"Word-of-AI" and Matching Quality: Evidence from a Natural Experiment on Online Review Platforms

Yanan Cheng, Baojun Gao, Ran (Alan) Zhang and Xitong Li ()
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Yanan Cheng: Wuhan University
Baojun Gao: Wuhan University
Ran (Alan) Zhang: Texas Tech University - Area of Information Systems and Quantitative Sciences (ISQS)
Xitong Li: HEC Paris

No 1545, HEC Research Papers Series from HEC Paris

Abstract: Many online platforms have recently integrated generative AI (GAI) generated content, yet its impact on the platform ecosystem requires careful investigation. By leveraging a unique policy of a leading online review platform that introduces GAI reviews summary (GAIRS), this study examines how GAIRS can affect the matching quality of online consumers purchasing products or services. Constructing a unique panel dataset of online reviews for a set of hotels on both TripAdvisor and Expedia, we apply a cross-platform difference-indifferences approach to estimate the impact of GAIRS. Our findings elucidate the positive effects of GAIRS on matching quality, manifested by increased consumer rating and decreased rating dispersion. This effect is driven by a decrease in unsatisfactory consumer experiences. Exploring potential mechanisms, we show that the positive effect of GAIRS on matching quality is more prominent in hotels with high uncertainty and when GAIRS is generated from a larger number of reviews, contains more content, or exhibits greater readability. We also present direct evidence supporting our mechanism by showing that the consumer reviews post-GAIRS display greater certainty and assertiveness in their content. Our further analyses rule out an alternative explanation for GAIRS's role being a form of top review, by showing evidence for the performance of solicited reviews, the absence of consumer imitation from GAIRS, and improvements in hotel performance. Finally, we employ transfer deep learning to further demonstrate that GAIRS can reduce uncertainty. Additionally, we find that improvements in experiential dimensions including rooms, value, noise level, and service drive the decline in unsatisfactory consumer experiences. This research highlights the potential of GAIRS, as a recent GAIempowered application in online platforms, in improving matching between online consumers and products, thereby contributing to the expanding discourse on the impacts of GAI in online markets.

Keywords: Generative AI reviews summary; matching quality; natural experiment; difference-in-differences; uncertainty reduction (search for similar items in EconPapers)
JEL-codes: C88 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2025-02-04
New Economics Papers: this item is included in nep-ain
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Persistent link: https://EconPapers.repec.org/RePEc:ebg:heccah:1545

DOI: 10.2139/ssrn.5052570

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