What Drives Sustainable Organizational Citizenship Behavior
Bibliographic record
Abstract
The purpose of this study is to examine the impact of the organizational justice variable, organizational commitment, job satisfaction on the sustainable organizational citizenship behavior (OCB) variable in food producers. A cross-sectional, self-administered questionnaire was distributed in this study, the sample was determined using a slovin approach, and the total number of respondents was 159. The structural equation modeling (SEM) software SmartPLS 3.3.3 was used as the analytical tool. Data for research was gathered through the distribution of online questionnaires. According to the findings of this study, organizational justice variables have a positive and significant impact on the OCB variable. This demonstrates that the higher the organizational justice variable given to food producers in Banten, the higher the employee's sustainable Organizational citizenship Behavior variable will be. The organizational commitment variable influences the OCB variable in a positive and significant way. This indicates that the higher the level of the employee's sustainable OCB variable, the higher the level of the employee's organizational commitment. Furthermore, job satisfaction has a positive and significant impact on long-term OCB. This shows that the higher the employee's job satisfaction, the higher the employee's sustainable Organizational citizenship Behavior variable will be.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".