Examining user migration intention from social Q&A communities to generative AI

Tao Zhou () and Xiaoying Wu
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Tao Zhou: Hangzhou Dianzi University
Xiaoying Wu: Hangzhou Dianzi University

Palgrave Communications, 2024, vol. 11, issue 1, 1-10

Abstract: Abstract As an emerging application, generative AI has attracted many users to conduct question and answer (Q&A), which may lead to their defection from social Q&A communities. Based on the push-pull-mooring (PPM) model, this research examined user migration intention from social Q&A communities to generative AI. Data were analyzed using a mixed method of SEM and fsQCA. The results revealed that migration intention is influenced by a combination of push factors (information overload and community fatigue), pull factors (perceived anthropomorphism, perceived accuracy, perceived trustworthiness, and flow experience), and mooring factor (social influence). The fsQCA results identified three main paths leading to migration intention. These results imply that Q&A communities need to reduce information overload and mitigate users’ fatigue in order to retain them and achieve a sustainable development.

Date: 2024
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DOI: 10.1057/s41599-024-03540-1

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