Bad, mad, or glad? Exploring the relationship between leaders' appraisals or attributions of their use of abusive supervision and emotional reactions
Bibliographic record
Abstract
Abstract A large body of research has documented the ill effects of abusive supervision. However, this begs the question of why these behaviors continue to occur. To address this question, we contend that scholars need to understand how leaders—the perpetrators of these actions—make sense of abusive supervision. Specifically, drawing upon theories of appraisal and attribution, this paper examines leaders' cognitions of who is accountable for incidents of abusive supervision (i.e., the leader or the subordinate) and their future expectations (i.e., are individuals likely to engage in the same behaviors subsequently or are capable of change) and how these appraisals interact to shape emotional reactions. We conducted three complementary studies: a pilot study to identify relevant emotions, an event‐based experience sampling study (Study 1), and a retrospective recall study (Study 2). Accountability appraisals were associated with emotions, such that appraisals that oneself (vs. one's subordinate) was more responsible for the incident were linked to higher levels of guilt and shame. Although growth mindset moderated associations between accountability appraisals and emotions, it did so for different emotions across the two studies (i.e., hostility in Study 1 and shame in Study 2). Implications for theory and practice are discussed.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".