Meta‐analytic tests of relationships between organizational justice and citizenship behavior: testing agent‐system and shared‐variance models
Bibliographic record
Abstract
Abstract Research on the unique effects of different types of perceived fairness on citizenship behavior that benefits individuals (organizational citizenship behavior (OCB‐I) and organizations (OCB‐O) has produced mixed results. We assert that how OCB‐O and OCB‐I are conceptualized affects the patterns of results, and we hypothesize that, when OCB is conceptualized appropriately, an agent‐system model is supported (interactional and procedural justice are the strongest unique predictors of OCB‐I and OCB‐O, respectively). We also hypothesize that shared variance among the justice types explains additional variance in OCB. Analyses of semi‐ partial correlations conducted on meta‐analytic coefficients supported our hypotheses. Theoretical and practical implications are discussed. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.136 | 0.265 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.032 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".