EconPapers    
Economics at your fingertips  
 

Generative AI-enabled music generation in marketing and consumer response

Philipp Iversen

Junior Management Science (JUMS), 2026, vol. 11, issue 1, 181-194

Abstract: Generative AI is revolutionizing the marketing industry by producing high-quality, cost-effective and time-efficient content. This study investigates the potential of AI-generated music in digital advertising. Two studies, a survey and a real-world A/B test, evaluate the different songs on chosen criteria: Overall, Melodiousness, Creativity, Naturalness, Correctness and Prompt following for the survey, and click-through rates (CTR) for the field experiment. The survey results show that AI-generated music can be comparable in quality to human compositions, even scoring significantly higher in the categories Prompt Following and Melodiousness. However, AI music showed significantly worse results in the category Creativity. The field experiment revealed no statistically significant difference in CTR between advertisements using AI-generated and royalty-free music, demonstrating that AI music can be a good substitute in supporting roles. This research underlines the possibility for AI-generated music to be used in hyper-personalized advertising, while addressing challenges related to perceived creativity and copyright. The findings contribute to understanding AI's disruptive potential in marketing and offers practical insights to integrate AI tools effectively.

Keywords: AI-generated music; music in advertising; AI consumer response (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/341435/1/1972104497.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:jumsac:341435

DOI: 10.5282/jums/v11i1pp181-194

Access Statistics for this article

Junior Management Science (JUMS) is currently edited by Dominik van Aaken, Gunther Friedl, Christian Koziol, Sascha Raithel

More articles in Junior Management Science (JUMS) from Junior Management Science e. V.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().

 
Page updated 2026-06-13
Handle: RePEc:zbw:jumsac:341435