Relationship Clean-Up Time: Using Meta-Analysis and Path Analysis to Clarify Relationships Among Job Satisfaction, Perceived Fairness, and Citizenship Behaviors †
Bibliographic record
Abstract
Although perceived fairness and job satisfaction predict organizational citizenship behaviors (OCB), researchers have pondered the conceptual relationships among these constructs. Using path analysis on meta-analytically derived coefficients, the authors compared four models: full mediation (job satisfaction mediates fairness-OCB relationships), partial mediation, independent effects, and a spurious effects model (the job satisfaction—OCB relationship is spurious because perceived fairness is a common cause). The authors found greatest support for the independent effects model: Job satisfaction and different types of perceived fairness accounted for unique variance in OCB dimensions. The article discusses implications for research and practice, and offers suggestions to advance theory in this area.
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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 | 0.096 | 0.191 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.044 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".