›› 2017, Vol. 29 ›› Issue (11): 74-88.

Previous Articles     Next Articles

Research on Cascading Failure Model of Organization-Routine Interdependent Technological Innovation Network

Wei Long, Dang Xinghua   

  1. Economics and Management School, Xi'an University of Technology, Xi'an 710054
  • Received:2015-04-28 Online:2017-11-28 Published:2017-11-25

Abstract:

Given the coupled interdependent relationship between organization and routine in innovation, this paper proposes an organization-routine interdependent technological innovation network architecture. Then this paper establishes a dynamic cascading failure model with the definition of node load, capacity and the failure mechanism between organization network and routine network by simulation, which is caused by uncertain risk with organization and routine failure. Lastly, the cascading failure model is illustrated through an example. The results show that organization and routine node have capability threshold. The uncertain risk may cause cascading failure in the interdependent technological innovation network when the capability value cannot reach a certain threshold; This threshold has approximate U-shaped correlation with coupling strength, so there is an optimal value of coupling strength; Symmetrical homogeneous topological structures can cause more damages than heterogeneous dependency structure; With load induced organization and routine node, the way of disassortative coupling can improve the robustness of organization-routines interdependent technological innovation network to defend against cascading failures; When coupling strength is less than the optimal value, highest-degree and random failure are more prone to lead cascading failures than lowest-degree failure. Otherwise, three failure modes have no significant difference. The results have significant meaning for improving the anti-risk capacity of technological innovation network.

Key words: technological innovation network, network organization, organizational routines, cascading failure, interdependent network