Mapping the Landscape of Green Human Resource Management (GHRM): A Bibliometric Approach
Bibliographic record
Abstract
This paper employs a bibliometric approach to comprehensively map the landscape of Green Human Resource Management (GHRM), addressing the absence of systematic analyses in the existing literature. GHRM, encompassing strategies fostering employee environmental awareness, has become pivotal as organizations recognize the need for sustainable practices among Asian countries. The study employs VOSviewer and Biblioshiny to analyze bibliographic data from the Web of Science (WoS) database from 2015 to 2024; search queries incorporating keywords such as “Green HRM”, “Eco-friendly HR practices”, and “Sustainable HRM” are formulated to capture the breadth of research in this field. We found 198 papers related to the GHRM in the Asian Context for analysis. They are revealing co-authorship networks, keyword co-occurrence, and citation patterns. Key objectives include identifying research themes, distribution across journals, and influential authors. Findings highlight the rise of GHRM as a pivotal component of sustainable business strategies, with China, Pakistan, and Malaysia leading in contributions. Central themes such as sustainability, green supply chain management, and environmental performance are underpinned by foundational works from prominent researchers and offer a nuanced understanding of GHRM’s current state and provide valuable insights for researchers, practitioners, and policymakers seeking to advance organizational sustainability. We found a forward-looking research agenda emphasizing interdisciplinary approaches, global inclusivity, and integration with corporate social responsibility and ESG frameworks. By consolidating existing knowledge and highlighting trends, this study contributes to the ongoing dialogue on creating green workplaces and eco-friendly practices, emphasizing the importance of integrating environmentally sustainable practices into HR strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.025 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".