Theoretical and Empirical Advances on Destructive Leadership
Bibliographic record
Abstract
Destructive leadership is an insidious and growing problem for organizations. While many strides have been made to understand how it emerges within organizations and its impact, scholars have pushed researchers to extend the literature by further unpacking processes and utilizing novel explanations to understand how to prevent destructive leadership and to understand its consequences to others and to these leaders themselves. This symposium addresses this research agenda with five theoretically-driven empirical papers. The contributions in this symposium do this by: (1) examining novel antecedents that reduce incidents of destructive leader behavior (i.e., awe, small self); (2) exploring different forms of destructive leader behavior (e.g., abusive supervision, unethical leadership), (3) unpacking psychological motives triggered by destructive leadership that shape reactions (self-blame and moral threat for targeted employees, and image threat and moral versus trajectory motives for destructive leaders), (4) shedding light on cognitive mechanisms that are triggered by destructive leadership (learning for targeted employees and problem-solving and affective rumination for destructive leaders), (5) examining various outcomes of destructive leadership—some that are employee focused (e.g., exhaustion, cheating, ethical behavior) and some that leaders themselves engage in from their destructive behaviors (e.g., impression management behaviors, continued abusive supervision or ethical leadership behaviors), and (6) highlighting critical moderators that influence destructive leadership processes that minimize dysfunctional outcomes (moral efficacy of targeted employees and perceived leader passion). Small self, big change: How awe reduces abusive supervision in leaders Author: Yang Bai; Peking University Author: Run Ren; Peking University Author: Huiwen Lian; Texas A&M University Author: Li Ma; Peking University Unethical leadership: Moral threat, learning, and outcomes Author: Gabriela Rivera; The Pennsylvania State University Author: Linda Klebe Trevino; The Pennsylvania State University Author: Anjier Chen; National University of Singapore My leader is abusing me, and it’s all my fault! Leader passion, abusive supervision and self-blame Author: Seungjae Yang; Yonsei University Author: Mijeong Kwon; Rice University Author: Boram Do; Yonsei University How supervisors manage their image following abuse: An image management view of abusive supervision Author: Abigail Fleri; University of North Carolina at Chapel Hill The battle between good and evil: Dynamic relations between abusive and ethical leadership Author: Wei Wang; University of Manitoba, Asper School of Business Author: Michelle K. Duffy; University of Minnesota
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".