Impacts of generative AI on user contributions: evidence from a coding Q &A platform

Xinyu Li () and Keongtae Kim ()
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Xinyu Li: The Chinese University of Hong Kong
Keongtae Kim: The Chinese University of Hong Kong

Marketing Letters, 2025, vol. 36, issue 3, No 19, 577-591

Abstract: Abstract This study investigates the short-term impact of generative AI, exemplified by the introduction of ChatGPT, on user contributions in a coding Q&A platform. We find that the introduction of ChatGPT led to a reduction in the number of high-quality answers provided by users, particularly among highly engaged contributors, despite an overall increase in answers. We identify two key mechanisms: (1) increased perceived question sophistication despite no actual change in content and (2) reduced motivation of loyal users in providing answers in the face of AI-generated alternatives. The findings suggest that while generative AI can facilitate value creation on user-generated content (UGC) platforms, it also poses challenges in retaining core contributors and managing content quality. The paper contributes to the literature on the impact of AI adoption on platforms and suggests practical implications for UGC platform management, such as the need for AI content disclosure measures to retain engaged users.

Keywords: Generative AI; User contribution; UGC platform; Content policy (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11002-024-09747-1

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