JOURNAL ARTICLE
The Value of Silence: The Effect of UMG's Licensing Dispute with TikTok on Music Demand.
Published In: Marketing Science (INFORMS), 2026, v. 45, n. 3. P. 493 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Cheng, Mengjie; Ofek, Elie; Yoganarasimhan, Hema 3 of 3
Abstract
This study analyzes the impact of the 2024 licensing dispute between TikTok and Universal Music Group (UMG) on music demand across streaming platforms. When UMG removed its music from TikTok between February and May 2024 due to disagreements over compensation, a Difference-in-Differences analysis comparing UMG tracks to those from Sony Music Entertainment and Warner Music Group found heterogeneous effects: tracks previously available on TikTok experienced a 2%–3% increase in Spotify and YouTube consumption (a substitution effect), while tracks not previously on TikTok saw a 1%–3% decrease in streams (a complementarity effect). The complementarity effect appears linked to TikTok’s role in promoting artist discovery, especially for artists with partial presence on the platform. Economic estimates suggest that UMG’s removal of music from TikTok could yield a net annual revenue gain of approximately $317 million from Spotify streaming, exceeding the compensation TikTok paid prior to the dispute. The findings highlight complex cross-platform dynamics with implications for licensing strategies, compensation models, and the interplay between social media and streaming services in the music industry.
Additional Information
- Source:Marketing Science (INFORMS). 2026/05, Vol. 45, Issue 3, p493
- Document Type:Article
- Subject Area:Music
- Publication Date:2026
- ISSN:0732-2399
- DOI:10.1287/mksc.2024.1170
- Accession Number:193623661
- Copyright Statement:Copyright of Marketing Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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