Innovation in tune: An empirical investigation of user acceptance of artificial intelligence-generated music

Abstract

This study investigates public perceptions and acceptance of artificial intelligence (AI)-generated singing, addressing a critical gap in technology acceptance research. Grounded in anthropomorphism theory—the attribution of human characteristics to non-human entities— this research explores how human-like qualities of AI singing voices influence empathy and acceptance. Utilizing a quantitative research design with 310 participants, this study employs partial least squares structural equation modeling (PLS-SEM) to analyze the effects of anthropomorphic factors, such as animacy, humanlike fit, and perceived sociability, on the likeability of AI-generated singing. The findings indicate that animacy and humanlike fit significantly enhance likeability, which, in turn, influences the intention to use AI-generated singing. Curiosity and word of mouth (WoM) emerged as key drivers of acceptance. This study builds on the traditional technology acceptance models (TAM, UTAUT2) by proposing a framework that integrates human-related factors, thereby demonstrating the necessity of adapting existing models to better evaluate AI systems in creative domains. These insights contribute to advancing the understanding of human-computer interaction, particularly within the evolving landscape of AI-driven creative processes. • Presents an acceptance model with anthropomorphism and human-related factors. • Shows impact of animacy and humanlike fit on AI singing voice acceptance. • Identifies curiosity and word of mouth (WoM) as acceptance drivers. • Offers insights to adapt acceptance models to AI creative contexts.

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