变革型领导力随着时间推移对员工留任和就业能力的动态影响:基于代理的模型

IF 1.8 4区 管理学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Sophia R. Thomas, S. R. Aurora
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引用次数: 0

摘要

对于各种规模的组织而言,留住员工都是一个问题。研究表明,变革型领导可以提高员工留任率并降低员工流失率;然而,对于变革型领导在考虑员工就业能力变化的同时,对员工留任率随时间推移而产生的影响却鲜有研究。我们使用基于代理的模型来展示这些不断变化的关系,同时考虑到现代组织的性质。我们的模型研究了变革型领导、个人就业能力和留任率之间的关系。该模型使用了早期研究的数据来定义这些变量的参数,展示了员工和领导者如何相互作用并影响就业能力和留任率,以及这些影响如何随时间推移而变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The dynamic effects of transformational leadership on employee retention and employability over time: an agent-based model

The dynamic effects of transformational leadership on employee retention and employability over time: an agent-based model

Employee retention is a problem for organizations of all sizes. Research has shown that transformational leaders improve retention and reduce turnover; however, there has been little research on the effects of transformational leadership on retention over time while also considering employees’ changing employability. We use agent-based modeling to demonstrate these changing relationships while considering the nature of modern organizations. Our model looks at the relationships between transformational leaders, individual employability, and retention. The model uses data from earlier research to define parameters for these variables, showing how workers and leaders interact and affect employability and retention and how these effects change over time.

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来源期刊
Computational and Mathematical Organization Theory
Computational and Mathematical Organization Theory COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
3.80
自引率
16.70%
发文量
14
审稿时长
>12 weeks
期刊介绍: Computational and Mathematical Organization Theory provides an international forum for interdisciplinary research that combines computation, organizations and society. The goal is to advance the state of science in formal reasoning, analysis, and system building drawing on and encouraging advances in areas at the confluence of social networks, artificial intelligence, complexity, machine learning, sociology, business, political science, economics, and operations research. The papers in this journal will lead to the development of newtheories that explain and predict the behaviour of complex adaptive systems, new computational models and technologies that are responsible to society, business, policy, and law, new methods for integrating data, computational models, analysis and visualization techniques. Various types of papers and underlying research are welcome. Papers presenting, validating, or applying models and/or computational techniques, new algorithms, dynamic metrics for networks and complex systems and papers comparing, contrasting and docking computational models are strongly encouraged. Both applied and theoretical work is strongly encouraged. The editors encourage theoretical research on fundamental principles of social behaviour such as coordination, cooperation, evolution, and destabilization. The editors encourage applied research representing actual organizational or policy problems that can be addressed using computational tools. Work related to fundamental concepts, corporate, military or intelligence issues are welcome.
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