Unraveling the impact of abusive supervision: a moderated mediation model of locus of control, team and individual efficacy

调解 心理学 滥用监督 适度 社会心理学 调解 心理干预 多级模型 心理安全 心理弹性 结构方程建模 控制源 应用心理学 核心自我评估 工作满意度 自我效能感 双灵巧性 角色冲突 工作表现 团队效能 控制(管理)
作者
Ali Tariq,Kashif Ammar,Tehreem Fatima,Saira Altaf,Bilal Javed
出处
期刊:Team Performance Management [Emerald Publishing Limited]
卷期号:31 (7-8): 532-556 被引量:3
标识
DOI:10.1108/tpm-10-2024-0118
摘要

Purpose Abusive supervision is a growing workplace concern, negatively impacting employee well-being and performance. Despite extensive research on job satisfaction and turnover, the influence of abusive supervision on performance remains underexplored. This study aims to investigate how abusive supervision affects individual and team performance, considering the mediating role of self-efficacy and team efficacy, with locus of control (LoC) as a moderating factor. Design/methodology/approach Data were collected from 175 teams (N = 524 individuals) in the banking sector using a structured questionnaire. Structural equation modeling via Smart PLS was used to analyze direct, indirect and moderation effects. In addition, multilevel mixed-effects modeling was used to rigorously account for the nested data structure and validate the hypothesized relationships at both individual and team levels. Findings The results indicate that abusive supervision negatively affects both individual and team performance, with self-efficacy and team efficacy fully mediating these relationships. In addition, LoC moderates the mediation effect, where individuals with a high internal LoC demonstrate greater resilience against the negative impact of abusive supervision. Originality/value This study extends team effectiveness literature by incorporating abusive supervision within team dynamics and leadership research. Integrating toxic leadership models and psychological resilience factors provides novel insights into leadership interventions aimed at mitigating the harmful effects of abusive supervision. Future research should explore longitudinal effects and industry-specific applications to enhance generalizability.

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