Knowledge sharing and unethical pro-organizational behavior in a Mexican organization
Bibliographic record
Abstract
Purpose This paper aims to investigate the relationship of knowledge sharing with unethical pro-organizational behavior (UPB) and the potential augmenting effects of two factors: employees’ dispositional resistance to change and perceptions of organizational politics. Design/methodology/approach Quantitative data come from employees in a Mexican manufacturing organization. The hypotheses tests use hierarchical regression analysis. Findings Knowledge sharing increases the risk that employees engage in UPB. This effect is most salient when employees tend to resist organizational change or believe the organizational climate is highly political. Practical implications Organizations should discourage UPB with their ranks, and to do so, they must realize that employees’ likelihood to engage in it may be enhanced by their access to peer knowledge. Employees with such access may feel more confident that they can protect their organization against external scrutiny through such unethical means. This process can be activated by both personal and organizational factors that make UPB appear more desirable. Originality/value This study contributes to organizational research by providing a deeper understanding of the risk that employees will engage in UPB, according to the extent of their knowledge sharing. It also explicates when knowledge sharing might have the greatest impact, both for good and for ill.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".