Abusive Workplace Behavior: Behavioral and Legal Insights
Notice bibliographique
Résumé
Workplace abuse is a widespread, worldwide social phenomenon and the topic is highly germane, given the established link between employment quality and mental and physical health and the changes developing in the labor market, including globalization processes, economic recession influenced by the COVID-19 pandemic, rapid technological innovation, and demographic variations that potentially cause fluctuations in the labor market. The primary objectives of this paper were to explore definitions and theoretical frameworks of workplace abuse and also to examine the international legal aspects of such abuse. We employed Tepper's Abusive Supervision Survey Questionnaire to detect manifestations of abusive behavior toward employees in two large organizations, one public and one private, and one smaller national organization in Israel, while expanding its scope beyond its current use in extant legislation and legal literature. Finally, we aimed to offer practical measures to cultivate an organizational culture that encourages employees to overcome the "trust gap" and speak up without fear of retribution. As this is an exploratory study, no hypotheses are propounded. Analysis of the data indicated that the incidence of abusive workplace behavior in the respondents' organizations was not high. Women managers exhibited fewer abusive behaviors than men, although the mean differences between men and women were very modest. This might be explained on the basis of the theory of "ethics of care", where women, in contrast to men, tend to support cooperation and shared decision-making rather than overly strict supervision. We further found that abusive behavior was more prevalent in the private concern than the public organizations. It is possible that regulation in public organizations contributes to the reduction of abuse in the workplace. In addition, we found that workers reported more cases of abuse than managers but that the mean differences between employees and managers were minor. Men reported more cases of abusive behavior than women, but the differences we found were insignificant. Indeed, the differences found in all the comparisons were insignificant and cannot serve to form conclusions indicating a trend. Nevertheless, we advocate that governments raise awareness of the issue and the desired outcomes while enhancing workers' protection and dignity. To that end, the relevant authorities should employ uniform terminology and promote applicable legislation.
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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 | 0,001 | 0,000 |
| 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 | 0,002 |
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 ».