Research on driving factors of consumer purchase intention of artificial intelligence creative products based on user behavior.

With the continuous advancement of artificial intelligence (AI) technology, AIGC (AI-generated content) has increasingly permeated various sectors, leading to a significant transformation in the design industry. This study aims to explore user purchase intention and the influencing factors of AI-generated cultural and creative products, thereby formulating strategies to enhance user satisfaction. Based on the stimulus-organism-response theory, the theory of planned behavior, the value adoption model, the innovation diffusion theory, and the unified theory of acceptance and use of technology 2, a comprehensive model is constructed. The model also incorporates external variables such as perceived value (PV), perceived price (PP), social influence, hedonic motivation (HM), and cultural experience (CE). Additionally, self-innovation is considered as a key moderator to explore its role in moderating the relationships between PV, PP, and user perceived behavioral control. Using 526 valid samples, this study employs structural equation modeling to conduct exploratory factor analysis and confirmatory factor analysis, and further verifies the importance of variables through artificial neural networks. The findings indicate that behavioral attitude, HM, PP, PV, and generative quality are the primary factors influencing user purchase intention. In the decision-making process, users not only consider the price and quality of the products but also place significant importance on the pleasurable experience and cultural uniqueness they offer. This study extends the theoretical application of AIGC in the field of cultural and creative consumption, enriches the user behavior research model, and provides practical insights for companies to optimize AI-generated cultural products, enhance user experience, and improve market acceptance.