Organizational justice and workplace aggression: the impact of power distance
Bibliographic record
Abstract
The two studies herein examine (1) the meditating role of psychological contract violation and (2) the moderating role of power distance in the relationship between organizational justice (i.e., procedural justice and interactional justice) and two targets of workplace aggression - one’s supervisor (i.e., direct aggression) and one’s coworkers (i.e., displaced aggression). Based on a North American sample, findings from Study 1 suggest that psychological contract violation fully mediates the impact of organizational justice on workplace aggression. Power distance moderates the relationship between procedural justice (but not interactional justice) and psychological contract violation, such that the relationship is stronger when power distance is lower. Power distance also moderates the between psychological contract violation and workplace aggression, such that the relationship is acerbated by power distance. Study 2 was conducted to replicate the relationships examined in Study 1, but this time using a cross- cultural sample with participants from Canada and China. The same pattern of findings emerged from Study 2 as in Study 1. Implications of the findings of these studies for theory and practice are discussed.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".