Vitality of work involvement in mediation: The effect of organizational justice on organizational citizenship behavior
Bibliographic record
Abstract
This paper performs an investigation on Organizational Citizenship Behavior (OCB) head of the biMBA-AI-UEO unit in DKI Jakarta Province, which is a franchise brand of kindergarten schools in Jakarta.The purpose of the research, is to know and analyze: the direct influence of Organizational Justice on OCB unit head biMBA-AIUEO; the direct influence of Work Involvement on OCB; the direct influence of Organizational Justice on the Work Involvement, and the indirect effect of Organizational Justice on OCB head of the biMBA-AIUEO unit, with Work Involvement as mediation.The study used 140 samples of head of the biMBA-AIUEO unit in DKI Jakarta Province, and collected data by distributing questionnaires.The research uses quantitative methods and data processing using the path analysis.The results of this study prove that Organizational Justice had a positive and significant direct effect on OCB.Organizational Justice that are treated well can be perceived positively for employees to increase their OCB.Research results from work involvement have positive and significant effects on OCB.Even, Work Involvement has the most influence on OCB.This confirms that, high work involvement will also increase their OCB.Likewise, the results of the organization justice have a positive effect on work involvement.This reflects, that employees who are treated fairly by the organization will feel happy and can be a stimulus for them to be more actively involved in work.Whereas Organizational Justice has a positive and significant indirect effect on OCB through the mediation of Work Involvement.Employees who are treated fairly will feel happy, so they can stimulate themselves actively involved in work.This condition then, inspires and stimulates subordinates willing to do extra work that reflects OCB.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".