Abstract
Since the beginning of the last century, the interest of economic science has been drawn to corporate interconnections, their connection with the financial system, the formation of alliances, and their relationship with political power. With the development of mathematics, especially graph theory, this interest was boosted. This chapter is intended as an introduction to matters related to the interconnection of corporates. Firstly, graph theory is analyzed at a rudimentary level, and its several forms are described that have been employed by economics, and social sciences in general. Two basic kinds of relationships are distinguished, which are then further specialized: interlocking directorate and interlocking ownership. An analysis is offered of these two aspects and their significance, along with an overview of the bibliography. The several forms of corporate interlocks existing worldwide are then presented in a broad outline. Following this, we focus on the cases found in Greek business networks. The lowest level of interconnections obtains, compared to the rest of Europe, and the similarities with the rest of the European countries are noted in the logic of the network’s articulation. Finally, the chapter concludes with the possibilities and prospects of research into social network analysis in business.
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Notes
- 1.
- 2.
In mathematics, graph theory is the study of graphs, i.e., mathematical structures used to produce models of the relationships between objects.
- 3.
A node’s linked part is that part of the graph comprised by the node itself and all the other nodes that it may reach through paths delineated along the edges. The distance between two nodes is the number of edges connecting them, while the shortest path connecting two nodes (in the sense of crossing the fewest edges) is known as geodesic.
- 4.
When the links approach the maximum number of connections between the nodes, then the network is designated as thick, otherwise as sparse.
- 5.
Such indicators are, the degree of centrality, closeness of centrality, betweenness centrality, and eigen vector centrality. The first concerns the sum total of tips that are connected inside a node; the second, the average length of the smallest route between the node and all the other nodes in the graph; the third defines the times when a node operates as a bridge along the shortest route between two other nodes; and the last one measures a node’s influence in the network, defined by the value of the nodes to which it is connected.
- 6.
In the mathematical field of graph theory, this constitutes a bipartite graph whose vertices can be divided into two separate and independent sets so that each edge connects a summit of one set with a summit of the other.
- 7.
In this type of network, the connections are generated always between nodes of a different type, but it is also possible, through the analysis of two distinctive unipartite graphs, to more fully depict the ties that develop inside the network: between corporations, or between directors (or owners) but also between the two.
- 8.
The two main qualities that characterize a small-world, according to the Watts-Strogatz model (Watts & Strogatz, 1998), which is a specific category of small-world networks of random graphs, are the concepts of the mean path length—which is to say randomly selected pairs of nodes that turn out to be unexpectedly close to one another—and high clustering—the tendency for nodes of the network to be in the same “neighborhood”. By contrast, according to the Erdős-Rényi model (Erdos & Renyi, 1959), the Poisson random graphs show a small index of clustering—inconsistent with the observed real social networks (Watts & Strogatz, 1998).
- 9.
Renowned psychologist Stanley Milgram (1933–1984) was the first to analyze the issue of the small-world (Milgram, 1967) through the mean number of connections between two individuals randomly selected from a population. From this research, the idea emerged that any two people (in the United States) could be connected through a chain of six contacts (at an average). An important offshoot of this work is the investigation of several sets of random graphs (Newman et al., 2001).
- 10.
These concerns ownership structures such as pyramid shareholding, one-sided shareholding, reciprocal shareholding, and cyclical shareholding.
- 11.
As a simple example of composite ownership structure, individual A owns part of the stocks of Company B, which is also the part owner of Company C. Even though A is not a shareholder of (so not directly interested in) Company C, he is an indirect “owner” and, thus, benefits from its revenue because of Company B.
- 12.
This rule can be summed as follows: if company A owns 10% of company B’s shares and company B owns 20% of company C, then, company A controls 2% of company C.
- 13.
The distinction concerns liberal market economies (LME) versus the coordinated market economies (CME). In LME, companies plan their activities mainly based on markets and hierarchies, while in CME, they are more dependent on the relationships outside the market. Also, in LME, companies turn to financial markets for investment capital and, as a result, transparency is important and stock prices are a primary criterion of company performance. By contrast, in CME, companies are funded by debt and banks play an important role, while there are close-knit ties between banks and industrial companies. In this case, reputation and trust, rather the price of stocks, are important criteria of company performance.
- 14.
The most characteristic case of “big linker” is that of the German Cromme G. who has a position on the boards of nine companies simultaneously.
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Vasilis, G. (2023). Networks in Ownership and Management Structures. In: Petrakis, P.E. (eds) Interconnections in the Greek Economy. The Political Economy of Greek Growth up to 2030. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-31335-6_4
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