Distributive Injustice: Leadership Adherence to Social Norm Pressures and the Negative Impact on Organizational Commitment
Bibliographic record
Abstract
The social norm theory suggests that leaders who rely on perceived norms (misperceptions) rather than actual norms may produce unfair work advantages. Furthermore, social norms alter ethical leadership behaviors. However, leadership adheres to social norms due to society's implied compliance in the absence of distributive injustice measurements. Therefore, distributive injustice may be a more salient predictor than distributive justice on affective organizational commitment. The aim of this study was to fill gaps in literature on distributive injustice and investigate negative influences on employees’ affective commitment. A distributive injustice scale was designed using employee perceptions of policies that create unfair advantages and meritless rewards. The distributive injustice scale consisted of 14 items. A survey was sent to 481 full-time employees in various industries throughout the U.S. Correlation and regression model output indicated that unfair advantages and meritless rewards had a negative relationship and influence on employees’ affective commitment. Social norm policies that create unfair advantages and meritless rewards can be perceived as a divisionary tacit that negatively impacts affective commitment.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".