Abusive Supervision Differentiation and Employee Outcomes: The Roles of Envy, Resentment, and Insecure Group Attachment
Bibliographic record
Abstract
When employees experience distressful events such as abusive supervision, they often rely on their workgroup for sense making and social support. However, research also shows that supervisors tend to differentially abuse members of the same group (i.e., abusive supervision differentiation, ASD). We argue that this behavior threatens an employee’s socioemotional bond with and reliance on the workgroup for support. Specifically, ASD drives negative comparisons of “self versus others” that diminish one’s socioemotional relationship with the group as a whole, particularly if one experiences more abuse than others. Drawing on attachment theory, we develop an individual-level conceptual model that links perceptions of ASD to employee outcomes through two forms of unhealthy person-group bonding—group attachment anxiety and group attachment avoidance. The results of two studies show that group attachment anxiety and avoidance uniquely explain the effects of ASD perceptions, over and above group identification. While both attachment patterns mediated the effects of ASD on psychological distress, group attachment avoidance primarily mediated the effects on quit intentions, and group attachment anxiety primarily mediated the effects on interpersonal deviance (Study 2). In addition, Study 2 demonstrates that resentment and envy towards other group members explain why ASD perceptions lead to group attachment anxiety, attachment avoidance, and subsequent outcomes. Lastly, we find some evidence that the indirect effects of ASD perceptions are more detrimental when one perceives greater (vs. less) personal exposure to abusive supervision. We conclude by discussing the implications of group attachment theory and targeted emotions for understanding ASD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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) | 0.003 | 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".