An Exploration of How Ethical Leaders Mitigate the Deviance of Dispositionally Dishonest Employees
Bibliographic record
Abstract
There is growing consensus among behavioral ethics scholars that ethical leadership is a potent solution for addressing unethical and deviant behaviors in the workplace. However, while the evidence to date shows that ethical leadership generally reduces employee misconduct, it is not clear how ethical leaders influence those most likely to engage in misconduct – i.e., dispositionally dishonest employees (or low Honesty-Humility, HH; Ashton & Lee, 2004). Drawing on Brown et al.’s (2005) theory of ethical leadership, we test two distinct theoretical explanations: a) the moral cognitive perspective, which argues that ethical leaders reduce the unethical behaviors of low HH employees by positively shaping their moral cognitions (e.g., moral attentiveness and awareness, moral judgment, moral motivation, and moral disengagement) of low HH employees or b) the trait suppression perspective, which argues that ethical leaders simply suppress or constrain low HH employees’ natural expression of unethical behavior through reinforcements. Across four studies investigating five moral cognitions, we found little support for the moral cognitive explanation. In contrast, we find evidence that ethical leaders primarily mitigate low HH employees’ unethical behaviors by influencing their perceptions that deviant behaviors are not tolerated (i.e., suppressed) in the workplace. We discuss the implications of these findings.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".