Cultural cognition in artificial intelligence generated content adoption among visual designers

Abstract

Artificial intelligence generated content (AIGC) is transforming visual arts by automating image design, illustration, and interface composition. Extending beyond traditional technology acceptance models (TAM), this study integrates cultural memory (CM) theory to develop a four-layer pathway model comprising CM perception, cultural cognition evaluation (cultural preservation (CP) and cultural transformation (CT)), functional assessment, and behavioral intention. Using partial least squares structural equation modeling (PLS-SEM) in SmartPLS, data from 238 visual designers were analyzed. The results show that CM significantly influences CP (β=0.564, p<0.001) and CT (β=0.166, p<0.01). CP strongly predicts CT (β=0.768, p<0.001), which in turn significantly affects perceived usefulness (PU) (β=0.423, p<0.001). However, CT does not directly predict adoption intention (UI) (β=0.069, p=0.158), indicating full mediation through PU (indirect effect =0.359, p<0.001). The model explains 81.4% of the variance in UI. These findings demonstrate that AIGC adoption is grounded in a hierarchical cultural cognitive process, which must be translated into functional utility perceptions before it can shape behavioral intention.