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首页> 外文期刊>Organizational Research Methods >Importance-Weighted Density: A Shared Leadership Illustration of the Case for Moving Beyond Density and Decentralization in Particularistic Resource Networks
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Importance-Weighted Density: A Shared Leadership Illustration of the Case for Moving Beyond Density and Decentralization in Particularistic Resource Networks

机译:重要性密度:在特定资源网络中超越密度和分散的情况下的共享领导信息

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摘要

Social network analysis has been increasingly used by researchers to operationalize team processes and emergent states. Despite their advantages over aggregate measures, the most frequently used network measures such as density and centrality are agnostic to potentially meaningful elements reflecting the patterns of ties between team members. Specifically, intangible resources transmitted within team networks are often more particularistic, such that the value of the shared resource is dependent upon who gives it. We use shared leadership as an exemplar case for explaining this issue and proposing a solution in the form of a new network measure, importance-weighted density (IWD). Combining logic from the principles of density, decentralization, and eigenvector centralization, IWD provides a more detailed understanding of network tie patterns by taking into account the degree to which ties emerge from members who are themselves well connected. We test the measure's validity in a series of Monte Carlo simulations and laboratory and field studies. We find that IWD has high convergent, discriminant, and criterion validities and discuss how this statistic might help to enhance the study of several other team constructs. We provide access to a downloadable tool for the calculation of IWD and other network statistics discussed within this article.
机译:研究人员越来越多地使用社交网络分析来运营团队流程和紧急国家。尽管它们优于总措施,但诸如密度和中心的最常用的网络措施是不可知的,对反映团队成员之间联系模式的潜在有意义的元素是不可知的。具体而言,在团队网络中传输的无形资源通常是更加特殊的,使得共享资源的值取决于谁给出它。我们使用共享领导作为解释此问题的示例性案例,并以新的网络测量,重要性加权密度(IWD)的形式提出解决方案。将逻辑从密度,分散化和特征传感器集中的原则中结合,IWD通过考虑到自己良好连接的成员的程度来提供对网络领带模式的更详细了解。我们在一系列蒙特卡罗模拟和实验室和田间研究中测试措施的有效性。我们发现IWD具有高收敛,判别和标准有效性,并讨论这种统计数据如何有助于加强对其他几个团队构建的研究。我们提供对计算IWD和其他网络统计数据的可下载工具的访问权限。

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