JOURNAL ARTICLE

The Impact of Green Human Resource Management on Employee Empowerment and Pro-Environmental Behaviour in Pakistan's Manufacturing Industry.

  • Published In: Journal of Environmental Assessment Policy & Management, 2023, v. 25, n. 3. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Shaukat, Hafiza Safia; Ong, Tian Soon; Cheok, Mui Yee; Bashir, Shahid; Zafar, Hassan 3 of 3

Abstract

This study examines the correlation between green human resource management (GHRM), environmental performance (EP), and pro-environmental behaviours (PEBs) among Pakistan's large-scale manufacturing industry employees. GHRM is a management approach to improve EP and encourage PEBs. The study assesses the influence of GHRM on EP and employee behaviours and the moderating effect of employee empowerment. Partial least squares (PLS) analysis evaluates the measurement model. The study finds a significant and positive association between GHRM and PEBs. However, there is no direct impact of GHRM on EP. Instead, the study reveals an indirect positive impact of GHRM on EP through PEBs. Employee empowerment moderates the direct impact of GHRM on EP. The findings highlight the importance of PEBs and employee empowerment in the relationship between GHRM and EP in Pakistan's manufacturing industry. The study suggests that manufacturing firms should adopt GHRM practices, incentivise and recognise PEBs, and empower employees to drive environmental initiatives. It has theoretical and practical implications for researchers and practitioners in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Environmental Assessment Policy & Management. 2023/09, Vol. 25, Issue 3, p1
  • Document Type:Article
  • Subject Area:Environmental Sciences
  • Publication Date:2023
  • ISSN:1464-3332
  • DOI:10.1142/S1464333223500151
  • Accession Number:173113478
  • Copyright Statement:Copyright of Journal of Environmental Assessment Policy & Management is the property of World Scientific Publishing Company 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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