Supervisors' exceedingly difficult goals and abusive supervision: The mediating effects of hindrance stress, anger, and anxiety
Bibliographic record
Abstract
Summary This study examined a contextual predictor of abusive supervision. Specifically, we hypothesized that job goals that are judged by supervisors to be exceedingly difficult to attain is a predictor of subordinate‐rated abusive supervisory behavior. Drawing on the cognitive theory of stress, we hypothesized that exceedingly difficult job goals assigned to supervisors predict abusive behavior directed at their subordinates, as mediated by the supervisors' hindrance stress and emotions (e.g., anger and anxiety). We collected data from employees and their immediate supervisors to test this theoretical model ( N = 215 matched pairs). The results of this multisource field study provided support for the hypothesized relationships. In particular, assigned job goals that were appraised by supervisors as exceedingly difficult to attain predicted their hindrance stress. Also, hindrance stress was positively related to anger and anxiety, which in turn predicted abusive supervision. Theoretically, these findings contribute to research on goal setting, stress, and abusive supervision. In addition, these findings are practically important in that they provide suggestions on how to minimize abusive supervision in organizations. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".