Factors Influencing the Effectiveness of Relationship Marketing

136Journal of MarketingVol. 70 (October 2006), 136–153

© 2006, American Marketing AssociationISSN: 0022-2429 (print), 1547-7185 (electronic)

Robert W. Palmatier, Rajiv P. Dant, Dhruv Grewal, & Kenneth R. Evans

Factors Influencing the Effectivenessof Relationship Marketing:

A Meta-AnalysisRelationship marketing (RM) has emerged as one of the dominant mantras in business strategy circles, though RMinvestigations often yield mixed results. To help managers and researchers improve the effectiveness of theirefforts, the authors synthesize RM empirical research in a meta-analytic framework. Although the fundamentalpremise that RM positively affects performance is well supported, many of the authors’ findings have significantimplications for research and practice. Relationship investment has a large, direct effect on seller objectiveperformance, which implies that additional meditated pathways may explain the impact of RM on performance.Objective performance is influenced most by relationship quality (a composite measure of relationship strength)and least by commitment. The results also suggest that RM is more effective when relationships are more criticalto customers (e.g., service offerings, channel exchanges, business markets) and when relationships are built withan individual person rather than a selling firm (which partially explains the mixed effects between RM andperformance reported in previous studies).

Robert W. Palmatier is an assistant professor, College of Business, Uni-versity of Cincinnati (e-mail: [email protected]). Rajiv P. Dant is FrankHarvey Distinguished Professor of Marketing, College of Business Admin-istration, University of South Florida (e-mail: [email protected]). DhruvGrewal is Toyota Chair of Commerce and Electronic Business and Profes-sor of Marketing, Babson College (e-mail: [email protected]).Kenneth R. Evans is Professor of Marketing and Associate Dean, Collegeof Business, University of Missouri (e-mail: [email protected]). Theauthors thank the Marketing Science Institute for its financial support ofthis research. They also thank Steven P. Brown, Elaine Allen, and Kent B.Monroe for their assistance on this project, as well as the anonymous JMreviewers for their constructive comments.

To read or contribute to reader and author dialogue on this article, visithttp://www.marketingpower.com/jmblog.

Relationship marketing (RM), both in business prac-tice and as a focus of academic research, has “expe-rienced explosive growth” in the past decade (Srini-

vasan and Moorman 2005). Morgan and Hunt (1994, p. 22)define RM as “all marketing activities directed towardsestablishing, developing, and maintaining successful rela-tional exchanges.” Most research and practice assumes thatRM efforts generate stronger customer relationships thatenhance seller performance outcomes, including salesgrowth, share, and profits (Crosby, Evans, and Cowles1990; Morgan and Hunt 1994), but some business execu-tives have been disappointed in the effectiveness of theirRM efforts (Colgate and Danaher 2000). Researchers havealso suggested that in certain situations, RM may have anegative impact on performance (De Wulf, Odekerken-Schröder, and Iacobucci 2001; Hibbard et al. 2001).

Overall, these findings indicate that the effectiveness ofRM efforts may vary depending on the specific RM strategyand exchange context; this inconsistency with regard to per-

formance suggests the need for a meta-analysis to integratethe abundance of accumulated empirical research and tounderstand better the RM strategies that are most effectivefor building strong relationships, the outcomes that are mostaffected by customer relationships, and the conditions inwhich RM is most effective for generating positive selleroutcomes. Advancing understanding of the primary driversof RM effectiveness can increase the return on firms’ RMinvestments dramatically and provide researchers withinsights into ways to build more comprehensive models ofthe influence of RM on performance (Reinartz and Kumar2003).

Using Dwyer, Schurr, and Oh’s (1987) seminal articleon relationships; Crosby, Evans, and Cowles’s (1990) intro-duction of relationship quality; and Morgan and Hunt’s(1994) key mediating variable theory of RM, most researchhas conceptualized the effects of RM on outcomes as fullymediated by one or more of the relational constructs oftrust, commitment, relationship satisfaction, and/or relation-ship quality. The existing literature offers a wide range ofantecedents for these relational mediators, and researchersdisagree about which one best captures the characteristicsof a relational exchange that influence performance. Forexample, Morgan and Hunt (1994) propose that trust andcommitment are both key to predicting exchange perfor-mance, whereas others suggest that either trust (e.g., Doneyand Cannon 1997; Sirdeshmukh, Singh, and Sabol 2002) orcommitment (e.g., Anderson and Weitz 1992; Gruen, Sum-mers, and Acito 2000; Jap and Ganesan 2000) alone is thecritical relational construct.

Another school of thought suggests that the global con-struct of relationship quality, as reflected by a combinationof commitment, trust, and relationship satisfaction, offersthe best assessment of relationship strength and providesthe most insight into exchange performance (e.g., De Wulf,

The Effectiveness of Relationship Marketing / 137

Odekerken-Schröder, and Iacobucci 2001; Kumar, Scheer,and Steenkamp 1995). These different relational mediatorshave been linked empirically to many antecedents and out-comes, which leads to the critical question, How does therelational mediated model vary across different relationalperspectives?

In this article, we systematically review and analyze theliterature on relational mediators in a meta-analytic frame-work (Figure 1) to provide insight into the following fourresearch questions: (1) Which RM strategies are most effec-tive for building customer relationships? (2) What outcomesare most affected by customer relationships? (3) Whichmoderators are most effective in influencing relationship–outcome linkages? and (4) How does the RM strategy →mediator → outcome linkage vary across differentmediators?

Conceptual FrameworkIn reviewing the literature pertaining to relational media-tors, we identified many constructs with similar definitionsthat operate under different aliases and constructs withsimilar names but different operationalizations. Thus, weuse a single construct definition (see Table 1) to code exist-ing research; we include a construct in the conceptualframework only if at least 10 effects emerge to support itsempirical analysis. Of the many constructs investigated,only 18 met these criteria and appear in the model. Ournomological placement of each construct is driven by boththeory and the frequency of placement in extant research.Of the studies that include hypothesized relationships withrelational mediators, more than 90% are consistent with thecausal ordering of constructs in our framework, with the

exceptions of conflict and cooperation, which agree withour nomological framework in approximately 70% of extantstudies.

Although a relationship is, by its very nature, two sidedand both parties typically share in the benefits of a strongrelationship, some antecedents and outcomes may have dif-ferential effects according to the measurement perspective(e.g., dependence). Thus, we adopt terminology to identifythe perspective of each construct relative to its relationalmediators. In this framework, “seller” refers to the partythat implements the RM effort in the hope of strengtheningits relationship with the “customer,” and the relationalmediator captures the customer’s perception of its relation-ship with the seller. For clarity and consistency, we usethese customer and seller perspectives even when the twoparties may not be engaged in a typical exchange transac-tion (e.g., a strategic alliance). Thus, we classifyantecedents and outcomes as “customer focused” when theyshare the same perspective as the relational mediator and as“seller focused” when they adopt a perspective oppositethat of the evaluation of the relational mediator. We developour conceptual framework in four parts, which roughly par-allel our research questions, by first reviewing the literatureon relational mediators, then investigating the antecedentsand outcomes of these mediators, and, finally, studyingpotential moderators of the impact of relational mediatorson outcomes.

Relational Mediators

Successful RM efforts improve customer loyalty and firmperformance through stronger relational bonds (e.g., DeWulf, Odekerken-Schröder, and Iacobucci 2001; Sirdesh-

FIGURE 1Relational Mediator Meta-Analytic Framework

aConstruct had sufficient reported effects to be included in the multivariate causal model.

138 / Journal of Marketing, October 2006

TABLE 1Review of Construct Definitions, Aliases, and Representative Studies

Constructs Definitions Common Aliases Representative Papers

Relational MediatorsCommitment An enduring desire to maintain a

valued relationshipAffective, behavioral, obligation,

and normative commitmentAnderson and Weitz 1992; Japand Ganesan 2000; Moorman,Zaltman, and Deshpandé 1992;

Morgan and Hunt 1994

Trust Confidence in an exchangepartner’s reliability and integrity

Trustworthiness, credibility,benevolence, and honesty

Doney and Cannon 1997;Hibbard et al. 2001; Sirdeshmukh,

Singh, and Sabol 2002

Relationshipsatisfaction

Customer’s affective or emotionalstate toward a relationship,

typically evaluated cumulativelyover the history of the exchange

Satisfaction with therelationship, but not overall

satisfaction

Crosby, Evans, and Cowles 1990;Reynolds and Beatty 1999

Relationshipquality

Overall assessment of thestrength of a relationship,

conceptualized as a composite ormultidimensional construct

capturing the different but relatedfacets of a relationship

Relationship closeness andstrength

Crosby, Evans, and Cowles 1990;De Wulf, Odekerken-Schröder,

and Iacobucci 2001

AntecedentsRelationship

benefitsBenefits received, including time

saving, convenience,companionship, and improved

decision making

Functional and social benefitsand rewards

Hennig-Thurau, Gwinner, andGremler 2002; Morgan and Hunt1994; Reynolds and Beatty 1999

Dependenceon seller

Customer’s evaluation of thevalue of seller-provided resources

for which few alternatives areavailable from other sellers

Relative and asymmetricdependence, switching cost,

and imbalance of power

Hibbard, Kumar, and Stern 2001;Morgan and Hunt 1994

Relationshipinvestment

Seller’s investment of time, effort,spending, and resources focusedon building a stronger relationship

Support, gifts, resources,investments, and loyalty

programs

De Wulf, Odekerken-Schröder,and Iacobucci 2001; Ganesan

1994

Sellerexpertise

Knowledge, experience, andoverall competency of seller

Competence, skill, knowledge,and ability

Crosby, Evans, and Cowles 1990;Lagace, Dahlstrom, and

Gassenheimer 1991

Communica-tion

Amount, frequency, and quality ofinformation shared between

exchange partners

Bilateral or collaborativecommunication, information

exchange, and sharing

Anderson and Weitz 1992; Mohr,Fisher, and Nevin 1996; Morgan

and Hunt 1994

Similarity Commonality in appearance,lifestyle, and status between

individual boundary spanners orsimilar cultures, values, and goals

between buying and sellingorganizations

Salesperson or culturalsimilarity, shared values, and

compatibility

Crosby, Evans, and Cowles 1990;Doney and Cannon 1997; Morgan

and Hunt 1994

Relationshipduration

Length of time that therelationship between the

exchange partners has existed

Relationship age or length,continuity, and duration with firm

or salesperson

Anderson and Weitz 1989; Doneyand Cannon 1997; Kumar,

Scheer, and Steenkamp 1995

Interactionfrequency

Number of interactions or numberof interactions per unit of timebetween exchange partners

Frequency of business contactand interaction intensity

Crosby, Evans, and Cowles 1990;Doney and Cannon 1997

Conflict Overall level of disagreementbetween exchange partners

Manifest and perceived conflictor level of conflict, but not

functional conflict

Anderson and Weitz 1992;Kumar, Scheer, and Steenkamp

1995


TABLE 1Continued

mukh, Singh, and Sabol 2002), but the literature offers var-ied perspectives on which relational constructs mediate theeffects of RM efforts on outcomes. Commitment and trustare most often studied; commitment is “an enduring desireto maintain a valued relationship” (Moorman, Zaltman, andDeshpandé 1992, p. 316), and trust is “confidence in anexchange partner’s reliability and integrity” (Morgan andHunt 1994, p. 23). Another relationship mediator, relation-ship satisfaction, is a customer’s affective or emotional statetoward a relationship. Relationship satisfaction reflectsexclusively the customer’s satisfaction with the relationshipand differs from the customer’s satisfaction with the overallexchange. Other researchers have suggested that thesemediators are merely indicators of the global mediator rela-tionship quality, which is “an overall assessment of thestrength of a relationship” and is conceptualized as a multi-dimensional construct that captures the many differentfacets of an exchange relationship (De Wulf, Odekerken-Schröder, and Iacobucci 2001, p. 36; see also Crosby,Evans, and Cowles 1990). Its structure and underlyingdimensions vary across empirical studies, but central to theconceptualization is the belief that no single dimension orrelational construct can fully define the “overall depth orclimate” of an exchange relationship (Johnson 1999, p. 6).

Thus, whereas the literature consistently conceptualizesa mediating model for the effects of RM on performance,the specific relational mediators or composite of mediatorsappear to be driven mainly by researcher discretion; empiri-cal comparisons of the differential effects of these relationalmediators are noticeably absent. For example, someresearchers propose trust as the critical relational mediator.

Berry (1996, p. 42) offers “trust as perhaps the single mostpowerful relationship marketing tool available to a com-pany,” and Spekman (1988, p. 79) suggests that trust is the“cornerstone” of long-term relationships. Alternatively,Gundlach, Achrol, and Mentzer (1995, p. 78) propose com-mitment as the “essential ingredient for successful long-term relationships,” and Morgan and Hunt (1994, p. 23)suggest “commitment among exchange partners as key toachieving valuable outcomes.” De Wulf, Odekerken-Schröder, and Iacobucci (2001) prefer the overall concept ofrelationship quality to any specific component. In summary,there is little agreement among researchers as to which indi-vidual or composite relational mediator best captures thekey aspects of a relationship that most affect outcomes. Toaddress this issue empirically, our meta-analytic frameworkcompares the relative effects of the different perspectives byanalyzing relational mediators separately and as a group.

Antecedents to Relational Mediators

Customer-focused antecedents. Customers may per-ceive value in a relationship when they receive relationshipbenefits from an exchange partner (e.g., time savings, con-venience, companionship), which increases their willing-ness to develop relational bonds. Relationship benefits havebeen shown to affect relational mediators positively (Mor-gan and Hunt 1994; Reynolds and Beatty 1999). Depen-dence on the seller reflects the customer’s evaluation of thevalue of seller-provided resources for which few alterna-tives are available (Hibbard, Kumar, and Stern 2001). Theliterature is mixed regarding the effect of a customer’sdependence, or relative dependence (i.e., the customer’s

Constructs Definitions Common Aliases Representative Papers

OutcomesExpectation

ofcontinuity

Customer’s intention to maintainthe relationship in the future,

which captures the likelihood ofcontinued purchases from the

seller

Purchase intentions, likelihoodto leave (reverse), andrelationship continuity

Crosby, Evans, and Cowles 1990;Doney and Cannon 1997

Word ofmouth

Likelihood of a customerpositively referring the seller to

another potential customer

Referrals and customer referrals Hennig-Thurau, Gwinner, andGremler 2002; Reynolds and

Beatty 1999

Customerloyalty

Composite or multidimensionalconstruct combining different

groupings of intentions, attitudes,and seller performance indicators

Behavioral loyalty and loyalty De Wulf, Odekerken-Schröder,and Iacobucci 2001; Hennig-

Thurau, Gwinner, and Gremler2002; Sirdeshmukh, Singh, and

Sabol 2002

Sellerobjectiveperform-ance

Actual seller performanceenhancements including sales,

share of wallet, profitperformance, and other

measurable changes to theseller’s business

Sales, share, saleseffectiveness, profit, and sales

performance

Reynolds and Beatty 1999;Siguaw, Simpson, and Baker

1998

Cooperation Coordinated and complementaryactions between exchange

partners to achieve mutual goals

Coordination and joint actions Anderson and Narus 1990;Morgan and Hunt 1994


dependence reduced by the seller’s dependence), on rela-tional mediators. Researchers find empirical support forboth positive and negative influences of relative dependenceon relational mediators (Anderson and Weitz 1989; Morganand Hunt 1994), which indicates that its impact may becontingent on the context.

Seller-focused antecedents. Researchers have investi-gated various RM strategies that sellers can employ tostrengthen relationships. Relationship investment refers tothe time, effort, and resources that sellers invest in buildingstronger relationships. Such investments often generateexpectations of reciprocation that can help strengthen andmaintain a relationship and, therefore, positively influencerelational mediators (Anderson and Weitz 1989; Ganesan1994). Seller expertise reflects the knowledge, experience,and overall competence of the seller. When customers inter-act with a competent seller, they receive increased value,their relationship becomes more important, and they investmore effort to strengthen and maintain it (Crosby, Evans,and Cowles 1990; Lagace, Dahlstrom, and Gassenheimer1991).

Dyadic antecedents. Customer- and seller-focusedantecedents are meaningful from one side of the exchangedyad, but other antecedents require the active involvementof both exchange partners and are equally meaningful fromboth perspectives. For example, communication, or theamount, frequency, and quality of information sharedbetween exchange partners (Mohr, Fisher, and Nevin 1996),requires both parties to exchange information. Communica-tion builds stronger relationships in an exchange by helpingresolve disputes, align goals, and uncover new value-creating opportunities (Morgan and Hunt 1994). Similarityis the commonality in appearance, lifestyle, and statusbetween individual boundary spanners or the similar cul-tures, values, and goals between organizations. Such simi-larities between people or organizations may provide cuesthat the exchange partner will help facilitate important goalsand has been shown to affect relational mediators positively(Crosby, Evans, and Cowles 1990; Doney and Cannon1997). Relationship duration is the length of time that therelationship between the exchange partners has existed,whereas interaction frequency refers to the number of inter-actions per unit of time between partners. Both providetrading partners with more behavioral information in variedcontexts, which allows for better predictions that shouldincrease each party’s confidence in its partner’s behavior(Anderson and Weitz 1989; Doney and Cannon 1997).Finally, conflict entails the overall level of disagreementbetween exchange partners; this is often termed “perceived”or “manifest” conflict. As conflict increases, the customer isless likely to have confidence in the long-term orientationof the seller or to invest in building or maintaining a rela-tionship; thus, conflict should negatively influence the cus-tomer’s trust in and commitment toward the seller (Ander-son and Weitz 1992).

Consequences of Relational Mediators

Customer-focused outcomes. Increased customer loyaltyis one of the most common outcomes expected from RM

efforts, but loyalty has been defined and operationalized inmany different ways. An expectation of continuity reflectsthe customer’s intention to maintain the relationship in thefuture and captures the likelihood of continued purchases.However, researchers have criticized this measure of loyaltybecause customers with weak relational bonds and littleloyalty may report high continuity expectations as a resultof their perceptions of high switching costs or their lack oftime to evaluate alternatives (Oliver 1999). Word of mouth(WOM) captures the likelihood that a customer will refer aseller positively to another potential customer and, there-fore, indicates both attitudinal and behavioral dimensions ofloyalty. Some studies operationalize customer loyalty as acomposite or multidimensional construct that includesgroupings of intentions, attitudes, and seller performanceindicators. We note that relational mediators positivelyinfluence global measures of customer loyalty, just as theydo its individual components (De Wulf, Odekerken-Schröder, and Iacobucci 2001; Sirdeshmukh, Singh, andSabol 2002).

Seller-focused outcomes. Possibly the most importantoutcome of RM efforts is seller objective performance,which captures the seller’s actual performance enhance-ments, including sales, profit, and share of wallet. Someresearchers have found empirical support for the influenceof relational mediators on seller objective outcomes (e.g.,Doney and Cannon 1997; Siguaw, Simpson, and Baker1998), but several other studies have failed to find any sig-nificant effects, which implies that the effect of RM onperformance may be context dependent (Crosby, Evans, andCowles 1990; Gruen, Summers, and Acito 2000).

Dyadic outcomes. Cooperation captures the level ofcoordinated and complementary actions between exchangepartners in their efforts to achieve mutual goals. Coopera-tion promotes value creation beyond that which each partycould achieve separately, but because one party oftenreceives its portion of the value earlier, the other party musthave enough trust in the relationship to wait for its futurereciprocation. Researchers have shown that trust and com-mitment between exchange partners are critical for cooper-ation (Anderson and Narus 1990; Morgan and Hunt 1994).

Moderators of Relational Mediators’ Influence onOutcomes

The RM model (RM strategies → relational mediator →outcomes) we conceptualize herein can be applied acrossmany different contexts in which business strategies mayhave varying effects. Therefore, an objective of our meta-analysis is to identify and empirically test the influence ofpotential moderators on the linkages in the RM model.

Contexts influencing relationship importance. Relation-ship marketing is based on the premise that building strongrelationships positively influences exchange outcomes, andresearchers recognize that exchanges vary across a spec-trum from transactional to relational (Anderson and Narus1991). For exchanges in which relationships are moreimportant, we expect that the relational mediators will havea greater impact on outcomes, whereas in highly transac-tional exchanges, the relationships between buyers and sell-


ers may have little influence on outcomes. Extant literatureidentifies three situations in which relationships may bemore important for the success of an exchange. First, ingeneral, services are perceived as less tangible, less consis-tent, and more perishable, and customers and sellers aremore involved in the production and consumption of ser-vices than they are for products (Zeithaml, Parasuraman,and Berry 1985). This close interaction between customersand sellers may make customer–seller relationships morecritical for services, and the intangibility of the offeringmay make the benefits of trust more critical because evalua-tions often are ambiguous.

Second, channel researchers tend to distinguish betweenchannel partner exchanges and direct seller–customer trans-actions. Exchanges between channel partners have higherlevels of interdependence, require coordinated action, andrely on the prevention of opportunistic behavior (Andersonand Weitz 1989). Thus, coordination improvements and thereduction of opportunistic behaviors through strong rela-tionships should be more important in a channel context,which should lead to a greater impact of relational media-tors on performance than their impact in direct exchanges.

Third, Anderson and Narus (2004, p. 21) differentiateconsumer and business markets on the basis of the impor-tance of relationships, maintaining that a “firm’s success inbusiness markets depends directly on its working relation-ships.” If a working relationship is more critical for a cus-tomer’s success in business markets, relationships shouldhave a greater impact on exchange outcomes in businessthan in consumer markets.

Individual versus organizational relationships. Cus-tomers may form a relationship with an individual boundaryspanner in the selling organization and/or with the sellingorganization as a whole. This issue of individual versusorganizational relationships also has significant managerialimplications as firms continue to try to increase their ser-vice efficiencies through the use of technology (e.g., cus-tomer relationship management). Experimental researchshows that when people evaluate another individual, theymake stronger, quicker, and more confident judgments thanwhen they evaluate a group; those judgments also are morestrongly related to outcomes and behaviors (Hamilton andSherman 1996). Accordingly, we expect that customers’judgments based on the relational characteristics of an indi-vidual boundary spanner (e.g., trust in the salesperson) willbe stronger, more confident, and more strongly linked tooutcomes than their judgments based on the relational char-acteristics of a selling firm (e.g., trust in the firm). Post hocfindings support this premise. Doney and Cannon (1997, p.45) report that “the process by which trust develops appearsto differ when the target is an organization … as opposed toan individual salesperson,” and Iacobucci and Ostrom(1996, p. 69) find that “[i]ndividual-to-firm relationships[are] also typically short-term and less intense in compari-son to individual-level dyads.” Thus, the positive effect ofrelational mediators on outcomes will be greater when therelational mediator is targeted toward an individual memberof the selling organization than when it is targeted towardthe organization.

1A list of the articles used in our empirical meta-analysis isavailable on request.

2Before applying the sample weights, we first converted thereliability-adjusted r’s to variance-stabilizing Fisher’s z scores(Rosenthal 1994; Shadish and Haddock 1994). Following standardprocedures (Shadish and Haddock 1994, p. 268), we reconvertedthem back to r’s to report the sample-weighted reliability-adjustedr and the 95% CIs.

MethodCollection and Coding of StudiesThe key impetus for RM research was Dwyer, Schurr, andOh’s (1987) seminal article, so we searched empiricalresearch for the mediators of interest during the period1987–2004. We employed various methods in our literaturesearch, including (1) a search of the ABI/Informs,PsycINFO, and Business Source Premier databases for eachrelational mediator; (2) a search of the Social Sciences Cita-tion Index, using the seminal articles for these constructs;(3) manual shelf searches of journals that contain researchon relational mediators; and (4) e-mails sent to researchersin the domain asking for their published and unpublishedworks. Our search generated more than 100 published andunpublished studies, each of which we evaluated for mea-sures of the relationships among antecedents, outcomes,and the four relational mediators. Because correlations werethe most common metric included in these studies (>95%),we e-mailed authors to request the correlation matrices forany studies in which they were not provided. Two indepen-dent coders, who were not familiar with the study, used thedefinitions in Table 1 to code the studies, and any differ-ences (overall agreement >95%) were resolved through dis-cussion (Szymanski and Henard 2001). When a single studyprovided more than one effect size estimate for the samerelationship, we calculated an average. In cases in which themultiple effect size estimates from the same study wereindependent, we included them as separate effect size esti-mates. This procedure prevents the bias that may occur as aresult of multiple counts of dependent effect size estimatesand enables us to code moderators that vary across subsetsof a sample in a single study (e.g., Brown and Peterson1993). Ultimately, we combined 637 correlations from 111independent samples drawn from 94 different manuscriptsto yield a combined N of 38,077 with which to calculate thepairwise effect size estimates.1

Univariate Analyses

We began our analysis by adjusting our basic input mea-sure, correlations (r), for corrections due to measurementerror (scale reliability differences); we report the correlationadjusted for reliability (Hunter and Schmidt 1990). We thenadjusted for sampling error (sample size differences), andwe report the sample-weighted reliability-adjusted r and its95% confidence intervals (CIs).2 We calculated the chi-square test (d.f. = 1) for association and then addressed thefile-drawer problem by computing the classical file drawerN (Rosenthal 1979) and the Q statistic test of homogeneity


3To estimate the publication bias associated with publishedstudies, we employed multiple methods: (1) Rosenthal’s (1979)well-known file drawer method; (2) Orwin’s (1983) failsafe N,which represents the number of missing studies (set to .05) thatwould bring the effect to .075, or less than the .10 effect level thatCohen (1977) classifies as signifying a low effect; and (3) compre-hensive meta-analysis software (http://www.meta-analysis.com/html/stat_analysis_overview.html) to compute funnel plots for thevarious relationships. Funnel plots offer a simple scatterplot–based visual tool for investigating publication bias in meta-analyses (Sterne and Egger 2001). Overall, the funnel plots cor-roborate the inferences we drew from the file drawer N andOrwin’s failsafe N; namely, the data we use in the meta-analysesdo not display any evidence of publication bias.

(Cheung and Chan 2004; Hunter and Schmidt 1990) foreach relationship.3

We performed such analyses for the influence of eachantecedent on the four relational mediators (provided thatthere were four effects for each antecedent), which enablesus to compare the influence of each antecedent on eachmediator. Although these mediators measure differentaspects of a relationship, researchers have argued that theyare highly related and difficult to distinguish and, therefore,can be combined into a composite construct (Crosby,Evans, and Cowles 1990; De Wulf, Odekerken-Schröder,and Iacobucci 2001; Smith 1998). To facilitate the compari-son of relative effects among the different antecedents onthe overall relationship, we duplicated these analyses for theeffects of each antecedent on all four mediators as a group.

Causal Model

In addition to the pairwise analyses, we aggregated thestudies to test the nomological causal model implicit in Fig-ure 1. This multivariate technique has the advantage of ana-lyzing all linkages simultaneously, but it also needs signifi-cantly more data because the effects (i.e., correlationcoefficients) must be available between each construct inthe model and all other constructs, not just the pairwiseeffects for constructs with proposed relationships (Brownand Peterson 1993). Thus, causal models typically are lim-ited to only the most commonly studied constructs. Wedetermined the average-adjusted intercorrelation among allconstructs in the framework whose required correlationcoefficients were reported in three or more studies (Table2). Furthermore, to increase the number of constructs thatmet this requirement and to provide a concise synthesis ofthe literature, we grouped all relational mediators together,and thus we propose a fully mediated model (Morgan andHunt 1994). Of the 14 antecedent and outcome constructswe include in Figure 1, only 6 met this criterion and couldbe evaluated in the causal model.

ResultsAfter we report the results of our causal model estimationprocedure (Cheung and Chan 2005; Furlow and Beretvas2005), we provide the results of the pairwise and casualmodel analyses structured around our four focal researchquestions. Because the first two questions focus on the

4The median sample size from our meta-analysis of the studiesincluded in the causal model is 2839. Modification indexes indi-cate that a direct path from relationship duration to seller objectiveperformance could improve the model fit, but our evaluation of theparsimony-adjusted fit indexes suggests that the slight improve-ment in fit is more than offset by a loss in parsimony. The signifi-cance and pattern of effects do not change with this additionalpath, so we do not add it.

effectiveness of antecedents that influence relational media-tors (Table 3) and relational mediators that influence out-comes (Table 4), we report the findings beginning with themost influential constructs, and we concentrate on theaggregate results for all mediators (i.e., last row of eachconstruct in Tables 3 and 4). Next, we report the results ofthe moderator analyses to understand the context in whichRM is most effective. Finally, we concentrate on the lastresearch question, namely, how the RM mediating modelvaries across different mediators. For this question, we nolonger focus on the aggregate results but instead evaluatethe effects that pertain to each mediator and thus provideinsight into how the effects vary across mediators.

Causal Model Estimation

The fit indexes from the structural model testing of thecausal model indicate that this model fits the data poorly:χ2(5) = 322.27, p < .01; comparative fit index = .87;goodness-of-fit index = .95; and root mean square error ofapproximation = .19 (for an example of this technique, seeBrown and Peterson 1993). Modification indexes suggest arevised causal model that includes direct paths from depen-dence on seller and relationship investment to seller objec-tive performance. The revised model results in indexes thatindicate a good fit to the data: χ2(3) = 12.15, p < .01; com-parative fit index = .89; goodness-of-fit index = .99; androot mean square error of approximation = .04.4

Which RM Strategies Are Most Effective forBuilding Customer Relationships?

As we show in Table 3, not all RM strategies (antecedents)are equally effective for building relationships. The averageof the sample-weighted reliability-adjusted correlationsamong antecedents and mediators is .41, and they rangefrom .13 for relationship duration to the largest absoluteeffect of –.67 for conflict. All paths from antecedents torelational mediators are supported in the pairwise analyses,except for the path from interaction frequency to relation-ship satisfaction. Most of these findings appear to be robustwith regard to the number of null studies needed to renderthe observed effects zero (mean file-drawer N is 3152).Only three linkages appear susceptible to a file-drawerproblem: interaction frequency → relationship satisfaction(N = 2), relationship duration → relationship satisfaction(N = 18), and relationship duration → relationship quality(N = 25). In the Q-statistic test for homogeneity, with oneexception (i.e., seller expertise → relationship satisfaction),all the tests for homogeneity are significant.

Several insights can be drawn from the evaluation of therelative impact of different RM strategies on building strong


Constructs RBEN DEPS RINV COMM RDUR RMED SOP

Relation Benefits (RBEN) [.87]SDNumber of studies 0000.00 0000.00 0000.00 0000.00 0000.00 0000.00 0000.00Cumulative sample size

Dependence on Seller (DEPS) .12 [.85]SD .29Number of studies 0004Cumulative sample size 0886

Relationship Investment (RINV) .42 .13 [.82]SD .08 .21Number of studies 0007 05Cumulative sample size 1911 1273

Communication (COMM) .49 .28 .47 [.85]SD .15 .21 .13Number of studies 0010 0012 0009Cumulative sample size 2380 3260 2893

Relationship Duration (RDUR) .15 .14 .02 .18 [.99]SD .03 .17 .06 .13Number of studies 0003 07 0007 0006Cumulative sample size 1097 2150 3496 2282

Relational Mediator (RMED) .43 .19 .45 .51 .11 [.85]SD .18 .25 .18 .19 .13Number of studies 0018 0033 0024 0038 00,026Cumulative sample size 5108 9296 8564 9803 10,720

Seller Objective Performance (SOP) .23 .29 .44 .30 .12 .35 [.92]SD .04 .31 .31 .30 .12 .22Number of studies 0003 0008 0007 0012 00,008 00,047Cumulative sample size 0600 2860 2813 3149 0,4293 16,469

TABLE 2Average Reliability-Adjusted Intercorrelations Among Constructs in Causal Model

Notes: Entries on the diagonal in brackets are weighted mean Cronbach’s alpha coefficients. We included constructs in the causal model whenthree or more correlation coefficients were available among that construct and all other constructs in the model.

customer relationships. Conflict (r = –.67) has the largestabsolute impact on the relational mediators of allantecedents, in support of the importance of resolving prob-lems and disagreements to prevent relationship-damagingconflicts (alternatively, the presence of conflict may seri-ously undermine the effect of other RM antecedents). Thatthe largest effect is negative extends to the RM domain thefinding that people pay more attention to negatives than topositives (Shiv, Edell, and Payne 1997); this warrants fur-ther investigation. The seller expertise (r = .62) and commu-nication (r = .54) antecedents have the greatest positiveinfluence on relational mediators. The great impact of sellerexpertise suggests the importance of training boundaryspanners and the potential detriments of staffing call centerswith inexperienced or unskilled employees. The influenceof seller expertise also seems to apply across all four rela-tional mediators, in support of Vargo and Lusch’s (2004, p.3) claim that “skills and knowledge are the fundamentalunit of exchange,” such that sellers’ skills and knowledgeare the most important value-creating attributes. Similarly,the large positive effect of communication on all mediatorsis consistent with its role in both uncovering value-creatingopportunities and resolving problems.

Relationship investment (r = .46), similarity (r = .44),and relationship benefits (r = .42) are the next most influen-tial RM strategies. The strong positive impact of the seller’srelationship investments and customer relationship benefitsindicates that managers should engage in proactive RMspending. The importance of similarities between buyersand sellers suggests that without common reference points,exchange partners may find it difficult to move theexchange from a purely economic or transactional basis to arelational basis. The last three antecedents—dependence onseller (r = .26), interaction frequency (r = .16), and relation-ship duration (r = .13)—have notably smaller effects onrelational mediators.

The causal model analysis generates the same rankorder of relative effects of the antecedents on relationalmediators as the pairwise analyses, which increases ourconfidence in the univariate results. Communication (β =.29, p < .01), relationship investment (β = .23, p < .01), rela-tionship benefit (β = .18, p < .01), and dependence on seller(β = .05, p < .01) all have significant, positive effects onrelational mediators, but relationship duration (β = .02) failsto influence relational mediators significantly in the multi-variate analysis.


TAB

LE

3R

esu

lts:

Des

crip

tive

Sta

tist

ics

and

Infl

uen

ce o

f A

nte

ced

ents

on

Rel

atio

nal

Med

iato

rs

Pro

po

sed

Rel

atio

nsh

ips

Nu

mb

ero

f R

awE

ffec

tsTo

tal

N

Sim

ple

Ave

rag

er

Ave

rag

e r

Ad

just

edfo

r R

elia

bili

ty

Sam

ple

-W

eig

hte

dR

elia

bili

ty-

Ad

just

edA

vera

ge

r

χχ2fo

rA

sso

cia-

tio

n

(d.f

.= 1

)L

ow

erB

ou

nd

Up

per

Bo

un

d

File

Dra

wer

N(U

sin

gTw

o-

Taile

dTe

st)

QS

tati

stic

fo

r H

om

og

enei

tyTe

st (

d.f

.)

Rel

atio

nshi

p be

nefit

s →

com

mitm

ent

1131

62.3

7.4

5.5

198

7.50

.48

.53

2670

501.

29 (

10)

Rel

atio

nshi

p be

nefit

s →

trus

t13

3633

.31

.34

.33

420.

99.3

0.3

614

3366

.61

(12)

Rel

atio

nshi

p be

nefit

s →

rela

tions

hip

satis

fact

ion

720

57.4

1.4

6.4

549

0.59

.43

.49

916

53.5

6 (6

)R

elat

ions

hip

bene

fits

→re

latio

nshi

p qu

ality

820

91.3

3.3

9.3

635

3.47

.35

.43

749

60.2

7 (7

)R

elat

ions

hip

bene

fits

→al

l med

iato

rs39

10,9

43.3

5.4

0.4

2

Dep

ende

nce

on s

elle

r →

com

mitm

ent

1646

70.3

3.4

0.3

770

0.04

.35

.40

3206

530.

30 (

15)

Dep

ende

nce

on s

elle

r →

trus

t26

5935

.14

.17

.21

261.

01.1

8.2

313

5445

2.24

(25

)D

epen

denc

e on

sel

ler

→re

latio

nshi

p sa

tisfa

ctio

n3

1076

.13

.13

.27

——

——

—D

epen

denc

e on

sel

ler

→re

latio

nshi

p qu

ality

216

04.0

7.0

8.0

8—

——

——

Dep

ende

nce

on s

elle

r →

all m

edia

tors

4713

,285

.20

.24

.26

Rel

atio

nshi

p in

vest

men

t →

com

mitm

ent

1565

44.3

6.4

3.3

482

1.86

.32

.36

4200

381.

19 (

14)

Rel

atio

nshi

p in

vest

men

t →

trus

t17

4601

.38

.45

.45

1053

.73

.42

.47

4455

161.

70 (

16)

Rel

atio

nshi

p in

vest

men

t →

rela

tions

hip

satis

fact

ion

1026

91.4

2.4

7.5

288

2.85

.49

.55

2217

276.

14

(9)

Rel

atio

nshi

p in

vest

men

t →

rela

tions

hip

qual

ity9

2635

.49

.57

.58

1125

.80

.55

.60

2750

245.

40

(8)

Rel

atio

nshi

p in

vest

men

ts →

all m

edia

tors

5116

,471

.40

.47

.46

Sel

ler

expe

rtis

e →

com

mitm

ent

117

7.7

8.9

0.9

0—

——

——

Sel

ler

expe

rtis

e →

trus

t12

3464

.49

.59

.52

1121

.22

.49

.54

3988

279.

45 (

11)

Sel

ler

expe

rtis

e →

rela

tions

hip

satis

fact

ion

510

49.4

9.5

6.5

641

3.64

.52

.60

473

1.20

(4

)S

elle

r ex

pert

ise

→re

latio

nshi

p qu

ality

110

09.9

0.9

8.9

8—

——

——

Sel

ler

expe

rtis

e →

all m

edia

tors

1956

99.5

3.6

2.6

2

Com

mun

icat

ion

→co

mm

itmen

t25

5840

.45

.53

.55

2244

.96

.54

.57

13,7

1232

3.37

(24

)C

omm

unic

atio

n →

trus

t29

7948

.43

.51

.56

3146

.74

.54

.57

21,9

6293

9.32

(28

)C

omm

unic

atio

n →

rela

tions

hip

satis

fact

ion

617

27.4

6.5

1.5

154

6.15

.48

.55

870

34.7

8 (5

)C

omm

unic

atio

n →

rela

tions

hip

qual

ity7

1907

.35

.40

.43

407.

26.4

0.4

770

982

.39

(6)

Com

mun

icat

ion

→al

l med

iato

rs67

17,4

22.4

3.5

1.5

4

Sim

ilarit

y →

com

mitm

ent

338

6.5

4.6

6.6

3—

——

——

Sim

ilarit

y →

trus

t10

2562

.50

.56

.41

475.

20.3

8.4

418

8434

7.42

(9

)S

imila

rity

→re

latio

nshi

p sa

tisfa

ctio

n1

151

.30

.34

.34

——

——

—S

imila

rity

→re

latio

nshi

p qu

ality

110

09.2

2.2

4.2

4—

——

——

Sim

ilarit

y →

all m

edia

tors

1541

08.4

8.5

4.4

4

95%

CI


TAB

LE

3C

on

tin

ued

Not

es:O

pera

tiona

lly,

we

atte

mpt

ed c

alcu

latio

ns o

nly

whe

n th

ere

was

a m

inim

um o

f fo

ur r

aw e

ffect

s as

soci

ated

with

a r

elat

ions

hip.

A d

ash

indi

cate

s th

at t

his

cond

ition

was

not

met

.

Pro

po

sed

Rel

atio

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ips

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f R

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tal

N

Sim

ple

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rag

er

Ave

rag

e r

Ad

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r R

elia

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ty

Sam

ple

-W

eig

hte

dR

elia

bili

ty-

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just

edA

vera

ge

r

χχ2fo

rA

sso

cia-

tio

n

(d.f

.= 1

)L

ow

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ou

nd

Up

per

Bo

un

d

File

Dra

wer

N(U

sin

gTw

o-

Taile

dTe

st)

QS

tati

stic

fo

r H

om

og

enei

tyTe

st (

d.f

.)

Rel

atio

nshi

p du

ratio

n →

com

mitm

ent

1366

38.1

2.1

3.1

182

.43

.09

.14

312

241.

99 (

12)

Rel

atio

nshi

p du

ratio

n →

trus

t20

8201

.12

.12

.14

162.

63.1

2.1

667

479

.73

(19)

Rel

atio

nshi

p du

ratio

n →

rela

tions

hip

satis

fact

ion

515

42.0

9.0

9.1

324

.15

.08

.17

1811

.14

(4)

Rel

atio

nshi

p du

ratio

n →

rela

tions

hip

qual

ity5

1830

.11

.11

.11

22.9

5.0

7.1

625

25.1

5 (4

)R

elat

ions

hip

dura

tion

→al

l med

iato

rs43

18,2

11.1

1.1

2.1

3

Inte

ract

ion

freq

uenc

y →

com

mitm

ent

272

4–.

03–.

03–.

03—

——

——

Inte

ract

ion

freq

uenc

y →

trus

t10

2198

.30

.33

.30

210.

97.2

6.3

568

029

4.63

(9

)In

tera

ctio

n fr

eque

ncy

→re

latio

nshi

p sa

tisfa

ctio

n4

965

.11

.11

.04

1.47

–.02

.10

227

.81

(3)

Inte

ract

ion

freq

uenc

y →

rela

tions

hip

qual

ity3

1124

–.03

–.03

–.03

——

——

—In

tera

ctio

n fr

eque

ncy

→al

l med

iato

rs19

5011

.17

.19

.16

Con

flict

→co

mm

itmen

t10

4339

–.41

–.49

–.71

3331

.03

–.72

–.69

5496

1145

.07

(9)

Con

flict

→tr

ust

929

06–.

54–.

63–.

6517

69.5

2–.

68–.

6340

3320

6.98

(8

)C

onfli

ct →

rela

tions

hip

satis

fact

ion

195

–.27

–.31

–.31

——

——

—C

onfli

ct →

rela

tions

hip

qual

ity0

——

——

——

——

—C

onfli

ct →

all m

edia

tors

2073

40–.

46–.

55–.

67

95%

CI


TAB

LE

4R

esu

lts:

Des

crip

tive

Sta

tist

ics

and

Infl

uen

ce o

f R

elat

ion

al M

edia

tors

on

Ou

tco

mes

Pro

po

sed

Rel

atio

nsh

ips

Nu

mb

er o

fR

awE

ffec

tsTo

tal

N

Sim

ple

Ave

rag

er

Ave

rag

e r

Ad

just

edfo

rR

elia

bili

ty

Sam

ple

-W

eig

hte

dR

elia

bili

ty-

Ad

just

edA

vera

ge

r

χχ2fo

rA

sso

cia-

tio

n

(d.f

.= 1

)L

ow

erB

ou

nd

Up

per

Bo

un

d

File

Dra

wer

N(U

sin

gTw

o-

Taile

dTe

st)

QS

tati

stic

fo

rH

om

og

enei

tyTe

st (

d.f

.)

Com

mitm

ent

→ex

pect

atio

n of

con

tinui

ty16

4215

.45

.54

.53

1447

.81

.51

.55

5895

210.

88 (

15)

Trus

t →

expe

ctat

ion

of c

ontin

uity

2466

32.4

7.5

5.5

828

89.9

5.5

6.6

015

,456

274.

10 (

23)

Rel

atio

nshi

p sa

tisfa

ctio

n →

expe

ctat

ion

of c

ontin

uity

518

79.5

0.5

8.5

777

8.08

.54

.60

949

21.3

6(4

)R

elat

ions

hip

qual

ity →

expe

ctat

ion

of c

ontin

uity

317

33.5

0.5

5.5

4—

——

——

All

med

iato

rs →

expe

ctat

ion

of c

ontin

uity

4814

,459

.47

.55

.56

Com

mitm

ent

→W

OM

636

74.5

2.6

1.6

421

11.5

2.6

2.6

627

0714

7.41

(5)

Trus

t →

WO

M5

3507

.48

.56

.62

1833

.48

.60

.64

1804

61.0

5(4

)R

elat

ions

hip

satis

fact

ion

→W

OM

310

54.4

8.5

0.5

3—

——

——

Rel

atio

nshi

p qu

ality

→W

OM

317

33.5

8.6

1.6

0—

——

——

All

med

iato

rs →

WO

M17

9968

.51

.58

.61

Com

mitm

ent

→cu

stom

er lo

yalty

1245

88.4

5.5

4.5

819

96.0

1.5

6.6

054

4715

1.79

(11

)Tr

ust

→cu

stom

er lo

yalty

2063

28.4

4.5

1.5

422

48.5

1.5

2.5

510

,572

308.

98 (

19)

Rel

atio

nshi

p sa

tisfa

ctio

n →

cust

omer

loya

lty9

2781

.35

.39

.41

522.

61.3

8.4

411

8812

9.89

(8)

Rel

atio

nshi

p qu

ality

→cu

stom

er lo

yalty

928

51.4

0.4

6.4

775

0.86

.45

.50

1722

55.0

4(8

)A

ll m

edia

tors

→cu

stom

er lo

yalty

5016

,548

.42

.48

.52

Com

mitm

ent

→se

ller

obje

ctiv

e pe

rfor

man

ce20

7342

.30

.35

.27

549.

90.2

5.2

934

8940

7.99

(19

)Tr

ust

→se

ller

obje

ctiv

e pe

rfor

man

ce32

10,3

06.2

9.3

3.3

513

33.2

8.3

3.3

610

,108

924.

64 (

31)

Rel

atio

nshi

p sa

tisfa

ctio

n →

selle

r ob

ject

ive

perf

orm

ance

716

05.3

2.3

7.3

217

2.58

.27

.36

376

84.6

2(6

)R

elat

ions

hip

qual

ity →

selle

r ob

ject

ive

perf

orm

ance

635

17.2

8.3

1.6

319

30.1

8.6

1.6

519

862,

641.

29(5

)A

ll m

edia

tors

→se

ller

obje

ctiv

e pe

rfor

man

ce65

22,7

70.2

9.3

4.3

5

Com

mitm

ent

→co

oper

atio

n16

4436

.41

.50

.64

2509

.07

.62

.66

7385

418.

42 (

15)

Trus

t →

coop

erat

ion

2461

92.5

6.6

7.7

353

40.8

0.7

2.7

428

,898

476.

68 (

23)

Rel

atio

nshi

p sa

tisfa

ctio

n →

coop

erat

ion

593

1.4

5.5

5.6

863

0.30

.64

.71

468

17.3

3(4

)R

elat

ions

hip

qual

ity →

coop

erat

ion

0—

——

——

——

——

All

med

iato

rs →

coop

erat

ion

4511

,559

.50

.60

.70

95%

CI

Not

es:O

pera

tiona

lly,

we

atte

mpt

ed c

alcu

latio

ns o

nly

whe

n th

ere

was

a m

inim

um o

f fo

ur r

aw e

ffect

s as

soci

ated

with

a r

elat

ions

hip.

A d

ash

indi

cate

s th

at t

his

cond

ition

was

not

met

.


5We carried out the moderator analysis using the procedure thatBrown (1996) and Grewal and colleagues (1997) employ. How-ever, the results we report in Table 5 must be interpreted cau-tiously because in the majority of the nonsignificant cases, thepower of the test is relatively small (Cohen 1977; Fern and Mon-roe 1996). On the basis of our power analysis, we have flagged therelationships in Table 5 that we believe researchers would be pre-mature in dismissing.

What Outcomes Are Most Affected by CustomerRelationships?

In Table 4, we show that customer relationships do notequally influence all exchange outcomes. The average ofthe correlations among relational mediators and outcomes is.55, ranging from a low of .35 for seller objective perfor-mance to a high of .70 for cooperation. All paths from rela-tional mediators to outcomes are supported. None of theseresults appears to be susceptible to a file-drawer problem;all paths would require more than 375 null studies to gener-ate a zero effect, with a mean file drawer N of 6153. AllQ-statistic tests for homogeneity are significant, demon-strating statistical heterogeneity and supporting a moderatoranalysis.

Relational mediators have the largest combined influ-ence on the dyadic outcome of cooperation (r = .70), fol-lowed by WOM (r = .61). This finding reinforces the impor-tance of relationship building for a high level of customercooperation. The greater impact of relational mediators onWOM (r = .61) than on the expectation of continuity (r =.56) or on customer loyalty (r = .52) lends support to Reich-held’s (2003, p. 48) premise that WOM may be the bestindicator of “intense loyalty.” Only customers who havestrong relationships with sellers are willing to risk their ownreputation by giving a referral.

Of the five outcomes, relational mediators have the leastinfluence on seller objective performance (r = .35). Thus,although customer relationships positively influence perfor-mance outcomes, in support of efforts put into RM strate-gies, the actual effect on performance is lower than that onthe other four outcomes. This finding is not surprising; rela-tional mediators are more closely related to loyalty andcooperation than is objective performance, which oftendepends on other, nonrelational factors (e.g., the economy).

The causal model includes only one outcome, but weconfirm the significant influence of relational mediators onseller objective performance (β = .16, p < .01). In addition,although the impact of relationship benefits and communi-cation strategies on seller objective performance is fullymediated by the relational mediators, the influence ofdependence and relationship investment is only partiallymediated; both dependence on seller (β = .22, p < .01) andrelationship investment (β = .34, p < .01) also have large,direct effects on seller performance.

Which Moderators Are Most Effective inInfluencing Relationship–Outcome Linkages?

In Table 5, we present the influence of moderators on thelinkage between relational mediators and outcomes.5 Thepremise that customer relationships have a greater impacton exchange outcomes in situations in which relationshipsare more critical to the success of the exchange is supported

for the impact of all mediators on customer loyalty amongservices, channels, and business customers. The correlationof all mediators with customer loyalty is .58 for service ver-sus .43 for product-based exchanges (p < .05), .65 for chan-nel versus .46 for direct interactions (p < .01), and .56 forbusiness versus .46 for consumer markets (p < .05). We finda similar effect in business markets for the impact of rela-tionships on seller objective performance, for which theinfluence of all mediators is r = .36 in business markets ver-sus r = .25 in consumer markets (p < .01). In summary, thesignificant moderation of the influence of relationships oncustomer loyalty across services, channels, and businessmarkets, as well as on performance in business markets,provides support for our premise that customer relation-ships have a greater impact on exchange outcomes in situa-tions in which relationships are more critical to success.

Contrary to our expectations, relational mediators’influence on the expectation of continuity is greater in con-sumer than in business markets, mostly because of commit-ment’s influence on the expectation of continuity (r = .46business, r = .71 consumer; p < .05). Because commitmenttaps a customer’s desire to maintain a valued relationship,whereas the expectation of continuity captures a customer’sintent to maintain the relationship, consumers may be betterable to convert their attitudes or desire into an intention thanare business buyers because consumers have a higherdegree of control over their actions. Consistent with thetheory of planned behavior, the link between an attitude andan intention should be stronger as control increases (Ajzenand Fishbein 1980). Thus, the stronger impact of commit-ment on the expectation of continuity (which results fromhigher levels of control) in consumer markets than in busi-ness markets may offset the typically greater importance ofrelationships in business markets.

As we proposed, relationships have a greater impact oncustomer loyalty when the target of the relationship is anindividual person (r = .56) than when the target is an orga-nization (r = .46; p < .05). Similarly, the impact of relationalmediators on cooperation is greater (r = .68 for interper-sonal, r = .55 for interorganizational; p < .05) when the cus-tomer’s relationship is targeted toward a person employedby the seller than when it is targeted toward the seller over-all. We provide additional support for this finding in Table5, in which we show that of the 16 moderation tests, 81%are in the expected direction, and the impact of all media-tors on seller objective performance is significant at the p <.10 level (r = .40 for interpersonal, r = .31 forinterorganizational).

How Does the RM Strategy → Mediator →Outcome Linkage Vary Across Mediators?

The preceding research questions focus on the effects of thefour mediators as a group. In this subsection, we investigatethe individual linkages to identify when mediators operatedifferently; we begin with the front half of the model: theRM strategy → relational mediator linkage (Table 3). Theeffectiveness of RM strategies varies across different rela-tional mediators. We consider the differential effects of rela-tionship investments and benefits on mediators togetherbecause they are logically related. Sellers’ relationship


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investments normally generate customer relationship bene-fits, but in some cases, an investment may not be desired orgenerate any actual benefit. Relationship investment has theleast impact on commitment (r = .34) of all the relationalmediators (mean of other relational mediators, r = .52), withno overlap in the CIs. Thus, sellers can strengthen theiroverall relationships through investments (possibly by gen-erating feelings of reciprocity), but the relative impact oncustomer commitment is minimal. Alternatively, customerrelationship benefits have the greatest impact on customercommitment (r = .51), especially compared with customertrust (r = .33, no overlap in CI), which suggests that cus-tomers value these benefits and want to maintain them. Thisdiscrepancy may occur because many relationship invest-ments do not generate value for the customer and thereforedo not lead to customer commitment. Although investmentsthat do not generate customer value may strengthen rela-tionships by generating debts of reciprocity, they will notnecessarily generate an enduring desire to maintain a valuedrelationship.

As we might have expected, dependence has a greaterpositive effect on commitment (r = .37) than the other medi-ators (mean of other relational mediators, r = .19, no over-lap in CI), which reflects customers’ desire to maintain arelationship with the seller on which they are dependent.The relatively limited effect of dependence on customertrust (r = .21) may be due to customers’ concerns that sell-ers will take advantage of their dependence.

Although similarity often is hypothesized to influencetrust by reducing uncertainty and serving as a cue to facili-tate goals, we find that similarity actually has a greaterimpact on commitment (r = .63) than on trust (r = .41). Thisgreater impact on commitment might be explained byresearch on stereotype behaviors, which suggests thatpeople want to strengthen and maintain relationships with“in-group” members and that similarity is a proxy for cus-tomers’ perceptions of a seller’s fit with their in-group(Devine 1995).

The influence of interaction frequency on trust is muchgreater (r = .30) than that of the three other mediators (meanof other relational mediators, r = –.01, no overlap in CI). Ascustomers interact more frequently with sellers, they appearto gain more information about their partner, which reducestheir uncertainty about future behaviors and improves trust;however, the frequency of their interaction has little effecton other relational mediators.

We now turn our attention to the back half of the model,the relational mediator → outcome linkage (Table 4), forwhich we find that relational mediators have differentialeffects on most of the outcomes studied. Commitment (r =.58) has the greatest influence on customer loyalty (mean ofall other relational mediators, r = .47, no overlap of CI), aswe might expect from these two similar constructs.

Moreover, relationship quality has the greatest influenceon objective performance (r = .63), followed by trust (r =.35), relationship satisfaction (r = .32), and commitment(r = .27), and the CIs of relationship quality, trust, and com-mitment do not overlap. These findings indicate that RMresearchers may need to take a multiple mediator or com-posite view when they measure customers’ relationships to

capture their impacts on objective performance. Differentdimensions of a relationship may be synergistic, and supe-rior performance may be possible only when the relationshipis sufficiently strong on all critical aspects. Trust (r = .73) ismost critical for cooperation compared with the other medi-ators (mean of other relational mediators, r = .66, no overlapof CI), in support of its role in coordinating actions amongpartners to create value and achieve mutual outcomes.

DiscussionWe provide evidence that the intervening role of relationalmediators between RM strategies and exchange outcomes ismore complex than is currently suggested in the extantresearch, but the fundamental premise that RM and strongrelationships positively affect performance is well sup-ported. Several of our findings offer important implicationsfor improving the effectiveness of RM research and practice(for a summary of key findings and implications, see Table6).

First, RM strategies/antecedents have a wide range ofeffectiveness in terms of generating strong relationships,though specific strategies appear to be most effective forstrengthening specific aspects of a relationship. Overall,expertise and communication are the most effectiverelationship-building strategies across all elements of a rela-tionship, whereas the other strategies often have differentialeffects across the different mediators. For example, generat-ing relationship benefits, promoting customer dependency,and increasing similarity to customers are more effectivestrategies for increasing customer commitment than forbuilding trust, whereas relationship investment and interac-tion frequency have the opposite effect. Therefore, whencomparing the relative effectiveness of RM strategies, theresults depend on the relational mediator under investiga-tion. These findings indicate that RM may be improved bytaking a more fine-grained approach in which managers tar-get RM strategies at specific relational weaknesses.

Second, we find that objective performance is influ-enced most by relationship quality (a composite measure ofrelationship strength) and least by commitment, which sup-ports a multidimensional perspective of relationships inwhich no single or “best” relational mediator can capturethe full essence or depth of a customer–seller relationship(Hennig-Thurau, Gwinner, and Gremler 2002; Johnson1999). Previous research (Berry 1996; Doney and Cannon1997; Spekman 1988) that offers either commitment or trustas the key, central, or cornerstone relational mediator maybe focused too narrowly; a relationship may be truly effec-tive only when most or all of its key aspects are strong.Therefore, research that focuses only on commitment andgeneralizes from its impact on customer intention or inter-mediate behaviors to its effect on seller performance mayprove misleading. For example, commitment has the great-est impact on customer loyalty and the smallest impact onobjective performance.

Third, the large, direct effects of dependence and rela-tionship investment on seller objective performance in thecausal model suggest that these antecedents influence per-formance through alternative, mediated pathways. Although


Key Findings Research and Managerial Implications

AntecedentsRelationship marketing strategies/antecedents have a

wide range of effectiveness for generating strongrelationships. Expertise and communication are mosteffective, then relationship investment, similarity, andrelationship benefits; dependence, frequency, andduration are relatively ineffective.

Selection and training of boundary spanners is critical;expertise, communication, and similarity to customers are

some of the most effective relationship-buildingstrategies. Expertise’s impact supports Vargo and Lusch’s(2004) premise that “skills and knowledge” are the most

important seller value-creation attributes.

The negative impact of conflict is larger in magnitudethan the positive effect of any other RM strategy.

All proactive RM efforts may be wasted if customerconflict is left unresolved.

Specific RM strategies appear most effective forstrengthening one aspect of a relationship.Relationship benefits, customer dependency, andsimilarity are more effective for increasing commitmentthan for building trust; the opposite is true forrelationship investment and frequency.

Relationship marketing may be improved through a fine-grained approach that targets specific relationalweaknesses. The relative effectiveness of RMstrategies depends on the relational mediator

investigated.

OutcomesRelationship quality (a composite measure of relationship

strength) has the greatest influence on objectiveperformance, and commitment has the least.

No single relational mediator captures the full essence ordepth of a customer–seller relationship; the findings

support a multidimensional perspective of relationships.Extant research focused on a single relational mediator

may provide misleading guidance.

Surprisingly, relationship investment has a large, directeffect on seller objective performance, in addition to itsfrequently hypothesized indirect mediated effect.

The classic mediating model of RM (Morgan and Hunt1994) should be adapted to include alternative mediated

pathways (e.g., reciprocity).

Dependence has a large, direct effect on seller objectiveperformance but a relatively small impact on relationalmediators.

Dependence is not an effective relationship-buildingstrategy but can improve performance in other ways,possibly by increasing switching costs and barriers to

exit.

Of all outcomes, relationships have the greatest influenceon cooperation and WOM and the least on objectiveperformance.

Relationship marketing efforts may be effectivelyextended across many other nontraditional buyer–sellerinteractions (e.g., interdepartmental groups) for which

cooperation is often critical for success. Word-of-mouthbehaviors may be the best discriminator of true customer

loyalty (Reichheld 2003).

ModeratorsRelationship marketing is typically more effective when

relationships are more critical to customers, such as for(1) service versus product offerings, (2) channel versusdirect exchanges, and (3) business versus consumermarkets.

Researchers must take care when extending findingsacross contexts in which relationship importance may

vary. Managers might target RM expenditures tocustomer segments with the highest desire for strong

relationships to improve returns.

Customer relationships often have stronger effects onexchange outcomes when their target is an individualperson than when it is a selling firm.

Researchers should differentiate the effects of customerrelationships with boundary spanners from those with

firms. Strategies such as team selling, salespersondisintermediation, and the use of call centers should be

evaluated in light of the impact of interpersonalrelationships.

TABLE 6Summary of Key Findings and Implications

dependence is not very effective at building relationships, itcan improve performance by increasing switching costs andbarriers to exit, which may make it an effectiveperformance-enhancing strategy but not an effective RMstrategy. However, relationship investment both builds cus-tomer relationships and directly improves performance,which suggests that the extant relational-mediated frame-work is not comprehensive and that additional mediators

(e.g., reciprocity) must be investigated to explain the impactof RM on performance fully. The importance of capturingthe direct effect of relationship investment (β = .34) is rein-forced by its greater impact on objective performance thanthe effect of the relational mediators (β = .16) in the causalmodel.

Fourth, the findings that strong relationships appear tobe more effective for building customer loyalty and improv-


ing seller performance for (1) service versus product offer-ings, (2) channel versus direct exchanges, and (3) businessversus consumer markets lend support to the premise thatRM may be a more effective strategy in situations in whichrelationships are more critical. This finding calls into ques-tion sellers’ efforts to force RM strategies in contexts inwhich the customer’s relational needs are unclear; it alsomay explain the less-than-desirable results of RM on per-formance that have been documented in previous studies(e.g., Reinartz and Kumar 2003). Because these situationalmoderators are coarse proxies for customers’ relationshipneeds, RM effectiveness likely varies across other factorsthat influence customers’ needs for strong relationships. Inturn, researchers must take care when extending RMresearch to these different contexts.

Fifth, the results suggest that customer relationshipshave stronger effects on exchange outcomes when their tar-get is an individual person than when their target is a sellingfirm. Thus, RM strategies focused on building interpersonalrelationships between boundary spanners (e.g., dedicatedsalesperson, social entertaining) may be more effective thanthose focused on building customer–firm relationships (e.g.,team selling, frequency-driven loyalty programs). Socialpsychology’s individual and group judgment theory(Hamilton and Sherman 1996, p. 336), which posits “differ-ences in the outcomes of impressions formed of individualand group targets, even when those impressions are basedon the very same behavioral information,” has severalimplications for the marketing domain and may provide aparsimonious explanation for previous marketing research(Doney and Cannon 1997; Iacobucci and Ostrom 1996).The post hoc finding that conflict has a more negativeimpact (p < .01) on customer–firm relationships than oncustomer–individual relationships is also consistent withthis theory because judgments about individuals are moreresilient to disconfirming events than are judgments aboutgroups (Hamilton and Sherman 1996). Thus, managers maywant to use boundary spanners or salespeople rather thencentralized service centers to resolve conflicts because cus-tomers’ relationships with salespeople may withstand con-flict better than their relationships with selling firms.

Managerial Implications

Most promising for managers is that five of the strategieswith the greatest impact are either seller focused or dyadic,in support of the effectiveness of proactive relationship-building strategies undertaken by sellers. Business execu-tives focused on building and maintaining strong customerrelationships should note that the selection and training ofboundary spanners is critical; expertise, communication,and similarity to customers are the most effectiverelationship-building strategies. The next most effectivestrategy is for managers to make relationship investmentsand generate relationship-based benefits for customers; fur-thermore, relationship investment has the added benefit ofinfluencing performance directly. However, managers mustrecognize that these proactive efforts will be wasted if theyleave customer conflict unresolved because the negativeinfluence of conflict on customer relationships is greater inmagnitude than that of any other strategy. Thus, some firms

could generate higher returns by reallocating their RMinvestments to conflict resolution. Extending service recov-ery research into the RM domain to develop strategies for“relationship recovery” also might be worthwhile. A strat-egy of increasing customer dependence does not appear tobe an effective way to build relationships, but it seems toinfluence seller performance directly. Neither relationshipduration nor interaction frequency is a good driver of strongcustomer relationships.

Of all the outcomes we analyze, relationships have thegreatest influence on cooperation and WOM. The impact oncooperation implies that RM efforts may be effectivelyextended across many nontraditional buyer–seller interac-tions (e.g., alliances, interdepartmental groups); in thesesituations, cooperation is often critical for success. Simplystated, firms that depend on WOM strategies for new cus-tomers should implement effective RM programs.

Some results indicate that a more targeted effort mayimprove RM efficiency. Because RM strategies appear tooperate through different mediators that affect outcomesdifferentially, a manager who desires cooperation betweentwo groups after a merger and who recognizes that trust isthe relational mediator with the greatest influence on coop-eration should select RM strategies that influence trust best(i.e., communication and interaction frequency). Marketerswith a portfolio of customers, channels, and products couldimprove the return on their RM expenditures by targetingtheir spending toward segments in which RM is more likelyto pay off, such as customers who purchase more services,channel versus direct customers, and business versus con-sumer segments.

Managers may also want to leverage the potentiallystronger impact on customer loyalty and seller performancein relationships that involve an individual boundary span-ner. For firms that experience low turnover, focusing theirRM efforts on building customer–salesperson bonds may bea productive strategy, though developing strong relation-ships may prove difficult for firms that want to move cus-tomers from dedicated salespeople to offshore call centersfor various reasons. These firms should recognize that alack of seller expertise, dissimilarities between boundaryspanners and customers, ineffective communication, andshifts from interpersonal to person–firm relationships cannegatively affect customer–seller relationships.

Limitations

Meta-analyses have several strengths, but they also containinherent limitations. First, the constructs we include are con-strained to variables for which sufficient primary data areavailable. Thus, our framework should be considered a sum-mary of the most commonly studied RM constructs, not anexhaustive list or even a list of the most important con-structs. For example, mutual dependence, seller disclosure,and functional conflict have been shown to be importantconstructs for RM, but because of data unavailability, wecould not include them in our meta-analysis. Second, hetero-geneity in effect sizes remained even after we accounted forany variability due to the moderator variables in the study,which indicates that the effect sizes we report should be con-sidered averages and may vary with the inclusion of unmea-


sured moderating conditions. Third, because of the limitednumber of studies for some moderator variables, our studyhas limited power to reject null hypotheses.

Future Research DirectionsAfter nearly two decades of RM research, marketers’ effortsmay need to shift from significant testing to identifyingwhich, and in what conditions, RM strategies generate thehighest return on RM investment. Our synthesis of theextant literature identifies several avenues that require fur-ther study.

Research should expand the constructs included in ourRM-mediated framework and determine which aspects ordimensions should be included to obtain a multifacetedview of relational exchanges. Although commitment andtrust play critical roles, other candidates might include rela-tionship satisfaction, exchange efficiency, equity, relationalnorms, and reciprocity. The absence of any measure of reci-procity between exchange partners is especially notablebecause it has been identified as “the core of marketingrelationships” (Bagozzi 1995, p. 275) and may help explainthe pattern of effects surrounding the impact of relationshipinvestments and benefits on relational mediators. Integrat-ing reciprocity into the relational-mediating framework mayalso explain the large, direct effect of relationship invest-ment on performance, such that people’s inherent desire torepay “debts” generated by sellers’ investments may lead toperformance-enhancing behaviors, independent of trust orcommitment.

In addition to taking a multidimensional perspective ofrelationships, the scope of RM research should expand toinvestigate potential interactions among the relational medi-ators and identify relational synergies. For example, thestrong linkage between relationship quality and objectiveperformance may be due to interactions among the differentfacets of a relationship.

Even some of the high-impact antecedents and impor-tant outcomes in our framework appear in relatively fewprimary studies (i.e., conflict, seller expertise, and WOM),which suggests the need for additional research. Seller

expertise, beyond product-specific expertise, might includeoverall customer knowledge, industry expertise, creativity,process knowledge, and intraorganizational facilitation.Strategies that remedy conflict-laden events, such as serviceor relationship recoveries, also are critical to incorporateinto both practice and further research.

The relatively small correlations between customerfocal antecedents (relationship benefit, dependence onseller) and relational mediators are surprising because wetook the relational mediators from the customer’s perspec-tive as well. This finding may be due to a misspecification;we may not have studied some critical customer-focusedantecedents. Thus, researchers should investigate othercustomer-focused antecedents, such as perceived exchangeefficiency, perceived relationship investments, and liking, toidentify other key drivers of a strong relationship from thecustomer’s perspective.

The heterogeneity across nearly all linkages, even afterwe account for the moderators we included, demandsresearch to determine other moderators that may influenceRM effectiveness (e.g., relationship age, customer control,customer involvement, relationship orientation of the cus-tomer). For example, as the customer’s need for a relation-ship increases, RM strategies may become more effective.Thus, researchers should develop a measure of the relation-ship orientation of the customer to support the segmentationof RM efforts. Marketers could then target their RM effortstoward customers with the highest susceptibility for RM.Contrary to most existing RM research, our results andsocial psychology theory suggest that researchers shoulddifferentiate between individual–individual and individual–firm relationships.

In summary, we provide insight into the most effectiveRM strategies, the conditions that moderate this effective-ness, and how the links between both antecedents and con-sequences of relational mediators depend on the mediatorbeing investigated. These insights provide managers withopportunities to improve the returns on their RM invest-ments and researchers with directions to build more robustmodels of the influence of RM on outcomes.

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Factors Influencing the Effectiveness of Relationship Marketing