The impact of GHRM practices on corporate sustainability dimensions: a mediation and moderation analysis
Bibliographic record
Abstract
Purpose This study examines the relationship between green human resource management (GHRM) practices, internal environmental management practices (IEM) and sustainable performance of Tunisian manufacturing firms. Moreover, it considers ethical leadership style (ELS) as a moderator of the relationship between GHRM practices and sustainable performance. Design/methodology/approach The authors used a survey questionnaire to collect data from 180 Tunisian manufacturing companies; partial least square structural equation modeling (PLS-SEM) was used to analyze the collected data. Findings This study shows that GHRM practices positively influence both IEM practices and environmental performance. In addition, IEM practices positively affect the social and environmental performance of Tunisian companies. IEM practices partially mediate the relationship between GHRM practices and environmental performance and fully mediate the relationship between GHRM practices and social performance. Finally, ELS has a significant moderating effect on the relationship between GHRM practices and environmental performance. Research limitations/implications The results of this study are specific to manufacturing companies in Tunisia and may not be generalizable to other sectors (e.g. services) or countries. Nevertheless, the findings provide valuable insights for manufacturing managers in Tunisia. Originality/value Given the scarcity of research integrating GHRM and IEM practices within manufacturing firms in developing countries, this study offers empirical evidence on the relationship between the above concepts in Tunisian manufacturing firms as well as their combined effect on sustainability, under the lens of resource-based view (RBV) theory.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".