Four theoretical perspectives currently dominate attempts to understand the drivers of successful interorganizational relationship performance: (1) commitment-trust, (2) dependence, (3) transaction cost economics, and (4) relational norms. Each perspective specifies a different set and distinct causal ordering of focal constructs as the most critical for understanding performance. Using four years of longitudinal data (N = 396), the authors compare the relative efficacy of these four perspectives for driving exchange performance and provide empirical insights into the causal ordering among key interorganizational constructs. The results demonstrate the parallel and equally important roles of commitment-trust and relationship-specific investments as immediate precursors to and key drivers of exchange performance. Building on the insights gleaned from tests of the four frameworks, the authors parsimoniously integrate these perspectives within a single model of interfirm relationship performance consistent with a resource-based view of an exchange. Managers may be able to increase performance by shifting resources from "relationship building" to specific investments targeted toward increasing the efficacy or effectiveness of the relationship itself to improve the relationship's ability to create value. Moderation analysis indicates that managers may find it productive to allocate more relationship marketing efforts and investments to exchanges in markets with higher levels of uncertainty.

No abstract

The dynamic components of relational constructs should play an important role in driving performance. To take an initial step toward a theory of relationship dynamics, the authors introduce the construct of commitment velocity—or the rate and direction of change in commitment—and articulate its important role in understanding relationships. In two studies, the authors demonstrate that commitment velocity has a strong impact on performance, beyond the impact of the level of commitment. In Study 1, modeling six years of longitudinal data in a latent growth curve analysis, the authors empirically demonstrate the significance of commitment velocity as a predictor of performance. In Study 2, the authors use matched multiple-source data to investigate the drivers of commitment velocity. Both customer trust and dynamic capabilities for creating value through exchange relationships (i.e., communication capabilities for exploring and investment capabilities for exploiting opportunities) affect commitment velocity. However, trust and communication capabilities become less impactful as a relationship ages, while investment capabilities grow more important. The authors offer three post hoc tenets that represent initial components of a theory of relationship dynamics that integrates two streams of relationship marketing research into a unified perspective.

Understanding how relationships are damaged is a critical component in building and preserving strong distribution channels. Using longitudinal data from a Fortune 500 firm and its channel members, this research shows that perceived unfairness truly acts as "relationship poison" by directly damaging relationships, aggravating the negative effects of both conflict and opportunism, and undermining the benefits of using contracts to manage channel relationships. Surprisingly, at low levels of perceived unfairness, conflict and opportunism have small or even insignificant effects on channel member outcomes, which implies that research investigating the negative impact of conflict and opportunism on exchange outcomes may need reevaluation because these effects are contingent and may vary depending on the levels of perceived unfairness. In addition, the findings support the premise that using contracts to manage channel relationships represents a double-edged sword that suppresses the negative effects of conflict and opportunism while aggravating the negative effect of unfairness.

Firms routinely engage in relationship marketing (RM) efforts to improve their relationships with business partners, and extant research has documented the effectiveness of various RM strategies. According to the perspective proposed in this article, as customers migrate through different relationship states over time, not all RM strategies are equally effective, so it is possible to identify the most effective RM strategies given customers' states. The authors apply a multivariate hidden Markov model to a six-year longitudinal data set of 552 B2B relationships maintained by a Fortune 500 firm. The analysis identifies four latent buyer-seller relationship states, according to each customer's level of commitment, trust, dependence, and relational norms, and it parsimoniously captures customers' migration across relationships states through three positive (exploration, endowment, recovery) and two negative (neglect, betrayal) migration mechanisms. The most effective RM strategies across migration paths can help firms promote customer migration to higher performance states and prevent deterioration to poorer ones. A counterfactual elasticity analysis compares the relative importance of different migration strategies at various relationship stages. This research thus moves beyond extant RM literature by focusing on the differential effectiveness of RM strategies across relationship states, and it provides managerial guidance regarding efficient, dynamic resource allocations.3 Understanding and managing customer relationships is fundamental to marketing. Accordingly, firms spend in excess of $12 billion annually on customer relationship management, in efforts to understand how to target and sell to customers across various relationship stages (Gartner Research 2012). Substantial research in the relationship marketing (RM) domain also has proposed multiple relational constructs and frameworks to better understand the nature of the buyer-seller relationship (Mullins et al. 2014;Samaha, Beck, and Palmatier 2014). Yet much of this literature treats relationships as temporally homogenous, implying that all relationships respond in similar ways to RM initiatives, independent of the relationship stages or states. More recent research using hidden Markov models instead suggests the importance of acknowledging customer relationship states as a means to understand customer behavior, such that certain marketing actions might be more effective in some states than in others (Luo and Kumar 2013;Netzer, Lattin, and Srinivasan 2008). This concept is particularly important in business-tobusiness (B2B) settings, where customer relationships take longer to develop, last longer, exhibit higher switching costs, and have greater impacts on outcomes than in business-to-consumer settings (Zhang, Netzer, and Ansari 2014). Relationships are dynamic in nature, and as customers move across relationship states, certain RM strategies might be more effective than others or even represent a waste of resources in some situations. Yet very little rese...

No abstract

No abstract

No abstract