The adoption behavior of fashion designers toward AIGC: The interaction of psychological motivation and perceived risk
Mengyun Yang, Jiabing Jin · Acta Psychologica · 2026
With the significant advantages of Artificial Intelligence Generated Content (AIGC) in creativity, efficiency, and cost control, its application in the fashion design industry is rapidly advancing. However, designers' adoption behavior is not only influenced by these advantages but is also increasingly affected by psychological motives and perceived risk. This study constructs a comprehensive model integrating Self-Determination Theory (SDT), Technology Acceptance Model (TAM) and Perceived Risk Theory (PRT) to systematically explore the behavioral mechanisms of AIGC adoption among fashion designers. Based on data collected from 352 respondents, the study employed Partial Least Squares Structural Equation Modeling (PLS-SEM), incorporating control variable analysis to enhance model robustness. The results show that autonomy, competence, and relatedness significantly enhance designers' perceived usefulness and ease of use, which mediate their adoption intention, while perceived risk exerts a significant negative effect on perceived usefulness and behavioral intention. Prior AIGC experience also has a positive influence on adoption intention, confirming the robustness of the proposed model. These findings provide empirical evidence and practical guidance for optimizing the design and promotion of AIGC tools in the fashion industry.