Competitive channel relationship management:When resellers establish competing manufacturerrelationships

Alberto Sa Vinhas & Richard Gibbs

Published online: 5 April 2012# Springer Science+Business Media, LLC 2012

Abstract The focus of this study is the impact of competitive relationships on theeffectiveness of channel relationship management strategies. We argue that thecharacteristics of a reseller’s relationship with alternative manufacturers influencehis/her evaluation of the relationship with a focal manufacturer. We extend previousresearch by suggesting that the relative levels of channel conflict and informationexchange are critical determinants of relationship outcomes. Building on previousliterature on customer satisfaction, we argue that this relation is asymmetric; improve-ments in the levels of channel conflict and information exchange will have a greatereffect when these are below the competitor’s comparative level than when these areabove. We find support for these hypotheses in a sample of 491 observationscorresponding to different resellers selling a manufacturer’s product line acrossseveral countries. Our results underscore the need for suppliers to go beyond conflictminimization and information exchange policies across relationships and considerindividual-level competitive effects for each relationship.

Keywords Distribution channels . Business-to-business marketing . Channelmanagement . Competitive effects

In today’s competitive world, business-to-business (B2B) manufacturers face importantchallenges when managing their distribution channel relationships. High levels of com-petition are prevalent in several B2B industries, both at the manufacturer level and at thereseller level. Manufacturers frequently compete with other manufacturers for a reseller’sbusiness. A reseller’s reactions to a manufacturer’s channel relationship initiatives maydepend on the characteristics of its relationships with alternative manufacturers.

Mark Lett (2012) 23:645–659DOI 10.1007/s11002-012-9168-3

A. Sa Vinhas (*)Washington State University, College of Business, 14204 NE Salmon Creek Avenue, Vancouver,WA 98686-9600, USAe-mail: asavinhas@vancouver.wsu.edu

R. Gibbs (*)London, United Kingdome-mail: richardkgibbs@hotmail.com

While the existing literature provides wide support for a relation between channelrelationship management constructs (e.g., level of conflict in the relationship) andchannel outcomes (Palmatier et al. 2006), insights on how competition affects theeffectiveness of channel relationship initiatives remain scarce. Most of the previousrelationship management research focuses on the relationship between a supplier and areseller. We contribute to this research by looking at how a competitive relationshipoutside of the focal supplier–reseller dyad influences relationship outcomes at the focaldyad level.

The literature supports the notion that a B2B supplier’s performance vis-à-viscompetition impacts relationship outcomes (e.g., Qualls and Puto 1989). However,most of this literature focuses on product, price, and service attributes, rather thanrelationship attributes. For instance, Bowman and Narayandas (2004) show that theeffect of the level of responsiveness of a B2B supplier on a buyer’s satisfaction with thesupplier depends on the focal vendor’s performance, in terms of that attribute comparedwith that of the closest competitor in the account. Anderson and Narus (1984) show thatthe level of outcomes derived from a focal relationship, compared to those availablefrom alternative suppliers, impacts a reseller’s cooperation and satisfaction with therelationship. Turning to relationship attributes (e.g., the level of conflict in a relation-ship), it is unclear how and whether relative performance on these attributes compared toalternative suppliers influences the reseller’s reactions to a supplier’s relationshipmanagement initiatives. Do resellers assess less tangible relationship managementattributes, such as the level of conflict and information exchange in a relationship, froma relative or absolute standpoint?

Our initial interviews with distribution managers in the information technol-ogy industry revealed that managers struggled with this question. For instance,they wondered whether they should strive to achieve low(er) uniform levels ofchannel conflict across all of their main channel relationships or, alternatively,implement conflict management strategies at the individual reseller level. Theywondered how the characteristics of the relationships between individualresellers and alternative manufacturers were relevant to this analysis. Mostgathered relationship information for their own channel relationships but notfor competitive relationships.

We address these questions and build on previous literature on the antecedents ofsatisfaction (e.g., Anderson and Sullivan 1993; Mittal et al. 1998) to argue thatcompetitive relationships will serve as a “reference point” that will influence thereseller’s reactions to the manufacturer’s relationship management initiatives. Weargue that the relation between the relative levels of relationship attributes (e.g., levelof conflict in a relationship) and relationship outcomes (e.g., relationship quality orsatisfaction) is asymmetric, i.e., increases in the levels of a relationship attribute willhave a greater effect when this level rests below the competitor’s in the relationshipwith the same reseller rather than when it is above.

The paper proceeds as follows. The next section presents the conceptualframework and hypotheses. Then, the research method is described, includingthe empirical test of the hypotheses. We test our hypotheses in a cross-sectionalsample of resellers representing an information technology manufacturer inseveral countries in Western Europe. The final section discusses the implica-tions for theory and practice.

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1 Theoretical framework

Much of the existing relationship management and governance literatures predomi-nantly focus on individual dyadic relationships between firms (Wathne and Heide2004). We contribute to the growing literature that argues that, to fully understand thenature of dyadic relationships, greater attention must be directed to the embeddedcontext with which dyadic business relationships take place (e.g., Anderson et al.1994). For instance, Heide and John (1988) describe how an agent’s bonding effortsin one (customer) relationship serve to discourage opportunism in another (i.e., by aprincipal).

In this paper, we also go beyond the dyad and investigate how the relationshipmanagement characteristics of a parallel relationship (i.e., between the focal resellerand an alternative supplier) influence relationship management outcomes at the focalsupplier–reseller dyad.

We concentrate on two main relationship attributes: the level of manifest conflictin a relationship and the quality of the information exchange between the manufac-turer and the reseller. The level of manifest conflict in a relationship is defined as theoverall level of disagreement in the relationship between the reseller and the manu-facturer (Anderson and Narus 1990). Frequent and intense disagreements ultimatelyhinder the parties’ ability to reach its goals (Etgar 1979; Geyskens et al. 1996). Thequality of information exchange represents the degree to which the reseller values theinformation shared by the manufacturer (Anderson and Weitz 1992; Morgan andHunt 1994). In a meta-analysis, Palmatier et al. (2006) show that these are two maindeterminants of relationship outcomes, with conflict having the largest absoluteimpact of all the relationship constructs analyzed in their study.

In terms of relationship outcomes, we focus on two main constructs: relationshipsatisfaction and relationship quality. Relationship satisfaction is defined as a reseller’spositive affect state resulting from the appraisal of all aspects of a firm’s workingrelationship with the manufacturer (Anderson and Narus 1990). Geyskens et al.(1996) argue that satisfaction is the most popular construct in models of channelrelationships. Relationship quality is the reseller’s overall assessment of the strengthof a relationship with the manufacturer, taking into consideration all the differentaspects of its interaction with the manufacturer. Consistent with previous literature (e.g., Crosby et al. 1990; De Wulf et al. 2001) we conceptualize relationship quality as ahigher-order construct consisting of several distinct, though related dimensions.Although there still exists discussion on which dimensions make up relationshipquality, prior conceptualizations mainly emphasize the critical importance ofrelationship satisfaction, trust, and relationship commitment as indicators ofrelationship quality. Therefore, we assume that a better quality relationship isaccompanied by a greater satisfaction, trust, and commitment. We note that ourtwo main dependent variables are not independent from each other, as satisfac-tion is one of the dimensions of relationship quality. We estimate one separatemodel for each dependent variable to provide further evidence of convergentvalidity. Relationship trust is defined as the extent to which a firm believes thata partner is honest, i.e., will keep its promises. Relationship commitment isdefined as a desire to continue the relationship in the future and a willingnessto make short-term sacrifices to maintain the relationship.

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The relationships among these constructs (information exchange, relationshipconflict, relationship satisfaction, and relationship quality) are well established inthe literature. We treat the quality of information exchange as an antecedent of thelevel of conflict in a relationship (c.f. Etgar 1979; Anderson and Narus 1984). Wheninformation exchange between the manufacturer and the reseller is used to helpresellers and support and nurture relationships with channel members, the interestsof the parties align with disagreements becoming less likely (Mohr et al. 1996).Ineffective communications frequently lead to misunderstandings and mutual feelingsof frustration (Etgar 1979). Information exchange builds stronger relationships in anexchange by helping resolve disputes (Morgan and Hunt 1994).

Previous literature provides strong evidence for a negative effect of the level ofconflict in a relationship on relationship outcomes (Palmatier et al. 2006). Relationaldisagreements tend to block achievements of the firm’s goals and elicit frustration,thereby causing feelings of unpleasantness and dissatisfaction with the relationship(Anderson and Narus 1990; Frazier 1983; Gaski 1984). Figure 1 provides a graphicalrepresentation of our framework.

This literature establishes a clear relation between our main constructs. However,most studies implicitly assume that a reseller’s assessment of a relationship is basedon the absolute levels of relationship attributes. However, information exchange andrelationship conflict are abstract, intangible attributes that are difficult to evaluate.Decision makers are thus likely to face uncertainty when determining what are “goodlevels” for these attributes. We argue that comparative levels in parallel relationships

Information Exchange

Conflict

Reseller Relationship Satisfaction

Relative Conflict

Relative Information Exchange

Absolute Levels of Relationship Attributes

Impact of Competitive Relationships: Relative Relationship Attributes

Outcomes Given Alternatives

Previous Research

Reseller Perceptions of Relationship Quality

Relationship Outcomes

Fig. 1 Main model: determinants of relationship outcomes in a manufacturer–reseller relationship

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with alternative suppliers will serve as a reference point, which will then serve as abaseline for the reseller’s evaluation of the relationship with the focal supplier.

We base this prediction on the literature looking at the impact of attribute levelperformance on consumer satisfaction (e.g., Anderson and Sullivan 1993; Mittal et al.1998; Sirdeshmukh et al. 2002). This literature builds on prospect theory (e.g.,Kahneman and Tversky 1979), which postulates that the perceived context associatedwith a given decision (i.e., the decision frame) affects the outcome of the decision-making process. In particular, the past and present context of experience defines anadaptation level (or reference point) relative to which incoming stimuli are perceivedand compared (Helson 1964; Qualls and Puto 1989). People’s judgments displayreference dependence, i.e., carriers of value are gains and losses from this referencepoint. The relationship with the best alternative supplier will provide a particularlyrelevant comparison point as it is likely to be more salient in the reseller’s mind.Business customers normally establish concurrent relationships with multiple suppli-ers to ensure supply and competition among vendors to keep prices in check, amongother reasons. The reseller will likely compare the performance of the focal supplieron different relationship attributes with this alternative supplier to decide on how toallocate its business among the two suppliers. This is also consistent with the socialexchange literature arguing that the present and past experiences with a similarmanufacturer influence a reseller’s expectations in a focal manufacturer relationship(e.g., Thibaut and Kelley 1959; Anderson and Narus 1984). Thus:

H1: When evaluating a relationship with a given supplier, in terms of the level ofinformation exchange and relationship conflict, a focal reseller will use the compar-ative levels of these variables in a relationship with the best alternative manufactureras a reference point.

Consistent with the prospect theory literature, we also expect one unit of negativerelative performance for a given relationship attribute to have a greater effect onrelationship outcomes than a corresponding unit of positive relative performance.This is based on the notion of loss aversion, i.e., a unit loss is weighted more than anequal amount of gain (Einhorn and Hogarth 1981; Hogarth 1987). We define therelative value of a relationship attribute in the following way: as how the level of anattribute in a relationship between the manufacturer and a focal reseller compares tothe level of the same attribute in the relationship between the focal reseller and analternative supplier. We argue that that this relative value will have an impact onrelationship outcomes above and beyond its absolute effects. We propose an asym-metric gain–loss framework for understanding the effect of relative relationshipattribute performance and relationship outcomes. In summary, we treat the qualityof information exchange as an antecedent of conflict and expect an asymmetricrelation among the two constructs. We also expect an asymmetric effect of conflicton relationship outcomes. Thus:

H2: Negative performance compared to the main alternative supplier in terms of thequality of information exchange between the supplier and a focal reseller will have agreater impact on the level of relationship conflict than positive performance on thesame attribute.

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H3: Negative performance compared to the main alternative supplier in terms of thelevel of conflict in the relationship between the supplier and a focal reseller will havea greater impact on relationship satisfaction and quality than positive performance onthe same attribute.

2 Methodology

2.1 Research design

Our hypotheses were tested empirically through a cross-sectional survey of resellersacting in several European countries, reporting on their supplier relationship. Thesupplier was a major manufacturer of Information Technology products with annualsales in Europe in excess of US$1.5 billion, selling to approximately 6.500 resellers.Resellers typically handled logistics and marketing to large and small commercialcustomers within their target market.

The supplier used several routes to market for equipment sales to serve differentcustomer segments. Given our focus on interbrand competition, we excluded fromour sample channels selling exclusively the supplier’s product. These resellers do nothave an “alternative supplier” to serve as a relevant reference point. Distributors soldseveral manufacturer brands to second tier intermediaries. Corporate IT Resellerswere multibrand resellers selling to large corporate companies. These resellers gavehigh emphasis to providing value-added services to their customers. Small andmedium business resellers bought frequently from the manufacturer and had anaccount manager assigned. We have excluded from our analysis the resellers whopurchased infrequently from and had no established relationship with the supplier.

2.2 Data collection and questionnaire design

Our universe was the set of the 783 intermediaries from 16 European countries whohad purchased from the manufacturer organization within the 12-month periodpreceding the survey. They also had an ongoing relationship with the manufacturer,which was demonstrated by the allocation of a dedicated sales manager. Our universerepresented over 90% of all sales revenue generated by the manufacturer throughnon-exclusive channels in the territories being considered. We used a stratifiedsample across countries, surveying a total of 592 intermediaries. The stratificationhas worked bottom upwards from a country level, such that within each country arepresentative cross-section of all intermediaries should be surveyed. A marketresearch company administered a telephone questionnaire on behalf of the researchersthat included measures of the variables of interest.

We received a total of 580 responses. These respondents were asked to evaluatetheir relationships with the manufacturer and their best alternative supplier. A total of521 out of the 580 respondents reported on a second manufacturer, representingseveral competitor brands. Thus, our final response rate was 88% (521/592). Amultivariate test of differences in the means of the study variables failed to rejectthe null hypothesis of group equality (i.e., between respondents reporting and those

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refusing to report on a second manufacturer). More than 80% of the respondentsreported on one of the other three main brands in the market, each with market sharesabove 20%. There were no significant differences in the response rate acrosscountries. Respondents represented 55% of the manufacturer sales through resellersand approximately two thirds of sales through distributors in Western Europe.

The supplier’s primary contact at each firm, the individual with whom thesupplier interacted on a regular basis, acted as our informant. They weretypically managing directors, marketing and/or sales directors. Questionnaireswere translated and back-translated into 14 languages through a third-partytranslation firm to assure a common understanding of the survey questionsacross cultures and languages (Brislan 1970; Sekaran 1983). Local employees ofthe supplier organization assessed the modified questionnaires in each languagevariant to ensure comprehension of the question set.

2.3 Measures

Constructs were defined conceptually based on the past literature and our theoreticalframework. Next, we developed a set of items for each construct based on pastmeasures. The measures and internal consistency estimates are presented in the“Appendix.”

As described in the theoretical section, we conceive relationship quality as ahigher-order construct composed of the relationship satisfaction, trust and commit-ment constructs. We captured our global evaluations of relationship satisfactionthrough a single item. This is consistent with previous satisfaction research (e.g.,Anderson and Narus 1990; Anderson and Sullivan 1993; Bowman and Narayandas2004; Mittal et al. 1998). LaBarbera and Mazursky (1983) show that, in large scalesurvey research, the use of multi-item scales for satisfaction does not significantlyincrease the quality of measurement compared to single-item measures. To avoidoverburdening or annoying respondents, multiple items were not used for thisconstruct, as per Rossiter (2002). We operationalized trust and commitment throughmulti-item scales based on existing research. For convergent validity, we askedrespondents to describe the strength of the overall relationship with the manufacturer,taking into consideration all of the different aspects of their relationship with themanufacturer (based on a seven-point scale scaled from “very weak” to “verystrong”). A high correlation between this single item and our overall measure ofrelationship quality (0.82, p<.01) provides further support for the validity of ourrelationship quality measure. To be consistent with the previous satisfaction literatureand to demonstrate convergent validity, we also estimate a second model withsatisfaction as the dependent variable.

The constructs of level of conflict and information exchange were operationalizedthrough multi-item scales based on existing research. We captured these constructsfor both the focal manufacturer and a competitor and computed its relative levelsbased on these answers. In the particular case of relationship conflict, our itemscapture the frequency and intensity of disagreements. These appear to be the bestindicators of the level of overt conflict that can be obtained in field surveys (Brownand Day 1981). The distribution of observations for the relative relationship variableswas the following: 37% of the observations had a conflict level below the competitive

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relationship and 33% had values above. For information exchange the respectivevalues were 39% and 36%.

We included some covariates in our analysis. We define the level of outcomesgiven alternatives as the reseller’s cognitive assessment of the outcomes obtained intheir relationship with the manufacturer compared to the ones obtained in a relation-ship with their best alternative manufacturer (Anderson and Narus 1984). Theprovision of outcomes influences reseller satisfaction through the economic payoffsthat are made available (Anderson and Narus 1990; Kumar et al. 1995). Previousliterature suggests that this construct may provide an important reference point whenevaluating competing relationships (Qualls and Puto 1989). We also controlled for thelength of the channel relationship, i.e., the number of years that the reseller has beenworking with the supplier. We followed the typical classification adopted by themanufacturer and captured this construct using a categorical variable with fivecategories (with a minimum value of “from less than 1 year” and a maximum valueof “more than 10 years.”).

2.3.1 Measure purification

An exploratory factor analysis indicated that all of the measures were unidimensional.A confirmatory factor analysis was used to assess the convergent and discriminantvalidity of the measures. The CFA model has a goodness-of-fit index of 0.94, acomparative fit index of 0.98, a root mean square error of approximation of 0.073 anda standardized root mean square residual of 0.045, which indicates a reasonable fit tothe data. Each of the observable indicators loaded significantly on their intendedfactors, which indicated convergent validity among the items of each scale. Discrim-inant validity was assessed according to Fornell and Larcher’s (1981) criterion. Allpairs of factors met this criterion, providing evidence of discriminant validity. Reli-ability was assessed through calculation of coefficient alpha for each item set, all ofwhich were acceptable. Table 1 presents the descriptive statistics for the set ofvariables.

A relevant concern in cross-national research is whether the constructs of interestexhibit cross-national equivalence (Singh 1995; Steenkamp and Baumgartner 1998).

Table 1 Variable means, standard deviations, and correlations

Means S.D. Min Max V1 V2 V3 V4 V5 V6

Relationship satisfaction (V1) 4.91 1.35 1.00 7.00 1.00

Relationship quality (V2) 4.82 1.58 1.00 7.00 0.73* 1.00

Relationship conflict (V3) 3.21 1.41 1.00 7.00 −0.63* −0.64* 1.00

Information exchange (V4) 4.60 1.23 1.00 7.00 0.55* 0.60* −0.51* 1.00

Outcomes given alternatives(V5)

4.86 1.36 1.00 7.00 0.51* 0.53* −0.46* 0.49* 1.00

Relationship length (V6) 3.39 1.19 1.00 5.00 −0.03 −0.02 −0.03 0.07 −0.03 1.00

V variable* p<.05

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To improve functional and conceptual equivalence, we conducted an initial set ofqualitative interviews with local employees of the manufacturer organization toensure that the concepts served the same function from country to country and thatthe way the concepts were expressed in each country was the same. We alsoconducted a set of empirical tests after the data collection process to assess whetherour measures were cross-nationally invariant. Steenkamp and Baumgartner (1998)argue that when the purpose of a study is to relate the focal construct to otherconstructs in a nomological net, full or partial metric invariance has to be satisfiedbecause the scale intervals have to be comparable across countries. Metric invarianceexists when the different scores on the item can be meaningfully compared acrosscountries, and these observed item differences are indicative of similar cross nationaldifferences in the underlying construct. We performed a set of tests to assess metricinvariance. The configural invariance model was estimated first. It is the baselinemodel against which other models can be compared. The fit of the configuralinvariance model was satisfactory, with an RMSEA of .07 and a CFI of 0.95. Wethen compared the fit of the metric invariance model with the configural invariancemodel. We rejected the null hypothesis of full metric invariance for the entire sample(chi-square difference of 235.36 with 135 degrees of freedom). However, we failed toreject the hypothesis of full metric invariance for a subset including the largest Europeancountries. We then conducted a set of stepwise tests, adding one country at a time to oursample. We rejected the null hypothesis of metric invariance when adding four (out ofthe 16) countries. Conversely, we failed to reject the null hypothesis of full metricinvariance for a subsample of 12 European countries representing 87% of the observa-tions (chi-square difference of 104.86 with 99 degrees of freedom, p<.05). Thus, ouranalysis suggests that lack of measurement equivalence is not a significant validitythreat in our study. We estimate our model using all of the observations and con-firmed that the results would not change if we excluded the four countries for whichwe failed to find evidence of metric equivalence.

Another possible concern associated with empirical studies that use primary data isthe possible presence of common method bias. We first employed Harman’s one-factor test (Podsakoff et al. 2003). We compared our measurement model with amodel where all items for our latent variables were entered into a single factor usingCFA procedures. Our multi-factor model significantly improves fit, suggesting thatinter-item correlations are not driven purely by method bias. Second, Lindell andWhitney (2001) suggest that the smallest correlation among the manifest variablesprovides a reasonable proxy for CMV. The authors suggest using the second smallestpositive correlation as a more conservative estimate. Following their suggestion, thisindicator of CMV was assumed to have a constant correlation with all of themeasured items. We calculated CMV-adjusted correlations between the variablesunder investigation—partialling out the CMV coefficient from the unadjusted corre-lations—and found that there were no differences in terms of size and patterns ofsignificance between the unadjusted and CMV-adjusted correlations.

2.4 Model estimation and results

There were 491 observations available because we eliminated 30 questionnaires as aresult of excessive missing answers. We test for hypothesis 1 by looking at absolute

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fit improvement (in terms of improvements in R-square) and by computing an F-testcomparing the variance explained by each of the two models (with and without“relative” effects). We then test for asymmetric effects following the empiricalspecification adopted in previous satisfaction research (e.g., Anderson and Sullivan1993). The asymmetric framework has been defined in our model as having both anegative and a positive component, depending on the specific situation for a partic-ular reseller relationship, with separate effects on satisfaction and relationship quality.The asymmetric effect is tested by constraining the coefficients for (1) negative and(2) positive relative relationship attributes to be equal, determining whether theconstraint can or cannot be rejected, and reporting whether coefficient (1) is biggerthan coefficient (2).1 The variance inflation factors were all less than 3, whichsuggests that multicollinearity is not a concern.

We control for channel specific effects by including dummy variables for largecustomer resellers and small customer resellers. Since we capture our data acrossseveral countries, we also controlled for possible country-specific effects by addingcountry-specific dummies. We also estimated a hierarchical linear model (HLM) toaccount for a lack of independence across observations from the same geographicalterritory and found consistent results with our OLS model.

2.4.1 Results of hypothesis tests

Table 2, column 1, presents a summary of our results for relationship conflict. We finda negative effect of the quality of information exchange on the level of conflict in therelationship (−0.41, p<.01). Consistent with H2, we find that improvements in thequality of information exchange when the manufacturer is below the competition inthat attribute (−0.15, p<.05) have a larger impact on conflict than when the value isabove (0.03, non-significant). However, we do not find evidence of asymmetriceffects (the Wald test was non-significant). We also find that higher levels of out-comes given alternatives lead to lower levels of relationship conflict (−0.28, p<.01).In addition, we find that adding the relative information exchange variables does notsignificantly improve model fit (negligible improvement in R-square and a non-significant F-test 0 1.97). We thus fail to support hypothesis 1 for the relationshipconflict model.

Columns 2 and 3 present a summary of our results for the two relationshipoutcomes. Regarding the main effects of the two relationship attribute variables, wefind that the level of conflict in the manufacturer–reseller relationship has a negativeeffect on reseller satisfaction (−0.24, p<.01) and relationship quality (−0.30, p<.01).The quality of information exchange has a positive impact on relationship outcomes(0.25, p<.01 and 0.34, p<.01, respectively). We find strong support for an asymmet-ric effect of relationship conflict on relationship outcomes, as per H3. Improvements(decreases) in relationship conflict have a greater positive impact on relationshipquality when the manufacturer is poorer than the competition in that attribute, i.e., has

1 In a first step, a Wald test indicates whether the equality of coefficients constraint can be rejected. If thisnull hypothesis is rejected (i.e., the two coefficients are not equal), then an asymmetric effect is present ifthe coefficient for “negative relative relationship attribute performance” is higher than the “positive relativerelationship attribute performance” coefficient.

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higher levels of relationship conflict (−0.23, p<.01 and −0.15, p<.01, respectively)than when it is better or has lower levels of relationship conflict (−0.11, p<.05 and−0.05, non-significant). For both models, we rejected the null hypothesis that thepositive (conflict below competition) and negative (conflict above competition)coefficients were equal (Wald statistics of 21.55, p<.01 and 12.28, p<.01, respec-tively). We fail to find evidence of an asymmetric effect of information exchange onrelationship outcomes.

We find that adding the “relative” variables (for conflict and information ex-change) significantly improves model fit for the two relationship outcomes variables.For relationship satisfaction, the R-square increases from 0.509 to 0.552 with theintroduction of the two variables. The F-test comparing the two models (with andwithout the relative variables) is significant (11.1, p<.01). For relationship quality,the R-square increases from 0.695 to 0.71 with the introduction of the relativevariables. The F-test comparing the two models is also significant (5.86, p<.01).Thus, we find partial support for hypothesis 1. An analysis of these results alsosuggests that most of the improvements in model fit for the relationship quality modelcome from improvements in satisfaction, with the other two relationship qualitydimensions (trust and commitment) playing less of a role.

An examination of the covariates shows that relationship satisfaction and relationshipquality are influenced by outcomes given alternatives (0.19, p<.01 and 0.25, p<.01,respectively) and relationship length (−.06, p<.10 for relationship satisfaction).

Table 2 Estimation resultsa

Variable Relationshipconflict

Relationshipsatisfaction

Relationshipquality

Intercept 6.40*** 4.02*** 3.22***

Relationship conflict −0.24*** −0.30***

Relative level of relationship conflict(higher levels of conflict)

−0.23*** −0.15***

Relative level of relationship conflict(lower levels of conflict)

−0.11** −0.05

Information exchange −0.41*** 0.25*** 0.34***

Relative level of information exchange(below competition)

−0.15** 0.08 0.05

Relative level of information exchange(above competition)

+0.03 0.06 0.01

Outcomes given alternatives −0.28*** 0.19*** 0.25***

Relationship length −0.01 −0.06* −0.04Dummy for channel type 1 −0.07 −0.14 −0.43***

Dummy for channel type 2 0.09 −0.12 −0.20R2 0.36 0.55 0.71

Number 491 491 491

a Country dummies omitted

Significant results (p<.10) in bold, all results based on two-tailed tests* p<.10; ** p<.05; *** p<.01

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3 Discussion

In today’s competitive environments, B2B manufacturers need motivated chan-nels, which focus their efforts on promoting their brands, rather than on sellingcompetitive brands. However, the current literature provides limited insights onhow competition impacts the effectiveness of channel relationship managementinitiatives. Focusing on interbrand competitive effects, we show that the abilityto improve relationship outcomes may depend on the characteristics of relation-ships (directly) connected to the focal manufacturer–reseller relationship. Inparticular, we demonstrate that the effectiveness of a manufacturer’s channelrelationship strategies depends on the characteristics of the focal reseller’srelationship with an alternative manufacturer. In general, this study furtheremphasizes the importance of broadening the unit of analysis in relationshipresearch (e.g., Anderson et al. 1994; Wathne and Heide 2004).

We find support for our hypothesized asymmetric effects of relationshipattributes on relationship outcomes. Previous research usually conceptualizedthe relations among the quality of information exchange, relationship conflictand relationship outcomes constructs as linear and symmetric. We find that theimpact of improvements in the level of conflict on relationship satisfaction andrelationship quality is greater if the manufacturer’s performance on that attributeis inferior (i.e., higher level of conflict) to that of the closest competitor in theaccount.

These results have important managerial implications: these findings implythat it is better for firms to err on the high side in determining the “optimal”levels of relationship conflict. A manufacturer that falls short of the compet-itor’s levels will be less likely to maintain strong relationships with itsresellers and keep them satisfied. Higher levels of relationship attributesmay not increase relationship outcomes significantly, but low relative levelsmight have a deleterious impact on relationship outcomes. The strategicimplication of our results is that trying to maximize attribute-level perfor-mance across relationships ignores relationship-level competitive effects. Inorder to maximize overall channel satisfaction and relationship quality, man-agers should tailor their efforts at the level of each individual reseller. A“weaker” relationship (i.e., with high absolute levels of conflict) may ceterisparibus generate lower returns (in terms of improvements in relationship quality) thana “stronger” relationship, if (1) the levels of conflict in the “weaker” relationship aresignificantly below those for a comparative relationship (between the reseller and analternative supplier) and (2) the levels of conflict in the “stronger” relationship aresignificantly above those for a comparative relationship (between the reseller and analternative supplier).

Our results suggest that comparative performance on relationship character-istics (in particular, relationship conflict) plays an important a role as adeterminant of reseller satisfaction. However, it is less relevant for otherdimensions of relationship quality (such as trust and commitment). This isan interesting insight, suggesting that manufacturers should take into consid-eration different factors, depending on the relationship quality dimension theyare focusing on.

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4 Limitations and directions for further research

Our results are subject to limitations, which suggest opportunities for further research.Every reseller in the study referred to its relationship with the same supplier in asingle industry, even though they also reported on different alternative manufacturers.It would be important to assess the generalizability of our findings across suppliersand industries. We studied an industry that is relatively competitive with no dominantbrand. It would be relevant to assess whether the competitive relationship manage-ment effects identified in this research are less salient in less competitive markets.However, the data collection challenges are formidable.

Our analysis is also limited by the cross-sectional nature of the data. We cannotshow causality. We observe one moment in time for what is undoubtedly a dynamicprocess of relationship development. Longitudinal data would be more appropriate. Alongitudinal perspective could address interesting issues. For instance, since relation-ship outcomes such as trust and commitment take a long time to build, it would beinteresting to investigate how resellers react to short term fluctuations in terms ofrelationship characteristics (e.g., a competitor increasing its levels of informationexchange).

Several other avenues for future research appear promising, including an investi-gation of the role of culture on the impact of competitive relationship effects onrelationship outcomes. Are competitive effects more relevant in more competitivecultures? While we did not find any evidence for country or cultural effects, it ispossible that this was due to the characteristics of our sample, which included alimited set of countries.

Acknowledgments We would like to thank the participating supplier for the assistance in the design anddata collection. We would also like to thank Erin Anderson, Doug Bowman, Hubert Gatignon, Sandy Jap,and Dimitri Kapelianis for the valuable comments on elements of this project. The usual disclaimer applies.

Appendix

Table 3 Measures

Level of manifest conflict in the relationshipa (alpha 0 0.79)

Based on Morgan and Hunt (1994), Brown and Day (1981), Etgar (1979)

1. We have very few disagreements with the manufacturer (R)

2. Disagreements, if they occur, are resolved quickly and smoothly (R)

3. Any “differences of opinion” with the manufacturer are simply treated as a part of business (R)

Quality of information exchange between the manufacturer and the resellera (alpha 0 0.75)

Based on Anderson and Narus (1990)

1. Information provided about products and services is timely and accurate

2. The information provided about programs and policies is relevant to my business

3. We regularly receive good quality sales leads from the manufacturer

Level of outcomes compared to alternativesa (alpha 0 0.75)

Based on Anderson and Narus (1984)

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1. In comparison with our main supplier the strength of the manufacturer’s brand significantly helpsgenerate sales

2. In comparison with our main supplier the features and functionality of the manufacturer makes themeasy for me to sell

Length of the relationshipb

1. How long has your company been selling the manufacturer’s products?

Relationship satisfactionc

1. Taking everything into account, how satisfied are you, overall, with the manufacturer?

Relationship trusta (alpha 0 0.82)

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1. The manufacturer always lives up to its promises

2. The manufacturer demonstrably supports our business development

3. I have belief in the manufacturer’s ability to deliver on what they have told me they plan to do

Relationship commitmenta (alpha 0 0.82)

Based on Kim and Frazier (1997)

1. I see our relationship with the manufacturer as important to the longer term growth of our company

2. We have common goals with the manufacturer and see them as a business partner

3. The overall contribution to our business of the manufacturer makes it important that the relationshipcontinues

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