The Role of Islamic Work Ethics and Organizational Citizenship Behavior in Green Human Resource Practices and Environmental Performance of Indonesian Food SMEs
Bibliographic record
Abstract
Many Small and Medium Enterprises (SMEs) in the food processing sector generate waste and exhaust emissions from food residue burning, and consume excessive electrical energy during cooking, leading to environmental pollution and damage.This study proposes the application of Green Human Resource Management (GHRM) practices (e.g., selection, training, performance management, and compensation) and Organizational Citizenship Behavior towards the Environment (OCBE) to improve the Environmental Performance (EP) of food processing SMEs, with the implementation of Islamic Work Ethics (IWE) as a driving factor.Utilizing a purposive sampling method, a total of 500 owners of food processing SMEs in West Sumatra, Indonesia were selected as research samples.The data were analyzed using Partial Least Squares Structural Equation Modeling.The study's findings demonstrate that OCBE fully mediates the relationship between IWE and EP.These insights are crucial for helping food processing SMEs to enhance their environmental protection efforts.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".