JOURNAL ARTICLE

Play it Again, Sam? Reference-Point Formation and Product Differentiation in the Music Industry.

  • Published In: Management Science (INFORMS), 2025, v. 71, n. 10. P. 8304 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Deshmane, Abhishek; Martínez-de-Albéniz, Victor 3 of 3

Abstract

This article develops a theoretical and empirical framework to analyze how product differentiation in new musical releases influences audience reactions, focusing on the distinct preferences of radio stations and music critics. Using data on sonic attributes and genre affiliations from Spotify and Deezer, radio play counts across 26 European countries, and aggregated critic ratings, the study identifies three key reference sets shaping audience expectations: within-artist past works, proximal peers in similar genres, and dominant market designs (chart-toppers). Findings reveal that radio stations favor consistency with an artist’s previous style and similarity to popular market leaders, showing attachment to familiar sounds, while critics prefer novelty and musical evolution relative to the artist’s own past, exhibiting satiation toward repetition. The research highlights the moderating roles of genre labeling and artist career tenure in managing audience expectations and suggests that data-driven decision support can help artists strategically balance commercial success and critical acclaim. The framework is proposed as applicable beyond music to other cultural markets characterized by dual audience structures and complex reference effects.

Additional Information

  • Source:Management Science (INFORMS). 2025/10, Vol. 71, Issue 10, p8304
  • Document Type:Article
  • Subject Area:Music
  • Publication Date:2025
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2022.03319
  • Accession Number:188352041
  • Copyright Statement:Copyright of Management 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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