Search in service of choice: An agenda for comparing, extending, and integrating consumer search models. (2025). International Journal of Marketing.
Abstract: Understanding consumer search behavior is crucial for businesses and policymakers seeking to design effective product offerings and interventions. Consumer search behavior is a key topic in marketing, economics, and psychology. Across fields, distinct research traditions have emerged, each with its own foundational assumptions and data requirements. Some of these differences stem from conflicting assumptions (e.g., search is Bayesian optimal vs heuristic) while others arise from contextual factors (e.g., search in a visual space vs search in memory). This article proposes an agenda for comparing and extending models of consumer search to support practitioners and researchers in selecting, applying, and refining these models. Ultimately, this may contribute to a more integrated understanding of consumer search. We review five prominent theoretical models, each with distinct terminologies. For each model, we review its background, aims, core assumptions, and provide illustrative examples. Our contribution is twofold: (1) we initiate a synthesis aimed at resolving terminological differences and propose an agenda for model comparison, extension, and integration; and (2) we identify opportunities for future research to advance a more comprehensive understanding of consumer search and choice.
Modeling eye movements and response times in consumer choice. (2015). Journal of Agricultural and Food Industrial Organization.
Abstract: Peoples’ choices are not instantaneous, nor are they perfectly self consistent. While these two facts may at first seem unrelated, they are in fact inextricably linked. Decision scientists are accustomed to using logit and probit models to account for “noise” in their choice data. But what is the driving force behind these behavioral inconsistencies? Random utility theory (RUT) provides little guidance in this respect. While providing a mathematical basis for dealing with stochastic choice, RUT is agnostic about whether the noise is due to unobserved characteristics of the decision maker and/or the choice environment, or due to actual “mistakes.” The distinction is important because the former implies that from the point of view of the decision maker, her choices are perfectly consistent, while the latter implies that the decision maker herself may be surprised by her set of choices. Here we argue that non-choice (“process”) data strongly favors the latter explanation. Rather than thinking of choice as an instantaneous realization of stored preferences, we instead conceptualize choice as a dynamical process of information accumulation and comparison. Adapting “sequential sampling models” from cognitive psychology to economic choice, we illustrate the surprisingly complex relationship between choice and response-time data. Finally, we review recent data demonstrating how other process measures such as eye-tracking and neural recordings can be incorporated into this modeling approach, yielding further insights into the choice process.