AI Summaries and Online Review Contributions: Effects on Modality Choice and Content Novelty

33 Pages Posted:

See all articles by Guoqing Zhou

Guoqing Zhou

School of Management, University of Science and Technology of China

J. Leon Zhao

The Chinese University of Hong Kong, Shenzhen

Gao Chaoyue

affiliation not provided to SSRN

Xiang (Shawn) Wan

Santa Clara University - Leavey School of Business

Zhuoxin Li

University of Wisconsin-Madison - Wisconsin School of Business

Date Written: May 31, 2026

Abstract

Online platforms increasingly use generative AI to summarize user-generated content and facilitate consumer decision making, yet little is known about how different forms of summaries shape users' content contribution behavior. We examine the effects of two types of AI summaries on review contributions: textbased AI summaries (TAIS), which summarize textual content only, and multimodal AI summaries (MAIS), which jointly summarize review text and images. Drawing on social comparison theory, we conceptualize AI summaries as salient comparison benchmarks that motivate contributors to differentiate their reviews from AI-generated representations of existing content and avoid redundant contributions. We propose a dual-path differentiation framework, in which contributors, motivated by a need for uniqueness, respond by (i) shifting contributions toward the modality not emphasized by AI summaries (modality differentiation) and (ii) increasing the semantic novelty of content within the modality emphasized by AI summaries (semantic differentiation). Leveraging quasi-natural experiments surrounding the introduction of TAIS and MAIS on two leading online travel agency platforms in China, we find support for this dual-path differentiation framework. Specifically, TAIS increases the proportion of multimodal reviews (i.e., reviews that combine text and images) while increasing the semantic novelty of text-only reviews. In contrast, MAIS decreases the proportion of multimodal reviews, shifting contributions toward text-only reviews, while increasing the semantic novelty of images within multimodal reviews. Together, these findings demonstrate how AI summaries affect reviewer behavior by shaping how reviewers allocate effort across modalities and differentiate from existing content. Our findings offer new principles for digital platforms to understand and manage user-generated content in the presence of AI summaries.

Keywords: Text-based AI Summaries, Multimodal AI Summaries, Social Comparison Theory, Modality Differentiation, Semantic Differentiation

Suggested Citation

Zhou, Guoqing and Zhao, J. Leon and Chaoyue, Gao and Wan, Xiang (Shawn) and Li, Zhuoxin, AI Summaries and Online Review Contributions: Effects on Modality Choice and Content Novelty (May 31, 2026). Available at SSRN: https://ssrn.com/abstract=

Guoqing Zhou

School of Management, University of Science and Technology of China ( email )

Hefei, Anhui
China

J. Leon Zhao

The Chinese University of Hong Kong, Shenzhen ( email )

Gao Chaoyue

affiliation not provided to SSRN ( email )

Xiang (Shawn) Wan (Contact Author)

Santa Clara University - Leavey School of Business ( email )

500 El Camino Real
Santa Clara, CA California 95053
United States

HOME PAGE: http://sites.google.com/view/shawnwan

Zhuoxin Li

University of Wisconsin-Madison - Wisconsin School of Business ( email )

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
3
Abstract Views
6
PlumX Metrics