ABSTRACT
Objective:
the literature on board interlocks has increased in recent years, focusing on understanding board composition and its relationships with other companies’ boards. Such studies usually require multiple procedures of data extraction, handling, and analysis to create and analyze social networks. However, these procedures are not standardized, and there is a lack of methodological instructions available to make this process easier for researchers. This tutorial intends to describe the logical steps taken to collect data, treat them, and map and measure the network properties to provide researchers with the sources to replicate it in their own research. We contribute to the literature in the management field by proposing an empirical methodological approach to conduct board interlocks’ research.
Proposal:
our tutorial describes and provides examples of data collection, directors’ data treatment, and the use of these data to map and measure network structural properties using an open-source tool - R statistical software.
Conclusions:
our main contribution is a tutorial detailing the steps required to map and analyze board interlocks, making this process easier, standardized, and more accessible for all researchers who wish to develop social network analysis studies.
Keywords:
board interlocks; social network analysis; tutorial
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This paper describes these overall steps for the conducting of board interlock research using the databases of Brazilian companies.
This figure presents the centrality degree of our network provided by #Step10. It can be replicated with script “324/degree network graph.”
This figure shows the distribution of degree centrality in our sample, as provided by #Step10. It can be replicated with script “342/degree histogram.”
This figure shows those companies with higher betweenness centrality as more prominent nodes. These companies act as a bridge in the network, joining different companies by sharing their directors. It can be replicated using #Step10 with script “350/betweenness network graphs.”
This figure presents the companies with higher closeness centrality as more prominent nodes. These companies have many direct connections with other companies by sharing their directors. This can be replicated using #Step10 with script “368/closeness network graphs.”