JOURNAL ARTICLE

You Get What You Pay For! An Economic Analysis of the Impact of Data Sponsorship on Content Production.

  • Published In: Information Systems Research (INFORMS), 2026, v. 37, n. 1. P. 341 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Wang, Xin; Wang, Chong; Qiu, Liangfei; Weng, Xi 3 of 3

Abstract

This article analyzes the impact of sponsored data services—where content providers (CPs) pay internet service providers (ISPs) so users can access their content without using personal data allowances—on content quality, company profits, consumer surplus, and social welfare. Using a game-theoretical model that endogenizes CPs' content quality decisions, the study finds that the efficiency of content production critically shapes outcomes: when content production efficiency is low, sponsored data increases ISP profits but intensifies competition among CPs, harming some providers; when efficiency is high, sponsored data benefits CPs and consumers by reducing content competition but lowers ISP profits. The research further suggests that allowing only one CP to sponsor data maximizes consumer surplus and social welfare under high production efficiency, challenging traditional net neutrality views. Given technological advances such as generative artificial intelligence that enhance content production efficiency, the study recommends that ISPs and policymakers reconsider data sponsorship regulations to balance the interests of ISPs, CPs, and consumers.

Additional Information

  • Source:Information Systems Research (INFORMS). 2026/03, Vol. 37, Issue 1, p341
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2026
  • ISSN:1047-7047
  • DOI:10.1287/isre.2022.0002
  • Accession Number:192724197
  • Copyright Statement:Copyright of Information Systems Research (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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