🤖 AI Summary
This study investigates how advertising displays—such as “featured recommendations”—on digital platforms influence consumers’ stochastic choice behavior. By extending the Luce multinomial logit model, the authors propose a hybrid attention mechanism wherein consumers either focus on advertised items or consider the full choice set, and they develop a generalized framework in which advertising simultaneously affects both attention and preference. The work innovatively disentangles these dual effects for the first time, establishing identification conditions and a data-driven decomposition method based on observed choices. Building on random utility theory and integrating parameter identification, econometric inference, and optimization algorithms, the model primitives are uniquely identified, enabling clear separation of attention and preference effects. This approach yields implementable rules for optimizing the design of advertising subsets to maximize platform profit.
📝 Abstract
We study how advertised products (e.g., Top Picks, Recommended, Featured) affect consumer choice on digital platforms and retail interfaces by extending the Luce (1959) (or multinomial logit) model. A consumer either focuses on the advertised items or considers the full menu, then chooses among the considered alternatives according to the Luce/logit rule. We characterize this model and show that its underlying primitives are uniquely identified from choice data. We also study a managerially important advertisement-design problem, in which a platform or retailer chooses the advertised subset to maximize expected profit, and we derive implementable design rules. We then introduce a richer framework in which advertising can influence both attention and preference. For this more general model, we provide a characterization and show how choice data can be used to separate the attention effect from the preference effect.