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Paper Number
1965
Paper Type
Short
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.
Recommended Citation
Chan, Fun Yi; Gao, Chaoyue; Leung, Alvin; Fang, Yulin; and Ye, Qiang, "ChatGPT-empowered Product Recommendation and Online Word-of-Mouth: Evidence from Online Travel Agency" (2024). ICIS 2024 Proceedings. 27.
https://aisel.aisnet.org/icis2024/user_behav/user_behav/27
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.
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Comments
21-UserBehavior