Abusive Supervision and Retaliation: A Self-Control Framework
Bibliographic record
Abstract
There are conflicting perspectives on whether subordinates will or will not aggress against an abusive supervisor. To address this paradox we develop a self-control model of retaliatory behavior, wherein subordinates' self-control capacity and motivation to self-control influence emotional and retaliatory reactions to provocations by enabling individuals to override their hostile impulses. In Study 1, we demonstrate that self-control capacity, motivation to self-control (supervisor coercive power), and abusive supervision interact in such a way that the strongest association between abusive supervision and supervisor-directed aggression occurs when subordinates are low in self-control capacity and perceive their supervisor to be low in coercive power. In Study 2, we extend this finding, testing a moderated mediation model, wherein hostility toward a supervisor represents the hostile impulse resulting in retaliatory behavior, mediating the relation between abusive supervision and supervisor-directed aggression. Results from Study 2 indicate that self-control capacity allows individuals to regulate the hostile feelings experienced following abusive supervision, while self-control capacity and supervisor coercive power jointly moderate the tendency to act on one's hostile feelings toward an abusive supervisor. We discuss implications for retaliatory behaviors at work.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| 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".