Heterogeneous effects of generative artificial intelligence (GenAI) on knowledge seeking in online communities
Martin Quinn () and
Dominik Gutt
Additional contact information
Martin Quinn: Rotterdam School of Management - RSM Erasmus University, Erasmus University Rotterdam
Dominik Gutt: Rotterdam School of Management - RSM Erasmus University, Erasmus University Rotterdam
Post-Print from HAL
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 user-generated data, providing a nuanced view of GenAI's disruptive influence.
Keywords: Generative AI; GAI; GenAI; Knowledge seeking; Commitment-based theory; Information foraging theory; Knowledge-sharing communities (search for similar items in EconPapers)
Date: 2025-06-22
References: Add references at CitEc
Citations:
Published in Journal of Management Information Systems, 2025, 42 (2), pp.370-399. ⟨10.1080/07421222.2025.2487313⟩
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05789452
DOI: 10.1080/07421222.2025.2487313
Access Statistics for this paper
More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().