JOURNAL ARTICLE

Demand-Side Effects of Open Innovation: The Case of Cryptocurrency Forking.

  • Published In: Management Science (INFORMS), 2025, v. 71, n. 8. P. 7136 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Sharma, Vasundhara; Agarwal, Ashish; Barua, Anitesh 3 of 3

Abstract

This article examines how forked products—new cryptocurrencies created by copying (forking) the open-source codebase of existing "parent" cryptocurrencies—affect the demand for their parent coins within the open-innovation model. Analyzing data from 42 major cryptocurrencies between 2011 and 2021, the study distinguishes between transaction coins (used mainly for payments) and platform coins (which also host applications via smart contracts). Results show that forks generally reduce demand for transaction-type parents, with popularity mitigating but not eliminating this effect. In contrast, platform-type parents do not experience a net demand loss from forks; instead, compatibility and complementarity with forked entrants increase smart contract transactions, offsetting substitution effects. The study finds no evidence that forks lead to developer attrition or increased innovation in parent coins and highlights the importance of product type and popularity in shaping competitive dynamics in open-source ecosystems. These findings offer insights for firms and investors regarding strategic product design and competition management in open-innovation settings.

Additional Information

  • Source:Management Science (INFORMS). 2025/08, Vol. 71, Issue 8, p7136
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2025
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2021.03132
  • Accession Number:187706386
  • 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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