Mitigating the negative effect of perceived organizational politics on organizational citizenship behavior: Moderating roles of contextual and personal resources
Bibliographic record
Abstract
Abstract Based on the job demands–resources model, this study considers how employees’ perceptions of organizational politics might reduce their engagement in organizational citizenship behavior. It also considers the moderating role of two contextual resources and one personal resource (i.e., supervisor transformational leadership, knowledge sharing with peers, and resilience) and argues that they buffer the negative relationship between perceptions of organizational politics and organizational citizenship behavior. Data from a Mexican-based manufacturing organization reveal that perceptions of organizational politics reduce organizational citizenship behavior, but the effect is weaker with higher levels of transformational leadership, knowledge sharing, and resilience. The buffering role of resilience is particularly strong when transformational leadership is low, thus suggesting a three-way interaction among perceptions of organizational politics, resilience, and transformational leadership. These findings indicate that organizations marked by strongly politicized internal environments can counter the resulting stress by developing adequate contextual and personal resources within their ranks.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".