A Convergence Study on Factors Influencing User Acceptance of AI-Generated Music - Taking Suno AI Users as an Example -

AI-Generated music, which combines artificial intelligence technology with music creation, has garnered significant attention in both academia and industry in recent years. Platforms like Suno AI offer accessible music creation tools for ordinary users and provide fresh creative insights for professional musicians. However, the factors that influence user acceptance of AI-Generated music remain underexplored. This study, based on data from 302 valid questionnaires, utilizes SmartPLS and SPSS for structural equation modeling to investigate the impact of product heterogeneous preference on user acceptance. The study also examines the mediating role of sense of competence and the moderating effects of consumer expertise and AI personification. The findings reveal that product heterogeneous preference significantly reduces user acceptance of AI-Generated music, both directly and through its impact on the sense of competence. However, AI personification can mitigate this negative effect. These insights deepen our understanding of user interaction mechanisms within the creative domain of generative AI and offer practical guidance for enhancing AI-Generated music platforms. By addressing the psychological factors influencing user acceptance, this research provides a theoretical foundation for optimizing AI-Generated music systems to better align with user preferences and improve overall user experience. Additionally, the study offers valuable perspectives on how artificial intelligence is reshaping human aesthetic experiences and creative practices.