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

Business

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.... show more
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.
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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