A study on factors influencing music enthusiast's behavioral intention to use generative AI for music composition based on an expanded technology acceptance model.

This study aims to understand the intentions of music enthusiasts in using generative artificial intelligence (GenAI) for music composition and to elucidate the factors influencing those intentions. A covariance-based structural equation modeling approach is applied to construct a technology acceptance model (TAM). A total of 429 valid questionnaire responses were analyzed to explore music enthusiasts' acceptance mechanisms regarding GenAI tools. The sample encompasses diverse age groups, educational backgrounds, and experience levels, thus ensuring the broad applicability of the findings. The results show that relationships within the original TAM structure remain robust. Specifically, perceived AI risk negatively impacts intention to use (IU) and perceived ease of use (PEOU); however, it does not correlate significantly with perceived usefulness (PU). Perceived trust positively influences PEOU and PU but does not directly affect IU. Content quality is positively correlated with PEOU, PU, and IU. Social influence is correlated only with PU and does not affect PEOU or IU. Perceived enjoyment enhances both PEOU and PU but has no effect on IU. Demographic analysis reveals no differences in GenAI tool evaluations among music enthusiasts in terms of gender, age, or education level. Overall, this report provides essential theoretical and practical insights for applying GenAI in music creation by integrating traditional and novel factors.