Designers' AI disengagement intention in the era of generative AI: The triple anxiety transmission mechanism

The rapid rise of generative AI (GenAI) is reshaping the design industry but also triggers deep-seated anxieties among practitioners. Many designers report a cyclical "AI withdrawal" impulse, deliberately avoiding AI tools during certain creative stages to regain control. This study develops and empirically tests a "Source of Anxiety-Anxiety Transmission-Disengagement Intention" model to explain this phenomenon. Using survey data from 382 design professionals and Partial Least Squares Structural Equation Modeling (PLS-SEM), the results show that job replacement anxiety, skill obsolescence anxiety, and creative autonomy anxiety jointly drive AI disengagement intention, with skill obsolescence anxiety having the strongest impact. Technology iteration speed intensifies job replacement anxiety but does not directly increase skill obsolescence anxiety. Creative path deviation amplifies both job replacement and creative autonomy anxieties, while skill upgrading pressure significantly heightens skill obsolescence anxiety without affecting creative autonomy. This study reveals the psychological mechanism underlying designers' AI disengagement, offering new insights for AI developers and managers to reduce anxiety and improve human-AI collaboration in design practice.