a medical technician across the
globe. The technicians are educated, many at the finest United States universities. This practice
represents a new threat to employment in the United States. An equally qualified employee thousands
of miles away working for lower wages may soon replace accountants, engineers and executives
working in the United States.
Bradley addressed the employment decline in Pennsylvania and said it was the result of offshore
production. He said a similar decline is occurring in other parts of the United States. Bradley stated
that large gaps in the employment figures are the result of increased offshore sourcing and a resulting
loss of United States jobs. Exports increased by 62% from 1994 to 2000 in the United States.
(Bradley, 2002) Further, United States exports do not create jobs domestically; they are processed in
other countries and sent back to the United States. (Bradley, 2002)
Griswold discussed the opposite viewpoint. He did not believe the increase in imports has a
negative impact on the economy. Griswold studied United States manufacturing imports and exports
between the years 1989-2004. A growth in manufacturing imports is strongly correlated with a growth
in domestic manufacturing output. (Griswold, 2005) Manufacturing imports, which include raw
materials and capital goods as well as consumer goods, benefit the economy. This definition is from
the U.S. Commerce Departments “National Income and Product Accounts”, Bureau of Economic
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Analysis. (Griswold, 2005) United States companies process these raw materials domestically and
then turn around and export them. His theory (Table 1) is supported with a graphic presentation of a
linear regression model with a right sloping line, suggesting that this is shows a positive not negative
affect.
(Griswold, 2005).
Table 1
Low priced labor in many foreign countries has been a reason that some companies continue to
outsource. (Griswold, 2005) Raw materials imported from lower wage countries, are benefiting the
United States economy. Goods processed domestically are then exported. Imports are a cheaper source
of supply enabling manufacturers to remain competitive.
Other researchers supported the theory of marketing playing an important part in helping to drive
the new global economy. Manufacturing has become retailer and consumer driven. (Gereffi, 2001)
Developing countries in the Far East and Central America realize the importance of technology and
skills when it comes to the products they export. The emphasis in the past was on simply supplying
cheap products. For companies and countries to succeed in today’s global environment, they need to
develop strategies to improve their products. (Gereffi, 2001) In other words, cheaper is no longer best
in the import arena. China is excelling in manufacturing telecommunications and computers today.
(Plunkett Research Ltd, 2005) Consumers and retailers are seeking quality goods to satisfy everyone’s
needs. This will lead to higher quality goods for consumers to buy in retail outlets.
The literature suggested solutions for dealing with the import problem. One solution is
compensating unemployed workers for lost wages when products are outsourced. (Kletzer, 2004)
Most of this responsibility falls on women, who have traditionally worked in industries that affected by
job loss. Trade related job loss is job loss because of imports. Kletzer suggested insurance similar to
unemployment that would compensate workers laid off because of job loss because of imports. This
program would be difficult to finance. The United States government is faced with difficulties in
meeting the debts from an overburdened social security system. It would be unrealistic for the
government to take on this debt. This program would need to be funded by employers who are
responsible for displacing the workers.
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Bonacich discussed the economic and social problems of organizing apparel workers to build a
strong industry. This would force manufacturers to keep production in the United States. China has
become the leader in manufacturing of apparel and by the year 2010 will control nearly 50% of the
world market. (Plunkett Research Ltd, 2005) Most of the apparel workers in the United States are from
Mexico and Central America. If the workers are strongly supported by a union, then clothing
companies have no choice but to produce domestically. (Bonacich, 1998) However, unions do not have
the strength they once had in the United States. It may be difficult to set up a union that would be
strong enough to dictate where production would take place. The immigrant workers are at the mercy
of the large apparel manufacturing companies. The total garment industry in Los Angeles has about
150,000 workers, the same size as the movie industry. (Bonacich, 1998) Many of these workers accept
the jobs without objections, as they fear loosing the jobs if the manufacturers decide to move overseas.
(Bonacich, 1998) The average pay scales for apparel works across the world (Plunkett Research Ltd,
2005) with China paying about $73 a month, $75 in Indonesia and $300 in Honduras.
Research Questions:
From the problem discussed and the literature review, the following research questions are presented.
Research Questions:
1. Is there a relationship between profitability of companies importing products, rather than
producing them domestically?
2. What is the relationship between companies importing products and employment of domestic
workers?
The following is being proposed for study:
Hı (Alternative) The increase in imports does impact the profitability of domestic manufacturers
and their workforce.
Ho (Null) The increase in imports does not impact the profitability of domestic manufacturers and their
workforce.
The independent variables to be examined are volume of imports, quality of imported products,
transportation costs, and source country of the imports. There are other variables that could be included
in the study; however, it will be limited to those mentioned. The dependent variables are profitability
of the domestic companies and employment of their domestic workers.
II. Data and Methodology
The study will analyze two groups of manufacturers:
1. Those who are importing products
2. Those who do not import but manufacture domestically.
The hypothesis will test the profitability of those importing verse those not importing. It will also
test the affect on employment for those who are importing verse those companies who are
manufacturing domestically. The hypothesis developed identifies both the independent and dependent
variables. The researcher can manipulate the independent variables. For example, if the volume of
imports were adjusted either up or down, this would manipulate the variable. The dependent variables
112

in the study are the “profitability of the manufacturers” and the “employment for the current workers of
the domestic manufacturers”. They may or may not be impacted by the increase in imports. The
assumption is these results would yield a normal distribution. The following chart explains the set up
of the study.
Imports vs. NonImports into the U.S. –MANOVA
Group One Group Two
Manufacturers Importing Manufacturers
Not Importing
Independent
Variables: Volume of imports, quality of imports,
Transportation costs for imports, Source country
of imports
Dependent
Variables: Profitability of the domestic companies,
Employment of the domestic workers
A MANOVA (Multivariable analysis of variance) is used to test the hypothesis as the study
involves two dependent variables and several independent variables. A MANOVA is used when there
are two dependent variables and the researcher wants to determine how the independent variables
impact one or both dependent variables. (French, Paulson and Yu, 2002) In this case, a MANOVA will
decide if one or both dependent variables are affected by manipulating the independent variables.
A sample size of n=364 is needed based on the total manufacturers in the Los Angeles market.
Participants will be selected on a random basis using a purchased mailing list. To get a smaller
representation of the population, we must decrease the degree of confidence or increase the sample
size. (Sternstein, 2004) For this study p= .05 with a 95% confidence level.
The research for this study will be gathered using a written survey to save the cost of conducting a
personal interview. A 15-question survey instrument will be mailed to manufacturers in the Statistical
Metropolitan Marketing Area for Los Angeles. This area is being selected by the researcher because of
the diversity of business in the market. The questionnaires will be written using a Likert response
system to help simplify tabulation of responses. (Isaac and Michael, 1995) The questions will include
information on dollar sales volume, manufacturing category, geographic data and number of
employees in the facility. In addition, questions will address the company’s attitudes toward imported
products and their quality perceptions. The target date for sending out these first surveys is October 1,
2006. A cover letter will be sent with the questionnaires to explain the importance the respondents
taking part in the survey.
Empirical Findings
This will be reported after finishing the research.
III. Conclusions and Future Studies
There is no doubt the market continues to face declines because of the increase in outsourcing and
offshoring for all segments of the economy. No industry is immune. Some industries are faced with
sharper declines than others. The apparel and computer industries are affected with
113

telecommunications following close behind. Much of the literature suggests reasons and solutions for
this problem. One possible solution that consistently surfaces is imposing strict tariffs on goods
entering the United States. The use of tariffs to control imported products from entering a country has
been used for thousands of years. The affect that tariffs would have on goods entering the United States
is an important one for American business to consider. Tariffs may create a trade war. Some may argue
that we have a duty as a society to keep American workers employed perhaps by using tariffs on goods
entering the United States. Others argue that we need to forgo patriotism to secure the best products at
the cheapest prices.
However, it may be a matter of business simply finding inexpensive sources of products where they
are available. If a manufacturer is able to find a less expensive supplier domestically, why should this
practice be any different when offshoring? It is this author’s opinion that business will continue to
source where it is cheapest…regardless of the location. American business needs to learn to work
cooperatively with other countries to create a global economy. Further studies need to be done which
examine American consumers opinions about imported products and how they compare with
domestically produced items. The subject presents many challenges to the United States both
economically as well as socially. Solutions must be found so the United States can remain competitive
and keep its workforce employed in a changing global economy.
References
[1] Bevins J., Scott, R., and Weller, C. (2003). Mending manufacturing: reversing poor policy
decisions is the only way to end current crisis. Economic Policy Institute Briefing Paper, pp. 1-
11.
[2] Bonacich, E., (1998) Organizing immigrant workers in the Los Angeles apparel industry.
Journal of World System Research, 4 (1) pp.10-19.
[3] Bradley, D.H., (2002) Trade and Pennsylvania: distorted trade patterns translate into job loss
for commonwealth. Keystone Research. October, pp. 2-26.
[4] Clark, E ., (2005). China share rises again. Footwear News, Vol. 61, p. 49.
[5] French, A., Poulsen, J., and Yu, A., (2002) Multivariate Analysis of Variance (MANOVA)
retrieved from http: www.sfsu.edu/~efc/classes/bio710/manova/manoview.htm.
[6] Gereffi, G., (2001). Beyond the producer-driven/buyer-driven dichotomy: the evolution of
global value chains in the internet era. IDS Bulletin, 32 (B), 30-40.
[7] Griswold, D., (2005). A package deal: U.S. manufacturing imports and output rise and fall
together. Free Trade Bulletin, Vol. 17
[8] Isaac, S., & Michael, W. B., (1995). Handbook in research and evaluation (3rd ed.). San
Diego, California: EdITS.
[9] Kirkegaard, J.F., (2002). Outsourcing –stains on the white-collar? Institute for International
Economics, pp 1-19.
[10] Kletzer, L.G., (2004). Trade –related job loss and wage insurance: a synthetic review. Review of
International Economics. 12 (5), p. 25
[11] Anonymous (2005). Outsourcing and offshoring industry trends. Plunketts Research Ltd.
Retrieved from http://www.plunkettsresarch.com.
[12] Sternstein, D. (2004) Statistical Inference. In Barron’s How to prepare for the advanced
placement statistics (p.268) Hauppauge, NY: Barron’s Educational Services.
[13] Tariff from Wikipeida, the free encyclopedia. (2005). Retrieved from http:en.wikipedia.org/
wiki/tariff.
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International Bulletin of Business Administration
ISSN: 1451-243X Issue 1 (2006)
© EuroJournals, Inc. 2006
http://www.eurojournalsn.com
Applying Benchmarking Practices in Small Companies:
An Empirical Approach
Emmanouil Stiakakis
Department of Marketing
Alexander Technological Educational Institution of Thessaloniki, Greece
E-mail: steiakakis@hol.gr
Tel: + 2310618777
Ioannis Kechagioglou
Department of Electronics
Alexander Technological Educational Institution of Thessaloniki, Greece
E-mail: yannis_tei@yahoo.com
Tel: + 2310436856
Abstract
Benchmarking is nowadays a powerful management tool that stimulates innovative
improvement through exchange of corporate information, performance measurement, and
adoption of best practices. It has been used for years to improve productivity and quality in
leading manufacturing organizations. More recently, companies of different sizes and
business sectors are getting involved in benchmarking activities. Despite the differences of
benchmarking practices between smaller and bigger organizations, a successful
benchmarking project could also enhance small business competitiveness. The participants
in this empirical study are small companies from many different sectors, located in
Northern Greece. As far as benchmarking is concerned, the perceptions of small companies
and the methods they adopt have been delineated herein. Some noticeable findings of this
study are: i) best performers in the same industry are mainly chosen for benchmarking by
small companies, ii) benchmarking subjects seem to relate to business sizes, and iii)
benchmarking is not considered particularly time-consuming and expensive process for
small companies.
Key words: benchmarking, small business competitiveness, performance assessment,
business practices
Introduction
As management of change and continuous improvement have become the foremost business issues of
the 21 st century, appropriate tools and techniques need to be adopted by firms in order to survive and
prosper in a particularly competitive environment. Fast paces of change force businesses to be
committed in continuous improvement through performance monitoring, goal setting and knowledge
acquisition. Today’s successful businesses are outward looking, market oriented and knowledge
driven. An approach that many companies adopt to align with these characteristics and obtain
sustainable advantages over their competitors is benchmarking.

The term ‘benchmark’ originally comes from the land surveying terminology, where a mark is used
as a reference point. Benchmarking could be defined as “the process of improving performance by
continuously identifying, understanding and adapting outstanding practices and processes found inside
and outside the organization and implementing the results” (American Productivity and Quality Center,
1997). However, prior to that definition Xerox Corporation had suggested the term ‘competitive
benchmarking’ in 1979 to describe a process “used by the manufacturing function to revitalize itself by
comparing features, assemblies and components of its products with those of competitors” (Camp,
1989). Since then benchmarking has not been restricted to manufacturing operations, but it has been
extended to include areas like service operations, marketing, finance and human resources
management. The ever-growing literature on benchmarking indicates a wide spread of benchmarking
applications across geographical and industrial borders (Jarrar and Zairi, 2001). It has also become one
of the most popular management tools in the world as a primary instrument in firms’ total quality
management, knowledge management and process improvement efforts (Vorhies and Morgan, 2005;
Rigby, 2001; Anderson, 1999; Garvin, 1993).
The main objectives of benchmarking include performance assessment, goal setting and the study
of best practices. Once a business has measured its performance, comparisons have to be made with
some kind of standards. Using historical standards, a company compares its current against past
performance measures. However, with the competition getting more and more intense, reaching or
exceeding those standards does not necessarily mean that current performance can be regarded as
satisfactory. Benchmarking involves the comparison of a company’s achieved performance to the
corresponding performance of its competitors. In this way, a firm can determine the level of its
competitive abilities. Looking at the competitors’ performance, companies can also set realistic goals.
If benchmarking is carried out by looking at best-in-class companies, these goals are likely to lead to a
considerable improvement in performance and learning (Roth et al, 1994). The success of
benchmarking however depends strongly on the ability of a company to learn lessons about how best
performance is accomplished. Rather than merely measuring best performance, benchmarking is a
means of identifying and understanding the practices needed to reach new goals. By identifying ways
that superior companies organize their processes, a company could try to adopt and adapt these
practices (Voss et al, 1997). Therefore benchmarking is both a means by which new practices are
discovered and understood, as well as a goal setting process (Camp, 1989).
Although large firms were the first practitioners and are responsible for the latest advances in
benchmarking, the role of small-and medium-sized enterprises should not be underestimated.
According to the European Commission, SMEs constitute 99.8 percent of all companies, provide 66
percent of total employment and attain 65 percent of business turnover in the European Union
(European Commission, 1995). SMEs have the most to gain from benchmarking since analysis of the
performance gap helps them sustain a competitive advantage (Balm, 1996).
The objective of this paper is to examine benchmarking process from a small company’s point of
view and identify the main differences of benchmarking between small and leading organizations.
Relevant literature on this topic is reviewed in the second section, while the third section focuses on the
description of the research methodology. The research findings are presented in the fourth section, and
finally the conclusions of our study are discussed in the fifth section.
Literature Review
Several types of benchmarking methods exist but all these fall into four main categories: internal,
competitive, functional and generic (Zairi and Leonard, 1994; Camp, 1989). A company may well
choose to study data or practices from different departments or factory sites that belong to its own total
organization. This process is called internal benchmarking. In competitive benchmarking, comparisons
are made between companies that are direct competitors. In contrast, functional benchmarking involves
the study of practices that are used by businesses which do not directly compete in the same markets.
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When Motorola was trying to speed the delivery process of its cellular phones, it paid visits to
Domino’s Pizza and Federal Express (Hollings, 1992). The most typical example of functional
benchmarking is the comparison of picking and packing functions between Xerox Corporation and
L.L. Bean, which activate in completely different industries. Finally, generic (or innovative)
benchmarking is an extension of functional benchmarking, but differs from it in that the search is not
restricted to a common application. Instead, it looks to adopt a method or practice that someone
employs for doing something completely different (Tenner and DeToro, 1997).
The popularity of benchmarking in recent years has led to an increasing number of conferences,
associations, and journals devoted to this subject. As a result, a lot of research work has been
undertaken with the aim of providing answers to key questions concerning the area of benchmarking.
Methodologies are also proposed to assist companies in implementing this approach. Using sample
data from over 600 European manufacturing companies, Voss, Ahlstrom, and Blackmon (1997) have
proposed a relationship between learning, benchmarking, understanding, and performance, as shown in
Figure 1. Benchmarking is a vital part of the learning company’s repertoire for performance
improvements and learning organizations will be more likely to use benchmarking. In addition to that,
benchmarking promotes higher performance directly through helping a company to identify practices
and set challenging performance goals. Benchmarking also increases a company’s understanding of its
strengths and weaknesses in relation to the competitors. This understanding in turn benefits
performance, since improvement agendas will be focused on real needs.
Figure 1: Relationship between learning, benchmarking, understanding, and performance.
Learning
orientation
Benchmarking
Manufacturing
performance
Business
performance
Understanding
Many attempts have been made to find out the extent to which firms have adopted benchmarking.
Companies that use benchmarks within their own organization or business group are considered to
attain average levels in terms of benchmarking activities. In contrast, the best practitioners of
benchmarking are those who use regular and documented benchmarks against their competitors but
also against world class standards from other industries. An interesting common conclusion coming
from these attempts is that benchmarking is strongly associated with an organization’s learning
orientation, confirming Voss and his colleagues’ research model. This means that companies which are
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outward looking and committed to organizational learning are more likely to employ benchmarking
methods.
Another key question concerns the existence of a relation between business size and benchmarking.
According to some authors, small-and medium-sized businesses do not seem to engage in
benchmarking activities to a large extent, claiming that benchmarking is expensive and time
consuming (Micklewright, 1993). Small-and medium-sized enterprises are therefore reluctant to
participate in benchmarking practices due to the lack of time, financial and personnel resources (Nelder
and Skandalakis, 1999). Recent global studies however are opposed to the above statement, finding a
clear spread of benchmarking world-wide and across various industries and business sizes (Jarrar and
Zairi, 2001).
Companies less familiar with the practice of benchmarking usually regard the way they do things
as uniquely suited to their operations and dismiss findings not invented in-house. They also support
that their operations are unique and therefore not comparable to those of benchmarked units (Stauffer,
2003). When Siemens started benchmarking in the ‘80s, they were not willing to accept the results.
They said “there are cost differentials, but we are better” (Heinrich von Pierer, Siemens CEO, 2005).
Managers need to recognize that all operations, no matter how well managed, are capable of
improvement (Slack, Chambers and Johnston, 2001).
A procedure for implementing benchmarking has been proposed by Dawar, and Vandenbosch
(2004). The selection of variables, which have to be monitored and measured, is the first step of the
procedure. Issues of importance to customers have to be seriously considered in the selection process.
The authors claim that the choice of variables deserves considerable thought, not just because it will
determine the success of the benchmarking process, but also because there is a first-mover advantage
to capturing the most salient measures. Data collection on variables follows and constitutes a difficult
and time-consuming task. The next step involves data aggregation and analysis, where comparisons
can help diagnose problems and adjust internal processes and systems. Correlation measures and
models based on key variables can also be obtained to enable simulation of possible outcomes
according to different decision variables. In the deployment step, the solutions developed through data
analysis are adapted and applied to the company processes. Finally, feedback and updating are
employed to ensure that the whole process is designed to learn and adapt. Otherwise its value will
vanish after the first suggestions are delivered. Continuous feedback from the application of solutions
and updating of the benchmarking data are required in order to develop a sustainable competitive
advantage. Attention should be paid to all the aforementioned steps, since they require a lot of effort in
order to be planned and implemented.
Various benchmarking approaches have been adopted by industry leaders (for example, Motorola,
Bristol – Myers, AT&T), but they mainly differ only in the number of stages involved. The most
widely accepted approach has been proposed by benchmarking’s leading authority, Dr. Robert Camp
(1995). It includes the following ten steps: 1) identify the benchmark subject, 2) identify benchmark
partners, 3) collect data, 4) determine the gap, 5) project future performance, 6) communicate results,
7) establish goals, 8) develop action plans, 9) implement plans and monitor results, and 10) recalibrate
benchmarks. The first three steps refer to the planning phase, the 4 th and 5 th to the analysis phase, the
6 th and 7 th to the integration phase, and the last three to the action phase.
A major issue that will affect benchmarking in the future is the advances in IT. The concept of
benchmarking is strongly related to the exchange of corporate information, which is greatly facilitated
by the use of information systems and the Internet. Significant advances in information technology
make benchmarking more effective and available to smaller organizations. A study (Jarrar and Zairi,
2000) reported that there are currently over 500 Internet sites devoted to benchmarking education and
best practices spread and transfer.
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Research Methodology
The research methodology enabled the extraction of information regarding the application of
benchmarking practices in small sized companies. The main issues of concern were how these
companies perceive and apply benchmarking methods. Based on relevant literature an attempt was
made to find relationships between variables of interest by testing a number of hypotheses:
H1: Benchmarking is the most important practice for improving a small company’s competitiveness.
H2: Benchmarking is a particularly time-consuming and expensive process for small companies.
H3: Small companies mainly apply competitive benchmarking methods, preferring competitors from the
same industry.
H4: Benchmarking subjects relate to business sizes (how important a benchmarking subject is for a
small company relates to its business size).
H5: All the implementation steps of a benchmarking project for a small company have the same degree
of difficulty.
The statistical population in our research consists of the entire set of small companies functioning
in Northern Greece (for reasons of convenience in accomplishing personal interviews) and employing
some of the benchmarking methods. The sample size is 94 companies, which are classified as follows:
19 percent (18/94) are companies with primary activity in the manufacturing industry, 18 percent
(17/94) handicrafts, 17 percent (16/94) wholesalers, 20 percent (19/94) retailers, and finally 26 percent
(24/94) are companies dealing with services provision. Attention has been paid for all aforementioned
industries to be equally represented in our sample. The business sizes of the sample are listed below,
on the basis of the companies’ annual turnover per employee:
• 0 – 99,000 € → 50 percent (47/94)
• 100 – 199,000 € → 31 percent (29/94)
• 200 – 299,000 € → 8 percent (7/94)
• 300 – 399,000 € → 5 percent (5/94)
• ≥ 400,000 € → 6 percent (6/94).
The survey has been carried out by means of personal interviews with senior managers of the
companies sampled for a period of approximately four months, starting in January 2005. Close-ended
questions (i.e. predetermined choices in each question) have been included in the questionnaire and the
variables used have been mostly qualitative – ordinal-scaled, since the respondents have been asked to
rank the importance degree of these predetermined choices.
Research Findings
Benchmarking was assessed as the most important practice for a small company to improve its
competitiveness, getting an 88.5 percent (as “very important” and “quite important” practice) [1] of the
respondents’ preferences. It is worth to point out that the managers of the companies sampled were
given a great number of possible practices for improving a company’s competitiveness, as it is shown
in Figure 2. Our intention was not only to assess benchmarking as a business tool for achieving a
sustainable competitive advantage, but mainly to compare it with the most typical contributing factors
to that purpose [2]. The respondents classified training programs (76.9 percent as “very important” and
“quite important” practice), upgrading of technological equipment (74.4 percent), and improved
staffing systems (71.8 percent) to the second, third, and fourth position respectively. Consequently,
benchmarking is believed to be the most reliable choice in today’s competitive environment,
confirming the hypothesis H1.
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Figure 2: Assessment of the practices for improving small business competitiveness.
Where:
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1 2 3 4 5 6 7 8 9 10 11 12
Very important
Quite important
Somewhat important
Not important
1=benchmarking 7=development and monitoring of
2=training programs for staff development
competitiveness indices
8=company’s participation in domestic and
international fairs, conferences etc.
3=motivation schemes 9=adoption of modern sales techniques (e.g.
telemarketing, electronic sales via Internet)
4=improved staffing systems (e.g. staff
requirements planning, modern recruitment
techniques)
10=establishment of competitiveness prizes for
the personnel
5=technological equipment’s upgrading 11=writing and distribution of informative
material to the personnel, concerning the ways
for improving competitiveness
6=information system’s upgrading 12=assignment
consultants
of this task to external
Regarding the hypothesis H2, the statement that benchmarking is a particularly time-consuming and
expensive process for small companies is not in agreement with the findings of this research. It was
found that benchmarking is applicable by any organization, irrespectively of its size. Because the
above statement is considered as a main difference of benchmarking between smaller and bigger
organizations, it was compared in the framework of our research with some other main differences, and
the results were as follows: the possibility for a big organization to have specialized personnel for
benchmarking was assessed as the most important difference between small and bigger enterprises
(87.2 percent as “very important” and “quite important”), as illustrated in Figure 3. The fact that big
organizations usually apply their own benchmarking models (84.6 percent) and the possibility for them
120

to develop co-operations aiming at information exchange (78.2 percent) are classified in the second and
third position respectively. The fourth difference in order of importance is the fact that benchmarking
is a continuous process for bigger organizations (71 percent), whereas the hypothesis H2 gathered only
60.3 percent (as “very important” and “quite important” difference) of the respondents’ preferences.
Additionally, in order to retest our hypothesis, correlation analysis was carried out. The variables
examined were i) the statement that benchmarking is a particularly time-consuming and expensive
process for small companies, and ii) the annual turnover per employee, categorized as mentioned in the
“Research Methodology” section. The dependent as well as the independent variable is ordinal, so the
correlation coefficient Spearman rs was used. No relation between the two variables was found (rs=-
0.071, p=0.534 > a=0.05) [3], leading to the rejection of the hypothesis H2.
Figure 3: Assessment of the differences of benchmarking between smaller and bigger organizations.
Where:
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1 2 3 4 5
Very important
Quite important
Somewhat important
Not important
1=big organizations apply their own benchmarking models, while small companies do not
2=big organizations have specialized personnel for benchmarking, while small companies do not
3=big organizations can more easily develop co-operations aiming at information exchange
4=benchmarking is a continuous process for big organizations, while small companies employ it
occasionally
5=benchmarking is a particularly time-consuming and expensive process for small companies
Companies considered as being best performers in the same industry, meaning that their products,
services or practices have the broadest acceptance in that industry, are chosen for benchmarking by the
majority of the companies sampled (51 percent). The companies of relative business size in the same
industry are classified in the second position (26 percent), whereas the companies with the greatest
market share in the same industry are classified in the third position (15 percent). Perusal of Figure 4
121

eveals that only direct competitors are chosen for comparison, while companies from different
business sectors seem to be ignored, as far as the benchmarking process of small companies is
concerned. Small enterprises are therefore engaged in competitive benchmarking, which confirms the
hypothesis H3. As shown in the Figure, foreign companies are not used as standards of comparison by
small companies, and this is another point that differentiates benchmarking between bigger and smaller
organizations. This leads to the conclusion that although small companies are engaged in some kind of
benchmarking activities, they only attain average levels in this field, meaning that they do not use
benchmarking as a strategic tool and they do not fully exploit it.
Figure 4: Groups of companies which are chosen for benchmarking.
Where:
3
26%
4
1%
5
7%
6
0%
1
15%
2
51%
1=companies with the greatest market share in the same industry
2=companies considered to be the best in the same industry, in relation with their products,
practices etc.
3=companies of relative business size in the same industry
4=companies applying similar processes, but activating in a different industry
5=foreign companies in the same industry
6=foreign companies in different industries
The challenge for the benchmarking team of a small company is to select the subject for which
benchmarking can provide the most significant advantage. This is, because the number of
benchmarking projects that could be accomplished by a small company is particularly limited. Figure 5
depicts the percentages of the most likely benchmarking subjects, in order of importance for a small
company. All respondents agree that quality of delivered products or services is a “very important” or
“quite important” subject, whereas strategy development, process performance, and product/service
pricing also attained high percentages (94.8, 93.6, and 92.3 percent respectively) as “very important”
and “quite important” subjects. The ranking order of the rest subjects is as follows: 5) technological
122
1
2
3
4
5
6

equipment (80.8 percent), 6) financial performance (79.5 percent), 7) information system (76.9
percent), 8) staff training (75.6 percent), 9) promotional activities (73.1 percent), 10) staff expertise
(70.6 percent), and finally 11) innovation development (65.4 percent). The hypothesis H4 is concerned
with the relation of benchmarking subjects to business sizes, meaning that the importance of the
subject for a small company depends upon its business size. The prerequisites for testing this
hypothesis are the same as in the case of the hypothesis H2 (both variables are qualitative, ordinalscaled),
so the correlation coefficient Spearman rs was used again. Based on nonparametric correlation
measures, strategy development relates proportionally [4] to business size (rs=0.341, p=0.038 <
a=0.05). In addition to that, promotional activities (rs=0.24, p=0.003 < a=0.05), staff training
(rs=0.274, p=0.027 < a=0.05), and staff expertise (rs=0.356, p=0.019 < a=0.05) relate proportionally
to business size, as well. On the contrary, innovation development relates inversely proportionally to
business size (rs=-0.256, p=0.024 < a=0.05), implying that a smaller and therefore more flexible
organization is more likely to conceive and convert a new idea to an innovation. No relation between
the remaining benchmarking subjects and business sizes was found.
Figure 5: Assessment of benchmarking subjects.
Where:
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1 2 3 4 5 6 7 8 9 10 11
Very important
Quite important
Somewhat important
Not important
1=strategy development 7=promotional activities
2=product/service quality 8=staff training
3=product/service pricing 9=staff expertise
4=process performance 10=technological equipment
5=financial performance
6=innovation development
11=information system
The managers of the companies sampled were asked to grade the five steps of the benchmarking
procedure [5], as it has been proposed by Dawar and Vandenbosch. The selection of this procedure is
due to the limited number of steps, the simplified description of the procedure which leads to a
123

complete understanding of the comprising steps, and the fact that it is not only applied to a specific
organization. Figure 6 shows the mean values of grades of benchmarking steps. The application of
solutions to the company (mean value: 3.33) and selection of variables (3.32) are the steps requiring
the most possible attention in order to be carried out. The third step in terms of difficulty order is data
analysis / comparisons (3.09), whereas feedback and updating of data (2.87) seems to have been
underestimated, according to the respondents’ preferences. Finally, data collection during the
benchmarking procedure (2.37) is rather considered as a time-consuming but not difficult task to be
carried out. Therefore, the hypothesis H5 has to be rejected.
Figure 6: Assessment of the steps of the benchmarking procedure.
3,5
3
2,5
2
1,5
1
0,5
0
selection of
variables
data collection data analysis /
comparisons
application of
solutions to the
company
feedback and
updating of
data
Conclusions
The practice of comparing a company’s products, services or processes to those of competitors has
been mostly used by big organizations. However, this could also be a particularly beneficial tool for
small companies. According to the findings of our survey research, benchmarking is considered to be
the most important practice for enhancing a small company’s position in today’s competitive
environment. Even though a long list of practices leading to superior performance was given to senior
managers of the companies sampled, benchmarking is recognized as the best practice that could
improve small business competitiveness.
Considering the time and resources required to complete a benchmarking project, the researchers’
opinions are quite different. It is not clear whether such a project is affordable for a small company’s
capabilities. The statement that benchmarking is a time-consuming and expensive process was
compared in our research with some other main differences of benchmarking between smaller and
bigger organizations. Our findings reveal that the possibility for bigger organizations to have
specialized personnel for benchmarking and apply their own benchmarking models are much more
important differences than the above statement. Nevertheless, sixty percent of the responding managers
argue that lack of time and limited resources characterize benchmarking in smaller organizations.
Another noticeable point of this paper is that only competitors from the same industry are chosen
for benchmarking by small companies. However, it is a fact that looking outside one’s own industry to
organizations performing similar activities could assist small companies to obtain a more integrated
image of their business environment. Of course, this does not mean that competitive studies should be
124

skipped. The benchmarking subjects chosen by small companies range from issues of strategic
consideration to more common business issues. The importance of a subject for a small company
seems to be dependent upon its business size, with strategy development, promotional activities, staff
training, and staff expertise to be preferred by more substantial firms.
Small companies do not develop their own benchmarking models. Instead, they apply methods that
could be described in general terms by the procedure proposed by Dawar and Vandenbosch (see
“Literature Review”). Indeed, selecting the variables to be measured, collecting data, analyzing them,
applying the solutions to the company, and finally updating the data are the basic stages involved in a
typical benchmarking project. The research findings reflect the application of solutions to the company
and the selection of variables as the most difficult stages to be accomplished, whilst data collection
seems to be the easiest part of a benchmarking project. Managers of small companies need to
understand the whole process and focus on the benchmarking steps which are more difficult to achieve
and require more resources.
References
[1] Anderson B. (1999) Industrial Benchmarking for Competitive Advantage. Human Systems
Management 18(3/4):287-296.
[2] American Productivity and Quality Center (1997) What is Benchmarking?. APQC Report
USA.
[3] Balm J. (1996) Benchmarking and Gap Analysis: What is the Next Milestone?. Benchmarking
for Quality Management Technology.
[4] Camp R.C. (1995) Business Process Benchmarking: Finding and Implementing Best Practices.
Found in Tenner A.R. and DeToro I.J. (1997) Process Redesign, The Implementation Guide for
Managers. Addison – Wesley pp 212-227.
[5] _____ (1989) Benchmarking: The Search for Industry Best Practices That Lead to Superior
Performance. ASQC Quality Press, Milwaukee WI.
[6] _____ (1989) Benchmarking: The Search for Best Practices Which Lead to Superior
Performance – Parts 1 to 5. Quality Progress Jan – May.
[7] Dawar N. and Vandenbosch M. (2004) The Seller’s Hidden Advantage. MIT Sloan
Management Review Winter:83-88.
[8] European Commission (1995) Small-and Medium-Sized Enterprises: A Dynamic Source of
Employment, Growth and Competitiveness in the European Union. Report CSE(95)2087: A
report presented by the European Commission for the Madrid Council, Madrid Spain.
[9] Garvin D. (1993) Building a Learning Organization. Harvard Business Review 71(July-
August):78-91.
[10] Heinrich von Pierer (2005) Transforming an Industrial Giant. Interviewed by Stewart T. and
O’Brien L. Harvard Business Review February:114-122.
[11] Hollings L. (1992) Clearing up the Confusion. Total Quality Management Magazine 4(3):149-
151.
[12] Jarrar Y. and Zairi M. (2001) Future Trends in Benchmarking for Competitive Advantage: A
global Survey. Total Quality Management 12:906-912.
[13] _____ (2000) Benchmarking – How IT is Transforming Benchmarking. European Centre for
TQM Report Series, Bradford.
[14] Micklewright M.J. (1993) Competitive Benchmarking: Large Gains for Small Companies.
Quality Progress June:67-68.
[15] Nelder G.P. and Skandalakis A. (1999) Diagnostic Benchmarking for Small-and Medium-Sized
Enterprises. Proceedings of the Institution of Mechanical Engineers 213(Part B):323-327.
125

[16] Rigby D. (2001) Management Tools and Techniques: A Survey. California Management
Review 43(2):139-160.
[17] Roth A.V., Marucheck A.S., Kemp A. and Trimble D. (1994) The Knowledge Factory for
Accelerated Learning Practices. Planning Review 22(3):26-46.
[18] Slack N., Chambers S. and Johnston R. (2001) Operations Management 3 rd Ed Financial Times
– Prentice Hall.
[19] Stauffer D. (2003) Is Your Benchmarking Doing the Right Work?. Harvard Management
Update September:3-6.
[20] Tenner A.R. and DeToro I.J. (1997) Process Redesign, The Implementation Guide for
Managers. Addison – Wesley pp 213-214.
[21] Vorhies D.W. and Morgan N.A. (2005) Benchmarking Marketing Capabilities for Sustainable
Competitive Advantage. Journal of Marketing 69(1):80-94.
[22] Voss C., Ahlstrom P. and Blackmon K. (1997) Benchmarking and Operational Performance:
Some Empirical Results. International Journal of Operations and Production Management
17(10):1046-1058.
[23] Zairi M. and Leonard P. (1994) Practical Benchmarking: The Complete Guide. Chapman &
Hall, Oxford.
Footnotes
1. The respondents were asked to classify 12 predetermined practices for improving a small
company’s competitiveness, according to the following scale of four categories: “very important”,
“quite important”, “somewhat important”, and “not important”.
2. Apart from the predetermined choices, there was also the possibility for an open-ended choice.
3. A level of significance a=0.05 was accepted in the hypothesis test.
4. As the business size increases the importance of strategy development also increases.
5. The managers who participated in the survey research had to assign 5 points to the step which is
considered the most difficult to be accomplished, up to 1 point to the easiest step.
126

International Bulletin of Business Administration
ISSN: 1451-243X Issue 1 (2006)
© EuroJournals, Inc. 2006
http://www.eurojournalsn.com
An Empirical Analysis of the Federal Emergency
Management Agency 12
Gbolahan S. Osho
Texas Southern University
oshogs@tsu.edu
Michael O. Adams
Texas Southern University
adams_mo@tsu.edu
Richelle N. Jones
Texas Southern University
jonesrn@tsu.edu
Ihekwoaba D. Onwudiwe
Texas Southern University
onwudiweid@tsu.edu
Abstract
FEMA is the Federal Emergency Management Agency created by the United States to
respond to, take action for and head recovery efforts from disasters that occur. It was
officially created by a 1979 executive order by President Jimmy Carter and is a former
independent agency. In March 2003, Congress reduced the agencies independent status to
become apart of the newly formed Department of Homeland Security. Because of the slow
response of FEMA to Hurricane Katrina victims, the competency of the Federal
Emergency Management Agency has been questioned. In this research, the response of
FEMA to past disasters will be examined. Case studies will be investigated to research
what methods are in place and what possible methods can be introduced to enhance the
ability of FEMA to swiftly respond to disaster victims in the future.
I. Introduction
FEMA is the Federal Emergency Management Agency created by the United States government to
respond to, take action for and head recovery efforts from disasters that occur. It was officially created
by a 1979 executive order by President Jimmy Carter and is a former independent agency. In March
2003, Congress reduced the agencies independent status to became apart of the newly formed
1 Project: We wish to acknowledge the support of Texas Southern University and the Office of Sponsored Programs for project entitled
“An Empirical Analysis of the Federal Emergency Management Agency (FEMA)”.
2 The authors wish to acknowledge the office of research for their efforts and contributions towards this seed grant. We are most grateful
to Professor Robert Ford, associate provost for commenting on an earlier version of this seed grant and Dr. Linda Gardiner of the Office
of Sponsored Programs

Department of Homeland Security. The logic was that the new department needed emergency-response
experts for any terrorist incident, and FEMA brought this hard-won experience. 1
Because of the slow response of FEMA to Hurricane Katrina victims, the competency of the
Federal Emergency Management Agency has been questioned. Created to manage federal responses to
disasters, FEMA morphed under President Reagan into a Cold War agency, preparing for a potential
Soviet nuclear attack and planning how to keep a government going post-apocalypse. While its Cold
War mission grew, its natural-disaster focus withered. In 1989 and three years later, FEMA was unable
to respond quickly enough to East Coast hurricanes. The low point was 1992, when Hurricane Andrew
cost 26 lives in Florida and Louisiana and did an estimated $25 billion in damage. Thousands went
without shelter for days, and the federal government was widely regarded as slow to react. Florida
voters responded by helping vote in Bill Clinton. In Washington, FEMA caught heavy blame. By 1993,
there were calls on Capitol Hill to abolish the agency. The Clinton administration revitalized it instead.
New FEMA chief James Lee Witt adopted what’s known as an all-hazards approach, based on the
theory that responding to a disaster is essentially the same whether it is natural or manmade. The focus
turned FEMA into an agency widely regarded as one of the government’s most effective—responding
swiftly, for instance, to the 1995 Oklahoma City bombing. Mr. Clinton soon gave the agency cabinetlevel
status. That lofty perch changed after 9/11, as Congress moved to create a department to confront
the terrorism threat and put FEMA into it. 2
The problems with FEMA stem back to its creation. The initial concept and task of FEMA was to
respond to, plan for, recover from and mitigate against disasters. 3 However, the creators and successors
of FEMA failed to evaluate and notice the discrepancies of the agency. FEMA is no longer an
independent agency and operates under the Department of Homeland Security. Once this occurred, the
staff and employee morale declined due to the introduction of new policies and procedures that were
incorporated under Homeland Security. FEMA has also failed in the leadership category. The
individuals chosen to lead FEMA have lacked experience in disaster and emergency management. Not
to mention, the emergency management capabilities of FEMA are inadequate to respond to and take
action for any disasters that may occur. FEMA continues to lack the controls and key information
necessary to ensure that personal property is properly accounted for. 4 For example, in 2004 when
Hurricane Frances struck Florida, the agency ran afoul of federal auditors after it paid $31 million to
residents of Miami-Dade, which was 100 miles south of the hurricane’s eye. 5 The agency released
funds to an area in Florida where the value of the disaster claims was considerably less than the actual
money paid to Miami-Dade residents. A critical analysis must be done to evaluate the competency of
FEMA. From past actions, the agency as a whole has been unable to accurately assess disaster areas
and distribute disaster relief money effectively.
This research project will examine the response of FEMA to past disasters. Case studies will also be
investigated to research what methods are in place and what possible methods can be introduced to
enhance the ability of FEMA to swiftly respond to disaster victims in the future. Why is a crisis agency
(FEMA) in a crisis? In examining the structure of FEMA, as well as its practices and procedures, the
1 Block, Robert. “Identity Crisis: Hurricane Tests Emergency Agency At Time of Ferment; Now Under
Homeland Security, FEMA Has Lost Clout, Managers on Ground Say; Terrorist With 145 MPH
Winds.” Wall Street Journal (Eastern Edition). New York, N.Y.: Aug. 16, 2004. pg. A. 1.
2 Block, Robert. “Identity Crisis: Hurricane Tests Emergency Agency At Time of Ferment; Now Under
Homeland Security, FEMA Has Lost Clout, Managers on Ground Say; Terrorist With 145 MPH
Winds.” Wall Street Journal (Eastern Edition). New York, N.Y.: Aug. 16, 2004. pg. A. 1.
3 http://www.fema.gov/about/history.shtm
4 United States Government Accountability Office. GAO-04-819R FEMA Accountability Over Property. “Federal Emergency
Management Agency: Lack of Controls and Key Information for Property Leave Assets Vulnerable to Loss or Misappropriation.
5 Thompson, Mark. “3 The Director: Why did FEMA and its chief, Michael Brown, fail their biggest test? (An American Tragedy/Where
the System Broke Down).” Time. Sept. 19, 2005. Volume 166. Issue 12. Pg. 39.
128

article Transforming Government: The Renewal and Revitalization of the Federal Emergency
Management Agency, written by R. Steven Daniels and Carolyn L. Clark-Daniels, was helpful in
investigating how FEMA operates. This study gave the history of FEMA and examined how it
functioned under various presidential administrations.
During the Reagan and Carter administrations, FEMA was able to maintain its status as a legitimate
emergency management agency. However, the degree of natural disasters the agency was required to
tackle were not as disastrous as the disasters that occurred after Reagan left office. At the start of the
Bush administration, FEMA was an agency with serious organizational problems that functioned
adequately through the Carter and Reagan administrations only because the disasters between 1979 and
1988 were not catastrophic enough to exceed the agency’s limited capacity. Unfortunately, the
historical experience of the agency under the previous administrations provided President Bush with
little incentive to overcome FEMA’s organizational weaknesses. 1
After FEMA’s creation in 1979, it faced organizational structure complexities and widespread
criticism. This criticism led to a microscopic examination of FEMA. The study was performed by
FEMA’s Inspector General Office, the United States States Government Accounting Office, and the
National Academy of Public Administration. From these investigations, four specific problems were
found with FEMA. These problems included:
1. the inconsistency of presidential support;
2. the “stovepiping” of FEMA;
3. the circumvention of FEMA;
4. and the reactive versus proactive response.
Inconsistency of Presidential Support
Because of the Cold War, FEMA was not put as a top priority. War was deemed more important than
recovery, help, and aide from disasters. The agency was not getting the support and structural attention
it needed from the presidency. At this point, FEMA was in its beginning stages and was not
functioning properly.
The “Stovepiping” of FEMA
The various functions and organizations within FEMA never fully integrated after the creation of the
agency in 1979. The absence of vision and mission prevented the development of core organizational
values, which in turn precluded the agency’s constituent parts from consolidating into a workable
organization. The lack of core values only reinforced the “stovepiping” of agency functions, the
division of the agency into independent and poorly synchronized directorates. 2
The Circumvention of FEMA
The circumvention of FEMA refers to the attempt by government to act efficiently in a disaster.
However, in the study by Daniel and Clark-Daniel, the acts of government during natural disasters
were never headed by the emergency management agencies. For example, following the Alaskan
Earthquake in 1964, the Johnson administration and Congress created the Alaskan Reconstructions
Commission (ARC) to fund the rebuilding of the Alaskan cities and towns destroyed by the earthquake
and subsequent tsunami. Although the director of the Office of Emergency Planning (OEP), as it was
referred to then; was on the Alaskan Reconstructions Commission, OEP had little responsibility or the
recovery operation. 3
1 Daniels, R. Steven and Carolyn L. Clark-Daniels. Transforming Government: The Renewal and
Revitalization of the Federal Emergency Management Agency. PricewaterhouseCoopers Endowment for The Business of Government.
April 2000. Page 10.
2 Daniels, R. Steven and Carolyn L. Clark-Daniels. Transforming Government: The Renewal and
Revitalization of the Federal Emergency Management Agency. PricewaterhouseCoopers Endowment for The Business of Government.
April 2000. Page 11.
3 Ibid.
129

Reactive versus Proactive Response
Reactive versus proactive response deals with the response of FEMA during catastrophic natural
disasters. During the Bush administration, federal law, FEMA regulations, and FEMA policy limited
the agency’s ability to anticipate disasters for which there was adequate warning. In 1989, Hurricane
Hugo hit North Carolina, South Carolina, Puerto Rico, and the Virgin Islands. In 1992, Hurricane
Andrew hit Florida and Louisiana. Although FEMA officials were in place 24 hours prior to landfall of
both Hurricanes Hugo and Andrew, resources took much longer to deploy. Sufficient quantities of food
and clothing did not arrive in Charleston, South Carolina, until six days after Hugo’s landfall.
Following Hurricane Andrew, FEMA found itself unable to respond quickly despite administrative
changes. The bulk of the federal aid effort did not arrive until August 29, again, days after the disaster.
Even after administrative changes, FEMA could not act efficiently, not to mention they lacked
presidential support, which eventually led to a bad political reputation and loss of support by
Congress. 1
Once President Clinton took office, he appointed James Lee Witt as the director of FEMA. Even
though he received a considerable amount criticism, Witt chose to focus on organizational changes,
which included:
1. the reestablishment of FEMA’s authority in disaster management;
2. the appointment of senior executives with extensive emergency management experience;
3. the redefinition of FEMA’s missions and goals
4. the restructuring of the agency along functional lines;
5. the redesign and reinterpretation of the Stafford Act and supporting legislation;
a. The Stafford Acts purpose was to provide an orderly and continuing means of
assistance by the Federal Government to State and local governments in carrying out their
responsibilities to alleviate the suffering and damage which result from such disasters by:
i. revising and broadening the scope of existing disaster relief programs;
ii. encouraging the development of comprehensive disaster preparedness and
assistance plans, programs, capabilities, and organizations by the States and by local governments;
iii. achieving greater coordination and responsiveness of disaster preparedness and
relief programs;
iv. encouraging individuals, States, and local governments to protect themselves by
obtaining insurance coverage to supplement or replace governmental assistance;
v. encouraging hazard mitigation measures to reduce losses from disasters,
including development of land use and construction regulations; and
vi. providing Federal assistance programs for both public and private losses
sustained in disasters. 2
6. the creation of effective media and political linkages;
7. and the development of a proactive strategy for disaster response. 3
Even though, James Lee Witt sought to make FEMA a better agency, problems in FEMA’s finances
persisted. Beyond ambiguity in the declaration process, FEMA’s financial system was not operating up
to federal standards until 1995. An audit by the FEMA Inspector General of the Disaster Relief Fund in
July 1995 revealed unreliable fund financial data, unclear standards of appropriateness for
1
Daniels, R. Steven and Carolyn L. Clark-Daniels. Transforming Government: The Renewal and
Revitalization of the Federal Emergency Management Agency. PricewaterhouseCoopers Endowment for The Business of Government.
April 2000. Page 12.
2
http://www.fema.gov/library/stafact.shtm
3
Daniels, R. Steven and Carolyn L. Clark-Daniels. Transforming Government: The Renewal and
Revitalization of the Federal Emergency Management Agency. PricewaterhouseCoopers Endowment for The Business of
Government. April 2000. Page 13.
130

expenditures, inadequate grants management, irregular and incomplete loan data, and, in several
instances, inefficient and uneconomical field operations. 1
In sum, Daniels and Clark-Daniels address problems pertinent to researching the foundation of the
Federal Emergency Management Agency. Their published work aides in finding further information on
studies that examine FEMA in its entirety. Deep examination into the problems and issues they address
are essential to the crisis facing the Federal Emergency Management Agency.
Data Analysis
According to Time magazine, 192,424 hotel rooms were used to house Katrina evacuees, at a cost of
$11 million a day to FEMA; however, FEMA did not budget for these accommodations. In addition,
$2 billion was allotted to buy as many as 300,000 mobile homes and trailers. As of the week of
October 16-22, 2005, only 7,308 were occupied. 2 Because of the slow response of FEMA to Hurricane
Katrina victims and the inappropriate use of federal monies, the competency of the Federal Emergency
Management Agency has been questioned. The following data show the impact of past disasters. The
following chart is a list of the top 19 states where hurricanes directly hit the mainland United States
coastline 1851-2004.
Figure 1. FEMA Regions. http://www.fema.gov/regions/
Table 1. Hurricane direct hits on the mainland U.S. coastline and for individual states 1851-2004
Category Number All
Area 1 2 3 4 5 (1-5)
United States 109 72 71 18 3 273
Texas 23 17 12 7 0 59
(North) 12 6 3 4 0 25
(Central) 7 5 2 2 0 16
(South) 9 5 7 1 0 22
Louisiana 17 14 13 4 1 49
Mississippi 2 5 7 0 1 15
Alabama 11 5 6 0 0 22
1 Daniels, R. Steven and Carolyn L. Clark-Daniels. Transforming Government: The Renewal and
Revitalization of the Federal Emergency Management Agency. PricewaterhouseCoopers Endowment for The Business of
Government. April 2000. Page 18.
2 Time, Oct 24, 2005, v166 i17 p28.
131

Florida 43 32 27 6 2 110
(Northwest) 27 16 12 0 0 55
(Northeast) 13 8 1 0 0 22
(Southwest) 16 8 7 4 1 36
(Southeast) 13 13 11 3 1 41
Georgia 12 5 2 1 0 20
South Carolina 19 6 4 2 0 31
North Carolina 21 13 11 1 0 46
Virginia 9 2 1 0 0 12
Maryland 1 1 0 0 0 2
Delaware 2 0 0 0 0 2
New Jersey 2 0 0 0 0 2
Pennsylvania 1 0 0 0 0 1
New York 6 1 5 0 0 12
Connecticut 4 3 3 0 0 10
Rhode Island 3 2 4 0 0 9
Massachusetts 5 2 3 0 0 10
New
Hampshire 1 1 0 0 0 2
Maine 5 1 0 0 0 6
This chart describes the most costly hurricane disasters, ranging from 1954 to 2004. The data shows
that higher the category of the hurricane, the more money was spent on damages.
Table 2. The 30 costliest tropical cyclones to strike the U.S. mainland, with addendum for Hawaii,
Puerto Rico, and the US Virgin Islands. Damages are listed in US dollars and are not adjusted for
inflation.
Rank Hurricane Year Category Damage
1 Andrew (SE FL, SE LA) 1992 5 26,500,000,000
2 Charley (SW FL) 2004 4 15,000,000,000
3 Ivan (AL/NW FL) 2004 3 14,200,000,000
4 Frances (FL) 2004 2 8,900,000,000
5 Hugo (SC) 1989 4 7,000,000,000
6 Jeanne (FL) 2004 3 6,900,000,000
7 Allison (N TX) 2001 TS a Floyd (Mid-Atlantic & NE
5,000,000,000
8 U.S.) 1999 2 4,500,000,000
9 Isabel (Mid-Atlantic) 2003 2 3,370,000,000
10 Fran (NC) 1996 3 3,200,000,000
11 Opal (NW FL, AL) 1995 3 3,000,000,000
12 Frederic (AL, MS) 1979 3 2,300,000,000
13 Agnes (FL, NE U.S.) 1972 1 2,100,000,000
14 Alicia (N TX) 1983 3 2,000,000,000
132

15 Bob (NC, NE U.S.) 1991 2 1,500,000,000
16 Juan (LA) 1985 1 1,500,000,000
17 Camille (MS, SE LA, VA) 1969 5 1,420,700,000
18 Betsy (SE FL, SE LA) 1965 3 1,420,500,000
19 Elena (MS, AL, NW FL)
Georges (FL Keys, MS,
1985 3 1,250,000,000
20 AL) 1998 2 1,155,000,000
21 Gloria (Eastern US) 1985 3 900,000,000
22 Lili (SC LA) 2002 1 860,000,000
23 Diane (NE U.S.) 1955 1 831,700,000
24 Bonnie (NC, VA) 1998 2 720,000,000
25 Erin (NW FL) 1995 2 700,000,000
26 Allison (N TX) 1989 TS a 500,000,000
27 Alberto (NW FL, GA, AL) 1994 TS a 500,000,000
28 Frances (TX) 1998 TS a 500,000,000
29 Eloise (NW FL) 1975 3 490,000,000
30 Carol (NE U.S.) 1954 3 461,000,000
Figure 2. Top Ten Natural Disasters, Ranked By FEMA Relief Costs
$ (Billions)
Top Ten Natural Disasters Reported By FEMA
10
Northridge
Earthquake
Hurricane
Georges
5
0
1
6.961
2.251
Disasters
Northridge Earthquake
Hurricane Georges
Hurricane Ivan
Hurricane Andrew
Hurricane Charley
Hurricane Frances
Hurricane Jeanne
Tropical Storm Allison
Hurricane Hugo
Midwest Floods
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Table 3. Types of Disasters
Earth-
Quake Flood Accident Landslide
Volcanic
Eruption Fire Storm Epidemic Drought Civil Strife
Temblor
Sweeping
Flood Ship Disaster Avalanche Lava Flow Forest Fire Hurricane Polio Famine
Food
Civil War
Tremor Flash Flood Ferry Disaster Mudslide Typhoon Yellow Fever Shortage Revolution
Border
Tsunami Snow Melt Rail Disaster
Truck
Cylcone Cholera
Conflict
Tidal Bore
Disaster Tornado Equine Encephalitis War
Earthquakes Oil Spill Tropical Storm Cerebral Meningitis Expulsion
Mine Disaster Wind Storm Measles
Explosion Snow Storm Meningitis
Air Disaster Ice Storm Plague
Cold Wave Bubonic Plague
Heat Wave Smallpox
Malaria
Hemorrhagic Fever
Influenza
Typhus
e 4. City Summary Report on Hotel Use - Hurricanes Katrina and Rita as of November 28, 2005.
Number of rooms in various cities across the United
States.http://www.fema.gov/pdf/press/katrina_after/cityhotel.pdf
Table 5. City Summary Report on Hotel Use - Hurricanes Katrina and Rita as of December 13, 2005.
Number of rooms in various states across the United States.
http://www.fema.gov/pdf/press/katrina_after/hotel-use-report-121305.pdf
134

135