Combat poison with “poison”: Leader-targeted negative team gossip mitigates the detrimental team consequences of abusive supervision climate.
Bibliographic record
Abstract
Existing research presents mixed perspectives on the impact of abusive supervision climate on team processes and effectiveness. This discrepancy prompts an important question: when, why, and how does abusive supervision climate become more or less detrimental to teams? By integrating the social functional perspective of gossip with recent theoretical advancements on abusive supervision climate, we develop a novel theoretical model to explain how leader-targeted negative team gossip-defined as the extent to which team members share negative evaluations of the leader's behaviors with each other when the leader is absent-can mitigate the adverse effects of abusive supervision climate on teams. Our model posits that leader-targeted negative team gossip serves its social function in two key ways: (a) It diminishes team members' perception of the leader as a role model, thereby reducing the influence of abusive supervision climate on team aggressive behavior, and (b) it fosters perceived similarity among team members regarding their negative attitudes toward the leader, which lessens the impact of abusive supervision climate on team affective trust. We further argue that these buffering effects of leader-targeted negative team gossip have significant downstream implications for team effectiveness, specifically in terms of team performance and team voluntary turnover. Our model was tested using two multiwave, multisource field studies employing a round-robin design, with samples of 111 and 237 work teams, respectively. The results largely supported our model. We conclude by discussing the theoretical and practical implications of our findings. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".