The Role of Jealousy in Predicting the Effects of Informational Unfairness on Helping and Gossiping
Bibliographic record
Abstract
We theorize that jealousy will influence the effects of informational unfairness on supervisor-directed helping behavior and spurs negative supervisor-targeted gossip. We test our hypothesis across three studies. In Studies 1 and 2, we conducted an experiment with 72 employees in Pakistan (Study 1) and 88 individuals in France (Study 2). In Study 3, the findings from a field study with 226 employee-supervisor dyads in Pakistan indicated that employees’ exposure to informational unfairness invokes employee jealousy. In reaction, we find that employees stop extending a helping hand to their supervisor and spread negative gossip about them. Moreover, we find that perceived informational unfairness is more salient and perceived as hurtful for individuals who compare themselves unfavorably to their colleagues in terms of leader-member exchange quality. The findings of these studies provide practical insights to both organizations and managers about the importance of clear and fair communication, and the tangible, detrimental effects of jealousy in the workplace.
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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 | 0.004 | 0.031 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".