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Promoting pro‐environmental behavior through organizational identity and green organizational climate.

  • Published In: Asia Pacific Journal of Human Resources, 2023, v. 61, n. 2. P. 483 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zafar, Hina; Ho, Jo Ann; Cheah, Jun‐Hwa; Mohamed, Rosmah 3 of 3

Abstract

This study was conducted to explore the influence of green human resource practices on employees' voluntary pro‐environmental behavior through the sequential mediation path of green organizational climate and organizational identity. A total of 459 employees from the textile industry in Pakistan participated in the study. The results were collected using two sources (managers and employees) at two time points. The proposed research model of the study was tested using structural equation modeling. The results validated the significant positive relationship between green organizational climate and organizational identity. We also found support for the serial mediation of green organizational climate and organizational identity in the green human resource management–voluntary pro‐environmental behavior relationship. All the hypotheses were significant. Overall, this study explains why and how green human resource management practices lead to voluntary pro‐environmental behavior. The implications for theory and practices that will enable organizations to encourage voluntary pro‐environmental behavior among their employees are discussed. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Asia Pacific Journal of Human Resources. 2023/04, Vol. 61, Issue 2, p483
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:1038-4111
  • DOI:10.1111/1744-7941.12347
  • Accession Number:162878275
  • Copyright Statement:Copyright of Asia Pacific Journal of Human Resources 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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