How AI-generated review summary transparency shapes online purchase intention: The dual mediating roles of perceived diagnosticity and algorithm trust

E-commerce platforms are increasingly integrating artificial intelligence (AI)-generated review summaries (AGRSs) to help consumers process large volumes of online review information. Despite this growing trend, limited academic attention has examined the association between AI-generated review summary transparency (AGRST) and purchase intention. The study uses signaling theory and S-O-R (Stimulus-Organism-Response) to examine whether AI-generated review summary transparency is positively associated with online purchase intention through the mediating roles of algorithm trust and perceived diagnosticity. The study employed a scenario-based quantitative survey design, with data collected from 450 adult online shoppers residing in Saudi Arabia who had recently purchased products online and used online consumer reviews when evaluating products. After analyzing the valid responses through PLS-SEM, AGRST was found to be positively associated with higher levels of algorithm trust, perceived diagnosticity, and purchase intention. Perceived diagnosticity and algorithm trust were also found to be positively related to purchase intention, as well as partially mediate the relationship between AGRST and purchase intention. Overall, the findings indicate that AI-generated review summary transparency may provide consumers with clear informational cues that are associated with lower uncertainty, more favorable assessments of relevant product-related data, and higher confidence in using an automated system. Ultimately, this research provides additional insight into how transparency is associated with consumers’ purchase intention within AI-mediated e-commerce environments.