Actor centrality in Network Projects and scientific performance: an exploratory study

André Luiz Lemes Alarcão,Mário Sacomano Neto

doi:10.1016/j.rai.2016.03.002

André Luiz Lemes Alarcão, Mário Sacomano Neto

Open Access

https://doi.org/10.1016/j.rai.2016.03.002

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Abstract

This study analyzed the relationship between actor centrality of Network Projects and scientific productivity performance using a method known as Social Network Analysis. Social Network Analysis and its respective properties are able to analyze actors’ positions in the structure and existing social interactions in networks. Thus, this method generates indicators to understand the format of collaborative structures of projects and their respective performances in scientific productivity. In order to carry out this proposal, models for multimodal analysis were used, taking into consideration different centrality measures. The behavior of centrality metrics has proven to be significantly different for analyses. Furthermore, the correlations between these metrics and scientific productivity performance have shown to be important in achieving project goals. This shows that the more centrality there is, the greater the chance the project has to achieve its goals.

Highlights

Social Network Analysis (SNA) is based on methods deriving from graph theory (KILDUFF & TSAI, 2003, p. 38) and can organize structures and interactions from actors and represent them in a graph
Descriptive and inferential statistical studies were conducted to verify if there were any significant differences between the Degree Centrality (DC), Betweenness Centrality (BC) and Harmonic Closeness Centrality (HC-c) metrics
As part of the method that make up these structural characteristics (WASSERMANN & FAUST, 1994), the centralities in this research have analytical differences in their use, which infers that the complexity of the project structures can influence the definition of which centrality is to be used in different analytical contexts in networks

Summary

Introduction

Social Network Analysis (SNA) is based on methods deriving from graph theory (KILDUFF & TSAI, 2003, p. 38) and can organize structures and interactions from actors and represent them in a graph. Social Network Analysis (SNA) is based on methods deriving from graph theory SNA generates individual indicators from actors or even groups and networks as a whole. These indicators can associate the nature of the structures and relations from the network to phenomena, such as power, knowledge transmission, information flow, etc. According to Borgatti & Everett (1997), SNA studies attributes of pairs of individuals (or dyads), sub-groups or networks whereas in traditional social science the focus is on attributes of individuals. SNA examines structural and relational aspects in dyads, sub-groups and relationship networking (SACOMANO NETO & TRUZZI, 2009) and is known as a meso level of analysis method. SNA examines structural and relational aspects in dyads, sub-groups and relationship networking (SACOMANO NETO & TRUZZI, 2009) and is known as a meso level of analysis method. Borgatti & Everett (1997, p. 243) highlight the importance of “pairs of individuals” in SNA, which they call dyadic attributes, instead of focusing on the individual itself

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