Workplace Envy and Organizational Citizenship Behaviour: The Mediating Effect of Cynicism and the Moderating Effect of Organizational Support
Bibliographic record
Abstract
ABSTRACT Employees frequently engage in social comparisons in work environments, which can give rise to envy, a painful emotional response to perceived inferiority. Although previous envy research has mainly focused on its consequences within dyadic domains, the present study examines its broader organizational implications by investigating how envy influences organizational citizenship behaviour. Drawing on conservation of resource theory, this study investigated the mediating role of cynicism as emotion‐focused coping mechanism as well as the moderating role of perceived organizational support in this relationship. Data were collected from 221 full‐time employees across 3 time‐lagged intervals. According to the results, envy was found to positively relate to cynicism, which in turn negatively associated with citizenship behaviour. Perceived organizational support was found to moderate the relationship between envy and cynicism, mitigating the indirect effect of envy on organizational citizenship behaviour. This study extends envy research by highlighting cynicism as an emotion‐focused coping mechanism that affects organizational outcomes and identifies organizational support as a key resource in mitigating envy's negative effects. Theoretical and practical implications are also discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".