Social and technical drivers of traditional craftspeople’s continuance intention to use AI-generated content systems: a mixed SEM–fsQCA study

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This study investigates the interplay between social and technological factors affecting traditional craftspeople’s ongoing willingness to utilize generative artificial intelligence (AIGC) tools, specifically text-based models and image generators, aiming to elucidate the cognitive and affective mechanisms underlying their behavior, grounded in social influence theory and the stimulus–organism–response (SOR) model. This study uses a mixed-methods approach combining partial least squares structural equation modeling (PLS-SEM) with fuzzy-set qualitative comparative analysis (fsQCA) to analyze data from 209 traditional craftspeople in China. The study examined the linear and group effects of social influences and technical attributes on perceived value, trust, and willingness to sustain use. PLS-SEM results indicate that industry support, peer influence, content quality, and performance significantly enhance craftspeople’s perceived value and trust, which, in turn, positively affect their continued willingness to use. The effects of social identity and platform technology on trust are not significant. Mediation effect analysis indicated that perceived value partially mediated multiple paths, whereas trust more often appeared as a full mediator. fsQCA further revealed six sufficiently grouped paths with high consistency, which were categorized into three dominant mechanism types: content–performance compensation-driven, identity–peer reinforcement-driven, and social endorsement–technology synergy. This study integrates and applies social influence theory and the SOR model to the study of AIGC adoption behavior among traditional craftspeople. The results demonstrate that for culturally embedded and digitally vulnerable traditional craftspeople, social and technical cues operate through distinct cognitive and affective routes, with trust playing a particularly central role in driving continuance intention. This provides a nuanced view of the behavioral drivers within this traditional group of craftspeople.

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Related topics: AI in Service Interactions · Technology Adoption and User Behaviour · Digital Transformation in Industry