Currently, there is an important debate about the relative merits of models with discrete and continuous representations of consumer heterogeneity. In a recent JMR study, Andrews, Ansari, and Currim (2002; hereafter AAC) compared metric conjoint analysis models with discrete and continuous representations of heterogeneity and found no differences between the two models with respect to parameter recovery and prediction of ratings for holdout profiles. Models with continuous representations of heterogeneity fit the data better than models with discrete representations of heterogeneity. The goal of the current study is to compare the relative performance of logit choice models with discrete versus continuous representations of heterogeneity in terms of the accuracy of household-level parameters, fit, and forecasting accuracy. To accomplish this goal, the authors conduct an extensive simulation experiment with logit models in a scanner data context, using an experimental design based on AAC and other recent simulation studies. One of the main findings is that models with continuous and discrete representations of heterogeneity recover household-level parameter estimates and predict holdout choices about equally well except when the number of purchases per household is small, in which case the models with continuous representations perform very poorly. As in the AAC study, models with continuous representations of heterogeneity fit the data better.

This study aims at formulating and testing a model of store choice dynamics to measure the effects of consumer characteristics on consumer grocery store choice and switching behavior. A dynamic hazard model is estimated to obtain an understanding of the components influencing consumer purchase timing, store choice, and the competitive dynamics of retail competition. The hazard model is combined with an internal market structure analysis using a generalized factor analytic structure. We estimate a latent structure that is both store and store chain specific. This allows us to study store competition at the store chain level such as competition based on price such as EDLP versus a Hi-Lo pricing strategy and competition specific to a store due to differences in location. Competition in the retailing industry has reached dramatic dimensions. New retailing formats appear in the market increasingly more rapidly. A focus on a particular aspect of the retail mix (e.g., service or price) means that retailers can compete on highly diverse dimensions. Scrambled merchandising and similar developments have implied that particular retailers are now competing against retailers they did not compete with in the past.

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.