Abusive Supervision Dispersion and Team Resilience: The Moderating Role of Team-Member Exchange
Bibliographic record
Abstract
In this study, we explore the idea of abusive supervision dispersion (ASD), which refers to the variability in team members’ perceptions of supervisory abuse, and its implications for team performance and cohesion. While past researchers have mainly adopted a compositional perspective to study abusive supervision, treating it as a uniform experience, we employed a dispersion lens to highlight the heterogeneity within teams. Drawing on Social Exchange Theory (SET), which emphasizes the importance of reciprocity and fairness in relationships, we argue that ASD disrupts these relational norms, eroding team trust and cohesion. Using multi-source, multi-level data from 62 work teams across diverse industries, we found that ASD has significant negative effects on team outcomes, including reduced performance and diminished morale. These effects, however, are not uniform and are moderated by team-member exchange (TMX)—the quality of interpersonal relationships within the team. Specifically, in high-TMX teams, the detrimental effects of ASD are mitigated as strong social bonds provide a buffering effect, promoting resilience and cohesion despite discrepancies in supervisory experiences. Conversely, in low-TMX teams, the negative effects of ASD are amplified, leading to greater dysfunction, conflict, and reduced productivity. These findings advance leadership research by challenging traditional assumptions of homogeneity in abusive supervision experiences and underscore the role of contextual factors like TMX in shaping team dynamics. Practically, fostering high TMX through team-building activities and reducing inconsistencies in supervisory behavior are critical organizational strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".