题　目： GenAI Review Summaries and Consumer Decision-Making on Digital Platforms: Evidence from a Large-Scale Randomized Field Experiment

嘉　宾： Shiyi Wang（王施懿）, Ph.D. Candidate，Nanyang Technological University

主持人：韩雪雯，助理教授，上海交通大学安泰经济与管理学院

时　间：2026年12月2日（周三）10:00-11:30

地　点：上海交通大学安泰经济与管理学院包兆龙图书馆A303

内容简介:

Generative AI (GenAI) is transforming how digital platforms organize and present user-generated information, creating new opportunities to improve platform operations while reducing consumers’ information-processing burden. A prominent example is GenAI review summaries, which are rapidly being deployed across major digital platforms

to help consumers navigate increasingly overwhelming online reviews. Despite their widespread adoption, little is known about whether these AI-generated summaries improve marketplace performance by facilitating consumer decision making. We investigate this question using a large-scale randomized field experiment conducted on one of the world’s largest online travel booking platforms. We find that providing consumers with GenAI review summaries significantly increases browsing engagement and purchase rates. Behavioral trace data further reveal the underlying mechanism: GenAI review summaries improve information-processing efficiency by reducing consumers’ reliance on reading individual reviews while encouraging broader exploration of hotel alternatives. These effects are strongest for consumers facing greater information-processing demands, including highly active platform users and hotels with more complex review environments characterized by larger review volumes, richer multimedia content, lower ratings, and greater rating dispersion. A follow-up randomized field experiment further demonstrates that incorporating moderate negative information into otherwise positive GenAI summaries enhances their effectiveness by further reducing review browsing without diminishing consumer engagement or purchase rates. Taken together, Our findings suggest that GenAI can serve as an operational

layer that improves marketplace efficiency by increasing the efficiency with which consumers process user-generated information.

演讲人简介：

Shiyi Wang is a Ph.D. candidate in Information Systems at Nanyang Business School, Nanyang Technological University. Her research examines information provision on digital platforms, focusing on how the content, timing, and format of platform information shape user decisions. She draws on field experiments, lab experiments, and causal inference to address these questions. Her work has been recognized with the AOM 2024 Best Paper Proceedings, the PACIS 2024 Best Paper in Track, and the PACIS 2023 Best Complete Paper. She also received the TP Liang Young Scholar Award. She expects to complete her Ph.D. in June 2027.