Stakeholders have long pressured firms to provide societal benefits in addition to generating shareholder wealth. Such benefits have traditionally come in the form of corporate social responsibility. However, many stakeholders now expect firms to demonstrate their values by expressing public support for or opposition to one side of a partisan sociopolitical issue, a phenomenon the authors call “corporate sociopolitical activism” (CSA). Such activities differ from commonly favored corporate social responsibility and have the potential to both strengthen and sever stakeholder relationships, thus making their impact on firm value uncertain. Using signaling and screening theories, the authors analyze 293 CSA events initiated by 149 firms across 39 industries, and find that, on average, CSA elicits an adverse reaction from investors. Investors evaluate CSA as a signal of a firm’s allocation of resources away from profit-oriented objectives and toward a risky activity with uncertain outcomes. The authors further identify two sets of moderators: (1) CSA’s deviation from key stakeholders’ values and brand image and (2) characteristics of CSA’s resource implementation, which affect investor and customer responses. The findings provide new and important implications for marketing theory and practice.
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...
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