reciprocity in marketing relationships a dissertation
TRANSCRIPT
RECIPROCITY IN MARKETING RELATIONSHIPS
A Dissertation presented to the Faculty of the Graduate School
University of Missouri
In Partial Fulfillment Of the Requirements for the Degree
Doctor of Philosophy
by DONALD J. LUND
Dr. Lisa Scheer, Dissertation Supervisor
DECEMBER 2010
This dissertation is dedicated to my family. To my children, I’m sorry that I’ve missed out on much during the last few years. Thank you for understanding. Very few people know the sacrifices you have made. To my mom, I wish you could be here in person for me to thank for all of your support, love and caring throughout my life. I know you have always been with me, and I will never forget you. To my father, I hope that you have stopped trying to make up for whatever it was you were supposed to do while I was in college – because I am honestly done with school now! Your encouragement, support, and guidance have been invaluable. You have helped make me the man I am. And how could I forget: Doctor, Dentist or Lawyer!
ACKNOWLEDGEMENTS
I would like to thank my dissertation committee, Detelina Marinova, Chris
Groening and Peter Klein for all of their help, guidance and time during the creation,
revision and completion of this dissertation. Each of you offered a unique perspective
throughout the entire process that helped make this a better project. I will be forever
grateful for your time and interest in helping make this process a success. Additionally I
would like to thank Thomas Rose, Karen Nazario and Laurel Youmans for their help in
navigating the dissertation process at the University of Missouri.
I would especially like to thank my dissertation chair, Lisa Scheer, for her the
extensive amount of time she put in to improving my experience during my four years at
the University of Missouri. Lisa helped me by focusing on professional development,
academic rigor, career planning, and managing my personal life. I could not have asked
for a better mentor for this process. I look forward to being able to earn the peer status
you have already begun to treat me with.
I would also like to thank a few organizations that made my dissertation
financially possible; the Robert J. Trulaske, Sr. College of Business provided financial
support for a number of conference presentations, statistical software, and for my PhD
program in general. Additional funding that helped pay for survey printing and postage
was provided by Professor Lisa Scheer’s Emma S. Hibbs Professorship. Finally, Boone
County National Bank provided financial support for printing and postage of a survey for
this research. Without the financial support acknowledged above, this research would
not have been able to be completed.
I feel lucky and blessed to have chosen the University of Missouri for my doctoral
program. The faculty, staff, administration and students all combined to build an
environment that has allowed me to develop and grow. I have no doubt that my
training is far superior to my expectations when considering different programs for my
PhD.
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Table of Contents ACKNOWLEDGEMENTS .................................................................................................................... ii
LIST OF FIGURES ..............................................................................................................................vii
LIST OF TABLES ................................................................................................................................ ix
ABSTRACT ........................................................................................................................................xii
CHAPTER 1: INTRODUCTION ............................................................................................................ 1
1.1 Reciprocity in Marketing Theory ................................................................................................ 1
1.1.1 Reciprocity in Interpersonal Relationships ......................................................................... 1
1.1.2 Reciprocity in Interorganizational Relationships ................................................................ 5
1.1.3 Reciprocity in the Relationship Marketing Literature ......................................................... 7
1.2 Primary Motivating Research Questions ................................................................................. 11
1.3 Organization of the Dissertation .............................................................................................. 14
CHAPTER 2: LITERATURE REVIEW .................................................................................................. 15
CHAPTER 3: A GENERAL THEORY OF EPISODIC RECIPROCITY IN MARKETING RELATIONSHIPS ... 21
3.1 Episodic Reciprocity Abstract ................................................................................................... 21
3.2 Episodic Reciprocity Introduction ............................................................................................ 21
3.3 Episodic Reciprocity Conceptual Development ....................................................................... 23
3.3.1 Reciprocal Action – Debt Response Cycles ....................................................................... 27
3.3.2 Factors that impact the formation of Reciprocal Debt ..................................................... 34
3.3.2.1 Nature of the Action .................................................................................................. 34
3.3.2.2 Responsibility for Action ............................................................................................ 36
3.3.2.3 Justifiability of Action ................................................................................................. 38
3.3.2.4 Nature of the Focal Party ........................................................................................... 39
3.3.2.5 Nature of the Relationship ......................................................................................... 42
CHAPTER 4: RESEARCH ON EPISODIC RECIPROCITY ...................................................................... 51
4.1 Survey Research Model ........................................................................................................... 51
4.1.2 Operationalizations ........................................................................................................... 53
4.1.2.1 Pre-Scenario Questionnaire ....................................................................................... 53
4.1.2.2 Experimental Scenarios .............................................................................................. 56
4.1.2.3 Post-Scenario Questionnaire ..................................................................................... 57
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4.1.3 Sample Selection ............................................................................................................... 60
4.1.4 Measurement Model and Scale Development ................................................................. 63
4.1.4.1 Directional Reciprocal Debt ....................................................................................... 63
4.1.4.2 Exploratory Factor Analyses....................................................................................... 63
4.1.4.3 Confirmatory Factor Analysis ..................................................................................... 71
4.1.4.4 Manipulation Checks ................................................................................................. 74
4.1.5 Hypothesis Testing ............................................................................................................ 77
4.1.5.1 Effects of Partner Actions on Reciprocal Debts ......................................................... 77
4.1.5.2 Moderating Impacts on the Creation of Reciprocal Debt .......................................... 81
4.1.6 Discussion ......................................................................................................................... 84
4.2 Laboratory Experiment ............................................................................................................ 88
4.2.1 Research Model ................................................................................................................ 88
4.2.2 Operationalizations ........................................................................................................... 93
4.2.3 Sample Selection ............................................................................................................... 96
4.2.4 Measurement Model and Scale Creation ......................................................................... 96
4.2.4.1 Exploratory Factor Analyses....................................................................................... 96
4.2.4.2 Confirmatory Factor Analyses .................................................................................... 98
4.2.4.3 Manipulation Checks ............................................................................................... 101
4.2.6 Hypothesis-Testing .......................................................................................................... 102
CHAPTER 5: RELATIONAL RECIPROCITY IN MARKETING RELATIONSHIPS: THE ROLE OF COMPLEX RECIPROCITY ................................................................................................................................ 125
5.1 Relational Reciprocity Abstract .............................................................................................. 125
5.2 Relational Reciprocity Introduction ....................................................................................... 126
5.3 Theoretical Model Development ........................................................................................... 128
5.3.1 Complex and Simple Reciprocity .................................................................................... 134
5.4 Hypotheses ............................................................................................................................ 137
5.4.1 Outcomes of Complex Reciprocity .................................................................................. 137
5.4.2 Antecedents of Relational and Discrete Reciprocity ...................................................... 140
5.4.2.1 Characteristics of the Buyer and Seller .................................................................... 141
5.4.2.2 Characteristics of the Exchange ............................................................................... 143
5.5 Survey Research on Complex Reciprocity .............................................................................. 146
5.5.1 Research Model .............................................................................................................. 146
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5.5.2 Operationalizations ......................................................................................................... 147
5.5.3 Sample Selection ............................................................................................................. 155
5.5.4 Measurement Model and Scale Creation ....................................................................... 156
5.5.5 Hypothesis Testing .......................................................................................................... 165
5.5.7 Discussion ....................................................................................................................... 169
5.5.7.1 Episodic Reciprocity ................................................................................................. 169
5.5.7.2 Complex reciprocity ................................................................................................. 173
5.5.7.3 Summary .................................................................................................................. 174
CHAPTER 6 CONCLUSIONS ........................................................................................................... 176
6.1 Contributions to Theory ......................................................................................................... 176
6.2 Contributions to Practitioners ............................................................................................... 178
6.3 Future Research Directions .................................................................................................... 179
APPENDIX ..................................................................................................................................... 181
Exhibit #1 – University of Missouri Email Cover letter ................................................................ 181
Exhibit #2 UAB Email Cover Letter ............................................................................................... 182
Exhibit #3 – Customer Survey ...................................................................................................... 183
BIBLIOGRAPHY ............................................................................................................................. 187
VITA .............................................................................................................................................. 195
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LIST OF FIGURES
Figure Page
1. The Domain of Marketing Interactions ........................................................................ 12
2. Positive Reciprocal Debt Responses ............................................................................ 30
3. Negative Reciprocal Debt Responses .......................................................................... 33
4. Dependence Configurations ........................................................................................ 45
5. Process Model of Positive Reciprocity ......................................................................... 49
6. Process Model of Negative Reciprocity ....................................................................... 50
7. Process Model of Reciprocity ...................................................................................... 51
8. Random Assignment to Beneficial Conditions ............................................................. 52
9. Random Assignment to Detrimental Conditions ......................................................... 52
10. Plots of mean differences in OwnOut and RO based on the Valence manipulation ......................................................................................... 75
11. Plots of mean differences in automaker and other based on the attribution manipulation ............................................................................... 76
12. Scatterplot of RDLocus per OwnOut ............................................................................ 80
13. Estimated Marginal Means of RDLocus ....................................................................... 83
14. Experimental Research Model Tested ......................................................................... 88
15. Experimental Process Flowchart .................................................................................. 89
16. Random Assignment to Conditions ............................................................................. 90
17. Manipulations: Partner’s Initial Allocation Offer ........................................................ 92
18. Conceptual Framework for Beneficial Actions ............................................................ 103
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19. Conceptual Framework for Detrimental Actions ......................................................... 103
20. Impact of Valence on Locus (ANOVA) .......................................................................... 104
21. Locus*Valence Crosstabulation ................................................................................... 106
22. Locus*Magnitude Crosstabulation .............................................................................. 106
23. ANOVA Results of PartnerOut at Different Locus ........................................................ 112
24. ANOVA Results for Positive Norm of Reciprocity’s Impact on RD and RDLocus ....................................................................................... 119
25. Plots of RD based on Perceived Impact and the Positive Norm of Reciprocity .................................................................................................. 119
26. ANOVA Plots of NR on RD and RDLocus ...................................................................... 120
27. Moderating Impact of Negative Norm of Reciprocity ................................................. 122
28. ANOVA Plot of the moderating impact of PR .............................................................. 124
29. Relational Reciprocity Conceptual Framework............................................................ 145
30. Final Structural Model – Significant Paths ................................................................... 167
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LIST OF TABLES
Table Page
1. Reciprocity Definitions in Marketing Interpersonal Relationships .............................. 5
2. Reciprocity Definitions in Marketing B2B Relationships ............................................. 6
3. Approaches to Reciprocity taken in Seminal Works .................................................... 20
4. Experimental Scenario Components ........................................................................... 57
5. Recruitment Regions .................................................................................................... 61
6. Summary of Individual EFA’s for 4 Latent Constructs ................................................. 64
7. Separate EFA on RD and Just ....................................................................................... 64
8. Overall Means, Standard Deviations and Correlations for Study Variables ................ 65
9. Means, Standard Deviations and Correlations for Participants in the Beneficial Condition ............................................................................................ 67
10. Means, Standard Deviations and Correlations for Participants in the Detrimental Condition ........................................................................................ 67
11. CFA Results ................................................................................................................... 72
12. ANOVA results for OwnOut and RO Valence manipulation checks ............................. 74
13. 12a. ANOVA results for automaker and other – attribution manipulation checks ..... 76
14. Mean difference in Locus based on valence manipulation ......................................... 78
15. Coefficients from regression of Locus on manipulation factors .................................. 78
16. Coefficients from regression of RDLocus on OwnOut ................................................. 80
17. Tests of Between-Subjects Effects ............................................................................... 82
18. Planned Contrast Results (K Matrix) Justifiability ........................................................ 83
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19. Test Results for planned contrast ................................................................................ 83
20. Car Dealer Survey Hypothesis Summary ..................................................................... 84
21. Combined EFA on 4 Latent Constructs ........................................................................ 97
22. Summary of Individual EFA’s for 4 Latent Constructs ................................................. 98
23. CFA Results ................................................................................................................... 100
24. ANOVA’s with the Valence manipulation as the Independent variable ...................... 101
25. ANOVA’s with the Magnitude manipulation as the independent variable ................. 102
26. General Linear Model Test of Mean Differences ......................................................... 105
27. Parameter Estimates .................................................................................................... 107
28. Classification Results of Multinomial Regression for Locus ......................................... 107
29. Spline Regression Results ............................................................................................ 109
30. Spline Regression Results with no Intercept ............................................................... 110
31. Contrast Coefficients ................................................................................................... 112
32. Contrast Tests .............................................................................................................. 112
33. Spline Regression for RDLocus effect on PartnerOut .................................................. 113
34. Spline Regression for the moderating impact of relative outcomes ........................... 115
35. Regression of PR and OwnImpact on RDLocus ............................................................ 116
36. Regression of OwnImpact and PROwnImpact on RDLocus ......................................... 117
37. Norm of Positive Reciprocity’s effect on Reciprocal Debt (ANOVA) ........................... 117
38. Between-Subjects Factors ........................................................................................... 118
39. Tests of Between-Subjects Effects ............................................................................... 118
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40. ANOVA results for Negative Norm of Reciprocity’s impact on RD and RDLocus ................................................................................................... 120
31. Between-Subjects Factors ........................................................................................... 121
32. Tests of Between-Subjects Effects ............................................................................... 121
33. Between-Subjects Factors ........................................................................................... 122
34. Tests of Between-Subjects Effects ............................................................................... 123
35. Experimental Research Hypothesis Summary ............................................................. 124
36. Dimensions of Complex and Simple Reciprocity ......................................................... 135
37. Antecedent Variables EFA ............................................................................................ 156
38. EFA for Relational Reciprocity ...................................................................................... 157
39. Outcome variable EFA Results ..................................................................................... 158
40. Overall CFA results for all items in the theoretical model ........................................... 162
41. Means, Standard Deviations and Correlations Between Study Variables ................... 163
42. Structural Model Results ............................................................................................. 165
43. Mediation Analyses for the Structural Path Model ..................................................... 168
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RECIPROCITY IN MARKETING RELATIONSHIPS
Donald J. Lund
Dr. Lisa Scheer, Dissertation Supervisor
ABSTRACT
Marketing researchers have often cited reciprocity as an important aspect of relational
exchange; however the extant research has not conceptualized or measured reciprocity in
marketing relationships. This research is a first attempt to explicate the role of reciprocity in
these exchange relationships. Reciprocity is first conceptualized as a component of discrete
transactions. A theoretical model is built and tested in (a) a laboratory setting and (b) through a
survey of car dealership managers. The role of reciprocal debt is established and shown to
impact exchange relationships. Next, reciprocity is conceptualized as a multidimensional
element of ongoing exchange relationships. Through a survey of business customers of a bank,
the existence of relational reciprocity is established and shown to impact numerous indicators of
relationship success. Implications for both theory and practitioners are presented, and future
research directions are discussed.
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CHAPTER 1: INTRODUCTION
1.1 Reciprocity in Marketing Theory
“I regard reciprocity as an essential feature of self-regulation and the problem of coordinating mutual actions for parties in a marketing relationship.”(Bagozzi 1995) Marketing as a discipline has failed to adequately incorporate the concept of reciprocity
in any theory of customer relationships. The term reciprocity appears in much of the marketing
literature, especially those streams focusing on relationship marketing (both consumer and
B2B), however very few researchers have attempted to conceptualize or measure reciprocity at
all. Reciprocity research within marketing can be categorized as two distinct types. The first is
explicit-reciprocity research, where the researchers attempted to measure reciprocity and/or
offer a specific definition, and subsequently place it into their conceptual framework and/or
hypotheses. The second category is implicit-reciprocity research, where the term reciprocity is
used, but is not measured nor explicitly defined. The former section is minute compared to the
latter. The following is an overview of the explicit marketing reciprocity research. This research
can be segregated depending on the focus of the reciprocal interaction – interpersonal versus
interorganizational.
1.1.1 Reciprocity in Interpersonal Relationships
The earliest explicit use of reciprocity located in the marketing literature drew almost
exclusively from legal cases. Moyer (1970) discusses “Reciprocal Buying” and describes
reciprocity as doing business with one’s friends (see definition in table #1). “You scratch my
back, and I’ll scratch yours” (p. 47). The focus of this line of research was on coercive
reciprocity, where partner A threatens reduced purchasing if partner B didn’t increase or
maintain purchase levels from A. Relationships like that described above led to the
development of anti-trust laws to attempt to prevent “strong-arming”, and the potential harm it
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could have on consumers (not to mention the less powerful partner). Finney (1978) later did a
follow up intending to examine if this coercive type of reciprocity continued to exist in
businesses. Finney found that although it probably continued to occur in some relationships,
the prevalence had decreased substantially since Moyer’s research in 1970. This concept of
reciprocal buying points out an important fact about reciprocal behavior: it has the potential to
influence the behavior of a relational partner. This is reminiscent of Howard Becker’s
description of the role of reciprocity in the social learning process. Do marketing relationships
exhibit goal oriented (positive or negative) reciprocal behavior intended to mold the response of
their partner?
Houston and Gassenheimer (1987) extend the conceptual work of Bagozzi (1975) in
developing exchange theory as the key component of marketing. Houston and Gassenheimer
argue that an exchange relationship is created when “…one party is expected to defer receipt of
value into the future…” (p. 10). They go on to explain the role of reciprocity in exchange
relationships in great detail, arguing that reciprocity is present in every exchange relationship,
and that its implementation varies depending on the “social distance” of the parties involved.
While they do not define social distance, it appears that minimum social distance is implied by a
familial relationship, intermediate would involve friendships and business relationships, and
maximum social distance is presumably strangers. Interestingly, Houston and Gassenheimer
theorize that negative reciprocity is only engaged in with strangers, citing an example of bribery.
Additionally, they suggest that with a minimum social distance (e.g. parental relationships),
partners give willingly, “the value to A being in the giving of the product”, which is more in line
with altruistic behavior when no expectation of future return of benefits exists. More recent
work has interpreted the parent-child relationship differently, suggesting that the level of late-
adult healthcare provided by the child may depend on child perceptions of parental investments
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during the child’s youth, suggesting that altruism may not be the only explanation of this type of
giving. While this paper does not specifically measure reciprocity, the authors do offer a
definition and explicitly place reciprocity as a functional component of exchange relationships
(see Table #1).
Marketing researchers have approached reciprocity from both a consumer perspective,
as well as from an interorganizational perspective. Dawson (1988) found that reciprocal
motivations were one of four motivations for consumer charitable giving. His definition of
reciprocity (see Table #1) could potentially be applied to any plausible exchange being that it
only encompasses a future benefit expectation from a donor. The measures used to capture
reciprocity focused on past benefits received (by the donor or their family) and on the expected
benefits to be gained by the donor from the charitable organization in the future. Frenzen and
Davis (1990) find that existing “social debts” increased the likelihood of purchase in socially
embedded markets (e.g. Mary Kay sales parties among friends). After controlling for the
economic utility of purchases made, they found that attendees at sales parties were more likely
to purchase from a host who had previously purchased from them. Miller and Kean (1997) find
that when local business owners exhibit interpersonal reciprocity (defined as emphasizing
concern for others or strong attachment to others), rural consumers are more likely to behave
reciprocally by purchasing locally. As we can see by this approach, the focus of reciprocity is on
cooperation, a concept that is definitely related to, but not the same as, reciprocity. In a
consumer-retailer exchange, Dahl, Honea and Manchanda (2005) find that consumers may
experience guilt if they are not able to reciprocate the social interaction (through a purchase).
Subsequently, they are more likely to return and purchase from that salesperson to assuage the
feeling of guilt.
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Within the framework of interpersonal relations, there were two other articles
attributable to marketing academics, one focused on research response rates and the other on
teacher evaluations. Cialdini and Rhoads (2001) review a large quantity of past research which
investigates the role of reciprocity in the process of social influence. The authors suggest that
reciprocity is one of six psychological principles (also including scarcity, authority, consistency,
liking, and consensus) that impacts persuasion, citing research on the granting of concessions to
individuals in order to increase participation in research (e.g., reducing the expected survey
duration to 20 minutes from an hour, etc.). Here again, we see how reciprocity might be
implicated in altering people’s behavior. The last interpersonal marketing paper investigated
the role of reciprocity in student evaluations of teacher effectiveness (Clayson 2004). The
author found that past grades received did in fact impact future evaluations of teacher
effectiveness, however given that the student’s were evaluating classes from 1-2 semesters
before the research was conducted, it is not clear if the same relationship would hold if the
typical student evaluations (given during the semester) were explored.
Reviewing the definitions of reciprocity (see table #1 on the following page) offered in
the marketing research on interpersonal relationships suggests two important facts: (1) there is
little consensus on the definition of what reciprocity is in marketing relationships; and (2) there
is a lack of research investigating the role of negative reciprocity in marketing relationships.
These are the first of many gaps in the literature that need to be addressed.
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1.1.2 Reciprocity in Interorganizational Relationships
In the B2B relationship framework, Kumar, Scheer and Steenkamp (1998) investigated
the existence of reciprocal punitive actions in auto-dealers’ relationships with their suppliers.
While these authors did not define reciprocity explicitly, the implied definition was evident in
the existence of higher rates of punitive dealer actions as a response to higher levels of supplier
punitive actions. Johnson and Sohi (2001) measured and found support for their
conceptualization of reciprocity as returning good for good between relational partners. They
developed a 6-item scale that captured the “positive reciprocity” as an outcome of interfirm
connectedness. Palmer (2002) attempted to measure reciprocity as a predictor of effectiveness
in cooperative marketing associations, however his measure failed to predict effectiveness.
Expanding on the lack of significance, Palmer suggests that his measures (being drawn from the
power/dependence literature) failed to capture reciprocity. Lee et al. (2008) modeled
Moyer, 1970; Finney, 1978
Journal of Marketing
Reciprocal purchasing - both the use of purchasing power to obtain sales and the practice of preferring one's customers in purchasing
Houston and Gassenheimer,
1987
Journal of Marketing
Reciprocity - the process whereby a mutual exchange of acceptable terms is actualized; it is a social interaction in which the movement of one party evokes a compensating movement in some other party
Dawson, 1988J. Health
Care MKT.Reciprocity - a cultural norm whereby individuals enter into an exchange with the anticipation of receiving personal benefits
Frenzen and Davis, 1990
JCRNorm of reciprocity - use of a purchase occasion in the short term to repay outstanding social debtsReciprocity - the degree to which individuals expect cooperative actionInterpersonal Reciprocity - emphasizing concern for others or strong attachment to othersInstitutional Reciprocity - having a built-in system for calculating the costs versus the benefits involved in the exchange
Dahl, Honea and Manchanda, 2005
JCPNorm of reciprocity - an obligation for people to return in kind what they've received from others
Cialdini and Rhoads, 2001
Marketing Research
Reciprocity - an obligation for people to return in kind what they've received from others
Clayson, 2004 MERReciprocity - evidence that student written teacher evaluations are related to grades received
Table #1. Reciprocity Definitions in Marketing Interpersonal Relationships
Miller and Kean, 1997
Psychology and
Marketing
6
reciprocity as the outcome of mutualistic benevolence and altruistic benevolence in a
framework that is questionable in relation to the existing understanding of relationship
marketing. Troubled not only by the definition (see Table #2), this research is also challenged
with an inadequate conceptualization. The measure of reciprocity was the combination of two
questions: (1) Has your company helped this foreign exporter on at least one occasion; and (2)
Has this foreign exporter returned the favor on at least one occasion? While this measure quite
possibly captures one instance of a reciprocal interaction, it does little to explicate the role of
reciprocity in an ongoing relationship. Are we to assume that one beneficial act reciprocated by
a partner implies a healthy social exchange over the duration of the relationship?
Some more recent work by Pervan and Johnson attempts to develop Gouldner’s
conception of reciprocity. In their first paper (Pervan and Johnson 2003), the authors develop a
marketing based definition of reciprocity from the literature and theory on reciprocity in social
relationships (see Table #2). Their second paper (Pervan et al. 2009), attempts to conceptualize
Frazier and Colleagues, 1986,
1989 and 1991JM and JMR
Reciprocity - (implied definition) the actions taken by one party in response to the actions taken by the other party in an exchange relationship
Kumar, Scheer and Steenkamp, 1998
JMRReciprocity - (implied definition) evidenced by the existence of higher levels of dealer punitive actions in response to higher levels of suplier punitive actions
Johnson and Sohi, 2001
IJRMReciprocity - partner response is contingent on actions; mutually contingent exchange of benefits; ensures long run gratification for partners
Palmer, 2002J. Strat.
Marketing
Reciprocity - a disposition to return good for good in proportion to what they receive; to resist evil, but to do no evil in return; and to make reparation for the harm we do
Lee, Jeong, Lee and Sung, 2008
IMMReciprocity - mutual exchange of helping behaviors between importers and exporters
Pervan and Johnson, 2003
JCRReciprocity in RM is an expectation that good is returned for good in a fitting and proportional manner, resist negative acts but not return negative acts, and make reparation for any harm we do.
Pervan, Bove and Johnson, 2009
IMM Same as above.
Table #2 Reciprocity Definitions in Marketing B2B Relationships
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and develop a scale to measure reciprocity in B2B relationships. In attempting to develop a
scale to capture four proposed dimensions (make reparations, non-retaliation, returning good
for good and resisting evil) they found a 2 dimension scale explained the responses the best.
These dimensions were called Exchanging good and Response to Harm. In reality most of the
components of the exchanging good dimension appear to be based on equality of outcomes,
which should be related to reciprocity, but definitely is not the same as reciprocity. Additionally,
the response to harm dimension appears to capture more about cooperation between partners
than it does reciprocal exchanges. Again, cooperation should be related to reciprocity; however
reciprocity being a time delayed response to an action by a partner does not necessarily imply
concurrent cooperation.
1.1.3 Reciprocity in the Relationship Marketing Literature
Relationship marketing (RM) entails a substantial stream of literature in the field of
marketing. In a compelling article on the relationship development process, Dwyer, Schurr and
Oh (1987) describe five stages that social actors engage in: (1) awareness, (2) exploration, (3)
expansion, (4) commitment, and (5) dissolution. Further, they explain that the exploration stage
is sub-divided into 5 processes including (1) attraction, (2) communication and bargaining, (3)
development and exercise of power, (4) norm development, and (5) expectation development.
While reciprocity is implicitly included in more stages than just the communication and
bargaining phase of the exploration stage, these authors specifically discuss the role of
reciprocity during this stage of the relationship. At that point in the relationship, both parties
have some interest in furthering the relationship, they have mutually decided to exchange over
some indeterminate period of time, and are in the process of exploring exactly how receptive
their counterpart will be to a mutually beneficial relationship. It is expected that parties will try
to reveal specific information about themselves, their needs, and or their resources. It is
8
suggested that if the relationship is to endure, “intimate disclosure (of information) must be
reciprocated” (p. 16). Further it is suggested that later in the relationship, there may be a lesser
need for strict-reciprocal accounting in that the future holds ample opportunity for, and
expectations of, the balancing of outcomes over time.
Dwyer, Schurr and Oh (1987) also implicate reciprocity during the second phase of the
relationship building process, the exploration stage. While they acknowledge that one party
may have more power over (ability to mediate the rewards of) their partner, they detail that the
unjust use of power (for the selfish gain of the more powerful partner) will lead to quick
dissolution, whereas legitimate or just uses of power which benefit both parties can actually
strengthen the relationship. Further, during the norm and expectation development stages, it is
clear that norms and expectations which support mutual gains will develop a stronger
relationship than those which are self-serving. These authors suggest that trust begins to
develop during these stages, and is a critical component to relationship continuation. Trust
leads to higher levels of risk taking which encourage the norm of reciprocity (granting
concessions, proposing compromises, etc.).
The third relationship phase is the expansion phase. Here norms and behavioral
expectations have led to a trusting relationship. During the expansion phase, trusting partners
risk to expand the benefits provided to their partners (and themselves) which has the effect of
increasing the interdependence of the actors on one another. Increased interdependence of
both parties can lead to higher levels of trust, commitment and reduced conflict between the
parties (Kumar et al. 1995a). Finally, the last stage of continuing relationships is proposed to be
the commitment phase. Now existing relational norms, expectations of future behaviors, trust,
higher levels of risk taking and increased interdependence create an expectation and a desire
for both parties to maintain (or expand) the relationship as it provides beneficial outcomes to
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both partners in both an absolute sense, and in a relative sense (Thibaut and Kelley 1961).
While Dwyer, Schurr and Oh only briefly mention reciprocity in their theory, it is clear that it is
implied throughout the relationship development process. Each party makes investments
exhibiting increasing levels of risk, over time, to communicate their role as an effective partner
and build commitment and dependence from the other in a series of repeated interactions and
exchanges. Clearly this is the development of reciprocity between two social actors.
Shortly thereafter, Morgan and Hunt (1994) developed and tested their Commitment-
Trust theory of marketing relationships, also referred to as the Key-Mediating Variable model
(KMV), due to the key roles of commitment and trust as mediators in marketing relationships.
One of the most highly cited articles in marketing (Cited by 4488 according to Google Scholar),
Morgan and Hunt built on Dwyer, Schurr and Oh by suggesting that all relational investments
were mediated through commitment and trust which in turn impacted all relational outcomes.
Morgan and Hunt find support for the majority of their hypotheses. Some notable exceptions
were that relationship benefits did not tend to lead directly to commitment and that
opportunistic behavior exhibited a direct path to conflict (+) and uncertainty (+). They also note
the possibility that additional mediators may exist that they have failed to capture. Additionally,
their results suggest that firms acquiesce to their partner not necessarily for fear of power (prior
assumption) but because they are committed to their partner. Numerous extensions of the
KMV model have consistently found support for the general framework; however some more
recent work suggests that commitment and trust may not be the only mediators at play in
marketing relationships.
In a meta-analysis investigating the effectiveness of relationship marketing strategies,
Palmatier et al. (2006a) explore the Morgan and Hunt framework by compiling and analyzing
empirical results from 94 different published and unpublished manuscripts. In broad terms their
10
framework was motivated by both Dwyer Schurr and Oh (1987) and Morgan and Hunt (1994) in
that they tested a model of RM strategies → relational mediators → outcomes. The meta-
analysis broadly finds support for the framework; however some interesting findings suggest
that there may be some missing mediators in this framework. Examples include a finding that
beyond the path through the relational mediators, dependence and relationship investments
had a direct effect on seller performance. It is difficult to suggest that this direct effect is not
mediated by some relational variable, as ceteris paribus financial investments in the relationship
should not lead directly to positive outcomes for the investor. One plausible path these
investments might take leading to outcomes for the investor is if their partner reciprocates the
investment through increased purchase, spreading positive word of mouth, or reductions in
opportunistic behavior. Similarly one would not expect that higher levels of dependence on a
seller would necessarily lead directly to better performance for the seller. Much research has
suggested that greater levels of asymmetric dependence (favoring a seller in this case) actually
lead to higher levels of conflict and possibly relationship dissolution without some compensating
mechanism to increase the dependence of the other partner (seller on the buyer). These
findings lead the authors to suggest that “… the extant relational-mediated framework is not
comprehensive and that additional mediators (e.g., reciprocity) must be investigated to explain
the impact of RM on performance fully” (p. 150). Additional support for this extension is
provided by Morgan and Hunt (1994) who find that opportunistic behavior displayed the largest
effects on outcome variables (both direct and indirect). While Morgan and Hunt argue that it
might be beneficial to allow for direct effects of opportunistic behavior, another interpretation
is that opportunistic behavior is not only mediated through commitment and trust, but possibly
another relational construct like reciprocity. Do relational partners punish when they feel their
partner has appropriated too much of the benefits of the relationship?
11
Extending this work, Palmatier et al. (2009) find support for the role of gratitude and
gratitude based reciprocal behavior as mediators in the relationship marketing framework.
While this finding does have implications for an overall role of reciprocity, it is important to note
that they measure felt gratitude (described as short-lived) and resulting reciprocity based
behaviors. This research is positioned within RM episodes or cycles whereby the seller can see
the gratitude based response of the buyer over a series of individual exchanges. The authors
find support for the role of gratitude based reciprocal actions as a mediator between RM
investments and firm outcomes. Two important questions are brought up by the
conceptualization in this research: (1) is gratitude a necessary component for reciprocal
exchange; and (2) what might impact the gratitude based reciprocal response within a
relationship?
1.2 Primary Motivating Research Questions
An additional concern that is raised by extant marketing research is that the response to
negative or detrimental actions is not often considered in this research. It is unclear whether
the appropriate response would be some sort of punishment or retaliation, or whether
marketers should always “turn the other cheek” when faced with a detrimental situation. What
about the evidence for the role of negative reciprocity? Are we to assume that in all marketing
relationships, the best (only?) response to a partner’s self interested behavior is exit from the
relationship? Doesn’t reciprocity include the response to harmful actions as well as to beneficial
actions?
Figure #1 charts the domain of reciprocity research in marketing. The common
assumption in marketing (either explicitly, or implicitly) is that relationships are either
cooperatively built, based on positive reciprocity (Pervan et al. 2009), or that they will end up
dissolving. There are exceptions to this general trend; Kumar, Scheer and Steenkamp (1998)
12
investigate the reciprocation of punitive actions in supplier-dealer relationships. Frazier and
Rody (1991), and Frazier and Summers (1986) find evidence of reciprocity by significant
correlations between supplier and dealer actions (both coercive and non-coercive strategies),
however Frazier, Gill and Kale (1989) find no evidence of reciprocal coercive actions from
dealers who have very few alternative suppliers. Interestingly, these findings might provide
some preliminary support for the idea that negative reciprocity (or returning a punitive act in
response to one) will result in the degradation of marketing relationships. We see that higher
levels of supplier punitive or coercive actions lead to similar responses from dealers. It appears
from these studies that responding to an “evil” with an “evil” may lead to a spiral of increased
retaliation and revenge. But does this finding always hold? What boundary conditions might
make retaliation less likely when one party commits a “punitive act”?
Figure #1. The Domain of Marketing Interactions
"Recipient" Response
Positive Negative
Perceived "Donor" Action
Positive Most
Marketing Research
Negative
Kumar et al. '98,
Frazier et al. *
Shaded Region - Reciprocity according to Becker * 1986, 1989, 1991
We all know from experience that any relationship has challenges and problems. Figure
#1 shows that the marketing reciprocity research has only studied reciprocity in a very small
13
area of the possible interactions between exchange partners. The interesting interactions have
been left to behavioral economists in sterile laboratory settings. What happens when one
partner suspects he is being taken advantage of? Does a negative response to an unjustified
negative action have beneficial outcomes for the relationship or does it lead to the degeneration
of the social interaction? When is it viable to strategically avoid responding to a partner’s
actions? There are numerous questions which need to be addressed with respect to the role of
reciprocity in marketing relationships.
While there are numerous directions that would help build a better understanding of
reciprocity in marketing relationships, this dissertation focuses on two distinct research
perspectives. The first investigates the role of episodic reciprocity between relational partners.
This perspective approaches reciprocity as part of a single interaction between actors in a
marketing interaction. The specific research questions addressed are:
1) Is reciprocal debt created in marketing relationships?
2) What characteristics of an exchange will impact the reciprocal response to a given action?
3) How do business partners respond to detrimental partner-actions, and what factors
will mitigate any potential retaliation? The second perspective positions reciprocity as an ongoing relational process. This
approach proposes that reciprocal behavior in ongoing relationships is a multi-dimensional
process that can enhance relational outcomes. While it is important to investigate a response to
a given action, relationships happen over time and over the course of numerous interactions.
This research investigates the role of reciprocation over time within ongoing relationships and
asks some very important questions:
1) What is the role of reciprocity in the development of ongoing marketing relationships?
2) Which characteristics of a relationship and the business transactions impact reciprocity over time?
14
1.3 Organization of the Dissertation
The remainder of this dissertation is organized into six chapters. The next chapter
provides a literature review of reciprocity theory and research from other disciplines. Chapter
three develops a general theory of episodic reciprocity. Chapter four describes the survey and
experimental research completed to investigate episodic reciprocity. Chapter five develops a
general theory of ongoing or relational reciprocity in marketing relationships, and provides
results from a survey designed to test the research hypotheses. Finally, chapter six discusses
the conclusions and implications of the dissertation research. References and appendices
including survey materials and cover letters are attached at the end of the document.
15
CHAPTER 2: LITERATURE REVIEW
“… we ought to be disposed, as a matter of moral character, to make reciprocity a moral obligation.” - Lawrence C. Becker
If 20 people were asked to define reciprocity, like a lot of abstract constructs, there
would be some general agreement between the definitions; however none of them would
adequately define the entire conceptual domain of the term. This problem is highlighted when
the academic literature on reciprocity is reviewed. Not only is there a lack of consensus of a
definition of reciprocity, but there is very little agreement on the way reciprocity is
conceptualized as well. Reciprocity has been a cultural phenomenon throughout recorded
history in nearly every society, as can be seen in ancient writings from a number of cultures, for
example:
“Never impose on others what you would not choose for yourself” - Analects of Confucius (circa 500 B.C., as cited in Wikipedia 2009)
Also, the Golden Rule – Do unto others as you would have done unto you - was adapted
from the Bible verse: “Thou shalt not avenge, nor bear any grudge against the children of thy
people, but thou shalt love thy neighbor as thyself…(Leviticus 19:18)”. In fact nearly every
religion espouses some version of this edict. Buddhism, Hinduism, Islam, Judaism and Taoism all
promote some derivative of the Golden Rule. While the Golden Rule itself is not the same as
reciprocity, the fundamental concept, that we can expect behavior similar to our own from
those we interact with, definitely relates to a general understanding of reciprocal behavior.
One of the most highly cited articles on reciprocity is Gouldner’s (1960) “The Norm of
Reciprocity”. Gouldner distinguishes between three distinct conceptual domains of reciprocity,
(1) reciprocal behavior, (2) existential folk beliefs in reciprocity, and (3) a moral norm of
reciprocity. Gouldner argues that the moral norm of reciprocity is defined by two important
16
edicts: (a) people should help those who have helped them; and (b) people should not injure
those who have helped them. His argument is based on the principal that reciprocity promotes
stable and enduring relationships. The fact that a recipient of help should in turn help those
who contributed in the first place ensures some enduring debt that people rely on in the future
of the relationship. Further, the idea that the indebted partner should not harm the debtor, at
least until the debt is repaid, helps to create peace and order within society. This brings up an
interesting question, what role does punishment or retaliation (negative reciprocity) have in
reciprocal relationships? Gouldner argues that since social debts are never paid entirely in full,
there is always a reciprocal debt remaining and thus harm of the partner should be constrained.
While there is value in this position, we all know that in reality people do take revenge and often
to those closest to them (e.g., “Hell hath no fury like a woman scorned”).
In 1956, Howard Becker collected his introduction to sociology lectures from the
University of Wisconsin and printed them in a book titled “Man in Reciprocity” (Becker 1956). In
this lecture series, Becker relates the development of all human social structures to reciprocity.
Coining the term Homo Reciprocus, Becker relates the mores of ancient civilizations, the
development of the division of labor, teaching a child to be obedient, and everything in between
to reciprocity between social actors and or social institutions. Becker argues that all social
interactions can be interpreted based on the concept of reciprocity, although he intentionally
fails to offer a definition of reciprocity, leaving it to the reader to formulate their own. Becker
viewed reciprocity as responsible for teaching children socially acceptable behavior through
parental responses to both proper and improper actions. Warm smiles or a slap on the wrist
were responses to behavior that condition the child to repeat actions which were rewarded, and
avoid actions that resulted in punishment. Thus Becker acknowledges that reciprocation to
“negative” actions can help reinforce socially acceptable behavior.
17
Another Becker, this one a philosopher with a first name of Lawrence (LB) disagrees
with Howard Becker, not on the prominence of reciprocity in social relationships, but rather on
the role of negative reciprocity in social relationships. LB’s book Reciprocity (1986) argues that
reciprocity is a moral norm. To gain a solid understanding of this philosophical definition of
reciprocity, I want to reprint the maxims from LB’s thesis which illustrate this normative
position, and the disagreement with the sociologist:
1. Good received should be returned with good. 2. Evil received should not be returned with evil. 3. Evil received should be resisted. 4. Evil done should be made good. 5. Returns and restitution should be made by the ones who have received the good or
done the evil, respectively. 6. Returns and restitution should be fitting and proportional. 7. Returns should be made for the good received – not merely for good accepted or
requested. 8. Reciprocation, as defined by 1-7, should be made a moral obligation.
LB’s position is argued based on the idea that anything that promotes good (rather than evil) is a
moral norm of which social actors should abide. LB argues that if evil is returned for evil
received, the result is an escalation of revenge and punishment leading to the eventual
degeneration of society as a whole. We can see in maxim #5 that LB proposes an alternative
reaction to “evil received” in that the evil-doer should make up for the evil deed by providing
“fitting and proportional” restitution to the injured party. Would that it was so, our entire legal
system might be out of work. Importantly to our building discussion on the definition of
reciprocity, we see that this normative definition suggests that negative reciprocity should not
be a part of healthy social relationships. How do we reconcile these different perspectives?
Perceptions of the recipient of a punishment must be an important consideration in
considering “evil” actions. If LB’s maxims were completely correct, society would have
degenerated by now. Instead, parents continue to punish children (even if not by spanking),
marital partners punish through withholding affection or attention, business partners can
18
spread negative word of mouth, and if the perception is that the punishment is justified for prior
actions, we expect to see these relationships persist. LB hints at the value of recipient
perceptions in maxim #6 by stating that returns and restitution should be “fitting and
proportional”. While this is not a statement about negative reciprocity, it is a hint that
perceptions about the appropriateness of reciprocal reaction are important.
Gouldner supports this view that recipient perceptions are important in the valuation of
reciprocal responses to positive actions. He describes the valuation process of the help (and
hence the debt) received, and argues that it is affected by:
1. The intensity of the recipient’s need at the time the benefit is bestowed 2. The resources of the donor 3. The motives imputed to the donor by the recipient 4. Nature of constraints perceived to exist (costs to the donor)
Later empirical work has offered support for much of Gouldner’s theory. Motives were found to
reduce reciprocal responses when they were perceived as inappropriate (Lerner and Lichtman
1968; Schopler and Thompson 1968). Additionally, subjects who were intentionally under-
rewarded tended to reciprocate less, and evaluated their exchange partners more poorly, than
subjects who were under-rewarded “by chance” (Leventhal et al. 1969). Importantly, this
suggests that reciprocal responses to actions will vary depending on the recipient’s perceptions.
Another perspective on reciprocity is that some people exhibit a resistance to take on a
reciprocal debt. Brehm and Cole (1966) termed this concept psychological reactance which is
defined as the resistance to a felt loss of freedom, resulting in hostility and acting out.
Berkowitz (1973) found that reactance is increased when a request for help is perceived as
“improper”. A vast amount of literature in psychology focuses on the impact of recipient
perceptions on reciprocal (positive and negative) responses and ratings of the donor.
To review, there is much agreement that reciprocity includes the return of some
benefits for benefits received. There is disagreement about the role of negative reciprocity, or
19
retaliation. Some theorists suggest that returning evil for evil received should be avoided
(Becker 1986; Gouldner 1960), others suggest that punishing can actually be beneficial to the
development of acceptable social norms (Becker 1956; Michaels 1983). There is much
agreement that reciprocal responses involve perceptual valuation of the action (positive or
negative) received (Bolton et al. 1998; Eisenberger et al. 1987), and the motives at play by the
donor (Leventhal et al. 1969; Michaels 1983; Rabin 1993). There are definite implications that
reciprocity occurs over time, and many authors suggest that the waiting time until reciprocal
debts are repaid actually supports a positive social interaction as both parties have an interest in
not harming the other at least until the debt has been repaid. Evidence also suggests that
reciprocal responses can vary depending on the situation, and that some people (or situations)
will exhibit a resistance to accepting a reciprocal debt. However, very few articles offer any
explicit definition of the term, and the few that do only capture some subset of the concepts
discussed to this point (see Table #3 below).
Au
tho
rD
efi
nit
ion
Dis
cip
lin
eR
ole
of
reci
pro
city
Pro
acti
veR
eac
tive
Ho
war
d
Be
cke
r 19
56n
on
eSo
cio
logy
Man
is b
orn
inh
um
an, o
nly
th
rou
gh t
he
p
roce
ss o
f le
arn
ing
reci
pro
city
(i
nte
rpe
rso
nal
, in
ter-
inst
itu
tio
nal
, in
ter-
gro
up
, etc
.) d
o h
om
o s
apie
ns
be
com
e
hu
man
(h
om
o-r
ecip
roco
us
)
Ind
ivid
ual
s ad
he
re
to n
orm
s to
avo
id
ne
gati
ve r
esp
on
ses
asso
ciat
ed
wit
h
no
rm v
iola
tio
n
Pe
op
le in
po
siti
on
s o
f au
tho
rity
pu
nis
h
for
no
rm v
iola
tio
ns
to r
ein
forc
e
"pro
pe
r" b
eh
avio
r
Go
uld
ne
r 19
60
A p
atte
rn o
f m
utu
ally
co
nti
nge
nt
exc
han
ge
grat
ific
atio
ns
Soci
olo
gy
The
re
cip
ien
t o
f "h
elp
" h
as a
mo
ral
ob
liga
tio
n t
o r
etu
rn t
he
he
lp t
o t
he
d
on
or.
Th
e d
on
or
of
"he
lp"
has
a m
ora
l ri
ght
to r
ece
ive
he
lp f
rom
th
e r
eci
pie
nt
in t
he
fu
ture
.
Re
cip
roci
ty a
cts
as a
"s
tart
ing
me
chan
ism
" to
e
nab
le in
itia
l e
xch
ange
The
ob
liga
tio
n t
o
no
t h
arm
th
ose
wh
o
hav
e h
elp
ed
yo
u
cre
ate
s so
cial
so
lid
arit
y
Mac
ne
il
1980
, 198
6
Ge
ttin
g so
me
thin
g b
ack
for
som
eth
ing
give
n -
ge
ne
rali
zed
, n
on
-sp
eci
aliz
ed
, an
d
spe
cial
ize
d
Law
an
d
Eth
ics
All
ow
s m
an t
o b
alan
ce h
is c
on
flic
tin
g co
nce
rns
in li
fe f
or
self
-in
tere
st a
nd
fo
r so
cial
so
lid
arit
y. W
ith
ou
t re
cip
roci
ty,
man
wo
uld
no
t b
e a
ble
to
re
solv
e
inte
rnal
co
nfl
ict
ove
r th
ese
tw
o
con
cern
s.
Re
cip
roci
ty e
nab
les
hu
man
s to
be
ab
le
to "
give
" to
oth
ers
, w
ith
th
e
exp
ect
atio
n o
f re
turn
Law
ren
ce
Be
cke
r 19
86
8 m
axim
s -
see
pag
e
12 in
th
e t
ext
Ph
ilo
sop
hy
A s
oci
al m
ech
anis
m t
hat
en
able
s th
e
de
velo
pm
en
t o
f h
eal
thy
soci
al r
ela
tio
ns,
an
d f
urt
he
r d
isco
ura
ges
de
stru
ctiv
e
soci
al in
tera
ctio
ns
A r
esp
on
se b
y b
oth
th
e s
en
de
r an
d
rece
ive
r o
f go
od
an
d e
vil a
ctio
ns
Feh
r an
d
Gac
hte
r 19
98
Po
siti
ve -
be
kin
d t
o
tho
se w
ho
hav
e b
ee
n
kin
d t
o u
s. N
ega
tive
-
'an
eye
fo
r an
eye
'
Be
hav
iora
l Ec
on
om
ics
A m
ech
anis
m t
hat
en
cou
rage
s e
qu
ity
(of
som
e s
ort
) in
exc
han
ges,
evi
de
nce
d b
y p
eo
ple
s' w
illi
ngn
ess
to
incu
r p
ers
on
al
cost
s to
re
war
d o
r p
un
ish
oth
ers
A r
esp
on
se b
y a
reci
pie
nt
of
eit
he
r a
po
siti
ve o
r n
ega
tive
ac
t
Tab
le #
3. A
pp
roac
he
s to
Re
cip
roci
ty t
ake
n in
Se
min
al W
ork
s
21
CHAPTER 3: A GENERAL THEORY OF EPISODIC RECIPROCITY IN MARKETING RELATIONSHIPS
3.1 Episodic Reciprocity Abstract
Relationship marketing research relies on the implicit inclusion of reciprocation. One
relational member (typically the seller) performs some action that is intended to elicit a
response from their customer. The assumption is that investments in relationship marketing
programs will be reciprocated back to the seller through increased purchases, increased loyalty,
or improved relationship quality. In reviewing the extant marketing literature, it is obvious that
reciprocity is neither clearly defined, nor accurately measured within marketing relationships.
Furthermore, the role of reciprocity in the process of relational exchange has been overlooked in
the marketing literature. This research will address these issues. Building on social exchange,
reciprocity and relationship marketing theories, a process model of reciprocal exchange is
proposed. Factors which should influence reciprocal responses to both beneficial, and harmful,
actions are proposed. The role of the norm of reciprocity, and its influence on reciprocal
responses will be tested. Further, both positive and negative reciprocal debts are
conceptualized and measured as outcome variables in marketing relationships. This research
will be empirically tested through both a survey in a B2B setting, and a lab based experiment to
test the proposed process model of episodic reciprocal exchange.
3.2 Episodic Reciprocity Introduction
In proposing the Commitment-Trust theory of relationship marketing (RM), Morgan and
Hunt (1994) developed a framework that described how relational characteristics between
partners lead to trust and commitment, and how these key relational mediators impact dyadic
outcomes. This approach focused less on specific behaviors and more on the overall dynamics
22
that define the marketing relationship. These are more enduring characteristics of the
relationship including the dependence structure, shared values, communication and
opportunistic behavior measured at a global relationship level. While it is clear that
commitment and trust are highly correlated with beneficial relational outcomes, the
commitment-trust theory did little to suggest specific actions business partners could utilize to
improve the relationship outcomes.
More recently, RM researchers have approached relationship marketing as a strategy
enacted by relational partners to build and strengthen long term business relationships (Bagozzi
1995; Cannon and Perreault Jr 1999; Gronroos 1999; Jap et al. 1999; Palmatier 2008; Palmatier
et al. 2007a). These researchers attempt to categorize investments in RM and to quantify
differential impacts of RM investments on financial outcomes (usually for the “selling” firm) of
the dyad. RM categories have been defined as financial, structural and social (Berry 1995;
Palmatier et al. 2008; Palmatier et al. 2007a), and research investigating the financial impact of
these different categories of RM suggest that they have unique effects on financial outcomes
(Palmatier et al. 2006b). For instance, social RM efforts had the largest direct effect on
customer specific returns (a proxy for the contribution margin a rep firm earns from a specific
customer), structural RM investments had the largest impact for firms that had frequent
interactions, and financial RM investments did not exhibit any direct effects on customer
specific returns.
While both perspectives are valuable for understanding business relationships, both the
Commitment Trust and the Relationship Marketing approaches fail to capture the process which
transforms one member’s actions (investments, behavior, etc.) into financial outcomes for
either themselves, or their partner. RM assumes that financial, social or structural investments
will result in financial benefits not only for the investor, but also for their partner. This research
23
argues that it is important to understand the process which translates these investments into
objective performance outcomes for either partner. Even if trust and commitment are directly
impacted by RM investments, what process extracts financial returns from trust and
commitment? In order for any business to increase their outcomes based on relationship
investments, their partner must in some way repay that effort. RM research implicitly relies on
reciprocal exchange of investment and return from marketing relationships (e.g., Doney and
Cannon 1997; Li and Dant 1997; Morgan and Hunt 1994); however that process is rarely defined
nor explicitly measured in the extant literature. In fact, authors of a recent meta-analysis
suggest that “integrating reciprocity into the relational-mediating framework may also explain
the large, direct effect of relationship investment on performance, such that people’s inherent
desire to repay “debts” generated by sellers’ investments may lead to performance-enhancing
behaviors, independent of trust or commitment” (Palmatier et al. 2006a, pg. 152).
This research posits that the process of reciprocity is a critical component to marketing
exchange. While reciprocity is mentioned often in the marketing literature, the failure to
accurately conceptualize, define or measure the role of reciprocation has resulted in a lack of
understanding of the process of reciprocation in the development of marketing relationships.
Building off of reciprocity, relationship marketing and social exchange theories, this research will
investigate the role of reciprocal behavior, norms of reciprocity, and relational characteristics
proposed to affect reciprocal exchange.
3.3 Episodic Reciprocity Conceptual Development
Relationships do not happen at one point in time; they are dynamic, evolving and
occurring over time. Reciprocal exchange occurs over several interactions within a given
relationship (Bagozzi 1995; Macneil 1980). Given this understanding, it would be appropriate to
consider all actions within a relationship as reciprocal actions. In fact Gouldner (1960) suggests
24
that the norm of reciprocity encourages the first trusting act by one party to a potential
relationship by reducing the risk that their efforts will be taken advantage of; every action after
this first effort is considered a reciprocal response to prior actions.
What types of actions are being referred to? Given that relationships are dynamic and
occurring over time, expectations are developed for future interactions based on prior exchange
occasions. The status quo becomes the expected range of actions over time as the relationship
develops and partners become more familiar and comfortable with each other in the exchange
process (Bagozzi 1975; Dwyer et al. 1987; Morgan and Hunt 1994). Actions can easily be
categorized any number of ways. This research distinguishes between beneficial actions and
detrimental actions. Beneficial actions are defined as any action which is perceived to have a
more positive effect than expected given the status quo of the relationship (e.g., an unexpected
price discount offered to a customer, supplier investments in an integrated distribution system
that increases customer profitability, etc.). Similarly, a detrimental action is defined as any
action which is perceived to have a more negative effect than expected given the status quo of
the relationship (e.g., unexpected reductions in product quality due to cost increases, reduction
in sales force which causes service quality deterioration, etc.). Obviously these actions do not
cover the normal interactions involved with making repeated purchases, paying accounts
receivable, or making a regular periodic sales call on a customer.
Relationship marketing research has focused on the beneficial actions as investments
(of financial or non-financial resources) that a seller makes to build a stronger relationship with
its customers (Anderson and Weitz 1992; Bowman and Narayandas 2004; Palmatier et al.
2006b). Other research has examined how detrimental actions can affect relational outcomes
(Frazier and Rody 1991; Hibbard et al. 2001; Kumar et al. 1998). While much of this research
suggests that beneficial actions lead to more positive exchange outcomes, and detrimental
25
actions lead to more negative outcomes, the extant marketing literature fails to capture the
process which enables specific actions to affect firm outcomes, or what relationship specific
characteristics might impact the response to beneficial or detrimental actions by one’s business
partner.
Business relationships involve prior social history, and expectations of future exchange
(Jap et al. 1999; Weitz and Jap 1995), however much (if not all) of the empirical work dedicated
to measuring the role of reciprocity in social exchange has examined interactions between
strangers with no social history, and no expectation of future interactions. The primary context
for the existing reciprocity research is a sterile lab environment, where researchers often
intentionally eliminate any chance of prior social interactions. Research set in the context of
existing social relationships would provide a fertile ground to test theory about the role of
reciprocity in social interactions. Building off social exchange theory, and incorporating
empirical results from reciprocal dyadic exchanges (Fehr and Gachter 2000; Fehr and Gachter
1998; Loch and Wu 2008) into the theoretical RM framework of social relationships, this
research will investigate factors which impact reciprocal responses to a partner’s action within
the framework of established marketing relationships.
Substantial empirical evidence exists for various factors which affect reciprocal
exchange, however very little investigates reciprocity within a social relationship. That is, most
empirical research involves interactions between total strangers. Acknowledging that a social
history may impact reciprocal behavior, Loch and Wu (2008) used a manipulation in a lab setting
to simulate a social history. In the social condition, they allowed participants a brief
introduction to get acquainted and then asked each participant to read the following paragraph:
“You have already met the person with whom you will play the game. Now the person is no longer a stranger to you. You can imagine that the other player is a good friend. You have a good relationship and like each other” (p. 1837).
26
In the control condition, players interacted anonymously, separated throughout the study. In a
sequential move game, even with this weak manipulation, they find higher levels of trust and
positive reciprocation in the social history condition than the control condition. On the other
hand, Fehr and Gachter (2000) find that (lab manipulated) “partners” actually punish more
severely for norm violations than do strangers, and Chen, Chen and Portnoy (2009) find that
friends (existing student friendships) respond less positively to unfavorable, inequitable offers
than do complete strangers. All of these examples illustrate that a history of social interactions
(even if those interactions are imagined) will impact future reciprocation between “partners”.
Does this suggest that the existence of a social relationship simply magnifies the level of
reciprocal reactions to partner efforts? Without understanding perceptions of the actions, and
the underlying relationship dynamics, it is difficult to determine with certainty how these
findings will apply to marketing relationships.
Additionally, we know very little about the existence or role of negative reciprocity in
marketing relationships. The relationship marketing literature advocates the philosophical,
normative positive role of reciprocity, suggesting that evil not be responded to with evil (Bagozzi
1995; Pervan et al. 2009), but without testing responses to negative actions within functional
relationships it is difficult to assume that avoiding punishment always provides the best
solution. In fact, some analytical work from the behavioral economics literature suggests that
“unconditional cooperation” can result in overall deterioration of relational networks as those
parties who tend to reciprocate positively will eventually tire of being taken advantage of (Sethi
and Somanathan 2003). These inconsistencies suggest that the true range and role of reciprocal
responses in marketing relationships is not fully understood. Does negative reciprocity impact
marketing relationships? How will partners react to detrimental actions by their counterpart?
What relational dynamics will affect these reciprocal responses? The majority of evidence for
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influences on reciprocal responses lies within the field of economics, where unfortunately social
histories and expectations of future interactions have been designed away (Eisenberger et al.
1987; Falk et al. 1999).
3.3.1 Reciprocal Action – Debt Response Cycles
Reciprocity has been defined a variety of different ways in the marketing literature.
Numerous authors have defined reciprocity in line with Becker’s (1986) conceptualization of
returning good for good received, and not harming a relational counterpart, even if you have
been harmed by them (Bagozzi 1995; Palmer 2002; Pervan et al. 2009; Pervan and Johnson
2003). Others have defined reciprocity either explicitly or implicitly (through their
measurements) as the existence of similar tactics used by members of a marketing relationship
(Clayson 2004; Kumar et al. 1998; Lee et al. 2008). Interestingly, in a stream of research by
Frazier and colleagues (Frazier et al. 1989; Frazier and Rody 1991; Frazier and Summers 1986),
the authors define reciprocity as actions taken by one party in response to the actions taken by
the other party in an exchange relationship, however their empirical evidence for the existence
of reciprocity is a correlation between buyers and sellers’ use of coercive tactics. While they
define reciprocity as being any action which is induced by the action of a partner, they measure
it by only “in-kind” returns of potentially destructive behavior.
This research will adopt the definition of reciprocity offered by Frazier and colleagues
as: actions taken by one party in response to the actions taken by their counterpart in an
exchange relationship. This definition of reciprocal behavior is also supported by Houston and
Gassenheimer (1987), who define reciprocity as “a social interaction in which the movement of
one party evokes a compensating movement in some other party” (pg 11). Importantly this
definition of reciprocal behavior does not require that the response to any action is necessarily
an action “in-kind.” Note that is also does not imply that reciprocation necessarily takes place
28
immediately. In fact, the time lapse between the acceptance and repayment of a reciprocal
debt may be an important component of reciprocity (Becker 1956; Becker 1986).
Reciprocity theory argues that actions by one member of a relationship create in their
counterpart a reciprocal debt that must be repaid in the future in order for the relationship to
endure (Cialdini and Rhoads 2001; Gouldner 1960; Macneil 1980). When this reciprocal debt
involves the repayment of some beneficial action, it is suggested that the existence of a debt can
actually help to build social solidarity, or the degree of integration in society. This is so because
during the elapsed time between the incurrence and the repayment of the debt, it is expected
that both parties will avoid harming the other (Macneil 1980). Gouldner (1960) claims that
during this time period, when debts have yet to be repaid, the person owing the debt is “morally
constrained to manifest their gratitude toward, or at least to maintain peace with, their
benefactors” (pg 174). When the reciprocal debt involves the return of a benefit or favor, it is
contrary to the self interest of both parties to harm the other: the debtor avoids harming the
creditor for fear of reduced chances of gaining a benefit in future interactions; and the creditor
because it may reduce the chances of the existing debt being fulfilled. It is exactly this process
which Macneil (1986) argues enables reciprocity to satisfy the dual, conflicting interest of
“schizophrenic” man: self interest and social solidarity. Cialdini (2009) claims that the social
debt, or future obligation, created by the rule of reciprocity is directly responsible for the unique
social advantages of the human race.
Evidence suggests that these social debts are actually consciously recognized by
relational partners, and have predictive power over behavior in future exchange (Frenzen and
Davis 1990). Frenzen and Davis (1990) find that individuals who claim to owe a social debt to a
sales host are more likely to purchase in a home selling context than are those who claim no
existing debt. In this study, the debts referred to were prior home selling events during which
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the current host either purchased from the individual (causing a social debt needing to be repaid
in the future), or did not purchase (and hence created no reciprocal debt). Further evidence for
the existence of reciprocal debts is shown through an experimental selling context, during which
customers actually reported feeling guilty if they did not make a purchase from a salesperson
when social connectedness exists, and often returned to make a future purchase from that
salesperson to satisfy their reciprocal debt (Dahl et al. 2005). This feeling of guilt was also found
in a consumer relationship management context (Palmatier et al. 2009) when consumers were
unable to repay reciprocal debts. Eisenberger, Cotterell and Marvel (1987) find that recipients
of help or favors experience a feeling of obligation and indebtedness. Cialdini (2009) argues that
any benefits received from a relationship create debts which require reciprocation to be
fulfilled.
Based on reciprocity theory, positive reciprocal debt is defined as: the conscious
recognition that a benefit is owed to a relational partner in repayment of a benefit received. It
is important to note that there are response options available to someone after they receive
some beneficial treatment from there partner that would fulfill the reciprocal debt that is
created. The assumption of course is that in a given relationship, if person A does something
that benefits person B, then at some point in the future B will do something to benefit A. This
would be a mirrored response strategy. Alternatively, it is possible that A could eliminate B’s
debt through some counter-valenced compensatory action. For example maybe A takes more
than their fair share in a future interaction with B. Extant research has focused on the mirrored
response strategies, but a counter-valenced strategy is also a possibility. These possible
responses are illustrated below in figure #2:
30
While it is acknowledged that reciprocity can work both upstream and downstream in a
marketing relationship, consistent with the relationship marketing literature this research will
focus on a selling firm’s actions and their customer’s response. Based on the prior discussion,
the following hypothesis is offered:
H1: Beneficial actions by a selling firm will lead to a positive reciprocal debt felt by their customer. So what are the expected outcomes to these positive reciprocal debts? Theorists agree
that the existence of positive reciprocal debts helps to ensure the health of the relationship
(Becker 1986; Gouldner 1960; Macneil 1980). Positive reciprocal debts serve at least two
beneficial purposes in a relationship, the first is that at some point the debt will be repaid,
helping enforce the norm of reciprocity and a healthy exchange relationship (Macneil 1986;
Macneil 1980). Additionally, it is argued that the existence of positive reciprocal debts create a
barrier to harmful actions in that both parties are motivated to maintain the peace, at least until
the debt has been repaid (Gouldner 1960). It has also been suggested that in most
relationships, the balance of existing reciprocal debts is never exactly paid so that there always
remains a reciprocal debt to help ensure the health of the relationship (Becker 1986).
Relationship marketing theory relies on customer reciprocation of seller relationship
investments (Morgan and Hunt 1994). Selling firms invest in financial, social or structural
relationship marketing programs relying on the idea that their customer will reciprocate those
A’s Action Benefits B
B’s Positive Reciprocal Debt
B’s Action Benefits A
A’s Action Benefits A
or
Figure #2 Positive Reciprocal Debt Responses
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efforts through increased loyalty, order frequency and quantity, and possibly through positive
word of mouth (Palmatier et al. 2006a). While the role of reciprocal debts has not been
explicitly investigated in marketing research, evidence suggests that relationship marketing
investments do provide returns to selling firms through increased sales, share of wallet, and
reduced customer switching behavior. If seller initiated beneficial actions do translate into
improved seller performance, the existence and subsequent satisfaction of reciprocal debts
must explain some of the gains for selling firms. Therefore, I expect that:
H2: Customer positive reciprocal debt will lead to increased selling firm outcomes.
But what should be expected of detrimental actions? We have established that
reciprocity does not only refer to the return of benefits gained for benefits received. Based on
the definition of reciprocal behavior as actions in response to those of a relational partner, we
must also consider how a customer will respond to detrimental actions in a relationship. Becker
(1986) argues that evil actions should not be responded to with evil, however empirical
evidence suggests otherwise. Numerous marketing researchers have found that potentially
harmful actions by one partner are often correlated with similar harmful actions by their
counterpart, and interestingly, these relationships have survived despite the apparent negative
reciprocation. Kumar, Scheer and Steenkamp (1998) find that the use of punitive capabilities by
a channel member is positively correlated with similar use by its channel partner. Similarly,
Frazier and colleagues (Frazier and Summers 1984; Frazier and Summers 1986) find that dealers
and manufacturers’ use of coercive tactics are highly correlated. In an interpersonal setting,
Clayson (2004) finds that students who received grades lower than they expected in class
tended to evaluate their professors more severely than those whose expectations were met,
suggesting a type of punishment for the perceived mistreatment.
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Experimental evidence also suggests that people will punish for perceived detrimental
actions. For example, Greenglass (1969) found that participants whose task performance was
hindered were likely to exert effort to hinder “similar-others” in the future. In another
experiment, when the experimenter divulged that participants were to be intentionally
underpaid for their participation, these individuals punished the experimenter by under-
performing compared to those who were “unintentionally” underpaid (Michaels 1983).
Evidence suggests that this tendency to punish for detrimental actions persists even when the
absolute outcomes favor the individual, for example Keysar, Converse, Jiunwen and Epley
(2008) find that when the same outcomes are framed as a partner taking some of their
counterpart’s endowment, those counterparts tend to punish in future interactions, however if
the exchange is framed as the partner giving some of their own endowment, future exchange
shows evidence of positive reciprocity. This suggests that in response to a perceived
detrimental action, and given the opportunity to do so, people may retaliate or punish their
partner. While it is possible that this response happens immediately, it should not always be the
case. The opportunity when a fitting and proportional punishment is available may take time to
develop.
Negative reciprocal debt is therefore defined as: the intent to respond to a detrimental
action through either (a) punishing the responsible party, or (b) by acquiring extra benefits from
the responsible party at some point in the future. It should be noted that the two options
offered above provide two distinct routes to the satisfaction of the reciprocal debt. The first is a
mirrored response strategy whereby the harmed party punishes their partner in the future.
Alternatively, they would expect their partner to make up for the detrimental treatment by
providing some compensation to them in the future. In both cases, the focal partner has been
slighted and expects something to occur to make up for the mistreatment. However it is
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acknowledged that the route to the satisfaction of that reciprocal debt may depend on
characteristics of the person, and of the situation. These response strategies are illustrated
below in Figure #3:
Based on the prior discussion, this research proposes the following hypothesis regarding
negative reciprocal debts:
H3: Detrimental actions by a selling firm will lead to a negative reciprocal debt felt by their customer. While customer positive reciprocal debt is expected to lead to improved selling firm
outcomes, the opposite is true for negative reciprocal debts. Becker (1986) argues that negative
reciprocity will actually lead to the deterioration of social exchange. While I acknowledge that
negative reciprocity could lead to exit strategies, the reality is that all relationships experience
detrimental actions at one time or another. People also have some expectation that negative
reciprocation is a possible outcome if they harm their partner in a relationship (Becker 1956;
Perugini et al. 2003). Importantly if a customer’s negative reciprocal debt is resolved or
satisfied, the process of resolution will cause costs to the selling firm. These costs may or may
not be financial in nature, but over the course of the relationship, increased levels of negative
reciprocal debts should result in decreased relational outcomes for the selling firm.
H4: Customer negative reciprocal debt will lead to decreased selling firm outcomes.
A’s Action Harms B
B’s Negative Reciprocal Debt
B’s Action Harms A
A’s Action Benefits B
or
Figure #3 Negative Reciprocal Debt Responses
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But these first two hypotheses are really uninteresting aren’t they? Isn’t this the
intuitive understanding of reciprocity? With the exception of introducing a measurable,
conscious debt created by a partner firm, this is exactly what we would expect in relationships:
people return benefits received with a similar magnitude of benefits, and they also punish
“misbehavior” in social relationships. The more interesting implications of reciprocity remain:
Why would responses differ in magnitude from the action they are meant to reciprocate? What
aspects of the action and relationship will enhance, or suppress one party’s intent to repay a
positive or a negative debt? When is it likely that a specific reciprocal debt is not recognized?
These are the interesting questions that will be dealt with in the remainder of this research.
3.3.2 Factors that impact the formation of Reciprocal Debt
In line with past marketing research (Anderson and Weitz 1992; Jap and Ganesan 2000) I
view perceptions to be a key determinant of behavior in marketing relationships. In discussing
the valuation of reciprocal responses, Becker (1986) argues that creditor perceptions of the
value of a reciprocal response are important to satisfying the existing reciprocal debt. Gouldner
(1960) views perceptions from the opposite side of the transaction, the debtor’s, but would
agree, stating that obligations to repay beneficial acts are contingent on the perceived value of
the benefit received.
3.3.2.1 Nature of the Action
It is important to highlight that perceptions of any action will be affected by the
absolute value (or cost in the case of detrimental actions), but could also be impacted by some
relative valuation. Gouldner argues that the recipient’s perception of the value of a benefit
impacts the magnitude of debt she feels towards the creditor. He elaborates by suggesting that
perceptions of the value of the benefit will be impacted by the resources available to the
creditor, the motives imputed to the creditor, and the nature of constraints perceived to exist or
35
be absent. This suggests that there could be multiple relative valuation processes taking place.
For example, even a small gift from a child may be priceless to a parent, whereas an expensive
wedding gift from a rich relative may not be valued as highly by the recipient. Experimental
evidence supports this contention as well: Eisenberger, Cotterell and Marvel (1987) find that
experimental participants responded more favorably when the same amount given represented
a larger proportion of their partner’s available resources.
In a business relationship, it is possible that the relative outcomes for the recipient
when compared with the outcomes to the benefactor of a gift may be the most important
perception affecting the valuation of any action. In this context, beneficial relational
investments may often directly or indirectly benefit both partners. An example may be the
investment of an integrated distribution system by a selling firm. From the customer’s
perspective, the value of this investment may significantly depend on their perceptions of how
much it will benefit their own firm relative to the selling firm. When they perceive the selling
firm as benefitting as much (or more) than themselves, it is likely that the perceived value of this
relational investment will be reduced. Experimental research has shown that individuals do not
respond positively to gifts which they perceive as a direct attempt to benefit their partner
through the creation of a reciprocal debt (Rynning 1989). Additionally, Bolton and Ockenfels
(2000) find that a common strategy in games is to strive for equity in the relative payoff division.
In the context of games, this suggests that if one party benefits more from the transaction than
the other, equity considerations reduce the likelihood of positive reciprocation. Therefore the
magnitude of any positive reciprocal debt may depend not only on the absolute magnitude of
the benefit received, but also on the relative benefit to the customer. As the customer
perceives the selling firm to benefit more from a given action than the customer, it is likely that
their perceived reciprocal debt will be diminished, suggesting that:
36
H5a: As the customer’s perceived relative benefits increase, the link between a beneficial action and a positive reciprocal debt will be enhanced. When faced with a detrimental action instigated by the selling firm, it seems likely that
the perceived relative costs of the action will also be important factors. Hibbard, Kumar and
Stern (2001) defined a destructive act as “an action that is perceived by the aggrieved channel
member as having a significant negative impact on the viability or functioning of the affected
firm” (pg. 46). This implies that some additional costs have been incurred by the affected firm;
examples could include increased financial costs, decreased convenience, or any change that is
viewed as detrimental to the firm. It is likely that the selling firm may experience similar
increased costs due to necessary policy changes. An example may be increased costs of supply
that must be carried down the channel to the customer. If the customer perceives that the
selling firm has also experienced a decrease in profit due to higher supply prices, they may
object less to the increased prices they face. On the other hand, if the customer perceives that
the selling firm has encountered minimal additional costs (financial or otherwise) relative to
their own, they may view this new policy as a destructive act and their negative reciprocal debt
may increase. As with beneficial acts, it is expected that the response to detrimental acts will be
affected not only by the absolute costs incurred by the act, but also by the relative costs the
customer faces compared to the selling firm:
H5b: As the customer’s perceived relative costs increase, the link between a detrimental action and a negative reciprocal debt will be enhanced. 3.3.2.2 Responsibility for Action
Both beneficial and detrimental actions can occur for a number of reasons. Partners to
a relationship may see a positive outcome due to intentional efforts by their counterpart, or
environmental factors which naturally affect both members. More importantly, customers may
perceive that the control of these relational outcomes can be attributed to the selling firm, or
37
possibly some external entity. How would we expect reciprocity to interplay with these
perceptions? In a buyer-seller relationship, selling firm-attributed actions may include
relationship specific investments that benefit both parties (positive) or an unexplained price
increase (negative). Alternatively, a customer may attribute the control of a particular action
externally, for example the elimination of minimum pricing policies could be attributed to
governmental regulation instead of the seller. Obviously it should be expected that these
attributions of responsibility for inter-relational actions would impact reciprocal responses.
Becker’s (1986) 5th maxim states returns and restitution should be made by the ones who
have received the good or done the evil, respectively. This suggests that positive and negative
reciprocal debts should be directed at a specific target: that entity deemed responsible for the
beneficial or detrimental act. In marketing, perceived salesperson control has been found to
increase the positive effect of RM programs on the customer’s relationship quality with the
salesperson, and similarly, RM programs attributed to the firm tend to increase relationship
quality with the firm (Palmatier et al. 2007a). Cialdini (2009) suggests that reciprocal debts will
be repaid to the perceived provider of the benefit. It is clear that attributions about who is
responsible for relationship building activities will impact relational outcomes. This implies that
obligations to repay (either good or evil) will be directed towards the perceived responsible
party. However, it is possible that both beneficial and detrimental actions could be partially
attributed to external entities. Even if a beneficial action is mediated by the selling firm (e.g. the
partial pass through of a price reduction by the selling firm’s supplier), it is likely that the
positive reciprocal debt felt towards the selling firm will be reduced. With respect to
detrimental actions, Hibbard, Kumar and Stern (2001) find that actions attributed to external
forces are more likely to be responded to by passive acceptance and less likely to be responded
to with detrimental behavior (defined here as disengagement or venting), suggesting that
38
external attributions can reduce the negative reciprocal debt directed towards the selling firm.
Therefore the following hypotheses are offered:
H6a: As customer external attribution of the control of beneficial acts increases, the link between the beneficial action and a positive reciprocal debt will be suppressed. H6b: As customer external attribution of the control of detrimental acts increases, the link between the detrimental action and a negative reciprocal debt will be suppressed. 3.3.2.3 Justifiability of Action
“A conspicuous slight without justification produces anger and an impulse toward revenge, whose fulfillment produces pleasure” (as quoted in Eisenberger et al. 2004, pp. 787) The justifiability of a destructive act is also an important perception which will impact
reciprocal response. Recent reciprocity research (Pervan et al. 2009) defined whether an action
is evil based on the perceptions of how justifiable the action is. In an organizational context, it is
easy to imagine that a price increase could be perceived as a harmful action, however if the
customer understands that the seller’s price increase was necessary to continue offering the
same quality of service, they may view that “evil” action as justified. Support for the importance
of this perception has also been shown empirically when investigating civilian opinions about
prison abuse during wartime (Eder et al. 2006). In this study, Eder and colleagues found that
when civilians believed that prisoner abuse was justified during wartime, they believed that
punishing the American soldiers responsible for the abuse was inappropriate. Conversely, those
who viewed the prison abuse as unjustified felt that punishing those responsible was an
appropriate response. Detrimental actions could be perceived as justifiable if they are a
response to a failure to act within the norms of a given relationship.
Becker (1956) highlights this fact when discussing the role of punishments in
conditioning children to behave within acceptable bounds. Additionally, detrimental acts could
be perceived as justifiable if external factors are to blame. Clearly if an action, even a
detrimental action, is perceived as justified, the expected or appropriate response will be
39
different than for one that is perceived as unjustified. It would not be surprising to find that a
measure of justifiability may be highly correlated with a measure of “external control” when
considering a detrimental action. In fact, some researchers have defined justifiable, detrimental
actions as such. Importantly this research will not rule out the possibility that other factors (e.g.,
expectations based on past behavior) might influence a perception that a detrimental action is
justified. Also note that beneficial actions could be perceived as inappropriate (as in a bribe),
however it is not expected that this concept will be as prevalent in a business relationship, and
particularly not with the research design as planned, therefore justifiability is only considered
for destructive acts.
H7: As the customer’s perception of the justifiability of a detrimental act increases, the link between detrimental actions and negative reciprocal debt will be suppressed. 3.3.2.4 Nature of the Focal Party
Macneil (p. 38, 1980) refers to norms as “A principle of right action binding upon the
members of a group and serving to guide, control, or regulate proper and acceptable behavior.”
One implication is that norms provide guidelines to influence the behavior of members of a
relationship. It is important to realize that norms do not define actual behavior; rather they
portray what behavior ought to be in a particular context, and influence individuals to either act
appropriately, or to inhibit their actions through self-regulatory mechanisms (Bagozzi 1992).
This is an important distinction, members of social relationships can and do choose to behave in
ways inconsistent with accepted norms of behavior, including the norm of reciprocity; however
we should expect that strongly held social norms will ultimately influence social behavior.
Becker’s (1986) norm of reciprocity (as discussed previously) focuses on responding to
beneficial actions, and avoiding evil, and therefore is referred to here as the positive norm of
reciprocity. It focuses on the use of beneficial or positive reactions in a relationship to build the
strength of the relationship over time. Based on the understanding that norms guide behavior,
40
it should be expected that as an individual’s belief in the positive norm of reciprocity increases,
they will be more likely to acknowledge a positive reciprocal debt. Thus, an individual with a
strong belief in the norm of reciprocity should be more likely to consciously recognize the need
to reciprocate beneficial actions to their partner.
H8a: Customer belief in the positive norm of reciprocity will enhance the link between beneficial actions and a positive reciprocal debt. Although Bagozzi (1995) calls for future relationship marketing research to utilize
Becker’s (1986) positive norm of reciprocity, other researchers have extended the definition of
reciprocity beyond this utopian conception of consistent beneficial reactions. Eisenberger and
colleagues (2004) find support for a negative norm of reciprocity as an individual difference
variable which helps explain why some people tend to punish more severely than others. They
define the negative norm of reciprocity as “a unitary set of beliefs favoring retribution as the
correct and proper way to respond to unfavorable treatment” (p. 788). Gouldner (1960) also
incorporates retaliation in his explication of the norm of reciprocity, noting that reciprocity can
refer to the return of harms instead of benefits.
In fact, just as we see evidence of the Golden Rule in both the Bible and the writings of
Confucius, Eisenberger and colleagues (2004) discuss the origins of the concept of retribution,
not only from the Bible, “And if any mischief follow, then thou shalt give life for life, eye for eye,
tooth for tooth…” (Exodus 21:23-25, King James Version), but also in the Hammarabian code,
which is replete with examples of punishments for various crimes which serve no purpose save
to discourage evil acts by sentencing severe punishments on the perpetrators of evil. Cialdini
(2009) states that “… the rule for reciprocation assures that, whether the fruit of our action is
sweet or bitter, we reap what we sow” (p. 21), and goes on to quote the poetry of W.H. Auden:
41
I and the world know What every schoolboy learns, Those to whom evil is done Do evil in return. (1939)
Evidence for retribution as a moral imperative remains in our penal system, which while some
argue is designed to rehabilitate offenders, others maintain that its primary purpose is to punish
criminal offenders through a socially sanctioned process so that victims of crime gain some
feeling of catharsis through retributive justice (Carlsmith et al. 2002; Logan and Gaes 1993).
These proponents of the penal system as a punitive mechanism do not consider rehabilitation a
goal of incarceration.
This research will utilize a slightly less restrictive conceptualization of the norm of
reciprocity developed by Perugini, Gallucci, Presaghi and Ercolani (2003). Recognizing that the
norm of reciprocity includes not only positive reciprocation, but also can include retributive
punishment, or negative reciprocity as a moral imperative, these authors conceptualized the
norm of reciprocity as a two part internalized social norm. The “Personal Norm of Reciprocity”
scale measures individual adherence to a norm of both positive reciprocity and negative
reciprocity. The authors find evidence that individuals with higher positive or negative
reciprocity norms behave differently in interactions with others depending on the perceived
valence of the others’ past behavior. In other words, individuals exhibit different levels of
adherence to the norm of positive and negative reciprocity, and these norms influence their
behavior differentially in relationships.
In development of their scale, Perugini et al. (2003) measured the two distinct norms of
reciprocity; along with a third scale which measured a general belief in reciprocal behavior. The
normative scales measured opinions about what the respondent should do in response to a
particular action (e.g., positive – If someone is helpful with me at work, I am pleased to help
him/her; and negative – I am willing to invest time and effort to reciprocate an unfair action),
42
while the “Beliefs in reciprocity” scale captured general beliefs about what others might do in
response to the respondent’s action (e.g., positive – If I work hard, I expect it will be repaid; and
negative – I avoid being impolite because I do not want others being impolite with me). One
might argue that the positive and negative norms of reciprocity are at opposite ends of the
same spectrum, as they deal with actions of an opposing valence. If this were true, we might
expect to see a negative correlation between these norms in individuals, where (as Becker
argues) people should always try to avoid returning evil for evil, however empirical evidence
suggests this is not the case. In an Italian sample, no relationship was found between the two,
and the two norms were positively associated in an English sample (Perugini et al. 2003).
Further, in both samples the correlation between the general belief in reciprocity, and both the
positive and negative norm of reciprocity scales was positive. This suggests that people can and
do exhibit unique agreement to both a positive norm (returning good for good) and a negative
norm (an eye for an eye…) of reciprocity. In fact, Eisenberger et al. (2004) find that people with
a stronger belief in negative reciprocity are more responsive to mistreatment than those with a
lesser belief in the negative norm of reciprocity. Further, it was found that the belief in a
negative norm of reciprocity enhanced the impact of a mistreatment on negative outcomes
including anger, disagreement and ridicule directed at the experimenter. This suggests that in
exchange situations involving a detrimental action, the negative norm of reciprocity will be more
salient in affecting the response behavior.
H8b: Customer belief in the negative norm of reciprocity will enhance the link between detrimental actions and a negative reciprocal debt. 3.3.2.5 Nature of the Relationship
Defined as the need to maintain a relationship with a partner in order to achieve one’s
goals, dependence has a long tradition in the marketing literature. Early dependence research
positioned dependence as a key variable for the quality of relationships (Emerson 1962).
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Research found that more dependent parties were subjected to greater coercion from their
partner (El-Ansary and Stern 1972; Stern 1967). Later work investigated the role of
interdependence and dependence asymmetry as predictors of relational outcomes (Kumar et al.
1995a; Kumar et al. 1998). Recently the role of dependence as a predictor of performance in
marketing relationships has been questioned. In a meta-analysis on the effectiveness of various
relationship factors, dependence was found to be one of the least influential constructs in the
relationship marketing framework (Palmatier et al. 2006a). It seems unlikely that the degree a
firm’s success relies on their partner is not an important aspect of marketing relationships.
The extant marketing research has measured dependence in a number of ways, without
respect to how these various measurements might impact the empirical results. For instance,
dependence has been conceptualized as a firm’s relative dependence compared to their partner
(Anderson and Narus 1990; Stern and Reve 1980); interdependence, or the aggregate
dependence among both parties (Gundlach and Cadotte 1994; Kumar et al. 1995a; Kumar et al.
1998); dependence asymmetry or the difference (and direction of that difference) between the
focal firm and their partner (Anderson and Weitz 1989; Stern and Reve 1980); or the absolute
level of dependence of the focal party (Frazier et al. 1989). While there has been research that
finds significant impacts using these measures of dependence, researchers have recently
ascertained that dependence is not a one-dimensional construct.
Recent research suggests that dependence is actually a two-dimensional construct
comprised of cost based and benefit based dependence, which may have unique impacts on
relationships (Scheer et al. 2008b). For example, decomposing dependence resulted in a direct
positive impact of benefit-based dependence on customer relational loyalty, however while
cost-based dependence positively influenced insensitivity to competitive offerings, it showed no
effect on relational loyalty (Scheer et al. 2008a). In a meta-analysis investigating the empirical
44
evidence for dependence in the marketing literature, Scheer, Palmatier and Miao (2008) find
support for breaking dependence into two dimensions, cost-based dependence (CBD) and
benefit-based dependence (BBD).
CBD is defined as the need to maintain the relationship because of the unrealized costs
that would be incurred if the relationship ended (termination costs). BBD is the need to
maintain the relationship because of the irreplaceable, unique net value that would be forfeited
if the relationship ended (relationship benefits received). The findings of the meta-analysis with
respect to the roles of CBD and BBD are highlighted when the authors state “Benefit-based,
cost-based, and overall dependence display strikingly different patterns of relationships with the
other relational constructs in our framework.” The authors find that while both CBD and BBD
have a positive relationship with commitment, CBD has a more positive correlation than does
BBD. This suggests that high anticipated termination costs can act as stabilizing mechanisms in a
relationship. The results also show that as a customer’s CBD increases, the supplier is able to
exercise greater influence over the customer; however the same cannot be said of BBD.
So how should cost-based dependence and benefit-based dependence affect reciprocal
behavior? It is important to examine how cost based and benefit based dependence may differ
within relationships. While it is likely that the two will be positively correlated, it is not
necessary that the correlation be strong. BBD deals with the benefits mediated through the
partner firm, while CBD deals with the latent termination costs if the relationship were to end.
It is possible that in some situations, BBD is very high; however multiple suppliers (or customers
from the seller’s perspective) are available who would be equally able to provide similar benefits
should this relationship end, suggesting that CBD is very low. Similarly, a firm could have a high
level of CBD given a lack of available alternatives for a partner firm that provides very small
amounts of benefits through the relationship.
45
Reciprocity can be viewed as a behavioral norm for independent interactions that
transpire between any two individuals in a one-shot episodic transaction (Macneil 1980), and
also as a mechanism that enables the development of enduring social relationships that deepen
in value over time (Bagozzi 1995; Gouldner 1960; Macneil 1986). As Gouldner (1960) points out,
the valuation of an individual exchange is dependent on the perceptions of the actors involved.
Ongoing exchange creates expectations for future exchange. Given that actions have been
defined as significant deviations from the status quo in a relationship, we might expect that the
same action could have a unique impact in two separate relationships depending on the history
of the relationship, and hence the expectations for future interactions within each relationship.
Figure #4 illustrates different configurations of cost-based and benefit-based dependence which
should be expected to affect the reference point in a given relationship, and hence the response
to any beneficial action within that relationship.
Figure #4. Dependence Configurations
Benefit Based Dependence
Low High
Cost Based Dependence
Low
Quadrant A Quadrant C
Low Benefits, Low Latent
Costs
High Benefits, Low Latent
Costs
High
Quadrant B Quadrant D
Low Benefits, High Latent
Costs
High Benefits, High Latent
Costs
It seems that in a relationship described by low benefit-based dependence (quadrants A and B in
Figure #4) a given beneficial action would be perceived as more positive when compared to a
relationship described by a high level of BBD. In the first case (low BBD) the action may be
perceived as a significant positive deviation from the expected outcomes. In the latter case
(high BBD), the same action may not make much of an impact as the recipient is already
46
accustomed to greater benefits mediated through their partner. This suggests the following
hypothesis:
H9a: Customer benefit-based dependence will suppress the impact of beneficial actions on positive reciprocal debts. Cost-based dependence is expected to impact the response to detrimental actions in a
similar way. CBD is directly related to the need to maintain a relationship because of the latent
costs if the relationship were to end. It does not necessarily depend on the benefits accrued
directly from the relationship, but rather on how costly it would be to replace this relational
partner. Frazier and colleagues (Frazier and Rody 1991; Frazier and Summers 1984) find that
coercive manufacturer tactics are reciprocated by dealers in the U.S., however they find that in a
developing country, coercive manufacturer tactics are less likely to be reciprocated by their
dealers (Frazier et al. 1989). They argue that this is the case because in developing markets,
firms have fewer alternatives for suppliers, and hence a higher level of dependence on their
supplier than similar firms in a developed country. In the U.S., that is not the case. Dealers in
the U.S. have multiple alternatives, and hence their CBD is relatively lower than those in
developing countries.
It seems logical that all else equal, higher switching costs (Quadrants B and D in Figure
#4) would encourage channel members to tolerate more negative treatment from their
counterparts, at least to some extent. In fact, channels researchers generally agree that firms
lacking alternatives, and hence higher cost-based dependence, are likely to focus less on equity
in their relationship and also more likely to endure detrimental actions than those in low
dependence relationships (Bucklin 1973; Frazier 1983). Additionally, Scheer, Palmatier and
Miao (2008b) find that cost-based dependence has a very strong positive relationship with
commitment, relationship continuity and cooperation. It seems that the anticipation of high
costs if a relationship is terminated can have a stabilizing effect, and possibly reduce the
47
tendency to actively respond to negative actions. Overall it is expected that cost based
dependence will exhibit the following impact on negative reciprocal debts:
H9b: Customer cost-based dependence will suppress the impact of detrimental actions on negative reciprocal debts. Kumar, Scheer and Steenkamp (1998) cite bilateral deterrence theory (Lawler and
Bacharach 1987) in their prediction that increasing total dependence in a relationship will lead
to the avoidance of punitive tactics as the parties are motivated by loss avoidance. While this
research will focus on the customer’s dependence on its selling firm, I believe the impact of
bilateral deterrence theory will obtain. While this theory is focused on loss avoidance, it applies
to both BBD and CBD in with respect to benefit based dependence a party may be motivated to
refrain from doing anything that would cause a reduction in benefits through that relationship.
On the other hand, avoiding loss with respect to cost-based dependence suggests avoidance of
realizing the latent costs that would be incurred should the relationship terminate. Bilateral
deterrence theory examines interactions at a broad level, including the dependence from both
parties, and a general trend of interactions over time. For example, Kumar, Scheer and
Steenkamp (1998) examine the overall use of punitive actions in channel relationships, and find
that as relationship interdependence increases the use of punitive action decreases. The reason
that high levels of overall dependence may have had this suppressing effect on punitive action
may be due to high levels of both cost-based and benefit-based dependence, suggesting that
when both are high we might expect to see the lowest levels of punitive or detrimental actions.
Unlike Kumar, Scheer and Steenkamp, this research will focus on one particular exchange
between a customer and their supplier firm. If we examine one single response to an action
through the scope of bilateral deterrence theory, understanding that we are not capturing the
entire relationship, we would expect that as one party’s total dependence increases (Quadrant
48
D in Figure #4) they will be less likely to acknowledge a negative reciprocal debt when faced
with a detrimental action by their partner, and hence expect that:
H10: The customer’s negative reciprocal debt will be lowest when the customer CBD and BBD are both high.
Figure #5 (below) presents a process model of the reciprocation of beneficial actions,
while figure #6 presents a model of negative reciprocation. In brief, seller actions lead to
positive or negative reciprocal debts. It is proposed that the path from the seller action to the
customer reciprocal debt is moderated by: (1) the customer’s perception of relative outcomes;
(2) the customer’s attributions of control of the seller action; (3) the customer’s norms of
reciprocity; (4) the customer’s perception of the dependence structure of the relationship; (5)
and the customer’s judgment of the justifiability of seller detrimental actions.
Selle
r Ini
tiate
d B
enef
icia
l A
ctio
n (B
A)
Posit
ive
Rec
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Deb
t
Posit
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of R
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efit
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Ben
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H1
+
H5a
+
H6a
-
H8a
+
H9a
-
Figu
re #
5Pr
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s Mod
el o
f Pos
itiv
e Re
cipr
ocity
Partn
er
Out
com
esH
2 +*
* -t
este
d in
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l res
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e N
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Partn
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4 -*
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este
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l res
earc
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ly
51
CHAPTER 4: RESEARCH ON EPISODIC RECIPROCITY
4.1 Survey Research Model
A survey was administered to address the front end of the theoretical framework (up
until the creation of reciprocal debt) developed to address episodic reciprocity (see figure #7
below).
The survey was organized such that respondents (customers) first answered questions about
their relationship with the seller during the pre-scenario questionnaire. After completing these
relationship scales, participants were exposed to an experimental scenario which placed them in
a hypothetical situation during which their selling firm enacted some strategic action that would
presumably have an impact on the customer firm. After reading the hypothetical scenario,
participants responded to a number of questions gauging their perceptions (both research
variables and manipulation checks) of the strategic action, and questions that measured the
Perceived Relative
Outcomes
Norms of Reciprocity
Customer Attribution of Control
Seller Action
Reciprocal Debt
Customer Dependence
Customer Perceived
Justifiability
Figure #7 – Process Model of Reciprocity
52
dependent variable for this portion of the research – reciprocal debt. Finally, participants were
asked to complete demographic information about themselves and about their firm.
The survey was administered using Qualtrics online survey software. Participants were
randomly assigned to one of 8 conditions in a 2 [valence: detrimental vs. beneficial] x 2
[magnitude: low vs. high] x 2 [attribution of control: seller vs. external] full factorial
experimental design. Figures #8 and 9 below summarize the random assignment.
Figure #8
Random Assignment to Beneficial Conditions (N = 81)
Magnitude
High n = 24
(females = 4) (males = 20)
n = 20 (females = 2) (males = 18)
Low n = 18
(females = 3) (males = 15)
n = 19 (females = 2) (males = 17)
Internal External
Attribution of Control
Figure #9
Random Assignment to Detrimental Conditions (N = 81)
Magnitude
High n = 19
(females = 0) (males = 18)
n = 18 (females = 0) (males = 18)
Low n = 19
(females = 0) (males = 19)
n = 25 (females = 3) (males = 22)
Internal External
Attribution of Control
53
The 8 conditions were created by putting together various components into a hypothetical
scenario that involved a car manufacturer (selling firm) announcing a pricing change that either
increased (beneficial) or decreased (detrimental) the car dealer markup. The dealer markup is
the portion of retail vehicle sales that is adds to the dealership’s bottom line – making it an
appropriate marketing variable to manipulate for our study. The actual scenario components
will be discussed in detail in the measures section of this document.
4.1.2 Operationalizations
Measures were captured by Qualtrics and included attitudinal and perceptual scales
which can be separated into two distinct questionnaires: pre-scenario questionnaire; and post-
scenario questionnaire. Although scales will be discussed that measured participants’
perceptions about the scenario in the post-scenario questionnaire, the experimental scenarios
will be discussed in detail in this section as well.
4.1.2.1 Pre-Scenario Questionnaire
A battery of questions capturing the participants’ adherence to the positive norm of
reciprocity, their adherence to the negative norm of reciprocity and their dealership’s
dependence on their primary automaker was asked prior to exposure to the experimental
scenarios. When possible, scale items were adapted from existing scales.
Norms of Reciprocity
The norms of reciprocity were conceptualized as multi-item reflective latent constructs.
Items were adapted from Perugini et al (2003) to capture both the positive and negative norms
of reciprocity. 6 items were used to capture the participant's adherence to the positive norm of
reciprocity (pr1 - pr6) and to the negative norm of reciprocity (nr1 - nr6) using 7-point Likert
scales ranging from 1 (Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor
Agree” labeling the midpoint.
54
Positive Norm of Reciprocity
1. When I profit from the actions of someone else, I will do something that benefits him/her in return.
2. When someone does me a favor, I feel committed to repay the favor. 3. If someone does a favor for me, I will return the favor. 4. I am ready to incur personal costs to help someone who has helped me. 5. I go out of my way to help a person who has been kind to me. 6. I'm willing to do things I don't enjoy to return someone's assistance.
Negative Norm of Reciprocity
1. If someone purposely injures me, I will try to injure him/her as well. 2. If somebody takes action that damages me, I will go to great lengths to inflict damage
on him/her. 3. If somebody is impolite to me, I become impolite in return. 4. I am willing to invest time and effort to reciprocate a harmful action. 5. If someone puts me in a difficult position, I will do the same to him/her. 6. I will be unfair to someone who has been unfair to me.
Primary Automaker – Control Variable
One question was asked of participants to focus their attention on one automaker for
the scenario and other relationship questions:
“Please select the automaker that represents the largest share of your dealership’s sales”
Acura Ford Lincoln Porsche Audi Honda Mazda Scion BMW Hyundai Mercedes Suzuki Buick Infiniti Mercury Toyota Cadillac Jeep Mini Volkswagen Chevrolet Kia Mitsubishi Volvo Chrysler Lexus Nissan Subaru Dodge
Dependence
Dependence was conceptualized as a two-dimensional construct comprised of (1) cost
based dependence and (2) benefit based dependence. Cost based dependence is defined as the
need to maintain the relationship because of the unrealized costs that would be incurred if the
relationship ended (Scheer et al. 2009). Benefit based dependence is defined as the need to
55
maintain the relationship because of the irreplaceable, unique net value that would be forfeited
if the relationship ended. Both cost based and benefit based dependence were conceptualized
as multi-item reflective latent constructs. Additionally, a “global” dependence single item was
asked of participants, along with a single relative dependence item so that these measures could
be included as controls in the analysis. Finally, share of wallet was measured with one item.
The items for all of these measures are included below, with “Porsche” being used where text
was piped into questions by Qualtrics based on the primary automaker answer.
Benefit Based Dependence – captured with 3 items using 7-point Likert scales ranging from 1
(Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor Agree” labeling the
midpoint.
1. If we were no longer a “Porsche” dealer, it would be difficult to maintain our current sales volume.
2. My dealership receives benefits as a “Porsche” dealer that couldn’t be fully duplicated by another automaker.
3. If my dealership stopped selling “Porsche”, our other options would not be as profitable.
Cost Based Dependence– captured with 3 items using 7-point Likert scales ranging from 1
(Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor Agree” labeling the
midpoint.
1. My dealership would incur many costs to end its business relationship with “Porsche” and switch to another automaker.
2. It would be costly for my dealership to search for and locate an alternative automaker to replace “Porsche”.
3. It would be costly for my dealership to end its relationship with “Porsche”.
Global Dependence
1. How dependent is your dealership on “Porsche”?
Captured with a 7 point semantic differential scale anchored by (1) Not at all dependent
and (7) Extremely dependent.
56
Relative Dependence
1. In your market, is your dealership more dependent on “Porsche” or is “Porsche” more
dependent on your dealership?
Captured with a 7 point semantic differential scale anchored by (1) They are more
dependent on us and (7) We are more dependent on them.
Share of Wallet – Control Variable
1. Consider all your dealership’s sales from all makes you carry. What share of your dealership’s total sales (# of vehicles) are “Porsche” vehicles?
Please enter the % of total number of vehicles sold that Porsche represents _________
4.1.2.2 Experimental Scenarios
In order to manipulate the valence, attribution of control and relative outcomes of the
seller action, participants were randomly assigned to view one of 8 scenarios in a 2 [valence:
detrimental vs. beneficial] x 2 [magnitude: low vs. high] x 2 [attribution of control: seller vs.
external] full factorial experimental design. The scenarios were presented as “What If’s” and
participants were asked to think about how they would react if their primary automaker actually
made the strategic decision described in the scenario. The three factors were manipulated
within the scenarios using standard components embedded within the text of the hypothetical
situations. As with many of the preceding survey questions, the name of the primary automaker
was piped in by Qualtrics such that each participant viewed a personalized scenario. Porsche
again will be used as the example automaker for the scenario components. Table #4 (on the
following page) contains the components of the scenarios that were designed to manipulate the
various factors. Figures 8 and 9 (section 4.1) showed the number of participants assigned to
each condition.
57
Two sample scenarios are recreated below for illustrative purposes.
Scenario #1 – Valence = Beneficial; Magnitude = Low; Attribution = Porsche
Porsche announced that because of decreased costs of production, 3rd quarter 2010 dealer prices for all passenger cars will be decreased. Due to the current economic environment, MSRP’s will remain the same. The price change will increase your effective dealer markup by 1%.
Scenario #2 – Valence = Detrimental; Magnitude = High; Attribution = External
Porsche announced that because of changes in governmental regulations, 3rd quarter 2010 dealer prices for all passenger cars will be increased. Due to the current economic environment, MSRP’s will remain the same. The price change will decrease your effective dealer markup by 2%.
4.1.2.3 Post-Scenario Questionnaire
After reading the hypothetical scenario, participants answered a number of questions
about their perceptions of the strategic decision presented to them during the scenario.
Partner Actions
Each participant assessed the impact of the automaker’s decision in the scenario on
their dealership’s outcome and the automaker’s outcomes by completing two questions using 7-
point semantic differential scales.
Table #4 - Experimental Scenario Components
Common Statements Valence Magnitude Attribution of Control
Beneficial Detrimental High Low Porsche External
Porsche announced that because of
decreased increased
costs of production,
changes in governmental regulations,
3rd quarter 2010 dealer prices for all passenger cars will
be decreased.
be increased.
Due to the current economic environment, MSRP's will remain the same. The price change will
increase decrease your effective dealer markup
by 2%.
your effective dealer markup
by 1%.
58
• Own Impact (dealerimpact): How will the decision made by Porsche impact your dealership? [1 = “Very negative effect on my dealership;” 7 = “Very positive effect on my dealership”]
• Partner Impact (makerimpact): How do you think the decision by Porsche will impact Porsche? [1 = “Very negatively;” 7 = “Very positively”]
• Relative Outcomes (ROut) were computed by subtracting makerimpact from dealerimpact. As the two items were assessed consecutively on consistent scales, I believe that this calculated variable inherently reflects the participant's perception of the extent to which his/her outcomes were relatively greater or lower than the partner's.
Reciprocal Debts
Reciprocal debt was conceptualized as a multi dimensional reflective latent construct.
Extant reciprocity literature was referenced to help develop items to assess the participant's
perceptions regarding reciprocity debts for this research. A large pool of possible scale items
was developed to capture the perceived reciprocal debt. After further consideration and a
pretest to examine measurement properties, a smaller set of measures was selected to assess
these latent constructs. Three items capturing reciprocal debt were measured using 7-point
Likert scales [1 = "Strongly Disagree;" 4 = "Neither Disagree nor Agree;" 7 = "Strongly Agree"]
and one item assessed the locus of the debt using forced choice.
• Existence of Reciprocal Debt (RD): Indicate your agreement or disagreement with the
following statements.
1. An obligation has been created that should be repaid. 2. Something should be done to make up for this situation. 3. Porsche’s decision has created an imbalance in our relationship that needs to be
addressed.
• Locus of Reciprocal Debt (Locus): Given the outcome of the game you just played, which
statement below best captures your opinion of what should happen?
1. Porsche should do something in response to this situation. 2. My dealership should do something in response to this situation. 3. Either Porsche or my dealership or both should do something in response to this
situation, but I don’t have a strong opinion about which should act.
59
4. Neither Porsche nor my dealership should do anything in response to this situation. Justifiability
Justifiability was conceptualized as a multi-item reflective latent construct. Participants
were asked to rate their agreement with four statements to capture their perceptions of the
justifiability of Porsche’s action.
“Overall, I believe that Porsche’s pricing decision is…”
1. … completely justified. 2. … appropriate under current circumstances. 3. … a viable strategic option by Porsche. 4. … a good business decision.
Responses to these 4 statements were captured using 7-point Likert scales [1 = "Strongly
Disagree;" 4 = "Neither Disagree nor Agree;" 7 = "Strongly Agree"].
Attribution of Control
Participants were asked to respond to a single question about their perceptions of
responsibility for (a) the automobile manufacturer, (b) external forces and (c) their own
dealership. Using these 3 questions, both relative and absolute attribution of control measures
can be calculated.
“How much responsibility do the following parties have for Porsche’s action?”
• Porsche • External forces (competition, government, etc.) • My dealership
Answers were recorded for each of the three choices on a 5-point Likert scale using a dropdown
list to select the appropriate answer:
1. No responsibility 2. Slight responsibility 3. Moderate responsibility 4. Primary responsibility 5. Full responsibility
60
4.1.3 Sample Selection
We decided to survey car dealership managers about their relationships with car
manufacturers. This is a fertile context to investigate the response to selling-firm actions given
the recent turmoil in the automobile industry (dealership closings, government interventions,
safety recalls, etc.). A database was purchased from www.autodealerlist.com that included
approximately 23,000 contacts (owners, CEO’s, parts managers, sales managers, etc.) from
12,000 new car dealers across the United States. The database included the contact’s name,
phone number, email address and job title. It also included some information about the
dealership, including automaker(s) represented, annual sales volume ($) and number of
employees.
Because the database included numerous contacts per dealership, ranging from
assistant managers to owners, decisions had to be made about who would be best qualified as
both an informant and respondent for this survey. The ideal participants would be those who
(1) have a direct interest in the financial outcomes of the dealership’s relationship with an
automaker; (2) have a reasonable amount of direct interactions with an automaker – to be
qualified to respond about those relationship dynamics; and (3) have decision making authority
at the dealership. Owners and executive officers were eliminated because of the uncertainty of
their day-to-day operational experience at the dealership. Parts and service managers were
eliminated because of the uncertainty of their meeting any of the above criteria. We decided to
only approach general managers and sales managers at the car dealerships because of the
likelihood that they would meet all three criteria we specified for our participants. The final
sample we selected included 17,081 sales and general managers at 9,437 unique car dealerships
throughout the United States.
61
Given the online data collection procedure, cost savings concerns, and the availability of
the email contact list from the purchased database, potential participants were recruited
through email. After unsuccessful negotiations with the host research university, a multi-
method recruitment process ensued. In order to bolster response rates as much as possible, the
email cover letters were customized with letterhead from the two Universities involved with this
research: the University of Missouri, and the University of Alabama – Birmingham (UAB); see
exhibit 2 and 3 in the appendix. The total sample was split geographically into 3 regions: (1)
University of Missouri Region; (2) UAB Region; and (3) the rest of the United States. Details for
each region are included in Table #5 below.
Table#5 - Recruitment Regions
Region States Included
Email Contacts
Bouncebacks Email
Delivery System
Responses Response
Rate
MU
Arkansas, Colorado, Iowa, Illinois, Kansas,
Nebraska, Oklahoma, Texas
3102 737 *
Researcher's Personal
Email Account
119 3.76%
UAB
Alabama, Florida, Georgia,
Louisiana, Mississippi,
South Carolina, Tennessee
2869 681 ** Qualtrics 58 2.65%
USA All Others 11,110 2,640 ** Qualtrics 157 1.85%
Total 17,081 4058 ** 334 2.56%
* - actual tracked bounced emails ** - estimated # of bounced emails based on MU Region
(23.76%)
62
The researchers endeavored to email potential participants using their own email
accounts to minimize the number of emails that would end up in junk email boxes (every test
email – no matter the designation of a return email address – sent from Qualtrics ended up in
the researcher’s junk email folder), however found that it was not possible to do so with the
entire sample. Because of the proximity of the MU region to the university, and the likelihood
that response rates would be higher in that area, the MU region was selected as the one region
that would receive emails directly from the researchers instead of any mass email system.
However, limitations in email capacities required that recruitment emails be sent out in batches
of < 250 per day. So the MU region received emails at a rate of ~250 per day, followed by a
reminder email after the entire MU region sample had been emailed their first recruitment
letter. The UAB and USA regions were mass emailed from Qualtrics with a follow up reminder
email sent out approximately 10 days after the first email.
Participants were offered a summary report of the research findings, and entry into a
drawing for one of 10 $100 gift cards as an incentive for their participation. A link to the survey
was included in the email cover letter so that participants could click the link in the email and be
taken directly to the survey hosted by Qualtrics.
Although it was impossible to estimate or track the number of emails that ended up in
junk email folders, using a personal email account to send out the emails to the MU region
allowed tracking of bounced emails that were returned due to spam tracking at the receivers
end, or bad email addresses (or any other reason). As table #5 shows the bounce back rate for
emails in that sample was 23.76%. This bounce back rate was assumed for the other two
regions, which should be a very conservative estimate given that emails sent from a mass email
system like Qualtrics have a much higher probability of being flagged as spam by the recipient’s
email server than those sent from an individual’s account. Assuming the same bounce back
63
rate, the response rates were realistic considering the medium used to solicit participation: MU
region – 3.76%; UAB region – 2.65%; USA region – 1.85%; for a total response rate of 2.56%.1
4.1.4 Measurement Model and Scale Development
4.1.4.1 Directional Reciprocal Debt
RDLocus was calculated by multiplying the multi-item latent reciprocal debt measure by
the locus measure. The locus measure was The RDLocus variable was coded negatively when the
participant felt the automaker (Porsche in our examples) was responsible, when they thought
that their own dealership was responsible, the RDLocus variable was positive. If they said
“neither” party should do anything, the RDLocus = 0 – which makes sense since they obviously
feel that there is no reciprocal debt. The “either” option was a bit more complicated, but
ultimately we decided on dropping them from the analysis. While they may have thought
something should be done (based on the RD measure), it is unclear how to analyze these
responses without making assumptions about the locus. RDLocus therefore represents the
intensity and directionality of the perceived reciprocal debt, ranging from strong participant
reciprocal debt (at high positive values of this variable) to strong partner reciprocal debt (at high
negative values of this variable).
4.1.4.2 Exploratory Factor Analyses
Exploratory factor analyses were conducted on each latent construct separately, and the
results are summarized in Table 6 (Pre-Scenario items) and Table 7 (Post-Scenario items). Each
separate EFA resulted in all items loading onto one factor. These exploratory factor analyses
results provide preliminary evidence for the validity of the latent constructs.
1 Plans exist to use “snail-mail” to recruit car dealership managers from the state of Missouri to participate in our online survey which should serve 2 purposes: (1) increase our total sample for this survey; and (2) allow comparisons of response rates from email versus mail based recruitment to solicit participation for an online survey. However those results will not be included at this point in the analysis.
64
Table#6 – Summary of Individual EFA’s for 4 Latent Constructs
Eigenvalue 2.985 3.909 1.628 1.683 % Var. Extracted 49.74% 65.15% 54.25% 56.10% # of factors with Eigenvalue > 1
1 1 1 1
PR1 .711 PR2 .816 PR3 .620 PR4 .704 PR5 .728 PR6 .634 NR1 .769 NR2 .704 NR3 .825 NR4 .874 NR5 .876 NR6 .781
BBD1 .750 BBD2 .681 BBD3 .775 CBD1 .754 CBD2 .845 CBD3 .634
Extraction Method: Principal Component Analysis.
Table#7 – Separate EFA on RD and Just
Eigenvalue 2.436 3.510 % Var. Extracted 81.21% 87.75% # of factors with Eigenvalue > 1
1 1
RD1 .874 RD2 .933 RD3 .896 Just1 .934 Just2 .923 Just3 .943 Just4 .948
Extraction Method: Principal Component Analysis.
Ta
ble
#8 -
Ove
rall
Mea
ns, S
tand
ard
Dev
iati
ons
and
Corr
elat
ions
for
Stud
y V
aria
bles
1 2
3 4
5 6
7 8
9 10
11
12
13
14
15
16
17
(1) V
alen
ce
(man
ip)
μ =
.500
σ
= .5
01
r
p
N
(2) M
agni
tude
(m
anip
) μ
= .5
00
σ =
.501
r .0
86
p
.274
N
162
(3) A
ttri
buti
on
(man
ip)
μ =
.494
σ
= .5
01
r .0
49
.074
p
.533
.3
49
N
162
162
(4) O
wnO
ut
μ =
4.05
σ
= 2.
16
r .6
29
.097
.0
80
p
.000
.2
19
.310
N
162
162
162
(5) O
ther
Out
μ
= 4.
16
σ =
1.85
r .3
68
.067
-.0
06
.484
p
.000
.3
98
.943
.0
00
N
162
162
162
162
(6) R
elat
iveO
ut
μ= -.
111
σ
= 2.
06
r .3
31
.042
.0
90
.617
-.3
90
p
.000
.5
95
.257
.0
00
.000
N
162
162
162
162
162
(7) P
R μ=
23.5
1
σ =
2.40
r .0
01
.002
.0
78
.014
-.0
16
.029
p
.990
.9
76
.326
.8
61
.837
.7
11
N
162
162
162
162
162
162
(8) N
R μ=
11.6
6
σ =
5.59
r .0
54
.031
.0
26
-.048
-.0
99
.039
-.0
24
p
.497
.6
92
.743
.5
47
.211
.6
25
.760
N
162
162
162
162
162
162
162
(9) B
BD
μ =
8.83
σ
= 2.
23
r -.1
44
.154
.0
03
-.085
-.0
49
-.046
.0
09
-.110
p
.068
.0
51
.973
.2
81
.537
.5
63
.912
.1
62
N
162
162
162
162
162
162
162
162
(10)
CBD
μ=
10.4
2
σ =
2.56
r -.1
14
-.055
-.0
93
-.015
-.0
62
.040
.0
61
-.111
.5
34
p
.149
.4
86
.240
.8
49
.432
.6
13
.442
.1
61
.000
N
162
162
162
162
162
162
162
162
162
(11)
Dep
ende
nce
μ=19
.25
σ
= 4.
20
r -.1
46
.048
-.0
55
-.054
-.0
64
.000
.0
42
-.126
.8
57
.894
p
.064
.5
44
.486
.4
91
.419
.9
99
.598
.1
10
.000
.0
00
N
162
162
162
162
162
162
162
162
162
162
(12)
Rec
. Deb
t μ=
11.4
3
σ =
4.45
r -.4
46
-.064
-.0
99
-.503
-.2
50
-.305
.0
51
.224
.0
67
.112
.1
04
p
.000
.4
21
.211
.0
00
.001
.0
00
.516
.0
04
.396
.1
55
.188
N
162
162
162
162
162
162
162
162
162
162
162
(13)
RD
Locu
s μ=
-5.9
0
σ=10
.02
r .5
65
.184
.1
88
.488
.3
10
.230
.0
73
.001
.0
40
-.043
-.0
04
-.536
p
.000
.0
39
.035
.0
00
.000
.0
10
.418
.9
90
.653
.6
31
.964
.0
00
N
126
126
126
126
126
126
126
126
126
126
126
126
(14)
Loc
us
μ= -.
325
σ
= .8
37
r .5
98
.174
.2
07
.526
.3
35
.247
.1
57
.009
.0
38
-.063
-.0
17
-.544
.9
44
p
.000
.0
51
.020
.0
00
.000
.0
05
.079
.9
18
.670
.4
87
.852
.0
00
.000
N
126
126
126
126
126
126
126
126
126
126
126
126
126
(15)
Just
μ=
15.4
6
σ =
6.67
r .4
90
.063
.1
94
.637
.4
69
.248
.1
36
-.228
-.0
47
-.080
-.0
74
-.539
.5
26
.531
p
.000
.4
22
.013
.0
00
.000
.0
01
.085
.0
04
.549
.3
10
.349
.0
00
.000
.0
00
N
162
162
162
162
162
162
162
162
162
162
162
162
126
126
(16)
aut
omak
er
resp
onsi
bilit
y μ
= 3.
98
σ =
1.05
r -.0
71
.024
.0
47
-.063
.0
02
-.068
.1
19
.033
-.1
15
-.098
-.1
21
.032
-.0
56
-.015
-.2
00
p
.369
.7
65
.552
.4
29
.979
.3
92
.132
.6
75
.146
.2
13
.126
.6
87
.533
.8
72
.011
N
162
162
162
162
162
162
162
162
162
162
162
162
126
126
162
(17)
oth
er
resp
onsi
bilit
y μ
= 2.
65
σ =
1.12
r .0
17
-.006
-.1
98
-.003
-.0
39
.032
.0
72
-.148
.0
33
.049
.0
47
-.160
.0
71
.046
.1
51
-.380
p
.834
.9
44
.012
.9
69
.626
.6
91
.364
.0
60
.678
.5
34
.549
.0
43
.427
.6
06
.056
.0
00
N
16
2 16
2 16
2 16
2 16
2 16
2 16
2 16
2 16
2 16
2 16
2 16
2 12
6 12
6 16
2 16
2
(18)
dea
ler
resp
onsi
bilit
y μ
= 1.
66
σ =
.953
r .2
40
-.071
.0
28
.192
.0
45
.161
-.0
72
-.023
-.0
23
-.080
-.0
61
-.114
.1
98
.178
.2
31
-.083
.0
15
p
.002
.3
66
.723
.0
15
.568
.0
41
.363
.7
68
.770
.3
14
.442
.1
50
.026
.0
47
.003
.2
92
.845
N
162
162
162
162
162
162
162
162
162
162
162
162
126
126
162
162
162
Ta
ble
#9 -
Mea
ns, S
tand
ard
Dev
iati
ons
and
Corr
elat
ions
for
Part
icip
ants
in t
he B
enef
icia
l Con
diti
on
1
2 3
4 5
6 7
8 9
10
11
12
13
14
15
16
17
(1) V
alen
ce
(man
ip)
μ =
1.00
σ
= .0
00
r
p
N
(2) M
agni
tude
(m
anip
) μ
= .5
43
σ =
.501
r .a
p
.
N
81
(3) A
ttri
buti
on
(man
ip)
μ =
.518
σ
= .5
03
r .a
.0
59
p
. .6
02
N
81
81
(4) O
wnO
ut
μ =
5.41
σ
= 1.
92
r .a
-.0
12
.063
p
. .9
15
.575
N
81
81
81
(5) O
ther
Out
μ
= 4.
84
σ =
1.78
r .a
-.0
69
.038
.3
52
p
. .5
39
.734
.0
01
N
81
81
81
81
(6) R
elat
iveO
ut
μ =
.568
σ
= 2.
11
r .a
.0
47
.025
.6
14
-.522
p
. .6
74
.822
.0
00
.000
N
81
81
81
81
81
(7) P
R μ
= 23
.51
σ
= 2.
51
r .a
.0
11
.104
.0
58
.080
-.0
14
p
. .9
24
.354
.6
05
.477
.8
98
N
81
81
81
81
81
81
(8) N
R μ
= 11
.96
σ
= 5.
80
r .a
-.0
27
-.031
-.1
17
-.110
-.0
14
.002
p
. .8
08
.784
.3
00
.330
.9
02
.987
N
81
81
81
81
81
81
81
(9) B
BD
μ =
8.51
σ
= 2.
27
r .a
.1
46
.096
.0
81
.036
.0
44
-.123
-.1
96
p
. .1
94
.393
.4
70
.752
.6
95
.272
.0
79
N
81
81
81
81
81
81
81
81
(10)
CBD
μ
= 10
.13
σ
= 2.
77
r .a
-.0
42
-.156
.1
81
.165
.0
26
-.012
-.1
99
.482
p
. .7
08
.164
.1
05
.142
.8
14
.915
.0
75
.000
N
81
81
81
81
81
81
81
81
81
(11)
Dep
ende
nce
μ =
18.6
4 σ
=
4.35
r .a
.0
49
-.049
.1
58
.124
.0
40
-.072
-.2
29
.829
.8
89
p
. .6
63
.662
.1
58
.272
.7
23
.523
.0
39
.000
.0
00
N
81
81
81
81
81
81
81
81
81
81
(12)
Rec
. Deb
t μ
= 9.
44
σ =
4.39
r .a
-.0
76
-.004
-.3
48
-.178
-.1
67
.034
.3
31
.032
.1
13
.088
p
. .4
99
.969
.0
01
.111
.1
36
.765
.0
03
.779
.3
17
.433
N
81
81
81
81
81
81
81
81
81
81
81
(13)
RD
Locu
s μ
= .8
31
σ =
9.04
6
r .a
.1
90
.162
.2
88
.333
-.0
03
-.007
-.1
07
.250
.0
88
.193
-.3
57
p
. .1
78
.251
.0
38
.016
.9
83
.963
.4
49
.074
.5
37
.170
.0
09
N
52
52
52
52
52
52
52
52
52
52
52
52
(14)
Loc
us
μ =
.269
σ
= .8
19
r .a
.1
04
.212
.3
51
.309
.0
80
.111
-.1
28
.203
.0
42
.138
-.5
08
.919
p
. .4
63
.131
.0
11
.026
.5
73
.432
.3
65
.148
.7
69
.328
.0
00
.000
N
52
52
52
52
52
52
52
52
52
52
52
52
52
(15)
Just
μ
= 18
.72
σ
= 5.
43
r .a
.0
24
.197
.4
61
.428
.0
60
.217
-.2
91
.007
-.0
75
-.044
-.3
94
.421
.4
38
p
. .8
28
.078
.0
00
.000
.5
97
.052
.0
08
.948
.5
03
.695
.0
00
.002
.0
01
N
81
81
81
81
81
81
81
81
81
81
81
81
52
52
(16)
aut
omak
er
resp
onsi
bilit
y μ
= 3.
90
σ =
1.14
r .a
.1
39
.178
.2
31
.209
.0
34
.206
-.0
37
-.147
-.1
07
-.145
-.1
15
.107
.1
62
.134
p
. .2
15
.111
.0
38
.062
.7
62
.065
.7
41
.190
.3
40
.195
.3
07
.450
.2
51
.233
N
81
81
81
81
81
81
81
81
81
81
81
81
52
52
81
(17)
oth
er
resp
onsi
bilit
y μ
= 2.
67
σ =
1.17
r .a
-.0
28
-.360
-.2
05
-.062
-.1
35
.120
-.1
28
.030
-.0
17
.005
-.1
33
.068
.0
93
-.001
-.3
16
p
. .8
02
.001
.0
66
.583
.2
30
.286
.2
56
.793
.8
82
.966
.2
37
.634
.5
13
.991
.0
04
N
81
81
81
81
81
81
81
81
81
81
81
81
52
52
81
81
(18)
dea
ler
resp
onsi
bilit
y μ
= 1.
89
σ =
1.05
r .a
-.0
98
-.055
-.0
52
-.104
.0
40
-.188
-.0
72
.062
-.1
15
-.041
-.0
80
.089
.0
33
.098
.0
43
-.203
p
. .3
85
.624
.6
47
.358
.7
22
.092
.5
26
.583
.3
08
.718
.4
76
.532
.8
19
.382
.7
02
.069
N
81
81
81
81
81
81
81
81
81
81
81
81
52
52
81
81
81
Ta
ble#
10 -
Mea
ns, S
tand
ard
Dev
iati
ons
and
Corr
elat
ions
for
Part
icip
ants
in t
he D
etri
men
tal C
ondi
tion
1 2
3 4
5 6
7 8
9 10
11
12
13
14
15
16
17
(1) V
alen
ce
(man
ip)
μ =
.000
σ
= .0
00
r
p
N
(2) M
agni
tude
(m
anip
) μ
= .4
57
σ =
.501
r .a
p
.
N
81
(3) A
ttri
buti
on
(man
ip)
μ =
.469
σ
= .5
02
r .a
.0
82
p
. .4
69
N
81
81
(4) O
wnO
ut
μ =
2.69
σ
= 1.
42
r .a
.1
48
.065
p
. .1
88
.562
N
81
81
81
(5) O
ther
Out
μ
= 3.
48
σ =
1.67
r .a
.1
52
-.094
.3
53
p
. .1
76
.406
.0
01
N
81
81
81
81
(6) R
elat
iveO
ut
μ =
-.7
90
σ =
1.77
r .a
-.0
25
.141
.4
68
-.662
p
. .8
26
.210
.0
00
.000
N
81
81
81
81
81
(7) P
R μ=
23
.51
σ
= 2.
30
r .a
-.0
07
.049
-.0
43
-.131
.0
89
p
. .9
51
.665
.7
00
.243
.4
29
N
81
81
81
81
81
81
(8) N
R μ=
11
.36
σ
= 5.
38
r .a
.0
86
.082
-.0
89
-.148
.0
69
-.055
p
. .4
48
.467
.4
28
.186
.5
42
.627
N
81
81
81
81
81
81
81
(9) B
BD
μ =
9.15
σ
= 2.
16
r .a
.1
93
-.081
-.1
00
-.031
-.0
51
.161
.0
01
p
. .0
85
.473
.3
74
.785
.6
50
.152
.9
96
N
81
81
81
81
81
81
81
81
(10)
CBD
μ=
10
.71
σ
= 2.
31
r .a
-.0
50
-.007
-.1
02
-.259
.1
63
.157
.0
16
.586
p
. .6
55
.952
.3
65
.019
.1
45
.161
.8
90
.000
N
81
81
81
81
81
81
81
81
81
(1
1) D
epen
denc
e μ=
19
.86
σ
= 3.
98
r .a
.0
75
-.048
-.1
13
-.167
.0
67
.178
.0
09
.882
.8
99
p
. .5
05
.672
.3
13
.135
.5
51
.111
.9
34
.000
.0
00
N
81
81
81
81
81
81
81
81
81
81
(12)
Rec
. Deb
t μ=
13
.41
σ
= 3.
55
r .a
.0
31
-.188
-.2
74
-.004
-.2
16
.091
.2
09
-.033
.0
04
-.015
p
. .7
83
.093
.0
13
.971
.0
53
.421
.0
61
.770
.9
69
.892
N
81
81
81
81
81
81
81
81
81
81
81
(13)
RD
Locu
s μ=
-10
.64
σ
= 7.
74
r .a
.0
58
.170
.1
20
-.103
.1
86
.015
.0
60
.068
-.0
09
.031
-.4
49
p
. .6
23
.148
.3
08
.385
.1
13
.896
.6
14
.562
.9
40
.790
.0
00
N
74
74
74
74
74
74
74
74
74
74
74
74
(14)
Loc
us
μ =
-.7
43
σ =
.550
r .a
.1
00
.175
.0
83
-.086
.1
42
.109
.1
24
.116
-.0
07
.058
-.2
40
.928
p
. .3
98
.135
.4
84
.465
.2
27
.354
.2
93
.323
.9
51
.624
.0
39
.000
N
74
74
74
74
74
74
74
74
74
74
74
74
74
(15)
Just
μ=
12
.19
σ
= 6.
21
r .a
.0
24
.194
.5
38
.293
.1
54
.098
-.2
95
.045
.0
20
.036
-.4
41
.212
.1
58
p
. .8
30
.082
.0
00
.008
.1
69
.385
.0
08
.693
.8
56
.750
.0
00
.070
.1
79
N
81
81
81
81
81
81
81
81
81
81
81
81
74
74
(16)
aut
omak
er
resp
onsi
bilit
y μ
= 4.
05
σ =
.947
r .a
-.1
01
-.102
-.4
35
-.196
-.1
63
.007
.1
34
-.101
-.1
07
-.117
.1
71
-.071
-.0
33
-.533
p
. .3
71
.366
.0
00
.079
.1
47
.952
.2
35
.370
.3
40
.298
.1
26
.549
.7
79
.000
N
81
81
81
81
81
81
81
81
81
81
81
81
74
74
81
(17)
oth
er
resp
onsi
bilit
y μ
= 2.
63
σ =
1.07
r .a
.0
16
-.022
.2
62
-.032
.2
40
.014
-.1
74
.042
.1
42
.105
-.2
21
-.007
-.1
19
.324
-.4
64
p
. .8
84
.848
.0
18
.777
.0
31
.900
.1
20
.707
.2
08
.350
.0
47
.955
.3
13
.003
.0
00
N
81
81
81
81
81
81
81
81
81
81
81
81
74
74
81
81
(18)
dea
ler
resp
onsi
bilit
y μ
= 1.
43
σ =
.790
r .a
-.0
94
.113
.2
43
.030
.1
67
.089
.0
11
-.058
.0
42
-.007
.1
12
.008
-.0
16
.182
-.2
46
.326
p
. .4
02
.316
.0
29
.792
.1
37
.428
.9
23
.608
.7
11
.950
.3
18
.948
.8
93
.104
.0
27
.003
N
81
81
81
81
81
81
81
81
81
81
81
81
74
74
81
81
81
71
Table#8, 9 and 10 (above) summarize means, standard deviations and correlations between the
research variables for the entire sample, participants in the beneficial and the detrimental
conditions respectively.
4.1.4.3 Confirmatory Factor Analysis
An overall measurement model including all six latent constructs (positive norm of
reciprocity – PR, negative norm of reciprocity – NR, benefit based dependence – BBD, cost based
dependence – CBD, reciprocal debt – RD and justifiability – Just) was estimated using
confirmatory factor analysis (CFA) in EQS 6.1 for windows. The measurement model was
estimated using maximum likelihood estimation with elliptical reweighted generalized least
squares (ERLS) because ERLS estimates are equivalent to maximum likelihood for normally
distributed data, and superior to ML for non-normal data (Zou and Cavusgil 2002).
The measurement model fit the data very well. All items loaded on the a priori factors,
with positive and significant variances, and factor loadings of at least 0.465. The measurement
model had acceptable fit statistics: χ2 = 363.132 (d.f. = 260), p < .001; Bentler-Bonnett Normed
Fit Index = .892; Bentler-Bonnett Non-Normed Fit Index = .961; CFI = .966; Standardized RMR =
.059; RMSEA = .042. Factor weighted combinations of the final scale items were used in
subsequent analyses based on the factor loadings in table#11 (next page). The combination of
the EFA’s already performed and these CFA’s provide solid evidence for the discriminant validity
of the latent constructs in this study.
T
able
#11
- CF
A R
esul
ts
Fact
ors
and
Item
Loa
ding
s
Item
It
em W
ordi
ng
Posi
tive
Re
cipr
ocit
y N
egat
ive
Reci
proc
ity
Bene
fit
Base
d D
epen
d.
Cost
Ba
sed
Dep
end.
Re
cipr
ocal
D
ebt
Just
ifiab
ility
pr1
Whe
n I p
rofit
from
the
actio
ns o
f som
eone
els
e, I
will
do
som
ethi
ng th
at b
enef
its h
im/h
er in
ret
urn.
0.
657
pr2
Whe
n so
meo
ne d
oes
me
a fa
vor,
I fe
el c
omm
itted
to
repa
y th
e fa
vor.
0.
830
pr3
If so
meo
ne d
oes
a fa
vor f
or m
e, I
will
ret
urn
the
favo
r.
0.49
7
pr4
I am
read
y to
incu
r pe
rson
al c
osts
to h
elp
som
eone
w
ho h
as h
elpe
d m
e.
0.62
0
pr5
I go
out o
f my
way
to h
elp
a pe
rson
who
has
bee
n ki
nd
to m
e.
0.64
2
pr6
I'm w
illin
g to
do
thin
gs I
don'
t enj
oy to
retu
rn
som
eone
's a
ssis
tanc
e.
0.56
1
nr1
If so
meo
ne p
urpo
sely
inju
res
me,
I w
ill tr
y to
inju
re
him
/her
as
wel
l.
0.57
6
nr2
If so
meb
ody
take
s ac
tion
that
dam
ages
me,
I w
ill g
o to
gr
eat l
engt
hs to
infli
ct d
amag
e on
him
/her
.
0.53
0
nr3
If so
meb
ody
take
s ac
tion
that
dam
ages
me,
I w
ill g
o to
gr
eat l
engt
hs to
infli
ct d
amag
e on
him
/her
.
0.68
1
nr4
I am
will
ing
to in
vest
tim
e an
d ef
fort
to r
ecip
roca
te a
ha
rmfu
l act
ion.
0.90
7
nr5
If so
meo
ne p
uts
me
in a
diff
icul
t pos
ition
, I w
ill d
o th
e sa
me
to h
im/h
er.
0.
923
nr6
I will
be
unfa
ir to
som
eone
who
has
bee
n un
fair
to m
e.
0.
728
BBD
1 If
we
wer
e no
long
er a
XXX
dea
ler,
it w
ould
be
diff
icul
t to
mai
ntai
n ou
r cu
rren
t sal
es v
olum
e.
0.56
8
BB
D2
My
deal
ersh
ip re
ceiv
es b
enef
its a
s a
XXX
deal
er th
at
coul
dn't
be fu
lly d
uplic
ated
by
anot
her
auto
mak
er.
0.46
5
BBD
3 If
my
deal
ersh
ip s
topp
ed s
ellin
g XX
X, o
ur o
ther
opt
ions
w
ould
not
be
as p
rofit
able
.
0.
652
CBD
1 M
y de
aler
ship
wou
ld in
cur m
any
cost
s to
end
its
busi
ness
rel
atio
nshi
p w
ith X
XX a
nd s
witc
h to
ano
ther
au
tom
aker
.
0.55
7
CBD
2 It
wou
ld b
e co
stly
for m
y de
aler
ship
to s
earc
h fo
r an
d lo
cate
an
alte
rnat
ive
auto
mak
er to
repl
ace
XXX.
0.76
4
CBD
3 It
wou
ld b
e co
stly
for m
y de
aler
ship
to e
nd it
s re
latio
nshi
p w
ith X
XX.
0.
501
RD1
An
oblig
atio
n ha
s be
en c
reat
ed th
at s
houl
d be
rep
aid.
0.
768
RD2
Som
ethi
ng s
houl
d be
don
e to
mak
e up
for
this
si
tuat
ion.
0.
954
RD3
XXX'
s de
cisi
on h
as c
reat
ed a
n im
bala
nce
in o
ur
rela
tions
hip
that
nee
ds to
be
addr
esse
d.
0.82
8
Just
1 O
vera
ll, I
belie
ve th
at X
XX's
pri
cing
dec
isio
n is
co
mpl
etel
y ju
stifi
ed.
0.
893
Just
2 O
vera
ll, I
belie
ve th
at X
XX's
pri
cing
dec
isio
n is
ap
prop
riat
e un
der c
urre
nt c
ircu
mst
ance
s.
0.
874
Just
3 O
vera
ll, I
belie
ve th
at X
XX's
pri
cing
dec
isio
n is
a v
iabl
e st
rate
gic
optio
n by
XXX
.
0.93
9
Just
4 O
vera
ll, I
belie
ve th
at X
XX's
pri
cing
dec
isio
n is
a g
ood
busi
ness
dec
isio
n.
0.
948
χ2 =
363.
132
(d.f.
= 2
60),
p <
.001
Be
ntle
r-Bo
nnet
t Nor
med
Fit
Inde
x =
.892
; Ben
tler-
Bonn
ett N
on-N
orm
ed F
it In
dex
= .9
61
CFI =
.966
; Sta
ndar
dize
d RM
R =
.059
; RM
SEA
= .0
42
74
4.1.4.4 Manipulation Checks
Valence
Valence was the first manipulation in the scenario. In this analysis, valence is coded as 1
for beneficial scenarios and 0 for detrimental scenarios. We should expect that participants
would associate the beneficial valence condition with greater own outcomes, and also greater
relative outcomes compared to their automaker. To test that this manipulation had the
hypothesized effect, 2 ANOVA’s were performed using PASW 18.0 with relative outcomes (RO)
and own outcomes (OwnOut) as the dependent variables. Table #12 below shows that both
ANOVA’s were significant (OwnOut – F = 104.637, d.f. = 161, p < .001; RO – F = 19.678, d.f. =
161, p < .001), and figure #10 below shows that the manipulation had the desired effect – when
presented with a beneficial scenario participants perceived greater own outcomes and greater
relative outcomes than when given the detrimental scenario. Therefore, it appears that the
valence of the action was adequately manipulated.
Table #12 – ANOVA results for OwnOut and RO Valence manipulation checks
Sum of Squares df Mean Square F Sig.
OwnOut Between Groups 298.765 1 298.765 104.637 .000 Within Groups 456.840 160 2.855 Total 755.605 161
RO Between Groups 74.691 1 74.691 19.678 .000 Within Groups 607.309 160 3.796 Total 682.000 161
75
Figure #10 – Plots of mean differences in OwnOut and RO based on the Valence manipulation
Magnitude
The magnitude manipulation may not be as straight forward to check as was the
valence. It is unclear that we would expect similar patterns of results from the high magnitude –
beneficial valence condition as we would from the high magnitude – detrimental valence
condition. Given this concern, it may be valuable to split the sample and investigate participants
with a beneficial manipulation separately from those with a detrimental manipulation. ANOVA’s
performed with the entire sample, and again with a split sample based on the valence of the
manipulation failed to produce any significant differences in own outcomes, relative outcomes
nor in the measure of reciprocal debt. This suggests that the magnitude manipulation did not
successfully manipulate participants’ perceptions of the magnitude of an action. Magnitude will
not be considered further in this analysis.
Attribution of Control
Three questions were asked of participants about the control of the locus of
responsibility for the strategic decision made in the scenario. They were asked to rate the level
of responsibility that the (1) automaker, (2) their own dealership, and (3) external forces had for
the pricing decision they saw in the scenario. If the attribution manipulation were successful,
we would expect to see higher levels of responsibility assigned to the automaker in the
76
“Internal” (coded 1) condition than in the “external” (coded 0) condition. We also would expect
to see the reverse attributions with respect to (3) the responsibility of external forces. Table
#12a below shows that one of these ANOVA’s supports our expectations.
Table #12a – ANOVA results for automaker and other – attribution manipulation checks
Sum of Squares df
Mean Square F Sig.
automaker Between Groups .390 1 .390 .356 .552 Within Groups 175.511 160 1.097 Total 175.901 161
other Between Groups 7.870 1 7.870 6.522 .012 Within Groups 193.074 160 1.207 Total 200.944 161
When participants were in the internal-attribution condition, they assigned less responsibility to
external (other) sources than when they received the external-attribution condition (F=6.522,
d.f. = 161, p = .012). Figure #11 below shows that directional support was given by the
responsibility attributions for the automaker; however the differences were not significant.
Figure #11 – Plots of mean differences in automaker and other based on the attribution manipulation
While the evidence is somewhat inconclusive, the directional support from automaker
combined with the significant differences in attributions of responsibility to external sources
provides some support that the attribution of control was successfully manipulated.
77
4.1.5 Hypothesis Testing
4.1.5.1 Effects of Partner Actions on Reciprocal Debts
The first hypotheses concern the impact of the automaker’s beneficial or detrimental
actions on the participant's perception of reciprocal debts. Specifically:
H1: Beneficial actions by a selling firm will lead to a positive (participant) reciprocal debt felt by their customer. H3: Detrimental actions by a selling firm will lead to a negative (automaker) reciprocal debt felt by their customer.
As has been previously discussed, the term “participant reciprocal debt” is used instead of
“positive reciprocal debt” because it is assumed that in response to a beneficial action, the
participant will perceive that they owe their partner something. Likewise, the “automaker
reciprocal debt” implies that the participant perceives their automaker to owe them something
in the future. I examine these hypotheses using various analyses, first focusing on the locus of
reciprocal debt and then examining the level or intensity of the reciprocal debt.
Locus of Reciprocal Debt
An ANOVA was performed to investigate the participant’s perceptions of locus based on
the valence of the condition they were exposed to. The ANOVA was significant (see table #13
below – F = 68.888, d.f. = 125, p < .001), and the difference in mean locus (Locus) was in the
expected direction. When participants received a detrimental scenario they had significantly
lower average locus score (-0.743) than when they received a beneficial scenario (0.269),
suggesting that car dealership managers were more likely to perceive that their dealership owed
the automaker when presented with a beneficial outcome for their dealership, and also that
they were more likely to expect that the automaker should make up the difference when they
had made a decision that negatively impacted the dealership.
78
Table #13 – Mean difference in Locus based on valence manipulation
Sum of Squares df Mean Square F Sig.
Between Groups 31.306 1 31.306 68.888 .000 Within Groups 56.352 124 .454 Total 87.659 125
While the ANOVA provides some support for the expected relationship between valence
and locus, regression analyses can be used to examine the specific impact of a given variable on
the participant’s perception of the locus of the debt. A regression was performed with Locus as
the dependent variable. Locus was regressed on the three dichotomous manipulation variables
– Valence, Magnitude and Attribution. While only the valence manipulation needs to be
included to test H1 and H3, the magnitude and attribution variables were included as controls to
examine if the impact of the valence manipulation was significant controlling for the other 2
manipulations. Support for H1 and H3 would be provided by a positive and significant impact of
Valence on Locus. The overall model is significant (F = 25.527, d.f. = 125, p < .001), explaining
approximately 37% of the variance in the Locus (adj R2 = .371). Table #14 shows the estimated
coefficients, importantly the coefficient for Valence (standardized β = .572) is positive and
significant (t = 7.935, p < .001) suggesting that those subjected to the beneficial manipulation
were more likely to assume that their own dealership owed the automaker in the future.
Table #14 – Coefficients from regression of Locus on manipulation factorsa Model
Unstandardized Coefficients Standardized Coefficients
t Sig. B Std. Error Beta 1 (Constant) -.912 .108 -8.429 .000
Valence .970 .122 .572 7.935 .000 Magnitude .122 .120 .073 1.013 .313 Attribution .250 .119 .150 2.096 .038
a. Dependent Variable: Locus
79
Interestingly, the attribution manipulation appears to impact the locus as well (standardized β =
.150, t = 2.096, p < .038) which means that when participants were given the internal attribution
condition, they were more likely to perceive the locus of the reciprocal debt with their own
dealership, rather than assign it to the automaker.
Thus, H1 and H3 receive support regarding the essential locus of the reciprocal debt.
Level or Intensity of Reciprocal Debt
It was expected that the manipulation of the magnitude of the automaker’s beneficial or
detrimental action would be associated with a greater level or intensity of reciprocal debt. But
greater intensity or level of a participant reciprocal debt is not equivalent to lower automaker
reciprocal debt. As it is possible that a participant reciprocal debt may not be conceptually and
functionally the converse of an automaker reciprocal debt, pooling the data across the two
valence conditions could mask differences or effects unique to each condition. That is, a simple
magnitude main effect would be unlikely, even if magnitude had been effectively manipulated.
Given that magnitude was not effectively manipulated, the participants’ perception of the
impact of the action on their own outcomes (OwnOut) is a theoretically sound proxy for the
level or intensity of the action in the scenario. Thus, I examine the impact of OwnOut on the
amount and locus (RDLocus) of reciprocal debt created. Figure #12 below shows a scatterplot of
the RDLocus values based on OwnOut perceptions.
80
Figure #12 – Scatterplot of RDLocus per OwnOut
RDLocus was regressed on OwnOut using PASW 18.0. Support for H1 and H3 would be
found by a significant positive coefficient of OwnOut on RDLocus. The regression was significant
(F = 38.716, d.f. = 125, p < .001) and explained approximately 23% of the variance in RDLocus
(adj R2 = .232). Table #15 shows that the standardized coefficient for OwnOut was .488
(t=6.222, p < .001).
Table #15 – Coefficients from regression of RDLocus on OwnOuta Model
Unstandardized Coefficients Standardized Coefficients
t Sig. B Std. Error Beta 1 (Constant) -14.404 1.575 -9.148 .000
OwnOut 2.333 .375 .488 6.222 .000 a. Dependent Variable: RDLocus
Overall, both ANOVA based analysis of the locus variable, and regression based analysis
of the RDLocus variable have provided support for H1 and H3. Thus we can conclude that when
a beneficial action is perceived, the participant believed they owed their automaker. Further
the greater benefits they perceived from the action translated into a greater debt they felt they
81
owed the automaker. Conversely, detrimental actions resulted in the participant’s being more
likely to believe the automaker owed them in the future, and the more they felt slighted by the
automaker’s decision, the greater the intensity of reciprocal debt they perceived.
4.1.5.2 Moderating Impacts on the Creation of Reciprocal Debt
The hypotheses about moderating impacts of various variables were tested using
multiple procedures. Regressions were run with interaction terms for each of these moderating
variables proposed in the model. Unfortunately, none of the moderators were significant.
Interaction terms for each of these variables were multiplied by the OwnOut variable to test the
hypothesized moderation. The following regression equation was used to test these moderating
hypotheses:
RDLocus = α + β1OwnOut + β2Xi + β3XiOwnOut + e
Where xi = the proposed moderating variables
Feasible variations of this regression were also tested by dropping the xi term and only testing
the interaction. Additionally, the sample was split based on the valence manipulation to
investigate if there were differential effects of any of the variables for the detrimental
conditions than there were for the beneficial conditions. In no case were any of the interaction
terms significant. A stepwise regression was performed with all of the interaction terms and
main effects terms, only 2 variables were found to significantly impact the creation of RDLocus –
Valence and Justifiability.
82
Table #16 – Tests of Between-Subjects Effects Dependent Variable:RDLocus Source Type III Sum
of Squares df Mean Square F Sig. Corrected Model 2934.194a 3 978.065 12.394 .000 Intercept 1715.297 1 1715.297 21.736 .000 OwnOutHi 286.896 1 286.896 3.635 .059 JustHi 694.201 1 694.201 8.797 .004 OwnOutHi * JustHi 134.917 1 134.917 1.710 .193 Error 9627.749 122 78.916 Total 16953.669 126 Corrected Total 12561.943 125 a. R Squared = .234 (Adjusted R Squared = .215)
After completing the regression analyses described above, all of the proposed
moderators were tested with ANOVA’s after median splitting the moderator variables. In no
case were any of the moderators significant in predicting the mean difference of RDLocus.
Justifiability had a direct effect on RDLocus, table #16 (above) shows the results of a General
Linear Model explaining the variance in RDLocus based on OwnOutHi – a variable created by
performing a median split on the OwnOut measure, JustHi – median split on justifiability, and
the interaction of those 2 dichotomous variables. As can be seen, both variables have a
significant main effect (OwnOutHi F = 3.635, ρ = .059; JustHi F = 8.797, ρ = .004) however the
interaction term fails to reach significance (F = 1.71, ρ = .193). Planned contrasts showed that
when justifiability was high (JustHi = 1), the link between OwnOut and RDLocus was significantly
different (F = 8.797, ρ = .004), providing some support for the moderating impact of justifiability
(see tables #17 and 18 below), however as was already discussed, the interaction term in the
General Linear Model was not significant (see table #16 above).
83
Table #17 – Planned Contrast Results (K Matrix) Justifiability JustHi Simple Contrasta Dependen
t Variable RDLocus
Level 1 vs. Level 2 Contrast Estimate -7.047 Hypothesized Value 0 Difference (Estimate - Hypothesized) -7.047 Std. Error 2.376 Sig. .004 95% Confidence Interval for Difference
Lower Bound -11.751 Upper Bound -2.344
a. Reference category = 2
Table #18 - Test Results for planned contrast Dependent Variable:RDLocus Source Sum of
Squares df Mean Square F Sig. Contrast 694.201 1 694.201 8.797 .004 Error 9627.749 122 78.916
Figure #13 below illustrates the difference in RDLocus given different values for the OwnOutHi
and JustHi variables.
Figure #13 – Estimated Marginal Means of RDLocus
84
Therefore, none of the moderating hypotheses can be supported. Possibly a larger sample size
or more diverse group of respondents might help induce some variance which would help
detect the proposed relationships. A summary of the hypotheses tested in this section is
provided in table #19.
Hypothesis Hypothesis Wording Supported?
H1 Beneficial Action (+) → Customer Reciprocal Debt H3 Detrimental Action (+) → Seller Reciprocal Debt
H5aParticipant Relative Benefits moderates (+) link between Beneficial Action and Customer Reciprocal Debt
H5bPartner Relative Benefits moderates (+) the link between Detrimental Action and Seller Reciprocal Debt
H6aExternal Attribution of Control moderates (-) the link between Beneficial Action and Customer Reciprocal Debt
H6bExternal Attribution of Control moderates (-) the link between Detrimental Action and Seller Reciprocal Debt
H7Perceived Justifiability moderates (-) the link between Detrimental Action and Seller Reciprocal Debt
H8aPositive Norm of Reciprocity moderates (+) the link between Beneficial Action and Customer Reciprocal Debt
H8bNegative Norm of Reciprocity moderates (+) the link between Detrimental Action and Seller Reciprocal Debt
H9aBenefit-Based Dependence moderates (-) the link between Beneficial Action and Customer Reciprocal Debt
H9bCost-Based Dependence moderates (-) the link between Detrimental Action and Seller Reciprocal Debt
H10Benefit-Based Dependence enhances the moderating impact of Cost-Based Dependence
Table #19 - Car Dealer Survey Hypothesis Summary
4.1.6 Discussion
With such disappointing results from the analyses of moderation hypotheses, it seems
appropriate to address some possibilities that may exist in the current data, or may be
possibilities for future research. While this discussion section addresses theory issues that were
not proposed in my dissertation, it seems valuable to at least try to make some sense of the
85
non-findings. In that spirit, three possible future directions will be discussed that might help
explain the lack of findings of this research.
The first topic can be addressed with data collected in this survey. This research was
designed with the idea or plan of investigating variables that would enhance feelings of
reciprocal debt – debt owned by either the respondent, or their business partner. The
hypotheses were mostly in line with that thinking. It seems logical that if people have a greater
personal norm of positive reciprocity, they would be likely to respond more strongly when
someone does something that benefits them. This thinking assumes that the baseline response
for business partners is minimal, or non-existent. Only when the large group of moderating
variables line up to enhance the reciprocal debt will we see greater magnitude or strength of
response. Alternatively, it could be that the baseline, or norm, for these partners is a
reasonable reciprocal response. This alternative assumption suggests that the moderators
proposed in this research really don’t do much to enhance the reciprocal response because the
reciprocal response will already be substantial. If this is the case, it might be valuable to look at
variables that might minimize the need or desire for partners to reciprocate. For instance, when
the respondent has a great deal of trust in their automaker, maybe the detrimental action will
not be reciprocated. This suggests that some relational variables may actually diminish negative
reciprocation between business partners. Other variables that might exhibit similar diminishing
effects on reciprocation might include: commitment, loyalty, and the duration of the
relationship. All of these variables were measured in this survey. Analyses will be conducted in
the future to examine if these relational variables have any moderating impact on the creation
of reciprocal debt that might help better explain differences in reciprocal responses.
A second topic came up during the planning of this research that was not addressed in
these analyses. One challenge that was originally conceptualized with this research was the
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difficulty in finding specific reciprocal responses within an ongoing relationship. One reason this
is problematic is that it is possible that an existing reciprocal debt in the relationship might
impact future responses to impactful actions. Secondly, and more importantly, as a relationship
develops and becomes more relational in nature, it is expected that the relationship partners
will focus less and less on responding to specific actions but instead view the relationship in its
entirety. Actions that are within some normal operating range will be basically overlooked as
they are viewed as just a part of the relationship. In this case, it would take a major event that
might alter the dynamics of the relationship in order to capture substantial instances of
reciprocation. It is possible that our scenarios were not viewed as any major event by the car
dealers. Some evidence for this argument exists in the fact that the magnitude manipulation
appeared to be totally unsuccessful in affecting the participants. While a number of brief
interviews were conducted with car dealership managers during the planning and development
of this research, it is possible that a better – more impactful – scenario might be able to better
capture reciprocal responses in this context.
Finally, the car dealership – automaker context was probably not the best context for
this research. Originally we had planned on doing this survey in a B2B setting that involved large
investments of personal interactions by both parties – a marketing firm and their customers.
That context was probably a much better context to study the reciprocation of business partners
as more interpersonal relationships would be created as members of both firms had to work in
close proximity, and rely on members of the other firm to accomplish their own goals. It is
unclear in the automotive industry how prevalent interpersonal relationships are between car
dealer managers and automaker representatives. If these interpersonal relationships are not
perceived as being important for the entities involved, then the likelihood of finding much
variance in the response to an impactful action would be much lower. We knew from the
87
beginning that contractual relationships between car dealerships and auto manufacturers would
not be the best context for this research; however the timing and costs of finding a more fitting
context (after our original company fell through) were prohibitive for this research. Ideally a
B2B services setting would be explored where interpersonal relationships were more common
and more important to the functioning of the relationship.
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4.2 Laboratory Experiment
4.2.1 Research Model
Figure #14 below illustrates the research model tested with the laboratory experiment.
The primary purpose of this research is to test the mediation of reciprocal debt leading to
changes in partner outcomes.
Figure #15 (next page) summarizes the stages of the laboratory experiment –
Preliminary Procedures, Game One – Dictator Game, and Game Two – Investment Game. Each
stage involved several steps and points of data capture, which are clearly illustrated in Figure
#15. Descriptions of each of these stages, and the data collected follow Figure #15.
Partner Action
Recipient Relative
Outcomes
Participant Reciprocal Debt
Partner Outcomes
Recipient Norm of Reciprocity
Figure #14 – Experimental Research Model Tested
Prel
imin
ary
Proc
edur
es
Pre-
expe
rim
enta
l Q
uest
ionn
aire
Gam
e 2
Dir
ectio
ns
Gam
e 2
Allo
catio
n D
ecis
ion
Acce
pt/R
ejec
t Al
loca
tion
Figu
re #
15:
Expe
rim
enta
l Pro
cess
Flo
wch
art
Gam
e 1
Allo
catio
nEx
peri
men
ter
Act
ion
Part
icip
ant
Act
ion
Post
Gam
e 1
Que
stio
nnai
re
Dat
a Co
llect
ed
•Nor
m o
f Pos
itive
Re
cipr
ocity
•Nor
m o
f Neg
ativ
e Re
cipr
ocity
•Acc
epta
nce
•Ow
n Im
pact
•Par
tner
Impa
ct•R
elat
ive
Out
com
es•R
ecip
roca
l Deb
t•L
ocus
of D
ebt
•Par
tner
At
trac
tiven
ess
•Par
tner
Out
com
es
90
Preliminary Procedures
Random Assignment to Conditions. Each participant was randomly assigned to one of 4
conditions in the 2 [valence: detrimental vs. beneficial] X 2 [magnitude: low vs. high] full
factorial experimentation design. A total of 175 undergraduate students participated in this
experiment for extra credit. Figure #16 below summarizes the random assignment.
Low (0) High (1)
n = 42 n = 45
(females = 20) (females = 20)
(males = 22) (males = 25)
n = 44 n = 44
(females = 29) (females = 17)(males = 15) (males = 27)
N = 175
Random Assignment to Conditions
Figure #16
Beneficial (+1)
MAGNITUDE
VALENCE
Detrimental (-1)
The participants entered the experimental lab and took their seats.
Preliminary Questionnaire. First, participants completed the pre-experiment questionnaire--a
battery of questions capturing demographic information, their adherence to the positive norm
of reciprocity and their adherence to the negative norm of reciprocity (same items as prior
description).
Preliminary Instructions. Participants were told:
• they would be engaging in a series of bargaining, negotiation, and decision-making games; • with one randomly assigned Game Partner among the other participants in the same room; • they would interact with that paired Game Partner via a computer-mediated exchange that would record their decisions;
91
• although the procedures for each game would vary, each game would begin with one Game Partner being given $10 and that each round would involve an allocation offer splitting that $10 from one Game Partner and either acceptance or rejection of that offer by the paired Game Partner; • although the participants were asked to accept or reject the initial allocation, the only clear explanation of the ramifications of rejecting the allocation was that neither party would get any of the $10 during the first game; • the number of rounds within each game could vary from only one round up to several rounds and that neither Game Partner knew when each game would end; • the amount of money the participant had when each game ended would be banked; and • the primary objective was to maximize the amount banked across all games.
The interactions were actually generated by the experimental software (MediaLab v2008).
Participants engaged in two distinct games--first, the Dictator Game in which the mythical
counterpart was the dictator and second, the Investment Game in which the participant made
the opening allocation decision. Each game will be discussed below.
Game One - Dictator Game
Background Information. Each participant was told that:
• His/her counterpart had been given $10 with instructions to divide that sum between himself/herself and the paired Game Partner, i.e. the participant. The Game Partner would make the Initial Allocation Offer. • The participant would then make his or her Offer Response -- whether to accept or reject that offer. • An initial allocation offer and subsequent offer response constituted one round of the Dictator Game. Participants were told the game was over after 1 round.
Manipulations: Partner's Initial Allocation Offer. The Dictator Game began. After a delay of 20
seconds – during which the participant was told their Game Partner was making their allocation
decision -- each participant received an initial allocation offer based on the condition to which
they were randomly assigned, as indicated in Figure #17 below.
92
Low (0) High (1)
Participant: $3 Participant: $1
Game Partner: $7 Game Partner: $9
Participant: $7 Participant: $9
Game Partner: $3 Game Partner: $1
Figure #17Manipulations: Partner's Initial Allocation Offer
MAGNITUDE
VALENCE
Detrimental (-1)
Beneficial (+1)
Thus, in the detrimental action conditions, it appeared that the Game Partner kept the majority
of the $10; the participant was offered less than the Game Partner would keep. In the beneficial
action conditions, it appeared that the Game Partner allocated the majority of $10 to the
participant. Two levels of the magnitude of the disparity were examined. Ultimately, each
participant received an initial allocation offer from the Game Partner of either:
$9 (Game Partner would keep $1) in the high/beneficial condition, $7 (Game Partner would keep $3) in the low/beneficial condition, $3 (Game Partner would keep $7) in the low/detrimental condition, or $1 (Game Partner would keep $9) in the high/detrimental condition.
Offer Response. After being presented with the proposed allocation, the participant was asked
to either accept or reject the offered split. The software captured the participant's offer
response -- acceptance or rejection.
Post-Game Questionnaire. Following the offer response, each participant completed a post-
game questionnaire assessing perceptions about the positive or negative impact of the
proposed division of $10 and about the reciprocity debt created by this interaction.
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The participant was then told that the first game was over and the participant and Game Partner
would bank the funds resulting from Game 1. Game 2 would begin fresh with a new $10
available for allocation.
Game Two -- Investment Game
As with the first game, Game 2 involved splitting $10 between the paired game
partners. Unlike in Game 1, where the Game Partner made the initial allocation, in Game 2, the
participant had control of the initial allocation. The participant could divide the $10 in any way
desired, knowing that the Game Partner would then be able to return a portion of their
allocation to the participant. Although Game 2 continued after this point, the final measure
taken was the participant's allocation decision. The amount the participant allocated to the
Game Partner is the focal dependent variable in this experiment -- partner outcomes.
4.2.2 Operationalizations
Measures were captured by the experimental software and included both attitude
scales and reports of decisions/actions made by the participant.
Preliminary Questionnaire
The preliminary questionnaire included a battery of questions capturing demographic
information, the participants’ adherence to the positive norm of reciprocity and their adherence
to the negative norm of reciprocity. Items were adapted from Perugini et al (2003) to capture
both the positive and negative norms of reciprocity. 6 items were used to capture the
participant's adherence to the positive norm of reciprocity (pr1 - pr6) and to the negative norm
of reciprocity (nr1 - nr6) using 7-point Likert scales ranging from 1 (Strongly Disagree) to 7
(Strongly Agree) with “Neither Disagree nor Agree” labeling the midpoint.
Positive Norm of Reciprocity
1. When I profit from the actions of someone else, I will do something that benefits him/her in return.
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2. When someone does me a favor, I feel committed to repay the favor. 3. If someone does a favor for me, I will return the favor. 4. I am ready to incur personal costs to help someone who has helped me. 5. I go out of my way to help a person who has been kind to me. 6. I'm willing to do things I don't enjoy to return someone's assistance.
Negative Norm of Reciprocity
1. If someone purposely injures me, I will try to injure him/her as well. 2. If somebody takes action that damages me, I will go to great lengths to inflict damage on him/her. 3. If somebody is impolite to me, I become impolite in return. 4. I am willing to invest time and effort to reciprocate a harmful action. 5. If someone puts me in a difficult position, I will do the same to him/her. 6. I will be unfair to someone who has been unfair to me.
Allocation Response -- Offer Acceptance or Rejection
Each participant's response to the Game Partner's initial allocation in Game 1(Dictator Game)
was recorded. The participant's affirmative response (acceptance of the offer) was coded "1,"
while a negative response (rejection of the offer) was coded "0."
Post-Game Questionnaire
Immediately following the participant's acceptance response, the participant completed a
questionnaire assessing perceptions and reactions regarding the Game Partner's actions and
their own reactions.
Partner Actions
Each participant assessed the impact of the Game Partners’ actions during Game 1 on the
participant's outcome and the partner’s outcomes by completing two questions using 7-point
Likert scales.
• Own Impact (OwnImpact): How have the outcomes of this game affected you? [1 = “Very negative effect on me;” 7 = “Very positive effect on me”] • Partner Impact (PartnerImpact): How do you think the outcomes of this game affected your partner? [1 = “Very negative effect on my partner;” 7 = “Very positive effect on my patner”] • Relative Outcomes (ROut) were computed by subtracting PartnerImpact from OwnImpact. As the two items were assessed consecutively on consistent scales, I believe
95
that this calculated variable inherently reflects the participant's perception of the extent to which his/her outcomes were relatively greater or lower than the partner's.
Reciprocal Debts
Extant reciprocity literature was referenced to help develop items to assess the participant's
perceptions regarding reciprocity debts for this research. A large pool of possible scale items
was developed to capture the perceived reciprocal debt. After further consideration and a
pretest to examine measurement properties, a smaller set of measures was selected to assess
these latent constructs. Three items capturing reciprocal debt were measured using 7-point
Likert scales [1 = "Strongly Disagree;" 4 = "Neither Disagree nor Agree;" 7 = "Strongly Agree"]
and one item assessed the locus of the debt using forced choice.
• Existence of Reciprocal Debt (RD): Indicate your agreement or disagreement with the following statements.
1. An obligation has been created that should be repaid. 2. Something should be done to make up for this situation. 3. This game has created an imbalance between my partner and myself that needs to be addressed.
• Locus of Reciprocal Debt (Locus): Given the outcome of the game you just played, which statement below best captures your opinion of what should happen?
1. My partner should do something to balance out the outcomes. [coded -1] 2. I should do something to balance out the outcomes. [coded +1] 3. Neither my partner nor I need to do anything to balance out the outcomes. [coded 0]
Directional Reciprocal Debt (RecipDebt) was calculated by multiplying the multi-item latent
reciprocal debt measure by the coded locus dummy variable. This variable represents the
intensity and directionality of the perceived reciprocal debt, ranging from strong participant
reciprocity debt (at high positive values of this variable) to strong partner reciprocity debt (at
low negative values of this variable).
Partner Attractiveness
Four items measured the perceived the attractiveness of the Game Partner. These items were
created for this research, and were designed to capture the degree to which a participant liked
96
their partner. Participants rated their agreement or disagreement with all items on 7-point
Likert scales [1 = "Strongly Disagree;" 4 = "Neither Disagree nor Agree;" 7 = "Strongly Agree"].
1. I could see myself becoming good friends with this person. 2. I think this person and I are a lot alike. 3. I like my partner very much. 4. I think my partner is nice.
Participant's Allocation Decision in Game 2 -- Partner Outcomes
After reciprocal debt was measured, the participant began Game 2 with the same Game
Partner. If a reciprocal debt was created during Game 1, the participant's control of the initial
allocation decision in Game 2 provides an opportunity for the participant to resolve that
reciprocal debt. Therefore, the $ amount allocated to the partner is used to measure partner
outcomes (PartnerOut).
4.2.3 Sample Selection
Participants were undergraduate students at a Midwestern University. Participants
received extra credit in a marketing class for their participation in the study. Three separate
classes were recruited to participate in these experiments.
4.2.4 Measurement Model and Scale Creation
4.2.4.1 Exploratory Factor Analyses
Exploratory factor analyses using SPSS 17.0 was conducted with all 4 latent constructs
(positive norm of reciprocity, negative norm of reciprocity, reciprocity debt and partner
attractiveness). The analysis summarized in Table #20 revealed 5 factors with all items loading
on the a priori latent constructs except for the positive norm of reciprocity, which loaded onto 2
factors. Follow-up EFA on each separate construct summarized in Table #21 confirmed
unidimensionality for these 4 latent constructs. Again, the analysis of the items assessing the
positive norm of reciprocity revealed 2 factors with items 1-3 loading on one factor and items 4-
97
6 loading on a separate factor. After reviewing the items for the positive norm of reciprocity, it
is not surprising that the factor structure exhibits some multi-dimensionality. Items 1-3 are
much more concrete in nature, specifying particular responses to particular actions by an
exchange partner, while items 4-6 are much more subjective in nature – e.g., “I’m willing to do
things I don’t enjoy to return someone’s assistance”. Although further analysis is needed to
determine the best factor structure for the positive norm of reciprocity, I conclude that the EFAs
provide strong evidence for the discriminant validity of the latent constructs, but confirmatory
factor analyses were conducted to further examine the measures.
1 2 3 4 5
Eigenvalue 3.842 3.551 2.873 1.412 1.022
nr1 .737nr2 .822nr3 .622nr4 .775nr5 .793nr6 .758pr1 .726pr2 .733pr3 .779pr4 .536 .501pr5 .850pr6 .829rd1 .646rd2 .755rd3 .871pa1 .865pa2 .785pa3 .910pa4 .881
a. Rotation converged in 5 iterations.
Table #20: Combined EFA on 4 Latent Constructs
Rotated Component Matrixa
Component
Extraction Method: Principal component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
98
Eigenvalue 3.842 3.056 2.871 1.023 1.975% Var. Extracted 57.23% 76.41% 47.85% 17.06% 65.84%# of factors with Eigenvalue > 1
1 1 1 of 2a 2 of 2a 1
nr1 .724nr2 .812nr3 .637nr4 .766nr5 .807nr6 .779pr1 .732pr2 .759pr3 .783pr4 .582pr5 .876pr6 .839rd1 .811rd2 .838rd3 .784pa1 .893pa2 .786pa3 .918pa4 .893
Table #21: Summary of Individual EFA's for 4 Latent Constructs
Extraction Method: Principal component Analysis.
a. Rotation Method: Varimax with Kaiser Normalization.
4.2.4.2 Confirmatory Factor Analyses
An overall measurement model including all four latent constructs (positive norm of
reciprocity - PR, negative norm of reciprocity - NR, reciprocal debt - RD and partner
attractiveness - PA) was estimated using confirmatory factor analysis (CFA) in EQS 6.1 for
windows. This measurement model was estimated using maximum likelihood estimation with
elliptical reweighted generalized least square (ERLS) because ERLS estimates are equivalent to
ML for normally distributed data, and superior to ML for non-normal data (Zou and Cavusgil
2002).
99
Upon examination of the initial CFA, scales were purified to develop more internally
consistent and valid measures of the latent constructs. Four items were retained from the
original NR (nr1, nr2, nr4 and nr5) scale; nr3 and nr6 were eliminated due to factor loadings
below .5 or R2 of less than .3. Three items were retained from the PR scale (pr4, pr5 and pr6),
the three which loaded on the second factor in the exploratory factor analyses. While the
exploratory factor analyses appeared to suggest that pr1-pr3 might be the best items (explained
the most variance in the latent construct), pr1-pr3 were dropped due to R2 values of
approximately .3 and lower standardized factor loadings. While this was an iterative process,
the best fitting model used pr4-pr6. All three RD items were retained. Three of the four PA items
were retained; pa2 was dropped based on the modification indices. Further consideration of
the conceptual content of these items helped solidify the decision to drop pa2 as it asked a
substantively different question than the other three partner attractiveness items.
The final measurement model incorporating the final items for each construct had
acceptable fit statistics: χ2 = 120.82 (d.f. = 59), p < .001; Bentler-Bonnett Non-Normed Fit Index
= .903; CFI = .927; RMSEA = .078 (see table #22 below). All items loaded on the a priori factors,
with positive and significant variances, and factor loadings of at least .541. Factor weighted
combinations of the final scale items were used in subsequent analyses based on the factor
loadings in table #22.
Ta
ble
#22
– CF
A R
esul
ts
Fact
or L
oadi
ngs
Item
#
Item
Po
sitiv
e Re
cipr
ocity
N
egat
ive
Reci
proc
ity
Reci
proc
al
Deb
t Pa
rtic
ipan
t A
ttra
ctiv
enes
s pr
4 I a
m re
ady
to in
cur
pers
onal
cos
ts to
hel
p so
meo
ne w
ho h
as h
elpe
d m
e.
0.57
7
pr5
I go
out o
f my
way
to h
elp
a pe
rson
who
has
bee
n ki
nd to
me.
0.
785
pr
6 I'm
will
ing
to d
o th
ings
I do
n't e
njoy
to re
turn
som
eone
's a
ssis
tanc
e.
0.75
4
nr1
If so
meo
ne p
urpo
sely
inju
res
me,
I w
ill tr
y to
inju
re h
im/h
er a
s w
ell.
0.
754
nr2
If so
meb
ody
take
s ac
tion
that
dam
ages
me,
I w
ill g
o to
gre
at le
ngth
s to
in
flict
dam
age
on h
im/h
er.
0.
888
nr4
I am
will
ing
to in
vest
tim
e an
d ef
fort
to r
ecip
roca
te a
har
mfu
l act
ion.
0.67
2
nr
5 If
som
eone
put
s m
e in
a d
iffic
ult p
ositi
on, I
will
do
the
sam
e to
him
/her
.
0.62
7
rd
1 A
n ob
ligat
ion
has
been
cre
ated
that
sho
uld
be r
epai
d.
0.81
4
rd2
Som
ethi
ng s
houl
d be
don
e to
mak
e up
for
this
situ
atio
n.
0.69
8
rd3
This
gam
e ha
s cr
eate
d an
imba
lanc
e be
twee
n m
y pa
rtne
r an
d m
ysel
f th
at n
eeds
to b
e ad
dres
sed.
0.
541
pa1
I cou
ld s
ee m
ysel
f bec
omin
g go
od fr
iend
s w
ith th
is p
erso
n.
0.
834
pa3
I lik
e m
y pa
rtne
r ver
y m
uch.
0.90
8 pa
4 I t
hink
my
part
ner
is n
ice.
0.88
9
χ2
= 12
0.82
(d.f.
= 5
9), p
< .0
01
Be
ntle
r-Bo
nnet
t Non
-Nor
med
Fit
Inde
x =
.903
CFI =
.927
; RM
SEA
= .0
78
101
4.2.4.3 Manipulation Checks
An ANOVA was conducted using SPSS’s General Linear Model module to investigate if
the experimental conditions effectively manipulated the experimental variables of valence and
magnitude of the partner's actions. The participant's perceptions regarding the impact of the
partner's initial allocation in Game 1 was used as a manipulation check.
Valence Manipulation. With respect to all 3 measures (OwnImpact, PartnerImpact and
ROut), the valence manipulation had a significant main effect (see table #23). Thus, I
conclude that the valence of the partner's action was successfully manipulated.
Magnitude Manipulation. In contrast, magnitude had neither a direct nor indirect effect
on any of the three perceived outcome measures (see table #24). Thus, I conclude that
magnitude was not successfully manipulated.
Table #23 – ANOVA’s with the Valence manipulation as the independent variable.
Sum of Squares df Mean Square F Sig.
RD Between Groups 213.198 1 213.198 31.870 .000
Within Groups 1157.317 173 6.690
Total 1370.514 174
RDlocus Between Groups 7958.746 1 7958.746 206.900 .000
Within Groups 6654.717 173 38.467
Total 14613.463 174
OwnImpact Between Groups 287.344 1 287.344 204.928 .000
Within Groups 242.576 173 1.402
Total 529.920 174
PartnerImpact Between Groups 113.324 1 113.324 30.914 .000
Within Groups 634.184 173 3.666
Total 747.509 174
ROut Between Groups 761.573 1 761.573 135.724 .000
Within Groups 970.736 173 5.611
Total 1732.309 174
102
Table #24 – ANOVA’s with the Magnitude manipulation as the independent variable.
Sum of Squares df Mean Square F Sig.
RD Between Groups .718 1 .718 .091 .764
Within Groups 1369.796 173 7.918
Total 1370.514 174
RDlocus Between Groups 54.093 1 54.093 .643 .424
Within Groups 14559.370 173 84.158
Total 14613.463 174
OwnImpact Between Groups .003 1 .003 .001 .975
Within Groups 529.917 173 3.063
Total 529.920 174
PartnerImpact Between Groups .239 1 .239 .055 .814
Within Groups 747.269 173 4.319
Total 747.509 174
ROut Between Groups .295 1 .295 .030 .864
Within Groups 1732.013 173 10.012
Total 1732.309 174
This has two implications for hypothesis-testing analyses. First, as it is possible that partner's
actions could impact reciprocal debt formation or the participant's subsequent actions even
without conscious awareness of partner initiating action, I will use ANOVA to examine the
impact of both valence and manipulation factors. However, it could be argued that it is the
participant's perception of the partner's action that is critical, for those perceptions will
determine subsequent reactions. Participants' perceptions of the impact of their partner’s
action (OwnImpact) vary within the sample (μ = 4.76, σ2 = 3.046). Therefore, I will also examine
the participants' perceptions of the impact of the partner's actions (OwnImpact) on other
variables using regression analysis.
4.2.6 Hypothesis-Testing
Figures #18 and 19 illustrate the hypotheses tested in this experimental portion of the
research.
103
Effects of Partner Actions on Reciprocal Debts
The hypotheses were adapted to the experimental context, and hence the wording has
changed slightly, however they are substantively the same hypotheses as have been discussed
previously. The first hypotheses concern the impact of the partner's beneficial or detrimental
actions on the participant's perception of reciprocal debts. Specifically:
Beneficial Partner Action
Participant’s Positive Norm of
Reciprocity
Participant’s Reciprocal Debt
H1+ Partner Outcomes
H8a+
H2+
Participant’s Perceived Relative
Benefits
H5a+
Figure #18 – Conceptual Framework for Beneficial Actions
Detrimental Partner Action
Participant’s Negative Norm of Reciprocity
Partner’s Reciprocal Debt
Partner Outcomes
H3+
H8b+
H4-
Perceived Partner Relative
Benefits
H5b+
Figure #19 – Conceptual Framework for Detrimental Actions
104
H1: Beneficial partner actions will lead to a participant reciprocal debt.
H3: Detrimental partner actions will lead to a partner reciprocal debt.
As has been previously discussed, the term “participant reciprocal debt” implies that the
participant will perceive that they owe their partner something. Likewise, the “partner
reciprocal debt” implies that the participant perceives their partner to owe them something in
the future. I examine these hypotheses using various analyses, first focusing on the locus of
reciprocal debt and then examining the level or intensity of the reciprocal debt.
Locus of Reciprocal Debt
First, basic ANOVA’s were conducted examining the impact of the experimental factors
(valence and magnitude of partner actions) on the locus of the reciprocal debt which ranges
from participant's debt (+1) to partner's debt (-1). As can be seen in Figure #20, participants in
the detrimental condition (valence = -1) were much more likely to assign a reciprocal debt to
their partner (locus = -1), while those in the beneficial condition (valence = 1) assumed that they
owed their partner a debt (locus = 1; F = 222.051, d.f. = 173, ρ < .001).
Figure #20 – Impact of Valence on Locus (ANOVA)
105
This was not the case when investigating the impact of the magnitude manipulation. A
simple ANOVA revealed no impact of the magnitude manipulation (F = .468, d.f. = 173, ρ = .495).
However, since the magnitude manipulation was crossed with the valence manipulation, a
simple ANOVA would be meaningless if the relationship between the conditions and the
outcome (here locus) is nonlinear. In order to verify that the magnitude manipulation had no
effect on the locus of a debt, the Univariate General Linear model was used in SPSS 17.0 which
allows the comparison of mean levels of the dependent variable based on multiple independent
variables and their interactions (see table #25 below). The magnitude manipulation failed to
provide any direct effects (F = .591, d.f. = 174, p = .443), and further did not have an indirect
effect when the interaction of valence and magnitude was considered (F = .918, d.f. = 174, p =
.339).
Table #25 - General Linear Model Test of Mean Differences
Dependent Variable:Locus
Source Type III Sum of
Squares df Mean Square F Sig.
Corrected Model 82.711a 3 27.570 74.317 .000
Intercept 1.707 1 1.707 4.601 .033
Magnitude .219 1 .219 .591 .443
Valence 82.134 1 82.134 221.398 .000
Magnitude * Valence .341 1 .341 .918 .339
Error 63.438 171 .371
Total 148.000 175
Corrected Total 146.149 174
a. R Squared = .566 (Adjusted R Squared = .558)
Further examination of the data supports the hypothesized relationship between the
valence of an action and the locus of the reciprocal debt. Crosstabs showed that the debt was
more likely to be perceived as the participant's own debt when the partner's actions were
beneficial (+1) than when they were detrimental (-1). In the beneficial condition, 84%
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considered the participant responsible for resolving the debt. In the detrimental condition, only
10% considered the participant responsible, but 69% consider the partner responsible
(compared to 5.7% in the beneficial condition) for resolving the debt (see figure #21 below). No
such differences were found for the magnitude manipulation (see figure #22 below).
Figure #21 Locus * Valence Crosstabulation
Count
Valence
Total -1.00 1.00
Locus -1.00 60 5 65
.00 18 9 27
1.00 9 74 83
Total 87 88 175
Figure #22 Locus * Magnitude Crosstabulation
Count
Magnitude
Total .00 1.00
Locus -1.00 31 34 65
.00 11 16 27
1.00 44 39 83
Total 86 89 175
This suggests that in the beneficial condition, the participant perceived a reciprocal debt
that they owed their partner -- and that it is primarily the participant's responsibility to take
action to resolve that debt. The converse was true in the detrimental condition; the participant
was more likely to perceive a reciprocal debt owed by their partner -- and that the partner had
greater responsibility to resolve that debt.
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Although the ANOVA and crosstabs presented above provide substantial support for H1
and H3, regression based analysis was also conducted to provide additional support for the
hypotheses. First, because the locus variable was a trichotomous indicator (-1, 0 and 1), a
multinomial logistic regression was performed in SPSS 17.0 with locus as the dependent
variable, and the valence manipulation as the independent variable. This model with only one
explanatory variable provided a better fit than a null model (Δχ2 = 115.994, d.f. = 2, p < .001),
statistically significant coefficients for the impact of valence (see table #26 below), and
accurately predicted 76.6% of the locus classifications (see table #27 below).
Table #26 - Parameter Estimates
Locusa B Std.
Error Wald df Sig. Exp(B)
95% Confidence Interval for Exp(B)
Lower Bound
Upper Bound
-1.00 Intercept -2.695 .462 34.007 1 .000
[Bene=-1.00] 4.592 .584 61.778 1 .000 98.667 31.398 310.058
[Bene=1.00] 0b . . 0 . . . .
.00 Intercept -2.107 .353 35.617 1 .000
[Bene=-1.00] 2.800 .540 26.914 1 .000 16.444 5.710 47.361
[Bene=1.00] 0b . . 0 . . . .
a. The reference category is: 1.00.
b. This parameter is set to zero because it is redundant.
Table #27 - Classification Results of Multinomial Regression for Locus
Observed Locus
Predicted Locus
-1.00 .00 1.00 Percent Correct
-1.00 60 0 5 92.3%
.00 18 0 9 .0%
1.00 9 0 74 89.2%
Overall Percentage 49.7% .0% 50.3% 76.6%
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Thus, Hypotheses 1 and 3 receive strong support regarding the essential locus of the reciprocal
debt.
Level or Intensity of Reciprocal Debt
It was expected that the manipulation of the magnitude of the partner's beneficial or
detrimental partner action would be associated with greater level or intensity of reciprocal debt.
But greater intensity or level of a participant reciprocal debt is not equivalent to lower partner
reciprocal debt. As it is possible that a participant reciprocal debt may not be conceptually and
functionally the converse of a partner reciprocal debt, pooling the data across the two valence
conditions could mask differences or effects unique to each condition. That is, a simple
magnitude main effect would be unlikely, even if magnitude had been effectively manipulated.
Further, it is unclear how everyone would perceive the detrimental condition. For example,
when a partner splits up the $10 allocation and gives the participant $3, the planned
interpretation is that the participant got slighted by receiving a lesser amount. An equally valid
interpretation could be that the participant is lucky to receive anything because the $10 was
given to the partner and hence the $3 was a reasonable gesture at sharing the allotment. Thus,
I examine the participant's perceived impact of the partner's actions (OwnImpact) on the
amount of reciprocal debt created.
Because it is unclear the specific relationship we should expect to see across the entire
spectrum ranging from detrimental to beneficial actions and responses, an analysis technique
was chosen which would allow for the examination of beneficial and detrimental actions
possibly unique impacts on reciprocal debt. Spline regression allows for unique impacts of
separate “knots” of data without sacrificing the degrees of freedom that would be lost from
performing multiple distinct regressions on subsets of the data (Marsh and Cormier 2002). In
this analysis, the variable relative outcomes (Rout) was used as a break point to investigate the
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relationship between the participant’s perceptions of the impact of their partner’s actions on
their own outcomes (OwnImpact) and RDLocus – which should allow for the investigation of
how the magnitude of benefits received (or lost) will impact the participant’s (or partner’s)
reciprocal debt. Two knots were used from the overall data; the first was all participants who
perceived their relative outcomes to be less than 0 which signifies that their partner benefitted
more than they did – or the perception of a detrimental partner action. The second knot was all
those participants who perceived Rout to be positive – which signifies a beneficial partner
action. The following dummy variables were introduced to execute this analysis:
BenRout = 1 when Rout > 0; 0 otherwise
DetRout = 1 when Rout < 0; 0 otherwise
SplineBen = BenRout * OwnImpact
SplineDet = DetRout * OwnImpact
Support for H1 will be provided by SplineBen having a positive impact on RDLocus. Support for
H3 will be provided by a negative impact of SplineDet on RDLocus. The regression explained
approximately 34% of the variance in RDLocus (adj. R2 = .339), and the coefficient for SplineBen
was positive and significant (see table #28 below), however even though the coefficient for
SplineDet was negative as expected, it was not significant.
Table #28 - Spline Regression Resultsa
Model
Unstandardized Coefficients Standardized Coefficients
t Sig. B Std. Error Beta
1 (Constant) -2.812 1.265 -2.223 .028
SplineBen 1.572 .241 .530 6.533 .000
SplineDet -.482 .456 -.086 -1.058 .292
a. Dependent Variable: RDlocus
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Further examination of these results highlights the fact that the constant in this
regression is largely negative at -2.812. RDLocus has a mean value of 1.817 (σ = 9.16). It is not
clear why the intercept should be negative for this regression, nor is it clear what the intercept
means if the spline knot is at zero – when both SplineBen and SplineDet are equal to zero, we
would expect a zero value for reciprocal debt. Because of this, a second spline regression was
run without an intercept in the model. The overall regression model is significant (F = 47.354,
d.f. = 175, p < .001) with an adjusted R2 = .346 (interpret this with caution as the lack of
intercept in the model creates a different meaning for this measure). The standardized
coefficient (see table #29 below) for SplineBen is positive and significant (β = .536, t = 8.77, p <
.001) and for SplineDet is negative and significant (β = -.257, t = -4.205, p < .001) providing
support for both H1 and H3 when the level or intensity of the reciprocal debt is considered.
Table #29 - Spline Regression Results with no Intercepta,b
Model
Unstandardized Coefficients Standardized Coefficients
t Sig. B Std. Error Beta
1 SplineBen 1.116 .127 .536 8.777 .000
SplineDet -1.255 .298 -.257 -4.205 .000
a. Dependent Variable: RDlocus
b. Linear Regression through the Origin
Overall, both ANOVA based analysis of the locus variable, and regression based analysis
of the RDLocus variable have provided support for H1 and H3. Thus we can conclude that when
a beneficial action is perceived, the participant believed they owed their partner. Further the
greater benefits they perceived from the action translated into a greater debt they felt they
owed their partner. Conversely, detrimental actions resulted in the participant’s being more
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likely to believe their partner owed them in the future, and the more they felt slighted by their
partner, the greater the intensity of reciprocal debt they perceived.
Effect of Participant and Partner Reciprocal Debt on Partner Outcomes
The participant's allocation decision in Game 2 represents Partner Outcomes, that is, the
ramifications of the participant's response to the partner's actions in Game 1. Partner Outcomes
(PartnerOut) is measured as the dollars allocated by the participant to the partner in the first
round of Game 2, the partner's first opportunity to take action following the partner's beneficial
or detrimental action in Game 1. As with the prior analysis, H2 and H4 will be considered
together here. When considered together, these hypotheses suggest that when the participant
perceives that they owe their partner, PartnerOut will be larger than when they perceive that
their partner owes them. Both ANOVA’s and regression analyses will be performed to test these
hypotheses. The hypotheses are reprinted below:
H2: Participant reciprocal debt will lead to increased partner outcomes.
H4: Partner reciprocal debt will lead to decreased partner outcomes.
An ANOVA was performed comparing the level of partner outcomes based on the locus
of the reciprocal debt perceived by the participant. Planned contrasts were included as there
were three levels of the locus variable such that independent comparisons of the dependent
variable were analyzed at each of the three levels of locus. This ANOVA provides some support
for our hypotheses, as the mean of PartnerOut (4.546) when the participant perceive a partner
reciprocal debt (Locus = -1) was significantly lower than the mean of PartnerOut (5.831) when
they perceived that they owed their partner (Locus = 1). Neither partner nor participant locus
values were significantly different from the mean of PartnerOut when locus was set to 0. Figure
#23 below and tables #30 and 31 illustrate these findings.
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Figure #23 – ANOVA Results of PartnerOut at Different Locus
Table #30 - Contrast Coefficients
Contrast
Locus
-1.00 .00 1.00
1 -1 1 0
2 0 -1 1
3 -1 0 1
Table #31 - Contrast Tests
Contrast
Value of Contrast
Std. Error t df
Sig. (2-tailed)
PartnerOut
Assume equal variances
1 .528 .5328 .991 172 .323
2 .757 .5155 1.469 172 .144
3 1.285 .3854 3.335 172 .001
Does not assume equal variances
1 .528 .4972 1.062 53.002 .293
2 .757 .4844 1.563 49.746 .124
3 1.285 .3896 3.299 139.956 .001
The ANOVA results above provide ample support for H2 and H4; however it is possible
to consider that as the level or intensity of reciprocal debt increases we should expect an impact
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on partner outcomes. The preceding ANOVA simply tests that the mean value of partner
outcomes is greater when participants perceive their own reciprocal debt (locus = 1) rather than
their partner’s reciprocal debt (locus = -1). Regression analysis is required to examine if the
intensity of debt perceived has any impact on partner outcomes. Without theoretical reasoning
to assume a linear relationship between those who perceived their own reciprocal debt and
those who perceived a reciprocal debt from their partner, spline regression was again used.
New spline variables had to be created, as the spline knots now need to be separated based on
the locus of the debt rather than the relative outcomes used previously. The new variables that
were created are the following:
PosLocus = 1 when Locus > 0; 0 otherwise NegLocus = 1 when Locus < 0; 0 otherwise SplinePosRD = PosLocus * RDLocus SplineNegRD = NegLocus * RDLocus
PartnerOut was regressed on both SplinePosRD and SplineNegRD. Support for H2 would be
found by a significant positive coefficient for SplinePosRD. Support for H4 would be found by a
significant negative coefficient for SplineNegRD. The overall model was significant (F = 202.687,
d.f. = 175, p < .001), explaining approximately 70% of the variance in PartnerOut (adj R2 = .697).
The coefficient on SplinePosRD was positive and significant and the coefficient for SplineNegRD
was negative and significant (see table #32 below).
Table #32 - Spline Regression for RDLocus effect on PartnerOuta,b
Model
Unstandardized Coefficients Standardized Coefficients
t Sig. B Std. Error Beta
1 SplinePosRD .539 .032 .702 16.882 .000
SplineNegRD -.475 .043 -.456 -10.971 .000
a. Dependent Variable: PartnerOut
b. Linear Regression through the Origin
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Taken together, these analyses provide substantial support for H2 and H4. When
participants viewed their own reciprocal debt, they allocated a larger amount to their partner
than when they viewed the reciprocal debt as being their partner’s. Additionally, as the level or
intensity of the participant’s reciprocal debt increased, so did the amount they allocated their
partner. The converse is also true: as the participant perceived a greater reciprocal debt from
their partner, they allocated less to their partner when given the chance.
The Moderating Impact of Relative Benefits
Spline regression was again used to test these hypotheses. Some new spline variables
had to be created so that the differing impacts could be examined when relative outcomes were
either positive or negative. The first spline variables were also used which are copied below:
BenRout = 1 when Rout > 0; 0 otherwise DetRout = 1 when Rout < 0; 0 otherwise SplineBen = BenRout * OwnImpact SplineDet = DetRout * OwnImpact
In addition, the following variables were created:
SBRout = Rout * BenRout SDRout = Rout * DetRout
To investigate the moderating impact, the following interaction variable was created:
RoutOwnImp = Rout * OwnImpact
And to investigate the differential impacts of the moderation when relative outcomes are
greater than zero versus when they are less than zero:
SBRoutOwn = RoutOwnImp *BenRout SDRoutOwn = RoutOwnImp * DetRout
H5a: As the participant’s perceived relative benefits increase, the link between a beneficial action and a participant reciprocal debt will be enhanced. H5b: As the participant’s perceived relative benefits decrease, the link between a detrimental action and a partner reciprocal debt will be enhanced.
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The regression was significant (F = 31.847, d.f. = 175, p < .001), explaining approximately 51% of
the variance around the origin (adj R2 = .514). As for the hypotheses, we would find support for
H5a if SBRoutOwn had a positive, significant coefficient. Support for H5b would be found with a
negative, significant coefficient for the SDRoutOwn variable. As can be seen in table #33
(below), the coefficient for SBRoutOwn is positive and significant, supporting H5a. While the
coefficient for SDRoutOwn is in the expected direction, it failed to reach significance, therefore
we cannot support H5b.
Table #33 - Spline Regression for the moderating impact of relative outcomesa,b
Model
Unstandardized Coefficients Standardized Coefficients
t Sig. B Std. Error Beta
1 SplineBen 1.571 .322 .755 4.881 .000
SplineDet -.145 .803 -.030 -.180 .857
SBRout -9.815 1.494 -2.897 -6.570 .000
SDRout 2.039 .964 .393 2.115 .036
SBRoutOwn 1.398 .200 2.718 7.001 .000
SDRoutOwn -.170 .516 -.096 -.329 .743
a. Dependent Variable: RDlocus
b. Linear Regression through the Origin
The Moderating Impact of the Norms of Reciprocity
It was hypothesized that the norm of positive reciprocity would positively moderate the
impact of a beneficial action on the participant’s reciprocal debt:
H8a: Participant belief in the positive norm of reciprocity will enhance the link between beneficial actions and a participant reciprocal debt.
On the other hand, it is expected that the negative norm of reciprocity will moderate the link
between detrimental actions and the partner’s reciprocal debt:
H8b: Participant belief in the negative norm of reciprocity will enhance the link between detrimental actions and a partner reciprocal debt.
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While it is possible to test these hypotheses with spline regression, in reality they involve
different constructs – the positive norm of reciprocity in beneficial situations and the negative
norm of reciprocity in detrimental situations. Because they do not include the same constructs,
linear regressions were performed splitting the sample based on the relative outcomes measure
– those who considered the relative outcomes to be positive (beneficial, n = 97) versus those
who considered the relative outcomes to be negative (detrimental, n = 53). Those who
perceived a zero value (n = 25) on relative outcomes were removed. To test H8a, the following
regression was performed using only those participants who perceived a beneficial outcome:
RDLocus = α + PR + OwnImpact + PR(OwnImpact)
The results of this regression suggested that multicollinearity was a problem in the data
(VIF’s ranging from 29.8-75.9) and therefore alternate methods must be found for analyzing the
data. Because the interaction of PR and OwnImpact variable was highly correlated with both PR
and OwnImpact, a second regression was run with only PR and OwnImpact as explanatory
variables:
RDLocus = α + PR + OwnImpact
This regression was significant (F = 50.307, d.f. = 96, p < .001) and explained approximately 50%
of the variance (adj R2 = .507). PR was not a significant predictor of RDLocus in this regression;
however OwnImpact was a significant predictor of RDLocus (see table #34 below).
Table #34 Regression of PR and OwnImpact on RDLocusa,b
Model
Unstandardized Coefficients Standardized Coefficients
t Sig. B Std. Error Beta
1 (Constant) -20.238 4.486 -4.511 .000
PR -.013 .295 -.003 -.045 .964
OwnImpact 4.423 .442 .719 10.013 .000
a. Dependent Variable: RDlocus
b. Selecting only cases for which ROut > .00
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A third regression was run with the variables OwnImpact and the interaction of PR and
OwnImpact as explanatory variables:
RDLocus = α + OwnImpact + PR(OwnImpact)
Once again, the regression was significant (F=50.334, d.f. = 96, p < .001) and explained
approximately 50% of the variance in RDLocus (adj R2 = .507). The interaction variable failed to
produce a significant coefficient (see table #35 below), thus we cannot support H8a.
Table #35 Regression of OwnImpact and PROwnImpact on RDLocusa,b
Model
Unstandardized Coefficients Standardized Coefficients
t Sig. B Std. Error Beta
1 (Constant) -20.410 2.653 -7.693 .000
OwnImpact 4.332 .705 .704 6.147 .000
PROwnImpact .008 .048 .019 .168 .867
a. Dependent Variable: RDlocus
b. Selecting only cases for which ROut > .00
The preceding analysis was duplicated for the detrimental situations and similar results
were found. The negative norm of reciprocity did not moderate the response to detrimental
actions. Given these lack of findings, some ANOVA based analyses were conducted after
dichotomizing the variables of interest into high and low categories. We will first examine the
direct impacts of the positive norm of reciprocity on the creation of reciprocal debt (see Table
#36 below).
Table #36 - Norm of Positive Reciprocity’s effect on Reciprocal Debt (ANOVA)
Sum of Squares df Mean Square F Sig.
RD Between Groups 24.737 1 24.737 4.232 .043
Within Groups 450.087 77 5.845
Total 474.824 78
RDlocus Between Groups 112.864 1 112.864 4.968 .029
Within Groups 1749.173 77 22.717
Total 1862.037 78
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As with relative outcomes, the positive norm of reciprocity has a direct impact on the creation
of reciprocal debt whether that debt is measured without respect to the locus of the debt (F =
4.232, ρ = .043) or when the reciprocal debt is adjusted for the locus (F = 4.968, ρ = .029). Next
we will examine the hypothesized moderating impact of the norm of positive reciprocity. Table
#37 shows the sample size for the median split of OwnImpact (HiLoImpact) and on PR (HiLoPR).
Table #38 shows the results of the General Linear Model investigating the impact of HiLoImpact,
HiLoPR and the interaction of those 2 variables on RD. As can be seen in table #38, HiLoImpact
and HiLoPR both have a significant main effect on RD (FSplitOwn = 11.533, ρ = .001; FsplitPR = 4.961, ρ
= .029), however the interaction is not significant (ρ > .05). Figures #24 and 25 plot these direct
effects on RD.
Table #37 - Between-Subjects Factors
N
HiLoImpact .00 33
1.00 46
HiLoPR .00 37
1.00 42
Table #38 - Tests of Between-Subjects Effects
Dependent Variable:RD
Source Type III Sum of
Squares df Mean Square F Sig.
Corrected Model 85.006a 3 28.335 5.452 .002
Intercept 8009.636 1 8009.636 1541.034 .000
HiLoImpact 59.941 1 59.941 11.533 .001
HiLoPR 25.786 1 25.786 4.961 .029
HiLoImpact *HiLoPR .000 1 .000 .000 .993
Error 389.818 75 5.198
Total 9051.609 79
Corrected Total 474.824 78
a. R Squared = .179 (Adjusted R Squared = .146)
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Figure #24 – ANOVA Results for Positive Norm of Reciprocity’s impact on RD and RDLocus
Figure #25 – Plots of RD based on Perceived Impact and the Positive Norm of Reciprocity
It doesn’t appear that the norm of positive reciprocity moderates this relationship at all. While
the plots of the marginal means suggest a possible direct effect, the slopes of the high and low
PR participants appear to be exactly the same. Therefore, we fail to find support for H8a.
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Again, we first examine the direct effects of the negative norm of reciprocity (NR) on the
creation of reciprocal debt. Table #39 shows that a simple ANOVA investigating the impact of
the negative norm of reciprocity on both RD and RDLocus fails to provide any direct impact (FRD
= .027, ρ = .869; FRDLocus = 1.785, ρ = .185). Figure #26 plots the average value of RD and RDLocus
given the median split measure of NR (HiLoNR).
Table #39 – ANOVA results for Negative Norm of Reciprocity’s Impact on RD and RDLocus
Sum of Squares df Mean Square F Sig.
RD Between Groups .177 1 .177 .027 .869
Within Groups 549.875 85 6.469
Total 550.052 86
RDlocus Between Groups 70.123 1 70.123 1.785 .185
Within Groups 3339.108 85 39.284
Total 3409.231 86
Figure #26 – ANOVA Plots of NR on RD and RDLocus
Based on this analysis, it appears that the negative norm of reciprocity does not directly impact
reciprocal debt; however that doesn’t rule out an interactive impact. Table #40 shows the
crosstabs of participants after the data was median split on the participant’s rating of
OwnImpact (HiLoImpact) and their norm of negative reciprocity (HiLoNR). Table #41 shows the
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results of a General Linear Model investigating the impact of HiLoImpact, HiLoNR and their
interaction on RDLocus. As can be seen in table #41, none of the proposed variables is a
significant predictor of RDLocus.
Table #40 – Between-Subjects Factors
N
HiLoImpact .00 74
1.00 13
HiLoNR .00 44
1.00 43
Table #41 – Tests of Between-Subjects Effects
Dependent Variable:RDlocus
Source Type III Sum of
Squares df Mean Square F Sig.
Corrected Model 148.950a 3 49.650 1.264 .292
Intercept 743.819 1 743.819 18.936 .000
HiLoImpact 65.651 1 65.651 1.671 .200
HiLoNR 62.914 1 62.914 1.602 .209
HiLoImpact*HiLoNR 9.766 1 9.766 .249 .619
Error 3260.281 83 39.280
Total 5554.225 87
Corrected Total 3409.231 86
a - R Squared = .044 (Adjusted R Squared = .009)
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Figure #27 – Moderating Impact of Negative Norm of Reciprocity
Finally, figure #27 plots the marginal means of RDLocus given HiLoImpact and HiLoNR. We can
see that the plots of the marginal means suggest an interaction; however we fail to reach
statistical significance. Possibly with a larger sample size or a stronger manipulation of the
detrimental scenario we would see the hypothesized effects. Based on these plots, the
hypothesized relationship is directionally supported: those with a greater adherence to the
negative norm of reciprocity decreased their partners’ outcomes more than those with a low
negative reciprocity norm. We cannot support H8b however.
As an exploratory analysis, the positive norm of reciprocity was examined as a possible
antecedent to reciprocal debt in the detrimental situation.
Table #42 – Between-Subjects Factors
N
HiLoImpact .00 74
1.00 13
HiLoPR .00 43
1.00 44
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Table #43 – Tests of Between-Subjects Effects
Dependent Variable:RDlocus
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model 225.958a 3 75.319 1.964 .126
Intercept 689.896 1 689.896 17.988 .000
HiLoImpact 85.406 1 85.406 2.227 .139
HiLoPR 134.903 1 134.903 3.517 .064
HiLoImpact*HiLoPR 27.473 1 27.473 .716 .400
Error 3183.273 83 38.353
Total 5554.225 87
Corrected Total 3409.231 86
a. R Squared = .066 (Adjusted R Squared = .033)
Table #42 shows the crosstabs for these 2 variables. Table #43 explains the General Linear
Model that was run investigating the impact of these 2 variables on RDLocus. It seems that the
positive norm of reciprocity has an impact (F = 3.517, ρ = .064) on the reciprocal debt in the
detrimental manipulation. Those who are low in the positive norm of reciprocity tend to punish
their partners more severely for what they view as less beneficial treatment. Figure #28
illustrates this relationship as those high in the positive norm of reciprocity did not differ greatly
in their response to detrimental treatment.
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Figure #28 – ANOVA Plot of the moderating impact of PR
Table #44 summarizes the hypotheses tested from the experimental portion of this
dissertation.
Hypothesis Hypothesis Wording Supported?
H1 Beneficial Action (+) → Participant Reciprocal Debt H2 Participant Reciprocal Debt (+) → Partner Outcomes H3 Detrimental Action (+) → Partner Reciprocal Debt H4 Partner Reciprocal Debt (-) → Partner Outcomes H5a
Participant Relative Benefits moderates (+) link between Beneficial Action and Participant Reciprocal Debt
H5bPartner Relative Benefits moderates (+) the link between Detrimental Action and Partner Reciprocal Debt
H7aPositive Norm of Reciprocity moderates (+) the link between Beneficial Action and Participant Reciprocal Debt
H7bNegative Norm of Reciprocity moderates (+) the link between Detrimental Action and Partner Reciprocal Debt
Table #44 - Experimental Research Hypothesis Summary
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CHAPTER 5: RELATIONAL RECIPROCITY IN MARKETING RELATIONSHIPS:
THE ROLE OF COMPLEX RECIPROCITY
5.1 Relational Reciprocity Abstract
Marketing theorists and researchers have noted that transactional and relational
exchange differ on many dimensions. Social exchange and reciprocity theory suggest that the
process and nature of reciprocation differs as a relationship develops and deepens. Earlier
chapters of this dissertation focused on the reciprocation evident in a single exchange
transaction. The remainder of this dissertation focuses on the role of reciprocity in ongoing,
continuing relationships. The term Relational Reciprocity is adopted to distinguish this
reciprocity from the reciprocity evident in discrete transactions. While this portion focuses on
existing, continuing relationships, it is important to note that not all relationships are equal.
Many business relationships persist even as arms-length transactions which may appropriately
be viewed as a series of discrete transactions, while others may develop into more enmeshed,
relational exchange.
Relationships characterized as discrete should evidence a more simple reciprocity
through more immediate, direct and in kind reciprocal actions. On the other hand, relational
exchange should involve more complex reciprocity which is characterized by more comfort with
longer duration reciprocal debts, less exact accounting of reciprocal returns, and wider latitude
in the types of responses acceptable to qualify as reciprocation. This research seeks to examine
changes in relational reciprocation and how that may help explain the development of
relationships over time. A dyadic longitudinal survey, supplemented with secondary
performance data, is planned to test the theoretical model built based on this relational
reciprocity.
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5.2 Relational Reciprocity Introduction
Reciprocity theory suggests that the process of reciprocation is the necessary
component to the development of social ties within a society (Becker 1986). Reciprocity is
implicated as the driving force in the development of the human social system, enabling the
introduction of labor specialization, trust, informal social contracts and numerous other social
institutions we take for granted in everyday life (Macneil 1980). Becker (1956) goes so far as to
rename human beings as homo-reciprocus, suggesting that reciprocity is the key enabling
process in all social relationships. If we accept that reciprocity is the glue that binds together
social relationships, does this suggest that the process of reciprocation is the same in all
relationships? Or more likely, might there be differences in the process of reciprocation
depending on the strength of the social ties? While it is expected that reciprocity is a necessary
component of all relationships, it would be naïve to suggest that reciprocity functions the same
in a marriage or familial relationship as it does in a market transaction.
Bagozzi (1975) categorizes exchange relationships based on both the number of
participants to the exchange and, more importantly for this research, on the meaning of the
exchange process as either utilitarian, symbolic or mixed exchange. Utilitarian exchange focuses
on the anticipated use or tangible characteristics commonly associated with the objects of
exchange. Symbolic exchange is “the mutual exchange of psychological, social, or other
intangible entities between two or more parties” (p. 36). Bagozzi acknowledges that many
marketing interactions are based on the utilitarian meaning of the exchange. However, he also
argues the importance of the symbolic meaning of the exchange, and research has generally
shown support given that social aspects of the relationship like trust (Doney and Cannon 1997;
Fang et al. 2008), loyalty (Palmatier et al. 2007b), dependence (Kumar et al. 1995a), procedural
fairness (Kumar et al. 1995b), and power (Gaski 1984) all have important consequences for
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marketing relationships, not only impacting the quality of the relationship, but also more
objective outcomes like the financial returns to both parties. In fact, the premise behind the
relationship marketing field is that building stronger more enmeshed relationships will benefit
both firms through greater financial outcomes (Dwyer et al. 1987; Morgan and Hunt 1994;
Palmatier et al. 2006a).
But what determines whether a given marketing relationship relies more on the
utilitarian meaning of the exchange or on the symbolic meaning of the exchange? Dwyer,
Schurr and Oh (1987) argue that relationships develop over time and can transition from
discrete exchange into relational exchange (e.g., Macneil 1980). They posit that when at least
one of the parties has a high level of motivational investment (or expected net benefits from a
relationship), it may be managed as relational exchange. On the other hand, when the
motivational investment of both parties is low, the relationship is likely to remain as a series of
discrete exchanges over its duration. As numerous theorists have pointed out, the true
“discrete exchange” probably never happens, the common example being paying for fuel in cash
while traveling to a location the consumer is unlikely to return to, more likely most interactions
involve some degree of social interaction, relying on social norms and expectations of future
performance (Bagozzi 1975; Macneil 1980; Williamson 1996). This does not imply that all
relationships are and only relational in nature; however as is often pointed out (Bagozzi 1975;
Dwyer et al. 1987; Gouldner 1960; Macneil 1980), relationships can generally be categorized on
a continuum from discrete to relational exchange. As relationships favor either discrete or
relational exchange, theory suggests that the process of reciprocation should differ as well
(Cialdini 2009; Gouldner 1960; Macneil 1980).
Employing Macneil’s (1980) social exchange theory, this research will propose and
develop a scale that captures relational reciprocity behavior ranging from simple relational
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reciprocity (in more discrete exchange) to complex relational reciprocity (in relational exchange)
in nature. Theoretically, this research will help broaden the understanding of the role of
reciprocity in marketing relationships. Examining both simple and complex reciprocity will build
on existing reciprocity theory, and may help explain why recent attempts to explain reciprocity
in marketing relationships have failed to capture the entire domain of reciprocal exchange
(Pervan et al. 2009). From a managerial perspective this research should help to better
understand which relationship marketing strategies will be more effective in a given
relationship. Understanding of customer preference for complex or simple reciprocal
relationships will allow more judicious allocation of marketing resources to help build more
embedded relationships, or to help promote the convenience of exchange based more on
discrete transactions. Further, it is possible that certain trends may arise related to a customer’s
industry, purchase basket, or other signals which will help a selling firm delineate which
customers to approach with relational marketing tactics versus focusing on the discrete
exchange aspects the firm has to offer.
This section of the dissertation is organized as follows: the next section includes a brief
literature review and builds a theory of relational reciprocity. Hypotheses will then be
developed differentiating simple and complex relational reciprocity, and to suggest antecedents
that should impact a customer’s tendency towards either end of the spectrum. Additionally,
hypotheses will be offered to suggest outcomes of relational reciprocity. Finally, a research
project will be discussed that tests the theoretical model in real customer-selling firm
relationships.
5.3 Theoretical Model Development
Macneil (1980) differentiates exchange on a continuum ranging from discrete
transactions to relational exchange. While he admits that a true discrete transaction probably
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does not exist, he argues that the continuum is a useful categorization system to analyze how
factors of exchange will vary depending on which end of the continuum is favored. Macneil
argues that the human is basically a schizophrenic creature, always trying (and failing) to
balance self-interest with social solidarity. He relates discrete exchange and relational exchange
to these two opposing forces within humans, the predominantly discrete exchanges appealing
to the self-interest, while relational exchange functions to satisfy the human need for social
interaction. Importantly, he acknowledges that while exchange is typically more discrete or
more relational, in reality nearly every exchange has characteristics which could place it on both
the discrete and relational ends of the spectrum. Macneil defines discrete exchange as one in
which no relationship exists between the parties apart from the simple exchange of goods.
While he offers a definition of relational exchange as one involving relations other than a
discrete exchange, his definition does not adequately illustrate the difference between the two
concepts.
This research borrows from Macneil’s theory of relational exchange. Accepting that
marketing relationships can vary from more discrete to more relational in nature, this research
applies Macneil to build a theory of relational reciprocity and how it will differ depending on the
characteristics of the parties. Macneil examines 12 factors which should be substantially
different at opposing ends of the exchange continuum (also see Dwyer et al. 1987 for a concise
review of these factors). While it is possible relational reciprocity may exhibit differences based
on these 12 factors, it is noted that it may be possible that marketing relationships do not
require examination of all 12 of these dimensions (Dwyer et al. 1987). Eight of the 12 factors
which should be particularly relevant to the process of reciprocation are examined in this
research. The following is a discussion of these 8 dimensions of relational exchange.
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Personal Relations
The first of the factors of exchange is the level at which personal relations exist within
an exchange relationship. At the discrete end of the scale, there are no social relations.
Interactions between parties to an exchange are highly formalized, lacking informal or
noncritical communication. Discrete exchange partners have no need to interact on a social
level. The extent of their interaction is related to defining, monitoring and executing the terms
of the exchange. On the other hand, relational exchange involves social relationship whereby
individuals gain not only economic returns, but also psychological or social wellbeing (Dwyer et
al. 1987) through their interactions with their counterpart. Communications will include
extensive, informal as well as (or instead of) formal communications. Concern for the wellbeing
of the counterpart should be evidenced in relational exchange, whereas in discrete exchange,
self-interest is the primary motivator to exchange.
Measurement and Specificity
In a typical discrete exchange, the content exchanged will be some commodity with a
market value on the one hand and money on the other. Measurement is precise, and often
predetermined by market factors external to this specific exchange. The terms of the exchange
are very specific: one party delivers the agreed commodity while the other exchanges the
agreed upon price. In contrast, relational exchange is often much less clear on the terms of
performance. Measurement may be much less precise as the benefits of exchange cannot
always be determined by market forces. Additionally, the social exchange may allow for less
specificity in a given exchange because the parties to the exchange are committed to some level
of equity over the entire spectrum of the relationship, rather than with respect to one specific
interaction.
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Sources of Contractual Solidarity
Macneil (1980) defines contractual solidarity as “the social solidarity making exchange
work – that at a minimum holds the parties together enough so that they will not kill and steal in
preference to exchanging” (p. 14). In a discrete exchange, lacking any social relation, the only
sources of contractual solidarity must come from external enforcement: laws, rules, societal
norms, etc. The parties have no interest necessarily in continuing in the relationship, so
relationship-specific norms are nonexistent. Contractual solidarity must be enforced through
mechanisms external to this specific transaction. On the other hand, when considering
relational exchange, both parties have some interest in maintaining the relationship. Forces
from within the relationship may help promote contractual solidarity. Of course, external forces
including laws and industry regulations will also influence the interactions, but over the duration
of the relationship norms may develop to help promote healthy interaction (Heide and John
1992). Relational factors like trust, commitment and loyalty develop that also provide a source
of solidarity to both parties in the relationship.
Commencement, Duration and Termination
The discrete transaction has a clearly defined lifecycle. The transaction begins when
parties agree to the exchange, and terminates upon performance of the exchange. The duration
of the transaction will be relatively short, if not immediate upon consummation of the
transaction. Termination occurs when successful completion of the terms of the exchange has
been satisfied, which is a clear point in time because the measurement of performance has been
specifically delineated. Simply put, parties are both aware of the end of the interaction because
the terms clearly spell out the contractual obligations. Relational exchange tends to be of a
longer duration and have less well defined commencement and termination. Using the nuclear
family as an analogy, commencement begins at birth and termination may actually extend
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beyond death. Because of the vague measurement terms of the contract, parties may never be
100% sure that they have performed what is required, and in fact exchange may be managed
such that there is no clear termination of the exchange relationship.
Planning
In a discrete transaction, planning is complete, specific and binding. It is achieved by the
explicit consent of both parties to the terms of the exchange, and no planning is anticipated in
the future of the interaction. In relational exchange, planning may be an ongoing, long term
process with anticipation of amendments and adjustments in the future as the needs and
resources of both parties change. Recognizing that perfect information is nonexistent, relational
exchange allows for future changes to the plan and further may actually develop a less accurate
plan that accounts for externalities and contingencies in the relationship. Social norms (both
external and internal) may affect the planning, and often self-interest will be sacrificed in favor
of other goals (e.g., social solidarity).
Future Cooperation
Discrete transactions, by definition, do not require any future cooperation or
interaction. Parties to discrete transactions have no goals mediated by a desire for future
cooperation. Self-interest dictates, and if it weren’t for external sources of contractual solidarity
parties to exchange would appropriate as much of the outcomes as possible through this single
transaction. On the other hand, relational exchange involves an expectation of future
interaction and often requires joint efforts in the execution of the contract. Further, as the
planning allows for future adjustments and amendments as needed, future cooperation is
required in relational exchange.
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Sharing and Dividing of Benefits and Burdens
Macneil (1980) states that “in a discrete transaction benefits and burdens are sharply
divided; each party has his benefits, all his, and his burdens, also all his” (p. 16). The terms of
the transaction clearly specify which party is responsible for costs of the exchange, and how
much of the benefits are allocated to each party. There is no room for sharing of either. In
relational exchange, parties may plan on future negotiations to find equitable exchange terms
that are beneficial for both parties. In transaction cost economics, this process is referred to as
ex-post contracting and it is positioned as a necessary component to exchange due to the lack of
perfect information, managers’ bounded rationality and the consequent impossibility of
accurately planning for every contingency prior to a transaction (Williamson 2007).
Obligations – content, sources of obligations and specificity
Macneil (1980) argues that in discrete transactions the content of obligations originate
internally from the promises of parties to the transaction, whereas the obligations to perform
are external to the relationship. This causes the obligations to be clearly defined and very
specific such that external sources can verify, monitor and enforce the obligations where
necessary. In relational exchange, both the content and sources of obligations come from
within the relation itself. These could evolve out of relational norms over time, but importantly
they are likely to be less specific than those in a discrete transaction.
These 8 factors are argued to vary in the specified ways depending on whether an
exchange is more relational or more discrete. While reciprocity is specifically implicated by
Macneil (1980) when discussing these factors, given the expectation that reciprocity functions
differently in more embedded relationship than in a market based relationship, we should be
able to determine some important characteristics of relational reciprocity, and hence also
discrete reciprocity.
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5.3.1 Complex and Simple Reciprocity
The process of reciprocation is not the same in all relationships. As has been discussed
the exchange process itself differs, and it should not be surprising that as part of that process,
reciprocation will also differ depending on how discrete or relational the transaction is. In fact,
Cialdini (2009) goes so far as to suggest that “in its purest form reciprocity is unnecessary and
undesirable in certain long-term relationships such as families or established friendships” (p.
34). “Its purest form” refers to economic reciprocity where if you spend $20 to take me out to
lunch, it is understood that I owe you a $20 lunch in return. While Cialdini’s indictment of
reciprocity in long-term relationships appears ominous at first glance, he goes on to suggest that
instead of economically valued commodities being reciprocally exchanged in these relationships,
what is being reciprocated is the “willingness to provide what the other needs, when it is
needed” (p. 35). Importantly, his thesis suggests that reciprocity functions at multiple levels.
The traditionally understood form, where exchange is evaluated based on market rates,
reciprocal debts are repaid to the creditor of the debt, and at any given time both parties are
aware of who the debtor is, a market based valuation of how much they owe, and possibly
when the debt is expected to be repaid. On the other hand, in closer, longer-term exchange
relationships it is unlikely that either party keeps track of who owes who, how much is owed,
nor when it is expected to be repaid.
This view of relational reciprocal debts, as lacking in specificity, is shared by other
researchers as well. Bagozzi (1975) suggests that in symbolic exchange the motivations for
exchange are the “social and psychological significance of the experiences, feelings, and
meanings of the parties to the exchange” (p. 36). While he doesn’t suggest that all marketing
exchange occurs for symbolic reasons, he does posit that most exchange occurs for both
symbolic and utilitarian reasons. Given that this symbolic reason for exchange is impossible to
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value based on market factors, it seems likely that reciprocity with respect to these more
relational interactions will have less specificity in the measurement, definition and planning of
the actual terms of the exchange.
Dwyer, Schurr and Oh (1987) also discuss how reciprocal debts may change as a
relationship develops over time. One of the key factors that these researchers suggests impacts
this deepening of a relationship is the development of trust and commitment in the relationship.
They suggest that these relational qualities help give confidence to partners that their
investments in the relationship will be returned by their partner, and thus help build solidarity
between the partners. Additionally, they suggest that the process of reciprocation will change
as the relationship develops when they say “Later, when the parties are both projecting their
association into the future, there may be a lesser need for strict-reciprocity accounting in that
the future holds ample opportunity for and expectations of balancing” (p. 16). Clearly there are
implications for the specificity of any given exchange; more relational exchange should be
characterized by less specific accounting of reciprocal debts.
Reciprocity theorists also agree that while early in a relationship, reciprocity will exist at
a more discrete, simple level. These initial exchanges help build confidence and trust in the
partner so that in the future relational partners will relax the strict specificity and accounting of
the terms of exchange (Becker 1956; Becker 1986). Reciprocity becomes more complex and
flexible as exchange partners become more familiar with each other and develop more
confidence about the future of their relationship and the ability to even out benefits and
burdens of exchange over time (Gouldner 1960). This research proposes that relational
reciprocity is a multi-dimensional construct. Further, I propose five dimensions which should
distinguish between complex and simple relational reciprocity. These factors are summarized
in table #45 below.
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Table #45. Dimensions of Complex and Simple Reciprocity
Dimension Complex Simple
Comfort with own and other reciprocal debts
High Low
Presumption of Innocence
High Low
Valuation of reciprocal debts
Benefits provided Costs incurred
Repayment terms of reciprocal debts
Long term Short term
Accounting of reciprocal debts
Informal - nonexistant Formalized system
• Comfort with own and other’s reciprocal debt – complex reciprocity involves being more amenable to both accepting favors from, and doing favors for, their exchange partners, understanding that the relationship duration will allow them to repay favors in the future.
• Presumption of Innocence - Given greater levels of trust and commitment by both parties in relational exchange relationships, it seems unlikely that either party would jump to assigning blame or guilt to their counterpart when encountering a detrimental action. More likely they might presume innocence, at least until the offending action can be discussed and investigated.
• Valuation of reciprocal debts – is the valuation of relational inputs based on the benefits obtained, or the costs incurred to obtain them? Is the partner the focus of valuation, or is the self the focus? It should be expected that more complex reciprocity is characterized by partners being more concerned with their counterpart’s benefits than with their own costs in providing those benefits. This also agrees with Cialdini’s (2009) suggestion that in deeper relationships, the commodity reciprocated is the willingness to provide for the partner rather than the inputs exchanged themselves.
• Repayment terms of reciprocal debts – Macneil (1980) discusses the key distinction between discrete transactions and relational exchange as the expectation of repayment at sometime in the future. It seems that parties involved in more relational exchange should be willing to await repayment of reciprocal debts longer than those whose exchange is characterized as more discrete in nature.
• Accounting of reciprocal debts – Cialdini (2009) suggests that in longer-term, more committed relationships, “formal” reciprocity is unnecessary as parties are more concerned with the overall balance of benefits than with an exact accounting of who owes who what. It would seem that more discrete exchange would involve a strict, formalized accounting system for reciprocal debts, whereas a more relational exchange
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level might be characterized by only informal accounting, or even a lack of accounting of debits and credits to the relationship.
This research positions these dimensions as the differentiating dimensions for complex and
simple relational reciprocity.
5.4 Hypotheses
If reciprocity functions differently in relational exchange than it does in discrete
transactions, it should be expected that outcomes will differ depending on whether the
relationship is characterized by relational or discrete reciprocity. In fact, this understanding may
help explain the relationship marketing findings that financial relationship marketing programs
do not directly affect selling firm outcomes (Palmatier et al. 2006a). Based on the hypotheses
offered above, it seems that financial investments in relational exchange partners may be
valued less than other types of non-financial relational investments. It is possible that the type
of reciprocity functioning in a relationship has implications for which types of relationship
investments will have the greatest appeal to relational partners.
5.4.1 Outcomes of Complex Reciprocity
Marketing researchers have utilized the dependent variable of “share of wallet” as an
important outcome variable in marketing relationships (Reynolds and Beatty 1999; Siguaw et al.
1998). Unlike a measure of sales which can depend on the size of the customer, the industry
being investigated, or market factors like the general economy, share of wallet measures the
percentage of total purchases one customer obtains from a given supplier acknowledging that
customers often have multiple suppliers and hence alternative sources. When alternatives exist,
and a supplier offers multiple products or services that a customer could use, share of wallet
represents the customer’s choice to use this supplier for a greater proportion of their purchases.
In the relationship marketing literature, it is argued that increases in share of wallet should be
expected based on relationship building programs designed to develop a stronger, possibly
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more relational exchange relationship. This suggests that marketing relationships characterized
by relational reciprocity should be expected to have a higher share of wallet than those
characterized by discrete transactions. With discrete reciprocity, the driving force to exchange
is economic self-interest. Alternatively, relational reciprocity suggests that the focus also
includes the goals and success of the partner firm. Decisions will be made which benefit both
firms instead of seeking out the best economic alternative. Share of wallet might be witnessed
in a relationship characterized by discrete reciprocity; however the existence of discrete
reciprocity is not expected to be an antecedent to share of wallet. Specifically, it is expected
that:
H1: Relationships characterized by greater complex reciprocity will exhibit increased share of wallet over time. Discrete transactions are motivated by self-interest maximization (Macneil 1980). It is
expected that relationships characterized by discrete reciprocity will likely involve an ongoing
search for alternatives as the customer is constantly searching for the best economic outcomes.
On the other hand, when relational exchange is achieved the benefits being reciprocated in the
relationship are more complex than simple economic utility, including the social and
psychological benefits of the social interactions (Dwyer et al. 1987). The search for alternatives
is unlikely in relationships characterized by relational reciprocity, as trust, commitment and
interdependence have helped to bind the two parties together. These relational characteristics
essentially create exit barriers to the relationship. The only time we would see a search for
alternatives in this case is when the relationship has deteriorated and one party seeks to exit the
relationship. Otherwise, it is unlikely that either party would engage in any search for
alternatives, therefore:
H2: Relationships characterized by greater complex reciprocity will exhibit decreased search for alternatives.
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On the other hand, discrete reciprocity is motivated by self-interest. Terms of the
exchange are expected to be specified and agreed upon prior to execution. Trust and
commitment aren’t driving the solidarity of the relationship. When encountering a detrimental
act within the confines of the relationship, it is unlikely that the offended party will presume
innocence of their counterpart. Rather, they are more likely to assume that the negative
outcomes are caused by their partner attempting to increase their own positive outcomes at
their expense. Negative reciprocity may be the expected tactic in this situation. When your
partner injures you, and you expect that they do not share in the increased costs, you might
take action to even out the costs imposed by this new interaction in the relationship.
H3: Relationships characterized by less complex reciprocity will exhibit a higher level of negative reciprocal responses to detrimental actions. The preceding hypotheses suggest another outcome of relational reciprocity. If
relationships characterized by relational reciprocity are expected to exhibit an initial
presumption of innocence in the face of detrimental partner actions, while discrete reciprocal
relations are expected to respond with negative reciprocity, we would expect that constructive
conflict will differ between the two types of reciprocal relationships. Relational exchange
partners are not expected to presume innocence indefinitely in the face of detrimental partner
actions. Marriages end up in divorce if one member continually takes advantage of the other,
and I would expect the same for marketing relationships no matter how relational they are at a
given point in time. However, if there is an increased presumption of innocence, it should be
expected that higher levels of constructive conflict or the active response of voice will also exist
in relationships characterized by relational reciprocity. On the other hand, with discrete
reciprocity, the expected response lacks any communicative efforts to resolve the detrimental
action because the presumption is that the partner is attempting to appropriate more than their
agreed upon outcomes of the exchange. Therefore I expect that:
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H4: Relationships characterized by greater complex reciprocity will exhibit higher levels of constructive conflict in response to detrimental acts. The norm of reciprocity suggests that all reciprocal debts need to be repaid. It functions
as a social bonding norm which promotes social solidarity between anyone who receives a favor
or benefit from another person (Becker 1986). In fact, it has been shown that customers who
fail to reciprocate (purchase) a salesperson’s social interaction actually experience guilt (Dahl et
al. 2005; Palmatier et al. 2009). Based on the moral norm of reciprocity, we would expect that
some level of guilt will be felt whenever someone anticipates they will be unable to repay a
reciprocal debt. However, given that discrete reciprocity is governed by norms and rules
external to the relationship, while relational reciprocity is motivated by norms and expectations
developed internally, it should be expected that the anticipation of non-repayment of a
reciprocal debt will cause a greater feeling of guilt in relational partners than in discrete
transaction partners. Further, the expectation that the relationship will continue into the future
in relational exchange would also argue for an increased level of guilt; whereas in discrete
exchange a partner may anticipate a lack of future interaction and therefore while guilt may be
felt, they may not expect to have to face the creditor of that unpaid debt again in the future.
H5: Relationships characterized by greater complex reciprocity will exhibit a higher degree of guilt in the face of anticipated unpaid reciprocal debts. 5.4.2 Antecedents of Relational and Discrete Reciprocity
So what impacts the type of reciprocity engaged in by firms in a marketing relationship?
Can a firm enable the development of relational reciprocity with its channel partners? If
marketing relationships can be characterized as either more relational or more discrete in
nature, it is important to understand what determines the trajectory the relationship will take.
Acknowledging that both upstream and downstream parties in a relationship may make efforts
to develop relational exchange, consistent with prior marketing literature (De Wulf et al. 2001;
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Morgan and Hunt 1994; Palmatier et al. 2006b) this research will assume the role of the selling
firm as being motivated to develop stronger relational exchange with their customers. I argue
that characteristics of the parties of the relationship – the customer’s relationship orientation,
the selling firm’s relational investments, and the customer’s adherence to the positive and
norms of reciprocity, and characteristics of the exchange itself – degree of specialization
required, frequency of interactions, and contractual commitments may help determine whether
relational or discrete reciprocity will be favored.
5.4.2.1 Characteristics of the Buyer and Seller
It has been argued that some customers will prefer less relational exchange in their
marketing transactions (Bagozzi 1995), possibly going so far as to avoid or resist relational
efforts by their selling firms (Crosby et al. 1990). Alternatively, customers in some situations will
desire deeper relationships with their suppliers. Investigating the impact of this relationship
orientation, or desire to engage in a strong relationship with a current or potential partner to
conduct a specific exchange, Palmatier et al. (2008) found that a buyer’s relationship orientation
enhanced the impact of salesperson relationship marketing activities on buyer’s trust, which
lead to more favorable seller outcomes. This suggests that when a customer is predisposed to
building stronger relationships, relationship marketing efforts are more likely to be reciprocated
through increased purchase, higher share of wallet, and lower propensity to switch suppliers
(Palmatier et al. 2008). I expect that:
H6: Customer relationship orientation will lead to increased complex reciprocity. Relationship marketing is based on the idea that relational investments build stronger,
more trusting customer relationships (Morgan and Hunt 1994), which leads to improved selling
firm performance (De Wulf et al. 2001; Palmatier et al. 2006a). While relationship marketing
efforts do not always lead to the desired outcomes (Palmatier et al. 2008), it has been shown
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that relationship marketing efforts can improve the relationship quality (De Wulf et al. 2001; Jap
et al. 1999), relationship satisfaction (Caceres and Paparoidamis 2007; Ping 2003), and selling
firm outcomes (Jap 1999; Palmatier et al. 2006a). Applying these results to the study of
reciprocity in marketing relationships suggests that relational reciprocity may be enhanced by
the selling firm’s efforts at building a relationship. When a customer knows that the selling firm
is dedicated to building a stronger relationship, unless there are specific reasons to avoid
relational exchange, I would expect to see a higher degree of relational reciprocity resulting
from these efforts, therefore:
H7: The customer’s perception of selling firm relational investments will lead to increased complex reciprocity. Finally, the customer’s adherence to both positive and negative norms of reciprocity
may also be important in determining whether the relationship is characterized by relational
reciprocity or discrete reciprocity. Considering first the positive norm of reciprocity, individuals
who have a stronger belief in positive reciprocity are more likely to reciprocate all beneficial
actions by their partners (Becker 1986; Perugini et al. 2003). This includes not only economically
beneficial actions, but also more socially motivated actions. While they may feel some
reactance, or resistance to accepting certain reciprocal debts (Berkowitz 1973), if they have a
strong belief in the positive norm of reciprocity, they will feel compelled to reciprocate any
benefits obtained. This suggests that over time, as non-economic actions are reciprocated, the
relationship may tend more towards relational reciprocity, rather than discrete exchange.
H8: Customers scoring higher on the positive norm of reciprocity scale are more likely to exhibit greater complex reciprocity. What about the negative norm of reciprocity? Individuals who have a stronger belief in
negative reciprocity tend to react through negative reciprocity when encountering detrimental
actions. Retaliating or negatively reciprocating detrimental actions does not agree with the
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conceptualization of relational reciprocity. One of the components of relational reciprocity is a
presumption of innocence, and increased constructive conflict while detrimental actions are
investigated and appropriate responses determined. Negative reciprocation assumes the guilt
of the other party, and takes actions which can only be considered self-interested and possibly
not even economic self-interest, but rather with a goal of obtaining some catharsis through the
punishment of the offending party. Relational exchange assumes that externalities and
contingencies will affect the relationship in the future, and flexibility is allowed in response to
changing situations. Negative reciprocity is counterproductive to cooperation when dealing
with potentially detrimental actions. Instead of taking time to understand the counterpart’s
situation (e.g., increased costs, budget constraints, etc.), negative reciprocity advocates a fitting
punishment for a crime without consideration to the outcomes of the party as a whole.
Therefore, I expect that:
H9: Customers scoring higher on the negative norm of reciprocity scale are more likely to exhibit decreased levels of complex reciprocity. 5.4.2.2 Characteristics of the Exchange
It is also possible that certain characteristics of the exchange itself will push
relationships towards relational exchange. For example, a manufacturer purchasing
commodities that are commonly exchanged on the open market, with little need of
customization may not find relational exchange to be beneficial for his purchasing purposes. On
the other hand, it is argued that in services where the exchange is often co-produced
(Bendapudi and Leone 2003) a greater degree of integration, cooperation and flexibility are
required to consummate the exchange. Dwyer, Schurr and Oh (1987) suggest that duties and
performance are relatively more complex in relational exchange than in discrete transactions,
where all contingencies have been either eliminated or planned for in the transaction
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agreement. This suggests that as the content of exchange increases in complexity, we should
expect to see a higher prevalence of relational exchange.
H10: As the content of exchange increases in complexity, greater complex reciprocity will exist. Related to the complexity of the exchange content, the frequency of interaction is also
expected to impact the process of reciprocity within the relationship. It should be expected that
more complex exchange will likely involve more frequent interactions, however even more
simple exchanges may involve frequent interactions (e.g., buying gas for a commuter).
Increased frequency of interactions will promote relational exchange on multiple dimensions.
Interacting more often will increase partners’ confidence that reciprocity will happen sometime
in the future. It is likely that given a frequent exchange, parties may be more amenable to
waiting repayment of reciprocal debts longer as they anticipate frequent opportunities to collect
on those debts in the future. Additionally, more frequent interactions create an environment
that is conducive to relational reciprocity. On the other hand, if marketing partners interact
very infrequently, it may be difficult to develop the social ties outside of the formalized
communications directed at consummating the exchange. Therefore I expect that:
H11: As the interaction frequency increases, greater complex reciprocity will exist. These hypotheses are summarized in the conceptual model in figure #29 below:
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Figu
re #
29 –
Rela
tiona
l Rec
ipro
city
Con
cept
ual F
ram
ewor
k
Com
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5.5 Survey Research on Complex Reciprocity
This portion of the research was generously funded by Boone County National Bank
(BCNB). BCNB wanted to survey both their consumer and business customers, although only the
business customers are included in this report. Given the substantial investment in this
research, BCNB retained much input over issues that affected the costs of administering this
survey. In particular, we only sent out one full survey and one reminder postcard to the sample
because of both cost considerations, and BCNB’s concern about possibly upsetting their
customers by sending too much “junk mail.”
This survey was delivered on paper using postal mail services. BCNB customers received
two personalized cover letters – one from the VP of the bank, and one from the academic
researchers, the 2-page (front and back – see exhibit #4 in the appendix) survey, and a postage
paid return envelope in one package addressed to the account owner of record from the bank’s
database. Approximately 3 weeks after the surveys were mailed out, personalized reminder
postcards (see exhibit #5 in the appendix) were sent out to all non-respondents asking that they
fill out the survey that had already been sent. A summary report was offered as an incentive for
participation that compared the participant’s business with others responding on relational
characteristics and other variables of interest. The survey was split into four sections – section 1
dealt with the respondents’ personal norms of reciprocity; section 2 asked the respondent to act
as a informant about their firm’s relationship with BCNB; section 3 asked about the
respondent’s personal relationship with BCNB; and finally section 4 asked for some personal and
firm demographic information.
5.5.1 Research Model
This research will address the conceptual model illustrated in Figure #29 on the
preceding page.
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5.5.2 Operationalizations
Measures were captured through two distinct data capture techniques: the survey
items; and the bank’s database. Measures specific to each of these data collection methods will
be discussed separately. When possible, items were adapted from existing scales. Participants
were first asked about their norms of reciprocity in section I. Section II asked the participants to
be an informant reporting on their firm’s relationship with BCNB. Section III contained items to
capture aspects of the participant’s personal relationship with BCNB. And finally, section IV
captured some demographic and descriptive statistics about the participant and their firm.
Section I: Norms of Reciprocity
Items were adapted from Perugini et al (2003) to capture both the positive and negative
norms of reciprocity. 6 items were used to capture the participant's adherence to the positive
norm of reciprocity (pr1 - pr6) and to the negative norm of reciprocity (nr1 - nr6) using 7-point
Likert scales ranging from 1 (Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor
Agree” labeling the midpoint.
Positive Norm of Reciprocity
1. When I profit from the actions of someone else, I will do something that benefits him/her in return.
2. When someone does me a favor, I feel committed to repay the favor. 3. If someone does a favor for me, I will return the favor. 4. I am ready to incur personal costs to help someone who has helped me. 5. I go out of my way to help a person who has been kind to me. 6. I'm willing to do things I don't enjoy to return someone's assistance.
Negative Norm of Reciprocity
1. If someone purposely injures me, I will try to injure him/her as well. 2. If somebody takes action that damages me, I will go to great lengths to inflict
damage on him/her. 3. If somebody is impolite to me, I become impolite in return. 4. I am willing to invest time and effort to reciprocate a harmful action. 5. If someone puts me in a difficult position, I will do the same to him/her.
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6. I will be unfair to someone who has been unfair to me.
Section II: Firm’s Relationship with BCNB
Social Relationship Marketing – captured with 3 items adapted from Palmatier, Gopalakrishna
and Houston (2006b) using 5-point Likert scales with the points labeled Never (1), Rarely (2),
Sometimes (3), Often (4) and Always (5).
1. My firm receives special treatment or status from BCNB. 2. BCNB provides meals, entertainment, or gifts to me or my firm. 3. My firm receives special reports and/or information from BCNB.
Structural Relationship Marketing – captured with 4 items adapted from Palmatier,
Gopalakrishna and Houston (2006b) using 5-point Likert scales with the points labeled Never (1),
Rarely (2), Sometimes (3), Often (4) and Always (5).
1. BCNB provides special value-added benefits to my firm. 2. BCNB customizes special programs or services for my firm. 3. BCNB adapts its policies for my firm. 4. BCNB assigns dedicated personnel to my firm.
Financial Relationship Marketing – captured with 3 items adapted from Palmatier,
Gopalakrishna and Houston (2006b) using 5-point Likert scales with the points labeled Never (1),
Rarely (2), Sometimes (3), Often (4) and Always (5).
1. My firm gets free products or services from BCNB. 2. My firm gets special pricing or discounts from BCNB. 3. BCNB provides my firm with special financial benefits and incentives.
Bank Constructive Conflict – captured with 5 items using 7-point Likert scales ranging from 1
(Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor Agree” labeling the
midpoint.
1. BCNB employees and associates work with my firm to solve challenges. 2. BCNB employees and associates discuss mutual problems with my firm. 3. BCNB employees and associates suggest solutions to my firm, if there is a conflict. 4. BCNB employees and associates voice concerns to my firm so that we can resolve
disputes.
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5. BCNB employees and associates work with my firm to improve the situation, when complications arise.
Dependence
Dependence was conceptualized as a two-dimensional construct comprised of (1) cost
based dependence and (2) benefit based dependence. Cost based dependence is defined as the
need to maintain the relationship because of the unrealized costs that would be incurred if the
relationship ended (Scheer et al. 2009). Benefit based dependence is defined as the need to
maintain the relationship because of the irreplaceable, unique net value that would be forfeited
if the relationship ended. Additionally, a “global” dependence item was asked of participants,
along with a relative dependence item so that these measures could be included as controls in
the analysis. The items for all of these measures are included below.
Benefit Based Dependence – captured with 3 items using 7-point Likert scales ranging from 1
(Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor Agree” labeling the
midpoint.
1. If my firm ended its relationship with BCNB, it would be difficult to accomplish our banking needs.
2. My firm receives benefits from doing business with BCNB that couldn’t be fully duplicated with the next best alternative.
3. If my firm ended its relationship with BCNB, the replacement would not be as effective.
Cost Based Dependence – captured with 3 items using 7-point Likert scales ranging from 1
(Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor Agree” labeling the
midpoint.
1. My firm would incur many costs to end its business with BCNB and switch to the next best alternative.
2. It would be costly for my firm to search for and locate an alternative to BCNB. 3. It would be costly for my firm to end its business relationship with BCNB.
Global Dependence
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Captured with a 7 point semantic differential scale anchored by (1) My firm is not at all dependent on BCNB and (7) My firm is extremely dependent on BCNB.
Relative Dependence
Captured with a 7 point semantic differential scale anchored by (1) BCNB is more dependent on my firm, and (7) My firm is more dependent on BCNB.
Share of Wallet
Consider all of your firm’s financial services that could be provided by BCNB. What share of that total potential business does your firm currently do with BCNB? _________%
Search for Alternatives – captured with 3 items using 7-point Likert scales ranging from 1
(Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor Agree” labeling the
midpoint.
1. My firm does business with competitors of BCNB. 2. My firm actively examines potential business opportunities from competitors of
BCNB. 3. My firm continuously searches for alternatives to BCNB.
Section III: Your Personal Relationship with BCNB
Customer Constructive Conflict – captured with 5 items using 7-point Likert scales ranging from
1 (Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor Agree” labeling the
midpoint.
1. I discuss mutual problems with BCNB. 2. If we have a conflict, I suggest solutions. 3. I work with BCNB to solve challenges. 4. I voice my concerns to BCNB so that we can resolve disputes. 5. When complications arise, I work with BCNB to improve the situation.
Commitment – captured with 3 items using 7-point Likert scales ranging from 1 (Strongly
Disagree) to 7 (Strongly Agree) with “Neither Disagree nor Agree” labeling the midpoint.
1. I view my relationship with BCNB as a long-term partnership. 2. I am willing “to go the extra mile” to do business with BCNB.
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3. I feel committed to BCNB.
Relationship Orientation – captured with 3 items using 7-point Likert scales ranging from 1
(Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor Agree” labeling the
midpoint.
1. A close relationship with BCNB is important to my success. 2. A strong relationship with BCNB enables BCNB to provide higher quality services. 3. In my dealings with BCNB, a close relationship is important to me.
Bank-Owned Loyalty – captured with 4 items using 7-point Likert scales ranging from 1 (Strongly
Disagree) to 7 (Strongly Agree) with “Neither Disagree nor Agree” labeling the midpoint.
1. I say positive things about BCNB to my coworkers because of the overall quality of BCNB’s services.
2. I would be less likely to do business with BCNB if the associates I normally deal with were no longer there.
3. I plan to do business with BCNB in the future no matter who may be assigned to handle my account(s).
4. Even if the associates I normally deal with were no longer at BCNB, I would recommend BCNB to someone seeking financial services.
Trust – captured with 3 items using 7-point Likert scales ranging from 1 (Strongly Disagree) to 7
(Strongly Agree) with “Neither Disagree nor Agree” labeling the midpoint.
1. BCNB gives me a feeling of trust. 2. I think BCNB is trustworthy. 3. I trust BCNB.
Negative Word of Mouth – This measure asked the participant to consider a hypothetical
situation: “Imagine that BCNB made an error with one of your accounts, costing you significant
time and effort to get the error corrected. Following this situation, how likely would you be to
engage in the following actions? The construct was captured with 4 items using 6-point Likert
scales ranging from 1 (Not at all likely) to 7 (Extremely likely).
1. I would suggest to my friends that they avoid doing business with BCNB. 2. I would tell everyone about BCNB’s poor service. 3. I would encourage friends and relatives to consider other banks.
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4. I would discuss BCNB’s mistake with others, but I would also mention BCNB’s explanation of how and why it occurred.
Relational Reciprocity – This research proposes that relational reciprocity is a multi-dimensional
construct composed of 5 dimensions: presumption of innocence, comfort with reciprocal debts,
valuation of reciprocal debts, repayment horizon (time) and accounting of reciprocal debts. The
dimensions are conceptualized as reflective multi-item scales. The combination of these 5 first
order constructs results in a formative scale capturing the domain of relational reciprocity. The
whole range of relational reciprocity is anchored at one end by simple reciprocity and at the
other end by complex reciprocity. Larger scores on this multi-dimensional construct would
indicate increased levels of complex reciprocity.
Relational Reciprocity – Presumption of Innocence – captured with 3 items using 7-point Likert
scales ranging from 1 (Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor
Agree” labeling the midpoint.
1. In my relationship with BCNB and all of its employees and associates, I always seek an explanation before I assign blame.
2. When it appears that problems are caused by BCNB, I seek further explanation from BCNB before reacting.
3. In my dealings with BCNB, I give them a chance to explain situations that adversely affect me.
Relational Reciprocity – Comfort with Reciprocal debts – captured with 3 items using 7-point
Likert scales ranging from 1 (Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor
Agree” labeling the midpoint.
1. I am comfortable owing BCNB favors. 2. I am willing to recommend BCNB to others because I know BCNB will not let me
down. 3. In my relationship with BCNB and all of its employees and associates I am
comfortable being owed favors.
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Relational Reciprocity – Valuation of Reciprocal debts – captured with 2 items using 7-point
Likert scales ranging from 1 (Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor
Agree” labeling the midpoint.
1. In my relationship with BCNB and all of its employees and associates I know that a friendly gesture is appreciated, and is more important than the effort it takes to make it.
2. It is more important for BCNB and I to be willing to help each other out than to worry about who owes what to whom.
Relational Reciprocity – Repayment Horizon – captured with 2 items using 7-point Likert scales
ranging from 1 (Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor Agree”
labeling the midpoint.
1. When BCNB owes me a favor, I do not expect immediate payback. 2. I do not pressure BCNB to return favors quickly.
Relational Reciprocity – Accounting of Reciprocal debts – captured with 3 items using 7-point
Likert scales ranging from 1 (Strongly Disagree) to 7 (Strongly Agree) with “Neither Disagree nor
Agree” labeling the midpoint.
1. I keep track of the favors I owe to BCNB. 2. In the relationship with BCNB, I always know who owes what to whom. 3. If I do something that benefits BCNB (for example recommend a friend), I keep track
of the time and effort I spent on that activity. Bank Secondary Data on Business Customers
In addition to the survey items, data were collected from BCNB for each of the
responding business customers. These data are summarized below to illustrate the data
available for possible analysis.
Bank CVI – an index used by the bank to indicate some measure of quality of the customer. The
CVI is the sum of the following items for each customer:
1. Primary Checking Relationship 1 point 2. Primary Deposit Relationship and Primary Loan Relationship 1 point
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3. If number of primary deposit relationships plus primary loan relationships is: Between 2 and 5 1 point Greater than 5 2 points
4. Market Size – if percentage of primary customers/households reside in subjects zip code is greater or equal to 10% 1 point
5. Profit Percentile (weighted) – if profit percentile is: 40 – 69% 1 point 70 – 89% 2 points 90% plus 3 points
6. If number of years with BCNB is: 1 – 3 years 1 point 3 plus years 2 points Group Profit Contribution – the annual profit contribution for all accounts tied to this account in
dollars.
Group Profit Percentile – an index where the largest “Group Profit Contribution” = 100% and all
others are scaled accordingly.
SIC Code – Standard Industrial Classification Code from the U.S. Securities and Exchange
Commission.
Years with Institution – the # of years the account has been opened.
Max Years – the # of years that the longest account tied to this account has been open.
Profit Contribution – the annual profit contribution for this account.
Profit Percentile – an index where the largest “Profit Percentile” = 100% and all others are
scaled accordingly.
Profit Contribution per year – the Group Profit Contribution divided by the Years with
Institution.
Total Relationships (P) – Primary relationships assigned to the customer where they are
considered to be the primary owner.
Total Relationships (S) – Secondary relationship assigned to the customer where they are not
considered the primary owner.
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Services Used – Total service used in conjunction with this account. Services include the
following: CDs, Checking, Commercial Notes, Credit Cards, Credit Lines, Debit Cards, Installment
Loans, Investments, Money Markets, Mortgage Loans, On-line Banking, Retirement Accounts,
Safe Deposit Boxes, and Savings.
Service Used from All Accounts – Total services (same list as above) used from all accounts
linked to this account.
Average Deposit Balance – average monthly balance for all deposit services used by this
account.
Average Balances of the following services: loan balance; credit card; CD; credit line;
commercial note; checking; installment loan; investment balance; mortgage loan; money
market; retirement; and savings.
5.5.3 Sample Selection
The sample included a census of BCNB’s business customers. Rather than worry about
any bias associated with randomly sampling from within their customer database, the entire
database of approximately 4,400 business customers was included in the mailing. In addition to
the survey responses, BCNB provided us with some key performance indicators from their
database for all those customers who responded to the survey. Due to a lack of controls to
specifically address the situation, businesses owning multiple accounts at BCNB received one
survey for each account. While this could be problematic for data analysis, in reality only one
business returned multiple surveys. Because there was only one business that returned multiple
surveys, one of the two was randomly selected for deletion before the data analysis.
Surveys were received from March 3rd through April 14th. Surveys received after April
14th were not included in this analysis. In total 4,400 surveys were sent out to business
customers, 453 of which were returned as undeliverable due to bad addresses, closed accounts
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or other reasons. 492 surveys were returned from business owners, of these 28 were blank,
leaving 464 useable surveys (although some have some missing data) for a response rate of
11.76% (12.46% if blank surveys are included).
5.5.4 Measurement Model and Scale Creation
Separate exploratory factor analyses were performed on the antecedent variables, the
mediating variables and the outcome variables. Table #46 below shows the outcomes of the
exploratory factor analysis on the antecedent variables. As can be seen in Table #46, the
relationship orientation (RO1-3) and norms of reciprocity (PR1-6 and NR1-6) all loaded on their a
priori factors. The relationship investment variables (SoRM, StRM, and FRM) all loaded onto one
factor.
Table #46 - Antecedent Variables EFA
Component
1 2 3 4 Eigenvalue 6.097 4.470 2.979 1.638
PRN1 .633 PRN2 .862 PRN3 .791 PRN4 .829 PRN5 .760 PRN6 .609 NRN1 .808 NRN2 .743 NRN3 .847 NRN4 .844 NRN5 .842 NRN6 .719
SoRM1 .712 SoRM2 .640 SoRM3 .692 StRM1 .759 StRM2 .775 StRM3 .720 StRM4 .698 FRM1 .689
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FRM2 .763 FRM3 .819 RO1 .826 RO2 .758 RO3 .809
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
rotation converged in 5 iterations.
A second exploratory factor analysis was performed with the complex reciprocity
variables – comfort, presumption of innocence, accounting, valuation and repayment horizon.
The results of this EFA are reported in table #47 below.
1 2 3 4
Eigenvalue 2.905 2.482 1.711 1.047
CRC1 .707CRC2 .755CRC3CRPI1 .731CRPI2 .854CRPI3 .828CRA1 .796CRA2 .805CRA3 .691CRV1CRV2CRT1 .870CRT2 .861
Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.
Table #47 - EFA for Re la tiona l Rec iproc ity
Component
rotation converged in 7 iterations.
As we can see in Table #47, the factor analysis suggested only 4 (rather than 5) factors. The
valuation items did not seem to load on any of the factors, and the third comfort item (CRC3)
did not load onto any factors as well. Overall this EFA explained approximately 63% of the
variance in the complex reciprocity multidimensional construct.
158
Finally an EFA was performed on the outcome latent variables – customer constructive
conflict (CC1-CC5), search for alternatives (SFA1-3), bank owned loyalty (BL1-4) and negative
word of mouth (NWOM1-4). The results are reported in table #48.
1 2 3 4
Eigenvalue 5.046 2.767 1.784 1.271
BL1 .603
BL2 -.689
BL3 .791
BL4 .701
NWOM1 .863
NWOM2 .900
NWOM3 .898
NWOM4 .615
SFA1 .682
SFA2 .870
SFA3 .829
CC1 .781
CC2 .712
CC3 .825
CC4 .855
CC5 .843
Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.
Ta ble #48 - Outc om e va ria ble EFA Re s ults
Component
rotation converged in 5 iterations.
Based on these EFA results, BL1 was examined and found to be outside of the scope of the bank-
owned loyalty construct. Given the lack of fit into the theoretical construct, and the lack of
loading with the other BL items, BL1 will be dropped from further analysis. It should be noted
that BL2 is reverse coded – which makes sense given the negative loading on the bank-owned
loyalty component. These 4 factors explained approximately 67% of the variance in these items.
A measurement model was analyzed for the 6 antecedent latent constructs including:
positive norm of reciprocity – PR, negative norm of reciprocity – NR, relationship orientation –
RO, structural relationship marketing – StRM, social relationship marketing – SoRM, and
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financial relationship marketing – FRM was estimated using confirmatory factor analysis (CFA) in
EQS 6.1 for windows. The measurement model was estimated using maximum likelihood
estimation with elliptical reweighted generalized least squares (ERLS) because ERLS estimates
are equivalent to maximum likelihood for normally distributed data, and superior to ML for non-
normal data (Zou and Cavusgil 2002).
The measurement model fit the data very well. All items loaded on the a priori factors,
with positive and significant variances, and factor loadings of at least 0.487. The measurement
model had acceptable fit statistics: χ2 = 581.329 (d.f. = 260), p < .001; Bentler-Bonnett Normed
Fit Index = .931; Bentler-Bonnett Non-Normed Fit Index = .954; CFI = .960; Standardized RMR =
.052; RMSEA = .054.
Examination of the modification indices suggested some changes in items on certain
factors; however further examination of both the items and the suggested factors ruled out the
possibility of moving these items to another factor. Given that the relationship marketing
factors all loaded onto one factor in the EFA results reported above, it seemed valuable to
investigate whether this 6 factor model was more fitting for the data than a 4 factor model with
all the relationship marketing variables loaded onto the same factor (agreeing with the EFA).
This second CFA produced a χ2 = 750.486 (d.f. = 269). Using the χ2 difference test gives us a Δχ2
= 169.157 (d.f. = 9) which is a significant improvement favoring the 6 factor model (p < .0001).
Therefore the 6 factor model will be used.
A second measurement model was estimated for the latent constructs of complex
reciprocity – comfort, presumption of innocence, valuation, accounting and time horizon. While
the EFA suggested dropping the valuation construct, we will begin under the assumption that it
should be included. The first measurement model had marginal fit statistics (χ2 = 170.738 (d.f. =
55), p < .001; Bentler-Bonnett Normed Fit Index = .870; Bentler-Bonnett Non-Normed Fit Index =
160
.868; CFI = .907; Standardized RMR = .081; RMSEA = .070) and the modification indices
suggested a number of items that might better fit in other factors. The first item that would fit
better in a different factor was CRC2; however after reading the item, it was not conceptually
similar to the suggested factor. Additionally, the standardized factor loading of CRC2 was very
low (.329) and it only explained 10% of the variance in the factor. Given these outcomes, it was
decided to remove CRC2 from further analysis. Note that the final measurement model did
include the valuation construct – even though the EFA suggested otherwise. The new
measurement model exhibited improved fit statistics (χ2 = 118.105 (d.f. = 44), p < .001; Bentler-
Bonnett Normed Fit Index = .901; Bentler-Bonnett Non-Normed Fit Index = .902; CFI = .935;
Standardized RMR = .065; RMSEA = .063). Further, a χ2 difference test gives us a Δ χ2 = 52.633
(d.f. = 11) which is a significant improvement over the measurement model with CRC2 included
(p < .0001).
Finally, a measurement model was estimated for the outcome latent variables:
Customer constructive conflict (FCC1-5), search for alternatives (SFA1-3), bank owned loyalty
(BL1-4) and negative word of mouth (NWOM1-4). Repeated attempts at fitting a proper model
through dropping items, fixing the variance of particular items and allowing individual items to
correlate proved ineffective. The items measuring the latent constructs of search for
alternatives and bank owned loyalty simply did not fit properly into a CFA. Further examination
of these items suggests that the search for alternative variables actually should be considered a
formative scale measuring that construct. In this context, it is likely that the bank owned loyalty
measure, with its inclusion of references to the associates the customer normally deals with,
may be perceived as inappropriate by respondents. In fact, less than half (46.5%) of the
respondents answered that there was one primary person that they dealt with at the bank. It is
possible that the wording of the question confused the other respondents as they did not have a
161
familiar contact at the bank. The latent construct of commitment (C1-3) was used as a proxy for
loyalty.
A final measurement model was estimated for the outcome latent variables: customer
constructive conflict, negative word of mouth and commitment. The individual items in this
new measurement model all had positive and significant variances, and standardized factor
loadings of at least .478. The model exhibited acceptable fit statistics (χ2 = 132.879 (d.f. = 51), p
< .001; Bentler-Bonnett Normed Fit Index = .967; Bentler-Bonnett Non-Normed Fit Index = .973;
CFI = .979; Standardized RMR = .039; RMSEA = .060).
As a final step, all latent constructs were entered into one final measurement model.
Not surprisingly, all items had positive and significant variances. Standardized factor loadings
were at least .465. The overall model exhibited acceptable fit statistics (χ2 = 1672.711 (d.f. =
1036), p < .001; Bentler-Bonnett Normed Fit Index = .923; Bentler-Bonnett Non-Normed Fit
Index = .965; CFI = .969; Standardized RMR = .052; RMSEA = .039). This measurement model is
summarized in table #49 below.
Table #50 (below) summarizes the means, standard deviations and correlations
between all variables used in our model. Note that the construct “complex” represents a
formative composition of the 5 complex reciprocity measures. Services used, and Services Used
from all accounts are proxy variables for exchange complexity. Interaction frequency was not
captured in this survey, but will be in the next survey and tested in the model there. Negative
word of mouth is the proxy for “Negative Reciprocity” in the model. And as previously
mentioned, SFA was conceptualized as a formative measure – combining SFA2 and SFA3 to
capture the customer’s level of search for alternatives.
162
ItemN
orm
of P
osit
ive
Reci
proc
ity
Nor
m o
f Neg
ativ
e Re
cipr
ocit
y
Soci
al R
elat
ions
hip
Mar
keti
ng
Stru
ctur
al
Rela
tion
ship
M
arke
ting
Fina
ncia
l Re
lati
onsh
ip
Mar
keti
ng
Firm
Con
stru
ctiv
e Co
nflic
t
Com
mit
men
t
Rela
tion
ship
O
rien
tati
on
Ong
oing
Rec
ipro
city
- Co
mfo
rt
Ong
oing
Rec
ipro
city
- A
ccou
ntin
g
Ong
oing
Rec
ipro
city
- Pr
esum
ptio
n of
In
noce
nce
Ong
oing
Rec
ipro
city
- V
alua
tion
Ong
oing
Rec
ipro
city
- Ti
me
Neg
ativ
e W
ord
of
Mou
th
PRN1 0.584PRN2 0.854PRN3 0.775PRN4 0.834PRN5 0.695PRN6 0.483NRN1 0.728NRN2 0.635NRN3 0.782NRN4 0.857NRN5 0.854NRN6 0.700SoRM1 0.759SoRM2 0.640SoRM3 0.696StRM1 0.769StRM2 0.793StRM3 0.742StRM4 0.677FRM1 0.714FRM2 0.829FRM3 0.877FCC1 0.712FCC2 0.622FCC3 0.793FCC4 0.853FCC5 0.837
C1 0.765C2 0.864C3 0.812
RO1 0.831RO2 0.694RO3 0.739CRC1 0.769CRC3 0.578CRA1 0.867CRA2 0.675CRA3 0.489CRPI1 0.555CRPI2 0.771CRPI3 0.892CRV1 0.489CRV2 0.620CRT1 0.778CRT2 0.847
NWOM1 0.870NWOM2 0.872NWOM3 0.939NWOM4 0.465
Latent ConstructTable #49 - Overall CFA results for all items in the theoretical model
12
34
56
78
910
1112
1314
1516
1718
1920
r p N r.3
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.406
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.936
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p.1
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437
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447
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5.25
μ =
14.3
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7.11
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2.22
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= 1.
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3.14
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= 2.
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μ =
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88
Ta
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12
34
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1112
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.862
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544
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.628
.351
.368
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442
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r.2
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.307
.705
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.542
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844
144
043
843
444
143
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243
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244
244
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.177
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.113
.152
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.097
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-.057
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.291
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14.2
95.6
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07.0
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29.0
38.0
00.2
35.0
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00N
437
441
440
437
433
439
435
431
431
426
439
438
411
439
442
438
432
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440
r.3
74.2
82.3
22.2
63.3
29.2
45.0
32-.0
37.0
21.1
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17.4
01.0
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.668
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.000
.000
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242
442
342
241
842
341
941
341
341
142
342
339
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442
642
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742
742
742
7
μ =
53.8
8
σ =
7.51
μ =
25.0
8
σ =
5.85
μ =
3.05
σ
= 1.
39
μ =
15.1
7
σ =
4.08
μ =
13.0
0
σ =
5.66
μ =
7.22
σ
= 2.
83
μ =
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σ
= 3.
72
μ =
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(17)
CR
A
(18)
CR
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CR
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μ =
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9
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3.01
μ =
11.5
2
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μ =
9.97
σ
= 2.
54
(21)
ong
oing
(11)
SFA
(12)
FC
C
(13)
Gui
lt
(14)
C
omm
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(15)
NW
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(16)
CR
C
165
5.5.5 Hypothesis Testing
The following hypotheses were tested using Structural Equation Modeling.
H1: Relationships characterized by greater complex reciprocity will exhibit increased share of wallet over time.
H2: Relationships characterized by greater complex reciprocity will exhibit decreased search for alternatives.
H3: Relationships characterized by less complex reciprocity will exhibit a higher level of negative reciprocal responses to detrimental actions.
H4: Relationships characterized by greater complex reciprocity will exhibit higher levels of constructive conflict in response to detrimental acts.
H5: Relationships characterized by greater complex reciprocity will exhibit a higher degree of guilt in the face of anticipated unpaid reciprocal debts.
H6: Customer relationship orientation will lead to greater complex reciprocity.
H7: The customer’s perception of selling firm relational investments will lead to greater complex reciprocity.
H8: Customers scoring higher on the positive norm of reciprocity scale are more likely to be involved in relational reciprocity.
H9: Customers scoring higher on the negative norm of reciprocity scale are more likely to be involved in discrete reciprocity relationships.
H10: As the content of exchange increases in complexity, relational reciprocity will become more likely.
H11: As the interaction frequency increases, relational reciprocity will become more likely. – not tested in this research due to lack of information on interaction frequency.
The model depicted in Figure #29 was tested with the structural equation modeling
methodology in EQS 6.1 for windows. Items for each latent construct in the overall
measurement model (table 53) were used to estimate the structural model. As with the
measurement models, the structural path model was estimated with the procedure of
maximum likelihood (ML) followed by reweighted generalized least squares (ERLS). The original
path model demonstrated acceptable fit (χ2 = 960.385 (d.f. = 474), p < .001; Bentler-Bonnett
166
Normed Fit Index = .910; Bentler-Bonnett Non-Normed Fit Index = .946; CFI = .952; Standardized
RMR = .063; RMSEA = .055). Table #51 summarizes the structural model results.
Table #51 Structural Model Results (N = 345)
Effects Hypothesized Path Coef1 t-Value Conclusion H6 Relationship Orientation → Comp. rec. 1.687 6.075 Supported H7 Relationship Marketing → Comp. rec. -0.004 -0.034 Not supported H8 Pos. Norm of Reciprocity → Comp. rec. 0.251 1.280 Not supported H9 Neg. Norm of Reciprocity → Comp. rec. -0.089 -0.543 Not supported
H10 Exchange Complexity → Comp. rec. .314 1.188 Not supported H11 Interaction Frequency → Comp. rec. Na Na Not tested H1 Complex reciprocity → Share of Wallet 0.024 3.692 Supported H2 Complex reciprocity → Search For Alt. -0.254 -3.649 Supported H3 Complex reciprocity → Neg. Rec. -0.238 -5.691 Supported H4 Complex reciprocity → Firm Const. Conf. 0.308 6.272 Supported H5 Complex reciprocity → Guilt 0.032 1.134 Not supported
Complex reciprocity → Commitment 0.367 6.803 Post-Hoc test
1Standardized path coefficient
Because different types of relationship marketing have been shown to have unique
impacts on relationship outcomes (Palmatier et al. 2006b), an alternative model was compared
that separated social, structural and financial relationship marketing efforts to look for any
unique impacts on the complex reciprocity construct. This second structural model also
provided acceptable fit statistics, but there were no differences in significant paths from the first
model tested. Therefore, results of this alternative model are not discussed further.
Of the antecedents investigated, relationship orientation proved to have a major impact
on the process of reciprocation. In fact this was the only antecedent that had a significant
impact (β = 1.687) on the complex reciprocity construct. This was also the largest standardized
coefficient in the entire path model (see figure #30 below).
167
Of the outcomes, 5 of the 6 hypothesized relationships were supported. Complex reciprocity
has a positive impact on share of wallet (β = 0.024), customer constructive conflict (β = 0.308)
and commitment (β = 0.367). Complex reciprocity also reduced the customer’s search for
alternatives (β = -0.254), and likelihood of negative reciprocity (β = -0.238) given a detrimental
situation.
In addition to the structural path model reported in Figure #30 and Table #51, a
mediation analysis was conducted following Baron and Kenny (1986). Of the 6 hypothesized
antecedents, only one – relationship orientation (RO) was found to be significant. The mediator
proposed was complex reciprocity, which was found to be a significant mediator in the
structural model. Four of the five hypothesized outcome variables were significant, and
additionally a post-hoc test found that commitment was a significant outcome variable as well.
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For each of these 5 outcome variables the same mediation test was performed with ordinary
least squares regression. The results are summarized in table #52.
In the first step (Path 1), the mediator (complex reciprocity) was regressed on the independent
variable (RO) – this step verifies that the independent variable significantly predicts the
mediator (standardized β = 0.374, p < .001). The second path that was tested was to regress
each of the outcome variables on the independent variable (RO). In each case the coefficient
here was significant and in the predicted direction (see table #52). This step verifies that the
independent variable significantly predicts the outcome variable when the mediator is not
controlled for. The final step (Path 3) is to regress the dependent variables on both the
independent (RO) and the mediator (complex reciprocity) variables. In order to conclude that
the path is mediated the coefficients in step 1 and 2 need to be significant and in the
hypothesized directions – in every case this criteria was met. Additionally, a fully mediated path
is signified by the independent variable becoming insignificant when controlling for the
mediator (step 3) – none of the paths tested supported a fully mediated path.
Path 1 Path 2
RO → Ongoing Reciprocity
RO → Dependent Variable
Dependent Variable
βA RO βA RO βA RO βA Ongoing Reciprocity Conclusion
Share of Wallet
0.177 (ρ < .001)
0.140 (ρ = .008)
0.126 (ρ < .017)
Partial Mediation
Search for Alternatives
- 0.113 (ρ = .018)
- 0.163 (ρ = .002)
0.084 (ρ = .109)
Direct Effects
Negative Reciprocity
- 0.313 (ρ < .001)
-0.308 (ρ < .001)
- 0.034 (ρ < .494)
Direct Effects
Constructive Conflict
0.628 (ρ < .001)
0.560 (ρ < .001)
0.192 (ρ < .001)
Partial Mediation
Commitment0.759
(ρ < .001)0.731
(ρ < .001)0.085
(ρ = .012)Partial
MediationA - all coefficients reported are standardized coefficients
Table #52 - Mediation Analyses for the Structural Path ModelPath 1 and 2 combined
RO + Ongoing Recip → Dependent Variable
0.374 (ρ < .001)
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Partial mediation is shown by a significant path from the mediator and independent
variable to the outcome variable in step 3. As shown in table #52, 3 of the 5 paths suggested
partial mediation. The outcome variables Search for Alternatives and Negative Reciprocity
suggested a direct effects only path from the independent to the dependent variables, and
therefore did not support the hypothesized mediating role of complex reciprocity. The paths
from RO to (1) Share of Wallet, (2) Constructive Conflict, and (3) Commitment all supported a
partially mediated path.
5.5.7 Discussion
5.5.7.1 Episodic Reciprocity
So what has been learned about reciprocity? Prior to this research, there existed a large
quantity of research examining factors which impacted individuals’ reciprocal responses. All of
this research had been conducted in a laboratory environment, and the measures of
reciprocation were the objective outcomes of various tasks or economic games. Although
theory suggests an important psychological component of reciprocity – reciprocal debt – none
of the extant research had attempted to capture this perceptual aspect of reciprocation. In
order to better understand the process of reciprocation, it is imperative that this debt be
measured as it will help to better explain why people respond differently to the same situation.
What factors specific to the exchange, and which factors specific to the exchange parties will
impact the magnitude of this perceived reciprocal debt? Why do people differ in their
perception of the debt, and further why might they differ in actions when they attempt to
resolve the debt they perceive? These questions cannot be answered without first measuring
the reciprocal debt created by actions within a relationship.
170
This research developed a scale which was able to capture the reciprocal debt created
by impactful actions between exchange partners. Through both a lab experiment, and through
surveys administered to exchange partners in long term business relationships, this measure of
reciprocal debt performed well and was both reliably predicted by a partner’s action, and it also
was an important and significant predictor of relational outcomes. Evidence supported the role
of reciprocal debt as a mediator which helps determine the response an individual will have to a
given action, thus increasing our understanding of the exchange process.
While many of the proposed moderating relationships were not supported, there were
numerous factors which had an impact on the reciprocal debt created. The recipient’s
perceptions of the relative outcomes of the action appeared to moderate the impact of an
action on the debt created, and also had a strong direct effect. When good things happen, it is
important that the beneficial impact on the selling firm is not the focus of any communications.
The more a firm benefits, relative to their partner, the greater the reciprocal debt created which
should lead to improved outcomes over time. On the other hand, when a firm must make a
decision that has some negative impact on itself and its exchange partners, it is imperative that
all those affected understand that the focal firm is suffering as well. As the perceived disparity
between the negative impact on a partner firm and that on the focal firm decreases, exchange
partners feel less of a need for retribution or reparation. As an example if a selling firm must
increase prices to their customers because of increased costs or profit losses, it would be critical
that the customers understand the negative financial situation for the selling firm to minimize
any customer churn created by this detrimental situation.
This research was also the first to investigate the locus of negative reciprocal debts in
both ongoing relationships, and in a laboratory environment. While not hypothesized, it was
171
shown that most people expect that the responsible party should make up for a detrimental
situation. This finding is probably not that surprising in and of itself; however an important
implication for the lab-based reciprocity research, which often only allows participants the
opportunity to resolve these detrimental situations through punishment, is that future
laboratory designs should allow for the option/possibility of the wrong-doer making up for the
situation. It appears that most people would prefer to not have to punish their partner. It is
possible that punishment and retribution are thought of as last resorts only to be used if the
responsible party fails to reconcile the situation. It would be interesting to investigate what
factors might sway people’s preference for retribution over reparation in these situations. Are
there certain characteristics of the relationship or parties that impact this preference? Is the
decision made only on the basis of the offending action? Are some things too detrimental to
allow a partner a second chance? Relationship marketers would benefit from a better
understanding of when their partners’ preference will change with respect to punishment and
reparation. Could it be there is some time component that affects this choice?
Also important in the formation of reciprocal debt is the customer’s attribution of
control of the action. While the proposed moderating impact of the attribution of control was
not found, the direct effect of this important perception provides important implications for
relationship marketers. It is important that beneficial situations are presented as if they were
controlled by the partner organization, otherwise the feeling of a need to repay the partner will
be decreased. Similarly, if something bad happens that is not the focal firm’s fault, it is
important that they communicate that other organizations or forces were responsible for the
detrimental impact on their partner. If people realize that something outside the control of
their partner caused the negative impact, they will be more forgiving in their desire for
172
retribution or reparations from their partner. This finding helps to illustrate the importance of
quality communication in the face of challenging situations.
Similarly, the customer’s perception of the justifiability of a detrimental action can
reduce the desire to punish the responsible firm. If firms can present these strategic decisions
and the reasoning behind them, the negative impact on their existing relationships should be
reduced. This suggests that transparency and honesty are important in marketing relationships,
especially when potentially harmful decisions must be made. If the selling firm must make a
change in policy that adversely affects its customers, it is imperative that the customers
understand why that decision had to be made.
Finally the customer’s adherence to the positive norm of reciprocity also impacted the
creation of reciprocal debt. Those who scored higher on the positive norm of reciprocity tended
to reward their partners better given a beneficial situation. On the other hand, the negative
norm of reciprocity did not have any direct or interactive effect on the creation of reciprocal
debt. Is it possible that an overall business norm exists which defines the expected response
given a detrimental situation? Or could these non-findings be more illustrative of indecision
about the allocation of the locus of a reciprocal debt? Is it possible that those higher in the
negative norm of reciprocity are more likely to try to punish their partner? Future research
should work towards a better explanation of this process and how the locus and the negative
norm of reciprocity might interact to impact the perceived reciprocal debt.
The results of this research clearly suggest that reciprocal debt needs to be considered
in the process of exchange. Failure to do so leaves out an important mediating mechanism that
helps to shape the outcomes of any exchange. While support was not found for many of the
proposed moderators, most did directly impact the creation of reciprocal debt. Further,
173
reciprocal debt was shown to reliably predict the exchange outcomes. While theory had long
established the existence of this relational mechanism, this is the first research that has
conceptualized and measured this perceived debt. Future research should further investigate
factors which impact the magnitude of debt created, the perceived locus of that debt, and what
factors might impact the resolution of that debt within the relationship.
5.5.7.2 Complex reciprocity
The second research project investigated a multi-dimensional measure of complex
reciprocity, hypothesized to range from simple reciprocity in arms length transactions to more
complex reciprocity in closer, relational exchange. This project viewed reciprocity as a relational
enabling process which helps partners develop deeper, more enmeshed relationships consistent
with the social exchange literature. This research developed a multi-dimensional scale to
capture this complex reciprocity and positioned that complex reciprocity as a relational
mediator which encourages beneficial relationship outcomes in exchange relationships. The
new scale captured a range of characteristics that would describe reciprocity as partners
transition to relational exchange. Overall, five dimensions were captured that comprise
complex reciprocity: comfort with reciprocal debts; presumption of innocence; repayment
duration; accounting of reciprocal debts; and valuation of reciprocal debts.
Of the proposed antecedents to complex reciprocity, this analysis found that only the
customer’s relationship orientation directly impacted the level of complex reciprocity. As
participants viewed relational exchange as more beneficial for them, they tended to engage in
more complex complex reciprocity. Those who did not view relational exchange as beneficial
engaged in much simpler complex reciprocity (scored lower). It was surprising to see that this
was the only significant predictor of complex reciprocity in this analysis. Future research will
174
benefit by examining other possible antecedents, and possibly by modeling a different path to
complex reciprocity through these proposed antecedents. Importantly, this research found
evidence that complex reciprocity is an important relational mediator within ongoing business
to business relationships.
The outcomes of complex reciprocity include increased share of wallet, constructive
conflict and commitment, and decreased search for alternatives and negative reciprocity. This
suggests that as relationships can develop into more complex reciprocity, firms will benefit by
greater positive relational outcomes, and decreased punishment in the face of adversity and a
reduced chance that their customers will exit and find an alternative. Clearly in this context, it is
important for businesses to work towards developing more complex reciprocity with their
customers.
This brings up an important question for future research: Is complex reciprocity always
preferable? Are there conditions that make simple reciprocity more appealing and profitable?
Future research should investigate this, as clearly business relationships do endure for long
periods of time as “arms-length” transactions. Within the context of this research, it appears
that complex reciprocity is preferable, but are there other industries or contexts where a more
simple reciprocity is the preferred exchange process?
5.5.7.3 Summary
In total, this research clearly identified and measured the psychological component of
reciprocity – reciprocal debt. Reciprocal debt was shown to be an important mediating
mechanism in the process of episodic exchange, advancing both base theories of reciprocity and
the marketing literature which often mentions, but rarely measures, reciprocity in a relational
context. Important insights were gleaned about relational factors which impact the creation of
175
reciprocal debt, and other questions remain to be answered. Secondly this research examined
the characteristics of reciprocal exchange in ongoing business relationships. Proposed
reciprocity dimensions were shown to be important predictors of established relational
outcomes. Prior theory and empirical evidence has suggested that reciprocation plays an
important role as a mediating mechanism in social exchange, this research was the first to
conceptualize and measure how reciprocity differs based on McNeil’s Social Exchange Theory.
While little was clarified about the role to achieving complex reciprocity, it was clear that the
development of complex reciprocity has important and impactful implications for marketing
relationships. Overall this research has (1) advanced the base reciprocity theory; (2) improved
the understanding of reciprocity in marketing exchange relationships; and (3) provided
important managerial implications for the development of more successful marketing
relationships. Additionally, many avenues for future research are provided and these authors
hope that other researchers will continue to broaden our understanding of the reciprocation
process.
176
CHAPTER 6 CONCLUSIONS
This dissertation approached the process of reciprocation from two distinct
perspectives. The first half addressed the episodic reciprocity that occurs during the act of any
given interaction between two exchange participants. In that way it can be applied across a
broad range of contexts. The second approach looked at the role of relational reciprocity over
the course of an exchange relationship. This portion of the research measured the perceptions
and actions of real business partners to investigate how reciprocity may change as a relationship
broadens or develops over time. This chapter will summarize the contributions this dissertation
makes to both reciprocity theory, and to marketing practitioners.
6.1 Contributions to Theory
While numerous researchers, both in and outside of marketing, have discussed the
importance of reciprocity for business relationships, this is the first research to conceptualize
and measure a reciprocal debt as an important mediating mechanism connecting one party’s
action with their partner’s response. Prior marketing research had narrowly focused on finding
correlated strategies between channel partners as evidence of reciprocal behavior. This view is
overly narrow as it doesn’t allow for repaying reciprocal debts through alternative means.
While early reciprocity theory talked much about the debt that is created when someone does
something for us, the extant research has failed to measure that debt. This research developed
a scale and empirically validated the impact of creating reciprocal debt in an exchange partner.
In the investigation of relational reciprocity, this research broadened the view of the
role of reciprocity to position it as a mediating mechanism in the process of exchange over time.
Reciprocity theory clearly implicates a delay in the process of reciprocation, and actually
suggests that this delay helps to build stronger relationships, however extant marketing
177
literature fails to account for the process of reciprocation over time. While the majority of
existing reciprocity theory investigates the immediate response by one party to the actions of
another, this research conceptualized and measured relational reciprocity – a partner’s
perceptions and attitudes about the process of relational exchange – and found that it helps
explain many beneficial outcomes for exchange partners. The more complex is their relational
exchange, the more successful is the relationship as judged by numerous popular relationship
performance measures.
Extant reciprocity research has been primarily conducted in sterile laboratory settings.
Exchange partners typically do not know each other, nor do they have any expectation of a
continued relationship over time. When studying an exchange process like reciprocation – that
takes time to consummate – it would be more appropriate to study the process in real
relationships. This research extends the investigation of reciprocal behavior into real world
business relationships. By surveying customers of a real bank, this research was able to test
theoretical propositions in “the real world”, offering validity to the findings and the importance
of reciprocity for ongoing relationships.
Finally, this research clearly shows support for the role of reciprocity as a relational
mediator. Through three different studies, 2 different conceptualizations of reciprocity
(reciprocal debt and relational reciprocity) both were found to mediate the exchange process in
various types of settings and exchange contexts. Early research on relationship marketing
(Morgan and Hunt 1994), and a recent meta-analysis (Palmatier et al. 2006a) both find evidence
of a missing mediator. Reciprocity has been suggested as that missing mediator by a number of
marketing researchers. This is the first research to conceptualize and measure the role of
reciprocity as a relational mediator.
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6.2 Contributions to Practitioners
A clearer understanding of the role of reciprocity in marketing relationships will allow
managers to more effectively invest in relationship marketing programs. Establishing programs
that develop the greatest amount of positive reciprocal debt in your customers will prove to be
the best investments in relationship marketing. Researchers have continued to question the
effectiveness of relationship marketing investments, is it possible that companies are using
programs that reduce the chance to build a debt in their customers? What boundary conditions
will allow customers to repay that debt and benefit the company? Is there some business norm
that exists that will result in a mirrored response strategy? Clearly there are many questions left
unanswered, but this research is a first effort at establishing the importance of reciprocal debt in
marketing exchange. Throughout the research one consistent finding was that building a
greater feeling of debt in your exchange partner resulted in greater outcomes for you.
This research also informs a firm’s communication strategy. When a firm must make
some change in their policies that adversely affects their customers, this research suggests that
they can minimize the negative impact on themselves by explaining the reasoning behind the
harmful changes. Harmed parties reacted less harshly to actions they considered justified by
their exchange partners. If a selling firm must increase prices to remain profitable, transparency
with their customers may result in a reduction in lost sales due to the price increase. Also it is
important that the firm gets credit for strategies that help their customers. Be sure to take
credit for actions that will benefit your exchange partners and they will repay you in the future.
Obviously this may backfire if it appears to be taken advantage of, but being honest when your
firm is able to offer some benefit will result in improved relational outcomes.
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Finally, the work on relational reciprocity suggests one route to building a stronger,
more embedded customer base. While there are numerous dimensions of a successful
relationship, it is clear that the 5 dimensions of relational reciprocity can signal to your partner
that you are investing in building a long lasting, committed relationship. If they are willing to
reciprocate those actions it will result in improved outcomes for the firm. While there are many
strategies to building better customer relationships, doing favors and trusting your partner to
return them is one way to signal your commitment to them and to the future of the
relationship.
6.3 Future Research Directions
Of course this research raises some questions for the future of reciprocity theory. As
with all research, it will be important to test these findings in a number of different contexts.
Importantly, are there types of industries that lend themselves more to reciprocal relationships,
or do these findings hold in a wide variety of industries and business relationships? It seems
that the ideal context with which to impact a relationship through reciprocity is one where the
buyer and seller work together in a collaborative or highly interactive role such that there were
multiple opportunities to reciprocate your partner’s actions.
Many of the proposed moderators did not seem to affect the reciprocal response in this
research. Does that suggest that there is a standard reciprocal response, or does it suggest that
there may be other important moderators that impact the degree of reciprocation given a
particular action? On the flip side, what boundary conditions would decrease the chance of
reciprocation? Also, this research did not investigate what might make an exchange partner
prefer retaliation instead of compensation after they’ve been slighted. Understanding that
would be valuable in many business relationships.
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With respect to the relational reciprocity findings, it was interesting that relationship
orientation was the only significant antecedent to relational reciprocity. It is difficult to believe
that there aren’t any other antecedents to relational reciprocity. What other variables might
make a party more likely to engage in more complex reciprocity? On the other hand, what
conditions might make complex reciprocity less desirable? Many business relationships exist at
a more arms-length level, the naïve interpretation of our findings is that all relationships should
be pushed towards complex reciprocity, but is that necessarily so?
This research was the first to take a very close look at a very important relational
process – that of reciprocation. Marketing researchers have long cited reciprocity as an
important process, but have failed to include in their conceptual models and their empirical
studies. While it is possible that more questions were raised by this research than were
answered, it is the hope of these researchers that future relationship marketing researchers will
begin to include this important variable in their research. Reciprocity obviously has an
important position in marketing relationships.
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APPENDIX
Exhibit #1 – University of Missouri Email Cover letter
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Exhibit #2 UAB Email Cover Letter
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Exhibit #3 – Customer Survey
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VITA
Donald J. Lund was born on July 19, 1973 in Chicago, Illinois. He received both his
Bachelor’s of Science in Psychology, and his MBA with a focus in finance, from Missouri State
University. Prior to earning his Ph.D. degree from the University of Missouri in Business
Administration with an emphasis on marketing, he had 10 years of experience working in
numerous capacities for Domino’s Pizza. After managing multiple Domino’s stores in Southwest
Missouri, he built 3 Domino’s stores in Madison, Wisconsin. He has already joined the
University of Alabama – Birmingham as an assistant professor of Marketing.