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

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

T. Zhou and X. Wu

This research by Tao Zhou and Xiaoying Wu explores the motivations behind user migration from social Q&A communities to generative AI. By employing the push-pull-mooring model, the study reveals how factors like information overload and community fatigue drive users away, while trust in AI and a sense of flow attract them. Discover the vital strategies that Q&A communities can implement to retain their users.

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Playback language: English
Abstract
This research examines user migration intention from social Q&A communities to generative AI using the push-pull-mooring (PPM) model and a mixed method of SEM and fsQCA. Results indicate migration intention is influenced by push factors (information overload and community fatigue), pull factors (perceived anthropomorphism, accuracy, trustworthiness, and flow experience), and a mooring factor (social influence). Three main paths leading to migration intention were identified, highlighting the need for Q&A communities to address information overload and user fatigue to maintain sustainability.
Publisher
Humanities & Social Sciences Communications
Published On
Aug 24, 2024
Authors
Tao Zhou, Xiaoying Wu
Tags
user migration
social Q&A communities
generative AI
push-pull-mooring model
information overload
community fatigue
trust
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