Overcoming organizational politics with tenacity and passion for work: benefits for helping behaviors
Bibliographic record
Abstract
Purpose This study unpacks the relationship between employees' perceptions of organizational politics and their helping behavior, by explicating a mediating role of employees' affective commitment and moderating roles of their tenacity and passion for work. Design/methodology/approach Quantitative survey data were collected from 476 employees, through Amazon Mechanical Turk. Findings Beliefs that the organizational climate is predicated on self-serving behaviors diminish helping behaviors, and this effect arises because employees become less emotionally attached to their organization. This mediating role of affective commitment is less salient to the extent that employees persevere in the face of challenges and feel passionate about working hard. Practical implications For human resource managers, this study pinpoints a lack of positive organization-oriented energy as a key mechanism by which perceptions about a negative political climate steer employees away from assisting organizational colleagues on a voluntary basis. They can contain this mechanism by ensuring that employees are equipped with energy-boosting personal resources. Originality/value This study addresses employees' highly salient emotional reactions to organizational politics and pinpoints the critical function of affective commitment for explaining the escalation of perceived organizational politics into diminished helping behavior. It also identifies buffering effects linked to two pertinent personal resources.
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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 | 0.006 |
| 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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".