Evaluating Efficiency of Collective Learning in Innovation Networks: Simulation based Experiments in SKIN

Abstract

Background/Objectives: Innovation networks aim to increase collective learning and are a means of creating innovation. Different policies are made to increase the efficiency of these networks. These policies have several financial consequences for policy-makers. One of the most low-cost policies is a policy to increase collaborations and communications among network members, which leads to the improvement of collective learning and network efficiency. Methods: The present paper aimed to study the effect of increased network collaboration and communication on network efficiency through experiments based on simulations in the SKIN framework. In addition, the effect of density, as the notion of network complexity, is also examined in this regard. The analysis performed on simulated data using the structural equations method. Results: The results of simulation and structural equation modeling revealed that density is a variable with mediating and moderating effect on the relationship between support for collaboration and network efficiency. Application: The results of this research could be used by policy makers who want to use support for collaboration a policy to improve network efficiency. Although the increase in collaboration could increase efficiency but the more increase in it could increase network complexity and though decrease network efficiency.