The nexus between green HR practices and firm sustainable performance in Saudi Arabia manufacturing industry: The role of green innovation and green transformation leadership
Bibliographic record
Abstract
Environment concerns are now important for every business, especially manufacturing concerns due to imposing regulation regarding ecological performance. The quantitative study aims to investigate the impact of factors including green performance management and appraisal, green training and development and green compensation and reward on firm sustainable development of the manufacturing sector in Saudi Arabia. Furthermore, determine the mediating role of green innovation and green transformation leadership. Therefore, the role of these variables in causing actions of sustainability, through the purposive sampling we applied the administration of an online questionnaire to a sample of 350 employees of different levels from 40 manufacturing concerns. The study findings discovered that green human resource management practices positively influence firm sustainable performance. Additionally, the results indicated that green innovation and transformational leadership play an affirmative role in sustainable performance. Green innovation and transformational leadership partially mediate the link between green human resource management practices and firm sustainable performance. This study's findings provide a platform for policymakers and researchers in manufacturing firms to put green human resource-based approaches into practice to strengthen the employees’ environmental commitment and enhance sustainable performance. On the other hand, this study has given a holistic vision of green human resource management practices, green innovation, transformational leaders, and sustainable performance. This research can be reflected as a rock on which other research projects will be built and provide empirical evidence regarding the connection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".