This study explores how anthropomorphic factors influence the acceptance of AI-generated singing. By building upon classical acceptance models like TAM and UTAUT2, we incorporate concepts such as animacy, humanlike fit, and perceived sociability. Utilizing a quantitative survey with 310 participants and analyzing the data through PLS-SEM, the study finds that animacy and humanlike fit are significant predictors of likeability, which then positively influences the intention to use AI-generated singing. Word of mouth (WoM) and curiosity also emerge as critical drivers of acceptance.
The study suggests that listeners primarily respond to the expressive qualities of AI singing (animacy and humanlike fit), while perceived sociability remains largely irrelevant. This highlights a limitation of classical acceptance models, which do not account for the anthropomorphic appeal of AI-generated creative content. Future research should address these factors, especially as AI continues to advance in creative domains.
Full citation:
Bagratuni, M., Müller, P., & Planing, P. (2025). Innovation in tune: An empirical investigation of user acceptance of artificial intelligence-generated music. Computers in Human Behavior Reports, 18, Article 100660. https://doi.org/10.1016/j.chbr.2025.100660