Knowledge that Transforms

Features involving the taste, smell, touch, and sight of products, as well as attributes such as safety and confidence, are not easily measured in product research without respondents actually experiencing them. Moreover, product researchers often evaluate a large number of these attributes (e.g., >50) in applied studies, making standard valuation techniques such as conjoint analysis difficult to implement. Product researchers instead rely on ratings data to assess features for which the respondent has had actual experience. In this paper we develop a method of monetizing rating data to standardize product evaluations among respondents. The adjusted data are shown to increase the accuracy of purchase predictions by about 20% relative to existing methods of scale adjustment, leading to better inference in models using ratings data. We demonstrate our method using data from a large scale product use study by a packaged goods manufacturer. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0980 .

Experimental methods are critical tools in marketing, psychology, and economics to isolate the effects of key variables from vagaries intrinsic to field data. As such, they are often considered exempt from the sort of sample selectivity artifacts widely documented in empirical research, in part because participants are randomly assigned to experimental conditions. To conserve time and resources, experiments often focus on items participants have chosen or are familiar with, for example, postchoice satisfaction ratings, certain free recall tasks, or specifying consideration sets preceding brand choice. When consumer input even partially influences the items about which researchers request subsequent data, the potential for item selectivity arises. In such situations, analyses are contingent on both the choice context(s) of the experiment and the alternatives participants elect to evaluate, potentially leading to substantial item selectivity overall and to differing degrees across conditions. We examine situations in which a nonignorable “choose one of many” (polytomous) selection process limits which items offer up subsequent information, and develop methods to allow substantive results to pertain to the full set of items, not only those selected. The framework is illustrated via two experiments in which participants choose and then evaluate a frequently purchased consumer good as well as data first examined by Ratner et al. [Ratner RK, Kahn BE, Kahneman D (1999) Choosing less-preferred experiences for the sake of variety. J. Consumer Res. 26(1):1–15]. Results indicate substantial item selectivity that, when corrected for, can lead to markedly different interpretations of focal variable effects, such as large effect size changes and even sign reversal. Moreover, failing to flexibly account for item selectivity across experimental conditions, even in well-designed experimental settings, can lead to inaccurate substantive inferences about consumers’ evaluative criteria. We further demonstrate robustness to theoretically driven (but not overtly misspecified) selection rules and provide researchers with a simple, “two-step” exploratory procedure akin to a “control function” approach—involving just one additional variable added to standard models—to determine whether and to what degree item selectivity may be affecting their substantive results. Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2016.0991 .

We randomize advertising content motivated by the psychology literature on sympathy generation and framing effects in mailings to about 185,000 prospective new donors in India. We find a significant impact on the number of donors and amounts donated consistent with sympathy biases such as the “identifiable victim,” “in-group,” and “reference dependence.” A monthly reframing of the ask amount increases donors and the amount donated relative to daily reframing. A second field experiment targeted to past donors, finds that the effect of sympathy bias on giving is smaller in percentage terms but statistically and economically highly significant in terms of the magnitude of additional dollars raised. Methodologically, the paper complements the work of behavioral scholars by adopting an empirical researchers’ lens of measuring relative effect sizes and economic relevance of multiple behavioral theoretical constructs in the sympathy bias and charity domain within one field setting. Beyond the benefit of conceptual replications, the effect sizes provide guidance to managers on which behavioral theories are most managerially and economically relevant when developing advertising content. Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2016.0989 .

We introduce a new methodology that can capture and explain differences across a series of cohorts of new customers in a repeat-transaction setting. More specifically, this new framework, which we call a vector changepoint model, exploits the underlying regime structure in a sequence of acquired customer cohorts to make predictive statements about new cohorts for which the firm has little or no longitudinal transaction data. To accomplish this, we develop our model within a hierarchical Bayesian framework to uncover evidence of (latent) regime changes for each cohort-level parameter separately, while disentangling cross-cohort changes from calendar-time changes. Calibrating the model using multicohort donation data from a nonprofit organization, we find that holdout predictions for new cohorts using this model have greater accuracy—and greater diagnostic value—compared to a variety of strong benchmarks. Our modeling approach also highlights the perils of pooling data across cohorts without accounting for cross-cohort shifts, thus enabling managers to quantify their uncertainty about potential regime changes and avoid “old data” aggregation bias.

This paper studies the ability of competing retailers to form a cartel by sharing information with their mutual manufacturer. In a market characterized by demand uncertainty, colluding retailers wish to share information about the potential market demand to coordinate on the optimal collusive retail price. However, in light of potential exposure to antitrust investigations and possible sanctions, the retailers search for mechanisms to exchange information while avoiding the risks of scrutiny by the antitrust authorities. This paper examines such a mechanism: each retailer shares his private information with the mutual manufacturer; the wholesale price set by the latter is thereafter used by the retailers to infer the market condition and coordinate on the cartel’s price. Although a cartel at the retail level limits the manufacturer’s sold quantity, under certain conditions the manufacturer is better off accepting the retailers’ private information, thereby assisting the cartel formation. Moreover, vertical information sharing between the retailers and their mutual manufacturer can result in lower consumer surplus than that would have occurred had the retailers been permitted to collude directly.

We present an analytical framework of multimarket competition and supporting empirical analysis to explain why and when competing firms in an existing market may prefer an alliance entry over independent entry into a new market. Our findings suggest that an alliance entry is more profitable than an independent entry (i) when the new market is larger relative to the existing market, and (ii) when the competition in the existing market is stronger relative to the new market. We compare these key predictions with archival data from the regional shopping center industry in the United States and find that instances of alliance formation in this industry are consistent with our model-based predictions. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0988 .

Manufacturers in many industries frequently use vertical price policies, such as minimum advertised price (MAP), to influence prices set by downstream retailers. Although manufacturers expect retail partners to comply with MAP policies, violations of MAP are common in practice. In this research, we document and explain both the extent and the depth of MAP policy violations. We also shed light on how retailers vary in their propensity to violate MAP policies, and the depth by which they do so. Our inductive research approach documents managerial wisdom about MAP practices. We confront these insights from practice with a large empirical study that includes hundreds of products sold through hundreds of retailers. Consistent with managerial wisdom, we find that authorized retailers are more likely to comply with MAP than are unauthorized partners. By contrast to managerial wisdom, we find that authorized and unauthorized markets are largely separate, and that violations in the authorized channel have a small association with violations in the unauthorized channel. Last, we link our results to the literatures on agency theory, transaction cost analysis, and theories of price obfuscation. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0933.

Firms develop products by manipulating the attributes of offerings, and consumers derive utility from the benefits that the attributes afford. While the field of marketing has long been aware of the distinction between attributes and benefits, it has not developed methods for understanding how attributes and benefits are related. This paper develops a benefit-based model for conjoint analysis that assumes consumers satiate on attributes that are perceived to provide the same benefit. A latent-variable model is proposed that estimates the map between attributes and benefits, and is applied to data from two conjoint studies involving a durable product and a household consumable. The model is shown to fit the data better, provide improved predictions, and lead to different product design implications than the standard conjoint model. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.1003 .