Designer UGC (user-generated content) communities serve as the epicenter of contemporary innovation and creativity, offering a platform for a broad design community to showcase their talents, communicate, and collaborate. Grounded in Self-Determination Theory, this study constructs a research model aiming to delve deeply into the underlying driving factors influencing users’ intention to share their works within these communities. Through online surveys targeting UGC community users and employing structural equation modeling, this research validates the determinants affecting their sharing intentions and dissects the pathways of each influencing factor. The findings reveal that in designer UGC communities, factors such as autonomy, competence, relatedness, online social support, and value fit have a significant positive impact on users’ intention to share their works. This study offers profound insights into the intrinsic logic behind user behaviors in the design creativity domain, also providing robust guidance for the operation and management of online community platforms.

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.

Digital visual marketing increasingly relies on aesthetically modified social media content to attract consumers, yet such practices may also create authenticity risks when online representations are compared with actual experiences. This study examines how four visual modification cues in destination photos—light–shadow atmosphere, tone enhancement, framing angle, and focus prominence—shape post-visit destination trust, value co-destruction, and revisit intention, and whether persuasion knowledge moderates these effects. Survey data from 572 tourists who viewed Chongqing-related social media photos before visiting the city were analyzed using structural equation modeling, bootstrap mediation analysis, and hierarchical regression. Tone enhancement, framing angle, and focus prominence increased destination trust, whereas light–shadow atmosphere did not. Light–shadow atmosphere, tone enhancement, and framing angle increased value co-destruction, whereas focus prominence did not. Destination trust positively affected revisit intention, while value co-destruction had a negative effect. The two mediators operated as largely independent evaluative paths, with the trust pathway exerting the stronger effect. Persuasion knowledge weakened trust gains and amplified value co-destruction. These findings show that digital visual marketing produces asymmetric post-experience outcomes and highlight the need to balance aesthetic optimization with authentic representation when designing social media marketing content.

Generative artificial intelligence is reshaping how concept generation, visual exploration, and solution iteration are organized in design tasks, yet desig