Network Centrality and Performance: Effects in the Automotive Industry
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Cómo citar

Squarsado Ferreira, A., Sacomano Neto, M., Eduardo Alvarez Candido, S., & Mendonça Ferratti, G. (2022). Network Centrality and Performance: Effects in the Automotive Industry. RBGN Revista Brasileira De Gestão De Negócios, 23(4). https://doi.org/10.7819/rbgn.v23i4.4132

Resumen

Purpose – This paper seeks to assess and analyze the relationship between indegree centrality and organizational performance in the automotive industry. In other words, we look at whether the network position is related to the performance of the actors using production, revenue, and profit indicators.

Theoretical framework – This research uses social network analysis as a method/theoretical approach allied with relational capital.

Design/methodology/approach – A similarity assessment was carried out. Data were collected from 1359 relations across four specific governance structures between 2011 and 2013. Later, the same measures were implemented in subgroups detected with the Louvain method. The NodeXL, UCINET, and SPSS software were used for the graphs, metrics, and correlations, respectively.

Findings – The results show a moderate to strong correlation between the actors and the subgroups formed by them, with their respective revenue and the indegree centrality for the three years selected. Our conclusions were that the centrality of an automotive manufacturer is positively related to its performance.

Practical & social implications of research – This study might help organizations evaluate social network analysis usage as a tool for understanding their opportunities given their network.

Originality/value – This paper contributes to the literature by indicating that in the automotive industry, formed by alliances between different governance structures, the structural position a manufacturer occupies in the network is related to its performance indicators.

Keywords – automotive industry; interorganizational network; social network analysis; performance measures; similarity analyses.

https://doi.org/10.7819/rbgn.v23i4.4132
pdf (English)
pdf (Português (Brasil))

 

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