组织劳动力流网络和职业预测
Frank Webb1, Daniel Stimpson2, Miesha Purcell2
1Department of Computational and Data Sciences, George Mason University, Fairfax, VA 22030, USA.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
本研究介绍了组织劳动流网络,以建模内部员工流动. 这些网络揭示了类似于公司规模的权力法分布,为职业道路和经济结构提供了新的见解.
科学领域:
- 经济物理 经济物理
- 网络科学 网络科学
- 组织行为 组织行为
背景情况:
- 员工的移动在经济学和管理学中至关重要,但在经济物理学中未被充分研究.
- 现有的模型往往缺乏能够捕捉内部组织动态的分辨率.
研究的目的:
- 开发和测试高分辨率的内部劳动力市场网络.
- 应用网络分析来了解大型组织内的员工流动性.
主要方法:
- 构建内部劳动力市场的经验性校准网络.
- 根据职位 (操作单位,职业代码) 定义的节点和链接.
- 使用马尔科夫过程 (带有和没有有限的内存) 进行分析.
主要成果:
- 内部劳动力市场的网络描述显示出强大的预测能力.
- 基于操作单元的组织劳动流网络表现出了权力法特征.
- 这个权力定律反映了经济体中的公司规模分布,表明了广泛的适用性.
结论:
- 内部劳动力市场网络为研究职业提供了一种新的方法.
- 这些发现表明经济实体中普遍存在规律性,连接组织结构和经济结构.
- 这项工作跨越了研究职业道路的学科.
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