Does it matter who made the music? Evaluating the influence of artificial intelligence on music acceptability, source attribution, and listening gratifications through experimental manipulation of hip-hop, country, and synthwave tracks

This study examines how listeners evaluate human-composed and AI-generated music across three genres, hip-hop, country, and synthwave, using a 2 (source: human-created vs. AI-generated) × 3 (genre) mixed experimental design. Drawing on Uses and Gratifications Theory (UGT) and Authenticity Theory, this study examines the roles of perceived realism, listening satisfaction, ethical concern, AI familiarity, and gratification-seeking motivations in predicting music acceptability. Results show that perceived realism and listening satisfaction are the strongest and most consistent predictors of acceptability, together explaining 43% of variance in the dependent measure (averaged across genre). While participants ( N = 401) prefer music they believe to be human made, they systematically misattributed AI-generated country and synthwave tracks as human. Additionally, ethical concern negatively predicted acceptability for synthwave only. Furthermore, affective gratification-seeking positively predicted acceptability for hip-hop and synthwave. Findings suggest that experiential quality drives AI music acceptance more than source knowledge, with implications for AI music development, streaming platforms, and media psychology research.