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Governance factors in a network process approach

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  • Benassi, Mario

Abstract

Network approaches have been gaining growing support, since they seem to grasp the profile of modern organizations, which are increasingly embedded in a sea of relationships and alliances. However, most of these approaches are static, ignoring the time dimension and failing to explain how change and stability are intertwined in the evolution of networks. In this paper I suggest a focus on the dynamics of these complex systems and argue that a network-process approach provides a profitable avenue to a better understanding of firm profiles. In my view governance factors constitute a key building-block on the way towards a process approach, since they are responsible for change and stability in networks over time. Governance factors constitute rules and principles recursively used to model networks, which are neither perfectly planned nor spontaneous structures. Two main components of governance factors are distinguished: the network theory of the firm and the modes of control. These two variables generate a 2 x 2 matrix, in which four possible network patterns are identified. In conclusion it is claimed that governance factors can provide an elegant way of representing dynamic processes and identifying the patterns of different networks.

Suggested Citation

  • Benassi, Mario, 1995. "Governance factors in a network process approach," Scandinavian Journal of Management, Elsevier, vol. 11(3), pages 269-281, September.
  • Handle: RePEc:eee:scaman:v:11:y:1995:i:3:p:269-281
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    Cited by:

    1. Rachel LEVY & Damien TALBOT, 2010. "Control by proximity: evidence from Aerospace Valley competitiveness cluster (In French)," Cahiers du GREThA (2007-2019) 2010-08, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    2. Sara Saggese, 2016. "Governance of Industrial Districts: Bibliometric and Cluster Analyses," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(9), pages 1-36, August.
    3. Yu-Hsin Chang & Kuei-Kuei Lai & Chien-Yu Lin & Fang-Pei Su & Ming-Chung Yang, 2017. "A hybrid clustering approach to identify network positions and roles through social network and multivariate analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1733-1755, December.

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