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. Plain Language Summary When AI becomes a shopping advisor: A study on the impact of generative AI review on consumer purchase decision With the rapid development of artificial intelligence technology, Generative AI review, as an emerging form of online review, is gradually entering the field of vision of consumers and bringing innovation to the traditional online review model. This technology, based on big data and machine learning algorithms, combines users’ historical behavior data and real-time demand information to mine commonalities and differences from massive reviews, presenting a more comprehensive and objective product image to consumers. However, issues such as the usefulness, transparency of Generative AI reviews, and trust-building among consumers have also sparked widespread discussion. This study combines the Elaboration Likelihood Model with the multidimensional attributes of Generative AI reviews to explore how they influence consumer purchase decision-making behavior. The research results indicate that these attributes have a positive impact on consumer purchase decisions, and this impact ultimately acts on purchase decisions through perceived usefulness. At the same time, propensity to trust in artificial intelligence moderates consumers’ perceived usefulness of reviews of different lengths. This research not only enriches the research scope in the field of online reviews and expands the research boundaries of Generative AI reviews, but also has important practical implications for improving the information ecosystem of e-commerce platforms and consumer purchase decisions.