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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".