When Justice Conflicts with Care: Navigating Leaders’ Moral Dilemma in the Face of Employees’ UPB
Bibliographic record
Abstract
Research on unethical pro-organizational behavior (UPB) has predominantly focused on its consequences for the employees engaged while overlooking how observing such behavior might affect their leaders’ psychological experiences and downstream responding behaviors. Integrating moral heuristics theory and social exchange theory, we argue that leader moral identity and relationship quality with the UPB conductor (i.e., LMX) may interact with the leader’s perception of employee UPB, leading to leader moral dissonance and influencing their subsequent helping and undermining behavior toward the UPB conductor. We examined the hypothesized relationships with two studies. The first study was a three-wave, multisource field study consisting of 684 employees across a total of 150 work teams and their 150 direct supervisors from a prominent pharmaceutical company located in northwestern China. The second study was an experimental study on an English platform, Prolific, recruiting 240 full-time employees from the United States, United Kingdom, and Canada. The results of both studies provide support for our predictions. Our research offers insights into moral literature and the consequences of UPB in organizations.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".