ScholarWorks@숙명여자대학교

초록

Literatures on open innovation have two major limitations. First, either on a firm level or on an industry level didthey analyze the open innovation issues. The results of a firm’s innovation can be diffused through the whole networkand the firm can learn back from the network knowledge. Prior literatures did not consider the feedback loop amongfirms and network in which the firms are involved. Second, most open innovation research had a static perspectiveon firm’s innovation performance. Since the diffusion, spill-over and learning among network members are involvedover time, the open innovation is intrinsically dynamic. From the dynamic perspective, we can appreciate the fundamentalattributes of the open innovation network which involves diverse firms, research institutes, and universities. In order to overcome the limitations, we suggest a dynamic model for open innovation network. We build an agentbasedmodel which consists of heterogeneous firms. The firms are connected through a scale-free network which isformed by preferential attachment. Through the diverse scenario of simulation, we collect massive data on the firmlevel and analyze them both on firm and industry level. From the analysis, we found that, on industry level, the overallperformance of open innovation increases as the internal research capability, absorptive capacity, and learning curvecoefficient increase. Noticeably, as the deprecation rate of knowledge increases, the variability of knowledge increases. From the firm level analysis, we found that the industry-level variables had a significant effect on the firm’s innovationperformance lasting through all the time, whereas the firm-level variables had only on the early phase of innovation.

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