Information Search Within a Web Page: Modeling the Full Sequence of Eye Movement Decisions, Subjective Value Updating, and First Clicks

Online retail settings often present shoppers with large, complex choice sets where they need to quickly and dynamically weigh the benefits and costs of search within each web page. We build a model of information search within a web page using eye-tracking data collected during two incentive-compatible online shopping experiments, in which participants browsed the websites of two different clothing retailers (Experiments 1 and 2), as well as previously reported data from a laboratory experiment involving choices among snack food assortments (Experiment 3). Our model incorporates features that build upon recent advances in descriptive and normative models of information sampling and search in psychology and economics. First, our model captures how people decide where to look by treating eye fixations on clickable options as a series of “split-second” decisions that depend on estimates of option attractiveness and navigation effort. Second, our model assumes that the value of each option is learned via Bayesian updating. Third, the choice to end search on the web page depends on a dynamic decision threshold. Our model outperforms benchmarks that assume random search, instant learning, fixed thresholds, nonheterogeneous thresholds, and stochastic accumulator stopping rules. Explicitly modeling the sequence of eye fixation decisions results in accurate counterfactual simulations of the effects of hypothetical product orderings on search duration and quality as verified using experimental manipulation, and it can be applied flexibly to a wide range of web-page layouts. Systematic differences across experiments highlight the importance of accounting for product familiarity, choice-set size, and the role of category outside options.