JOURNAL ARTICLE

Uncertainty and Green Innovation Nexus: The Moderating Influence of Ownership Structure and Product Market Competition.

  • Published In: Corporate Social Responsibility & Environmental Management, 2025, v. 32, n. 3. P. 3262 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Khan, Muhammad Arif; Meng, Bin; Ullah, Irfan 3 of 3

Abstract

Recently, there has been a growing interest among researchers, academics, and corporations regarding whether and how uncertainties influence corporate decision‐making. This study seeks to explore how various forms of uncertainty, including firm‐specific uncertainty (Fs_u), market‐based uncertainty (M_u), and economic policy uncertainty (Ep_u), influence corporate green innovation. This study also examines whether state ownership and product market competition moderate the relationship between uncertainty and green innovation. Using a sample of Chinese‐listed manufacturing firms from 2003 to 2020, the findings show that uncertainties (Fs_u, M_u, and Ep_u) negatively affect corporate green innovation. This negative effect is weakened by state ownership, whereas is strengthened by product market competition. The results remain consistent and valid following thorough robustness checks and additional analyses. This research contributes to real options theory by delineating how internal and external uncertainties impede green innovation. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Corporate Social Responsibility & Environmental Management. 2025/05, Vol. 32, Issue 3, p3262
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:1535-3958
  • DOI:10.1002/csr.3128
  • Accession Number:185030941
  • Copyright Statement:Copyright of Corporate Social Responsibility & Environmental Management is the property of Wiley-Blackwell 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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