User Acceptance Behavior Analysis of Multimodal Generative AI

Abstract

This study explores user acceptance behavior toward multimodal generative artificial intelligence (MGAI) by integrating the technology acceptance model (TAM) and media richness theory (MRT) into a cross-theoretical analytical framework. The proposed model examines the relationships among media richness, perceived ease of use, perceived usefulness, user attitude, and continued usage intention. Empirical analysis was conducted using structural equation modeling with a sample of 419 users who have experience of MGAI technologies. The results indicate that media richness significantly enhances perceived usefulness and user attitude; however, its impact on perceived ease of use is limited, which suggests that increased familiarity with technology may reduce the importance of interface simplicity. Additionally, perceived usefulness plays a critical mediating role between attitude and continued usage intention. This study extends the theoretical application of TAM and MRT in the context of multimodal AI, and provides practical recommendations for optimizing technology design and user experience.