The future of healthcare: green transformational leadership and GHRM’s role in sustainable performance
Bibliographic record
Abstract
Purpose Organizations have increasingly been compelled to engage in ecological businesses in recent decades, necessitating identifying environmental practices contributing to enhanced sustainability. One of the main reasons for doing this research is to see how far down the path to green transformational leadership (GTFL) in Green Human Resource Management (GHRM) practices in the healthcare industry in Pakistan. Additionally, this research aims to analyze how this change affects the long-term success of businesses in sustainable performance (SP). Design/methodology/approach To identify factors related to the study variables, the research utilized master journals, as well as the Web of Science and Scopus databases. The ISM-DEMATEL (Interpretive Structural Modeling - Decision Making Trial and Evaluation Laboratory) technique was employed to establish a hierarchical model. This model facilitated the identification of cause-and-effect relationships among factors, which were further elucidated using the DEMATEL interrelationship diagram. Findings The analysis of the results indicates that Green Training (F4), Green Job Analysis (F1), Intellectual Stimulation (F10), and Green Product Innovation (F9) are the primary factors that have a significant impact on achieving Environmental Policies and Regulations (F13), and Subjective Environment Norms (F14) of SP factors. Research limitations/implications The study is implemented in the healthcare industry of Pakistan, with a focus on practical and managerial aspects. It encourages managers to develop and adapt their human resources policies and environmental strategies. Implementing safety health standards is crucial to mitigate the detrimental effects on the environment. The research was carried out during the period of the pandemic. The scope of this study was restricted to the healthcare industry in Pakistan. Originality/value In order to improve SP, this study presents a unique strategy combining sustainability into decision-making procedures with the function of GTFL in GHRM. Implementing safety health standards is crucial to mitigate the detrimental effects on the environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".