“Word-of-AI” and Matching Quality: Evidence from a Natural Experiment

2024年11月13日（周三）上午9:30

信管学院308会议室

上海财经大学信息管理与工程学院、人工智能与金融大模型双一流特区团队

高宝俊，武汉大学经济与管理学院教授、博士生导师，管理科学与工程系主任，管理科学与数据分析研究所所长。研究方向为信息系统、数字化平台中的用户行为与平台治理，商业数据分析，人工智能等新兴技术的商业影响。主持国家自然科学基金等项目多项，在Management Science, Decision support systems, Information & Mangement等期刊上发表了30余篇论文；其中7 篇论文入选 ESI 热点/高被引论文，获省级科技进步二等奖一次，省级人文社科一等奖两次。

“Word-of-AI” and Matching Quality: Evidence from a Natural Experiment

Many online platforms have recently integrated GAI-generated content into their systems, yet its impact on the platform ecosystem remains unclear. By leveraging a unique policy of a leading online review platform that introduces generative AI reviews summary (GAIRS), this study examines how GAIRS can affect the matching quality in the context of purchase. Constructing a unique panel dataset of online reviews for a matched set of hotels across TripAdvisor and Expedia, we apply a cross-platform difference-in-differences approach to assess the impact of GAIRS. Our findings elucidate the positive effects of GAIRS on matching quality, manifested in increased consumer rating and decreased rating dispersion. This effect is driven by a decrease in unsatisfactory consumer experiences. Notably, we also observed that the improvement in matching quality following the GAIRS launch comes at the expense of a decline in the number of review contributions. Exploring potential mechanisms, we show that the positive effect of GAIRS on matching quality is concentrated in hotels with high uncertainty, and in cases where GAIRS itself carries low perceived uncertainty. This research highlights the potential of GAI in shaping matching between consumer and product, thereby contributing to the expanding discourse on the impacts of GAI in online markets.

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