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GROUP-BASED RIVALRY:
A SOCIAL CONFLICT THEORY ON THE INFLUENCE
OF GROUP MEMBERSHIP ON COMPETITIVE BEHAVIOR

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Leslie McClintock Steel

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GROUP-BASED RIVALRY:
A SOCIAL CONFLICT THEORY ON THE INFLUENCE
OF GROUP MEMBERSHIP ON COMPETITIVE BEHAVIOR
By

Leslie McClintock Stoel

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Human Environment: Design and Management
Merchandising Management Specialization

1997

ABSTRACT
GROUP-BASED RIVALRY:
A SOCIAL CONFLICT THEORY ON THE INFLUENCE
OF GROUP MEMBERSHIP ON COMPETITIVE BEHAVIOR
By

Leslie McClintock Stoel

A new form of competition, group versus group, is emerging in many industries as ﬁrms
form groups to increase their competitive edge in the marketplace. This paper develops a
theory to explain the effect group membership has on inter-group competitive behavior
and its outcomes. Social conﬂict theory is used as the basis from which to derive the
proposed theory. Rivalry research provides support for propositions concerning behaviors
of individuals in competitive situations. Group research provides the foundation for
proposing the effects of group membership on behavior. The merging of social conﬂict
theory, rivalry research and group research provides a new method of examining the
inﬂuence of group membership on competitive behavior. The research found that, for
retail cooperative members, group member identiﬁcation with the group of afﬁliation
resulted in an increased awareness of goal conﬂicts with rivals, and, that feelings of group
identiﬁcation inspired action of group members, manifested in an increased use of
rivalrous behaviors. Contrary to expectations, increased awareness of goal conﬂict did
not inﬂuence the use of rivalrous behaviors, and higher levels of rivalrous behaviors did

not relate to ﬁnancial performance.

ACKNOWLEDGMENTS

A large part of my success in completing this degree is due to several important groups of
people. The ﬁrst group is my Committee. I’d like to thank Dr. Brenda Stemquist, the
Chair of my Committee, for her endless support of my teaching and research. Thanks to
Dr. Linda Good and Dr. Patricia Huddleston for being ten'iﬁc mentors throughout my
program. Thanks to Dr. Glenn Omura, in Marketing and Logistics, for his creativity and
his interest and generosity in helping turn creative ideas into real projects. Although not
on my committee, I must thank Dr. Roger Calantone, in Marketing and Logistics, for his

assistance with structural equation modeling problems.

The second group to acknowledge is my family. Thanks to my husband Dale, for
enduring this adventure with me, to my parents Russ and Carol McClintock, for their

uplifting support, and to Dale’s parents, Bob and Mary Stoel, for their support also.

And, the last group to mention is the rest of the Doctoral Students in the new
Merchandising Management program: Jae Eun Chung, Madeline Flaster, Barb Frazier,
Linda Niehm, Linda Plank, Vanessa Wickliﬂ‘e. Thanks to all of them for the camaraderie,

the “phiIOSOphical” discussions, and the support as we progressed through the program.

iii

TABLE OF CONTENTS

LIST OF FIGURES ................................................... vi
LIST OF TABLES ................................................... vii
Chapter 1
Introduction ......................................................... 1
Signiﬁcance of Study ............................................. 1
Problem Deﬁnition ............................................... 3
Chapter 2
Literature Review ..................................................... 5
Rivalry ........................................................ 5
Group Research ................................................. 6
Theoretical Framework ........................................... 7
Social Conﬂict Theory ...................................... 7
Theory Development ............................................. 8
Causes/Antecedents ........................................ 9
Behavior ................................................ 11
Outcomes ............................................... 14
Chapter 3
Methodology ........................................................ 18
Measures ..................................................... 18
Group Identiﬁcation ....................................... 18
Goal Conﬂict ............................................ 18
Behavior ................................................ l9
Outcome ............................................... 20
Sample ....................................................... 21
Data Collection ................................................ 22
Data Analysis .................................................. 25
Chapter 4
Results ............................................................ 26
Measurement Model ............................................ 26
Structural Model ............................................... 29
Hypotheses ................................................... 29

iv

Chapter 5

Conclusion ......................................................... 33
Implications ................................................... 34
Limitations and Future Research ................................... 35

References .......................................................... 38

Appendix A. Patterns and Dimensions of Rivalry ............................ 58

Appendix B. Questionnaire ............................................. 64

Appendix C. Formulas for Composite Reliability and Variance Extracted .......... 68

LIST OF FIGURES

Figure 1. General Conﬂict Model ........................................ 44
Figure 2. Theoretical Model ............................................ 45
Figure 3. Initial Model ................................................ 46
Figure 4. Revised Model ............................................... 47

LIST OF TABLES

Table 1. Item Content: Group Identiﬁcation ................................ 48
Table 2. Item Content: Goal Conﬂict ...................................... 49
Table 3. Item Content: Behavior ......................................... 50
Table 4. Item Content: Outcome ......................................... 51
Table 5. Sample Characteristics ......................................... 52
Table 6. Representativeness of Sample .................................... 53
Table 7. Non-response Bias ............................................ 54
Table 8. Measurement Model Results ..................................... 55
Table 9. Structural Model .............................................. 57

vii

Chapter 1

Introduction

Signiﬁcance of Study

In 1995, the top one hundred U.S. retailers averaged sales increases of 10.4% with proﬁt
decreases of 24.4% over 1994 (Schulz 1996a). Some industry observers contend that the
current environment is the most difﬁcult since the early 19705 (Johnson and Mendelson
1996). Because of an “intensive wave of bankruptcies, mergers and store closings” in
1995 (Schulz 1996a, p. 56), fewer, larger competitors remain to capture consumer dollars.
An examination of 1982, 1987 and 1992 retail concentration ratios does conﬁrm that
smaller players are losing market Share to larger players. The four largest retailers
controlled 5.5% of retail sales in 1982, 5.2% in 1987, and increased their share to 6.8% in
1992. The ﬁﬁy largest retail ﬁrms captured 19.8% of retail sales in 1982, 20.3% in 1987,
and 22.4% in 1992 (U .S. Department of Commerce 1995, 1990, 1985). Yet even large
size does not guarantee success in this trying environment. In the fourth quarter of 1995,
Wal-mart, the nation’s largest retailer, recorded its ﬁrst earnings decline in 99 quarters
(“State of the Industry,” 1996). What effect will such intense competition have on
retailers and the retail industry? What can retailers do to survive such intense

competition?

2
For many years, ﬁrms have used partnering strategies to compete in threatening
environments. These collaborative partnering efforts, often called strategic alliances, take
many different forms, ranging from “the simplest transactional relationship to licensing and
outsourcing to anything short of a merger” (Maynard 1996, p. 19). To help classify these
partnering strategies, Maynard proposed a spectrum of collaborative business relationships
based on degree of collaboration, level of formality and amount of expected added value.
For example, low levels of collaboration and formality characterize external, tactical
transactions, such as arm’s length agreements with vendors and equity investments, and
result in a small amount of added value to the partners. Extended partner networks, such
as buying cooperatives, franchise agreements and joint ventures, require higher levels of
formality and collaboration to execute than tactical transactions. We call these extended
relationships strategic alliances because the partnering ﬁrms rely on the capture of
interﬁrrn synergies to provide added value. Mergers and acquisitions are examples of
partnerships using internal growth to add value. These strategies allow partners to realize

synergies and share risks and beneﬁts by internalizing them (Gomez-Casseres 1994,

Maynard 1996).

Estimates show that between 1986 and 1996, the number of alliance partnerships, as
deﬁned in the spectrum proposed by Maynard, increased 25% annually (Maynard 1996).
The result of the popularity of alliance partnering for competing in diﬁcult environments
is that, in many industries, multiple groups are beginning to appear. In other words, a new
form of competition is emerging: “group versus group.” (Gomez-Casseres 1994, p. 62).

Industry observers believe that increased formation of alliance groups will intensify

3

industry competition (Gomez-Casseres 1994). The challenge of this new competitive
situation is to understand how group membership inﬂuences the competitive behaviors of
group members. Developing an understanding of group-based competition is a necessary
skill for survival in today’s marketplace. The purpose of this paper is to develop a theory
of inter-group competitive behavior that will enable groups in an industry to better

understand their competitive environment.

Problem Definition

In the ﬁerce economy of the 19905, organizations are turning to group-based methods of
competing to fully capitalize on their resources. These organizations are exploring largely
unknown territory because theories that explain and predict dynamic, group-based
competition do not exist. In addition, little empirical evidence exists to explain the effect
that group-based competition has on ﬁrms, groups or the industry. This paper seeks to
solve this problem by developing a theory that will propose the effects of group

membership on competitive behaviors and the outcomes of those behaviors.

Recent empirical studies in the strategy literature (Chen 1996a; Chen 1996b; Chen and
Hambrick 1995; Smith, et a1. 1997) have added to our understanding of competitive
behaviors of ﬁrms by analyzing the conduct of individual pairs of speciﬁc ﬁrms in a single
industry. Strategists term this dyadic behavior rivalry. We distinguish rivalry, the
competitive interactions of individual pairs of Speciﬁc ﬁrms, ﬁom competition, a general
term describing actions of nonspeciﬁc ﬁrms in a market. By distinguishing between rivalry

and competition, strategy researchers have provided a more effective method of explaining

4

and predicting causes and characteristics of competitive behavior than economic theories
and competitor analysis. This paper will apply the construct of rivalry to group-based

behavior to develop an effective method of analyzing group-based competition.

I derive the proposed theory from social conﬂict theory. Its use is particularly appropriate
for the development of a theory of intergroup rivalry because it examines the role of
conﬂict between groups in a social system. Social conﬂict theory provides a realistic
picture of the competitive climate faced by practitioners because it recognizes the
dynamics of group interdependence. In deriving this marketing-oriented theory of
intergroup rivalry from social conﬂict theory, the proposed theory will address the
following research questions: how does group membership inﬂuence the behavior of
group members, and, what impact does the behavior of group-oriented members have on

the outcomes of rivalrous interactions?

A review of relevant literature follows in Chapter 2. I review the concept of rivalry and
present its dimensions. Then I provide a review of group research, focusing on group
identiﬁcation and group cohesion. In the section entitled Theoretical Framework I explore
the relevance of social conﬂict theory to the development of the proposed theory. Finally,
the propositions and supporting text are presented in the Theory Development section.
Chapter 3 explains the methodology, including sampling, measures, instrument, data
collection and data analysis. Chapter 4 presents results of the analysis. A discussion of

results, limitations and implications for future research follow in Chapter 5.

Chapter 2

Literature Review

Competitive tension exists between individual ﬁrms in a market. Such tensions are the
unit of analysis in studies of individual rivalry. Competitive tensions also exist between
alliance groups. This tension between groups is the unit of analysis in studies of
intergroup rivalry. The merging of rivalry and group research provides a new method of
examining the inﬂuence of group membership on competitive behavior. Social conﬂict
theory effectively explains implications of group-based rivalry, which examines conﬂict
episodes between groups in a social system. This chapter begins with reviews of rivalry
and group research. In the last section of the chapter I explain the relevance of social

conﬂict theory and develop propositions for this group-based theory of rivalry.

Rivalry

Rivalry has been a tOpic of study in several disciplines (see Appendix A for rivalry
literature review table). Management strategists and economists deﬁne it as: ﬁrms
jockeying for incompatible market positions (Porter 1980; Scherer and Ross 1990), ﬁrms
jockeying for incompatible market positions and resources (Baum and Korn 1996 ), ﬁrms
jockeying for patronage of buyers (Brenner 1987), and competitive interactions between

speciﬁc pairs/sets of ﬁrms (Chen 1996a; Chen, Smith and Grim 1992; Smith, et al.

6

1997). Rivalry refers to the exchange of competitive moves between speciﬁc ﬁrms, which
distinguishes it from competition, a general concept describing a market or industry rather
than speciﬁc entities within it (Baum and Korn 1996; Chen 1996a; Chen 1996b; Chen and
Hambrick 1995; Chen and MacIVﬁllan 1992; Chen, Smith and Grim 1992; Smith, et a1.
1997). Sociologists describe rivalry as a competitive outcome, where the goal is to
minimize the absolute gains of the competitor (Knight and Kagan 1977). Social
psychologists deﬁne sibling rivalry as children in a family competing for parental attention
(Irish 1964). Studies of sibling rivalry suggest it to be a dimension of a relationship, which
results in conﬂict (F urrnan and Buhrmester 1985; Rafaelli 1992). Studies of rivalry
between individuals comprise the bulk of rivalry research. Strategic management studies
have examined inter-ﬁrm rivalry and behavioral science studies examined sibling rivalry.
Only a few studies of rivalry between groups have appeared, primarily in the strategic

group literature.

Group Research

Sociologists derive group identiﬁcation from social identity theory (Kelly and Kelly 1994),
which proposes that engaging in social comparisons of out groups motivates individuals to
achieve positive social identity through in group distinctiveness (Hogg, Terry and White
1995). They deﬁne group identiﬁcation as the desire an individual has to connect with or
work with a particular group (Kelly and Kelly 1994). Social conﬂict theorists propose
that the likelihood of intergroup conﬂict increases if groups develop a sense of identity

(Rubin, Pruitt and Kim 1994).

7

Group cohesion is a construct found in the psychology literature that is very similar in
meaning to group identiﬁcation. Psychologists deﬁne it as the attraction of a group to its
members (Roether and Peters 1972), the connectedness of the group (Budman, et al.

1989) and the willingness to continue to work together (Vinokur-Kaplan 1995).

Theoretical Framework
W
Social conﬂict theory explains disorder and change in society. In particular, it analyzes
”elements of inequality, power, domination and conﬂict in human societies" (Turner 1991,
p. 183). Social conﬂict theory proposes that conﬂict is a process that helps to maintain
the social system, which is composed of members vying for scarce resources (such as
power or competitive advantage). Mechanisms that generate change and disorder in
society, such as power differentials, scarce resources and different interest groups, cause

conﬂict to erupt and, therefore, are critical in understanding social systems.

The recent work of Thomas (1992) recognizes conﬂict as a basic process which
organizations must manage. Structural conditions of the system inﬂuence the process, it
has an internal logic, and it produces consequences for the accomplishment of tasks and
the maintenance of the social system. As a basis for a theory of group-based rivalry,
conﬂict theory has an advantage over competition theory because it studies conﬂicts of
interest between groups within a society and recognizes the interdependence of behaviors,
environment, and outcomes. Thus, conﬂict theory provides a unique viewpoint for the

development of propositions concerning group-based competition in an industry.

8

According to a general model of conﬂict (Thomas 1992), structural conditions of the
social system inﬂuence the conﬂict process, such as the characteristics of the parties, the
characteristics of the relationship between the parties, and the characteristics of the
environment (see Figure l). The important party characteristic in this study is the level of
group identiﬁcation found in an alliance group. According to the model, as a structural
characteristic, the level of group identiﬁcation may have an inﬂuence on the behaviors
exhibited by group members in a conﬂict situation. The conﬂict process includes
awareness or perception of a goal conﬂict between the groups and the behaviors exhibited
by those groups. A goal conﬂict exists when the results wanted by one party are
incompatible with the results wanted by the opposing party. Awareness/perception of
goal conﬂict serves as the link between the structural inﬂuences on the potential conﬂict
parties and the actual execution of behavior. The parties must perceive a conﬂict before
they take action. Outcomes are the results of the conﬂict episode. For organizations,
these results might be expressed as sales ﬁgures, proﬁt ﬁgures or other ﬁnancial data, or
as desirable attributes, such as innovation or creativity. This model serves as the basis for

the structural model derived from the proposed theory.

Theory Development

In the proposed theory, rivalry is the result of a goal conﬂict between a ﬁrm protecting
and one challenging the current distribution of scarce resources. It is based on a win-lose
outcome preference. Rivalry is the cause of two or more individuals/ﬁrms interacting to

capture scarce resources to which all parties believe they are entitled, at the expense of the

9

competitor. Two distinct characteristics indicate intergroup rivalry: a ‘win-lose’
orientation and an ‘us versus them’ stance. See Figure 2 for a model of the proposed

theory.

W

The conﬂict process developed by Thomas (1992) proposes that awareness triggers a
conﬂict episode. Awareness is the realization that goal conﬂicts exist between groups
concerning the distribution of scarce resources. Coser (1956) proposed that the existence
of unequal distribution of resources does not necessarily lead to rivalry. "Before a social
conﬂict between negatively and positively privileged groups can take place, before hostile
attitudes are turned into social action, the negatively privileged group must ﬁrst develop

the awareness that it is, indeed, negatively privileged" (Coser 1956, p. 37).

Some researchers studying rivalry between individual units have suggested that certain
motives cause rivalrous behavior. A study of the airline industry (Baum and Kom 1996)
identiﬁed a motive that inﬂuenced rivals to cooperate to avoid retaliation. MacCrimmon
and Messick (1976), in a theoretical paper, proposed social motives for behavior based on
outcomes and payoffs for self and other. Those inﬂuenced by self-interest and self-
sacriﬁce motives consider only payoffs to self. Others inﬂuenced by altruism and
aggression consider only payoﬁ's to other. Those inﬂuenced by cooperation and
competition motivations consider payoffs to self and other. Relative, rather than absolute,
payoffs characterize these motives. A study of competitive actions in the U. S. airline

industry identiﬁed three behavioral antecedents as positively related to interﬁrm rivalry:

10

awareness of interﬁrm relationships and action implications, motivation to act and

capability to act (Chen 1996b; Miller and Chen 1994).

Hare (1976) proposed that group members have many observations and facts available
from which to interpret reality and to problem solve. Opinions expressed in groups are
more thoughtful and prepared than opinions of individuals. Group discussion eliminates
some observations. Groups tend to make fewer, more effective decisions than individuals,
due to intra-group interaction. The wide knowledge range and better quality decisions
make groups more aware than individuals of goal conﬂicts between competitors. Hartford
(1971) proposed that group decisions are the result of exploration of various possibilities
of alternative issues, and a consensus happens only after much interaction. The

implication is that groups are more aware than individuals of potential conﬂict situations.

Struch and Schwartz (1989) found empirical evidence of a negative relationship between
group identiﬁcation of religious groups and perceived conﬂict in the prediction of
aggressive behavior. However, the authors note that strong social pressures existing at
the time of the study may have confounded perceptions of perceived conﬂict. Based on
social conﬂict theory and group research, I propose that group identiﬁcation, a structural
characteristic in the conﬂict process, will inﬂuence awareness of conﬂict. Thus, I propose:
P1: The greater the level of group identiﬁcation, the greater the perceived

awareness of goal conflict.

1 l
m
Coser (1956) proposes that when the existing distribution of scarce resources in a social
system is no longer considered acceptable, individuals with common interests will form
groups to Signal their dissatisfaction with the status quo. Dissatisfaction is expressed
through conﬂict behaviors. Rubin, Pruitt and Kim (1994) propose that group support
afﬁrms the legitimacy of a group’s goals, increasing the likelihood that the group will
initiate conﬂict behaviors to attain its goals. They also note that groups protest more than
individuals because the identity of individual members depends on the belief that one’s

own group is superior to other groups.

Hartford’s (1971) social work research led her to propose that an increased level of group
cohesion is associated with an increase in member motivation to participate in group tasks.
Insko et al. (1992) found empirical evidence that groups of students competing in
Prisoner’s Dilemma type games were more competitive than individuals, and that groups
became more aggressive in their competitive behaviors as individual members began to
identify with their group. In an experiment with schoolchildren, perceived similarity of
competing groups increased intergroup rivalry in a competitive situation as the groups
attempted to maintain their uniqueness and superiority (Brown 1984). Group
identiﬁcation was a signiﬁcant predictor of participatory behavior of union members (Kelly
and Kelly 1994). The above research suggests a direct, positive inﬂuence of group
identiﬁcation on behavior.

P2: The greater the level of group identiﬁcation, the greater the use of

rivalrous behaviors.

12

Thomas (1992) proposes that structural factors, such as characteristics of the parties,
characteristics of the environment, and characteristics of the relationship between conﬂict
parties inﬂuence the conﬂict process. Empirical studies show that these factors inﬂuence
behavior. Researchers proposed that numerous characteristics inﬂuence rivalry between
individual units. Empirical evidence in airline industry studies suggests that past
performance, experience breadth, and age of organization relate positively to the range of
rivalrous behaviors used (Miller and Chen 1996). In addition, ﬁrm size is a signiﬁcant
indicator of a ﬁrrn’s propensity to initiate competitive actions and ﬁrm propensity to
respond to competitive attacks (Chen and Hambrick 1995). Retailers use technology and
market criteria to develop cognitive categorizations deﬁning competitive relationships
between rivals, with managers deﬁning rivals as only those stores most similar to their
own organization in terms of self-deﬁned technology and market factors (Porac and
Thomas 1994). In the child development literature, children reported greater rivalry with
younger than older siblings. Also, feelings of rivalry were greater with wide-spaced
younger siblings in large families (Furman and Buhrmester 1985). Older children (twelfth
graders) reported less rivalry with older siblings than did younger children (third graders)

(Buhrmester and Furrnan 1990).

Characteristics of the individual units also inﬂuence group based rivalry. The group to
which participants belonged inﬂuenced rivalry between strategic groups. Behaviors
between members of the same strategic group were less competitive than behaviors

between members of different strategic groups (Peteraf 1993).

13

Studies concerning the inﬂuence of environmental characteristics on rivalry all examined
rivalry between individual units. Market diversity, breadth of customers and competitors
in a market, and market growth are inversely related to the range of rivalrous behaviors
initiated (Miller and Chen 1996). Market concentration relates to the incidence of
rivalrous behaviors in a U-shaped curve; when concentration is low, more ﬁrms enter the
market by initiating rivalrous behaviors, as concentration increases fewer ﬁrms enter the
market and market concentration drops (Baum and Korn 1996). Brahm (1995) proposes
that aggressive national targeting policies promote overinvestment in high technology

industries, which increases rivalrous interactions between ﬁrms.

Characteristics of the relationship between competitors also inﬂuence rivalry.
Interdependence, deﬁned in the management literatures as an overlap of customers,
markets and/or resources, is associated with a decrease in the initiation of rivalrous
behaviors between individual units (Baum and Kom 1996; Chen 1996b; Porac and

Thomas 1994; Porter 1980) and between strategic groups (Mehra 1996).

Absolute deprivation does not always result in conﬂict behaviors; it is the awareness of
relative positions that leads groups to initiate conﬂict behaviors (Turner 1991). Coser
(1956) proposes that when groups compare positions, the negatively privileged perceive a
position of relative deprivation and emulate and seek the resources possessed by groups
they consider to be relatively more privileged. In the attempt to gain the coveted
resources of the positively privileged groups, the less privileged initiate conﬂict behaviors.

Struch and Schwartz (1989) found a positive relationship between group identiﬁcation and

14

aggressive behavior, however, they noted an indirect link between the two constructs.
Their study of religious group conﬂict showed that perceived awareness of conﬂict
moderated the impact of group identiﬁcation on group behavior. Thus, I propose:

P3: Perceived goal conﬂict results in a greater use of rivalrous behaviors

in the presence of strong group identiﬁcation.

92mg.

The conﬂict process of Thomas (1992) proposes that behaviors inﬂuence outcomes.
Studies of rivalry between individual units conﬁrm Thomas, that characteristics of
actions/behaviors inﬂuence outcomes of rivalry. Failure to follow typical industry
behavior harms performance. Positive or negative deviations from group norms of action
execution speed hurt small airline performance and deviations from group norms of
propensity to initiate actions and response announcement speed hurt large airline
performance (Chen and Hambrick 1995). Other research on the airline industry by Chen
and his colleagues exposes numerous behavioral characteristics. Visibility and centrality
of rivalrous behaviors relate positively to the likelihood of retaliation while difﬁculty of
implementing response behaviors relates inversely to the likelihood of retaliation. The
authors suggest that avoiding retaliation increases success (Chen and Miller 1994). In the
airline industry, a narrow range of competitive activities results in poor performance in
periods of uncertainty and market grth (Miller and Chen 1996). The number of
competitors directly aﬁ‘ected by an attack and the degree that an action threatens a ﬁrm’s
key markets relate positively to the number of responses provoked by an attack. The

effort required to implement an attack relates inversely to the number of responses

15

provoked by the action, the more diﬂicult the implementation, the fewer the responses to
that action. Strategic actions have a negative inﬂuence on the number of competitive
response behaviors and tactical actions have a positive inﬂuence on the number of

competitive responses (Chen, Smith and Grim 1992).

Characteristics of responses, such as number of responses and length of response lag,
inﬂuence outcomes for the initiating and responding ﬁrms. The greater the number of
responses an action provokes, the lower the performance of the initiating ﬁrm (Chen,
Smith and Grimm 1992). The greater the lag between an action and a response, the
greater the performance of the responding ﬁrm. This unusual ﬁnding suggests that the
time spent on developing an effective response is productive (Smith, Grimm, Gannon and

Chen 1991).

Studies examining rivalry between individual ﬁrms found that intense and extended rivalry
negatively inﬂuences rate of return (Brahm 1995), risk-retum outcomes (Cool, Dierickx
and Jemison 1989), and return on assets and sales (Schul, Davis and Hartline 1995).
Intensity of rivalrous behaviors is associated with negative outcomes (Baum and Korn
1996). Excessive rivalry between college athletes and a sibling was found to result in poor
athletic performance (Hanna 1993). Irish (1964) suggests that sibling rivalry has
dysﬁrnctional effects on the socialization of children. It disrupts and hinders the child’s

development of the ability to function as a member of the family and society.

16

The social conﬂict theory of Coser (1956) proposes that conﬂict between groups provides
functional beneﬁts for the groups and for the social system. In contrast, rivalry between
strategic groups in the pharmaceutical industry negatively aﬂ’ects industry and ﬁrm proﬁts

(Cool and Dierickx 1993).

Group cohesion is a psychological concept that is equivalent to the sociological concept of
group identiﬁcation. Empirical results of early group research Show that cohesiveness
relates positively to productivity, that more cohesive groups are more productive (Hare
1976). Several more recent studies support the early research. Budman et al. (1989)
provided empirical evidence that the more cohesive the psychotherapy group an individual
patient participated in, the greater the patient’s improvement in symptoms. However, the
researchers noted that group process played an important role in the outcome; highly
cohesive groups exhibited more effective group therapy behaviors than less cohesive
groups, resulting in more positive outcomes. A strong positive relationship between
group cohesion and effectiveness of mental health teams was found, and group cohesion
and eﬂ’ective outcomes were dependent on the collaborative behaviors of group members
(Vinokur-Kaplan 1995). A study of female, collegiate golfers found that the level of
cohesion of the golf team related positively to performance (Williams and Widmeyer
1991). This study also noted a link with behavior, speciﬁcally, that more cohesive teams

demonstrated better communication and coordination skills during their golf matches.

A study of sexual offenders found those participating in cohesive groups were more likely

than those in less cohesive groups to experience treatment failure (Roether and Peters

17
1972). Hostile behavior during group therapy, rather than group cohesiveness, was
associated with treatment success for offenders. McGrath also found a negative cohesion-
performance relationship (1962) in a study of riﬂe teams. Since most research shows a
positive relationship between group identiﬁcation and performance, and notes behavior as
a direct antecedent to outcome, I propose:
P4: Rivalrous behavior produces positive outcomes in the presence of

strong group identiﬁcation.

In summary, the proposed theory suggests that the level of identiﬁcation alliance group
members feel toward their group is an important stnrctural factor that inﬂuences rivalry
with other groups. Strong feelings of group identiﬁcation heighten awareness of goal
conﬂicts with rival groups and increase the use of rivalrous behaviors. Heightened
awareness of goal conﬂicts also directly increases the use of rivalrous behavior. Following
the conﬂict theory of Lewis Coser (1956; 1967), I propose that increased use of rivalrous
behaviors produces positive outcomes for the alliance groups. The methodology used to

test this theory is presented in the next chapter.

Chapter 3
Methodology

Measures

Group Identiﬁcation

Group identiﬁcation measures the connectedness a group member feels toward the retail
cooperative group of afﬁliation, in comparison to other retail cooperative groups. This
construct is a ‘characteristic of the parties’ in the general conﬂict model. I used a scale
adapted for the marketing literature ﬁ'om social identity theory that measured group
identiﬁcation of art museum members (Bhattacharya, Rao and Glynn 1995). Respondents
in the cited study answered the six scale questions on a ﬁve-point Likert-type scale, and
the authors reported a Cronbach’s alpha of 0.87. For the current study, the questions
were adapted for use with cooperative groups by changing the word ‘museum’ to
‘cooperative group.’ This study used a ﬁve-point Likert-type scale (1=strongly disagree,

3=neutral, 5=strongly agree). See Table 1 for item content.

Goal Conﬂict

Goal conﬂict measures the extent to which a group member perceives a conﬂict between
his or her unit goals and the goals of his or her primary retail cooperative competitor.
Conﬂicting goals mean one party will achieve their goals only at the expense of the other

party. The perception of goal conﬂict is the precursor to behavior. Therefore it is a

18

19

critical link between the level of group identiﬁcation that a party feels and the behaviors
that result in a goal conﬂict situation. To measure this construct, I used a measure of
intergroup conﬂict developed by Rahim (1983; Rahim and Psenicka 1995). Respondents
indicated their level of agreement with six questions concerning conﬂicting versus
cooperative relationships with their primary retail cooperative competitor. Respondents
provided answers to the Likert-type questions on a ﬁve-point scale (l=strongly disagree,
3=neutral, 5=strongly agree). Questions 1, 3 and 5 were reverse scored. Rahim reported

an alpha reliability of .79 for this scale. See Table 2 for question content.

Beam

A scale from the entrepreneurial literature covering a broad range of competitive
behaviors typical of general business strategies comprises the behavior section of the
instrument (Covin 1991). The original scale asked respondents for their level of
agreement to twenty, ﬁve-point Likert-type questions (1=strongly disagree, 3=neutral,
5=strongly agree) concerning their competitive behaviors. Covin calculated separate
reliabilities for several sub-scales within the overall group of behaviors. Coeﬂicient alphas
by sub-scale include external ﬁnancing = .69, advertising = .84, product quality = .79,
operating efﬁciency = .73, external independence = .66, and industry awareness = .80.
Covin used the remaining questions as single-item measures. Craig, Martin and Honidge
(1997) recently used this scale and calculated an alpha of .76 for the entire twenty
questions. Their exploratory factor analysis showed the scale is composed of six
behavioral factors corresponding to advertising, quality, forecasting, cost, ﬁnancing and

service strategies. Pretests for the current study revealed that three questions (minimize

20

dependence on single supplier, minimize dependence on single customer, and oﬁ’er
superior warranties) from the original twenty were inappropriate for the population due to
the unique nature of the retail hardware cooperative business. I deleted these three
questions on the ﬁnal instrument. Only one question of Craig et. al’s (1997) cost factor
remained after the deletion. Based on Covin’s factors, I expected this question to load on
the quality factor, resulting in ﬁve behavioral factors, rather than six. The subtractions
resulted in a scale composed of seventeen questions. I asked that respondents indicate the
importance of each behavior in competing against their most important cooperative-
aﬂiliated competitor on a ﬁve-point scale (1=not at all important, 3=moderately important,
5=extremely important). See Table 3 for question content. Behavior is treated as a
second-order factor in the analysis, meaning it is composed of several ﬁrst-order factors.
These ﬁrst-order factors correspond to the speciﬁc types of competitive behaviors

contained in this scale.

Outcome

Relative performance, in terms of effectiveness (sales growth) and eﬂiciency
(proﬁtability), was the measure for outcome and was captured by a three-item scale ﬁ'om
the retailing literature (Smith, Venkatraman and Wortzel 1995). The authors of the scale
asked respondents how their sales and proﬁt results compared with competitors on three,
ﬁve-point Likert-type questions (l=much below competition, 3=same as competition,
5=much better than competition). Pretests for the current study found that retailers were
unable to assess their results in comparison to competitors. They were, however, aware

of industry averages. So, respondents compared sales and proﬁt results of their store to

21

industry average (l=much below industry average, 3=same as industry average, 5=much
above industry average). The authors of the scale reported a coefﬁcient alpha of .83. See

Table 4 for question content.

Sample

The sample for this study was chosen from the set of retail ﬁrms employing partnering
strategies. Retail buying cooperatives are a common partnering strategy (Maynard 1996).
Buying cooperatives are attractive because they allow small retailers to increase operating
efﬁciency, gain bargaining power with suppliers, and enhance market presence with

consumers (Ghosh 1994, Levvison 1994).

The study sample consists of members of retail buying cooperatives in the home
improvement industry. This industry is attractive for a study of intergroup rivalry for two
reasons. The ﬁrst reason is the recent competitive conditions experienced by industry
members. Since 1982, concentration in the home improvement industry has shifted from
small retailers to large retailers (U .S. Department of Commerce 1995, 1990, 1985). For
example, in 1982, the four largest building material and garden centers controlled 7.1% of
the market; in 1992, they controlled 16%. The four largest hardware stores increased
their share of the market from 7.9% in 1982 to 9.7% in 1992. The National Retail
Hardware Association estimated the top twenty-ﬁve home-improvement chains controlled
more than 35% of the market in 1995 (Quintaniﬂa 1996). This industry has experienced a
highly competitive environment in the 1990s. Hardware cooperatives are competing with

national hardware chains, such as Home Depot and Lowe’s, and with other retail

22

hardware cooperatives (Quintanilla 1996, Schulz 1996b, 1996c, Stankevich 1996). The
hardware industry is also a good choice for this study because several retail cooperatives
have national representation. A national sample avoids bias that may result from unusual

regional conditions, making the results of the study generalizable to a larger population.

The member list of the National Retail Hardware Association (NRHA) comprised the
sampling ﬁ'ame. This list contains 60,300 contact names ﬁ'om 26,200 retailers and NRHA
updates and audits the list monthly. NRHA includes members of all the national retail
hardware cooperatives, ensuring variability in the independent variables in the study. I
purchased a sample of 1700 names from the NRHA. They selected every ﬁfteenth store
ﬁom their list in a systematic random sampling routine. They included only names of
owners or presidents on the purchased list and they omitted duplicate contact names in the

sampling routine.

Data Collection

I developed and pretested a three-page questionnaire on a small group of twelve member
owners of retail hardware cooperative stores. A pretest of twelve to twenty-ﬁve cases is
generally sufﬁcient to reveal problems with an instrument (Rossi, Wright and Anderson
1983). I telephoned representative retail hardware cooperative owners to solicit
participation in the pretest, mailed a questionnaire to those agreeing to participate, and
made an appointment for pick up. I debriefed respondents at the pick up appointment,
noted actual respondent completion times and questionnaire problems and made

adjustments accordingly.

23

I mailed the ﬁnal instrument to a random sample of 1599 member owners (Appendix B).
From the list of 1700 purchased from NRHA, I deleted seven store owners who had been
contacted for the pretest. I also deleted 94 names ﬁ'om stores with multiple
owner/executive contacts. For stores with multiple contacts, only the ﬁrst name appearing
on the list received a questionnaire. A personalized letter from the researcher
accompanied questionnaires suggesting the relevance of the study to the participants. I
also invited respondents to request results of the survey. Approximately ten percent of

respondents did so.

From the initial mailing of 1599, I received 377 responses within four weeks. Six surveys
were returned as undeliverable and were deducted from the quantity initially mailed and
not replaced. A total of 377 received from a total of 1593 distributed resulted in a total
response rate of 24%. Over half of the stores (52%) in the sample have been in operation
twenty-ﬁve years or less. Respondents tended to operate only one store (82%), with ﬁve
or fewer full-time employees (44%), under 10,000 square feet (69%), with sales volume

under one million dollars in 1996 (49%). See Table 5 for sample characteristics.

The NRHA was unable to segment its list into cooperative versus non-cooperative
members. It also does not maintain records about the type of store each member operates.
All of the major retail hardware cooperatives have several different types of stores that sell
hardware, the most usual types being hardware stores, lumberyards, and home centers.
The three types of stores differ in annual sales volumes, square footage, number of

employees, and customer base (retail end-user versus contractor customers). Because it is

24

unknown whether owners of different types of stores differ on the key constructs in the
study, the instrument contained questions to subdivide respondents into the proper
segments for analysis. The questionnaire also contained several questions to discover the
competitive situation of the respondent, to ensure that they were eligible to participate.
Based on the screening questions, I grouped respondents into categories and analyzed
only hardware stores, who were members of a retail hardware cooperative, and recognized
a cooperative-aﬂiliated competitor from a different cooperative group. Out of 377
respondents, 38 (10%) were not members of a cooperative, 93 (25%) were members of a
non-hardware cooperative group, 67 (18%) did not recognize a competitor belonging to a
retail buying cooperative, and 32 (8%) cited a member of their own cooperative group as
the most important competitor (intra-group competition). Subtracting all of the above as
not eligible for the current study results in a usable sample size of 147 hardware retailers
who are afﬁliated with a retail hardware cooperative and recognize as an important

competitor a hardware retailer who is a member of a different hardware cooperative.

Representativeness of the usable sample was checked using a one-sample t-test on average
sales volume, average square footage and average number of employees. The one-sample
t-test tests the null hypothesis that there is no difference between the mean of a population
and a known test value. The NRHA provided industry averages for these three variables
for all three types of stores. I used the NRHA ﬁgures for hardware stores as the known
test values. Using .05 as the cutoﬁ’, the t-tests show that the 147 hardware stores in this
sample were not signiﬁcantly different from the average U. S. hardware store on any of the

three variables. See Table 6 for results.

25

A test for non-response bias was performed on the 147 hardware stores using independent
sample t-tests to probe for differences between early and late respondents. Comparing the
means of the summed scores of each of the key constructs in the study tested the null
hypothesis that the scores of early respondents are equal to the scores of late respondents.
T-tests Show no signiﬁcant difference between early and late respondents on any of the

constructs. See Table 7 for t-test results.

Data Analysis

I tested the proposed theory with a structural equation model. Following the two-step
approach recommended by Anderson and Gerbing (1988), I estimated the measurement
model ﬁrst and then continued with the structural model. To assess the measurement
model, I performed a conﬁrmatory factor analysis on the ﬁrst order factors, group
identiﬁcation, awareness of goal conﬂict, quality behavior, ﬁnancing behavior, forecasting
behavior, advertising behavior, customer service behavior, and performance outcome (see
Figure 3). I used the covariance matrix of the scale items as input for the measurement
model. Chapter 4 describes estimation and adjustment of the measurement model. The
covariances of the factors (phi matrix) produced by the revised measurement model served
as the input for the second stage of the analysis, the structural model. The ﬁnal model is

presented in Figure 4. Chapter 4 explains the results of the data analysis.

Chapter 4

Results

The ﬁrst section of this chapter explains the conﬁrmatory analysis performed on the ﬁrst
order factors in the measurement model. A discussion of the structural model follows and
the chapter closes with the results of the hypothesis testing. Figure 3 shows the initial
measurement model that was subjected to conﬁrmatory analysis. Figure 4 shows the ﬁnal
measurement model and the structural equation model tested in this analysis. I used

generalized least squares estimation in the EQS program (Version 3.00) to ﬁt the model.

Measurement Model

I began the process by estimating a conﬁrmatory factor analysis to test the hypothesized
relationships of the thirty-one observed variables to the nine latent constructs. Eight ﬁrst-
order constructs were speciﬁed with unit variances, as recommended by Gerbing and
Hunter (1982). These constructs were modeled as a six-item group identiﬁcation factor
(F 1: Group Id), a ﬁve-item awareness of goal conﬂict factor (F 2: Goal Conﬂict), a ﬁve-
item quality behavior factor (F3: Quality), a two-item ﬁnancing behavior factor (F4:
Financing), a three-item forecasting behavior factor (F5: Forecasting), a four-item
advertising behavior factor (F6: Advertising), a three-item customer service factor (F7 :

Customer Service), and a three-item performance outcome factor (F9: Outcome). This

26

27

model was under identiﬁed. To revise the model, I examined the pattern of standardized
residuals and the Lagrange multiplier tests (the equivalent of the chi-square difference
test), and deleted two variables from the behavior scale items that loaded onto multiple

factors.

The revised model produced a solution. However, the Lagrange multiplier test indicated
many problems with this measurement model. The Lagrange multiplier test evaluates the
effect of adding parameters to a model. For the model under consideration, the Lagrange
multiplier test showed that several indicators loaded onto more than one construct with
statistical signiﬁcance. Anderson and Gerbing (1988) note the importance in structural
equation modeling of unidirnensional constructs and propose that deleting indicators from
a model is preferable to relating one indicator to multiple factors because interpretation is
diﬂicult when indicators are assigned to multiple factors. Therefore, I deleted variables
with signiﬁcant multivariate probability in the Lagrange multiplier test from the model
sequentially until I achieved a parsimonious model. Deletion of eight behavioral indicators
resulted in the deletion of three ﬁrst-order behavior factors. This left three ﬁrst-order
behavioral factors, quality, control, and advertising, loading onto behavior, the second-
order factor. The ﬁnal model contains nineteen observed variables and seven underlying

constructs, one of which is the second-order factor (see Figure 4).

I assessed the measurement of each construct in the model for internal consistency.
Measures of composite reliability, Cronbach’s alpha, and variance extracted provide

evidence of internal consistency. See Appendix C for formulas used to calculate

28

composite reliability and variance extracted. Table 8 presents these ﬁgures for the revised
measurement model. All constructs exceed the .7 recommendation for composite
reliability and Cronbach’s coefﬁcient alpha, providing evidence of internal consistency.
Measures of variance extracted, which shows the amount of variance of the indicators
accounted for by the latent construct, exceed the recommended benchmark of .5 for all but
two constructs. Group identiﬁcation and awareness of goal conﬂict show variance
extracted measures of .43 and .46 respectively. These two constructs are derived from the
sociology literatures and the low variance extracted may indicate a need for ﬁrrther
measure reﬁnement. Evidence of convergent validity is provided when the coefﬁcient of
each indicator (variables with a V label in Figure 4) to its construct is signiﬁcant. The
parameter estimate for every indicator in the tested measurement model is signiﬁcant (see

Figure 4 and Table 8).

Table 8 also contains the overall results of the revised measurement model. The chi-
square value for the model is 160.553, based on 137 degrees of ﬁ'eedom, with a
probability of 0.083, indicating a good ﬁt. The chi-square value is an absolute measure of
ﬁt and can be sensitive to sample size and number of indicators in the model. The number
of indicators in this model is high, so ﬁt indices other than the chi-square should be
examined. Incremental ﬁt measures compare the ﬁt of a tested model to a null baseline
model. EQS provides two incremental ﬁt measures. The ﬁrst, the Bentler-Bonett
Normed Fit Index (NFI) is .982 for the model. The second, the Bentler-Bonett
Nonnormed Fit Index (NNF I), takes into account the degrees of freedom in a model. This

index is .997. Both of the incremental ﬁt indices indicate a good ﬁt of the data to the

29

measurement model. Parsimonious ﬁt measures are more precise than absolute ﬁt and
incremental ﬁt indices because they evaluate the ﬁt of a model in relationship to its
degrees of freedom and sample size. The parsimonious ﬁt index calculated in the EQS
program is the Comparative Fit Index (CPI). The Comparative Fit Index for the
proposed measurement model is .997 . The various ﬁt indices generally support the

revised measurement model.

Structural Model

Estimation of the structural parameters is the second step in the Structural equation
modeling technique recommended by Anderson and Gerbing (1988). The results of this
step are presented in Table 9. The overall ﬁt of the model is adequate, with chi-square of
12.414, 8 degrees of freedom, and p = .134. The Bentler-Bonett Normed Fit Index (NFI)
is .990, the Bentler-Bonett Nonnormed Fit Index (NNF I) is .993, and the Comparative Fit
Index (CFI) is .996. Behavior is a second-order factor in the structural model and the
three ﬁrst-order factors (quality, control and advertising) loading onto behavior were all

signiﬁcant. The loadings are included in Figure 4 and Table 9.

Hypotheses

Examination of the parameter estimates shows support for most of the linkages in the
proposed theory (see Table 9). I proposed a positive relationship between level of group
identiﬁcation and awareness of goal conﬂict in Proposition 1. The path between F2 (goal
conﬂict) and F1 (group identiﬁcation) represents this relationship in Figure 4. Results

show a signiﬁcant positive relationship between group identiﬁcation and awareness of goal

30

conﬂict (F 2 Fl = .146; p < .05). This suggests that the more a retail cooperative member
identiﬁes with his or her group of affiliation, the more likely he or she is to perceive a goal
conﬂict with a rival. Such a ﬁnding is consistent with Hare’s (1976) proposition that
groups are more aware than individuals of goal conﬂicts between rivals and with
Hartford’s (1971) proposition that groups are more aware of potential conﬂict (rivalrous)

situations.

Proposition 2 proposed a positive relationship between the level of group identiﬁcation
and the use of rivalrous behaviors. The path between F8 (behavior) and F1 (group
identiﬁcation) represents this relationship in Figure 4. The signiﬁcant F8 F1 link supports
this proposition (F8 F 1 = .345; p < .01), implying that the more a cooperative member
identiﬁes with the group, the greater the use of rivalrous behaviors. This ﬁnding is
consistent with propositions and empirical ﬁndings in the sociology literature. Rubin,
Pruitt and Kim (1994) proposed that group support increases goal-oriented group
behaviors. Hartford (1971) proposed increased levels of group cohesion to be associated
with increased motivation to participate in group tasks. Studies by Insko et al. (1992) and
Brown (1984) found that intergroup rivalry in student groups increased as the groups
developed identities. Findings in the current study also support those of Kelly and Kelly
(1994), who found that group identiﬁcation was a signiﬁcant predictor of participatory

behavior of union members.

I proposed a positive relationship between awareness of goal conﬂict and the use of

rivalrous behaviors in Proposition 3 and the path between goal conﬂict (F2) and behavior

31

(F 8) represents this relationship. The link between F8 and F2 is not signiﬁcant. This
ﬁnding suggests that a group member’s awareness of goal conﬂict with a rival does not
inﬂuence the use of rivalrous behaviors. This is contrary to the ﬁndings of Struch and
Schwartz (1989), who found that the greater a group member’s awareness of goal
conﬂict, the more aggressive the behaviors used. Perhaps this suggests a missing link in
the conﬂict process tested in the model. Perceived goal conﬂict may not directly cause
rivalrous behaviors, but may instead inﬂuence motives, which directly cause behavior. A
few respondents noted that their behaviors derive from their motives toward either their

customers or their competitors.

The last proposition proposed a positive relationship between rivalrous behavior of group
members and performance outcomes. A non-signiﬁcant link between F9 (outcome) and
F8 (behavior) in Figure 3 does not support this proposition. This implies that behaviors
arising from feelings of group identiﬁcation and awareness of goal conﬂicts with rivals do
not affect ﬁnancial performance of group members. This ﬁnding is inconsistent both with
management literature, which generally ﬁnd a negative relationship between rivalrous
behaviors and ﬁnancial performance (Cool and Dierickx 1993; Brahm 1995; Cool,
Dierickx and Jemison 1989; Schul, Davis and Hartline 1995), and sociology literature,
which propose a positive relationship between rivalrous behavior and performance
(Williams and Widmeyer 1991; Vinokur-Kaplan 1995; Budman et al. 1989). Such a
ﬁnding may suggest that ﬁnancial performance is not an appropriate measure for
behavioral results. In such a competitive market, retailers may have competed proﬁts

away, making other outcome measures, such as customer satisfaction or innovation more

32

appropriate.

Overall, the results of the current study show that group identiﬁcation relates positively to
awareness of goal conﬂict and the use of rivalrous behaviors. This infers that higher levels
of group identiﬁcation increase the awareness of goal conﬂict with a rival and the use of
rivalrous behaviors. Awareness of goal conﬂict is not signiﬁcantly related to the use of
rivalrous behaviors. The study did not ﬁnd a signiﬁcant relationship between the use of

rivalrous behaviors and ﬁnancial outcomes. I discuss these results in Chapter 5.

Chapter 5

Conclusion

The current study set out to examine characteristics of a type of competition gaining
prominence in the marketplace, competition between groups. Although group-based
methods of competing have been a part of the retail industry for many years, we should
not underestimate the effect of their recent gain in popularity. From the sample
responding to this study, 18% (67) of the total sample, and 28% (39) of the hardware
stores answered that their number one competitor was a member of another retail
cooperative group. While the majority of the respondents considered inter-type
competition, for example competition between cooperatives and Big Box retailers, the
biggest threat, the number of retailers considering group based competition to be a threat
was noteworthy. \Vrth more than 44,000 home improvement outlets operating in the US.
in 1996, and over 20,000 stores belonging to wholesaling cooperatives, the importance to
the retail industry of this rising form of competition becomes clear (“The Industry’s
Annual Report,” 1996). It is critical that hardware retailers understand the dynamics of
group-based behavior to survive in a competitive environment where they must compete

on two fronts, against category killers and group-based retailers.

33

34

I developed the proposed theory to explain how group identiﬁcation inﬂuences the
competitive behaviors of group members. Group identiﬁcation is a feeling of
connectedness that a member feels with other members of the group. Cooperative
management and fellow members can foster this feeling. Interaction of members is a key
factor in the development of group identiﬁcation. This theory proposed that group
member awareness of goal conﬂicts with rivals would be more acute because of the
interaction between group members. The analysis supported this proposition. A second
proposition of the theory is that feelings of solidarity and support among group members
inspire action, which results in increased rivalrous behaviors in groups with higher levels
of group identiﬁcation. The analysis also supported this proposition. Contrary to
expectations, the study found that increased awareness of goal conﬂict did not inﬂuence
the incidence of rivalrous behaviors, perhaps indicating that another construct, such as
motives, directly inﬂuences rivalrous behavior. The last ﬁnding of the study, that higher
levels of rivalrous behaviors do not relate to ﬁnancial performance, suggests that other
types of outcomes may be more important in a highly competitive environment. This
study has provided some useful empirical evidence about group-based competition in the

retail hardware industry upon which to build future research.

Implications

The inﬂuence of group membership on individual behavior raises some important
implications for this theory. Being a member of a retail cooperative will inﬂuence the
behavior of the independent owner. Owners expect certain rights as independents, but

also take on responsibilities and face behavioral norms as members of a cooperative.

35

Member owners and cooperative management need to be aware of and understand this

inﬂuence to operate effectively.

Group identiﬁcation should not be conﬁrsed with conformity. Social conﬂict theory warns
of the dangers of high levels of forced conformity to group goals and ideals. Such
conformity may lead to within-group conﬂict, ultimately resulting in a split of the group
into rival factions. So, while I advise that cooperative groups foster feelings of group
identiﬁcation within the group, the goal is to increase the interaction of group members,

not the conformity.

An implication that is sometimes considered counterintuitive is that cooperative groups
should encourage inter-group rivalry, rather than discourage it. Rivalry fosters innovation
and creativity of a group. If not carried to excess, increased inter-group rivalry may

improve operating results.

Limitations and Future Research

Several limitations of this theory exist. The generalizability of the study results is one
limitation. The ﬁndings may not apply to types of alliance groups other than retail
cooperatives. Retail cooperatives may possess unique levels of group identiﬁcation or
exhibit behaviors peculiar to their industry. Also, theoretical application may be limited to
the retail industry. Retailers face a distinct set of marketing mix decisions, which may
result in behaviors not applicable to other industries. Hardware retailers may currently be

facing a unique situation within the retail environment, making their situation unique in

36

comparison to retailers competing in other lines of business. Thus, we must undertake
careﬁrl consideration before generalizing the results of this study to populations other than

retail hardware cooperatives.

This theory proposes relationships between sociology-based constructs applied to a retail-
oriented business setting. To test the model, I adapted measures for several proposed
constructs from the sociology literature. Although I pretested these scales on a small
sample, reﬁnements are still needed, as evidenced by the reliability scores and variance
extracted of the adapted scales. Future research efforts should be made to reﬁne the

scales used in this study or develop new ones.

This theory proposes a dynamic process. Therefore, longitudinal measurement may add
more to our understanding of inter-group rivalry than one-time measurements. Future
replication projects are encouraged to learn more about the dynamic nature of the

inﬂuence of group membership on competitive retail behaviors and outcomes.

Antecedents to group identiﬁcation may be a salient research topic. Alliance groups in the
business community need to know what increases the identiﬁcation of members to the
group. Such knowledge can help groups attract and retain members, and increase the
effectiveness of the group. This is another area where we must adapt measures from

sociology to a business application.

37

The sociology literature suggests that level of group identiﬁcation is inversely related to
group Size, as membership increases, levels of group identiﬁcation suffer. Since level of
group identiﬁcation affects the perceptions of group members and the behaviors of group
members, cooperative management may be able to manipulate member identiﬁcation with
the group. Cooperative group managements may be interested in learning more about the
effect of size on level of group identiﬁcation as this form of competition continues to

increase in popularity and as groups merge to form larger groups.

Survey respondents suggested an additional area for ﬁrture research in notes on the
questionnaires. Some commented that their behaviors were driven not by their
competitors but by their customers. This suggests that the motives of retailers may differ,
with some concentrating primarily on their customers and others concentrating on their
competitors. Such a notion is consistent with the social conﬂict and sociology literatures
reviewed. However, before motive can be added to the model, researchers must develop a

measure of motive.

The ﬁndings of this study show that group identiﬁcation is an important sociological
concept that applies to the business setting. With the tremendous rise in the use of alliance
groups in the current economy, an understanding of factors inﬂuencing the behavior of
group members is critical to the success of the alliance effort. I have shown that group
identiﬁcation is one factor that inﬂuences the behavior of group members. While this
study has answered a few questions about the effect of group membership on behavior, it

has presented new questions that may serve as ﬁ'uitﬁrl research projects in the future.

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Figures

 

Structural Inﬂuences
Characteristics of:
Parties
Relationship between Parties
Environment

 

 

 

 

Conflict Process
Awareness of conﬂict
Behaviors

 

 

 

 

Outcomes

 

;

 

 

AClapted ﬁ'om Thomas (1992).

Figure 1. General Conﬂict Model

44

45

 

 

Behavior Goal Conﬂict

(htcome

Figure 2. Theoretical Model

46

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 3. Initial Model

47

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

WM StrmumlMxH
Warsaw X2= 160.553, df= 137, p= .083 )G= 12.414, df=8, p= .134
" p<.05 NFI=.982 NFI=.9SD
” p<.01 NNF1=.997 NNFI=.993
a ﬁmdforithniﬁmtim (F1=.997 (Fl=.996

Figure 4. Revised Model

Tables

Table 1. Item Content: Group Identiﬁcation

 

 

 

 

 

 

 

 

 

 

Scale Label Statements
Group Identification V5 When someone criticizes my coop, it feels like a personal insult.
V6 I am very interested in what others think about my coop.
V7 When I talk about my coop, I usually say we rather than they.
V8 My coop’s successes aremysuccesses.
V9 When someone praises my coop, it feels like a personal compliment.
V10 If a story in the media criticized my coop, I would feel annoyed.

 

48

 

49

Table 2. Item Content: Goal Conﬂict

 

 

 

 

 

 

 

 

 

Scale/Factor Statements
Goal Conflict V12 doesn’t play by the rules that allow each of us to proﬁtably

serve out respective market niches.

V13 The relationship between my store and is harmonious, allowing
each of us to proﬁtably serve our respective market niches.

V14 There is mutual antagonism between my store and

V15 There is implicit cooperation between my store and .

V16 has a winner takes all attitude towards my store.

 

 

 

50

Table 3. Item Content: Behavior

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Factor Label Statements
Quality V17 To Offer more high quality products/services
V18 To emphasize more strict quality control
V20 To improve employee productivity and operations efficiency
V21 To provide more extensive customer service
V22 To emphasize cost reduction in all facets of business operations
Finance V23 To maximize use of outside ﬁnancing to help gain advantage
V24 To ﬁequently explore for new sources of funds to help gain advantage
Forecasting V25 To actively attempt to predict industry trends to gain advantage
V26 To actively attempt to predict competitors’ moves to gain advantage
V27 To actively attempt to predict customer tastes and requirements to gain
advantage
Advertising V28 To use more sharply-focused advertising
V29 To use more frequent advertising
V30 To use advertising which clearly differentiates my products/services
V31 To sponsor more frequent promotional activities
Customer Service V32 To extend more generous customer credit
V33 To offer a wider range of products/services
V34 To Offer products/services at lower price

 

 

51

Table 4. Item Content: Outcome

 

 

 

 

 

 

 

Scale/Factor Label Statements

Performance Outcome V35 Proﬁtability of your store (ﬁscal 1996)
V36 Percent dollar sales growth of your store
V37 Percent proﬁt growth of your store

 

 

 

Table 5. Sample Characteristics

52

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Freq % Mean Freq '/o Mean
Years 35.61 Square Footage 13,687 .37
1-10 66 20% < 5,000 70 70%
11-25 102 32% 5,000 - 10,000 148 47%
26-50 80 25% 10,001 - 15,000 38 12%
51-75 35 11% 15,001 - 25,000 27 9%
76-100 26 8% 25,001 - 50,000 20 6%
> 100 13 4% > 50,000 9 3%
# Stores 1.31 1996 Sales Volume $2,233,1 17
1 253 82% < $500,000 54 20%
2 36 12% $500,000 - 999,999 84 29%
3 11 4% $1,000,000 - 1,499,999 51 18%
4 3 1% $1,500,000 - 4,999,999 71 25%
5 2 1% $5,000,000 - 9,999,999 16 6%
6 3 1% > $10,000,000 9 3%
7 l 0%
Full-Time
Employees 11.10
1-5 141 44%
6-10 92 29%
11-15 37 12%
> 15 48 15%

 

Table 6. Representativeness of Sample

53

 

 

 

 

 

 

 

 

 

 

 

 

One-sample t-tests

Variable # Cases Mean Test Value t-value df Sig—
average volume 1 15 996774 913498 1.10 1 14 .275
average square footage 132 7879 7000 1.93 131 .056
average # employees 103 9.27 9 .37 102 .713

 

 

Table 7. Non-response Bias

54

 

 

 

 

 

 

 

 

 

 

 

 

 

 

t-test for equality of means
Early Respondents Late Respondents
Scale Mean S.D. Mean S.D. t-value sig
Group Identiﬁcation 23.3025 4.445 22.2000 5.531 .85 .406
Goal Conﬂict 12.3193 3.659 12.4500 4.322 -. 13 .899
Behavior 67.8151 11.776 68.0000 1 1.21 1 -.O7 .947
Performance Outcome 9.5304 2.583 9.7000 2.203 -.31 .759

 

 

Table 8. Measurement Model Results

55

 

 

 

 

 

 

 

Internal Consistency
Composite Coefﬁcient Variance
Construct Reliability Alpha Extracted
Group Identiﬁcation .75 .80 .43
Awareness of Goa] Conﬂict .72 .77 .46
Quality Behavior .76 .79 .52
Control Behavior .75 .80 .51
Advertising Behavior .75 .72 .51
Performance Outcome .77 .81 .54

 

 

 

 

 

 

Table 8. (cont’d).

56

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Parameter Estimates
Standardized
Path Parameter Standardized Residual
Construct Labels Estimate t-value Estimate Variance
Group Identiﬁcation V5 F 1 .559 6.315" .626 .780
V6 F1 .534 6.683“ .647 .763
V9 F1 .737 9.102“I .803 .597
V10 F1 .689 8.962“ .801 .599
Goal Conﬂict V12 F2 .651 7.209" .713 .701
V14 F2 .726 8.517" .833 .553
V16 F2 .649 7.232" .685 .728
Quality Behavior V17 F3 .479 7.847" .700 .714
V20 F3 .602 10.042“I .868 .497
V21 F3 .464 9.406“ .820 .572
Control Behavior V23 F4 .559 5.088" .536 .844
V25 F4 .738 9.919" .866 .500
V26 F4 .789 9.986" .898 .440
Advertising Behavior V28 F6 .812 8.979“I .81 1 .585
V29 F6 .673 7.979" .754 .657
V30 F6 .782 9.604" .807 .590
Outcome V35 F9 .570 7.450“I .697 .717
V36 F9 .631 8.051 “ .743 .669
V37 F9 .798 11.546" .958 .288
"' p > .05
" p > .01
Overall Fit

 

Chi-squareI df I p

mlmlcml

 

I Measurement Model

 

160.55 I 137 I .083

 

.982 I .997 I .997 I

 

 

Table 9. Structural Model

57

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Overall Fit
I Chi-squareIdf pINFIINNFIICFI1
[Structural Model 12.41 I 8 .134 I .990 I .993 J .996 I
Second Order Factor: Behavior
Standardized
Path Parameter Standardized Residual
Parameter Labels Estimate t-value Estimate Variance
Behavior -r Quality Behavior F8 F3 .873 7.857“ .742 .671
Behavior -r Control Behavior F8 F4 .907 8.031" .770 .638
Behavior -r Advertising Behavior F8 F6 1000' .845 .534
Path Estimates
Standardized
Parameter Standardized Residual
Path Estimate t-value Estimate Variance
Group Identiﬁcation -* F1 F2 .151 1.651‘ .146 .989
Awareness of Goal Conﬂict
Group Identiﬁcation -* Behavior F1 F8 .303 3.608" .345 .934
Awareness of Goal Conﬂict -r F2 F8 .044 .564 .052
Behavior
Behavior -' Outcome F8 F9 .006 .056 .006 1.000

 

 

 

 

 

 

 

parameter ﬁxed for identiﬁcation purposes

"' p> .05
" p>.01

 

 

Appendices

Appendix A

 

 

 

 

 

 

Appendix A. Patterns and Dimensions of Rivalry
Patterns of Rivalry
Between Groups Within Groups Between Individual Units
I Causes
1 Factors Chen 1996 Interﬁrrn rivalry:
underlying awareness,
action motivation to
act, capability to
act
Motives MacCrimmon interpersonal

& Messick rivalry:

1976 aggression,
competition,
proportionate
competition

Baum & Korn interﬁrm rivalry:

1996 mutual
forbearance

 

 

 

 

 

 

 

 

58

 

59

 

Effects

Patterns of Rivalry

 

Between Groups

Within Groups

Between Individual Units

 

 

Effects on
performance

Cool &
Dierickx
1993

rivalry between
strategic groups
affects industry
& ﬁrm proﬁts

Cool &
Dierickx
1 993

rivalry within
strategic groups
affects industry
&. ﬁrm proﬁts

Brahm 1995

excessive
interfrrm rivalry
results in
industry ROR
below
competitive rate

 

Cool,
Dierickx &
Jernison 1989

intensity of
interﬁrrn rivalry
inﬂuences risk-
return outcomes

 

Schul, Davis
& Hartline
1995

extended
interﬁrm rivalry,
mediated by
strategy,
inﬂuences ﬁrm
ROA & sales

 

Hanna 1993

sibling rivalry
resulted in poor
athletic
performance

 

 

Functions of

rivalry

 

 

 

 

 

Irish1964

 

functions of
sibling rivalry
include:
socialization,
conﬂict
resolution, intro
to rights of
others, develop
role model

 

 

 

IhuunnsofRnwdnr
‘DﬁﬂﬁnChoups

 

 

hﬁﬂerar
Chenl996

 

Funnanih
Buhnnaﬂu'
1985

family
camﬂdhﬁon
vanabks:
rehﬁveagqqu
dﬁﬁnenca
ﬁnnﬂyshe,bhﬂr
onhu,gmuku

 

Chara:
Ihunbnck
1995

fhnruze,ﬁnn
wrench”
ﬁnn
nnponnvnmna
toactdrunpond,

 

Penmn&
Thcnum1994

cognﬁhw
caugoﬁznknrof
rehﬁonﬂﬁps
(uuuuunnytued
bymsr)

 

 

 

 

 

 

 

Peuuaf1993

 

rnenﬂxmﬂﬁp
dahutrﬂd
numﬂxusvusus
mavenUanm

 

 

61

 

 

 

 

 

Patterns of Rivalry
Between Groups Within Groups Between Individual Units
External - Miller & market
characteristics Chen 1996 characteristics:
of environment market diversity,
market growth,
market
uncertainty
Baum & Korn market
1996 concentration,
intensity of
rivalry
Cool, intensity of
Dierickx & rivalry
Jemison 1989

 

Brahm 1995 national
targeting policy

 

 

Schul, Davis supplier & buyer
& Hartline power & threat
1995 of substitutes

 

 

 

 

 

 

 

 

62

 

Patterns of Rivalry

 

Between Groups

Within Groups

Between Individual Units

 

Characteristics
of Relationship

Mehra interdependence:

1996 resources of
strategic groups,
customers of
strategic groups

Buhrmester & quality of sibling
Furman 1990 relationships:
warmth, power,
conﬂict

 

Chen 1996 interdependence:
market
commonality,
resource

similarity

 

Porac & interdependence:
Thomas 1994 technological
similarity,
market similarity

 

Baum & Kom interdependence:
1996 market domain
overlap, multi-
market contact

 

 

 

 

 

 

 

Porter 1980 interdependence:
market segments

 

 

 

63

 

 

 

 

 

 

Patterns of Rivalry
Between Groups Mthin Groups Between Individual Units
ﬂ —;
Characteristics Chen & speed & visibility
of Hambrick
Moves/Actions 1995

Chen & move

Miller 1994 characteristics:
visibility
diﬂiculty,
centrality

Chen, Smith characteristics of

& Grimm actions:

1992 competitive
impact, attack
intensity,
implementation
requirement,
type of action
(strategic,
tactical);
characteristics of
responses: # of
responses,
response I

Miller & simplicity,

Chen 1996 complexity

 

 

 

 

 

 

 

 

 

Appendix B

Appendix B. Questionnaire

Michigan State University
1997 Study of Retail Hardware Cooperatives

Section].
LAreyouamemberofaretailhardwarebuyingcooperative? yes no

lfno, please do not continue with the survey, but please do return it followingthe directions on the back page. Thank you.
2. Howmanyretailhardwarebuyingcooperativesdoyoubelongto?
3. Hmwhatretailhardwarebuyingcooperativedoyoubelongto?
4. lfmeﬂnnmmwhididoyoudependforthegreateupaeanofymupurelnses?
Pleaseamwerallquestionsinthismrveyabout‘yourcooperative’irrreferencetothecooperativeyouansweredfor#3or#4above.

 

 

Section II.
Toanswerthequestions inSection lelease cirelethemrrnberthatshowsyour level ofageementwiththestatement.
STRONGLY NEUTRAL STRONGLY

DISAGREE AGREE
1. Whensorneonecritieizesmycooperative, itfeelslikeaperaonalinsult. 1 2 3 4 5
2. Iamveryinterestcdinwhatothersthinkaboutmycooperative. l 2 3 4 5
3. Whenltalkaboutmycooperative, 1 usually say ‘we’ ratherthan ‘they'. 1 2 3 4 5
4. My cooperative’s successes aremysuccesses. 1 2 3 4 5
5. Whenaomeonepraisamywopaativeitfeeblikeapersonalcomplimem. l 2 3 4 5
6. lfastoryinthemediacriticizcdmycooperative,1wouldfeelamoyed. 1 2 3 4 5
7. lngeneraLlfavorastrmgerwhasisonthemarketingofuiedandu'ue l 2 3 4 5
productsoraervicea
8. lntlnpastSyearalhavemarketedwrymanynewlinesofwoductsor 1 2 3 4 5
aerviceainrnystore.
9. InﬂupastSyeargﬂieehangesinpmductsorservicesinmystorehave 1 2 3 4 5
beenmostlyofaminornature.
10. IngeneraLlhaveastrongtendencytochoosehigh—riskprojects(with 1 2 3 4 5
chanceaofveryhighretm'ns).
ll. IngeneraLIbelieveitisbesttoexplorethehardwanreuilenvirormerl 1 2 3 4 5

yadually via timid. incremental behaviors.

12. When confronted with decision-making situations involving mmrtainty, l 1 2 3 4 5
typicallyadoptabold aggressivepostureinordertomardmizethe
probability of exploiting potential opportunities.

SeetlonIII.
l. Inyourtown/cityorneighboringarea,isthereanotherhﬂwaustorewhoisamenherotaretaﬂhuylngeooperaﬂve?

._____Y“ ___no
Ifno,pleasegotopage3andcornplete SectionVIIonly. Ifyes, please complete all sections ofthe survey. Thankyou.
2. lfyawhatistbenameofthestore?(Iftherehmmﬂnnmwhichmeposesthebiggeaﬂneagrealorpotanial,myomstom?)
(m)
3. Whathardwareeooperatlveisthestoreinn aﬂiliatedwith?
(coowl‘lﬂvc)

 

 

 

65

SectionIV.
Pleancheledummbadmwinghowimpmtunachievingudiuatanauisfwyournm. Fora‘hlank’wlthlnaqrsestiomtﬂnlrof
thestoreyounentlonethecﬂonlqunesﬂonﬂ. ForenmpleifyouidanifyﬂappyﬂudwareasalocaLcooperative-aﬁliated
store,insertﬂappyﬂariwareintheblanks.
NOT AT ALL MODERATELY EXTREMELY
IMPORTANT IMPORTANT IMPORTANT

. To use more sharply-focused advertising than (store). 1 2 3 4 5
2. To use more frequent advertising than (store). 1 2 3 4 S
3. To use advertising which clearly diﬂ‘erentiates my products/services from 1 2 3 4 5
those of (store).

4. To sponsor more ﬁ'equent promotional activities (community events, how-to 1 2 3 4 5
clinics, celebrity appearances, etc.) than (store).

5. To sponsor more sharply-focused promotional activities than (store). 1 2 3 4 5

6. To sponsor promotional activities which clearly diﬂ‘erenttate' my store ﬁom l 2 3 4 5

(store).

To offer more big: quality products/services than (store). 1 2 3 4 5
To emphasize more strict quality control than (store). I 2 3 4 5
To provide a more attractive, customer-ﬁiendly physical store environment I 2 3 4 5
than (store).

10. To improve employee productivity and operations efﬁciency more than _ l 2 3 4 5
(store).

11. To provide more extensive customer service/support than (store). 1 2 3 4 5

l2. Toinitiatecustomerservice/mpportprognnumoreﬁequerulytlnn 1 2 3 4 5
(store).

13. Toactivelyatternpttopredictindustrytrendstogainadvantageover l 2 3 4 5
(“0")

l4. Toadivelyattempttopredictcompetitors’movestogainadvunageover l 2 3 4 5

(store).

15. Toactivelyanernpttopredictcustomertastesmdrequiremernstogain l 2 3 4 5

advantage over (store).

16. Toenphasizeconreductioninallfacetsofbusineasoperaﬁonsaolcannnke 1 2 3 4 5
moreproﬁtthan (store).

 

17. Tomaximizeuseofoutside ﬁnancingtohelpgainadvantageover 1 2 3 4 5
(store).

18. Toﬁequerulyexplorefornewsourcesofﬁmdstohelpgainadvantageover 1 2 3 4 5

(store).

19. To extend more generous customer credit than (store). 1 2 3 4 5

20. To offer a wider range of products/services than (store). 1 2 3 4 5

21. To offer a range of products/services which clearly differentiates my store 1 2 3 4 5
from __ (store).

22. To oﬂ‘er products/services at a substantially lower price than (store). 1 2 3 4 5

Section V.

Pleasecirclethemunbaﬂutshowsyourlevelofagreunemwiﬂithestatanau. Forqneatlonsl-4,whereyouencountera‘blanlr’
wﬂhlnaqnudomthhkofthecoognﬂveyoumﬂonethecﬂonnanesﬂonﬂ. Forexample,ifyouidentityllappyllardware
ualoeaLcoopuaﬁve—aﬂiliatednaemdﬂappyﬁudwmisambaofAceCoopauiw,insertAceintheblanks.

STRONGLY NEUTRAL STRONGLY

DISAGREE AGREE

l. lfeelastrongsemeofﬂrernandm”betweenmernbersofmyretail l 2 3 4 5
cooperativeandmembersof cooperative.

2. Youcanneverreallytrustmembersof cooperative. 1 2 3 4 5
1feelthatmembersofmyretailcooperativeandmembersof 1 2 3 4 5
cooperativearereallyonoppositesidesinmyindustry.

4. 1fmyretailcooperativegroupdidn’texist,membersof 1 2 3 4 5

cooperativewouldtrytodrivemembersofmycoopyoupoutofbusiness.

66

ForqnestioasS—l3,rrhereyonencomstera‘bhnk’wiﬂﬂlaquesﬂomtiﬂnkofﬂsestoreyonwrotehthebhnlriISectionIII,

question #2.
STRONGLY NEUTRAL STRONGLY

DISAGREE AGREE
5. There is unspoken agreement between my store and (store) 1 2 3 4 5
aboutourrespectivemarketnichesintheretailhardwareindustry.
6. (store) doesn‘t ‘play by the rules’ that allow each of us to 1 2 3 4 5
proﬁtably serve our respective market niches.
7. The relationship between my store and (store) is harmonious, 1 2 3 4 5
allowing each of us to proﬁtably serve our respective market niches.

. Thereismuuralantagonismbetweenmystoreand (store). 1 2 3 4 5
9. There is implicit cooperation between my store and (store). 1 2 3 4 5
10. (store)hasa‘wirmertakes all‘attitudetowardsmy 1 2 3 4 5

store.
11. Inrelationto (store),ltypicallyrespondtoactionswhich 1 2 3 4 5
12. Inrelationto (store),lamveryoﬁentheﬁrsttointroduce l 2 3 4 5
new products/services, operating technologies, etc.
13. In relation to (store), I typically seek to avoid competitive 1 2 3 4 S

clashes, preferring a ‘live-and-let-live‘ posture.

Section VI.
In comparison to other retail hardware cooperatives, please show your store’s and your cooperative’s performance on these dirnenaions:
MUCH BELOW SAME AS MUCH ABOVE
INDUSTRY INDUSTRY INDUSTRY
AVERAGE AVERAGE AVERAGE

1. Proﬁtability of your store (ﬁscal 1996) 1 2 3 4 5
2. Percent Dollar Sales Growth of your store (ﬁscal 1996) 1 2 3 4 5
3. Percent ProﬁtGrowthofyourstore(ﬁscal 1996) l 2 3 4 S
4. Proﬁtability of your cooperative group (ﬁscal 1996) 1 2 3 4 5
5. Percent Dollar Sales Growth of your cooperative group (ﬁscal 1996) 1 2 3 4 5
6. Percent Proﬁt Growth of your cooperative group (ﬁscal 1996) l 2 3 4 5
Section VII.

Please answer the following questions about characteristics of your store.
1. Moststoreshaveseveral competitors intheirtradingarea. Ofthecornpetitorsinyourtndingareawhatonestore isyourlnost
hportant competitor?
2. Whattypeofstore isthiscompetitor(circleone)?
Big Box Cooperative Independent National Chain Regional Chain Wholesale Voluntary Other

 

 

Howmanyyearshasyouroperationbeeninbusiness? years
Howmanystoreswerethereinyouroperationinﬁscal 1996?

Howmanypeople, on average, did you employ in your operation in ﬁscal 1996? full-time part-time
Whatistheapproximatesquarefootageofyourtotaloperation? sq.ﬁ.

Whatsales growth/decline didyourtotal operation achieve inﬁscal 1996? % growth, or %decline
qudieapprordmatesdesvolumeofyourtetalopaationinﬁscallm? S

”$9995"

 

67

 

Thankyonforparticipathginthisreaeareh.

Toreturnthequestionrrair'e.foldthebooklet intothirdsalongthehorizontallineswiththeremmaddressandpostageontheoutside,tapeor
stapleitclosed,andputitinthemail. Toensureconﬁdemialityﬁonotpdyourmreturnaddressonmebooldet.

 

Michigan State University

Department of Human Environment and Design
Merchandising Management

204 Human Ecology Building

East Lansing, MI 48824-1030

LESLIE STOEL

DEPARTMENT OF HUMAN ENVIRONMENT AND DESIGN
MERCHANDISING MANAGEMENT

204 HUMAN ECOIDGY BUILDING

EAST LANSING, MI 48824-1030

 

Appendix C

Appendix C. Formulas for Composite Reliability and Variance Extracted

Construct Reliability = LSum of Wings)’

(Sum of standardized loadings)2 + sum of indicator measurement error

Variance Extracted = Sum of squared standardized loadings

Sum of squared standardized loadings + sum of indicator measurement error

From: Hair, J.F., Jr., Anderson, R.E., Tatham, KL. and Black, WC. (1995). Multivariate Data sis with
Readings, Fourth Edition. Englewood Cliffs, New Jersey: Prentice Hall, p. 653.

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