Abstract

Online shopping platforms use AI-generated summaries to reduce cognitive load, but diversity in review content creates risks of generic, biased, or incongruent summaries that may mislead consumers. This research examines how AI review summaries influence shopping decisions as an interaction between consumers’ preferences and opaque summary algorithms. We propose a conditional AI offloading framework, arguing that AI summaries are most effective when their content aligns with users’ preferences and remains consistent with other product signals such as ratings and surrounding reviews. The framework also theorizes conditions under which AI summaries become counterproductive, extending theories of cognitive load and algorithmic appreciation and aversion in the online shopping context. The research utilizes experiments varying summary type, review congruence and valence, shopping orientation, and product type and advances theory and algorithmic management practices.

Download

Share

COinS