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!

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 ii

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. iii

ACKNOWLEDGEMENTS .................................................................................................................... ii
LIST OF FIGURES ..............................................................................................................................
vii
LIST OF TABLES ..............................................................................................................................
..
i x
ABSTRACT ........................................................................................................................................x
i i
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
i v

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
v

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
v i

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 v i i

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 v i ii

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 i x

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 x

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 x i

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. x i i

“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
1

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 lateadult healthcare provided by the child may depend on child perceptions of parental investments
2

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.
3

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.
4

Table #1. Reciprocity Definitions in Marketing Interpersonal Relationships
Moyer, 1970; Journal of Reciprocal purchasing - both the use of purchasing power to obtain
Finney, 1978
Houston and
Gassenheimer,
1987
Marketing
Journal of
Marketing sales and the practice of preferring one's customers in purchasing
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, 1988
J. Health
Care MKT.
Frenzen and Davis,
1990
Miller and Kean,
1997
Dahl, Honea and
Manchanda, 2005
JCR
Psychology and
Marketing
JCP
Reciprocity - a cultural norm whereby individuals enter into an exchange with the anticipation of receiving personal benefits
Norm of reciprocity - use of a purchase occasion in the short term to repay outstanding social debts
Reciprocity - the degree to which individuals expect cooperative action
Interpersonal Reciprocity - emphasizing concern for others or strong attachment to others
Institutional Reciprocity - having a built-in system for calculating the costs versus the benefits involved in the exchange
Norm of reciprocity - an obligation for people to return in kind what they've received from others
Cialdini and
Rhoads, 2001
Clayson, 2004
Marketing
Research
MER
Reciprocity - an obligation for people to return in kind what they've received from others
Reciprocity - evidence that student written teacher evaluations are related to grades received
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
5

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?
Frazier and
Colleagues, 1986,
1989 and 1991
Table #2 Reciprocity Definitions in Marketing B2B Relationships
Reciprocity - (implied definition) the actions taken by one party in
JM and JMR response to the actions taken by the other party in an exchange relationship
Kumar, Scheer and
Steenkamp, 1998
JMR
Reciprocity - (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
Palmer, 2002
Lee, Jeong, Lee and Sung, 2008
Pervan and
Johnson, 2003
IJRM
J. Strat.
Marketing
IMM
JCR
Reciprocity - partner response is contingent on actions; mutually contingent exchange of benefits; ensures long run gratification for partners
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
Reciprocity - mutual exchange of helping behaviors between importers and exporters
Reciprocity 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.
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
6

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
7

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
8

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
9

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 metaanalysis 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?
10

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?
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)
11

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”?
Positive Negative
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
12

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?
13

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.
14

“… 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
15

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.
16

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
17

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 underrewarded 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
18

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).
19

20

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.
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
21

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
22

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.
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
23

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
24

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).
25

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
26

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
27

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
28

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:
29

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
30

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.
31

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 underperforming 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
32

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.
33

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
34

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:
35

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
36

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) 5 th 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
37

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
38

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,
39

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:
40

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),
41

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).
42

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
43

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.
44

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
Cost Based
Dependence
Low
High
Low
Quadrant A
Low Benefits,
Low Latent
Costs
High
Quadrant C
High Benefits,
Low Latent
Costs
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
45

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
46

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
47

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.
48

49

50

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).
Figure #7 – Process Model of Reciprocity
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
51

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
Low n = 24
(females = 4)
(males = 20) n = 18
(females = 3)
(males = 15) n = 20
(females = 2)
(males = 18) n = 19
(females = 2)
(males = 17)
Internal External
Attribution of Control
Figure #9
Random Assignment to Detrimental Conditions (N = 81)
Magnitude
High
Low n = 19
(females = 0)
(males = 18) n = 19
(females = 0)
(males = 19) n = 18
(females = 0)
(males = 18) n = 25
(females = 3)
(males = 22)
Internal External
Attribution of Control
52

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 postscenario 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.
53

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
Audi
BMW
Buick
Cadillac
Chevrolet
Chrysler
Dodge
Ford
Honda
Hyundai
Infiniti
Jeep
Kia
Lexus
Lincoln
Mazda
Mercedes
Mercury
Mini
Nissan
Mitsubishi
Porsche
Scion
Suzuki
Toyota
Volkswagen
Volvo
Subaru
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
54

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.
55

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.
56

Common Statements
Porsche announced that because of
Table #4 - Experimental Scenario Components
Valence Magnitude
Beneficial Detrimental High Low decreased increased
Attribution of Control
Porsche External 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%.
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, 3 rd 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, 3 rd 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 7point semantic differential scales.
57

• 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.
58

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
59

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.
60

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 multimethod 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
MU
UAB
Arkansas,
Colorado, Iowa,
Illinois, Kansas,
Nebraska,
Oklahoma, Texas
Alabama,
Florida, Georgia,
Louisiana,
Mississippi,
South Carolina,
Tennessee
3102
2869
737 *
681 **
Researcher's
Personal
Email
Account
Qualtrics
Responses
Response
Rate
119
58
3.76%
2.65%
USA All Others 11,110 2,640 ** Qualtrics 157 1.85%
Total 17,081 4058 **
* - actual tracked bounced emails
** - estimated # of bounced emails based on MU Region
(23.76%)
334 2.56%
61

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
62

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.
63

Table#6 – Summary of Individual EFA’s for 4 Latent
Constructs
Eigenvalue
% Var. Extracted
# of factors with
Eigenvalue > 1
2.985
1
3.909
1
1.628
1
1.683
49.74% 65.15% 54.25% 56.10%
1
PR1
PR2
PR3
PR4
PR5
PR6
NR1
NR2
NR3
NR4
NR5
NR6
BBD1
BBD2
BBD3
CBD1
CBD2
CBD3
.711
.816
.620
.704
.728
.634
Extraction Method: Principal Component Analysis.
.825
.874
.876
.781
.769
.704
.750
.681
.775
.754
.845
.634
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
RD2
RD3
Just1
Just2
Just3
Just4
.874
.933
.896
.934
.923
.943
.948
Extraction Method: Principal Component Analysis.
64

65

66

67

68

69

70

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: χ 2 = 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.
71

72

73

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
OwnOut Between Groups
RO
Within Groups
Total
Between Groups
Within Groups
Total
Sum of
Squares
298.765
456.840
755.605
74.691
607.309
682.000 df Mean Square
1
160
161
1
160
161
298.765 104.637
2.855
3.796
F
74.691 19.678
Sig.
.000
.000
74

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
75

“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 automaker Between Groups other
Within Groups
Total
Between Groups
Within Groups
Total
Sum of
Squares
.390
175.511
175.901
7.870
193.074
200.944 df
1
160
161
1
160
161
Mean
Square
.390
1.097
7.870
1.207
F
.356
6.522
Sig.
.552
.012
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.
76

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.
77

Table #13 – Mean difference in Locus based on valence manipulation
Between Groups
Within Groups
Total
Sum of
Squares
31.306
56.352
87.659 df
1
124
125
Mean Square F
31.306 68.888
.454
Sig.
.000
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 R 2 = .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.
Model
1
Table #14 – Coefficients from regression of Locus on manipulation factors a
(Constant)
Valence
Magnitude
Unstandardized Coefficients
B
-.912
.970
.122
Std. Error
.108
.122
.120
Standardized
Coefficients
Beta
.572
.073 t
-8.429
7.935
1.013
Sig.
.000
.000
.313
Attribution a. Dependent Variable: Locus
.250 .119 .150 2.096 .038
78

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.
79

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 R 2 = .232). Table #15 shows that the standardized coefficient for OwnOut was .488
(t=6.222, p < .001).
Model
1
Table #15 – Coefficients from regression of RDLocus on OwnOut a
(Constant)
OwnOut
Unstandardized Coefficients
B
-14.404
2.333
Std. Error
1.575
.375
Standardized
Coefficients
Beta
.488 t
-9.148
6.222 a. Dependent Variable: RDLocus
Sig.
.000
.000
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
80

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 + β
1
OwnOut + β
2
X i
+ β
3
X i
OwnOut + e
Where x i
= the proposed moderating variables
Feasible variations of this regression were also tested by dropping the x i
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.
81

Table #16 – Tests of Between-Subjects Effects
Dependent Variable:RDLocus
Source
Corrected Model
Intercept
OwnOutHi
JustHi
OwnOutHi * JustHi
Error
Total
Corrected Total
Type III Sum of Squares
2934.194
a
1715.297
286.896
694.201
134.917
9627.749
16953.669
12561.943 df
3
1
1
1
1
122
126
125
Mean Square
978.065
1715.297
286.896
694.201
134.917
78.916
F
12.394
21.736
3.635
8.797
1.710 a. R Squared = .234 (Adjusted R Squared = .215)
Sig.
.000
.000
.059
.004
.193
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).
82

Table #17 – Planned Contrast Results (K Matrix) Justifiability
JustHi Simple Contrast a
Level 1 vs. Level 2 Contrast Estimate
Hypothesized Value
Difference (Estimate - Hypothesized)
Std. Error
Sig.
95% Confidence Interval for Difference a. Reference category = 2
Lower Bound
Upper Bound
Dependen t Variable
RDLocus
-7.047
0
-7.047
2.376
.004
-11.751
-2.344
Table #18 - Test Results for planned contrast
Dependent Variable:RDLocus
Source Sum of
Squares df Mean Square F
Contrast
Error
694.201
9627.749
1
122
694.201
78.916
8.797
Sig.
.004
Figure #13 below illustrates the difference in RDLocus given different values for the OwnOutHi and JustHi variables.
Figure #13 – Estimated Marginal Means of RDLocus
83

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
H1
Table #19 - Car Dealer Survey Hypothesis Summary
Hypothesis Wording
Beneficial Action (+) → Customer Reciprocal Debt
H3
H5a
H5b
H6a
H6b
H7
H8a
H8b
H9a
H9b
H10
Detrimental Action (+) → Seller Reciprocal Debt
Participant Relative Benefits moderates (+) link between
Beneficial Action and Customer Reciprocal Debt
Partner Relative Benefits moderates (+) the link between
Detrimental Action and Seller Reciprocal Debt
External Attribution of Control moderates (-) the link between
Beneficial Action and Customer Reciprocal Debt
External Attribution of Control moderates (-) the link between
Detrimental Action and Seller Reciprocal Debt
Perceived Justifiability moderates (-) the link between
Detrimental Action and Seller Reciprocal Debt
Positive Norm of Reciprocity moderates (+) the link between
Beneficial Action and Customer Reciprocal Debt
Negative Norm of Reciprocity moderates (+) the link between
Detrimental Action and Seller Reciprocal Debt
Benefit-Based Dependence moderates (-) the link between
Beneficial Action and Customer Reciprocal Debt
Cost-Based Dependence moderates (-) the link between
Detrimental Action and Seller Reciprocal Debt
Benefit-Based Dependence enhances the moderating impact of
Cost-Based Dependence





Supported?







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
84

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
85

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
86

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.
87

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.
88

89

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.
Figure #16
Random Assignment to Conditions
VALENCE
Detrimental (-1)
Low (0)
MAGNITUDE
High (1) n = 42
(females = 20)
(males = 22) n = 45
(females = 20)
(males = 25)
Beneficial (+1) n = 44
(females = 29)
(males = 15) n = 44
(females = 17)
(males = 27)
N = 175
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;
90

• 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.
91

Figure #17
Manipulations: Partner's Initial Allocation Offer
MAGNITUDE
Low (0) High (1)
Detrimental (-1) Participant: $3 Participant: $1
Game Partner: $7 Game Partner: $9
VALENCE
Beneficial (+1) Participant: $7 Participant: $9
Game Partner: $3 Game Partner: $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 postgame questionnaire assessing perceptions about the positive or negative impact of the proposed division of $10 and about the reciprocity debt created by this interaction.
92

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.
93

2.
3.
4.
5.
6.
When someone does me a favor, I feel committed to repay the favor.
If someone does a favor for me, I will return the favor.
I am ready to incur personal costs to help someone who has helped me.
I go out of my way to help a person who has been kind to me.
I'm willing to do things I don't enjoy to return someone's assistance.
Negative Norm of Reciprocity
1.
2.
If someone purposely injures me, I will try to injure him/her as well.
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.
6.
If someone puts me in a difficult position, I will do the same to him/her.
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
94

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.
2.
An obligation has been created that should be repaid.
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.
2.
My partner should do something to balance out the outcomes. [coded -1]
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
95

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.
2.
3.
4.
I could see myself becoming good friends with this person.
I think this person and I are a lot alike.
I like my partner very much.
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-
96

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.
Table #20: Combined EFA on 4 Latent Constructs
Rotated Component Matrix a
1
Eigenvalue 3.842
pr2 pr3 pr4 pr5 pr6 rd1 rd2 rd3 pa1 pa2 pa3 pa4 nr1 nr2 nr3 nr4 nr5 nr6 pr1
.737
.822
.622
.775
.793
.758
2
3.551
.865
.785
.910
.881
Component
3
2.873
.726
.733
.779
.536
4
1.412
Extraction Method: Principal component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
.646
.755
.871
5
1.022
.501
.850
.829
97

Table #21: Summary of Individual EFA's for 4 Latent Constructs
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 2 a 2 of 2 a 1 nr1 nr2 nr3 nr4 nr5
.724
.812
.637
.766
.807
.779
nr6 pr1 pr2 pr3 pr4 pr5 pr6 rd1 rd2 rd3
.732
.759
.783
.582
.876
.839
.811
.838
.784
pa1 pa2 pa3 pa4
.893
.786
.918
.893
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).
98

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 R 2 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.
99

100

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.

RD
Table #23 – ANOVA’s with the Valence manipulation as the independent variable.
Between Groups
Within Groups
Total
RDlocus Between Groups
Within Groups
Total
OwnImpact Between Groups
Within Groups
Total
PartnerImpact Between Groups
ROut
Within Groups
Total
Between Groups
Within Groups
Total
Sum of Squares
213.198
1157.317
1370.514 df
1
173
174
7958.746 1
6654.717 173
14613.463 174
287.344 1
242.576 173
529.920 174
113.324 1
634.184 173
747.509 174
761.573 1
970.736 173
1732.309 174
Mean Square
7958.746 206.900
38.467
287.344 204.928 .000
1.402
.000
113.324 30.914 .000
3.666
761.573 135.724 .000
5.611
6.690
F
213.198 31.870
Sig.
.000
101

RD
Table #24 – ANOVA’s with the Magnitude manipulation as the independent variable.
Between Groups
Within Groups
Sum of Squares
.718
1369.796 df
1
173
Mean Square
.718
7.918
F
.091
Sig.
.764
RDlocus
Total
Between Groups
Within Groups
Total
OwnImpact Between Groups
Within Groups
Total
PartnerImpact Between Groups
ROut
Within Groups
Total
Between Groups
Within Groups
Total
1370.514 174
54.093 1
14559.370 173
14613.463 174
.003 1
529.917 173
529.920 174
.239 1
747.269 173
747.509 174
.295 1
1732.013 173
1732.309 174
54.093
84.158
.643 .424
.003
3.063
.001 .975
.239 .055 .814
4.319
.295
10.012
.030 .864
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, σ 2 = 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.
102

Figure #18 – Conceptual Framework for Beneficial Actions
Participant’s
Perceived Relative
Benefits
H5a+
Beneficial
Partner Action
H1+
Participant’s
Reciprocal Debt
H2+
H8a+
Participant’s
Positive Norm of
Reciprocity
Partner
Outcomes
Figure #19 – Conceptual Framework for Detrimental Actions
Detrimental
Partner Action
H3+
Perceived
Partner Relative
Benefits
H5b+
H8b+
Participant’s
Negative Norm of Reciprocity
Partner’s
Reciprocal Debt
H4-
Partner
Outcomes
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:
103

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)
104

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
Corrected Model
Intercept
Magnitude
Valence
Type III Sum of
Squares
82.711
a
1.707
.219
82.134 df
Magnitude * Valence
Error
Total
Corrected Total
.341
63.438
148.000
146.149
1
171
175
174 a. R Squared = .566 (Adjusted R Squared = .558)
3
1
1
1
Mean Square F
27.570 74.317
1.707
.219
4.601
.591
82.134 221.398
.341
.371
.918
Sig.
.000
.033
.443
.000
.339
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%
105

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
Locus -1.00
Valence
-1.00
60
1.00
5
Total
65
Total
.00
1.00
18
9
87
9
74
88
27
83
175
Figure #22
Locus * Magnitude Crosstabulation
Count
Magnitude
Locus -1.00
.00
31
1.00
34
Total
65
Total
.00
1.00
11
44
86
16
39
89
27
83
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.
106

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 (Δχ 2 = 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
95% Confidence
Interval for Exp(B)
Locus a
-1.00 Intercept
B
Std.
Error Wald df Sig. Exp(B)
-2.695 .462 34.007 1 .000
Lower
Bound
Upper
Bound
[Bene=-1.00] 4.592 .584 61.778 1 .000 98.667 31.398 310.058
[Bene=1.00]
.00 Intercept
0 b . . 0 .
-2.107 .353 35.617 1 .000
.
[Bene=-1.00] 2.800 .540 26.914 1 .000 16.444
[Bene=1.00] 0 b . . 0 . . a. The reference category is: 1.00. b. This parameter is set to zero because it is redundant.
.
5.710
.
.
47.361
.
Table #27 - Classification Results of Multinomial Regression for Locus
Observed Locus
-1.00
.00
1.00
Overall Percentage
-1.00
60
18
9
49.7%
Predicted Locus
.00 1.00 Percent Correct
0
0
0
.0%
5
9
74
50.3%
92.3%
.0%
89.2%
76.6%
107

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
108

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. R 2 = .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 Results a
Model
1 (Constant)
Unstandardized Coefficients
B
-2.812
SplineBen 1.572
SplineDet -.482 a. Dependent Variable: RDlocus
Std. Error
1.265
.241
.456
Standardized
Coefficients
Beta t
-2.223
.530 6.533
-.086 -1.058
Sig.
.028
.000
.292
109

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 R 2 = .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
Intercept a,b
Model
1 SplineBen
Unstandardized Coefficients
B
1.116
SplineDet -1.255 a. Dependent Variable: RDlocus
Std. Error
.127
.298
Standardized
Coefficients
Beta
.536 t
8.777
-.257 -4.205 b. Linear Regression through the Origin
Sig.
.000
.000
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
110

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.
111

Figure #23 – ANOVA Results of PartnerOut at Different Locus
Contra st
1
2
3
Table #30 - Contrast Coefficients
-1.00
-1
0
-1
Locus
.00
1
-1
0
1.00
0
1
1
Partner
Out
Assume equal variances
Does not assume equal variances
Table #31 - Contrast Tests
Contrast
1
2
3
Value of
Contrast
Std.
Error t
.528 .5328 .991
.757 .5155 1.469
1.285 .3854 3.335
1
2
3
.528 .4972
.757 .4844
1.285 .3896 df
1.062 53.002
1.563 49.746
3.299 139.956
Sig. (2tailed)
172 .323
172 .144
172 .001
.293
.124
.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
112

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 R 2 = .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 PartnerOut a,b
Model
1 SplinePosRD
Unstandardized Coefficients
B
.539
Std. Error
.032
Standardized
Coefficients
Beta
.702 t
16.882
SplineNegRD -.475 a. Dependent Variable: PartnerOut b. Linear Regression through the Origin
.043 -.456 -10.971
Sig.
.000
.000
113

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.
114

The regression was significant (F = 31.847, d.f. = 175, p < .001), explaining approximately 51% of the variance around the origin (adj R 2 = .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 outcomes a,b
Model
1 SplineBen
SplineDet
SBRout
SDRout
Unstandardized Coefficients
B
1.571
-.145
-9.815
2.039
SBRoutOwn 1.398
SDRoutOwn -.170 a. Dependent Variable: RDlocus b. Linear Regression through the Origin
Std. Error
.322
.803
1.494
.964
.200
.516
Standardized
Coefficients
Beta
.755 t
4.881
-.030 -.180
-2.897 -6.570
.393 2.115
2.718
-.096
7.001
-.329
Sig.
.000
.857
.000
.036
.000
.743
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.
115

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 R 2 = .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 RDLocus a,b
Unstandardized Coefficients
Standardized
Coefficients
Model
1 (Constant)
PR
B
-20.238
-.013
Std. Error
4.486
.295
.442 OwnImpact 4.423 a. Dependent Variable: RDlocus b. Selecting only cases for which ROut > .00
Beta
-.003
.719 t
-4.511
-.045
10.013
Sig.
.000
.964
.000
116

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 R 2 = .507). The interaction variable failed to produce a significant coefficient (see table #35 below), thus we cannot support H8a.
Model
1
Table #35 Regression of OwnImpact and PROwnImpact on RDLocus a,b
Unstandardized Coefficients
Standardized
Coefficients
(Constant)
OwnImpact
B
-20.410
4.332
Std. Error
PROwnImpact a. Dependent Variable: RDlocus
.008 b. Selecting only cases for which ROut > .00
2.653
.705
.048
Beta
.704
.019 t
-7.693
6.147
.168
Sig.
.000
.000
.867
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)
RD Between Groups
Within Groups
Total
RDlocus Between Groups
Within Groups
Total
Sum of Squares
24.737
450.087
474.824
112.864
1749.173
1862.037 df
1
77
78
1
77
78
Mean Square
24.737
5.845
112.864
22.717
F
4.232
4.968
Sig.
.043
.029
117

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 (F
SplitOwn
= 11.533, ρ = .001; F splitPR
= 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
HiLoPR
1.00
.00
1.00
46
37
42
Table #38 - Tests of Between-Subjects Effects
Dependent Variable:RD
Source
Type III Sum of
Squares df
Corrected Model
Intercept
HiLoImpact
HiLoPR
HiLoImpact *HiLoPR
Error
Total
Corrected Total
85.006
a
8009.636
59.941
25.786
.000
389.818
9051.609
474.824 a. R Squared = .179 (Adjusted R Squared = .146)
1
1
75
79
78
3
1
1
Mean Square
28.335
F
8009.636 1541.034
59.941
25.786
.000
5.198
5.452
11.533
4.961
.000
118
Sig.
.002
.000
.001
.029
.993

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.
119

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 (F
RD
= .027, ρ = .869; F
RDLocus
= 1.785, ρ = .185). Figure #26 plots the average value of RD and RDLocus given the median split measure of NR (HiLoNR).
RD
Table #39 – ANOVA results for Negative Norm of Reciprocity’s Impact on RD and RDLocus
Between Groups
Sum of Squares
.177 df
1
Mean Square
.177
F
.027
Sig.
.869
Within Groups
Total
RDlocus Between Groups
Within Groups
Total
549.875
550.052
70.123
3339.108
3409.231
85
86
1
85
86
6.469
70.123
39.284
1.785 .185
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
120

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
HiLoNR
1.00
.00
1.00
74
13
44
43
Table #41 – Tests of Between-Subjects Effects
Dependent Variable:RDlocus
Source
Type III Sum of
Squares df Mean Square
Corrected Model
Intercept
HiLoImpact
HiLoNR
HiLoImpact*HiLoNR
148.950
a
743.819
65.651
62.914
9.766
3
1
1
1
1
Error
Total
3260.281
5554.225
83
87
Corrected Total 3409.231 86 a - R Squared = .044 (Adjusted R Squared = .009)
F
49.650 1.264
743.819 18.936
65.651
62.914
9.766
39.280
1.671
1.602
.249
121
Sig.
.292
.000
.200
.209
.619

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
HiLoPR
1.00
.00
1.00
13
43
44
122

Table #43 – Tests of Between-Subjects Effects
Dependent Variable:RDlocus
Source
Type III Sum of Squares df
Corrected Model
Intercept
HiLoImpact
HiLoPR
HiLoImpact*HiLoPR
225.958
a
689.896
85.406
134.903
27.473
Error
Total
3183.273
5554.225
83
87
Corrected Total 3409.231 86 a. R Squared = .066 (Adjusted R Squared = .033)
1
1
1
3
1
Mean Square F
75.319 1.964
689.896 17.988
85.406
134.903
27.473
38.353
2.227
3.517
.716
Sig.
.126
.000
.139
.064
.400
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.
123

Figure #28 – ANOVA Plot of the moderating impact of PR dissertation.
Table #44 summarizes the hypotheses tested from the experimental portion of this
Hypothesis
H1
H2
H3
H4
H5a
H5b
H7a
H7b
Table #44 - Experimental Research Hypothesis Summary
Hypothesis Wording
Beneficial Action (+) → Participant Reciprocal Debt
Participant Reciprocal Debt (+) → Partner Outcomes
Detrimental Action (+) → Partner Reciprocal Debt
Partner Reciprocal Debt (-) → Partner Outcomes
Participant Relative Benefits moderates (+) link between
Beneficial Action and Participant Reciprocal Debt
Partner Relative Benefits moderates (+) the link between
Detrimental Action and Partner Reciprocal Debt
Positive Norm of Reciprocity moderates (+) the link between
Beneficial Action and Participant Reciprocal Debt
Negative Norm of Reciprocity moderates (+) the link between Detrimental Action and Partner Reciprocal Debt
Supported?








124

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.
125

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
126

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
127

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.
Macneil (1980) differentiates exchange on a continuum ranging from discrete transactions to relational exchange. While he admits that a true discrete transaction probably
128

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.
129

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.
130

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
131

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.
132

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.
133

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
134

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.
135

Table #45. Dimensions of Complex and Simple Reciprocity
Dimension Complex Simple
Comfort with own and other reciprocal debts
High Low
Presumption of
Innocence
Valuation of reciprocal debts
Repayment terms of reciprocal debts
High
Benefits provided
Long term
Low
Costs incurred
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
136

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.
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
137

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.
138

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:
139

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;
140

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
141

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
142

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
143

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:
144

145

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.
146

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.
147

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.
148

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
149

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.
150

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.
151

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.
152

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
2.
Primary Deposit Relationship and Primary Loan Relationship 1 point
1 point
153

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%
5.
Profit Percentile (weighted) – if profit percentile is:
1 point
40 – 69%
70 – 89%
90% plus
6.
If number of years with BCNB is:
1 – 3 years
1 point
2 points
3 points
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.
154

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 3 rd through April 14 th . Surveys received after April
14 th 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
155

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
PRN4
PRN5
PRN6
.791
.829
.760
.609
NRN1
NRN2
NRN3
NRN4
NRN5
NRN6
SoRM1
SoRM2
.712
.640
.808
.743
.847
.844
.842
.719
SoRM3
StRM1
StRM2
StRM3
StRM4
FRM1
.692
.759
.775
.720
.698
.689
156

FRM2
FRM3
RO1
RO2
RO3
.763
.819
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser
Normalization. rotation converged in 5 iterations.
.826
.758
.809
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.
Eigenvalue
CRC1
CRC2
CRC3
CRPI1
CRPI2
CRPI3
CRA1
CRA2
CRA3
CRV1
CRV2
CRT1
CRT2
Ta b le #47 - EFA fo r Re la tio n a l Re c ip ro c ity
1 2
Component
3
2.905
2.482
1.711
4
1.047
.731
.854
.828
.796
.805
.691
.870
.861
.707
.755
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
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.
157

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.
Ta b le # 4 8 - Ou tc o m e va r ia b le EFA Re s u lts
1 2
Com ponent
3
2.767
1.784
Eigenvalue 5.046
BL1 .603
SFA1
SFA2
SFA3
CC1
CC2
CC3
CC4
CC5
BL2
BL3
BL4
NWOM1
NWOM2
NWOM3
NWOM4
.781
.712
.825
.855
.843
.863
.900
.898
.615
.682
.870
.829
4
1.271
-.689
.791
.701
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
rotation converged in 5 iterations.
Based on these EFA results, BL1 was examined and found to be outside of the scope of the bankowned 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
158

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 nonnormal 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: χ 2 = 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 χ 2 = 750.486 (d.f. = 269). Using the χ 2 difference test gives us a Δχ 2
= 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 (χ 2 = 170.738 (d.f. =
55), p < .001; Bentler-Bonnett Normed Fit Index = .870; Bentler-Bonnett Non-Normed Fit Index =
159

.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 (χ 2 = 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 χ 2 difference test gives us a Δ χ 2 = 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
160

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 (χ 2 = 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 (χ 2 = 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.
161

Table #49 - Overall CFA results for all items in the theoretical model
Latent Construct
FCC4
FCC5
C1
C2
C3
RO1
RO2
RO3
CRC1
CRC3
CRA1
SoRM2
SoRM3
StRM1
StRM2
StRM3
StRM4
FRM1
FRM2
FRM3
FCC1
FCC2
FCC3
Item
PRN1
PRN2
PRN3
PRN4
PRN5
PRN6
NRN1
NRN2
NRN3
NRN4
NRN5
NRN6
SoRM1
CRA2
CRA3
CRPI1
CRPI2
CRPI3
CRV1
CRV2
CRT1
CRT2
NWOM1
NWOM2
NWOM3
NWOM4
0.584
0.854
0.775
0.834
0.695
0.483
0.728
0.635
0.782
0.857
0.854
0.700
0.759
0.640
0.696
0.769
0.793
0.742
0.677
0.714
0.829
0.877
0.712
0.622
0.793
0.853
0.837
0.765
0.864
0.812
0.831
0.694
0.739
0.769
0.578
0.867
0.675
0.489
0.555
0.771
0.892
0.489
0.620
0.778
0.847
0.870
0.872
0.939
0.465
162

163

164

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 (χ 2 = 960.385 (d.f. = 474), p < .001; Bentler-Bonnett
165

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
H6 Relationship Orientation → Comp. rec.
Coef 1 t-Value Conclusion
1.687 6.075 Supported
H7 Relationship Marketing → Comp. rec.
H8 Pos. Norm of Reciprocity → Comp. rec.
-0.004 -0.034 Not supported
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.
H1 Complex reciprocity → Share of Wallet
H2 Complex reciprocity → Search For Alt.
H3 Complex reciprocity → Neg. Rec.
Na Na Not tested
0.024 3.692 Supported
-0.254 -3.649 Supported
-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
1 Standardized 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).
166

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.
167

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.
Table #52 - Mediation Analyses for the Structural Path Model
Path 1 Path 2 Path 1 and 2 combined
RO → Ongoing
Reciprocity
RO → Dependent
Variable
RO + Ongoing Recip → Dependent
Variable
Dependent
Variable
Share of
Wallet
Search for
Alternatives
Negative
β A
RO
β A
RO
β A
RO
β A
Ongoing Reciprocity
0.177 0.140
(ρ < .001)
(ρ = .018)
(ρ = .008)
(ρ = .002)
(ρ < .017)
(ρ = .109)
Conclusion
Mediation
Effects
Reciprocity
Constructive
0.374
(ρ < .001) (ρ < .001) (ρ < .001)
0.628
(ρ < .494)
0.560
Effects
Partial
Conflict (ρ < .001) (ρ < .001)
0.759
(ρ < .001)
0.731
Mediation
Partial
Commitment
(ρ < .001) (ρ < .001)
A - all coefficients reported are standardized coefficients
(ρ = .012) Mediation
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.
168

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.
169

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
170

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
171

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,
172

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
173

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
174

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.
175

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.
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
176

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.
177

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.
178

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.
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.
179

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.
180

181

182

183

184

185

186

Anderson, Erin and Barton Weitz (1989), "Determinants of Continuity in Conventional Industrial
Channel Dyads," Marketing Science, 8 (4), 310.
---- (1992), "The Use of Pledges to build and Sustain Commitment in Distribution Channels,"
Journal of Marketing Research, 29 (1), 18-34.
Anderson, James C. and James A. Narus (1990), "A Model of Distributor Firm and Manufacturer
Firm Working Partnerships," Journal of Marketing, 54 (1), 42-58.
Bagozzi, Richard P. (1975), "Marketing as Exchange," Journal of Marketing, 39 (4), 32-39.
---- (1995), "Reflections on Relationship Marketing in Consumer Markets," Journal of the
Academy of Marketing Science, 23 (4), 272-77.
---- (1992), "The Self-Regulation of Attitudes, Intentions, and Behavior," Social Psychology
Quarterly, 55 (2), 178-204.
Baron, R. M. and D. A. Kenny (1986), "The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations," Journal of personality and social psychology, 51 (6), 1173-82.
Becker, Howard (1956), Man in Reciprocity: Introductory Lectures on Culture, Society and
Personality. New York: Praeger Publishers, Inc.
Becker, Lawrence C. (1986), Reciprocity. New York: Routledge & Kegan Paul.
Bendapudi, Neeli and Robert P. Leone (2003), "Psychological Implications of Customer
Participation in Co-Production," Journal of Marketing, 67 (1), 14-28.
Berkowitz, Leonard (1973), "Reactance and the unwillingness to help others," Psychological
Bulletin, 79 (5), 310-17.
Berry, Leonard L. (1995), "Relationship Marketing of Services--Growing Interest, Emerging
Perspectives," Journal of the Academy of Marketing Science, 23 (4), 236-45.
Bolton, Gary E., Jordi Brandts, and Axel Ockenfels (1998), "Measuring Motivations for the
Reciprocal Responses Observed in a Simple Dilemma Game," Experimental Economics, 1 (3),
207-19.
Bolton, Gary E. and Axel Ockenfels (2000), "ERC: A theory of equity, reciprocity, and competition," The American Economic Review, 90 (1), 166.
187

Bowman, Douglas and Das Narayandas (2004), "Linking Customer Management Effort to
Customer Profitability in Business Markets," Journal of Marketing Research 41 (4), 433-47.
Brehm, Jack W. and Ann H. Cole (1966), "Effect of a favor which reduces freedom," Journal of
Personality & Social Psychology, 3 (4), 420-26.
Bucklin, Louis P. (1973), "A Theory of Channel Control," Journal of Marketing, 37 (1), 39-47.
Caceres, Ruben Chumpitaz and Nicholas G. Paparoidamis (2007), "Service quality, relationship satisfaction, trust, commitment and business-to-business loyalty," European Journal of
Marketing, 41 (7/8), 836-67.
Cannon, Joseph P. and William D. Perreault Jr (1999), "Buyer-Seller Relationships in Business
Markets," Journal of Marketing Research, 36 (4), 439-60.
Carlsmith, Kevin M., John M. Darley, and Paul H. Robinson (2002), "Why Do We Punish?
Deterrence and Just Deserts as Motives for Punishment," Journal of Personality & Social
Psychology, 83 (2), 284-99.
Chen, Ya-Ru, Xiao-Ping Chen, and Rebecca Portnoy (2009), "To whom do positive norm and negative norm of reciprocity apply? Effects of inequitable offer, relationship, and relational-self orientation," Journal of Experimental Social Psychology, 45 (1), 24-34.
Cialdini, Robert B. (2009), Influence: Science and Practice (5th ed.). Boston, MA: Pearson
Eduction, Inc.
Cialdini, Robert B. and Kelton V. L. Rhoads (2001), "Human Behavior and the Marketplace.,"
Marketing Research, 13 (3), 8-13.
Clayson, Dennis E. (2004), "A Test of the Reciprocity Effect in the Student Evaluation of
Instructors in Marketing Classes," Marketing Education Review, 14 (2), 11-21.
Crosby, Lawrence A., Kenneth A. Evans, and Deborah Cowles (1990), "Relationship quality in services selling: An interpersonal influence perspective," Journal of Marketing, 54 (3), 68.
Dahl, Darren W., Heather Honea, and Rajesh V. Manchanda (2005), "Three Rs of Interpersonal
Consumer Guilt: Relationship, Reciprocity, Reparation," Journal of Consumer Psychology, 15 (4),
307-15.
Dawson, Scott (1988), "Four Motivations for Charitable Giving: Implications for Marketing
Strategy to Attract Monetary Donations for Medical Research," Journal of Health Care
Marketing, 8 (2), 31-37.
De Wulf, Kristof, Gaby Odekerken-Schroder, and Dawn Iacobucci (2001), "Investments in consumer relationships: A cross-country and cross-industry exploration," Journal of Marketing,
65 (4), 33-51.
188

Doney, Patricia M. and Joseph P. Cannon (1997), "An examination of the nature of trust in buyer-seller relationships," Journal of Marketing, 61 (2), 35.
Dwyer, F. Robert, Paul H. Schurr, and Sejo Oh (1987), "Developing Buyer-Seller Relationships,"
Journal of Marketing, 51 (2).
Eder, Paul, Karl Aquino, Carl Turner, and Americus Reed (2006), "Punishing Those Responsible for the Prison Abuses at Abu Ghraib: The Influence of the Negative Reciprocity Norm (NRN),"
Political Psychology, 27 (6), 807-21.
Eisenberger, Robert, Norman Cotterell, and Joan Marvel (1987), "Reciprocation Ideology,"
Journal of Personality & Social Psychology, 53 (4), 743-50.
Eisenberger, Robert, Patrick Lynch, Justin Aselage, and Stephanie Rohdieck (2004), Who Takes the Most Revenge? Individual Differences in Negative Reciprocity Norm Endorsement:
Personality and Social Psychology Bulletin Vol 30(6) Jun 2004, 789-799.
El-Ansary, A. and L. W. Stern (1972), "Power and measurement in the distribution channel,"
Journal of Marketing Research, 9, 47-52.
Emerson, Richard M. (1962), "Power-Dependence Relations," American Sociological Review, 27
(1), 31-41.
Falk, Armin, Simon Gächter, and Judit Kovács (1999), "Intrinsic motivation and extrinsic incentives in a repeated game with incomplete contracts," Journal of Economic Psychology, 20
(3), 251-84.
Fang, Eric, Robert W. Palmatier, Lisa K. Scheer, and Ning Li (2008), "Trust at Different
Organizational Levels," Journal of Marketing, 72 (2), 80-98.
Fehr, Ernst and Simon Gachter (2000), "Fairness and retaliation: The economics of reciprocity,"
The Journal of Economic Perspectives, 14 (3), 159.
---- (1998), "Reciprocity and economics: The economic implications of Homo Reciprocans,"
European Economic Review, 42 (3-5), 845.
Finney, F. Robert (1978), "Reciprocity: Gone But Not Forgotten," Journal of Marketing, 42 (1),
54-59.
Frazier, Gary L. (1983), "Interorganizational Exchange Behavior in Marketing Channels: A
Broadened Perspective," Journal of Marketing, 47 (4), 68-78.
Frazier, Gary L., James D. Gill, and Sudhir H. Kale (1989), "Dealer Dependence Levels and
Reciprocal Actions in a Channel of Distribution in a Developing Country," Journal of Marketing,
53 (1), 50-69.
189

Frazier, Gary L. and Raymond C. Rody (1991), "The use of influence strategies in interfirm relationships in industrial product channels," Journal of Marketing, 55 (1), 52.
Frazier, Gary L. and John O. Summers (1984), "Interfirm Influence Strategies and Their
Applications within Distribution Channels," Journal of Marketing, 48 (3), 43-55.
---- (1986), "Perceptions of Interfirm Power and Its Use Within a Franchise Channel of
Distribution," Journal of Marketing Research, 23 (2), 169-76.
Frenzen, Jonathan K. and Harry L. Davis (1990), "Purchasing Behavior in Embedded Markets,"
Journal of Consumer Research, 17 (1), 1-12.
Gaski, John F. (1984), "The Theory of Power and Conflict in Channels of Distribution," Journal of
Marketing, 48 (3), 9-29.
Gouldner, Alvin W. (1960), "The Norm of Reciprocity: A Preliminary Statement," American
Sociological Review, 25 (2), 161-78.
Greenglass, Esther R. (1969), "Effects of prior help and hindrance on willingness to help another:
Reciprocity or social responsibility," Journal of Personality & Social Psychology, 11 (3), 224-31.
Gronroos, Christian (1999), "Relationship Marketing: Challenges for the Organization," Journal of Business Research, 46 (3), 327-35.
Gundlach, Gregory T. and Ernest R. Cadotte (1994), "Exchange Interdependence and Interfirm
Interaction: Research in a Simulated Channel Setting," Journal of Marketing Research, 31 (4),
516-32.
Heide, Jan B. and George John (1992), "Do norms matter in marketing relationships?," Journal of
Marketing, 56 (2), 32.
Hibbard, Jonathan D., Nirmalya Kumar, and Louis W. Stern (2001), "Examining the Impact of
Destructive Acts in Marketing Channel Relationships," Journal of Marketing Research, 38 (1), 45-
61.
Houston, Franklin S. and Jule B. Gassenheimer (1987), "Marketing and Exchange," Journal of
Marketing, 51 (4), 3-18.
Jap, Sandy D. (1999), "Pie-expansion efforts: Collaboration processes in buyer-supplier relationships," Journal of Marketing Research, 36 (4), 461-75.
Jap, Sandy D. and Shankar Ganesan (2000), "Control Mechanisms and the Relationship Life
Cycle: Implications for Safeguarding Specific Investments and Developing Commitment," Journal of Marketing Research 37 (2), 227-45.
190

Jap, Sandy D., Chris Manolis, and Barton A. Weitz (1999), "Relationship Quality and Buyer-Seller interactions in Channels of Distribution," Journal of Business Research, 46 (3), 303-13.
Johnson, Jean L. and Ravipreet S. Sohi (2001), "The influence of firm predispositions on interfirm relationship formation in business markets," International Journal of Research in Marketing, 18
(4), 299-318.
Keysar, Boaz, Benjamin A. Converse, Wang Jiunwen, and Nicholas Epley (2008), "Reciprocity Is
Not Give and Take: Asymmetric Reciprocity to Positive and Negative Acts," Psychological
Science, 19 (12), 1280-86.
Kumar, Nirmalya, Lisa K. Scheer, and Jan-Benedict E. M. Steenkamp (1995a), "The Effects of
Perceived Interdependence on Dealer Attitudes," Journal of Marketing Research 32 (3), 348-56.
---- (1995b), "The effects of supplier fairness on vulnerable resellers," Journal of Marketing
Research, 32 (1), 54-65.
---- (1998), "Interdependence, Punitive Capability, and the Reciprocation of Punitive Actions in
Channel Relationships," Journal of Marketing Research 35 (2), 225-35.
Lawler, Edward J. and Samuel B. Bacharach (1987), "Comparison of Dependence and Punitive
Forms of Power," Social Forces, 66 (2), 446.
Lee, Dong-Jin, Insik Jeong, Hyoung Tark Lee, and Hye Jin Sung (2008), "Developing a model of reciprocity in the importer–exporter relationship: The relative efficacy of economic versus social factors," Industrial Marketing Management, 37 (1), 9-22.
Lerner, Melvin J. and Rosemary R. Lichtman (1968), "Effects of Perceived Norms on Attitudes and Altruistic Behavior Toward a Dependent Other," Journal of Personality & Social Psychology,
9 (3), 226-32.
Leventhal, Gerald S., Thomas Weiss, and Gary Long (1969), "Equity, reciprocity, and reallocating rewards in the dyad," Journal of Personality & Social Psychology, 13 (4), 300-05.
Li, Zhan G. and Rajiv P. Dant (1997), "An Exploratory Study of Exclusive Dealing in Channel
Relationships," Journal of the Academy of Marketing Science, 25 (3), 201.
Loch, Christoph H. and Yaozhong Wu (2008), "Social Preferences and Supply Chain Performance:
An Experimental Study," Management Science, 54 (11), 1835-49.
Logan, Charles H. and Gerald G. Gaes (1993), "Meta-Analysis and the Rehabilitation of
Punishment," Justice Quarterly, 10 (2), 245-64.
Macneil, Ian R. (1986), "Exchange Revisited: Individual Utility and Social Solidarity," Ethics, 96
(3), 567-93.
191

---- (1980), The New Social Contract. Binghampton, N.Y.: Yale University Press.
Marsh, Lawrence C. and David R. Cormier (2002), Spline Regression Models. Thousand Oaks, CA:
Sage Publications.
Michaels, James W. (1983), "Equity and Reciprocity Motivations as a Function of the
Intentionality of Underpayment," Journal of Psychology, 114 (2), 179.
Miller, Nancy J. and Rita C. Kean (1997), "Reciprocal Exchange in Rural Communities: Consumers'
Inducements to Inshop," Psychology & Marketing, 14 (7), 637-61.
Morgan, Robert M. and Shelby D. Hunt (1994), "The Commitment-Trust Theory of Relationship
Marketing," Journal of Marketing, 58 (3), 20.
Moyer, Reed (1970), "Reciprocity: Retrospect and Prospect," Journal of Marketing, 34 (4), 47-54.
Palmatier, Robert W. (2008), Relationship Marketing. Cambridge, Mass.: Marketing Science
Institute.
Palmatier, Robert W., Rajiv P. Dant, Dhruv Grewal, and Kenneth R. Evans (2006a), "Factors
Influencing the Effectiveness of Relationship Marketing: A Meta-Analysis," Journal of Marketing,
70 (4), 136-53.
Palmatier, Robert W., Srinath Gopalakrishna, and Mark B. Houston (2006b), "Returns on
Business-to-Business Relationship Marketing Investments: Strategies for Leveraging Profits,"
Marketing Science, 25 (5), 477-93.
Palmatier, Robert W., Cheryl Burke Jarvis, Jennifer R. Beckhkoff, and Frank R. Kardes (2009),
"The Role of Customer Gratitude in Relationship Marketing," Journal of Marketing, Forthcoming.
Palmatier, Robert W., Lisa K. Scheer, Kenneth R. Evans, and Todd J. Arnold (2008), "Achieving relationship marketing effectiveness in business-to-business exchanges," Journal of the
Academy of Marketing Science, 36 (2), 174-90.
Palmatier, Robert W., Lisa K. Scheer, Mark B. Houston, Kenneth R. Evans, and Srinath
Gopalakrishna (2007a), "Use of relationship marketing programs in building customersalesperson and customer-firm relationships: Differential influences on financial outcomes,"
International Journal of Research in Marketing, 24 (3), 210-23.
Palmatier, Robert W., Lisa K. Scheer, and Jan-Benedict E. M. Steenkamp (2007b), "Customer
Loyalty to Whom? Managing the Benefits and Risks of Salesperson-Owned Loyalty," Journal of
Marketing Research, 44 (2), 185-99.
Palmer, Adrian (2002), "Cooperative marketing associations: an investigation into the causes of effectiveness," Journal of Strategic Marketing, 10 (2), 135-56.
192

Perugini, Marco , Marcello Gallucci, Fabio Presaghi, and Anna Paola Ercolani (2003), "The personal norm of reciprocity," European Journal of Personality, 17 (4), 251-83.
Pervan, Simon J., Liliana L. Bove, and Lester W. Johnson (2009), "Reciprocity as a key stabilizing norm of interpersonal marketing relationships: Scale development and validation," Industrial
Marketing Management, 38 (1), 60-70.
Pervan, Simon J. and Lester W. Johnson (2003), "Understanding Process in Relationship
Marketing: A Focus on Reciprocity," Journal of Customer Behaviour, 2, 85-109.
Ping, Robert A. Jr. (2003), "Antecedents of satisfaction in a marketing channel," Journal of
Retailing, 79 (4), 237.
Rabin, Matthew (1993), "Incorporating fairness into game theory and economics," The American
Economic Review, 83 (5), 1281.
Reynolds, Kristy E. and Sharon E. Beatty (1999), "Customer benefits and company consequences of customer-salesperson relationships in retailing," Journal of Retailing, 75 (1), 11.
Rynning, M. (1989), "Reciprocity in a Gift-Giving Situation," Journal of Social Psychology, 129 (6),
769.
Scheer, Lisa K., C. Fred Miao, and Jason Garrett (2008a), "The Effects of Supplier Capabilities on
Industrial Customers' Loyalty: The Role of Dependence," Journal of the Academy of Marketing
Science, Forthcoming.
Scheer, Lisa K., Robert W. Palmatier, and C. Fred Miao (2008b), "Rethinking the Role and Nature of Dependence in Marketing Relationships - A Meta Analysis."
---- (2009), "Rethinking the Role and Nature of Dependence in Marketing Relationships - A Meta
Analysis."
Schopler, John and Vaida D. Thompson (1968), "Role of attribution processes in mediating amount of reciprocity for a favor," Journal of Personality & Social Psychology, 10 (3), 243-50.
Sethi, Rajiv and E. Somanathan (2003), "Understanding reciprocity," Journal of Economic
Behavior & Organization, 50 (1), 1.
Siguaw, Judy A., Penny M. Simpson, and Thomas L. Baker (1998), "Effects of Supplier Market
Orientation on Distributor Market Orientation and the Channel Relationship: The Distributor
Perspective," The Journal of Marketing, 62 (3), 99-111.
Stern, Louis W. (1967), "The Concept of Channel Control," Journal of Retailing, 43 (2), 14.
Stern, Louis W. and Torger Reve (1980), "Distribution Channels as Political Economies: A
Framework for Comparative Analsysis," Journal of Marketing, 44 (3), 52-64.
193

Thibaut, John W. and Harold H. Kelley (1961), The Social Psychology of Groups. New York: John
Wiley & Sons, INC.
Weitz, Barton A. and Sandy D. Jap (1995), "Relationship Marketing and Distribution Channels,"
Journal of the Academy of Marketing Science, 23 (4), 305-20.
Wikipedia (2009), "Ethic of Reciprocity," Vol. 2009: Wikipedia.
Williamson, Oliver E. (1996), The Mechanisms of Governance. New York: Oxford University
Press.
---- (2007), "Transaction Cost Economics: The Process of Theory Development," in Great Minds in Management, Ken G Smith and Michael A. Hitt, Eds. Oxford: Oxford University Press.
Zou, Shaoming and S. Tamer Cavusgil (2002), "The GMS: A Broad Conceptualization of Global
Marketing Strategy and Its Effect on Firm Performance," Journal of Marketing, 66 (4), 40-56.
194

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.
195