ChatGPT-empowered Product Recommendation and Online Word-of-Mouth: Evidence from Online Travel Agency

ChatGPT has become increasingly prevalent in providing customer services in e-commerce platforms. Drawing upon reciprocity theory, this study examines the influence of ChatGPT-empowered recommendation on eWOM in online platforms. Leveraging a natural experiment conducted on leading online travel agencies (Expedia and Booking.com), along with a unique panel dataset of online reviews for a matched set of hotels across platforms, we employ a DID model to assess the impacts of ChatGPT. We find that ChatGPT-empowered recommendations decrease both review quantity and quality, evidenced by a reduction in the use of cognitive and analytical languages, and decreased readability indexes in review text. Additionally, we find a more pronounced impact on high-rating reviews. To further explore the underlying mechanisms, we assess Likes numbers and measures including prosocial behavior, fulfillment, and affiliation, all of which show a decline, reflecting a diminishing reciprocity norm.

Publication Details

Journal
Journal of the Association for Information Systems
Published
2024-12-15
Primary Topic
Atomic and Subatomic Physics Research
Type
article
Field-Weighted Citation Impact
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article

ChatGPT-empowered Product Recommendation and Online Word-of-Mouth: Evidence from Online Travel Agency

Journal of the Association for Information Systems
Atomic and Subatomic Physics Research
article

ChatGPT-empowered Product Recommendation and Online Word-of-Mouth: Evidence from Online Travel Agency

article en

Abstract

ChatGPT has become increasingly prevalent in providing customer services in e-commerce platforms. Drawing upon reciprocity theory, this study examines the influence of ChatGPT-empowered recommendation on eWOM in online platforms. Leveraging a natural experiment conducted on leading online travel agencies (Expedia and Booking.com), along with a unique panel dataset of online reviews for a matched set of hotels across platforms, we employ a DID model to assess the impacts of ChatGPT. We find that ChatGPT-empowered recommendations decrease both review quantity and quality, evidenced by a reduction in the use of cognitive and analytical languages, and decreased readability indexes in review text. Additionally, we find a more pronounced impact on high-rating reviews. To further explore the underlying mechanisms, we assess Likes numbers and measures including prosocial behavior, fulfillment, and affiliation, all of which show a decline, reflecting a diminishing reciprocity norm.

Journal of the Association for Information Systems
Openalex Percentile: Top 57%
Atomic and Subatomic Physics Research
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