Theoretical and Empirical Advances on Destructive Leadership
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».