Innovation in tune: An empirical investigation of user acceptance of artificial intelligence-generated music
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 hum...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Computers in Human Behavior Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2451958825000752 |
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| author | Mikael Bagratuni Patrick Müller Patrick Planing |
| author_facet | Mikael Bagratuni Patrick Müller Patrick Planing |
| author_sort | Mikael Bagratuni |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7851e16ed73044af9e00df7300fb3e2a |
| institution | OA Journals |
| issn | 2451-9588 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers in Human Behavior Reports |
| spelling | doaj-art-7851e16ed73044af9e00df7300fb3e2a2025-08-20T02:37:45ZengElsevierComputers in Human Behavior Reports2451-95882025-05-011810066010.1016/j.chbr.2025.100660Innovation in tune: An empirical investigation of user acceptance of artificial intelligence-generated musicMikael Bagratuni0Patrick Müller1Patrick Planing2Corresponding author.; University of Applied Sciences Stuttgart, Schellingstraße 24, 70174, Stuttgart, GermanyUniversity of Applied Sciences Stuttgart, Schellingstraße 24, 70174, Stuttgart, GermanyUniversity of Applied Sciences Stuttgart, Schellingstraße 24, 70174, Stuttgart, GermanyThis 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.http://www.sciencedirect.com/science/article/pii/S2451958825000752AITechnology acceptanceMusicHuman voicesUTAUT2Anthropomorphism |
| spellingShingle | Mikael Bagratuni Patrick Müller Patrick Planing Innovation in tune: An empirical investigation of user acceptance of artificial intelligence-generated music Computers in Human Behavior Reports AI Technology acceptance Music Human voices UTAUT2 Anthropomorphism |
| title | Innovation in tune: An empirical investigation of user acceptance of artificial intelligence-generated music |
| title_full | Innovation in tune: An empirical investigation of user acceptance of artificial intelligence-generated music |
| title_fullStr | Innovation in tune: An empirical investigation of user acceptance of artificial intelligence-generated music |
| title_full_unstemmed | Innovation in tune: An empirical investigation of user acceptance of artificial intelligence-generated music |
| title_short | Innovation in tune: An empirical investigation of user acceptance of artificial intelligence-generated music |
| title_sort | innovation in tune an empirical investigation of user acceptance of artificial intelligence generated music |
| topic | AI Technology acceptance Music Human voices UTAUT2 Anthropomorphism |
| url | http://www.sciencedirect.com/science/article/pii/S2451958825000752 |
| work_keys_str_mv | AT mikaelbagratuni innovationintuneanempiricalinvestigationofuseracceptanceofartificialintelligencegeneratedmusic AT patrickmuller innovationintuneanempiricalinvestigationofuseracceptanceofartificialintelligencegeneratedmusic AT patrickplaning innovationintuneanempiricalinvestigationofuseracceptanceofartificialintelligencegeneratedmusic |