Overall Organizational Justice Trajectories Among Newcomers: How Do Justice Perceptions Develop and Why Does It Matter?
Bibliographic record
Abstract
ABSTRACT While organizational justice perceptions are often thought to be stable, empirical evidence highlights substantial within‐person fluctuations over time. The development of these justice fluctuations may have important implications for newcomers' enactment of organizational citizenship behaviors (OCB). Building on fairness heuristics theory to consider the perception of justice as a dynamic phenomenon, we predict that the developmental nature of overall organizational justice (OOJ) perceptions varies between people following three trajectories. A Canadian sample of 103 participants responded to weekly surveys across 17 weeks, resulting in 986 observations. By means of latent class growth modeling, we identified one stable and two dynamic OOJ trajectories. These OOJ trajectories were differentially related to the enactment of OCB. Respondents characterized by the major negative change trajectory (decreasing levels of OOJ) had the lowest levels of OCB enactment, whereas respondents characterized by the stable and minor negative change OOJ trajectory had equally high levels of OCB enactment. Even when accounting for variable‐centered between‐person elements (primacy, recency and halo effects) when predicting OCB enactment at the end of the study, we found significant effects for the developmental nature of one's OOJ. We discuss the importance of these findings for organizational justice theory as well as their implications for practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".