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Motivational affordances and survival of new askers on social Q&A sites: The case of Stack Exchange network

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  • Minhyung Kang

Abstract

Social question‐and‐answer (Q&A) sites are platforms where users can freely ask, share, and rate knowledge. For the sustainable growth of social Q&A sites, maintaining askers is as critical as maintaining answerers. Based on motivational affordances theory and self‐determination theory, this study explores the influence of the design elements of social Q&A sites (i.e., upvotes, downvotes, edits, user profile, and comments) on the survival of new askers. In addition, the moderating effect of having an alternative experience is examined. Online data on 25,000 new askers from the top five Q&A sites in the Technology category of the Stack Exchange network are analyzed using logistic regression. The results show that the competency‐ and autonomy‐related design features of social Q&A sites motivate new askers to continue participating. Surprisingly, having an alternative experience shows a negative moderating effect, implying that alternative experiences increase switching costs in the Stack Exchange network. This study provides valuable insights for administrators of social Q&A sites as well as academics.

Suggested Citation

  • Minhyung Kang, 2022. "Motivational affordances and survival of new askers on social Q&A sites: The case of Stack Exchange network," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 90-103, January.
    • Handle: RePEc:bla:jinfst:v:73:y:2022:i:1:p:90-103
    DOI: 10.1002/asi.24548
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    References listed on IDEAS

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    Cited by:

    1. Qian Wu & Chei Sian Lee & Dion Hoe‐Lian Goh, 2023. "Understanding user‐generated questions in social Q&A: A goal‐framing approach," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(8), pages 990-1009, August.
    2. Wu, Qian & Zheng, Han, 2025. "Consumers' questions as nudges: Comparing the effect of linguistic cues on LLM chatbot and human responses," Journal of Retailing and Consumer Services, Elsevier, vol. 84(C).
    3. Tingting Zhao & Jie Lin & Zhenyu Zhang, 2022. "The Influence of Multi-Variation In-Trust Web Feature Behavior Performance on the Information Dissemination Mechanism in Virtual Community," Sustainability, MDPI, vol. 14(10), pages 1-21, May.

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