Research on user acceptance intention of artificial intelligence generated content for museum cultural and creative products - Zhang Dongjiao; Zhang Yunlong; Jiang Xu (PLoS ONE, 2026)
Recent advancements in the field of artificial intelligence have driven the rapid adoption of artificial intelligence generated content in the cultural and creative industries, making it a core driver of innovation. However, empirical research on user acceptance of artificial intelligence generated content remains limited. To address this research gap, this study, based on a review of the literature, constructs a four-dimensional analytical framework comprising technical functionality, cultural and aesthetic value, perceived psychological value, and user acceptance. Using structural equation modeling, this study analyzed data from 323 users with experience using artificial intelligence generated content to reveal the underlying relationships among these variables. The results indicate that technical functional experience has a significant positive impact on both user acceptance and cultural aesthetic value; cultural aesthetic value directly enhances psychological perceived value and positively influences user acceptance through a mediating effect; and psychological perceived value has a direct positive effect on user acceptance. The findings of this study provide empirical evidence to help stakeholders in the cultural and creative sector optimize their strategies for applying AI-generated content.