Heterogeneous Effects of Generative AI on Knowledge Seeking in Online Communities

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Generative AI (GenAI) may fundamentally reshape how users seek knowledge in online knowledge sharing communities. Although prior work found an overall decrease in knowledge seeking in online communities upon the availability of GenAI, the underlying dynamics across user groups have remained unexplored.
This study addresses that gap. Drawing on commitment-based theory, we hypothesize that casual users—motivated by cost-benefit considerations—are more likely to reduce their question-posting activity than highly committed members. Using a difference-in-differences analysis, we find that ChatGPT’s arrival leads to a substantial drop in questions on StackExchange, primarily driven
by casual users (about 18.2%). Motivated by information foraging theory, we reveal heterogeneous downstream effects of GenAI on question characteristics. In particular, we find that the questions by casual users become more complex and novel, while those by intensive and top users do not. These results highlight the importance of heterogeneous user motivations in shaping platform dynamics, underscoring that while GenAI may diminish overall participation, it may also
increase the value of the remaining content. Our study offers insights for knowledge sharing communities, managers, and stakeholders reliant on usergenerated data, providing a nuanced view of GenAI’s disruptive influence.
Original languageEnglish
JournalJournal of Management Information Systems
DOIs
Publication statusAccepted/In press - 2025

Bibliographical note

JEL Classification: D83, D22, D03, L86

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