JOURNAL ARTICLE

How Social Movements Catalyze Firm Innovation.

  • Published In: Organization Science (INFORMS), 2025, v. 36, n. 4. P. 1221 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Odziemkowska, Kate; Zhu, Yiying 3 of 3

Abstract

This article examines how social movements influence firm innovation through private politics, defined as activists’ direct engagement with firms either contentiously (e.g., protests, lawsuits) or cooperatively (e.g., collaborations). It finds that contentious private politics increases firms’ innovation on movement-advocated issues material to their performance by focusing managerial attention on these issues, leading to more patents generally related to the firm’s existing expertise. In contrast, cooperative private politics, involving collaborations with social movement organizations (SMOs), fosters innovation characterized by distant recombination—novel combinations of knowledge outside the firm’s usual domains—by providing access to new knowledge and reducing innovation risks. Using a matched sample of 500 large U.S. firms and data on interactions with 136 environmental SMOs on climate-related issues, the study shows that both forms of private politics catalyze innovation through distinct mechanisms, with collaborations following contention potentially diminishing these effects. This research contributes to understanding nonmarket stakeholder influence on technological innovation, particularly in addressing grand challenges like climate change.

Additional Information

  • Source:Organization Science (INFORMS). 2025/07, Vol. 36, Issue 4, p1221
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:1047-7039
  • DOI:10.1287/orsc.2023.17497
  • Accession Number:187706265
  • Copyright Statement:Copyright of Organization 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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