The impact of green human resource management on green pharmaceutical supply chain management practices
Bibliographic record
Abstract
Today's competition is among supply chains rather than enterprises as independent actors. Additionally, the success of the actors within the SC network, through the improvement of the global performance, depends on how well they handle the growing environmental problems. For this reason, much attention is given to the greening concept. This paper aims to investigate the Green Human Resource Management (GHRM) impact on Green Supply chain Management (GSCM) in a strategic sector, namely the pharmaceutical sector. Our interest to deal with this issue was aroused by the fact that there are no studies exploring the causality relationships in the pharmaceutical sector in Saudi Arabia. Based on deductive methodology, a questionnaire has been developed. A research sample included 109 pharmaceutical companies operating in KSA. The results highlight a significant impact of GHRM on GSCM practices. In other words, training and performance appraisal, recruitment, and eco-behavior-based rewards influence GSCM practices positively. Based on the results, the study provides new theoretical insights and practical suggestions.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".