How GHRM impacts employee OCBE: the role of emotions and value discrepancy
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the impact mechanism of green human resource management (GHRM) on employee organizational citizenship behavior for the environment (OCBE). The authors maintain that anticipated environmental pride and guilt serve as dual mediators on the relationship between GHRM and OCBE, while environmental value discrepancy between employees and coworkers of the employees serve as the moderator on this relationship. Design/methodology/approach For this study, 226 valid questionnaires were obtained from various industries (food, machinery, electronics, etc.) in China and a hierarchical regression analysis was performed. Findings The results revealed that GHRM exerts a direct influence on OCBE, as well as indirect effects through anticipated environmental emotions. Environmental value discrepancy moderates the relationship between GHRM and anticipated environmental emotions. Originality/value The contribution of this study is not only to investigate the emotional impact mechanism between GHRM and employee OCBE, but also to identify the boundary conditions for the effect of GHRM on employees’ anticipated environmental emotions. The authors' findings offer a new theoretical framework for future research on GHRM, as well as practical implications for researchers and managers in organizational environmental management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".