The Relationship between Green Human Resource Management Practices and Organizational Citizenship Behavior
Bibliographic record
Abstract
In an era of green environmental awareness, Green Human Resources Management (GHRM) uses human resources management practices to support green environmental functions. It embraces green environmental concerns by applying human resources initiatives to generate high performance and better efficiency in operations. Although the literature on GHRM is growing, a few studies investigated to what extent green innovative culture (GIC) moderates the relationship between green human resources management practices and organizational citizenship behavior towards the environment (OCBE). To address this research gap, the authors tested a new conceptual framework investigating the direct and interactive effects of GHRM practices and GIC on OCBE. A quantitative study uses a survey from a 174 convenient sample of employees selected from a manufacturing firm operating in Egypt. The research results revealed that (1) GHRM practices are crucial for encouraging employees to engage in green activities, in addition (2) there is a significant positive effect of GHRM practices on OCBE, while on the other hand, (3) the interaction of GHRM and GIC can foster employees’ engagement in OCBE. The research significance lies in identifying and validating the GHRM practices applied in the manufacturing firm, as it advances the previous studies by developing a research model that offers critical insights on how manufacturing organizations working in industrial and agricultural packaging solutions could strategically link their GHRM practices and green innovative culture to support their OCBE in creating a competitive advantage in the market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".