从属网络中嵌套性和模块化结构的解释:在软件项目团队形成的知识网络中的应用

Pub Date : 2021-01-01 DOI:10.4236/SN.2021.101001
Jorge Luiz dos Santos, R. Sampaio
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引用次数: 2

摘要

对组织背景下的知识创造和传播过程的理解是非常相关的。因为从这种理解,组织可以重组过程,重新定位团队和实施方法,以协助构建一个旨在可持续增长和创新的知识创造和传播的进化过程。复杂社会网络理论已被应用于多个领域,以帮助理解组织的认知过程。然而,这些方法仍然缺乏对所研究网络的嵌套性和模块化的分析。在本文中,我们提出了一种方法,旨在识别组织环境中项目中人员联系网络中的嵌套性和模块化模式。本研究试图在2006年至2013年期间在一家提供信息技术服务的公共组织的隶属关系网络中识别这些模式。这些模式的检测使用NODF (nesteness metric based on Overlap and递减填充)算法进行,该算法由[1]描述。嵌套性和模块化度量可以影响组织中为执行项目而构成的正式和非正式网络中的知识创造和传播模式。研究表明,研究期间的组织网络结构呈现出高度的嵌套性,并且可以识别出嵌套性和模块化的组合结构。
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Interpreting Nestedness and Modularity Structures in Affiliation Networks: An Application in Knowledge Networks Formed by Software Project Teams
An understanding of the knowledge creation and diffusion process in the organizational context is extremely relevant. Because from this understanding, organizations can restructure processes, reorient teams and implement methodologies to assist in the construction of an evolutionary process of knowledge creation and diffusion aimed at sustainable growth and innovation. The theory of complex social networks has been applied in several fields to help understand organizational cognitive processes. However, these approaches still insipiently consider the analysis of the nestedness and modularity of the studied networks. In this article, we presented an approach that sought to identify patterns of nestedness and modularity in networks of affiliation of people in projects in the organizational context. The study sought to identify these patterns in affiliation networks in a public organization providing information technology services in the period from 2006 to 2013. The detection of these patterns was performed using the NODF (Nestedness metric based on Overlap and Decreasing Fill) algorithm described by [1]. The nestedness and modularity metrics can influence patterns of knowledge creation and diffusion in formal and informal networks constituted for the execution of projects in organizations. This study showed that the network structures of the organization during the study period presented a high degree of nestedness, and it was possible to identify combined structures of nestedness and modularity.
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