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The Impact of AI-Generated Review Summaries on Hotel Booking Information Search
Date
2025
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Delaware
Abstract
AI-generated review summaries are increasingly used to streamline consumer decision-making by condensing large volumes of online reviews. Study 1 applies text analysis to examine sentiment alignment and topic overlap between AI summaries and user-generated reviews, finding that while AI captures customer feedback, discrepancies in sentiment and latent topics limit accuracy. Study 2 employs a quasi-experiment to assess the impact of AI summaries on rating dispersion, showing improved information search efficiency in the treatment group, though effectiveness weakens when sentiment divergence is high. Study 3 conducted an online survey to investigate how the concreteness level of AI-generated summaries influences consumers' perceived information overload and decision-making satisfaction. The results indicate that more concrete AI summaries increase perceived information overload, negatively affecting satisfaction, especially among prevention-focused individuals. These findings highlight both the potential and limitations of AI in enhancing decision-making in hotel booking, emphasizing the importance of content alignment, abstraction levels, and consumer motivational characteristics in optimizing AI-generated information.
Description
"At the request of the author or degree granting institution, this graduate work is not available to view or purchase until June 06 2026."--ProQuest abstract/details page.
Keywords
Decision-making, Hotel booking, Rating dispersion, Consumer motivation