ArticlePloS one2025
Analyzing the adoption of AIGC tools in fashion design: An S-O-R framework integrating task-technology fit.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Exploring fashion designers' acceptance of AIGC: A dual-pathway analysis from the stimulus-organism-response perspective.PloS one · 2026Article
- The influence of designer identity on design majors' intention to use AIGC tools: an SEM-fsQCA analysis in China.Frontiers in psychology · 2026Article
- Task-technology fit and digital textbook usage outcomes: The mediating role of positive emotion within an S-O-R framework.PloS one · 2026Article
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Authors and funding
2 authors.
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Abstract
AI-Generated Content (AIGC) tools have rapidly emerged in the field of apparel design in recent years, but how designers adopt these tools and the psychological mechanisms behind them are unclear. This study constructs a model based on the stimulus-organism-response (S-O-R) theory, aiming to reveal how external stimulus variables (perceived content quality (PCQ), personalized fit (PF), industry pressure (IP), perceived technological risk (PTR)) are adopted through psychological state variables (self-efficacy (SE), innovativeness (INN), and task technology fit (TTF)) influence apparel designers' AIGC adoption intentions. Based on the questionnaire data of 267 Chinese fashion designers, partial least squares structural equation modeling (PLS-SEM) was used for empirical analysis. The results showed that: PCQ and PF significantly enhanced SE and TTF, while PTR significantly inhibited the above psychological mechanisms; SE, INN, and TTF positively influenced adoption intention, with TTF having the most significant effect, and IP did not show a significant effect. The findings not only validate the applicability of the S-O-R theory in creative technology adoption, but also emphasize the key role of TTF matching and psychological-cognitive factors in promoting the application of AIGC tools, providing theoretical support and practical insights for subsequent tool optimization and user guidance.
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