JOURNAL ARTICLE

Flexible Revenue-Sharing Contract in a Digital-Content Supply Chain Under Heterogeneous Beliefs of the Parties.

  • Published In: Production & Operations Management, 2025, v. 34, n. 8. P. 2124 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Avinadav, Tal; Chernonog, Tatyana; Khmelnitsky, Eugene 3 of 3

Abstract

This article focuses on designing a novel, flexible revenue-sharing contract between digital content platforms and app developers that accounts for heterogeneous beliefs about future revenues. Unlike current fixed-percentage or tiered commission contracts, the proposed contract features a continuous commission function based on current-period revenue and is derived using game theory and calculus of variations under the Nash bargaining framework. The study demonstrates that heterogeneous beliefs lead to nonlinear, piecewise linear contracts that can significantly improve expected profits—by over 100% in some cases—compared to the best linear contract, while maintaining or reducing negotiation complexity. Extensive numerical analyses, including scenarios with normally and Erlang-distributed revenues, show that the flexible contract outperforms linear benchmarks in 99% of cases, particularly when the platform is more optimistic about future revenue than the developer. The findings also provide insights into real-world practices, such as Apple's and Steam's differing commission policies, and suggest that flexible contracts can better address challenges arising from revenue uncertainty and limited transaction transparency in the digital products supply chain.

Additional Information

  • Source:Production & Operations Management. 2025/08, Vol. 34, Issue 8, p2124
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2025
  • ISSN:1059-1478
  • DOI:10.1177/10591478241310220
  • Accession Number:186600184
  • Copyright Statement:Copyright of Production & Operations Management is the property of Sage Publications Inc. 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.