What makes a board director better connected? Evidence from graph theory

Laleh Samarbakhsh1 and Boža Tasić2

  1. Ted Rogers School of Business Management, Ryerson University
    350 Victoria Street, Toronto, ON, Canada M5B 2K3
    lsamarbakhsh@ryerson.ca
  2. Ted Rogers School of Business Management, Ryerson University
    350 Victoria Street, Toronto, ON, Canada M5B 2K3
    btasic@ryerson.ca

Abstract

We are interested in quantifying and uncovering the relationships that form between the board directors of companies. Using these relationships we compute three network centrality measures for each director in the network and employ them in the analysis of connectedness of directors. Our focus in this study is on the attributes that make a board member better connected. The biological, educational and experiential attributes are used as independent variables to develop a regression model measuring the impact on the three connectivity measures (degree, betweenness and closeness). Our results show that “Age” has a direct significant impact on all connectedness measures of a board member. We also find that female directors have a higher measure of degree centrality and betweenness centrality, but lower closeness. The number of foreign degrees increases the degree centrality and be- tweenness centrality but not closeness. The three identified characteristics of “Age”, “Gender”, and “Education” are supporting the idea that a high level of social connection can in part be expected by the characteristics of individual board members and can explain up to 25% of the board member’s connectivity.

Key words

Board of director networks, Centrality Measures

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS190628045S

Publication information

Volume 17, Issue 2 (June 2020)
Year of Publication: 2020
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

DownloadAvailable in PDF
Portable Document Format

How to cite

Samarbakhsh, L., Tasić, B.: What makes a board director better connected? Evidence from graph theory. Computer Science and Information Systems, Vol. 17, No. 2, 357–377. (2020), https://doi.org/10.2298/CSIS190628045S