The mediating role of environmental sustainability between green human resources management, green supply chain, and green business: A conceptual model
Bibliographic record
Abstract
This study aims to assess the influence of green HR management and Green Supply Chain on practices for initiating green business and environmental sustainability. The research also incorporated the mediation role of environmental sustainability between green HR practices, Green Supply Chain, and green business. A structural study methodology was used with 220 samples drawn from manufacturing companies in the province of Central Java. A questionnaire was used to collect data. The PLS(SEM) was also utilized to assess the constructs' reliability and validity as well as investigate their hypothesized links. The finding of this study indicates that environmental sustainability significantly mediates the relationship between green HRM, green supply chain, and green business. The research also finds a positive relationship between green HRM, environmental sustainability, and green businesses. Similarly, findings also found a significant relationship between green supply chain, environmental sustainability, and green industries. The results of this study can be applied to strengthen the resource based theory and literature of HRM and supply chain management. There are several practical managerial implications for improving the performance of manufacturing companies in Central Java. They provide recommendations for practitioners and managers to improve the business performance of companies based on green HRM, green supply chain, and environmental sustainability.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".