Analyzing the adoption of AIGC tools in fashion design: An S-O-R framework integrating task-technology fit

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.