JOURNAL ARTICLE

Investigating the role of the state in regulating corporate social responsibility: Evidence from the Gulf Cooperation Council countries.

  • Published In: Business & Society Review (00453609), 2023, v. 128, n. 3. P. 459 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: El‐Said, Osman Ahmed; Aziz, Heba; Mirzaei, Maryam; Smith, Michael 3 of 3

Abstract

The purpose of this research is to provide an overview of state governance for corporate social responsibility (CSR) in the countries of the Gulf Cooperation Council (GCC). A systemic literature review method is employed to collect 88 relevant publications, and a qualitative coding method is used to identify 98 governance instruments from those publications. These are grouped into 13 themes and then examined within three conceptual models. The findings reveal that most of the instruments are geared towards ethical expectations, internal and external social responsibility, raising awareness, and socio‐economic development. The findings are then explored within four theories. The results suggest that CSR governance in the GCC is at a moderate to high level of bureaucratization; reflects Islamic identity, national development targets, and business accountability; is between the stages of habitualization and objectification; and relies mostly on normative pressures. Recommendations for policymakers and company managers are then presented based on these findings. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Business & Society Review (00453609). 2023/09, Vol. 128, Issue 3, p459
  • Document Type:Article
  • Subject Area:Politics and Government
  • Publication Date:2023
  • ISSN:0045-3609
  • DOI:10.1111/basr.12322
  • Accession Number:172345975
  • Copyright Statement:Copyright of Business & Society Review (00453609) 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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