Direct and moderating effects of generative AI on new user retention in social Q&A platforms: evidence from Stack Overflow

Social question-and-answer (Q&A) platforms such as Stack Overflow have experienced a significant decline in user engagement, following the rise of generative artificial intelligence (AI) technologies, including ChatGPT. Grounded in self-determination and motivational affordance theories, this study examines five motivational features—upvotes, downvotes, collaborative edits, user profile completion, and comments—that influence new user retention. Generative AI is introduced as a novel external factor exerting both direct and moderating effects.

Hierarchical logistic regression was applied to a dataset of 10,000 newly registered Stack Overflow users to compare the user retention mechanisms before and after ChatGPT’s introduction. The results indicate that receiving upvotes or comments and user profile completion significantly promote continued participation, whereas downvotes and the emergence of generative AI reduce it. Following the advent of generative AI, the positive influence of user profile completion weakened, suggesting that autonomy-related needs may now be fulfilled through AI tools external to the platform.

By contrast, the effects of votes and comments remained stable, likely because generative AI cannot replicate these social interactions. This study extends the motivational affordance framework by integrating the influence of emerging external technologies and offers practical insights for enhancing new user retention on social Q&A platforms in the generative AI era. Department of Business Intelligence, School of Business, Ajou University, Suwon, Korea, Republic of The author declares no competing interests.

This study did not involve human participants in an experimental or interventional setting, nor did it involve primary data collection (such as user surveys or questionnaires). The research exclusively analyzed publicly available, fully anonymized digital trace data obtained from the official Stack Overflow data archive. According to the Bioethics and Safety Act and the Institutional Review Board (IRB) guidelines of Ajou University, research utilizing exclusively publicly available, anonymized secondary data is subject to automatic exemption from formal ethical review and does not require a separate IRB approval certificate.

All data retrieval and analysis procedures complied with the platform’s Terms of Service. This study exclusively utilized secondary data from a publicly accessible online archive, without direct recruitment of human subjects or collection of personally identifiable information; therefore, informed consent was not required. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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