Exploring User Intention to Use Generative AI in Music Composition: An SEM-ANN Methodology

Document Type

Article

Source of Publication

Studies in Computational Intelligence

Publication Date

7-5-2025

Abstract

Generative AI has emerged as a powerful tool in the creative industry, particularly in music composition, offering musicians new ways to enhance their creative processes. However, understanding the factors that influence user intention to adopt these AI-driven tools remains underexplored. This study investigates the user intention to use generative AI in music composition by integrating components from the Technology Acceptance Model (TAM) with perceived creativity and personal innovativeness. This paper addresses the need to identify the key factors which influence the adoption of generative AI for creative tasks, with a specific focus on its application in music composition. To achieve this, data was collected from 843 musicians, students, and music enthusiasts. A hybrid methodology combining Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) were employed to analyze both linear and non-linear relationships among the variables. The results highlight that perceived creativity, usefulness, and ease of use are significant predictors of user intention to adopt generative AI for music composition. Furthermore, user satisfaction and flow experience play a crucial role in enhancing adoption. The study’s implications are both theoretical and practical. Theoretically, it offers a comprehensive framework for understanding the adoption of AI technologies in creative fields, particularly music. The findings can practically guide AI tool developers and music professionals in designing user-centric tools that improve creativity and satisfaction. The integration of SEM and ANN methodologies also demonstrates their effectiveness in examining complex user behavior patterns.

ISSN

1860-949X

Publisher

Springer Nature Switzerland

Volume

1208

First Page

47

Last Page

65

Disciplines

Education

Keywords

Artificial neural networks (ANN), Creativity, Generative AI, Music composition, Structural equation modeling (SEM), User intention

Scopus ID

105010259455

Indexed in Scopus

yes

Open Access

no

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