MANAGING MULTIPLE RELATIONSHIPS IN INFORMATION-RICH
ENVIRONMENTS: THE POSITIVE AND NEGATIVE IMPACT OF INFORMATION
SYMMETRY IN NETWORKS

By
Hannah S. Lee

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Business Administration—Doctor of Philosophy
2014

ABSTRACT
MANAGING MULTIPLE RELATIONSHIPS IN INFORMATION-RICH
ENVIRONMENTS: THE POSITIVE AND NEGATIVE IMPACT OF INFORMATION
SYMMETRY IN NETWORKS
By
Hannah S. Lee
This dissertation, using a two essay format, focuses on the impact of information symmetry on
the multilateral management of inter-organizational relationships. The first essay, drawing on
structural network theory and social learning theory, investigates how a focal firm’s ability to
proactively manage the structural properties of a network— specifically, density of the network
and relative network centrality of the referent firm— as well as the influence strategies used in
dyads influence behavioral responses in an exchange network. The second essay examines the
context in which the symmetrical information environment can have negative consequences for a
focal firm in the management of multiple relationships within a network due to perceptions of
unfairness when partner firms anchor on the wrong comparative referent. Specifically, it
examines the efficacy of explanations in re-anchoring and lowering unfairness perceptions of a
partner firm building on equity theory and the anchoring and adjustment literature. Overall, the
research provides insight for marketing academics as well as marketing managers on how to
more carefully manage not only within, but across relationships, expanding our view of interorganizational relationships beyond the dyadic interaction and integrating the social contextual
motivations behind firm behavior.

Copyright by
HANNAH S. LEE
2014

ACKNOWLEDGEMENTS

This dissertation is the result of a challenging journey, upon which many people have contributed
and given their support.

First and foremost, I would like to express my deepest gratitude to my mentor and friend, Dr.
David A. Griffith, for his excellent guidance, genuine caring and concern, and his unwavering
faith in me. I am truly grateful to him for instilling in me the qualities of being a good researcher
of integrity. I’ll never forget the many wonderful lunches and research brainstorm sessions at the
round table.

I would also like to thank my committee members, Dr. Roger J. Calantone, Dr. Thomas J. Page,
and Dr. Cheri Speier-Pero. Their academic support and input and personal cheering are greatly
appreciated. Thank you. I will forever be thankful to my former college research advisor,
Professor Seokwon Lee, who guided me into the world of academia. I still think fondly of my
time as an undergraduate student at Ewha Woman’s University. Aim high, fly high!

A good support system is important to surviving and staying sane in grad school. I would like to
thank my cohort members, Praneet Randhawa and Chang Seob Yeo, with whom I have forged
very special bonds. I would have never made it through all the hurdles without all the Sansu
dinners, Chapelure coffee chats, and good company. Marketing ’09 gang forever!

iv

I know I always have my family to count on when times are rough. Special recognition and
appreciation goes to my loving husband whom I met the first day I set foot in the States again in
2008. I married the best person out there for me. Han has been a true and great supporter and has
unconditionally loved me during my good and bad times (and is the only person who knows how
to make me laugh until my stomach and cheeks hurt!). He has been non-judgmental of me and
instrumental in instilling confidence. He had faith in me and my intellect even when I felt like
digging hole and crawling into it because I didn’t have faith in myself.

I also thank my artistic little sister, Iris, who is about to start a graduate adventure of her own at
CalArts. I love her dearly and thank her for all her goofiness and cheer sessions (and for putting
up with her bossy big sis).

I thank my new family-in-law who welcomed me into the Lee household warmheartedly in
January 2012. They were always supporting me and encouraging me with their best wishes.

Of course no acknowledgments would be complete without giving thanks to my loving father
and mother, Dr. Chan Hong Chung and Jeong Hee Hong, for their wholehearted support.
Both have always expressed how proud they are of me and how much they love me. I too am
proud of them and love them very much. I am forever grateful to them and the opportunities they
provided me.

Finally, I thank my God, my good Father, for letting me through all the difficulties. I have
experienced Your guidance day by day. Thank you, Lord.

v

TABLE OF CONTENTS

LIST OF TABLES ....................................................................................................................... viii
LIST OF FIGURES ....................................................................................................................... ix
INTRODUCTION ...........................................................................................................................1
ESSAY ONE: CONTROLLING INFORMATION SYMMETRY IN THE NETWORK: THE
PROMOTION OF SOCIAL LEARNING FOR MULTILATERAL MANAGEMENT .................4
THEORETICAL BACKGROUND .............................................................................................9
Social Learning Theory..........................................................................................................9
Influence Strategies ..............................................................................................................13
The Structure of Networks ...................................................................................................16
HYPOTHESES DEVELOPMENT .........................................................................................18
Rewards and Punishment Usage and Compliance ...............................................................20
Network Density and Compliance .......................................................................................24
Referent Network Centrality: The Influence of Referent Prominence ................................27
Reward ...........................................................................................................................29
Punishment.....................................................................................................................31
METHODOLOGY ....................................................................................................................33
Research Context and Data Collection ................................................................................33
Measures ..............................................................................................................................36
ANALYSIS AND RESULTS .....................................................................................................38
Measurement Model Analysis..............................................................................................38
Measure Validation ........................................................................................................38
Assessment of Common Method Bias ...........................................................................40
Model and Estimation ..........................................................................................................40
Results ..................................................................................................................................42
DISCUSSION ............................................................................................................................45
Theoretical Implications ......................................................................................................46
Managerial Implications ......................................................................................................50
Limitations and Directions for Future Research ..................................................................52
ESSAY TWO: THE RE-ANCHORING EFFICACY OF EXPLANATIONS: LOWERING
PERCEPTIONS OF UNFAIRNES ................................................................................................54
THEORETICAL BACKGROUND .........................................................................................58
Equity Theory and Perceptions of Unfairness .....................................................................58
Re-anchoring through Explanation ......................................................................................61
HYPOTHESES DEVELOPMENT .........................................................................................65
The Re-anchoring Efficacy of Explanations ........................................................................65
Timing: Proactive vs. Reactive Explanations ................................................................65
Provision of a New Anchor ............................................................................................67
Re-anchoring and Perceived Unfairness ..............................................................................70
vi

METHODOLOGY ..................................................................................................................71
Research Context and Data Collection ................................................................................71
Pretest 1 ..........................................................................................................................72
Pretest 2 ..........................................................................................................................73
Pretest 3 ..........................................................................................................................74
Experimental Procedure .......................................................................................................75
Measures ..............................................................................................................................77
Dependent Measures ......................................................................................................77
Manipulation Checks .....................................................................................................78
Control Variables ...........................................................................................................78
Manipulation Checks ...........................................................................................................78
ANALYSIS AND RESULTS .....................................................................................................79
The Re-anchoring Efficacy of Explanations ........................................................................79
Re-anchoring and Perceived Unfairness ..............................................................................85
DISCUSSION ............................................................................................................................87
Theoretical Implications ......................................................................................................87
Managerial Implications ......................................................................................................91
Limitations and Directions for Future Research ..................................................................93
APPENDICES ...........................................................................................................................97
APPENDIX 1.1 SOLICITATION EMAIL TO DEALERSHIP MANAGERS ...................98
APPENDIX 1.2 MEASURES (ESSAY1) ...........................................................................99
APPENDIX 2.1 EXPERIMENTAL SCENARIO (PRETEST 1) ......................................102
APPENDIX 2.2 EXPLANATION MANIPULATIONS (PRETEST 1) ............................103
APPENDIX 2.3 MEASURES (PRETEST 1) ....................................................................104
APPENDIX 2.4 EXPERIMENTAL SCENARIO..............................................................105
APPENDIX 2.5 EXPLANATION MANIPULATIONS....................................................106
APPENDIX 2.6 MEASURES (ESSAY 2).........................................................................107
REFERENCES ........................................................................................................................109

vii

LIST OF TABLES

Table 1.1: Social Learning Theory Literature in Marketing ........................................................ 11
Table 1.2: Increasing Network Density: Enhancing Information Symmetry.................................25
Table 1.3A: The Effects of Relative Network Centrality (Reward)...............................................30
Table 1.3B: The Effects of Relative Network Centrality (Punishment) ........................................31
Table 1.4: Measure Statistics and Correlation Matrix ...................................................................39
Table 1.5: Estimation Results (H1 -H3 ) ..........................................................................................43
Table 1.6: Group Comparison Estimation Results (H4 : Reward) ..................................................44
Table 1.7: Group Comparison Estimation Results (H5 : Punishment) ...........................................45
Table 2.1: Target Firm’s Likelihood of Re-anchoring (H1 -H3 ) .....................................................68
Table 2.2: Logistic Regression Results for Explanation Factors ...................................................80
Table 2.3: Cross-Tabulation of Re-anchoring Percentages Given Explanation Timing ................81
Table 2.4: Cross-Tabulation of Re-anchoring Percentages Given Provision of a New
.Comparative Referent ...................................................................................................82
Table 2.5: Marascuillo Procedure Results for Equality of Proportions .........................................83
Table 2.6: Logistic Regression Results for Explanation Factor Combination Scenarios ..............84
Table 2.7: Perceived Unfairness ANCOVA Results.......................................................................85
Table 2.8: Perceived Unfairness Mean Values of Target Firms .....................................................86
Table 2.9: Multiple Comparisons of Perceived Unfairness Mean Values of Target Firms ............86
Table A.1: Measurements, Factor Loadings, Composite Reliabilities, and AVEs (Essay 1) .........99
Table A.2: Dependent Measures, Manipulation Checks, and Control Variables (Pretest 1)........104
Table A.3 Dependent Measures, Manipulation Checks, and Control Variables (Essay 2) ..........107

viii

LIST OF FIGURES

Figure 1.1: Illustration of Information Flow in Networks .............................................................18
Figure 1.2: Social Learning in Networks .......................................................................................21
Figure 2.1: External Social Comparison ........................................................................................60
Figure 2.2: Conceptual Model of Anchoring and Adjustment .......................................................62
Figure 2.3: Model of Comparative Referent Change through Explanations .................................65

ix

INTRODUCTION
Increased information symmetry within a network of multiple firms has substantively
complicated how a focal firm governs dyadic exchanges. In the past, firms engaged in
contractual agreements and other inter-organizational activities in a private manner. Members to
the exchange were only aware of how things were governed in their immediate relationship.
However, changes in information technology have created an ‘information-rich environment’
within which many inter-organizational actors interact within a firm’s wider business network,
stimulating social comparison between network members (Novemsky and Schweitzer 2004). For
example, a franchisor manages a ‘set’ of exchange relationships with its franchisees. While in the
past each relationship was able to be managed independently, increased information symmetry
across franchisees increasingly complicates the franchisor-franchisee management as franchisees
gain greater information about how the franchisor manages its relationship with other franchisees.
As exchange actors are now able to form judgments on the relationship with their partner not
only based upon how their partner treats them (i.e., internal comparison) but also based on
information of how their partner governs its other relationships (i.e., external comparison), firms
need to more carefully manage not only within, but across relationships (Wathne and Heide
2004).
Although the nature of information asymmetry is changing, it is important to note that
this change is derived both internally and externally; some being under a firm’s control that can
be used strategically to change behaviors and others being forced upon them by the environment
resulting in unintended negative consequences of perceived unfairness. For example, some
corporations have initiated internet-based exchanges establishing online links with partnering
firms (Dai and Kauffman 2002) or encourage interaction through regular conference meetings

1

(i.e., internally driven), whereas social network sites are often created among actors themselves
which further facilitate the flow of information (i.e., externally driven). This change in the nature
of information asymmetry necessitates a movement from a dyadic perspective to a network
perspective (Anderson, Hakansson, and Johanson 1994; Provan 1993). It has long been
recognized that dyadic economic transactions are embedded in a broader system of social
relationships. Prior to this information-rich environment, the consideration of dyadic relationship
management in isolation was not significantly questioned. However, the increase in the diffusion
of information and communication technologies (both initiated by the firm and thrust upon the
firm by the environment) necessitates a better understanding of how these changes may influence
the management of inter-organizational relationships.
To address this issue, this two essay dissertation examines the impact of increased
information symmetry on the multilateral management of inter-organizational relationships.
Essay One examines how a focal firm managing multiple relationships can effectively adjust
information symmetry and employ influence strategies in a particular relationship so as to
effectively manage another exchange relationship within the network, gaining compliance from
members of the network. Drawing on structural network theory and social learning theory
(Bandura 1977), this essay investigates how a focal firm’s ability to manage the structural
properties of a network— specifically, density of the network and relative network centrality of
the referent firm— as well as the influence strategies (i.e., rewards versus punishment) (Frazier
and Summers 1986; Payan and McFarland 2005) used in the focal dyads influence behavioral
responses (e.g., social learning) in an exchange network. This research demonstrates ‘social’
learning occurs where network actors (beyond a single dyadic relationship) learn and change
their behaviors following observations and receipt of referent information. A field survey was

2

administered to automotive dealerships within larger dealer networks to capture behavioral
responses of firms. This study argues for differential effects based upon the type of influence
strategy employed (i.e., rewards vs. punishments) as well as the observing firm’s relative
network centrality. Hence, this essay contributes to the literature by providing greater theoretical
understanding to inter-organizational management within a business network, expanding our
view of inter-organizational relationships beyond the dyadic interaction and integrating the social
contextual motivations behind firm behavior.
Whereas Essay One examines when information symmetry changes impact behavioral
spillovers, Essay Two examines the context in which the symmetrical information environment
(whether internally or externally created) can have negative consequences for a focal firm in the
management of multiple relationships within a network due to perceptions of unfairness (when
partner firms anchor on the wrong comparative referent). Specifically, it examines the efficacy of
explanations (Shapiro, Buttner, and Barry 1994; Bies 1987) in re-anchoring (i.e., changing the
comparable referent) and lowering unfairness perceptions of a partner firm that experienced a
negatively-perceived allocation event. Drawing on equity theory (Adams 1963) and the conflict
management literature, this essay investigates the factors that enhance or diminish the efficacy of
explanations in re-anchoring (e.g., timing and content) a target firm and demonstrates the effect
of explanation communications on lowering their subsequent unfairness perceptions (Samaha et
al. 2011) through an experimental survey of brand managers on their relationship with a larger
retailer. This essay contributes to the literature by extending the usage of explanations in
situations where differential inter-organizational management is inevitable but the information
environment facilitates external social comparisons.

3

ESSAY ONE: CONTROLLING INFORMATION SYMMETRY IN THE NETWORK:
THE PROMOTION OF SOCIAL LEARNING FOR MULTILATERAL MANAGEMENT
In November of 2009, General Motors (GM) reported to the Associated Press that it was suing
JTEKT North America, a major automotive supplier, for providing below-quality steering
systems used in the Chevrolet Cobalt, its best-selling small car. In the same year, General Motors
publically recognized companies, including GENTEX Corporation and Denso Corporation, with
its Supplier of the Year award, highlighting the significant contributions of these GM suppliers in
the company’s global product and performance achievements (GM News 2010). These two
public announcements not only publically punished an errant supplier and rewarded cooperative
suppliers, but also sent a message to other suppliers of GM of the behaviors GM does not
condone as well as what types of behaviors it desires. Like GM, many firms employ punishments
and rewards to elicit compliance which are often needed given the lack of alignment between
goals of transacting parties. However, reinforcement strategies used within a dyad are somewhat
limited in that they are targeted to a single partner at a time, and as such, firms are looking to
engage strategies that can be employed for effective ‘multilateral’ relationship management.
Specifically, firms are working to exploit the usage of influence strategies in one relationship to
create spillover effects in other relationships.
Reinforcement strategies utilizing rewards and punishments have been used extensively
by firms as influence mechanisms to gain compliance and elicit desired behaviors from channel
partners (Keith, Jackson, and Crosby 1990; Payan and McFarland 2005). By far, the predominant
focus has been on the bases of power (Stern 1969; Gaski 1984; Etgar 1978; Frazier and Summers
1984; Mohr and Nevin 1990) and the application of power via influence strategies (e.g. rewards
and/or punishments) (Ball, Trevino, and Sims 1994; Frazier and Sheth 1985; Molm 1988) to
control behavior (Heide 1994; Stump and Heide 1996) within dyadic exchanges. Yet, a critical
4

aspect of exchanges is that they are often embedded in a broader network of firms (Granovetter
1985). A dyadic relationship does not exist in isolation, impervious to external events.
In fact, over the last decade, researchers have been increasingly interested in the study of
the dynamics of effects beyond the dyad. For example, Antia and Frazier’s (2001) findings
suggest firms take into consideration network factors of density and centrality in enforcement
responses in channel relationships. Wathne and Heide (2004) demonstrate that a firm’s
management response in a dyadic relationship is contingent on how a related relationship outside
of the focal dyad (but in its immediate network context) is organized. Literature also
demonstrates how interfirm behavior can be socially motivated, resulting in spillover effects
from one dyadic relationship to an adjacent dyad (McFarland, Bloodgood, and Payan 2008).
While the majority of the research has focused on the relational or structural aspects of network
theory for either managing or coordinating a single or two adjacent dyadic relationships, little
research has been directed at the effect the structure of networks have in proactively
implementing more holistic (i.e., emphasizing the importance of the network of relationships as a
whole and the interdependence of its dyadic parts) and active influence efforts. A network is
defined as a composite of a larger number of connected actors (Iacobucci and Hopkins 1992).
Firms are embedded in networks (Grewal, Lilien, and Mallapragda 2006; Ibarra, Kilduff, and
Tsai 2005) in which sharing of information occurs between actors.
It has been typically assumed that parties in a marketing relationship possess asymmetric
amounts of information about the exchange. When one member is more or better informed than
the other, information asymmetry emerges1 (Bergen, Dutta, and Walker 1992; Eisenhardt 1989;

1

Information asymmetry can arise from two sources: information related to (1) conditions of a
particular transaction and (2) how other relationships are managed. Previous literature deals with
information asymmetry from the aspect of information related to a party’s true characteristics
5

Rindfleisch and Heide 1997). Different parties to a transaction often have different amounts of
information regarding the transaction and this information asymmetry has implications for the
terms of the transaction and the relationship between parties (Kirmani and Rao 2000). In this
essay, the information relates to the knowledge of how the focal firm is managing other dyadic
exchanges with partners in similar positions. Previously, information was distributed
asymmetrically in the sense that a particular firm was not able to discern its partnering firm’s
influence strategies towards other partners. Now with the changing information environment,
this information has become more symmetric, as it has become more transparent to other firms in
a network.
Taking into consideration the social embeddedness of exchange relationships, the focal
firm has the ability to control the characteristics of this broader environment to its advantage to
manage multilaterally as illustrated in the opening vignette regarding GM. Theoretically, the
ability to influence compliance behavior of others outside of reinforcement is founded on social
learning. Social learning theory (SLT) suggests that individual behavior is determined by both
the environment and an actor’s motivation to learn proactively from important referents (i.e.,
other parties in the network). Actors can engage in social learning by observing other referents
(Bandura 1977; Manz and Sims 1981). Social learning effects (behavior spillover) of other
members in the network occur as a result of increased observability of other actors in the
network (i.e., increased referent information availability). By observing the outcomes of the
behaviors of referent parties within the network, observers form outcome expectancies and thus
develop a propensity to either engage in or refrain from engaging in certain behaviors. When
and abilities (ex ante) and task performance (ex post) pertaining to the particular transaction
(Bergen, Dutta, and Walker 1992; Rindfleisch and Heide 1997). This essay focuses on the second
type of information regarding knowledge of other relationships.
6

observability is high among network members, social learning takes place, promoting
compliance and reducing noncompliance, thereby facilitating coordination in exchange settings.
To foster social learning, members of the network must be able to observe the influence
actions of the focal firm in a referent dyad. Today, given information and communication options,
firms can take on a more active strategic approach by adjusting the level of information
symmetry they desire within its partner network to manage their multiple relationships as a
whole. In the terminology of network theory, the extent of the interconnection among all the
actors of the network refers to network density (Coleman 1988). To strategically adjust network
density, firms can employ information system portals and/or public outlets. By doing so firms
can increase the connection and information flow among actors, thereby making their influence
strategies of rewards and punishments in a particular dyad more visible to its network, fostering
social learning (as in the prior GM examples). While firms can strategically enhance network
density, firms must also be aware that once network density is increased, it is difficult to retract.
For instance, once GM’s suppliers know that JTEKT North America supplies GM, it is difficult
to un-inform GM’s suppliers of this connection).
Network theory also identifies network centrality as key to understanding the operation of
a network. Network centrality refers to the position an organization occupies in a network and
denotes the extent to which the organization occupies a socially-defined position of prominence
by virtue of being involved in many ties (Wasserman and Faust 1994). Rewards and punishments
may be viewed differently when enforced upon a high centrality referent versus a low centrality
referent and also depending on the centrality of the observing firm. Therefore, the decision to
increase network information symmetry must be a well-contemplated decision, taking into

7

consideration multiple factors (e.g., type of influence strategy used, network centrality of the
referent and observing firms) that may differentially influence social learning in the network.
Utilizing SLT, this research examines how a focal firm managing multiple relationships
can effectively adjust information symmetry and employ influence strategies in a particular
relationship so as to effectively manage another exchange relationship within the network,
gaining compliance from members of the network. Specifically, the following research questions
are addressed:
(1) To what extent does social learning occur within a network, observing reward and
punishment of other members to a network?
(2) Are the effects of social learning consistent, or are there differential effects, when firms
employ rewards versus punishments?
(3) Are the effects of social learning consistent, or are there differential effects, based upon
the network centrality of the referent and observing firm of the influence strategy?
By addressing these questions, this research contributes to the field of marketing in three
distinct ways. First, this study extends our current understanding of social learning in networks
and its effectiveness in enhancing channel partner compliance across multiple interorganizational relationships. This research demonstrates ‘social’ learning occurs where network
actors (beyond a single dyadic relationship) learn and change their behaviors following
observations and receipt of referent information. This work extends the current interorganizational literature on influence strategies by recognizing the occurrence of information
flow and social observation activities among multiple inter-firm relationships.
Second, this study provides empirical evidence that reward and punishment usage may
have differential effects in motivating behavior of channel members. Rewards and punishments

8

were differentially effective in bringing about social learning.

Although the social learning

theory literature suggests similar behavioral responses from reward and punishment, the results
of this study suggests that this may change in a business context. This study provides a deeper
understanding of the differential behavioral responses to reward and punishment observation
within business networks and cautions practitioners in being aware of the unintended
consequences and being selective of their usage.
Third, the research contributes to the literature by identifying the contingent network
structural characteristics that may amplify or dampen the social learning effect, providing deeper
understanding of the differential consequences of the usage of influence strategies in network
environments. Moreover, depending on the relative network centrality of the referent firm
receiving the reward or punishment, observing firms may react differently. The study examines
the network factors that influence both the strategic management of the dyad and the network
(multiple dyads) as a whole and that the principal firm must be aware of these factors in making
holistic relationship management strategy decisions when simultaneously considering multiple
partners.
THEORETICAL BACKGROUND
Social Learning Theory

Social learning theory (SLT) (Bandura 1977) derives its name from the emphasis it places on
learning from the experience of others rather than personal experience — social learning. At the
heart of SLT is the premise that behavior results from the interaction of actors and situations.
Social learning is defined as behavioral change resulting from situational observations (Bandura
1977). Social learning broadly refers to any mechanism through which individuals learn from

9

others (Bikhchandani, Hirshleifer, and Welch 1998). It includes mechanisms in which
individuals learn from each other through formal or informal communications. It also includes
learning through simply observing the actions of others and/or the consequences of these actions
(also referred to as ‘observational learning’). The literature on social learning assumes that there
are multiple actors who are informationally linked, so that the actions and payoffs of one actor
provide information to other actors about the environment (Sobel 2000). Actions reflect
information and individuals’ behavior is impacted by their communication and/or observation of
the actions of others because of the information contained therein (Banerjee 1992; Bikhchandani,
Hirshleifer, and Welch 1998). Social learning therefore enables individuals to avoid needless and
costly errors (Bandura 1977; Manz and Sims 1981). As such, SLT suggests vicarious learning to
be a prominent element in acquiring behaviors (Bandura 1977). Most of the behaviors that
people display are learned either deliberately or inadvertently, through the influence of example
or models, that is, via observation of social referents.
Considerable research has demonstrated how people quickly reproduce the actions
exhibited by social referents. SLT has been widely employed to examine behavior in various
business environments (Bikhchandani, Hirshleifer, and Welch 1998; Sobel 2000) (see Table 1.1
for detailed findings). For example, marketing personnel were found to socially ‘learn’
marketing skills and strategic orientations by observing their environment and the behavior of
mentors (Hartline, Maxham, and McKee 2000; Lam, Kraus, and Ahearne 2010). Similarly,
researchers have found that consumers engaged in social learning processes before making
purchase decisions (Cai, Chen, and Fang 2009; Chen, Wang, and Xie 2011; Godes et al. 2005).

10

Table 1.1: Social Learning Theory Literature in Marketing
Authors
Bikhchandani,
Hirshleifer,
and Welch
1998

Sobel 2000

Davis and
Luthans 1980

Ginter and
White 1982

McKee,
Conant,
Varadarajan,
and Mokwa
1992

Hartline et al.
2000

Journal

Title

Arguments and Research
Findings
Argues that learning by observing
Learning from the
the past decision of others can help
Journal of
Behavior of Others:
explain human behavior. Theory of
Economic
Conformity, Fads, and
observational learning has much to
Perspectives Informational
offer economics and business
Cascades
strategy.
Classifies the learning setting on the
basis of the strategic environment,
Journal of
the way in which agents collect
Economists’ Models of
Economic
information, and the degree of
Learning
Theory
rationality of the agent. Examines
circumstances under which learning
leads to optimal decisions.
Social learning theory approach,
A Social Learning
incorporating the interactive nature
Academy of
Approach to
of all the variables of organizational
Management
Organizational
behavior—behavior, environment,
Review
Behavior
and person—is proposed for
enhanced managerial effectiveness.
Presents a social learning theory of
strategic management (SLTSM),
A Social Learning
offering a conceptual framework
Academy of Approach to Strategic
that links the interdependent
Management Management: Toward
elements of executive cognition,
Review
a Theoretical
stimulus and consequence
Foundation
environment, and strategic
behaviors through reciprocal
determinism.
Success-Producer and
Social learning perspective of skill
Journal of
Failure Preventer
development: Firms develop skills
Academy of
Marketing Skills: A
through the interaction of
Marketing
Social Learning Theory management cognition, strategic
Science
Interpretation
direction, and the environment.
The main “corridor” of influence in
Corridors of Influence
the dissemination of customerin the Dissemination of
oriented strategy is between
Journal of
Customer-Oriented
managers and customer-contact
Marketing
Strategy to Customer
employees, where employee
Contact Service
behavior development is greatly
Employees
influenced by mentor behavior.

11

Table 1.2 (cont’d)
Authors

Journal

Lam, Kraus,
and Ahearne
2010

Journal of
Marketing

Godes et al.
2005

Marketing
Letters

Cai, Chen, and
Fang 2009

American
Economic
Review

Chen, Wang,
and Xie 2011

Journal of
Marketing
Research

Title

Arguments and Research
Findings
The Diffusion of
Examines the diffusion of marketing
Market Orientation
orientation as a social learning
Throughout the
process. Identifies who the important
Organization: A Social work-group envoys are and under
Learning Theory
what conditions certain envoys are
Perspective
likely to be most effective.
Consumer tend to be influenced by
their social interactions with others
when they make purchase decision
The Firm’s
and firms can manage these social
Management of Social
interactions and take on a more
Interactions
active role as an (1) observer, (2)
moderator, (3) mediator, and (4)
participant.
Found that consumers engage in
observational learning in the context
Observational
of restaurant dining and this
Learning: Evidence
influences individuals’ behavior and
from a Randomized
subjective dining experience. The
Natural Field
observational learning effect was
Experiment
stronger among infrequent
customers.
Examines the effect of WOM and
observational learning on
consumers’
Online Social
purchase decisions and find that
Interactions: A Natural
negative word-of-mouth (WOM) is
Experiment on Word of
more influential than positive WOM
Mouth Versus
and positive observational learning
Observational Learning
(OL) information significantly
increases sales while negative OL
information has no effect.

Firm recognition of social learning effects have created opportunities for firms to actively
engage consumer markets. For example, in the consumer marketing literature (e.g., Chen, Wang,
and Xie 2011; Godes et al. 2005) advanced information technologies have been used to create
opportunities for firms to facilitate and manage consumer social interactions that drive consumer

12

choice. The marketing literature also demonstrates how the proactive establishment of learning
routes within firms promotes desired customer-oriented behavior of employees. For instance,
Hartline, Maxham, and McKee (2000) and Lam, Kraus, and Ahearne (2010) claim that workgroup socialization plays a pivotal role in the dissemination of a firm’s strategy from top
management to frontline employees and that firms need to understand the importance of creating
smaller work groups and provide more opportunities for expert peers, who act as the social
learning referents, to interact with other group members to facilitate peer-to-peer social learning.
Here, it is proposed that firms can also employ similar strategies within interorganizational relationships. Social learning is argued to facilitate the diffusion of behaviors
among members within multiple socially connected exchange dyads, i.e., a network. By
controlling the level of information provided to the network of the actions and consequences of
social referent firms, a focal firm is implementing a strategy for managing channel partner
compliance. The disclosure of information of actions in a particular exchange dyad within the
network facilitates social learning of other members. As such, a focal firm is able to utilize their
network to its advantage by adjusting the network structural factors to enhance the
contemporaneous social learning of multiple partners.
Influence Strategies

A general problem in managing exchange relationships is obtaining compliance among a
collection of firms who share only partially congruent objectives (Ouchi 1979). Research in the
inter-organizational marketing literature has provided some insights into the way ongoing interorganizational relationships can be effectively maintained and controlled (cf., Stump and Heide
1996). The earlier behavioral research paradigm’s primary focus was on the use of power and
design of mechanisms for controlling channel behaviors (Stern 1969; Gaski 1984). The concept
13

of power reflects one firm’s potential for influence on another firm’s beliefs, attitudes, and
behavior (El-Ansary and Stern 1972; Frazier 1983; French and Raven 1959; Hunt and Nevin
1974; Lusch 1976). Bases of power may be transformed into influence strategies. Influences
strategies are compliance-gaining tactics that firms use to achieve their desired actions from a
partner (i.e., referent firm) (Frazier and Summers 1984; Boyle et al. 1992). Compliance refers to
the channel member acting in accordance with the influence attempt from the source (Payan and
McFarland 2005).
Inter-organizational power is often equated to the authoritative control mechanisms and is
seen as synonymous with the use of pressure or force by its boundary personnel (Weitz and Jap
1995). Influence strategies represent the “means” or “instruments” a focal firm uses to exert
power over another firm (Dahl 1957) to induce or inhibit certain behaviors and gain compliance.
Influence strategies have been categorized as either coercive or noncoercive (Frazier and
Summers 1986). Coercive influence strategies motivate compliance or behavior change on the
basis of the influence mechanisms of source (focal firm)-controlled rewards and punishments
(Frazier and Rody 1991; Payan and McFarland 2005), whereas noncoercive influence strategies
operate by changing the attitude of the target about the desirability of the intended behavior
(Frazier and Summers 1994; 1996). This essay focuses on the influences strategies employing
reward and punishment.2

2

Wrong (1979. P.79-80) and French and Raven (1959, p.158) also suggest the possible
equivalence of unexercised rewards and punishment. Gaski and Nevin (1985) also examine the
differential effects of exercised and unexercised power sources. As Baldwin (1971) questions, “is
withholding a reward ever a punishment? Always a punishment? Is withholding a punishment
ever a reward?” (p. 23). He suggests resolution in terms of the subject’s “baseline of
expectations.” That which improves the value proposition relative to the baseline of expectation
is a reward (a positive sanction in Baldwin’s lexicon): a deprivation relative to this baseline is a
punishment, or negative sanction.
14

The provision of rewards and punishment is considered to be useful in proactively
obtaining social control (Coleman 1990; Stump and Heide 1996) and has been argued to
constitute a key aspect of relationship management (Heide and John 1992; Holmstrom and Tirole
1989). Typically, it is claimed that reward and punishment differ from other bases of power in
that the source firm mediates some consequences for the target (Frazier 1984; John 1984). A
positive influence attempt involves reward, the bestowal of consequences that the target
evaluates as desirable and a negative influence attempt involves punishment, the bestowal of
aversive consequences or penalties (Ball, Trevino, and Sims 1994; Busch 1980; Frazier and
Summers 1986; Mohr and Nevin 1990; Scheer and Stern 1992). These influence strategies
(whether positive or negative) motivate aligned behavior by linking an offer of instrumental gain
to performance (John and Weitz 1989; Williamson 1991). The rationale for the use of reward
and/or punishments is to manage a relationship in such a way that particular actions are explicitly
promoted or sanctioned (Heide and Wathne 2006; Telser 1980) and behavior is reinforced.
Previous research on influence strategies has demonstrated their effectiveness on
compliance and relational outcomes within dyadic channels of distribution (Boyle et al. 1992;
Frazier and Rody 1991; Frazier and Summers 1984; Kim 2000; Payan and Mcfarland 2005). Yet,
most work on management through social control of firms has been dyadic and has not examined
‘social’ learning where network actors can learn and change their behaviors following
observations and receipt of referent information. Members embedded within networks
experience social learning which influences behavior. This essay examines if and when exchange
members come to adopt certain compliance behaviors in the network system through observation
of the explicit usage of influence strategies (specifically, reward and punishments) on other
referents.

15

The Structure of Networks

Originating in the field of sociology, one use of network theory has been to investigate
relationships between individuals and organizations (Iacobucci and Hopkins 1992; Parkhe,
Wasserman, and Ralston 1996). Often, dyadic exchange relationships are embedded in a larger
set of exchange relations called a “network” (Iacobucci and Hopkins 1992). A network is defined
as a composite of a larger number of actors and the pattern of relationships that ties them
together (Iacobucci and Hopkins 1992). The social entities within a network are connected by a
set of linkages through which they exchange information or resources, or both (Borgatti and
Foster 2003). Grewal and Dharwadkar (2002) note that inter-organizational relationships are
embedded in larger social contexts and that the ubiquitous influence of the larger network
environment has been overlooked. Previous literature further suggests a strong motivating force
behind firm behavior which is socially based, embedded within interconnected organizational
relationships (Anderson, Hakansson, and Johanson 1994; Provan 1993) and points out the need
to study organizational behavior in situ, or from a larger ecological perspective (Davis and
Luthans 1980) at the network level. Network theory has grown in its use in the marketing
literature (Achrol and Kotler 1999), and has been applied to the structure of marketing
organizations (Achrol 1991), dyadic business relationships (Anderson, Hakanson, and Johanson
1994; Wathne and Heide 2004), strategic alliances (Rindfleisch and Moorman 2001;
Swaminathan and Moorman 2009), and relationship marketing (Hakansson and Snehota 1995).
Network models examine systems of dyadic interactions, capturing the influence of
multiple and interlinked exchange relationships on firm behavior (Rowley 1997). The most
distinctive feature of the network perspective is its focus on relationships among social entities
and the structures and implications of these relationships (Wasserman and Faust 1994). The
16

network perspective focuses on delineating the linkages among actors, assessing the structural
and motivational underpinnings of these linkages, and examining their outcomes (Galaskiewicz
and Wasserman 1994). In a network, multiple dyadic exchange relations are linked together in
such a way that events occurring in one location of the network have predictable consequences
for events occurring in other network locations (Cook and Emerson 1978; Yamagishi, Gillmore,
and Cook 1988). Network studies consist of two general streams: (1) the structural stream
focuses on the number and degree of connections within a network, and (2) the relational stream
emphasizes the importance of the relational bonds of network actors focusing on the strength-ofties literature3 (Granovetter 1973). Many researchers have urged the analysis of interorganizational relationships incorporating network factors to enhance the richness of theory
development of dyadic inter-organizational relationships (Anderson, Hakanson, and Johanson
1994; Antia and Frazier 2001). Taking a structural approach to network analysis (i.e., the
interconnectedness), this work examines a focal firm’s ability to manage the structural properties
of a network— specifically, density of the network and relative network centrality of the
observing firm— as well as the influence strategies used in the focal dyads as an initial step
toward understanding the structural influences of social learning in an exchange network. These
network factors influence the level and impact of referent information availability to observing
firms in the network and differentially influence socially learned behaviors of the members of the
network.

3

The relational stream draws attention to the fact that network linkages vary in terms of the
degree of reciprocity and emotional closeness shared by network actors. The strength of ties
literature is primarily concerned with the ‘nature’ of the relational bond between two or more
social actors, as well as the effect of this bond on their information sharing activities (Frensen
and Nakamoto 1993; Grannovetter 1973)
17

HYPOTHESES DEVELOPMENT

SLT examines both behavioral and cognitive processes in the environmental context in which
they take place (Mahoney 1977). SLT holds that behavior is determined by expectancies and
incentives (Bandura 1986). Economic actors are rational and calculate the best possible means to
engage in interaction and seek to maximize profit/returns from an exchange (Homans 1958). The
more often a particular action is rewarded through incentives the more likely a member to an
exchange will perform that action again in the future; when an exchange members’ action
receives unexpected punishment, the exchange member will aggressively avoid the action
(Homans, 1961; Michener and DeLamater 1999). Following the same logic, economic actors in a
network, observing others’ outcomes, are rational and calculate the best outcome for themselves.
They look at how others are treated, process the information, form ‘expectations’ of
consequences and consider the possibility of what is done to others could be done to them
(Bandura 1986).
In this work, it is argued that observation of influence strategies employed (i.e., a
stimulus) in a dyad will lead to a response in another dyad, resulting in social learning. A
common form of a network is where there are multiple firms that are connected to a dominant
hub firm (see Figure 1.1).

18

Figure 1.1: Illustration of Information Flow in Networks

Focal
Firm A

Dyadic
Exchange

Observing
Firm 1

Referent
Firm B

Observing
Firm 𝑛

Information
flow

While not all networks look like this, this work studies the problem of multilateral
management (McAfee and Schwartz 1994) where a focal firm (A) is simultaneously dealing with
several firms (B,

1-

).4 Examples of this relationship include a manufacturer selling to

distributors, a manufacturer purchasing from several suppliers such as GM sourcing from
multiple suppliers across the globe, a patent-holder licensing several producers, or a franchisor
with several franchisees. The focus of this work is on the structural importance of indirect
exchange relations (e.g., the second-order exchange relations related indirectly through a
common link—the focal firm). The focal firm (A) may use rewards to encourage compliance or
4

The structural network literature on social capital includes a discussion on ‘indirect ties’ that
examine the nature and importance of structural holes (Burt 1992), network closure (Coleman
1988), and “Simmelian” ties (Krackhardt 1999). For the purpose of this dissertation, the focus is
narrowed down to the effects of density (information symmetry) and centrality of firms of the
network; a focal firm works with multiple partner firms (constituting a ‘network’ of relationships
through which information flows).
19

punish noncompliance of the referent firm (B). The observing firm in the network (C), will
socially learn from this dyadic interaction by processing information of the consequences to the
referent firm (B). If the referent firm (B) was rewarded for compliance or punished for a
noncompliance, the observing firm (C) will increase their compliance with the expectancy of a
reward or to avoid similar punishment.
Rewards and Punishment Usage and Compliance

The use of influence strategies employing either reward or punishment provides the means to
influence another’s compliance (Frazier and Summers 1984; Frazier and Sheth 1985; Heide 1994;
Molm 1988). Reward is the administration of outcomes that are of positive value to reinforce
behavior whereas punishment is the presentation of a negative consequence in order to minimize
undesirable behavior (Ball, Trevino, and Sims 1994; Frazier and Sheth 1985). When punishing
or rewarding exchange partners, a firm may wish to purposefully disclose this information (and
increase referent information observability via changing the density of the network, e.g.,
establishing information portal systems, hosting industry conferences and/or making public
announcements) to other partnering firms to gain the benefits of social learning (see Figure 1.2).

20

Figure 1.2: Social Learning in Networks

Network Characteristics
 Network Density (H3 )
 Relative Network Centrality of
Observing Firm C (H4 -H5 )

Influence Strategies used on
Referent Firm B (𝐇𝟏 -𝐇𝟐 )
Reward
Punishment

Compliance of Observing
Firm C

Individuals learn from the consequences of their own behavior; thus, they are likely to
increase (decrease) the frequency of behavior that has resulted in positive (negative)
consequences. Positive reinforcement and punishment provide situational feedback that leads to
learning (Baumeister, Bratslavsky, Finkenauer, and Vohs 2001). Bandura (1976) states that
although behavior can be shaped into new patterns to some extent by rewarding and punishing
consequences (i.e., reinforcement learning), learning would be exceedingly laborious if it
proceeded only on a reinforcement basis. Social learning theorists have argued that “vicarious,
imitative learning seems to better explain the rapid transference of behavior than does the tedious
selective reinforcement of each discriminable response” (Davis and Luthans 1980, p. 283).
Social learning is expected to occur when the observing firm observes the interaction
between the focal firm and referent firm. By observing the outcomes of the noncompliance of the
referent firms within the network, observing firms form outcome expectancies of engaging in
similar behavior and thus develop a propensity to avoid the behavior. When a firm observes the
reward of compliance in a referent dyad, expectancies of similar rewards for compliance are
formed and the more likely the observing firm will increase compliance behavior. The focal
21

firm’s use of punishment and reward on the referent firm is more positively associated with the
observing firm’s compliance. Thus, it is hypothesized that:
H1 :

The focal firm’s use of reward on the referent firm is positively associated with
the observing firm’s compliance.

H1 :

The focal firm’s use of punishment on the referent firm is positively associated
with the observing firm’s compliance.

Researchers suggest that a given probability of punishment may be a greater deterrent
than an equal probability of rewards (Gray and Tallman, 1987; Kahneman, Slovic, and Tversky
1982). The greater power of bad events over good ones has been demonstrated by Baumeister, et
al. (2001). Theoretically, the behavioral literature argues for a cognitive positive-negative
asymmetry in which perception is guided more by negative than positive events (Anderson 1965;
Skowronski and Carlston 1989; Peeters and Czapinski 1990; Wright 1991). Actors appear to be
predisposed to learn more rapidly and easily about the correlates of negative than positive events
(Baumeister et al. 2001). In reinforcement learning contexts, the punishment of incorrect
responses was consistently found to be more effective than the reward of correct responses (e.g.,
Spence and Segner 1967; Tindall and Ratliff 1974). Punishment led to faster learning than
reward, across a variety of punishments and rewards (e.g., punishment contingencies resulted in
longer, more effective inhibition of responses than reward contingencies). Costantini and Hoving
(1973) suggest that underlying this bias is reasoning that the motivation to avoid losing
something is greater than the motivation of gaining something and this motivation influences
learning. Their findings indicate that learning and conditioning are more strongly influenced by

22

negative events than positive, even when the objective magnitude of positive and negative is
precisely equated.
This positive-negative asymmetric effect is closely aligned with the loss aversion
described in prospect theory (Kahneman and Tversky 1979) which holds that losses have more
impact than comparably sized gains in economic decision making. The theory says that people
make decisions based on the potential value of losses and gains (e.g., the expected consequences
of reward and punishment obtained through observation of social referents), and that people
evaluate these losses and gains asymmetrically. People value gains and losses differently and
losses hurt more than gains feel good (Kahneman and Tversky 1979). Research shows that
negative events elicit more cognitive work and causal attributional activity (Taylor 1991) than do
positive events (Peeters and Czapinski 1990) and counteracting processes are initiated to remove
the source of ‘threat’ (Taylor 1991). The evocative potential of negative events has ‘survival’
benefits (Taylor 1991) and this adaptive positive-negative asymmetry has evolved to maximize
the likelihood of a tendency to effectively respond to threats or costs. In a business context, it can
be theorized that firms will react more strongly when they observe the referent’s noncompliance
being punished than when they observe compliant behavior being rewarded. When a referent’s
action receives punishment, the observing network member will aggressively avoid the action in
the future to avoid the costs of punishment beyond the degree to which a referent observes or is
informed of a positive reward incident and performs the desired action. More elaboration is
given to negative information of punishment consequences and the observing firm will actively
engage in responsive behaviors to avoid similar consequences. The negatively-valenced
information triggers counteracting processes to remove the possible source of threat. It is
expected that the avoidance of costs is a stronger factor than the obtainment of rewards in

23

predicting behavior, because negative events trigger the need to respond and eliminate the threat.
Thus, it is hypothesized that:
H :

The positive relationship between the focal firm’s use of punishment on the
referent firm and the observing firm’s compliance is greater than the positive
relationship between the focal firm’s use of reward on the referent firm and the
observing firm’s compliance.

Network Density and Compliance

Network density is a characteristic of the whole network and indicates the degree of
interconnectedness among actors (Burt 1992; Coleman 1990). Information is more readily
disseminated in a dense network. As density increases, communication across the network
becomes more efficient (Antia and Frazier 2001) and information can be expected to flow more
freely among members (Haythorntwaite 1996; Swaminathan and Moorman 2009). While a focal
firm has many options of increasing network density and enhancing information symmetry in the
network, three are presented for illustration (see Table 1.2).
The first option is to establish intra-net type information systems that can connect the
multiple partnering members so that information flows more freely. The focal firm can also host
conferences or workshops where the partner firms come together to connect and interact. By
virtue of having many ties and increased information symmetry, the network structure facilitates
information exchange (Rowley 1997). Secondly, the focal firm may opt to selectively disclose
information by providing information to selective members it wishes to influence. Information
conveyed to the selective firms will also flow throughout the whole network (via the established
connections between firms).
24

Table 1.2: Increasing Network Density: Enhancing Information Symmetry

Examples

Point/subject of
information
Disclosure

Focal firm
control over
extent of
information flow

Establishment of
Intranet Systems/
Hosting Conferences

Selective (private)
Disclosure
of Information to
Certain Network
Members

Public Disclosure
of Information

Domino’s Pizza’s
Franchisee Association
(DFA), Pizza Patron’s
Annual Conference

GM’s notification to
certain partner of the
punishment consequences
of another partner firm

GM’s announcement of
Supplier of the Year,
McDonald’s public
lawsuit against franchisee

Information is disclosed
by other members of the
network (e.g., sharing
experiences). The focal
does not control the point
of information disclosure,
but only provides the
foundational structure for
information flow.

The focal firm partially
controls the point as well
as the content of
information disclosure.

The focal firm controls
the point as well as the
content of information
disclosure.

The focal firm controls to
whom the information is
initially disclosed.
The focal firm does not
have direct control of the
extent of information flow
nor the audience target.

This provides possibility
for future information
flow amongst members
(which the focal firm does
not have direct control
over).

The focal firm opts to
disclose the information
as widely as possible,
simultaneously disclosing
to a wider audience.

Low

High
The focal firm’s control over the point
of time of information disclosure and
information flow extent

25

A third option is to utilize information systems or public outlets to transfer information.
Announcements can be made on rewards and punishments through information systems that
have been set up by the focal firm or through public outlets such as newspapers or the court
system (e.g., lawsuits).
These options provide to the focal firm various tools (differing in how much control the
focal firm has over the information disclosure point and flow) to increase the density of a
relevant network. With a well-established network connecting multiple suppliers, information
exchange among network members more readily occurs (Frenzen and Nakamoto 1993) with the
enhanced flow of information spreading efficiently through the dense network. Firms are also
able to increase network density and the flow of information by notifying the members of the
network privately or publically.
Network density can be intentionally enhanced by connecting multiple partner firms. If
the focal firm makes the decision to make information of the influence strategies of a particular
relationship with a referent firm observable to other observing firms, it can increase the level of
referent information availability to the observing firms by disseminating information, employing
the established communication network. Highly dense networks are more conducive to social
learning because when network density increases, the information flow within the network
increases and the dyadic interactions between firms become more manifest and observable to
other members of the network. Other network members more readily observe other ‘referent’
firms as well as the consequences of engaging in particular behaviors. Thus, it is hypothesized
that:

26

H3 :

The focal firm’s use of punishment on the referent firm is more positively
associated with the observing firm’s compliance in high density networks than in
low density networks.

H3 :

The focal firm’s use of reward on the referent firm is more positively associated
with the observing firm’s compliance in high density networks than in low density
networks.

Referent Network Centrality: The Influence of Referent Prominence

Network centrality is defined as the position an organization occupies in a network. The
centrality of a firm denotes the extent to which the organization occupies a socially-defined
position of prominence by virtue of being involved in many ties (Wasserman and Faust 1994).
Centrality reflects the strength of an individual actor’s position in a network (Benson 1975) and
takes into consideration an individual actor’s involvement in network relations (Leavitt 1951).
The general notion of centrality encompasses an aspect of the ‘importance’ or ‘prominence’ of
actors within a network (Faust 1997) who maintain a network position-conferred advantage
(Cook and Whitmeyer 1992). The logic behind high centrality is that the actor is “wellconnected”- in terms of having many relations with other members of the network, capturing the
firm’s ability to access information and potentially influence network communication activity
(Freeman 1979). Podolny (1993) notes that firms occupy “socially defined” positions in the
market. A central position reflects more social capital (Burt 2000) and prominence (Benson
1975). Prominence (i.e., greater network centrality) indicates which actor/actors is/are “more or
less in demand” (Nohria 1992, p.6). The central position in the network makes highly central
firms of greater value and importance, also making their interactions more of interest to other
27

observing firms in their network. The referent firm could be a high centrality firm or a low
centrality firm. At the same time, observing firms will also have varying levels of network
centrality. It is expected that the level of the observing firm’s social learning will differ
depending on the network centrality of both the referent firm and the observing firm itself. The
signal that is sent through the reward and/or punishment of a referent and the behavior change of
the observing firm is theorized to be dependent on the relevant structural position the observing
firm occupies in the network. Four combinations emerge (see Table 1.3A and 1.3B): two
structurally equivalent combinations (high centrality and low centrality) and two structurally
non-equivalent combinations where the observant firm is either more or less central to the
referent firm.
Structural equivalence explains behavior on the basis of network position (Burt 1982),
which influences through indirect information and social comparison where actors who perceive
themselves as similar to others adopt similar practices (Cook and Whitmeyer 1992; Festinger,
Schachter, and Back 1963). Theoretically, structurally equivalent actors are those with identical
(or similar) relations to other actors in the network (Wasserman and Faust 1994) and enjoy
similar levels of positional advantage and social capital. Structurally equivalent people occupy
the same position in social network/structure and so are proximate to the extent that they have
the same patterns of relations with occupants of other positions within the network. Structural
equivalence serves as a source of similar behavior (Mizruchi 1990). Structural equivalence
predicts that “two people identically positioned in the flow of...communication will use each
other as a frame of reference for subjective judgments” (Burt 1982, p.1293) and are more likely
to behave similarly (Harkola and Greve 1995). Actors with similar levels of centrality view each
other as having similar positions of prominence (i.e., structural equivalence). Referent similarity

28

influences behavior adoption (Cook and Whitmeyer 1992). Firms engage in a process of
comparing their firm operations to those of salient referents. Structural equivalence increases the
relevance of the referent information available to the observing firm. The more similar actors are
in terms of their position (measured by similarity of degree centrality), the more likely they will
engage in similar behaviors resulting from the observation of consequences (Burt 1987) of a
referent firm’s action. The expectation of similar consequences (resulting from similar firm
actions and comparable position) is high.
The observing firm and referent firms are at structurally non-equivalent positions when
their respective network centrality levels differ. The observing firm could be a low centrality
firm and the referent firm holds a highly central position (Cell C). In contrast, the observing firm
could hold a more central position in the network relative to the referent firm (e.g., the observing
firm is a high centrality firm and the referent firm holds a lower centrality position in the
network) (Cell B). It is expected that the different combinations of the network centrality of the
observing firm and the referent firm will have differential effects on social learning across
reward and punishment.
Reward. It is expected that the social learning effect following rewards will be the largest
in the case of low-centrality structural equivalence. When a high centrality firm receives a
reward from the focal firm, other members of the network view it as due and expected, given the
prominence and importance of a highly central member of the network. It is expected that the
focal firm is more likely to grant merit to highly valued and important players in the network.
However, when a firm of low-centrality is rewarded, this information conveys socially relevant
information. To low centrality observing firms (i.e., low centrality-structural equivalence), this
signals that there is equal opportunity and high probability that other firms in the network can

29

earn similar rewards even without the prominent positional advantage of a highly central firm.
The information of reward of a low centrality referent firm has more impact on the other low
centrality observant firms than when a highly central firm is recognized (Table 1.3A; Cell D).

Table 1.3A: The Effects of Relative Network Centrality (Reward)
High Centrality Referent (R) Firm

High Centrality
Observing (O)
Firm

Low Centrality
Observing (O)
Firm

Low Centrality Referent (R) Firm

A: High Centrality-Structural
Equivalence (HSE)

B: Higher Observer Centrality
(HOBC)

Structural equivalence effect

Low social relevance of information

C: Lower Observer Centrality
(LOBC)

D: Low centrality-Structural
Equivalence (LSE)

Reward of a highly central referent is
expected. Information contains no
impact and relevance to observing firm

Structural equivalence effect +
Informational impact: Signals equal
opportunity to rewards and not limited
to just the prominent firms (reward of
low-centrality firm is less likely than
the reward of a highly central firm).

The social learning effect is expected to be largest in the following order: D > A > B, C.

However, we do expect social learning to occur among other high centrality observing firms
when a high centrality firm is rewarded (i.e., high centrality-structural equivalence). The
information of reward of a high centrality referent firm contains relevant information for a
structurally equivalent high centrality observing firm (Table 1.3A; Cell A). The expectation that
the probability of similar rewards (resulting from similar firm actions and comparable position)
is high. Observing firms are more likely to comply after they observe a similar referent firm
being rewarded.
It is expected that the social learning effect resulting from the use of rewards in the focal
relationship will be the smallest in structurally non-equivalent combinations, when a low
30

centrality observing firm observes a high centrality firm being rewarded, the information
contains less social relevance (Table 1.3A; Cell C). The reward of a high centrality firm does not
signal a similar potential reward for a low centrality and thus, less-valued firm. High centrality
observers will be indifferent to the information of a low centrality firm being rewarded due to
structurally different positions and the relative prominence they hold (Table 1.3A; Cell B). The
level of social learning is expected to be the lowest for these combinations. Thus, it is
hypothesized that:
H4 :

The focal firm’s use of rewards on the referent firm is most strongly positively
associated with the observing firm’s compliance in the following order: (a) LSE >
HSE, (b) HSE > HOBC, (c) HSE > LOBC, and (d) HOBC = LOBC.

Punishment. In contrast to punishments, it is expected that the social learning effect
following punishment of a referent firm will be the largest in the case when the observing firm is
less central relative to the referent firm (Table 1.3B; Cell C).
Table 1.3B: The Effects of Relative Network Centrality (Punishment)
High Centrality Referent(R) Firm

High Centrality
Observing (O)
Firm

Low Centrality
Observing (O)
Firm

Low Centrality Referent(R) Firm

A: High Centrality-Structural
Equivalence (HSE)

B: Higher Observer Centrality
(HOBC)

Structural equivalence effect

Low social relevance of information

C: Lower Observer Centrality
(LOBC)

D: Low Centrality-Structural
Equivalence (LSE)

Informational impact: Signals criticality
of non-compliant behavior prohibition
and possibility of larger punishment of
low-centrality observing firm

Structural equivalence effect

The social learning effect is expected to be largest in the following order: C > A, D > B.

31

The greater a referent firm’s centrality in a network of exchange relationships, the greater the
firm’s social capital, importance, and prominence. A focal firm will thus carefully weigh the
decision of whether or not to publicize the punishment of a highly central partner compared to a
less central partner. A highly central partnering firm is considered to be of more value and
importance to the focal firm. Thus, the punishment of a central firm is considered to be a less
probable and unexpected event compared to the punishment of a non-central (low centrality)
firm. Punishment of a low centrality firm would incur less cost to the focal firm compared to the
punishment of a highly central firm and thus, is more likely to happen. When a focal firm does
take actions toward a highly central firm, it is able to send a strong signal to all of the members
of the network (Antia and Frazier 2001). Because punishing a highly central firm demonstrates
that the focal firm is very much concerned with prohibiting the noncompliant behavior,
regardless of the value and importance of the exchange relationship. The message would be that
the prohibition of noncompliant behavior is so critical that even prominent firms in the network
are punished if they do not comply (Williamson 1983). The low centrality observing firms in the
network will immediately process the powerful message and learn to avoid engaging in
noncompliant behavior that will lead them to the possibility of facing the same (or possibly
larger) punishment.
Social learning also more readily occurs when the referent and observing firms are
structurally equivalent. Observing firms are more likely to engage in similar behaviors after they
observe a similar referent firm being punished (Table 1.3B; Cells A and D). It is expected that
social learning will occur among other high centrality observing firms when a high centrality
firm is punished (i.e., high centrality-structural equivalence). Similarly, the information of the

32

punishment of a low centrality referent firm contains relevant information for a structurally
equivalent low centrality observing firm. The expectation of similar punishments (resulting from
similar firm actions and comparable position) is high.
However, when a highly central observing firm receives information of punishment of a
low centrality referent firm, it is expected that this information will not have a strong impact on
behavior change or social learning of the observing firm. The information is not socially relevant
to the highly central firm who has a relative structural advantage compared to the punished
referent firm. The punishment of a low centrality firm does not signal a similar potential
punishment for a high centrality and thus, more prominent firm. The level of social learning is
expected to be the lowest for this combination (Table 1.3B; Cell B). Thus, it is hypothesized that:
H5 :

The focal firm’s use of punishment on the referent firm is the most strongly
positively associated with the observing firm’s compliance in the following order:
(a) LOBC > HSE, (b) HSE = LSE, (c) HSE > HOBC (d) LSE > HOBC.

METHODOLOGY
Research Context and Data Collection
Research Context. To test the hypotheses, relationships between automotive dealers and selected
manufacturers was examined. Auto dealerships within large networks were chosen for two
primary reasons. First, the dealership network reflects a similar network pattern where a focal
firm has similar relationships across multiple partnering firms in which the focal firm (i.e., the
manufacturer operates as a central hub). This provides an appropriate context for studying social
learning effects since the multiple dealers are indirectly connected through the manufacturer and
are able to observe or are exposed to information of other dealers within the network. Second,

33

previous literature demonstrates that the auto channel context has been appropriate in testing for
the effectiveness of influence strategies (cf. Lusch 1976; Lusch and Brown 1982; McFarland,
Bloodgood, and Payan 2008). The employment of rewards and punishment is widespread in the
auto dealer context and an inherent level of dependence exists in the manufacturer-dealer
relationship where the use of more coercive (reward and punishment) influence strategies is
common (Macaulay 1966). The survey instrument was pre-tested through an interview process
with six owners/general managers at automotive dealerships.5 Pre-testing focused on verification
and refinement of the study, specifically related to the relevance of the topic investigated and the
focal constructs and measurement items employed. The interviews provided insight into the
intricacies of the dealer network environment and the manufacturer’s usage of influence
strategies in the dealer-manufacturer relationships. The model was then tested using a crosssectional survey methodology employing a web-based mechanism administered to qualified key
informants at automotive dealerships, identified and incentivized through a market research firm.
Hypotheses 1ab, 2, and 3ab address the research question of social learning effects in
networks (i.e., the density of the network and resulting observations of reward and punishment of
others on dealer compliance). The goal of Hypotheses 4 and 5 is to investigate whether the
network centrality of the observing firm (relative to the referent firm) will have an effect on
social learning and if this will differ across reward and punishment observations. Regression
analysis (including multi-group analysis) was employed to examine the series of simultaneous
relationships among the key constructs.
Data Collection. A market research company was used to administer online survey

5

Owners/general managers of six auto dealerships were interviewed; three represented domestic
(U.S.) automotive manufacturers and three represented foreign automotive manufacturers. The
solicitation email is included in Appendix 1.1.
34

questionnaires to respondents who were part of its proprietary online panel. To enhance the
response rate, the respondents were compensated by the market research firm for participation in
this study. The unit of analysis was a multi-dyadic relationship among firms in which the auto
manufacturer has an ongoing relationship with other dealerships in the network. To ensure the
appropriateness of the respondents, the potential participants were screened based on their
managerial level and whether they were knowledgeable about their dealership’s relationship with
the focal manufacturer. Participants who fit all of the screening criteria were allowed to proceed
to the survey. The respondent was then asked to anchor his or her responses on an automobile
manufacturer with whom their dealership worked.
The data were collected over a period of two weeks. Follow- up emails containing
reminders were sent to non-respondents a week into the collection. In total, 397 questionnaires
were received. After careful examination of the returns, 158 responses were excluded due to poor
quality of responses or a large amount of missing data on key variables (i.e., incompletes).
Respondents who indicated that they had never observed any reward/punishments in the dealer
network (n=45) were excluded from the analysis as for social learning to occur observation must
exist. The final sample consisted of 194 completed and usable questionnaires.
The final sample represents auto dealerships that carried an average of 2.64 brands. The top
auto brand manufacturers represented in the sample include American (56.9%), Japanese (28.5%),
German (6.7%), South Korean (3.8%), British (1.3%), and Other (3.8%) brands. The median number
of employees (dealership size) is 52. The respondents selected auto manufacturers with whom they
have been doing business for an average of 26 years; on average, 72.05% of the dealership’s business
went to the selected auto manufacturer. The average size of the dealer network was 506.9 dealerships.
The responding managers had an average of 21.62 years of experience.

35

Nonresponse bias was assessed using Armstrong and Overton`s (1977) procedure by
comparing early and late respondents (i.e., those who responded after the reminder was sent were
considered to be late respondents (Mentzer and Flint 1997)) in terms of the key variables under
study. The results indicate that nonresponse bias is minimal because no significant differences (p
< 0.05) were found on any of the items used in the study.
Measures
Measures, when available, were based on existing scales modified to our setting (see Appendix
1.2). If pre-existing scales were not available, measures were developed on the basis of the
conceptual definitions of the constructs and pre-study interviews. The key constructs are
operationalized using reflective multi-item scales.
Network density is defined as the degree of interconnectedness among actors in a
network (Burt 1992; Coleman 1990) and reflects the average strength of relations in a network.
The items selected for network density are modifications of those employed by Antia and Frazier
(2001). The use of a five item seven point Likert-type scale (strongly disagree to strongly agree),
allows the respondent to indicate relative agreement with statements indicating the level of
connection, interaction, and communication of information within the network.
Network centrality is defined as the position an organization occupies in a network and
denotes the extent to which the organization occupies a socially-defined position of prominence
by virtue of being involved in many ties (Wasserman and Faust 1994). Utilizing a modification
of Antia and Frazier’s (2001) measure for network centrality, the respondent’s opinion of his or
her dealership’s centrality was be obtained through a six item seven point Likert-type scale
(strongly disagree to strongly agree) that allows the respondent to indicate relative agreement

36

with statements indicating the level of dealer importance and links it maintains with other dealers
in the network.
Reward usage is operationalized as the frequency of the manufacturer’s use of rewards
(i.e., administration of outcomes that are of positive value) on other dealers in the network. The
items selected were adapted from Lusch’s (1976) non-coercive sources of power. The six-item
seven point scale (never to very often) Likert-type scale allows for the respondent to indicate the
level of frequency of the manufacturer’s use of product, training, incentive, financial, and
advertising assistances as well as recognition of other dealers in the network.
Punishment usage is operationalized as the frequency of the manufacturer’s use of
punishment (i.e., the bestowal of aversive consequences or penalties) on other dealers in the
network. The items selected for conflict are modifications of those employed by Lusch (1976).
The seven-item seven point scale (never to very often) Likert-type scale allows for the
respondent to indicate the level of frequency of the manufacturer’s use of various sanctions on
other dealers of the network.
Relative network centrality is operationalized as the network centrality of the respondent
relative to another dealer in the network. First the respondent will be asked to indicate another
dealer in the network that has recently been rewarded (punished) by the manufacturer. The
respondents will be categorized into the four different combinations as denoted in Table 1.3A
and 1.3B. The respondents will be asked to indicate their relative position in the dealer network
(1= less, 4= equal, 7= more) to six statements indicating the level of dealer importance and links
it maintains with other dealers in the network compared to the selected rewarded or punished
referent dealer. “Less” indicates lower observer centrality and “more” indicates higher observer
centrality. The respondents who answer “equal” will then be matched with their previously

37

measured network centrality levels (low/high) to distinguish between low centrality-structural
equivalence and high centrality structural equivalence groups.
Compliance is operationalized as the respondent acting in accordance with a
manufacturer’s requests. The items selected for compliance are modifications of those employed
by Payan and McFarland’s (2005).The use of a five-item seven point Likert-type scale (never to
very often) allows for the respondent to indicate how frequently they comply to the
manufacturer’s requests.
Control Variables. As discussed, the main premise of the current study’s theoretical
argument is that the network density and observation of reward and punishment of other dealers
in the network increases compliance of the observing firm. Because factors such as the dealer
size and length of relationship could influence social learning, two control variables are included:
(1) dealership size (dealer annual sales) and (2) the length of relationship with the manufacturer
(in years).
ANALYSIS AND RESULTS
Measurement Model Analysis
Measure Validation. The measurement model was estimated using confirmatory factor analysis
with AMOS 18. The measurement model consisted of the reflective multi-item latent constructs
of reward observation, punishment observation, network density, and compliance. Appendix 1.1
represents the results of the measurement model analysis, together with item loadings, composite
reliabilities and average variance extracted (AVE). Descriptive indicators and correlations for
measures appear in Table 1.4.

38

Table 1.4: Measure Statistics and Correlation Matrix

1. Reward Usage
2. Punishment Usage
3. Network Density
4. Compliance
5. Dealership Size
6. Relationship Length

M
4.67
3.44
4.91
5.57
90.66
26.07

SD
1.16
1.27
1.12
1.03
113.750
16.98

1
(.80)
.16*
.40**
.18*
.07
.05

2

3

4

5

6

(.73)
-.10
-.15*
-.06
-.05

(.79)
.23**
.09
.07

(.87)
.07
.03

N.A.
.29**

N.A.

* p < .05; ** p < .01; The square roots of the AVE are on the diagonal. Single-item scales are denoted
with N.A.

Reliability of individual items is assessed by examining the loadings of the items with
their respective latent construct; loadings of less than .5 may represent poorly worded or
inappropriate items and thus should be eliminated from the model (Hulland 1999). As the
Appendix 1.1 reports, all measurement items exceed this threshold and load significantly on the
expected constructs (ranging from 0.60 to 0.95). Furthermore, all constructs have acceptable
levels of reliability, with the composite reliability coefficients ranging from 0.87 to 0.94 for each
construct, exceeding the .7 recommended threshold (Nunnally 1978). Convergent validity is also
evident, with the average variance extracted (AVE) for each construct ranging between .64
and .89, exceeding the .5 benchmark (Fornell and Larcker 1981). To test for discriminant validity,
we used Fornell and Larcker’s (1981) approach by examining whether the square root of the
AVE of each construct (shown in the diagonal in Table 1.4) was greater than the correlations
between variables. All constructs demonstrate discriminant validity.
The overall chi-square goodness-of-fit index for the model is 327.941 based on 199
degrees of freedom. The measurement fit indexes for the confirmatory measurement models all
meet the critical values for a model of good fit (Hu and Bentler 1999): the comparative fit index

39

(CFI) was 0.956, the root mean square error of approximation was (RMSEA) 0.058, and the
standardized root mean square residual (SRMR) was 0.0596.
Assessment of Common Method Bias. Cross-sectional surveys where both the
independent and dependent variables came from the same source are susceptible to common
method bias (Podsakoff et al., 2003). The potential presence of common method variance (CMV)
was tested in two ways. First, Harman`s one-factor test (McFarlin and Sweeney 1992, Sanchez
and Brock 1996) was conducted. This test entails entering all of the items of latent variables into
a single factor using confirmatory factor analysis. The fit statistics for this model were quite poor
(chi-square goodness-of-fit index of 1822.393 with 205 degrees of freedom; CFI= 0.453,
RMSEA= 0.202, and SRMR= 0.2182), indicating that common method bias is minimal. Second,
a marker variable was selected as a proxy for method variance (Lindell and Whitney 2001). The
variable selected was “job autonomy,” because it is theoretically unrelated to at least one of the
study constructs. A three-item seven point Likert type scale (Strongly Disagree to Strongly Agree)
allowed the respondent to indicate relative agreement with statements indicating the level of
autonomy the respondent has in their job tasks. This variable`s coefficient was partialled out
from the bivariate correlations. The partialled results were then compared against unadjusted
correlations. After partialling out the marker variable, all of the significant bivariate correlations
among key predictors and outcomes maintained their statistical significance. Collectively, the
results suggest that the risk of common method bias is minimal.
Model and Estimation
The model was estimated using multiple regression. To construct interactions with network
density, mean-centered variables were used. Thus, the coefficients (i.e., β’s) reflect the effect of
the predictor (i.e., reward and punishment usage) at the mean level of the moderator (e.g.,
40

network density). The mean-centering technique was employed for two reasons. First it avoids
analyzing individual effects at the zero-level of the moderator, which may be considered outside
of the relevant range of interest. Second, it aids in interpretation of the moderation effect of each
moderator through its range (i.e., above and below the mean). The model equation is:

(1) OMP = 𝛼0 + 𝛽1 𝑅𝐸𝑂 + 𝛽 𝑃𝑈𝑂 + 𝛽3 𝐷𝐸𝑁𝑆 + 𝛽4 𝑅𝐸𝑂 ∗ 𝐷𝐸𝑁𝑆 + 𝛽5 𝑃𝑈𝑂 ∗ 𝐷𝐸𝑁𝑆
+ 𝛽6 𝐷𝑆𝐼𝑍𝐸 + 𝛽7 𝐿𝐸𝑁𝐺 + 𝑒
where:

COMP = Dealer compliance;
REO = Reward usage;
PUO= Punishment usage;
DENS = Network density;
DSIZE = Dealership size;
LENG = Relationship length.

In order to compare the differential social learning effects of reward and punishment
usage, a regression using a multiplicative dummy variable was conducted. The model equations
are:
(2) OMP𝑖,𝑗𝑖 = 𝛼𝑖 + 𝛽1𝑖 𝑅𝐸𝑂𝑖,𝑗𝑖 + 𝛽 𝐷𝑆𝐼𝑍𝐸𝑖,𝑗𝑖 + 𝛽3 𝐿𝐸𝑁𝐺𝑖,𝑗𝑖 + 𝑒𝑖,𝑗𝑖
(3) OMP𝑖,𝑗𝑖 = 𝛼𝑖 + 𝛽1𝑖 𝑃𝑈𝑂𝑖,𝑗𝑖 + 𝛽 𝐷𝑆𝐼𝑍𝐸𝑖,𝑗𝑖 + 𝛽3 𝐿𝐸𝑁𝐺𝑖,𝑗𝑖 + 𝑒𝑖,𝑗𝑖

41

where, i =Relative Centrality Group (1 ≤ i ≤ 4) and 𝑗𝑖 = sample size for group i. The slope
coefficients were than compared using the statistical package R 3.0.3. The results of the multiple
comparisons are presented in Table 1.6 and 1.7.
Results
Table 1.5 contains the results for Equation (1). Beginning with tests of the hypotheses, H1
predicted that the focal firm’s use of reward on the referent firm is positively associated with the
observing firm’s compliance. The result of reward usage on compliance (β = .148, p < .05)
shows a significant positive effect. H1 is supported. H1 predicted that the focal firm’s use of
punishment on the referent firm is positively associated with the observing firm’s compliance.
The result of punishment usage on compliance (β = -.159, p < .05) shows a significant negative
effect. Thus, H1 is contradicted. In addition, although not specifically theorized, network
density had a significant positive effect on compliance (β = .148, p < .05).
In H , it was predicted that the positive relationship between the focal firm’s use of
punishment on the referent firm and the observing firm’s compliance is greater than the positive
relationship between the focal firm’s use of reward on the referent firm and the observing firm’s
compliance. The results in Table 1.5 demonstrate that punishment usage has a larger effect size
(𝛽 =- 1.59 vs. 𝛽1= .148), but a negative one on compliance. Thus, H is not supported.
In H3 , it was predicted that the focal firm’s use of reward on the referent firm is more
positively associated with the observing firm’s compliance in high density networks than in low
density networks. Similarly in H3 , it was predicted that he focal firm’s use of punishment on
the referent firm is more positively associated with the observing firm’s compliance in high
density networks than in low density networks. The addition of the moderation effects of
network density did not produce a significant change in the variance explained ( 𝑅 ,
42

F=.458,

p=.633). The individual coefficient estimates indicate non-significant moderation effects of
network density (DENS) (𝛽4 = .000, n.s., 𝛽5= .070, n.s.). Thus, H3 and H3 are not supported.

Table 1.5: Estimation Results (

-

)

Dependent Variable: Compliance
Independent Variables
Reward Usage (REO)
Punishment Usage (PUO)
Network Density (DEN)
Dealership Size (DSIZE)
Relationship Length (LENG)
Moderating Effects
Reward Usage x Network Density
(REO)x(DEN)
Punishment Usage x Network
Density
(PUO)x(DEN)

Baseline Model
Moderation Model
Effect
t-value
t-value
𝛽
𝛽
.148
1.896
.171
2.086
H1 (pos.)
-.159
-2.207
-.168
-2.231
H1 (neg.)
.148
1.909
.133
1.606
.038
.526
.043
.588
-.008
-.106
-.011
-.150
H3 (pos.)

.000

-.001

H3 (pos.)

.070

.920

= .087

= .092

Equations (2) and (3) were estimate to test the effect of relative network centrality on
social learning. In H4 , it was theorized that the positive effect of the focal firm’s use of reward
on the referent firm on the observing firm’s compliance would differ depending on the relative
centrality of the observing firm. Specifically, it was predicted that the focal firm’s use of rewards
on the referent firm would be most strongly positively associated with the observing firm’s
compliance in the following order: (a) LSE > HSE, (b) HSE > HOBC, (c) HSE > LOBC, and (d)
HOBC = LOBC. The results in Table 1.6 demonstrate that the focal firm’s use of rewards on the
referent firm is strongly positively associated with the observing firm’s compliance in the
following order: LOBC ( = .453), HOBC ( = .356), LSE ( = .257), and HSE ( = -.236). The

43

effect of reward usage on compliance in the LOBC and HSE groups were significantly different
from each other (|𝛽 difference| =.689, p < .05). Thus, H4 is partially supported (H4d ).
Table 1.6: Group Comparison Estimation Results (
Relative Centrality Group Ordering >
Reward Usage (𝛽1𝑖 )
Group Comparisons
c. LOBC — b. HOBC
c. LOBC — d. LSE
c. LOBC — a. HSE*
b. HOBC — d. LSE
b. HOBC — a. HSE
d. LSE — a. HSE

: Reward)

c. LOBC

b. HOBC

d. LSE

a. HSE

.435

.356

.257

-.236

|𝛽 difference|
.097
.196
.689
.099
.592
.493

t-value
.637
.684
2.051
.074
1.545
1.407

(H4d supported)
(H4 contradicted)
(H4 n.s.)
(H4 n.s.)

In H5 , it was theorized that the positive effect of the focal firm’s use of punishment on
the referent firm on the observing firm’s compliance would differ depending on the relative
centrality of the observing firm. Specifically, it was predicted that it was predicted that the focal
firm’s use of punishment on the referent firm is the most strongly positively associated with the
observing firm’s compliance in the following order: (a) LOBC > HSE, (b) HSE = LSE, (c)
HOBC, LSE > HOBC. The results in Table 1.7 demonstrate that the focal firm’s use of
punishments on the referent firm is strongly positively associated with the observing firm’s
compliance in the following order: HSE ( = .039), LOBC ( = -.031), LSE ( = -.050), and HOB
( = -.449). The effect of punishment usage on compliance in the HSE and HOBC groups were
significantly different from each other (|𝛽 difference| =.488, p < .05). Thus, H5 is not supported.

44

Table 1.7: Group Comparison Estimation Results (
Relative Centrality Group Ordering >
Punishment Usage (𝛽1𝑖 )
Group Comparisons
a. HSE — c. LOBC
a. HSE — d. LSE
a. HSE — b. HOBC*
c. LOBC — d. LSE
c. LOBC — b. HOBC
d. LSE — b. HOBC

: Punishment)

a. HSE

c. LOBC

d. LSE

b. HOBC

.039

-.301

-.050

-.449

|𝛽 difference|
.070
.089
.488
.019
.418
.339

t-value
.278
.341
1.954
.038
1.283
1.346

(H5 not supported)

DISCUSSION
This study was motivated by a desire to understand how a focal firm managing multiple
relationships can effectively adjust information symmetry and employ influence strategies in a
particular relationship so as to effectively manage another exchange relationship within the
network, gaining compliance from members of the network. Specifically, the following research
questions were addressed: (1) To what extent does social learning occur within a network, when
information symmetry is heightened? (2) Are the effects of social learning consistent, or are there
differential effects, when firms employ rewards versus punishments? (3) Are the effects of social
learning consistent, or are there differential effects, based upon the network centrality of the
referent and observing firm of the influence strategy? An integrated conceptual framework,
based on social learning theory and the network theory literature, focuses on how influence
strategies of reward and punishment influence compliance across multiple relationships and how
structural network factors impact social learning. Our findings offer initial insights into these
issues and provide significant implications for marketing academics and practitioners.

45

Theoretical Implications
First, this study extends our current understanding of the dynamics of effects beyond the dyad.
Previous studies have been typically based on the assumption that information was distributed
asymmetrically in the sense that a particular firm was not able to discern its partnering firm’s
influence strategies towards other partners. Now with the changing information environment,
this information has become more symmetric, as it has become more transparent to other firms in
a network.
Previous research on influence strategies has demonstrated their effectiveness on
compliance and relational outcomes within dyadic channels of distribution (Boyle et al. 1992;
Frazier and Rody 1991; Frazier and Summers 1984; Kim 2000; Payan and Mcfarland 2005). Yet,
most work on influence strategies in channel relationships has been dyadic and has not examined
‘social’ learning where network actors (beyond a single dyadic relationship) can learn and
change their behaviors following observations and receipt of referent information. This work
extends the current inter-organizational literature on influence strategies by recognizing the
occurrence of information flow and social observation activities among multiple inter-firm
relationships. The results demonstrate that reward usage has a positive effect on compliance of
other observers in the network. Thus, members to a network socially learn through the
observation of reward usage.
Contrary to expectations, the results indicate that the use of punishment results in less
compliance from observers in the network. Social learning theory would predict that the
observation of negative punishment would result in higher levels of compliance in order to avoid
being subject to the same punishment. Yet, the results demonstrate that particular strategies
employed to gain compliance may instead trigger resistance. According to reactance theory

46

(Brehem 1966; 1972), a person can be motivated to rebel or react when his/her behavioral
freedom is threatened. Education communication research (Plax et al. 1986) indicates that the
use of negative or punishment-oriented strategies can result in reactance or non-participation,
demonstrating that coercive techniques depleted morale in the classroom. The management
literature refers to the conventional wisdom that punishment should be avoided or used only as a
last resort because of its undesirable emotional and behavioral side effects (cf., Luthans and
Kreitner 1985). Defiance and resistance were found to be particularly relevant negative outcomes
of punishment events (Fisher. Locke, and Henne 1992). Similarly, the inter-organizational
literature demonstrates that conflict increases when coercive influence strategies (i.e.,
punishments) are used by partner channel members (Brown and Frazier 1978). The use of
coercive power decreases the target channel member’s social satisfaction (Geyskens and
Steenkamp 2000) and the target channel member is expected to feel tension and frustration
(Brown, Lusch, and Muehling 1983). Following this logic, it could be possible that the
observation of a punishment of another network member can arouse negative affect and
resistance and lower network morale, resulting in less compliance.
Our study provides empirical evidence that network characteristics matter in channel
relationships, both directly and in combination with other constructs. This research contributes to
the literature by identifying the contingent network structural characteristics that may amplify or
dampen the social learning effect, providing deeper understanding of the differential
consequences of the usage of influence strategies in network environments. Previous literature
further suggests a strong motivating force behind firm behavior which is socially based,
embedded within interconnected organizational relationships (Anderson, Hakansson, and
Johanson 1994; Provan 1993) and points out the need to study organizational behavior in situ, or

47

from a larger ecological perspective (Davis and Luthans 1980) at the network level.
The results indicate that network density has a direct positive effect on the level compliance.
This is consistent with findings in the current literature (e.g., Antia and Frazier 2001; Provan
1993) that indicates that information is more readily disseminated in a dense network as network
members are more interconnected. The denser the network where actions are more readily
observable, the more behaviors are ‘in check.” Provan (1993) demonstrated that network density
operated as a constraing mechanism against negative behaviors such as opportunism. The
enhanced flow of information in a network means that firms engaging in such behaviors will
likely get caught (Eisenhardt 1989; Fama and Jensen 1983). Social reputation can be so strong in
a dense network, reputations can be readily damaged (Granovetter 1985). Similarly, this study
indicates that dense networks are conducive to higher levels of compliance. Yet, no evidence of
the interaction effects of network density on the relationships between reward and punishment
usage and compliance were found. This may indicate that the observation of reward and/or
punishment usage is enough and the interconnectedness (and the higher flow of information)
may not operate as a necessary condition for social learning or behavioral reactance to occur.
Whatever the case, the nature of the relation between reward/punishment usage and network
density requires further exploration.
In addition, the results demonstrate that the relative centrality level between the
observer and referent firm has an effect on the behavioral response to observation of influence
strategies. The general notion of centrality encompasses an aspect of the ‘importance’ or
‘prominence’ of actors within a network (Faust 1997) who maintain a network positionconferred advantage (Cook and Whitmeyer 1992). It was expected that the level of the observing
firm’s social learning would differ depending on the network centrality of both the referent firm

48

and the observing firm itself. In terms of rewards, the social learning effect (i.e., the positive
influence of reward usage on compliance) was the strongest for the low observer centrality
(LOBC) group where the observer observed a higher-centrality referent firm being rewarded.
The observing firm can be seen as engaging in “benchmarking” by observing other important
players in the network and comparing outcomes. The central position in the network makes
highly central firms of greater value and importance, also making their interactions more of
interest (and conducive of social learning) to other observing firms in their network. Interestingly,
for observers that referred to the rewards of other highly central actors in the network (HSE
group), the social learning effect was dampened and lead to less compliance. This finding is
indicative of a sense of ‘entitlement’ that may spring from the prominent position the observer
holds in the network (Hochwater et al. 2007). Entitlement has been described as a characteristic
reflecting an inflated perception of worth, as well as a sense that one deserves more than others
(Campbell et al., 2004). Seeing another equally important actor being rewarded can lead to less
compliance as an attention-seeking reaction or resistance against the focal firm.
Punishment usage had a negative effect on compliance. This negative effect was found
to be the strongest when an observing firm referred to the punishment of a lower-centrality firm
(HOBC group). The effect of punishment observation in this group significantly differed from
the HSE group where actors observed the punishment of equally highly-central actors in the
network. Punishment observation was positively associated with compliance (i.e., resulted in
social learning). Social learning through punishment observation occurred when seeing someone
equally important experience negative outcomes. The informational impact (unlikely event of an
an important actor being punished) and the relevance of the information (a structurally
equivalent actor being punished) enhanced the learning mechanism. These results together

49

suggest that behavioral reactions (whether social learning or resistant reaction) differ across the
differing network positions of both the observer and referent firms, shedding light onto the
effects of structural network characteristics in dyadic interactions of firms.
Managerial Implications
From a managerial standpoint, the study presents insight into three important areas for marketing
managers who are involved in managing multiple inter-organizational relationships. First, the
implication of an information-rich environment is that influence strategies used in one dyadic
relationship is also observable to other connected members in the network and has behavioral
spillovers. Managers must be aware of the consequences of this new information-rich
environment in shaping their relationship management strategies. As network density increase,
the general level of compliance increases. From the perspective of the focal firm, this can be a
benefit in terms of decreasing opportunism and increasing the general level of compliance with
multiple channel members. Managers may wish to increase the density of their network of
relationships and foster information flow by better connecting partnering firms. This can be done
through conferences or workshops where the partner firms come together to connect and interact.
Second, the results of this research underscore the spillover benefits of reward usage in
this new environment. Previous literature demonstrates the effectiveness in gaining compliance
of individual channel members utilizing reward reinforcement strategy. Firms may wish to
purposefully disclose this information and increase referent reward information observability to
other partnering firms to gain the benefits of social learning. Recognizing high performers
through public awards as GM did through their Supplier of the Year Award or Preferred Dealer
Awards not only reinforces desired behavior of the recipient, but also enhances compliance of
other observing partners, maximizing the effect of reward usage beyond the dyad. Yet, the firm
50

may not want to publicize punishment usage as much as they do rewards. The study
demonstrated that punishments usage decreased the compliance of other observing firms. The
publicizing of negative punishments may arouse resistance and reactance from other network
members in the form of non-compliance.
Third, given the differential behavioral responses across influence strategies of reward
and punishment as well as network actors’ relative network centrality positions, managers may
consider different forms of information disclosure (i.e., enhancing network density). Although
network density had an enhancing effect on member compliance and reward usage demonstrated
positive social learning effects, firms must be aware of the possible side effects that follow. In the
case of rewards, although reward usage generally demonstrated a positive social learning effect,
if disclosed to an important network member, there is the possibility where this information
could backfire and bring about resistance rather than compliance. In addition, firms may wish to
only utilize punishments and information disclosure in extreme cases. The results demonstrate
that the possibility of negative response in the form of non-compliance in the network is high
when observing punishment activities of other members. Yet, further examination of relative
network centrality indicates that if punishment information of important network players is
disclosed to equally central firms, social learning effects can be seen. Practitioners are advised to
consider employing punishment as a ‘last resort’ option and to refrain from disclosing such
information within the network lest it triggers resistance. But when employed, punishment usage
can be a powerful signaling strategy and bring about social learning effects. It is important for
practitioners to be aware of the network contingent effects of social learning and behavioral
reactions to reward and punishment usage and rely on appropriate forms of network density
enhancement (i.e., whole network vs. selective).

51

Limitations and Directions for Future Research
Although this research provides insight into the how behavioral spillovers occur in multilateral
relationship management in an information-rich environment the following limitations need to be
considered when attempting to generalize the findings. First, the generalizability of our results is
in question. The literature suggests that coercive influence strategies may be most effective in
contexts where the target is highly dependent on the source (Payan and McFarland 2005).
Although we gathered data from a national research panel of automotive dealerships, the study
was limited to the auto dealership network context where the inherent asymmetry of dependence
is high. Different types of influence strategies should be examined within more balanced network
relationships to see whether they have similar spillover effects.
Second, specification error could be a problem, given the weak explanatory power of our
model. Because our study is an early attempt to build and test a conceptual framework of social
learning in the business context, important factors may have been omitted. Prior
reward/punishment experience, relationships characteristics of trust and commitment, long-term
orientation, all could have a significant bearing on current social learning and behavioral
responses to referent observations. In addition, a post hoc analysis of residuals identified
significant correlations in the error residuals of U.S. versus Japanese manufacturer brands,
indicating the possible influence of the recent ‘shifts’ in the U.S. auto industry. Extended
conceptual frameworks should be developed and examined empirically in the future.
Third, we relied on data from a single member of the dyad, the auto dealership.
Obtaining corresponding viewpoints from the upstream manufacturer would have made our
study stronger. Additional research based on data collected from both sides of the dyad would
yield insight into areas of divergence.

52

In addition to addressing the aforementioned limitations, the results of this research on
marketing strategy decision making suggests numerous avenues for future research. First,
following the current literature on influence strategies, this study measured reward and
punishment usage in terms of their frequency. The literature suggests that the not only the
frequency, but the magnitude or severity of rewards and punishments are important for
consideration (Bagozzi 1974). The magnitude of a reward or punishment may have an impact on
social learning. Bandura (1986) argued that for vicarious learning to have an impact on observer
behavior, it must be noticed and remembered. Trevino and Youngblood (1990) also found that
subjects seemed to be influenced by the reinforcement that was stronger than expected. Thus,
behavior may be significantly influenced only under circumstances in which the observable
reinforcement is unexpected or powerful (i.e., magnitude of the reward or severity of punishment
is high). Future research should explore the perceived severity or magnitude of these influence
strategies and how these observations impact observer behavior.
Second, the network characteristics that were expected to influence the social learning
process were captured from a social behavioral perspective (Antia and Frazier 2001). Although
the more social implications of network characteristics provide rich insight into the social
motivations behind behavioral responses in inter-organizational relationships, research looking
more strictly at the mathematical structural properties of the network may be interesting.
Research examining such interconnections could make important contributions to channels
research.

53

ESSAY TWO: THE RE-ANCHORING EFFICACY OF EXPLANATIONS: LOWERING
PERCEPTIONS OF UNFAIRNESS

In the summer of 2011, news spread among the duty free boutiques located in the Inchon
International Airport that Hotel Shilla (operator of the duty free facility) had offered Louis
Vuitton a preferential minimum rent to attract the firm to open a store within its facility. When
the Italian fashion brand Gucci, who maintained stores in this facility, became aware of this
situation, it immediately requested the same level of treatment as Louis Vuitton as it felt it wasn’t
being treated fairly. Their request was denied. Shilla’s explanation was that Louis Vuitton,
Chanel, Hermes, Gucci, and Prada are the usual top performers, recording an average of 10%
annual revenue growth. However, recently, Gucci had recorded a loss. Gucci withdrew two of its
major outlets in Shilla stores and moved to Lotte Duty Free (Shilla’s main competitor), in a
protest against Shilla’s alleged favorable treatment of Louis Vuitton (Chosun Ilbo 2011). As
illustrated by this incident, social comparison information external (i.e., Shilla-Louis Vuitton) to
the immediate dyad (i.e., Shilla-Gucci) is used when forming fairness judgments about exchange
relationships (Adams 1965; Ambrose, Harland, and Kulik 1991; Novemsky and Schweitzer
2004). These attributions lead to responsive behaviors and performance consequences. The
incident also demonstrates that explanations regarding allocation are not always effective and
suggests only those explanations with re-anchoring efficacy may be potent in restoring perceived
fairness. Would the perceptions of fairness and relationship outcome have been different had
Shilla communicated the explanation to Gucci before its offer of preferential minimum rent to
Louis Vuitton and directed Gucci towards a more appropriate comparative referent to anchor on?
Despite the growing academic and practitioner interest in fairness issues in interorganizational relationships (e.g., Samaha, Palmatier and Dant, 2011; Scheer, Kumar, and

54

Steenkamp, 2003), a review of the literature reveals several short-comings that have limited our
understanding. Specifically, although the information environment is becoming increasingly rich
and thereby making transparent management actions with others, the inter-organizational
literature has not addressed the issue of external comparison. Building on equity theory, the
literature demonstrates that perceptions of fairness based on internal comparisons (i.e., when a
firm compares their outcomes to their partner’s outcomes) significantly influence relationship
quality (Samaha, Palmatier and Dant, 2011; Scheer, Kumar, and Steenkamp, 2003) and limit the
extent of conflict (Kumar, Scheer, and Steenkamp, 1995; Brown, Cobb, and Lusch, 2006). The
implication of an information-rich environment is that a firm’s management of one partner has
become increasingly transparent to its other partners, facilitating external comparison.
Fundamental to this issue is that the focal firm managing multiple relationships does not see
these partners as comparable, but the partners themselves may do so and anchor on a noncomparable referent. The failure to account for the information-rich environment when managing
multiple relationships can be significantly problematic.
One approach that firms can engage in to address the issue of appropriate anchoring is to
educate their partners as to the difference between firms and therefore anchor the partner on the
right comparable referent (e.g., Shilla to re-anchor Guicci so that Gucci viewed themselves as
one of the other lower performing firms and, therefore separate from Louis Vuitton). Equity
theory focuses extensively on which referents are selected (Kulik and Ambrose 1992) and points
to the changing of the comparative referent as a means to resolve feelings of unfairness (Adams
1963). However, the literature has yet to examine how relatively stable referents are changed.
One approach to re-anchoring can be through the provision of an explanation that clarifies the
reasoning behind differential treatment. Research refers to explanations as a relatively

55

inexpensive conflict management tool (Greenberg 1990). Of specific importance is that the
literature notes that the provision of explanations has been linked to lowered unfairness
perceptions (Ball, Trevino, and Sims 1993; Colquitt 2001; Konovsky and Cropanzano 1991).
Unfortunately, the literature does not examine the factors that enhance or lessen the efficacy of
explanations in re-anchoring a target (i.e., the recipient of the explanation). The simple provision
of an explanation may not always work, as seen in Shilla’s experience with Gucci. The same
explanation can be accepted by the partnering firm as a justification in some cases, but as an
excuse in others. This leads one to question what factors influence the re-anchoring efficacy of
an explanation? Research suggests that the effectiveness of an explanation may differ across the
time of delivery (e.g., proactive vs. reactive) (Sitkin and Bies 1993) and the explanation content
(e.g., provision of new anchor) (Greenberg 1993; Shapiro, Butner, and Barry 1994) (refer to
Figure 2.1). The multiple factors surrounding the communication of the explanation provide
frames of reference and directional information through which the target receiving the
explanation interprets these explanations in different ways.
To address these issues, this study relies on equity theory and the anchoring and
adjustment literature to develop hypotheses on the effectiveness of explanations in re-anchoring
the target firm on a new comparative referent and lowering perceptions of unfairness against the
backdrop of a negatively perceived allocation event. Specifically, the following research
questions are addressed:
(1) How do factors surrounding the communication of an explanation influence the target
firm’s likelihood of re-anchoring their comparative referent?
(2) Are perceptions of unfairness lowered for target firms that successfully re-anchor
following an explanation?

56

By addressing these questions, this research contributes to the field of marketing in three
distinct ways. First, this study calls attention to the influence of external social comparison
activities in the information-rich environment and demonstrates that explanations can be
employed as an effective management tool in mitigating perceptions of unfairness. The interorganizational literature concerning fairness issues focus on situations where the comparative
referent is the partnering firm within the immediate dyad (e.g., Brown, Cobb, and Lusch, 2006;
Kumar, Scheer, and Steenkamp, 1995; Samaha, Palmatier and Dant, 2011; Scheer, Kumar, and
Steenkamp, 2003). Yet, as scholars note, the consideration of multiple dyadic relationships
(Wathne and Heide 2004) and balance of fairness concerns is necessary for effective
management in today’s information-rich environment. As firms work to engage multiple
relationships which necessitate differing governance among partner firms, they are also faced
with the issue of considerations of the perceptions of fairness of multiple partners which may
restrict their ability to govern relationships uniquely. This study extends the extant interorganizational fairness literature by considering the usage of explanations in situations where
differential treatment of partners is inevitable, but the information-rich environment facilitates
external social comparisons.
Second, this study extends the literature on conflict management into the interorganizational setting by examining the factors that enhance or diminish the re-anchoring
efficacy of explanations. The consensus among intra-organizational management researchers
examining the conflict management potential of explanations is that, it is not merely the
provision of explanations, but their perceived adequacy that matters (cf., Bies 1987; Shapiro,
Butner, and Barry 1994). The purpose of this present investigation is to provide important
guidance and caution to marketing managers for understanding the direct and interaction effects

57

of two main factors surrounding the communication of the explanation that influence reanchoring.
Third, this study contributes to the literature by demonstrating the effect of an
explanation on re-anchoring the target and lowering their subsequent unfairness perceptions. The
consumer anchoring and adjustment literature (Puto 1987; Rowe and Puto 1987) indicates that
initial reference points (anchors) are subject to change as additional information is
communicated and accepted to reach a new reference point. This study demonstrates that by
varying the reference point through explanations, the target changes their comparative referent
(i.e., a new anchor) and the same allocation outcome is perceived to be less unfair once the target
has appropriately re-anchored.
THEORETICAL BACKGROUND
Equity Theory and Perceptions of Unfairness
Equity theory describes an individual’s search for fairness or equity in social exchanges. Equity
theory postulates that members to a social exchange relationship compare the ratios of their
inputs into the exchange to their outcomes from the exchange (Huppertz, Arenson, and Evans
1978). Equity is said to exist when a member’s evaluation of their input-output ratio in the
relationship is equivalent to their partner’s input-output ratio (Pritchard 1969); inequity exists
when an imbalance exists (Cook and Hegtvedt 1983).6 Inputs refer to any and all factors that a
member has contributed to the relationship that they perceive as relevant for gaining a return,
whereas outputs refer to any and all factors perceived by a member as a valuable return from the

6

Two streams of the fairness literature exist. The first stream focuses on the comparison of
input-to-output ratios (e.g., Scheer et al., 2003) while the second stream focuses on an
individual’s evaluation/perception of their outputs given their inputs (e.g., Samaha et al., 2011).
This research is conducted under the latter approach.
58

relationship (Pritchard 1969). On the basis of the comparison with the referent, individuals feel
equitably (fairly) or inequitably (unfairly) rewarded with regard to the particular situation that
was contrasted (Adams 1965). Individuals use one or more methods to maintain and preserve
their feelings of equity or resolve their feelings of inequity. These methods include the focal
person’s altering his or her inputs or outcomes (either behaviorally or cognitively), leaving the
field, or changing comparative referents (Huseman, Hatfield and Miles 1987; Huppertz, Arenson,
and Evans 1978). This work focuses on changing the comparative referent.
Fairness judgments are strongly influenced by the social context of a given situation. One
important attribute of the social context is information about the procedures and outcomes of
other individuals. Social scientists argue that individuals evaluate outcomes in comparison to a
reference point they consider appropriate (Adams 1963) which can be an internal (i.e., within the
immediate dyad) or external (i.e., outside of the dyad). Kulik and Ambrose (1992) note that one
of the main factors in referent selection is the availability of information. Research up to now
mostly examines cases in which the selected referent was mostly internal to the exchange dyad
(e.g., Kumar, Scheer, Steenkamp 1995; Scheer, Kumar, and Steenkamp 2003), most often times
being the other party to the exchange. Yet, the information-rich environment has become
conducive to external comparisons with others in the immediate situation who are in some way
linked to an ongoing relationship with the person (Figure 2.1),. For example, researchers have
found that firms engage in external comparison (e.g., Novemsky and Schweitzer 2004). Similarly,
in the case of employees, Königstein, Kovács, EniköZala-Mezö(2003) demonstrated that when
work contracts were observable and one agent had information about another agent’s contract
offer, fairness perceptions were based on external information rather than just the information of
allocations of inputs and outputs of the internal dyad. Shah (1998) further argues that referents

59

are derived from an individual’s social network in which the individual is embedded and operates
as a source of social information. The person will compare his allocation situation directly with a
partner following an interaction with the partner or after they have mutually interacted with a
common third party (e.g., an employer) (Austin 1977, p.289).

Figure 2.1: External Social Comparison

Focal
Firm

Non-comparable
social comparison
referent
Comparable social
comparison
referent

Dyadic
Exchanges

Referent
Firm

External Social
Comparisons

Target
Firm

Referent
Firm

Firms may sometimes anchor on the wrong comparative referent when provided with
social information. The challenge for businesses is that different partners require different
treatment. Although the focal firm is not engaging in blind discrimination of its partnering firms,
the partnering firms may perceive so. The need to treat partners differently coupled with the
enhanced information symmetry (i.e., increased transparency) can create perceptions of
unfairness among multiple partners. The observation of a negatively perceived allocation event,
i.e., when a particular partner does not receive the exact same treatment or reward as another
‘favored’ referent firm, can lead to perceptions of unfairness (i.e., the partner views the allocation

60

as inequitable or unfair). The focal firm managing multiple relationships does not see these
partners as comparable, but the partners themselves do and anchor on a non-comparable referent.
As previously mentioned, the perception of fairness largely depends on the comparative
referent chosen, as this choice determines the relative favorability of outcome allocations. Adams
(1963) argued that once a referent is chosen as the object of comparison, individuals will be
highly resistant to changing it. In a sense, the comparative referent serves as a personal ‘anchor’
for the individual. Stepina and Perrewe (1991) demonstrate that the stability of referent choice
over time is very much stable and employees tend to anchor on and use the same comparative
referent in making compensation fairness judgments. In contrast to this position, it is possible
that individuals change referents to better understand their relative position or that referents are
changed whenever an individual experiences a sense of inequity. Adams (1963) also suggests
that although individuals are resistant to changing their comparative referent, it can be an
effective means of resolving inequity. When a target firm re-anchors its comparative referent
(Huseman, Hatfield, and Miles 1987), it changes the object of comparison. In other words, it
‘anchors’ on a different referent (Huppertz, Arenson, and Evans 1978).
Re-anchoring through Explanation
Anchoring and adjustment is the traditional explanation of how anchors (i.e., reference points)
influence judgment. The standard finding is that subjects anchor on the information that is most
directly available (Tversky and Kahneman 1973) and then adjust for information that is received
after exposure to the initial anchor. People use reference points as the basis for comparison (Puto
1987). Alternatives that exceed the reference point create positive judgment frames (i.e., gains)
and alternatives that fall short of the reference point create negative judgment frames (i.e., losses)
(Kahneman 1992). This initial reference is subject to a series of iterations as additional
61

information becomes available. The final reference point is the initial reference point as modified
by additional information and determines the valence of the decision frame (i.e., positive or
negative), which in turn influences evaluation (Puto 1987) (see Figure 2.2).

Figure 2.2: Conceptual Model of Anchoring and Adjustment
(Revised from Puto’s (1987) Buying Decision Framing Model), p. 303

Initial Reference
Point
Explanation
(Sales Message)

Final Reference
Point

Evaluation
(Choice)

Explanations can be used to influence the final reference point and re-anchor a partner
that has anchored on a wrong comparative referent. According to Merriam-Webster’s Collegiate
Dictionary (1998), an explanation is the act or process of “making something clear or
understandable.” The term implies revealing the reason for, or the cause of, some event that is
not immediately obvious or entirely known. Explanations have received considerable attention
recently in the literature (e.g., Elsbach 1994; Shapiro, Butner, and Barry 1994) which
consistently indicates that the provision of explanations mitigate a variety of negative attitudes
and conflictual behaviors (Bies and Moag 1986; Bies and Shapiro 1987; Shaw, Wild, and
Colquitt 2003; Sitkin and Bies 1993). Griffith and Lusch (2000) suggest that explanations and
62

feedback in channel relationships is essential for the development of positive relationship
outcomes.
In the event of a negatively perceived allocation (due to the target firm anchoring on the
wrong referent), an explanation provides the reasoning behind differential treatment, further
clarifying the input-to-output ratio of the relevant parties. The explanation demonstrates that the
input-to-out ratios are not comparable (or equal) and thus, the initial referent firm that the target
has anchored on cannot be considered as the appropriate comparative referent on which to base
fairness judgments. Explanations can be considered to be messages exchanged to communicate
one’s reference point and to influence the reference point of the other side (Kahneman 1992). By
varying the reference point, it is possible to create positive and negative frames for the same set
of alternatives (Puto 1987; Qualls and Puto 1989).
Research suggests that the effectiveness of an explanation may differ across various
factors inclusive of timeliness of delivery, the style of the explanation (e.g., sincerity or form)
(Sitkin and Bies 1993), as well as the specificity of the content (Greenberg 1993; Shapiro, Butner,
and Barry 1994). The multiple factors surrounding the communication of the explanation provide
‘frames of reference’ through which the target receiving the explanation interprets these
explanations in different ways and comes to different conclusions of the adequacy of the
explanation. A frame of reference refers to the overall context in which a problem or situation is
placed, viewed, or interpreted. The overall frame of reference under which an individual operates
can lead the individual to different perceptions or interpretations of the same stimulus (Epley et
al. 2004). Whether an explanation is adequate or not depends on the frame of reference the target
is relying on when interpreting the provided explanation. An explanation perceived to be
adequate in the target’s mind will be effective in educating and informing the target of the

63

mistaken prior social referent and induce the target to re-anchor. In contrast, an explanation
viewed as an excuse (i.e., inadequate) will not be effective in re-anchoring.7
When firms use social comparison information external to the focal exchange dyad in
forming fairness judgments, perceptions of unfairness arising from this ‘external’ comparison
become an issue in the maintenance of multiple dyadic relationships. Folger and Cropanzano’s
(2001) suggest that events related to unfavorable outcomes or procedures trigger effort to
understand the reasons behind the negative event. Partners subject to a negative allocation event
and perception of unfairness may demand an explanation behind allocation decisions. If a focal
firm fails to appropriately foresee and/or address these demands, the possibility of relationship
strain and relationship termination may increase due to the partner’s perceived unfairness.
Therefore, it is important to study explanation in the context of managing a network of multiple
relations because explanations can be a simple and cost-effective tool for managing (un)fairness
perceptions (McColl-Kennedy and Sparks 2003). The provision of explanations is a popular
management technique (Shapiro, Buttner, and Barry 1994) and can be employed in situations
where differential treatment is inevitable but the information-rich environment facilitates
external social comparisons, giving rise to possible perceptions of unfairness. The focal firm can
partially gain control over the referent selection process. Yet, the manager must be aware of what
aspects of an explanation that will enhance or lessen its efficacy in re-anchoring the target firm’s
initial comparative referent (see Figure 2.3).

7

Scott and Lyman’s (1968) distinguish between two types of explanations: excuses and
justifications. Past research has demonstrated that excuses are the least effective form of
explanation whereas justifications are viewed more favorably by audience targets (Elsbach 1994).
64

Figure 2.3: Model of Comparative Referent Change through Explanations
Explanation Factors
𝐇𝟏 -𝐇𝟑 (Table 2.1)
Timing
(Proactive vs.
Reactive)
Re-anchoring
Provision of New
Comparative Anchor
(Anchor vs. No
anchor)

𝐇𝟒

Perceived Unfairness

HYPOTHESES DEVELOPMENT
The Re-anchoring Efficacy of Explanations
Timing: Proactive vs. Reactive Explanations. The timing of an explanation refers to whether the
explanation is provided before or after an allocation event that could give rise to conflict.
Research on timing of explanation delivery in psychology has identified a simple main effect: the
later the delivery of the explanation, the less effective it will be in ameliorating negative
responses (Sitkin and Bies 1993). Shaprio, Buttner, and Barry (1994) also found that the
timeliness of an explanation influenced explanation adequacy. The consumer literature also
points to the importance of anticipation of price differences and proactively providing relevant
information in advance to influence buyers’ attributions for discrepancies (Collie, Bradley, and
Sparks 2002; Xia, Monroe, and Cox 2004). A proactive explanation is given prior to an
allocation event that could give rise to conflict. For instance, Ford decided to enter into new
65

long-term agreements with ‘select’ suppliers and reduce the current number of suppliers for key
high-impact parts and components by 50% (Media Ford.com 2005). Prior to rolling out the new
business model (i.e., selecting key partners and cutting others), Ford provided proactive
explanations through a public announcement, explaining the level of capability and commitment
that was expected of a selected partner supplier. In contrast, a reactive explanation is usually
given in response after an event as in the case of Shilla Duty Free reactively responding to
Gucci’s demands.
Proactive explanations are expected to positively influence the re-anchoring likelihood of
the target firm receiving the explanation. An empirical study by Van den Bos, Vermunt, and
Wilke (1997) found that people’s judgments are more strongly influenced by what information
they receive first; subsequent information is discounted and ‘assimilated’ toward the frame of
reference that is provided by the preceding information/event. Proactive explanations offered in
advance of experiencing a negative outcome provide a positive frame of reference for
interpreting the allocation event (Weaver and Conlon 2003). Similarly, experiencing a negative
allocation event prior to receiving an explanation may influence the way a person interprets and
responds to any reactive explanation offered following the event. Reactive explanations offered
after the allocation event constitute subsequent information and are framed by the negative
affective state resulting from the negative allocation event, which may serve as an anchor itself
that impedes the consideration of additional information, even when a credible explanation is
offered. The experience of negative outcome allocation creates a frame of reference that
categorizes any information following the event (including the explanation) in negative ways so
that even a legitimate explanation can be evaluated in a negative light and be perceived as an

66

excuse. Therefore, reactive explanations may be less effective than proactive ones in increasing
the target firm’s likelihood of re-anchoring its comparative referent. Thus, it is hypothesized that:
H1 :

The target firm is more likely to re-anchor a comparative referent when provided
a proactive explanation (offered prior) than when provided a reactive explanation
(offered after).

Provision of a New Anchor. The content of an explanation (Shapiro, Butner, and Barry 1994) has
also been found to influence its explanatory efficacy. Greenberg (1990) reported on the
importance of “informational integrity” in reducing perceptions of inequity. Informational
validity (inclusive of the amount and quality of the information) was found to reduce negative
response behaviors when subjects felt they were underpaid. Shapiro, Butner, and Barry (1994)
also demonstrate that adequacy judgments of explanations provided for bad news are influenced
by the specificity of the substance of the explanation. An explanation related to a negative
allocation event may or may not contain information on a new comparative referent. An
explanation can clarify the reasons behind an allocation event, but not necessarily provide a new
anchor for the target firm. In contrast, an explanation within which the focal firm provides a new
comparative anchor is more specific than one that does not provide a new reference point and
provides more thorough informational content to the target.
The provision of a specific new comparative anchor within the explanation will be
positively related to the re-anchoring likelihood of the target firm receiving the explanation. The
provision of a new comparative referent within the explanation regarding an allocation event
increases the specificity of the information content. The more directive the content of an
explanation, the more useful the explanation is in fulfilling its purpose of clarifying and

67

revealing the reasons behind some event that may not be immediately obvious. An explanation
with a new anchor provided is intended to guide the target firm toward a new anchor and ‘directs’
the target firm on a specific referent other. An explanation containing directive information on a
new comparative referent provides deeper clarification in interpreting an allocation event.
Accordingly, it is expected that explanations that do not contain a new comparative anchor may
be less effective than ones that do contain a new comparative referent in increasing the target
firm’s likelihood of re-anchoring its comparative referent. Thus, it is hypothesized that:
H :

The target firm is more likely to re-anchor their comparative referent when a new
comparative anchor is provided than when one is not provided.

Following H1 and H , is it expected that the re-anchoring likelihood of the target firm will be
the greatest when explanations are given proactively and contain a new comparative referent
(Table 2.1 Cell A).
Table 2.1: Target Firm’s Likelihood of Re-anchoring (

Proactive
Explanation

Reactive
Explanation

-

)

New Anchor Provided

New Anchor Not Provided

Cell A: Proactive-Anchor Provided
Highest likelihood of re-anchoring

Cell B: Proactive- No Anchor Provided

Cell C: Reactive-Anchor Provided
Directive information content of the new
comparative referent overrides the frame
of reference that arises from the timing of
the explanation

Cell D: Reactive- No Anchor Provided
Lowest likelihood of re-anchoring

The target firm’s re-anchoring likelihood is expected to be highest in the following order: A > C > B > D.

68

The positive framing effect of a proactive explanation and directional information of the
provision of a specific new comparative referent will work together to enhance the efficacy of
the explanation in clarifying the reasons behind an allocation event and increasing the reanchoring likelihood of the target firm. In contrast, the re-anchoring likelihood of the target firm
will be lowest when explanations are given reactively and do not provide a new comparative
referent (Cell D in Table 2.1). A less directive explanation which is also interpreted in a negative
light of a preceding allocation event will be least likely to contain re-anchoring efficacy.
The provision of a new comparative referent within the explanation regarding an
allocation event is intended to guide the target firm toward a new anchor and is proposed to have
a greater enhancement effect on the re-anchoring efficacy of the explanation. The specific and
directional information content of the new comparative referent is argued to override the ‘general’
frame of reference that arises from the timing of the explanation. Information regarding a new
comparative referent is a ‘component’ (Greenberg 1993; Shapiro, Buttner, and Barry 1994) of the
explanation provided. Shapiro, Buttner, and Barry (1994) found that when examining the relative
importance of content versus delivery, the content of the explanation’s substance accounted for
more variance in judgments as a factor of explanation adequacy. An explanation containing
directive information on a new comparative referent provides deeper clarification whereas the
timing of the explanation functions more as a ‘cue’ surrounding the explanation rather than being
a component. Therefore, since the explanation provides directive insight into the appropriate
comparative referent that the target should be anchoring on, the target firm is more likely to
adjust their comparative referent when it contains a specific new comparative referent regardless
of the timing of the explanation (Cell C > Cell B; refer to Table 2.1). Thus, it is hypothesized that:

69

H3 :

The target firm is more likely to re-anchor their comparative referent when a
reactive explanation contains a new comparative anchor than when a proactive
explanation does not contain a new comparative anchor.

Re-anchoring and Perceived Unfairness
Perceptions of fairness largely depend on the comparative referent chosen, because this choice of
anchor determines the evaluation of the relative favorability of outcome allocations. By varying
the reference point (re-anchoring through explanations), it is possible to create positive and
negative frames for the same set of outcome allocations (Puto 1987; Qualls and Puto 1989). The
successful re-anchoring of a target firm results in the change of the initial comparative referent
that led to a perceived negative allocation in the first place (Huppertz, Arenson, and Evans 1978;
Huseman Hatfield, and Miles 1987). As the comparative referent changes (i.e., the target firm reanchors its comparative referent), the same outcome allocation becomes more favorable, leading
to a weaker sense of unfairness (Brockner and Wiesenfeld 1996; Chen, Choi, and Chi 2002). The
input-to-output ratio of the target firm becomes more comparable with that of the new anchor,
resulting in perceptions of ‘equity.’ The target firm’s perceived unfairness related to the
previously ‘negative’ allocation event will be lowered for the target firm that has been
successfully re-anchored away from its initial anchor (Adams 1963). Thus, it is hypothesized that:
H4 :

Perceived unfairness will be lower for a re-anchored target firm.

70

METHODOLOGY
Research Context and Data Collection
The study was conducted using data collected from brand/store managers operating within a
store-within-a-store (SWS) arrangement for a larger retail chain in South Korea. In a SWS
arrangement, retailers (the department store chains) rent out retail space to brand manufacturers
and the stores (which are manufacturer-controlled space) are operated under a fair amount of
brand manufacturer autonomy over retail decisions (Kim et al. 2011). This retailing format
appears in an increasing number of large retailers worldwide (Kim et al. 2011; Jerath and Zhang
2010), especially in Asian countries. In SWS arrangements, multiple brand manufacturers do
business with a common partner (the retailer) and are also indirectly connected with other brand
manufacturers in the physical retail environment. The retailer rents a part of the retail space to be
used by a different company to run another, independent store and charges a percentage of sales
in return. Different brand manufacturers are charged differential rent for the retail space. Similar
to the opening example of the Shilla Duty Free-Louis Vuitton-Gucci relationship, social
comparison frequently occurs in this context and manufacturer experiences of negatively
perceived allocations are common, thus offering an ideal environment to test the hypotheses.
To benefit from insights of practitioners, the author contacted managers within large retail
corporations with experience in managing SWS with outside brand manufacturers. The
experimental scenarios were developed on the basis of in-depth interviews with practitioners
familiar with SWS arrangements. The experiment was originally developed in English and pretested with undergraduate students in the U.S. The English version of the scenario and
questionnaire were then translated into Korean and pre-tested with brand managers before the

71

final study. The final version was back-translated into English to ensure form and meaning
equivalence (Douglas and Craig 2007).

Pretest 1
The study was first pretested with undergraduate students in the U.S. to evaluate the
manipulations and dependent measures selected for this experimental design. The respondents
were informed that the purpose of the study was to understand the affective states and decision
behavior of managers in inter-organizational exchange settings. The scenario provided the full
background of the context containing imaginary brand names. In the scenario, the respondents
were given background information on their responsibility as a brand manager of a store within a
larger retailer. An initial comparative referent brand was specified as well as information of
another lower-category brand (see Appendix 2.1 for exact wording). Afterwards, the respondents
were asked to envision a situation where his or her brand is negotiating a commission charge
arrangement for the upcoming year and is charged a higher and unfavorable commission rate
compared to their initial comparative referent (i.e., a negatively perceived allocation event). An
explanation was provided by the retailer prior to [after] renewal meetings, indicating that brands
that did not reach a certain sales goal the past year were rated commission charges around 25%.
In addition, the respondent information that the respondent brand was in the same performance
category as the lower-category brand was included [not included] in the explanation (see
Appendix 2.2 for exact wording). The purpose of Pretest 1 was to confirm the differences
between the manipulations developed for the timing of the explanation and the new anchor
provision within the explanation. Forty seven undergraduates taking a marketing course served
as participants in Pretest 1. Each participant, using a pen and paper questionnaire, was randomly

72

assigned manipulations (see Appendix 2.3) and evaluated the dependent variables. Participants
responded to two seven-point manipulation checks. The results of the manipulation checks
confirmed the successful manipulation of each of the factors. Specifically, significant differences
were found for the manipulations of: (1) timing of the explanation (F(1,46) = 13.67, p < .001
Mpro

tive

= 4.77, Mre

tive =

(F(1,46) = 7.184, p < .010 M

2.60) and (2) the provision of a new comparative referent

n hor provided =

3.90, Mno

n hor provided

= 2.44). The participants

perceived the negative allocation event presented in the scenario to be unfavorable (M= 5.06,
SD= 1.86, t

l

= 3.93, p <.000, H0 : M = 4.00 (neither favorable nor unfavorable).

Based upon the results of Pretest 1, some minor refinements were made to the
experimental design and experimental procedure and were further translated into Korean.
Specifically, the experimental procedure was changed from the student pretest version so that the
respondents (now brand managers) were asked to anchor his or her responses on to their actual
relationship with the retailer and other brand manufacturers competing within the same product
category within the retail location. In addition, a no treatment condition to compare the overall
effect of any given explanation vs. no explanation was included. The scales of the manipulation
check for timing was reversed. Based on the consultation of retail managers, the given average
commission rate in the scenario (25%) was adjusted to reflect the actual commission rates
practiced in the Korean retail context (28~30%).

Pretest 2
The purpose of Pretest 2 was to identify refinements that need to be made to the experimental
design and/or experimental procedure with respect to (1) the language, (2) the experimental
manipulations, (3) the dependent variables, and (4) change in sample population. Conducting

73

Pretest 2 within a sample of the actual manager population allowed for any issues that may arise
when conducting the actual study to be uncovered. Eighty one brand/store managers at the
Korean retail location served as participants in Pretest 2. Each participant was presented with the
full experimental design as a pen and paper questionnaire, where participants evaluated the
dependent variables based upon their randomly assigned manipulations. The results of the
manipulation checks indicated that continuous manipulation checks were not appropriate in the
translated version of the instrument. Specifically, differences were found for the manipulations of
the provision of a new comparative referent (F(1,65) = 5.17, p < .05 M
Mno

n hor provided

n hor provided =

3.70,

= 2.82). There was no significant difference found for the timing of the

explanation manipulation (F(1,65)= 3.67, p =.06 Mpro

tive =

3.00, Mre

tive =

4.03). The

participants perceived the negative allocation event presented in the experimental scenario to be
unfavorable (M= 4.50, SD= 1.46, t

l

= 2.35, p < .005, H0 : M = 4.00 (neither favorable nor

unfavorable)). Based upon the results of Pretest 2, refinements were made to the experimental
design and procedure with the consult of retail managers. Specifically, the manipulation checks
for both timing and provision of a new comparative referent were modified to be dichotomous.
In addition, the given average commission rate in the scenario background information was set to
an exact percentage of 30% rather than the range (28~30%).

Pretest 3
The purpose of Pretest 3 was to confirm the differences between the manipulations (now
evaluated with discrete measures) and to identify refinements that need to be made to the
experimental design and/or experimental procedure with respect to (1) the language, (2) the
experimental manipulations, and (3) the dependent variables. Thirty eighty brand/store managers

74

at the retail location served as participants in Pretest 3. Each participant was presented with the
full experimental design as a pen and paper questionnaire, where participants evaluated the
dependent variables based upon their randomly assigned manipulations. A total of 87.5% of the
participants accurately processed the timing of the explanation manipulation. A total of 84.4% of
the participants accurately processed the anchor provision manipulation. The participants
perceived the negative allocation event presented in the experimental scenario to be unfavorable
(M= 4.97, SD= 1.46, t

l

= 4.168, p <.000, H0 : M = 4.00 (neither favorable nor unfavorable)).

Based upon Pretest 3, in order to improve the clarity of and the ease of which the experiment was
administered, small changes were made to the experimental procedure upon observing the
experience of the participants answering the distributed pen and paper questionnaire.
Experimental Procedure
The study was structured as a 2 x 2 between subjects experimental design including a no
treatment control condition (5 conditions total). Each cell represents a combination of the two
manipulations of interest, (1) the timing of the explanation (reactive or proactive) and (2) the
provision of a new comparative anchor (no provision or provision of new comparative anchor).
The respondents were informed that the purpose of the study was to understand the affective
states and decision behavior of managers in inter-organizational exchange settings. The
respondents were further asked to anchor his or her responses on to their relationship with the
retailer and other brand manufacturer competing within the same product category within the
department store. This approach is similar to that used by Ganesan et al. (2010), and is effective
for comparative analysis because it allowed for the manipulation of the explanation given in an
existing context (e.g., Figure 2.1) of the respondent’s choosing. In the experiment (see Appendix
2.4), after identifying a comparative initial anchoring brand and another lower-category brand,
75

the respondent was asked to envision a situation where his or her brand is negotiating a
commission charge arrangement for the upcoming year and is charged a higher and unfavorable
commission rate compared to their chosen comparative referent (i.e., a negatively perceived
allocation event).8 An explanation was provided by the retailer a week prior to [a week after]
renewal meetings, indicating that brands that did not reach a certain sales goal the past year
would be rated commission charges30%. In addition, the respondent information that the
respondent brand was in the same performance category as the selected lower-category brand
was included [not included] in the explanation (see Appendix 2.5 for exact wording). The
respondents were then asked to respond to the dependent measures (i.e., re-anchoring and
perceived unfairness) and manipulation checks (Appendix 2.6).
Within multiple locations of a single retailer, identified brand managers were visited and
asked to participate in a research study aimed at understanding the brand manufacturer’s
relationship with the retailer. Each invitation provided a brief summary of the topic area of the
study and the incentive amount. Upon electing to participate, managers were given the survey
instrument and sealable envelope in which to return the survey. A total of 543 brand managers
across 11 retail locations were initially contacted. One hundred and nine respondents declined. A
total of 434 surveys were distributed of which 302 (55.6%) were returned.

8

Through initial interviews with key informants, it has been identified that in the SWS setting,
external comparisons do occur and mostly in reference to terms of commission rate allocations
(other external comparison situations do occur in terms of store location, store size, display
purchase requirements made by the retailer, etc.). Comparisons (made by the manufacturing
brands) were found to be based on perceived brand power or performance of other manufacturer
brands whereas commission rate decisions (made by the retailer) were claimed to be made based
on actual sales performance. Negatively perceived allocations were found to commonly arise in
this setting.
76

Of the 302 completed surveys, 24 were determined to be unusable due to incomplete
answers, resulting in a sample size of 278 (No Treatment Scenario : 56, Scenario 1: 58 responses,
Scenario 2: 57 responses, Scenario 3: 52 responses, Scenario 4: 55 responses), an effective
response rate of 51.2%.
On average, respondents (72% female and 28% male) in the final sample represented
brand manufacturers which had been working with the retailer, on average, for 5.98 years. The
larger retailer, on average, accounted for 19.24% of the brand manufacturer’s total number of
retail locations. Respondents, on average, had 12.5 years of experience in retail operations and
were 41.5 years of age. The brand product categories represented in the sample include women’s
apparel (44.6%), men’s apparel (13.8%), sports/casual wear (30.4%), miscellaneous (9.1%);
which includes shoes, leather goods, and various accessories), and cosmetics (2.2%).
Because the experiment was taken in response to an in-person administration, the
collection time of each response was available allowing for examination of non-response bias.
The experiment was either collected at a first visit or at a later visit (per the request of the
respondent). Following Armstrong and Overton (1977), non-response bias was examined by
comparing early and late respondents (mean comparisons for the 229 surveys collected at the
first visit versus the 49 collected after a second visit) for key variables under study. No
significant differences (p < 0.05) were found, thereby suggesting that non-response bias is
minimal.
Measures
Dependent Measures. Re-anchoring is defined as the changing of the initial comparative anchor
to a different referent. Re-anchoring was measured by a dichotomous selection (forced choice
option) of the respondent’s comparative referent after the given experimental scenario where (0 =
77

initial comparative brand (unsuccessful re-anchoring) and 1 = new comparative brand
(successful re-anchoring).
Perceived unfairness is defined as the respondent’s view of the degree to which the
distribution of rewards (i.e., the commission rate) relative to its effort is inequitable (Samaha et
al. 2011). The respondent’s perception of fairness was obtained through a seven point Likert-type
scale (1= Very unfair, 7= Very fair) that allowed the respondent to indicate relative agreement
with a statement indicating the fairness of the commission rate given the explanation received in
the scenario. The scale was then reversed to obtain perceived unfairness.
Manipulation Checks. The respondents were asked to answer to manipulation checks
(Cook and Campbell 1979; Perdue and Summers 1986) (Appendix 2.3). Participants were asked
to evaluate the two manipulations regarding the timing of the explanation given (prior to/after)
and whether a new comparative anchor was provided (“yes/no”) which were measured with
dichotomous items.
Control Variables. Because respondents were asked to anchor on a real retailer and
competing brands (Ganesan et al. 2010), three control variables were be included in the model:
(1) relationship length, (2) dependence on the retailer (measured in terms of importance put on
the relationships with the retailer), and (3) trust in the retailer. In addition, to control for
individual factors that might influence the decision making of respondents, two control variables
were included: (1) years of retail experience and (2) gender. The measures for all of the control
variables are reported in Appendix 2.6.
Manipulation Checks
Manipulation checks reveal that the manipulations of the two factors — the timing of the
explanation and provision of a new comparative anchor — were successful. A total of 90.1% of
78

the participants accurately processed the timing of the explanation manipulation. A total of 88.7%
of the participants accurately processed the anchor provision manipulation. The participants who
did not accurately process at least one of the manipulation checks were distributed evenly over
the four experimental conditions, and the exclusion of those participants produced a similar
pattern of the main results. The participants perceived the negative allocation event presented in
the experimental scenario to be unfavorable (M= 5.56, SD= 1.49, t

l

= 17.54, p <.000, H0 : M

= 4.00 (neither favorable nor unfavorable)). Based on these results, it can be concluded that the
respondents did process the timing and content information presented to them.
ANALYSIS AND RESULTS
The Re-anchoring Efficacy of Explanations
The first set of hypotheses (H1 -H3 ) focused on the influence of factors surrounding the
communication of an explanation on the target firm’s likelihood of re-anchoring their
comparative referents. To test the relationship between explanation factors and the target firms’
re-anchoring of comparative referents, a logistic regression and subsequent chi-square goodness
of fit tests were conducted. The results of the logistic regression with the two explanation factors
and interactions are presented in Table 2.2.

79

Table 2.2: Logistic Regression Results for Explanation Factors
Predictor
Timing
Anchor Provision
Timing*Anchor Provision
Controls
Length of Relationship
Dependence
Trust
Gender
Respondent Retail Experience
Constant

1.495
.965
-.796

𝐞𝛃
4.461
2.626
.451

p-value
.000
.026
.187

-.038
.034
.058
-.621
-.014
-.517

.963
1.035
1.060
.537
.986
.596

.298
.745
.583
.083
.568
.525

𝛃

Overall Goodness of Fit:
-2 Log Likelihood= 262.550
Cox & Snell
= .114
Nagelkerke
= .152
Hosmer and Lemeshow Test: χ (8)= 8.311 , p= .404
In H1 , it was predicted that the target firm was more likely to re-anchor their
comparative referent when the explanation was provided proactively (offered prior to) than when
provided reactively (offered after). The results in Table 2.2 demonstrate the significant main
effects of explanation timing and the provision of a new anchor in the explanation. Thus, H1 is
supported.
In H , it was predicted that the target firm was more likely to re-anchor their
comparative referent when a new comparative anchor was provided than when one is not
provided. A target firm is 2.6 times more likely to likely to re-anchor their comparative referent
when information of a new comparative referent is included in the explanation than when the
explanation does not contain this new information. Thus, H is supported.
Subsequent chi-square goodness of fit analyses were conducted to get a better
understanding of the re-anchoring likelihood of target firms given different explanation factors.
80

The results of the chi-square goodness of fit analysis for the two explanation factors are
presented in Table 2.3 and 2.4.

Table 2.3: Cross-Tabulation of Re-anchoring Percentages Given Explanation Timing

Did Not Re-anchor
Comparative Referent
Re-anchored
Comparative Referent
Total
Statistical Testing
Test for Equal
Proportions

Explanation Given
Proactively
41

Explanation Given
Reactively
64

Total
105

71

45

116

112

109

221

χ (1) = 10.827

p = .001

The chi-square goodness of fit test for equal probabilities based on the timing of the
explanation was significant (χ (1) = 10.827, p <.001), indicating that the distribution of the reanchored target firms is not equivalent to random selection. Further, the percentage of firms that
re-anchored their comparative referent when the explanation was given proactively (61.2%) is
greater than the percentage of firms that re-anchored their comparative referent when the
explanation was given reactively (38.8%), confirming the previous findings for H1 .

81

Table 2.4: Cross-Tabulation of Re-anchoring Percentages Given Provision of a New
Comparative Referent

Did Not Re-anchor
Comparative Referent
Re-anchored
Comparative Referent
Total
Statistical Testing
Test for Equal
Proportions

Explanation Contained
Information on a New
Comparative Referent
(Yes Anchor)
43

Explanation Did Not
Contain Information on a
New Comparative
Referent (No Anchor)
62

Total

105

64

52

116

107

114

221

χ (1) = 4.462

p = .035

The chi-square goodness of fit test for equal probabilities based on the provision of a
new comparative referent within the explanation was significant (χ (1) = 4.462, p= .035),
indicating that the distribution of the re-anchored target firms is not equivalent to random
selection. Further, the percentage of firms that re-anchored their comparative referent when the
explanation contained information on a new comparative referent (55.2%) is greater than the
percentage of firms that re-anchored their comparative referent when the explanation did not
contain information on a new comparative referent (44.8%), confirming the previous findings for
H .
In H3 , it was predicted that the target firm is more likely to re-anchor their comparative
referent when the explanation is provided reactively, but contains a new comparative anchor than
when explanation is provided proactively, but does not contain a new comparative anchor. The
interaction between timing and anchor provision was non-significant.
The Marascuillo procedure was further conducted to simultaneously compare the
differences in multiple proportions and examine the difference between two groups (i.e.,

82

Proactive/No Anchor versus Reactive/Yes Anchor). The results of the Marascuillo procedure are
presented in Table 2.5. For an overall level of significance of 0.05, the critical value of the chisquare distribution having four degrees of freedom is χ0.95,4 = 9.488 and the square root of 9.488
is 3.080. Calculating the 10 absolute differences and the 10 critical values leads to the following
summary table.

Table 2.5: Marascuillo Procedure Results for Equality of Proportions
Contrast
|p0NO TREATMENT − p1REACTIVE /NO ANCHOR |
|p0NO TREATMENT − p PROACTIVE /NO ANCHOR |
|p0NO TREATMENT − p3REACTIVE /YES ANCHOR |
|p0NO TREATMENT − p4PROACTIVE /YES ANCHOR |
|p1REACTIVE /NO ANCHOR − p REACTIVE /NO ANCHOR |
|p1REACTIVE /NO ANCHOR − p3REACTIVE /YES ANCHOR |
|p1REACTIVE /NO ANCHOR − p4PROACTIVE /YES ANCHOR |
|p PROACTIVE /NO ANCHOR − p3REACTIVE /YES ANCHOR |
|p PROACTIVE /NO ANCHOR − p4PROACTIVE /YES ANCHOR |
|p3REACTIVE /YES ANCHOR − p4PROACTIVE /YES ANCHOR |

Difference Critical
Value
Range Value
0.044
0.251
0.400
0.262
0.326
0.274
0.404
0.265
0.356
0.267
0.282
0.279
0.360
0.269
0.074
0.289
0.004
0.280
0.078
0.291

The difference value (0.074) for the two groups of interest (Proactive/No Anchor versus
Reactive/Yes Anchor) does not exceed the critical range value (0.289). Consistent with the nonsignificant interaction of the logistic regression, there is not enough data to conclude that the
difference between two groups (i.e., Proactive/No Anchor versus Reactive/Yes Anchor) of focus
is significant. H3 is not supported.
Subsequent analyses were conducted to get a better understanding of the re-anchoring
likelihood of target firms given different explanation factors and their combinations. To examine
more fully the extent of the differences in re-anchoring likelihood given several control factors, a

83

logistic regression with 5 scenario groups (all possible explanation factor combinations and a no
treatment group) was conducted. The results of the logistic regression are presented in Table 2.6.

Table 2.6: Logistic Regression Results for Explanation Factor Combination Scenarios
Predictor
Scenario
No Anchor
Yes Anchor

𝐞𝛃

𝛃
Reactive
Proactive
Reactive
Proactive

Controls
Length of Relationship
Dependence
Trust
Gender
Respondent Retail Experience
Constant

.
.234
1.747
1.199
1.912

.
1.263
5.737
3.316
6.766

p-value
.000
.606
.000
.007
.000

-.042
.028
.091
-.701
-.012
-.810

.959
1.028
1.095
.496
.988
.445

.186
.778
.362
.027
.600
.272

Overall Goodness of Fit:
-2 Log Likelihood= 319.175
Cox & Snell
= .152
Nagelkerke
= .202
Hosmer and Lemeshow Test: χ (8)= 6.229, p= .622

The results in Table 2.6 demonstrate that generally, any given explanation (with the
exception of a reactive one with no information of a new comparative referent) is significantly
effective in increasing the re-anchoring likelihood (eβ > 3.316, p < .01) compared to when no
explanation is given (i.e., no treatment condition). The re-anchoring effect of an explanation is
largest when it is provided proactively and contains information on a new comparative referent
and smallest when provided reactively with no new information on a comparative referent (this
confirms the main effects timing and new anchor information content of H1 and H ). Contrary

84

to expectations (as hypothesized in H3), the re-anchoring efficacy of a proactive explanation that
doesn’t contain information on a new comparative referent (about 5.7 times higher than no
treatment) was higher than the re-anchoring efficacy of a reactive explanation that does include
information on a new comparative referent (about 3.3 times higher than no treatment).
Re-anchoring and Perceived Unfairness
The final hypotheses focused on the effectiveness of re-anchoring explanations in lowering
perceptions of unfairness. In H4 , it was predicted that perceived unfairness would be lower for a
re-anchored target firm. To compare the unfairness levels of the re-anchored versus not reanchored groups, an ANCOVA was conducted. The results of the one-way ANCOVA are
presented in Table 2.7.
Table 2.7: Perceived Unfairness ANCOVA Results
𝐅 − 𝐯𝐚𝐥𝐮𝐞 Significance
130.76
.000

Predictor
Re-anchor
Covariates
Length of Relationship
Dependence
Trust
Gender
Respondent Retail Experience

The difference in perceived unfairness was significant (F1,

2.15
.242
2.22
.260
.994

6

.144
.623
.137
.613
.320

= 130.76, MRE−ANCHORED = 3.58 vs.

MDID NOT RE−ANCHOR = 5.81, p < .000). H4 is supported.
Subsequent analyses were conducted to get a better understanding of perceived
unfairness levels of target firms given different explanation factors and their combinations. An
ANOVA was conducted with perceived unfairness as the dependent variable to examine
differences across the 4 explanation scenarios and control group (i.e., 5 groups). The results
85

indicated significant differences across the scenarios FUnf

irness,4 =

11.80 p <.000) (Please refer

to Table 2.8).

Table 2.8: Perceived Unfairness Mean Values of Target Firms
Scenario #

n

Explanation Factors

Scenario 0
Scenario 1
Scenario 2
Scenario 3
Scenario 4

56
58
57
52
55

No Treatment
No
Reactive
Anchor
Proactive
Yes
Reactive
Anchor
Proactive

Re-anchored Perceived
Percentage
Unfairness
23.2%
5.70
27.6%
5.36
63.1%
4.00
55.8%
3.87
63.6%
4.64

A post-hoc multiple comparison test was conducted to identify significant pair-wise
difference in perceived unfairness levels. A least significant difference (LSD) test (Table 2.9)
indicates that giving an explanation in most forms (with the exception of a reactive/no anchor
explanation) is significantly more effective in lowering unfairness perceptions compared to not
giving an explanation at all (Mno tre

tment =

5.70).

Table 2.9: Multiple Comparisons of Perceived Unfairness Mean Values of Target Firms
Dependent
Variable

(i) Scenario

(j) Scenario

Mean
Difference
(i-j)
Perceived
1 Reactive/No Anchor
.334
0 No treatment
Unfairness
2 Proactive/No Anchor
1.70*
3 Reactive/Yes Anchor
1.83*
4 Proactive/Yes Anchor
1.06*
2 Proactive/No Anchor
1.36*
1 Reactive/No Anchor
3 Reactive/Yes Anchor
1.50*
4 Proactive/Yes Anchor
.726*
3 Reactive/Yes Anchor
.135
2 Proactive/No Anchor
4 Proactive/Yes Anchor
-.636
4 Proactive/Yes Anchor
-.771*
3 Reactive/Yes Anchor
*: indicate there the pair of scenario groups’ means differ significantly at the p <.05
level.
86

Proactive explanations and explanations that contain information on a new comparative referent
are more effective in lowering unfairness perceptions. Interestingly, although the proportion of
re-anchored target firms is higher for proactive explanations containing new information on a
comparative referent (63.6%) compared to a reactive explanation that does contain information
on a new comparative referent (55.8%), unfairness perceptions were actually significantly higher
as well (Mdifferen

e

= −.771.*).
DISCUSSION

This study was motivated by a desire to understand both the theoretical nature of an informationrich environment where external social comparisons more readily occur and how explanations
could help in re-anchoring the target firm on a new comparative referent and lowering
perceptions of unfairness against the backdrop of a negatively perceived allocation event. As
such, three research questions were proposed: (1) Do firms engage in external social comparison
when making fairness judgments? (2) How do factors surrounding the communication of an
explanation influence the target firm’s likelihood of re-anchoring their comparative referent? (3)
Are perceptions of unfairness lowered for target firms that successfully re-anchor following an
explanation? An integrated conceptual framework, based on equity theory and the anchoring and
adjustment literature, focused on how various factors surrounding an explanation influenced reanchoring and unfairness perceptions. Our findings offer initial insights into these issues and
provide significant implications for marketing academics and practitioners.
Theoretical Implications
First, this study answers the question of whether firms engage in external social comparison
when making fairness judgments. Wathne and Hedie (2004) called for the consideration of
multiple dyadic relationships in examining inter-organizational relationships. The management
87

and economic psychology literature recognize that actors (i.e., employees) frequently engage in
external social comparisons (Konigstein, Kovacs, and Zala-Mezo 2003) and look to their social
network for comparative referents (Shah 1998). Similarly, firms maintain relationships with
many other firms within an information-rich environment. This study finds that firms do indeed
engage in social comparison. Specifically, they look to the treatment of comparative referents to
determine fairness, indicating that balance of fairness concenrs is necessary for effective
relationship management in such information-rich environments. This work extends the current
inter-organizational literature on fairness issues (e.g., Brown, Cobb, and Lusch, 2006; Kumar,
Scheer, and Steenkamp, 1995; Samaha, Palmatier and Dant, 2011; Scheer, Kumar, and
Steenkamp, 2003) by recognizing the occurrence of external social comparison activities among
multiple inter-firm relationships.
Second, the inter-organizational literature is advanced through the introduction of the
conflict management literature by examining the factors that enhance or diminish the reanchoring efficacy of explanations. Adams (1963) suggests that although individuals may be
resistant to changing comparative referents, it is an effective means of resolving inequity. Yet, the
stability of comparative referent choice within equity theory has not received research attention
in the past (Stepina and Perrewe 1991).The literature does not examine the factors that enhance
or lessen the efficacy of explanations in adjusting the initial reference point to re-anchor a target
and changing comparative referents. The consumer anchoring and adjustment literature (Puto
1987; Rowe and Puto 1987) indicates that initial reference points (anchors) are subject to change
as additional information is communicated and accepted to reach a new reference point. This
research demonstrates that explanations can be effective tools in responding to this change in the
business information environment. Specifically, the results demonstrate that the timing and the

88

content/specificity of an explanation have significant impact on re-anchoring. A proactive (given
prior) explanation was more effective in re-anchoring the target firm than a reactive (given after)
one (as found in H1 ). This is in line with the conflict management literature that identifies a
simple main effect: the later the delivery of the explanation, the less effective it will be in
ameliorating negative responses (Sitkin and Bies 1993). A proactively given explanation that is
given in advance to a foreseen negative allocation event provides a positive frame of reference
and is perceived as useful foretelling whereas a reactive explanation is perceived to be more of
an excuse that comes afterwards and is perceived to be a ‘quick-fix’ attempt by the focal firm
(Scott and Lyman 1968). This is suggestive of a framing effect of a proactively given explanation
wherein subsequent information (including the negative allocation event) is interpreted under a
positive light (Weaver and Conlon 2003).
The results also demonstrate that a more specific and directive explanation containing
information on a new comparative referent was more effective in re-anchoring a target firm (as
found in H ). By providing information of a new comparative referent within the explanation
regarding an allocation event increases the specificity of the information content. The more
directive the content of an explanation, the more useful the explanation is in fulfilling its purpose
of clarifying and revealing the reasons behind some event that may not be immediately obvious.
This is also consistent with management research that indicates that the content of an explanation
impacts explanatory efficacy (Greenberg 1990, Shapiro, Buttner, and Barry 1994).
Following existing studies on the adequacy of explanations (Shapiro, Buttner, and Barry
1994), it was expected that the content and specificity (thus, the substance of the explanation)
would be more effective in re-anchoring a target than the general structure of the explanation (i.e.,
the timing). Contrary to expectations, the results indicate that the timing of an explanation may

89

be slightly more potent in re-anchoring a target firm than a specific explanation that contains
information on a new comparative referent (as found in H3 ). This suggests that the general
frame of reference is more powerful in changing perceptions than the actual content of the
explanation itself, suggesting that targets may process the peripheral cues (much similar to the
elaboration likelihood model; Petty and Cacioppo 1986) over elaboration of the specifics of the
explanation when given alongside a negative allocation event. This underscores the importance
of the ‘signaling’ effect of a proactive explanation alongside its previously argued ‘framing’
effect.
This study demonstrates that by varying the reference point through explanations, the
target changes their comparative referent (i.e., a new anchor), indicating that firms can partially
control the information environment by influencing referent selection of partnering firms. This
finding contributes to the equity theory and inter-organizational literature by empirically
examining the strategic mechanisms that firms can employ to alter comparative referents. Finally,
the study answers to the question of whether perceptions of unfairness are lowered for target
firms that successfully re-anchor following an explanation. Perceptions of fairness largely
depend on the comparative referent chosen. Among the various means of resolving inequity
(Huseman, Hatfield and Miles 1987; Huppertz, Arenson, and Evans 1978), least attention has
been paid to the changing of the comparative referent. As Kulik and Ambrose (1992) note, the
availability of information is a crucial factor in the selection of referents. It was found that the
perceived unfairness levels were significantly lower for those who successfully re-anchored their
comparative referent after being given an explanation than those who did not re-anchor (as found
in H4 ). The study demonstrates that relatively stable comparative referents can be changed by
employing explanation strategies and this, following equity theory predictions, alleviates

90

unfairness perceptions by restoring the perceived equity imbalance. The target changes (adjusts)
their comparative referent to a new anchor and the same allocation outcome is perceived to be
less unfair as the input-to-output ratios are now at a more comparable level. The findings extends
the equity theory literature into the inter-organizational context by empirically demonstrating the
changing of referents (i.e., one of the theoretical inequity coping mechanisms) in changing
fairness perceptions.
Managerial Implications
From a managerial standpoint, the study presents insight into three important areas for marketing
managers who are involved in managing multiple inter-organizational relationships. First, the
implication of an information-rich environment is that a firm’s management of one partner has
become increasingly transparent to its other partners, facilitating external comparison, which
create problems for firms maintaining multiple relationships. The failure to account for the
change in the information environment when managing such relationships can significantly
influence perceptions of fairness among business partners and potentially lead to adverse
relationship consequences. The negative spillovers of an information-rich environment are
especially high in an SWS context where brand managers of multiple partnering firms of the
retailer are in extreme close proximity. Retailers are not able to treat all brand manufacturers the
same (i.e., provide the same level of outcomes) and it is very difficult for the retailer to control
the information environment. Despite the relatively asymmetrical dependence in power of the
retailer in such relationships, the constant discord and exiting of brands within the retail location
can hurt the retailer’s overall brand image as well, leading to profit loss. For sure, the retailer
should have strategies ready to alleviate unfairness perceptions that are bound to arise to
effectively balance these multiple relationships simultaneously.
91

Second, the results of this research underscore the importance of being ‘active’ in such
an information-rich environment. The literature points to the tendency for organizations to
withhold adequate explanations or information being concerned about revealing confidential
information or triggering damaging lawsuits (Shaw, Wild, and Colquitt, 2003). Yet, research
demonstrates that failure to give an explanation (or the use of an inadequate one) could have
adverse consequences. Providing an adequate explanation in a timely manner (i.e., foreseeing
possible negative allocation events and proactively providing explanations beforehand) with
substantive content and specificity (i.e., providing directive information on the appropriate
comparative referent) aids in recuperating any negative events through their ability to adjust
partnering firms’ reference points. The results of the study indicate that a proactively given
explanation has more re-anchoring (and thus, unfairness mitigating) power than a reactive
explanation. An explanation containing more specific and directive information on a new
comparative referent was more effective than a less specific explanation. A reactive explanation
that came with no directive information was no more effective in increasing the re-anchoring the
likelihood of a target firm than no explanation given at all with the backdrop of a negative
allocation event. Had Shilla communicated the explanation to Gucci before its offer of
preferential minimum rent to Louis Vuitton and directed them towards a more appropriate
comparative referent to anchor on due to its sub-par performance, it could have salvaged its
relationship with Gucci.
Third, managers must be aware that explanation communication strategies will be only
effective when they are plausible. The justification backing any allocation event must be
adequate meaning that the differential treatment across multiple brands must indeed be just. An
explanation is makes something not immediately obviously known more understandable. An

92

explanation is intended to provide the reasoning behind differential treatment and clarify the
input-to-output ratio of relevant parties. An explanation strategy intended to operate as an excuse
or cover-up of truly inequitable treatment may backfire. Before employing any type of
explanatory conflict-mitigating strategy, managers should have clear standards by which
allocations are established in dealing with multiple partners.
Limitations and Directions for Future Research
Although this research provides insight into the occurrence of external social comparisons and
how marketing managers can employ explanation communication strategies to respond to the
negative effects of the new information environment, the following limitations need to be
considered when attempting to generalize the findings.
The effects of explanations were examined through an experimental scenario regarding a
restricted hypothetical situation. Although this research design was purposefully selected and
designed based upon the extant literature and context to allow of the maximum amount of
control to be maintained over the explanation factors and information environment, this control
also limits the external generalizability of the findings. First, the experiment only examines the
two factors of timing and content of an explanation. The literature (e.g., Sitkin and Bies 1993;
Shapiro, Butner and Barry 1994) demonstrates many other potential factors (e.g.,
sincerity/empathy of delivery, format of delivery) surrounding explanations that can
enhance/lessen its efficacy. Second, the dependent variable was limited to measuring the
perceived unfairness of the commission rate arrangement in the experimental scenarios given the
explanation employing a single item measure. Measuring overall perception of unfairness
incorporating the larger relationship context (captured through multiple items) would provide a
more thorough picture of the relationship dynamics given explanation strategies.Third, the study
93

also restricts the comparative referents to two other brands in the location whereas in practice,
firms may refer to multiple referents simultaneously. Ordonez, Connolly, and Coughlan (2000)
point out that in many settings, more than one reference point is available and used. Thus, future
research could explore how explanations operate under the simultaneous impact of such multiple
reference points. In addition, the flow of information was restricted to the dyadic focal firmtarget relationship and other brand manufacturers within the retail location. Information can
come from a multitude of sources, including industry associations, rumors as well as information
from the mother company. Yet, the complexity of these environments is not fully reflected in the
scenarios. To address these limitations, the influence of explanatory communication strategies on
re-anchoring should be examined using a methodology where the influence of multiple
comparative referents and multiple sources of information can be incorporated. Research
addressing this topic could substantially contribute to the advancement of the social comparison
literature in inter-organizational relationship settings.
In addition to addressing the aforementioned limitations, the results of this research on
marketing strategy decision making suggests numerous avenues for future research. First, further
examination of the unfairness levels of successfully re-anchored firms suggest that the perceived
unfairness levels do not always correspond to the re-anchoring potent of different explanation
factor combinations. For example, the re-anchoring likelihood of a proactive explanation
containing information on a new comparative referent demonstrated the highest likelihood of
successfully re-anchoring a target firm. However, the resulting level of unfairness perceptions
was not the lowest (a reactive explanation containing information on a new comparative referent
had the lowest resulting perception of unfairness). This suggests that explanations could have
differing effects across different affective/emotional states towards the relationship that can

94

impact future events. For instance, trust and commitment in the relationship is found to have
significant impact on relationships performance (Morgan and Hunt 1994). Griffith, Harvey, and
Lusch (2006) find that perceptions of fairness influence long-term orientation and relational
behaviors that ultimately impact relationship performance. Future research might examine other
affective states of target firms by measuring/capturing the impact of explanation strategies used
on resulting attitudes and behaviors toward the relationship.
Second, it would be fruitful to examine how initial reference points are established at the
beginning of the relationship in a business context. When and how do firms form initial anchors?
The social comparison literature demonstrates a similarity bias where actors look to other
actors/entities that are similar for reference (Krackhardt and Brass 1994) or look to actors with
cohesive and interpersonal ties (Shah 1998). Can a focal firm have initial (albeit partial) control
over the information-rich environment by having the ability to set initial comparative referents?
It would be informative to examine whether the effect of an explanation change across similarity
referents versus cohesive referents or if the focal firm can influence the initial setting of the
anchor.
Finally, as Cropranzano (1998) stated, culture can be viewed as a lens through which
people interpret fairness. This study was conducted in a unique SWS setting in an East Asian
culture. Research demonstrates that that cross-cultural differences exist in consumer responses to
post-service recovery strategies such as explanations or compensations (Mattila and Patterson
2004; Patterson et al. 2006). It would be fruitful to examine whether the re-anchoring impact of
explanations are similar across different cultures. Re-anchoring responses may differ across
different levels and combinations of power distance, uncertainty avoidance,
individualistic/collectivistic cultural values (Hofstede 2001). In addition, the asymmetry of

95

dependence (in favor of the retailer) is inherent in SWS relationships in South Korea. Although
this aspect was controlled for in the analyses, it would be interesting to examine the study where
the dyadic relationships are more balanced in terms of power and dependence. Explanations
coming from a more equal partner (in terms of power in the relationship) may be more or less
effective in inducing re-anchoring compared to when they come from a much more powerful
partner. Further research that examines the relative influence of the underlying social relationship
factors would provide substantial insights into the inter-organizational marketing discipline.

96

APPENDICES

97

APPENDIX 1.1 SOLICITATION EMAIL TO DEALERSHIP MANAGERS
Email Title: Dealer network study (MSU)
Dear ******,
I am a Ph.D. candidate in the Department of Marketing at Michigan State University (Profile:
http://broad.msu.edu/facultystaff/chungh/). As a part of my degree requirements, I am working
on my dissertation regarding the information environment of the automotive dealer network and
how this influences dealer-manufacturer relationships.
The automobile dealer network is a critical asset the auto manufacturer. In order to build and
maintain effective dealer networks, it is necessary to understand the relationship management
strategies of manufacturers commonly observed in the dealer network today. I am writing to you
to request your help by participating in a very brief interview. This process will only take 15
minutes of your time and your responses will remain completely confidential. Insights gained
from these initial interviews will be used to put together a survey of the national auto dealership
network.
Only a select number of individuals will be contacted. Because of your experience and your
expertise in the auto industry, your participation is critical to the success of the research. I
understand how busy your daily schedule is, but a brief slice of your time will help me
tremendously to advance this research. I will call within the next few days to check your
availability and hopefully schedule the interview.
I am very much looking forward to your participation. Should you have any questions please do
not hesitate to contact me. Thanks in advance for your support.

Best regards,
Hannah S. Lee

98

APPENDIX 1.2 MEASURES (ESSAY 1)
Table A.1: Measurements, Factor Loadings, Composite Reliabilities, and AVEs (Essay 1)
This study focuses on automotive dealer-manufacturer relationships. When responding, think of
one particular automotive manufacturer that your dealer does business with. Other dealers that
work with this manufacturer and carry the brand are part of the “dealer network.”
Construct
Network Density
Adapted from
Antia and Frazier
(2001)

Description
(1= Strongly Disagree, 7= Strongly Agree)






Reward Usage
Adapted from
Lusch (1976)

CR/AVE
0.89/
0.62

0.75
0.81
0.89
0.76
0.73

(1= Never, 7= Very Often)

0.91/
0.64

How often do you observe other dealerships in the
network being rewarded by the manufacturer through
the following actions?
(Rewards broadly cover various actions that have
positive consequences and can range from the
manufacturer providing various assistances to the
dealership to awarding recognition as a preferred
dealer.)







Punishment
Usage
Adapted from
Lusch (1976)

Dealers of our system are well-connected.
There is a considerable amount of interaction
among the dealers.
Dealers of our system share frequent
communications.
Dealers of our system frequently discuss
common problems.
Information on how other dealers are treated
is readily available.

𝛌

Product Servicing assistances
Training assistances
Incentive assistances
Financial assistances
Advertising assistances
Preferred dealer recognition

(1= Never, 7= Very Often)
How often do you observe other dealerships in the
network being punished by the manufacturer through
the following actions?
(Punishments broadly cover various actions that have

99

0.82
0.78
0.84
0.80
0.80
0.75
0.87/
0.54

Table A.1 (CONT’D)
Construct

Description
negative consequences and can range from the
manufacturer “creating problems” for the dealership
to imposing formal sanctions or penalty.)







Slow payment on warranty work
Unfair distribution of vehicles
Turndowns on warranty work
Threat of termination
Bureaucratic red tape
Withholding of financial assistances

(1= Never, 7= Very Often)
Compliance
Adapted from
Payan and
Due to the rewards and punishment of other
McFarland (2005) dealerships in the network....
and Hunt, Mentzer,
and Dane (1987)
 We accommodate what the manufacturer
would like for us to do.
 When this manufacturer asks us to change,
we adjust accordingly.
 My dealership accommodates the desires of
this manufacturer
 My dealership complies to this
manufacturer’s requests.
 We satisfy the manufacturer’s demands.

𝛌

CR/AVE

0.71
0.69
0.71
0.60
0.82
0.86

0.94/
0.76

0.77
0.83
0.89
0.95
0.91

= 327.941, d.f.= 199 ; CFI= 0.956; RMSEA= 0.058; SRMR=0.0596.
Control Variables
Dealer Size
Length of
Relationship
Marker Variable
(Job Autonomy)
Adapted from
Hackman and
Oldham (1976)

Description


How many employees are there in your dealership?



How many years has your firm engaged in operations with the
manufacturer?
(1= Strongly Disagree, 7= Strongly Agree)




I have significant autonomy in determining how I do my job.
I can decide on my own how to go about doing my work.
I have considerable opportunity for independence and freedom
in how I do my job.

100

Table A.1 (CONT’D)
Grouping
Variables
Relative Network
Centrality
Adapted from Antia
and Frazier (2001)

Description
(1= Less, 4= Equally, 7= More)
For each statement below, please indicate the relative level of your dealer’s
position to the other dealer.
Reward Observation:
Think of another dealership in the network that has recently been
rewarded by the manufacturer. Compared to the rewarded dealership, my
dealership…
…is a (less, equally, more) crucial cog in the dealer network.
…maintains (less, equal, more) relations with other dealers.
… is (less, equally, more) active in the dealer network.
… has (less, equally, more) extensive links with other firms.
…is (less, equally, more) central to the dealer network.
… is (less, equally, more) of a leader in the dealer network.
Punishment Observation:
Think of another dealership in the network that has recently been
punished by the manufacturer. Compared to the punished dealership, my
dealership…
…is a (less, equally, more) crucial cog in the dealer network.
…maintains (less, equal, more) relations with other dealers.
… is (less, equally, more) active in the dealer network.
… has (less, equally, more) extensive links with other firms.
…is (less, equally, more) central to the dealer network.
… is (less, equally, more) of a leader in the dealer network.

(1= Strongly Disagree, 7= Strongly Agree)
Observing Firm
Network
My dealership…
Centrality
Adapted from Antia
…is a crucial cog in the dealer network.
and Frazier (2001)
…maintains many relations with other dealers.
… is a leader in the dealer network.
… is very active in the dealer network.
… has extensive links with other dealerships.
… is very central to the dealer network.

101

APPENDIX 2.1 EXPERIMENTAL SCENARIO (PRETEST 1)
Please read the following scenario very carefully and try your best to immerse yourself into
the imagined managerial position and situation.
Imagine you are the brand manager for Reneevon responsible for the management of the brand’s
retail location within the department store DK PLAZA. Reneevon stores offer classic styled suits,
separates, dresses, shoes and accessories targeted at affluent career women. The brand pays
commission charges (set as a percentage of sales) to DK PLAZA.
Within the DK PLAZA location, there are multiple brand shops in the same apparel category, but
with varying brand power. Franco Eva and Closet 9 compete with Reneevon directly in the
women’s apparel category. You consider your brand to be equal to Franco Eva in terms of brand
power. Industry reports indicate that Closet 9’s brand power is lower compared to Reneevon. All
brands within the store will undergo negotiations with the DK PLAZA representative to set the
terms of the renewal of the annual contract for the following period.
[Insert Manipulation for Proactive Scenarios]
During the contract renewal meeting, Reneevon receives a 25% commission charge for the next
contract period. You also become aware that Franco Eva received a much favorable and lower
commission charge at 15%.
[Insert Manipulation for Reactive Scenarios]
[Insert Dependent Variables; Followed by Manipulation Checks]

102

APPENDIX 2.2 EXPLANATION MANIPULATIONS (PRETEST 1)
Scenario 1: Reactive, No new anchor provision
After all renewal meetings have been completed, DK PLAZA explains that brands that did not
reach a certain sales goal the past year were rated commission charges around 25%.
Scenario 2: Reactive, New anchor provision
After all renewal meetings have been completed, DK PLAZA explains that brands that did not
reach a certain sales goal the past year were rated commission charges around 25%. The
department store representative also informs you that your brand is in the same performance
category as Closet 9.
Scenario 3: Proactive, No new anchor provision
Prior to renewal meetings, DK PLAZA announces that commission charges will vary across
brands and those brands that did not reach a certain sales goal the past year will be rated
commission charges around 25%.
Scenario 4: Proactive, New anchor provision
Prior to renewal meetings, DK PLAZA announces that commission charges will vary across
brands and those brands that did not reach a certain sales goal the past year will be rated
commission charges around 25%. The department store representative also informs you that your
brand is in the same performance category as Closet 9.

103

APPENDIX 2.3 MEASURES (PRETEST 1)
Table A.2: Dependent Measures, Manipulation Checks, and Control Variables (Pretest 1)
Dependent Measures
Re-anchoring

1) Do you consider Franco Eva as a comparative referent?
(1) Yes
(2) No
2) Do you consider Closet 9 as a comparative referent?
(1) Yes
(2) No

Perceived Unfairness

(1= Strongly Disagree, 7= Strongly Agree)
•

Manipulation Checks
Negative Allocation
Event

Given DK PLAZA’s explanation, I feel the commission
charge arrangement was fair.

(1= Extremely Favorable, 7= Extremely Unfavorable)
•

Timing of Explanation

How favorable was the commission your brand received
compared to the rate Franco Eva received?

(1= After, 7= Prior to)


Provision of New
Anchor

(1= Strongly Disagree, 7= Strongly Agree)
•

Control Variables
Gender

English Proficiency

DK PLAZA’s explanation regarding the commission charge
arrangement for the upcoming contract year was given
______________ the contract renewal meeting.

Within DK PLAZA’s explanation, information of a new
comparative referent brand was given.

What is your gender?
(1) Male
(2) Female
Did you attend high school in the United States?
(1) Yes
(2) No

104

APPENDIX 2.4 EXPERIMENTAL SCENARIO
The purpose of the study is to understand the behavior of firms (such as your brand
manufacturer) in inter-organizational exchange relationships.
Think about other brand/stores located within the retail location: Indicate the name of another
brand within the retail location that you consider to be equivalent to your brand in terms of brand
power: Brand A.____________________.
Now, indicate another brand within the retail location that you consider to be lower than your
brand in terms of brand power: Brand B.__________________.

***Please read the following scenario very carefully and try your best to immerse yourself
into the imagined managerial position and situation.***
You are the manager overlooking all brand operations with the current retailer. All brands within
the retail location pay commission charges (set as a percentage of sales) to rent retail space.
During the past three years, the commission rate for your brand product category has been on
average 30%. All brand manufacturers will undergo negotiations with the retailer representative
to set the terms of the renewal of the annual contract for the upcoming period. Imagine that you
were given complete authority from your brand manufacturer over contract negotiations with the
retailer.
[Insert Manipulation for Proactive Scenarios]
During the contract renewal meeting, your brand receives a 30% commission charge for the next
contract period. You also get to know through rumors that the brand that you consider to have
equivalent brand power (Brand A) received a much lower and favorable commission charge at
15%.
[Insert Manipulation for Reactive Scenarios]
[Insert Dependent Variables; Followed by Manipulation Checks]

105

APPENDIX 2.5 EXPLANATION MANIPULATIONS

Scenario 0: No Treatment/Explanation

Scenario 1: Reactive, No new anchor provision
A week later:
After all renewal meetings have been completed, the retailer explains as follows: “Commission
charges will vary across brands and those brands that did not reach a certain sales goal the past
year will be rated commission charges around 30%.”

Scenario 2: Proactive, No new anchor provision
Prior to renewal meetings, the retailer explains as follows: “Commission charges will vary across
brands and those brands that did not reach a certain sales goal the past year will be rated
commission charges around 30%.”
A week later:

Scenario 3: Reactive, New anchor provision
A week later:
After all renewal meetings have been completed, the retailer explains as follows: “Commission
charges will vary across brands and those brands that did not reach a certain sales goal the past
year will be rated commission charges around 30%.” Also, they explain “your brand is in the
same performance category as Brand B.”

Scenario 4: Proactive, New anchor provision
Prior to renewal meetings, the retailer explains as follows: “Commission charges will vary across
brands and those brands that did not reach a certain sales goal the past year will be rated
commission charges around 30%.” Also, they explain “your brand is in the same performance
category as Brand B.”
A week later:

106

APPENDIX 2.6 MEASURES (ESSAY 2)
Table A.3: Dependent Measures, Manipulation Checks, and Control Variables (Essay 2)
Dependent Measures
Re-anchoring
Given the department store’s explanation, Which brand do you
consider as your brand’s comparative referent?
(1) Brand A
(2) Brand B
Perceived Unfairness

(1= Strongly Disagree, 7= Strongly Agree)
•

Given the retailer’s explanation, I feel the commission
charge arrangement was fair.

Manipulation Checks
Negative Allocation
Event

(1= Extremely Favorable, 7= Extremely Unfavorable)
•

How favorable was the commission rate charged to
your brand compared to the rate Brand A received?

Timing of Explanation
The retailer’s explanation regarding the commission charge
arrangement for the upcoming contract year was given
______________ the contract renewal meeting.
(1) Prior to
(2) After
Provision of New
Anchor

Within the retailer’s explanation, was information of a new
comparative referent brand given?
(1) Within the retailer’s explanation, information of a new
comparative referent brand was given.
(2) Within the retailer’s explanation, information of a new
comparative referent brand was not given.

107

Table A.3 (CONT’D)
Control Variables
Relationship Length

How many years has your brand engaged in operations with the
retailer? ( ______) years

Trust in Retailer
Adapted from Doney and
Cannon (1997)

(1= Strongly Disagree, 7= Strongly Agree)

Dependence

•
•

My brand manufacturer trusts the retailer’s promises.
The retailer is honest and trustworthy.

•

My brand manufacturer has confidence in the retailer.

(1= Not at all Important, 7= Extremely Important)
•

How important is this relationship with the retailer to your
brand?

Years Experience
How many years of experience do you have in retailing?
About (______) years

Gender
What is your gender?
(1) Male
(2) Female

108

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