Abstract
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.
Highlights
Modeling based on complex network analysis has been used as a tool to answer questions and identify characteristics, behaviors and cause-and-effect relationships involving the systems studied in many areas of knowledge
We suggest an application of the nestedness and modularity measures in two-mode networks, in which one of the modes corresponds to people involved in software development and maintenance projects and the other mode corresponds to the projects to which these people are allocated
It is necessary to segment these relationships by year to facilitate the analyses so that we could have a view of the evolution of the nestedness and modularity formations over time
Summary
Modeling based on complex network analysis has been used as a tool to answer questions and identify characteristics, behaviors and cause-and-effect relationships involving the systems studied in many areas of knowledge. Some of these questions are not typically visible when studied from the perspective of other analytical approaches. An example of characteristics related to the interactions between the components of these networks that are not necessarily visible in the light of other approaches is the nestedness and modularity metrics, as described by [2] The study of these measures has helped to understand network structures formed in biological systems and has been applied to economic systems. In the field of economics, such studies were a complementary part of research that resulted in predictive models about economic development or about the emergence and disappearance of certain industries and companies in several countries, such as the studies conducted by [3] and by [4]
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