When AI Becomes a Shopping Advisor: A Study on the Impact of Generative AI Review on Consumer Purchase Decision

Abstract

With the swift advancement of artificial intelligence technology, generative AI reviews, as a novel form of online evaluation, are increasingly capturing consumers’ attention, thereby infusing innovation into the traditional online review paradigm. This technology, grounded in big data and sophisticated machine learning algorithms, seamlessly integrates users’ historical behavior data with real-time demand information. By meticulously excavating both commonalities and discrepancies from a vast corpus of reviews, it presents consumers with a more holistic and objective product representation. Nevertheless, the utility, transparency, and the fostering of consumer trust in generative AI reviews have precipitated extensive discourse. Drawing upon the Elaboration Likelihood Model, this investigation delves into the multifaceted attributes of generative AI reviews. Employing a questionnaire survey methodology, it systematically explores their influence on consumer purchase decision-making behavior. The findings reveal that the quality, emotional resonance, length, and credibility of generative AI reviews exert a positive influence on consumer purchase decisions. This impact is ultimately mediated through the perceived usefulness of the reviews. Furthermore, the inclination to trust artificial intelligence serves as a moderator, altering the perceived usefulness of reviews of varying lengths. This research not only enriches the landscape of online review studies and expands the horizons of generative AI review research but also bears substantial practical implications. It offers valuable insights for the refinement of the information ecosystem on e-commerce platforms and for enhancing consumer purchase decision-making processes.