The Hidden Cost of AI Summaries: AI-Generated Summaries Reduce Review Writing
(A2026-132408)

AUTHORS

Zhe Zhang, Ivey Business School; Yuxin Fu, University of Exeter; Matthew Thomson, Isenberg School of Business, University of Massachusetts Amherst; June Cotte, Ivey Business School, Western University

ABSTRACT

As AI-generated summaries (AIGS) become widely adopted across platforms (e.g., Yelp and Tripadvisor) to help consumers quickly digest large volumes of reviews, their use continues to grow. Yet despite AIGS’ growing prevalence, we show that they carry an overlooked downside: AIGS unintentionally undermine review writing, threatening the long-term sustainability of online review ecosystems. Across four studies, we show that AIGS have a negative effect on review writing. This occurs because writers anticipate that their contributions will receive less recognition when AI summarizes customer reviews, which leads to a deterioration in both their likelihood of leaving a review and the effort they put into it. We also identify a key boundary condition: these negative effects are attenuated when external incentives for writing reviews are provided.