A meta-analysis of incremental, comparative, and conditional motivations of unethical pro-organizational behavior
Bibliographic record
Abstract
Unethical pro-organizational behavior (UPB) was originally described as employees’ unethical acts to benefit the organization, driven by pro-social and organization-focused motivations of organizational identification (OI). However, subsequent perspectives suggest it can emerge from supervisor-focused pro-social motivations of high-quality leader-member exchange (LMX) and be facilitated by employees’ pro-self motivation enabled by moral disengagement (MD). We meta -analyzed the effects of OI, LMX, and MD on UPB (K = 262; N = 88,787) to compare these types of motivation and provide a descriptive update on UPB’s nomological network. MD explained variance in UPB beyond that explained by OI and LMX, suggesting the unique relevance of pro-self motivation. Additionally, OI explained twice the variance in UPB compared to LMX, underscoring the importance of different pro-social motivations. Moderation analyses revealed that country corruption amplified the relationships between pro-social motivations (OI and LMX) but not pro-self motivations (MD) and UPB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".