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Cited by 1,082

The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.

Green human resource management practices: scale development and validity

Tang, Guiyao, Chen, Yang, Jiang, Yuan, Paillé, Pascal, Jia, Jin·2018·Asia Pacific Journal of Human Resources

Abstract

Previous studies on green human resource management (GHRM) are mainly positioned at theoretical or qualitative level. There is urgent need to develop a valid measurement of GHRM and then to offer more insights into the implication of it on individual or organizational performance. The aim of this study was to propose and validate an instrument to measure GHRM. Based on exploratory analysis (study 1), it was established that GHRM includes five dimensions: green recruitment and selection, green training, green performance management, green pay and reward, and green involvement. Confirmatory factor analysis (study 2) was used to confirm the factor structure of study 1. The results indicated that the proposed measurement is valid. This study is the first and also the most comprehensive one to measure main human resource practices for environmental management, which can provide broader focus for further research and for practitioners.

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Metadata

Authors
Tang, G., Chen, Y., Jiang, Y., Paillé, P., & Jia, J.
Venue
Asia Pacific Journal of Human Resources
Publisher
Wiley
Published
2017-03-24
Volume
56
Issue
1
Pages
31-55
ISSN
  • 1038-4111 (ISSN-L)
  • 1744-7941
OpenAlex
W2600402146
Cited by
1,082
References
71
FWCI
62.39
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Green human resource management practices: scale development and validity. https://app.cochat.ai/papers/10.1111/1744-7941.12147
The paper
Tang, G., Chen, Y., Jiang, Y., Paillé, P., & Jia, J. (2018). Green human resource management practices: scale development and validity. Asia Pacific Journal of Human Resources. https://doi.org/10.1111/1744-7941.12147

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.