JOURNAL ARTICLE

Impact of corporate social responsibility attributions on employee's extra‐role behaviors: Moderating role of ethical corporate identity and interpersonal trust.

  • Published In: Corporate Social Responsibility & Environmental Management, 2023, v. 30, n. 2. P. 991 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Afridi, Sajjad A.; Afsar, Bilal; Shahjehan, Asad; Khan, Wajid; Rehman, Zia U.; Khan, Muhammad A. S. 3 of 3

Abstract

Interest in individual‐level outcomes of corporate social responsibility (CSR) is gaining momentum in academic and managerial circles. This study investigated whether employees attributed different motives to CSR efforts and if these motives influenced employee's extra‐role behaviors (proactivity, knowledge sharing, creativity, and adaptivity). We also tested the moderating role of interpersonal trust and ethical corporate identity on the link between CSR attributions and employee's extra‐role behaviors. Data were collected from 360 employees and 117 supervisors from the hotel industry of Pakistan. Using hierarchical regression analyses, results show that CSR attributions affected employee's extra‐role behaviors. Moreover, interpersonal trust and ethical corporate identity were found to moderate the relationship between CSR attributions and extra‐role behaviors. Directions for future research and implications for practice are discussed. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Corporate Social Responsibility & Environmental Management. 2023/03, Vol. 30, Issue 2, p991
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:1535-3958
  • DOI:10.1002/csr.2017
  • Accession Number:162403033
  • 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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