Examining the impact of ethical leadership and organizational justice on employees’ ethical behavior: Does person–organization fit play a role?
Bibliographic record
Abstract
Leadership studies on corporate ethical behavior and practices have grown considerably, contributing significant knowledge on ethical leadership challenges that are organizational and industry focused. However, complex socio-ecological systems are placing pressure on organizational culture and old patterns of leadership behavior that play a role in organizational justice. In this study, we argue that scholars of business ethics must consider the role of organizational justice and use person-organization fit (P–O fit). To address this, our study investigates the mediating effect of organizational justice on the relationship between ethical leadership and employees’ ethical behavior. We also examine the moderating role of P–O fit on the relationship between organizational justice and employee’s ethical behavior. The study survey focused on 295 employees belonging to organizations in Iraq. We show that ethical leadership positively influences employees’ ethical behavior, and this relationship is shaped by organizational justice. The findings reflect the positive impact of organizational justice on ethical behavior, and this relationship is more pronounced in employees with high rather than low P–O fit. This study clarifies the importance of employee’s P–O fit and its impact on organizational processes for creating a positive impact on ethical behavior in the workplace. We also share practical implications of the study and recommend systemic research that explores this area.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".