When Zeros Count: Confounding in Preference Heterogeneity and Attribute Non-attendance

Abstract

Identifying consumer heterogeneity is a central topic in marketing. While the main focus has been on developing models and estimation procedures that allow uncovering consumer heterogeneity in preferences, a new stream of literature has focused on models that account for consumers' heterogeneous attribute information usage. These models acknowledge that consumers may ignore subsets of attributes when making decisions, also commonly termed "attribute nonattendance" (ANA). In this paper, we explore the performance of choice models that explicitly account for ANA across ten different applications, which vary in terms of the choice context, the associated financial risk, and the complexity of the purchase decision. We systematically compare five different models that either neglect ANA and preference heterogeneity, account only for one at a time, or account for both across these applications. First, we showcase that ANA occurs across all ten applications. It prevails even in simple settings and high-stakes decisions. Second, we contribute by examining the direction and the magnitude of biases in parameters. We find that the location of zero with regard to the preference distribution affects the expected direction of biases in preference heterogeneity (i.e., variance) parameters. Neglecting ANA when the preference distribution is away from zero, often related to whether the attribute enables vertical differentiation of products, may lead to an overestimation of preference heterogeneity. In contrast, neglecting ANA when the preference distribution spreads on both sides of zero, often related to attributes enabling horizontal differentiation, may lead to an underestimation of preference heterogeneity. Lastly, we present how the empirical results translate into managerial implications and provide guidance to practitioners on when these models are beneficial.