Navigating the Sea of Reviews: Unveiling the Effects of Introducing AI-Generated Summaries in E-Commerce

Amidst the burgeoning incorporation of generative AI into commercial realms, numerous companies are exploring ways to leverage generative AI tools in practice. However, there is still a lack of empirical evidence to examine their effectiveness. In this research, we explore how AI-generated summaries (AIGS), a relatively new generative AI tool, influence consumer review behavior. While one may assume that AI-generated content diminishes users’ incentive to contribute by replacing user-generated content, we find the opposite: the introduction of AIGS actually leads to an increase in the volume of consumer reviews. We propose that this effect is driven by enhanced perceptions of collective efficacy. By highlighting the value of consumer reviews as a collective force, AIGS reinforces contributors’ belief that their individual input meaningfully contributes to a shared body of product knowledge, thus motivating participation. This mechanism is supported by an additional controlled laboratory experiment where we directly measure perceived collective efficacy and find that it mediates the effect of AIGS on the likelihood of leaving a review. Our moderation analysis results reveal that the positive effect of AIGS on review contributions is more pronounced among products that are well-reviewed (vs. under-reviewed) and polarizing (vs. non-polarizing). Moreover, a fine-grained review content analysis shows that reviews after the AIGS introduction exhibit enhanced content richness and provide complementary information beyond the AIGS content. Specifically, contributors discuss fewer product attributes already covered by AIGS, discuss more product attributes not covered by AIGS, and elaborate on AIGS product attributes rather than restating the summary. Taken together, these findings advance our understanding of how AI integration influences consumer review behaviors in e-commerce and offer valuable practical guidance for business strategies on employing generative AI.

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Publication Details

Journal
Information Systems Research
Published
2026-10-05
DOI
https://doi.org/10.1287/isre.2024.1282
Primary Topic
Digital Marketing and Social Media
Type
article
Field-Weighted Citation Impact
0.00
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article

Navigating the Sea of Reviews: Unveiling the Effects of Introducing AI-Generated Summaries in E-Commerce

Runyu Chen, Qili Wang, Liangfei Qiu, Yi SU
Information Systems Research
Digital Marketing and Social Media
article

Navigating the Sea of Reviews: Unveiling the Effects of Introducing AI-Generated Summaries in E-Commerce

Runyu Chen, Qili Wang, Liangfei Qiu, Yi SU
article en

Abstract

Amidst the burgeoning incorporation of generative AI into commercial realms, numerous companies are exploring ways to leverage generative AI tools in practice. However, there is still a lack of empirical evidence to examine their effectiveness. In this research, we explore how AI-generated summaries (AIGS), a relatively new generative AI tool, influence consumer review behavior. While one may assume that AI-generated content diminishes users’ incentive to contribute by replacing user-generated content, we find the opposite: the introduction of AIGS actually leads to an increase in the volume of consumer reviews. We propose that this effect is driven by enhanced perceptions of collective efficacy. By highlighting the value of consumer reviews as a collective force, AIGS reinforces contributors’ belief that their individual input meaningfully contributes to a shared body of product knowledge, thus motivating participation. This mechanism is supported by an additional controlled laboratory experiment where we directly measure perceived collective efficacy and find that it mediates the effect of AIGS on the likelihood of leaving a review. Our moderation analysis results reveal that the positive effect of AIGS on review contributions is more pronounced among products that are well-reviewed (vs. under-reviewed) and polarizing (vs. non-polarizing). Moreover, a fine-grained review content analysis shows that reviews after the AIGS introduction exhibit enhanced content richness and provide complementary information beyond the AIGS content. Specifically, contributors discuss fewer product attributes already covered by AIGS, discuss more product attributes not covered by AIGS, and elaborate on AIGS product attributes rather than restating the summary. Taken together, these findings advance our understanding of how AI integration influences consumer review behaviors in e-commerce and offer valuable practical guidance for business strategies on employing generative AI.

Information Systems Research
University of International Business and Economics (CN), University of Florida (US)
Openalex Percentile: Top 5%
Digital Marketing and Social Media
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