Recent trends of green human resource management: Text mining and network analysis.
Green human resource management
KNIME
Latent semantic analysis
Network analysis
TF-IDF
Text mining
VOSviewer
Journal
Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769
Informations de publication
Date de publication:
Dec 2022
Dec 2022
Historique:
received:
13
08
2021
accepted:
10
06
2022
pubmed:
6
7
2022
medline:
15
11
2022
entrez:
5
7
2022
Statut:
ppublish
Résumé
Issues of the environmental crisis are being addressed by researchers, government, and organizations alike. GHRM is one such field that is receiving lots of research focus since it is targeted at greening the firms and making them eco-friendly. This research reviews 317 articles from the Scopus database published on green human resource management (GHRM) from 2008 to 2021. The study applies text mining, latent semantic analysis (LSA), and network analysis to explore the trends in the research field in GHRM and establish the relationship between the quantitative and qualitative literature of GHRM. The study has been carried out using KNIME and VOSviewer tools. As a result, the research identifies five recent research trends in GHRM using K-mean clustering. Future researchers can work upon these identified trends to solve environmental issues, make the environment eco-friendly, and motivate firms to implement GHRM in their practices.
Identifiants
pubmed: 35790632
doi: 10.1007/s11356-022-21471-9
pii: 10.1007/s11356-022-21471-9
pmc: PMC9255839
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
84916-84935Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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