Knowledge that Transforms

Even though negative information about brands and companies is widely prevalent in the marketplace, except for case studies, there has been no systematic investigation of how consumers process negative information about the brands they like and use. In the three studies in this research, the authors attempt to bridge this gap. The findings of the first and second studies provide a theoretical framework for understanding how consumers process negative information in the marketplace. Commitment of the consumer toward the brand is identified as a moderator of negative information effects. In the third study, the authors use this theoretical framework to derive and test response strategies that companies can use to counter negative publicity for consumers who are high and low in commitment toward the brand.

Over the past two decades, validation of choice models has focused on predictive validity rather than parameter bias. In real-world validation of choice models, true parameter values are unknown, so examination of parameter bias is not possible. In contrast, the main focus of this study is parameter bias in simulated scanner-panel choice data with known parameter values. Study of parameter bias enables the assessment of a fundamental issue not addressed in the choice modeling literature—the extent to which the logit choice model is capable of distinguishing unobserved effects that give rise to persistence in observed choices (e.g., heterogeneity and state dependence). Although econometric theory provides some information about the causes of bias, the extent of such bias in typical scanner data applications remains unclear. The authors present an extensive simulation study that provides information on the extent of bias resulting from the misspecification of four unobserved effects that receive frequent attention in the literature—choice set effects, heterogeneity in preferences and market response, state dependence, and serial correlation. The authors outline implications for model builders and managers. In general, the potential for parameter bias in choice model applications appears to be high. Overall, a logit model with choice set effects and the Guadagni–Little loyalty variable produces the most valid parameter estimates.