AI-Generated Review Summaries and Consumer Decision-Making: Testing the Information Acceptance Model on Gen Z Online Shoppers

In the digital age, online customer reviews have become a critical source of information guiding consumer purchase decisions. As the volume of user-generated reviews increases, e-commerce platforms have begun to implement generative AI (GenAI) to synthesize these reviews into concise summaries. This study investigates how consumers perceive and adopt AI-generated review summaries and whether such adoption influences their purchase intention. Drawing on the Information Acceptance Model (IACM), the study examines the predictive role of information-related variables and consumer attitudes in the context of AI-mediated electronic word of mouth (eWOM). A quantitative survey of 153 Gen Z online shoppers was conducted and analyzed through structural equation modeling (SEM). The results indicate that, contrary to prior assumptions, information quality and credibility do not exhibit a significant influence on perceived usefulness. Instead, informational needs and consumers’ attitudes toward the information emerge as the primary drivers of perceived usefulness in the context of AI-generated review summaries. Moreover, perceived information usefulness emerges as a strong predictor of information adoption, which subsequently exerts a significant positive influence on purchase intention. These findings suggest that AI-generated summaries may trigger heuristic rather than systematic information processing, highlighting a shift in the determinants of eWOM effectiveness in AI-mediated environments.