The Gossiper's high and low: Investigating the impact of negative gossip about the supervisor on work engagement
Bibliographic record
Abstract
Abstract Although negative gossip is ubiquitous in the workplace, we know little about how negatively gossiping about the supervisor—who occupies a higher hierarchical position in the organization—influences gossipers themselves. To address this question, we draw on the conservation of resources theory to account for the resource‐consuming and resource‐generating impact of negative gossip about the supervisor on gossipers’ work engagement. Findings from three experience sampling studies show that negative gossip about the supervisor is a double‐edged sword for gossipers that seems to do more harm than good to their work engagement. On the one hand, spreading negative gossip about the supervisor evokes the resource‐consuming mechanism of image maintenance concerns, which impairs gossipers’ work engagement, especially when perceived organizational politics is higher. On the other hand, engaging in such gossip elicits the resource‐generating mechanism of sense of power, which only improves work engagement in Study 3 but not in Studies 1 and 2; contrary to our expectation, this effect is unaffected by perceived organizational politics. We conclude by discussing the theoretical and practical contributions of our research.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".