The mediating role of overall fairness and the moderating role of trust certainty in justice–criteria relationships: the formation and use of fairness heuristics in the workplace
Bibliographic record
Abstract
Abstract Theory suggests that perceptions of overall fairness play an important role in the justice judgment process, yet overall fairness is insufficiently studied. We derived hypotheses from fairness heuristic theory, which proposes that perceptions of overall fairness are influenced by different types of justice, are more proximal predictors of responses than specific justice types, and are used to infer trust when trust certainty is low. Results from Study 1 (N = 1340) showed that employees' perceptions of overall fairness in relation to a senior management team mediated the relationships between specific types of justice and employee outcomes (e.g., affective commitment). In Study 2 (N = 881), these mediated effects were replicated and trust certainty moderated the effect of overall fairness on trust as hypothesized. Study 2 also showed that, relative to procedural and informational justice, distributive and interpersonal justice had stronger effects on overall fairness. To explore how the organizational context may have influenced these findings, we performed qualitative analyses in Study 3 (N = 268). Results suggested that, consistent with the quantitative findings from Study 2, some types of justice were more salient than others. We discuss the implications of our findings for theory, research, and practice. Copyright © 2008 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 | 0.012 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".