Abstract
Organizations create networks with one another, and these networks may in turn shape the organizations involved. Until recently, such complex dynamic processes could not be rigorously empirically analyzed because of a lack of suitable modeling and validation methods. Using stochastic actor-oriented models and unique longitudinal survey data on the changing structure of interfirm production networks in the automotive industry in Japan, this paper illustrates how to quantitatively assess and validate (1) the dynamic micro-mechanism by which organizations form their networks and (2) the role of the dynamic network structures in organizational performance. The applied model helps to explain the endogenous processes behind the recent diversification of Japanese automobile production networks. Specifically, testing the effects of network topology and network diffusion on organizational performance, the novel modeling framework enables us to discern that the restructuring of interorganizational networks led to the increase of Japanese automakers’ production per employee, and not the reverse. Traditional models that do not allow for interaction between interorganizational structure and organizational agency misrepresent this mechanism.
Highlights
It has been recognized that the system of industrial production cannot be realistically reduced to the behavior of individual organizations, quantifiable by traditional atomistic approaches
We focus on the supply network aspect of the Japanese automakers’ interorganizational networks because supply relationships are crucial for organizations, automobile assemblers and manufacturers, that subcontract a large proportion of their production (Dyer 1996)
Stochastic actor-oriented modeling did not reveal any particular preference for creating and maintaining dense connections within separate network cliques, which would be expected if keiretsu considerations had continued to constrain procurement strategies
Summary
It has been recognized that the system of industrial production cannot be realistically reduced to the behavior of individual organizations, quantifiable by traditional atomistic approaches. Network conceptualizations of interorganizational relationships have become widely accepted (Borgatti and Foster 2003; Lomi and Pattison 2006; Trapido 2013; van de Bunt and Groenewegen 2007). Matous and Todo Applied Network Science (2017) 2:5 and that the researched systems are stationary or in equilibrium conditions (Ahuja et al 2011; Lomi and Pallotti 2012; van de Bunt and Groenewegen 2007). Since the first studies in the field, networks in organizational research have traditionally been treated either as independent variables explaining organizational outcomes or, less often, as dependent variables of organizational processes (Ahuja et al 2009, Borgatti and Foster 2003). The available tools were unable to simultaneously treat dynamic networks as both dependent and independent variables in organizational processes, and researchers had to adopt one of two perspectives: either assume environmental/structural determinism or emphasize organizational strategy and individual agency
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