Social Selection Mechanism in Corporate Governance Network

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This paper investigates network structures that evolved as a result of the executive and non-executive directorship selection processes in a Danish corporate governance context. Primarily, the paper focuses on discussion of legitimacy of a board and top management selection processes that emerge on two fronts – supervisory board selection of cooperatives, and their choice of executive directors (top-management). The paper utilizes the exponential random graph models on Danish corporate governance data for the period of five years (2010-2014) to reveal network structures in order to estimate tendencies for the social selection processes. This method is applied to enable unfolding of a discussion on how different interests and scarce resources over preferable social
actors (team members) create network dynamics, and how legitimate those processes are. Findings show that corporate governance network in Denmark tends to evolve around the most active top-managers and supervisory board members, while homophily effect related to knowledge and experience of actors plays significant role only within the supervisory board level, while not in the network between supervisory board members and top-managers.
Original languageEnglish
Publication date9 Sep 2019
Number of pages194
Publication statusPublished - 9 Sep 2019
Event4th European Conference on Social Networks - ETH, Zurich, Switzerland
Duration: 9 Sep 201912 Sep 2019
Conference number: 4


Conference4th European Conference on Social Networks
OtherThe 4th European Conference on Social Networks (EUSN 2019) will be held in Zurich, 9-12 September 2019. Continuing the traditions of previous conferences in Barcelona (2014), Paris (2016), and Mainz (2017), and the legacies of predecessors Applications of Social Network Analysis (ASNA) and UK Social Network Analysis (UKSNA), the conference will bring together researchers and practitioners from the social sciences in the broad sense as well as statistics, computer science, data science, physics, economics, humanities, and other areas dealing with network science.
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