JOURNAL ARTICLE

Voluntary Technology Sharing to Rivals.

  • Published In: Information Systems Research (INFORMS), 2026, v. 37, n. 1. P. 218 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Chen, Jianqing; Zeng, Weijun 3 of 3

Abstract

This article investigates a firm's incentive to voluntarily share its proprietary technology with a rival to enable the rival's development of a new competing product. Using a game-theoretic model of two firms producing horizontally differentiated products, the study finds that although the rival's new product increases competition, it also creates a cannibalization effect within the rival's multiproduct pricing that can soften competition and generate a positive externality for the original firm, motivating technology sharing. The firm is inclined to share technology when the new product's valuation is moderate—neither too high to cause excessive competition nor too low to eliminate cannibalization benefits—and the rival adopts the technology only when it is profitable, which is not always the case. The introduction of the new product generally enhances social welfare except when the existing product's valuation is high and the new product's valuation is low; consumer surplus increases only when the existing product's valuation is low. Compared to sharing technology with an independent third party, sharing with a rival is more likely due to the cannibalization externality, which also tends to yield greater social welfare improvements. These findings offer strategic guidance for firms and policymakers regarding technology sharing, new-product introduction, and their implications for market competition and social welfare.

Additional Information

  • Source:Information Systems Research (INFORMS). 2026/03, Vol. 37, Issue 1, p218
  • Document Type:Article
  • Subject Area:Technology
  • Publication Date:2026
  • ISSN:1047-7047
  • DOI:10.1287/isre.2024.1255
  • Accession Number:192724225
  • 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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