DM Seminar | The Impact of Generative AI–Assisted Answers on Askers’ Rewarding Behaviors in Social Question-and-Answer Communities
You are cordially invited to the lecture organized by Department of Management (DM), Faculty of Business and Management (FBM). Details of the lecture are as follows:
Topic: The Impact of Generative AI–Assisted Answers on Askers’ Rewarding Behaviors in Social Question-and-Answer Communities
Speaker: Prof. Lingyun QIU, Department of Management, Faculty of Business and Management, Beijing Normal-Hong Kong Baptist University
Time: 2:00-3:00pm, 3 December 2025
Venue: T1-108
Speaker:
Professor Lingyun QIU, Department of Management, Faculty of Business and Management, Beijing Normal-Hong Kong Baptist University.
Professor QIU’s research interests include user behavior in information systems, human-computer interaction, electronic commerce, and digital transformation. He has published multiple papers in leading academic journals, including MIS Quarterly, Journal of Management Information Systems, Information & Management, and Decision Support Systems. Before joining BNBU, he taught at the Guanghua School of Management, Peking University. He is the Vice President of the Information Systems Engineering Committee of the Chinese Society of Systems Engineering (CNAIS) and a Council Member of the China Information Economics Society (CIES).
Abstract:
Social question-and-answer communities (SQACs) have emerged as key venues for online knowledge exchange. The recent integration of generative artificial intelligence (GenAI) tools has fundamentally changed how contributors generate answers. While GenAI can improve content quality, it may also evoke algorithm aversion, prompting askers to distrust AI-assisted contributors. This research investigates how GenAI use affects askers’ rewarding behaviors—specifically, answer adoption and contributor following—and the underlying psychological mechanisms. Grounded in the Stereotype Content Model, we propose that GenAI use alters perceived contributor competence (expertise) and warmth (altruism), which jointly determine askers’ responses. Across four studies (including a supplementary study), we find that GenAI use reduces following intentions but has no significant effect on adoption. The negative effect on following is mediated by decreased perceptions of expertise and altruism, whereas the null effect on adoption arises from a compensatory mechanism between enhanced perceived answer quality and diminished perceived contributor competence. These effects depend on askers’ beliefs about GenAI’s capability and on contributor expertise disclosure. This research advances understanding of algorithm aversion and social perception in prosocial digital contexts and offers actionable insights for designing and governing GenAI-enabled knowledge-sharing platforms.
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