The Double-edged Effect of Banning Generative AI on Online Question-and-Answer Communities: Evidence from Stack Exchange

Yuanhong Ma, Qinglai He, Xitong Li and Lynn Wu
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Yuanhong Ma: Harbin Institute of Technology - School of Management
Qinglai He: University of Wisconsin-Madison - Department of Operations and Information Management
Xitong Li: HEC Paris
Lynn Wu: University of Pennsylvania - The Wharton School

No 1647, HEC Research Papers Series from HEC Paris

Abstract: We investigate how banning generative artificial intelligence-generated content (AIGC) affects knowledge seeking, knowledge contribution, and contribution efficiency in online question-and-answer communities. After the launch of ChatGPT in late November 2022, several Stack Exchange communities implemented official bans on AIGC over concerns such as less reliable and socially engaged content. Leveraging data from the full network of Stack Exchange communities, we employ a difference-indifferences (DID) approach to examine the impacts of these bans. Our results reveal a double-edged impact: while the AIGC ban increases knowledge seeking, as evidenced by a higher volume of posted questions, it simultaneously reduces contribution efficiency, reflected in a lower proportion of questions receiving satisfactory answers within the expected time frame. Notably, these impacts are only evident in non-STEM communities. We take a socio-technical perspective to explore information reliability and social interactivity as two plausible underlying factors driving the observed changes. Our mechanism exploration reveals that the AIGC ban spurs question volume in topics where AIGC is less reliable and where social interaction is highly expected. In contrast, the ban hampers answer efficiency in communities where LLMs are capable of producing reliable answers and where social interactivity is minimal. Additionally, our results indicate the increased human involvement from knowledge seekers and contributors following the ban. They adapt their behavior by posting questions and answers that are more informationally rich and socially engaging. Overall, our findings offer actionable implications for platform managers, community moderators, and policymakers of online Q&A communities.

Keywords: Generative AI; Online Q&A Community; AIGC; Large Language Models; Knowledge Exchange; Platform Competition (search for similar items in EconPapers)
JEL-codes: C23 D83 L86 O33 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2026-07-24
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Persistent link: https://EconPapers.repec.org/RePEc:ebg:heccah:1647

DOI: 10.2139/ssrn.7052340

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