Technological Innovation Performance Analysis Using Multilayer Networks: Evidence from the Printer Industry

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Technological Innovation Performance Analysis Using Multilayer Networks: Evidence from the Printer Industry
Lee, Jung-Min
Woo, Han Gyun
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Graduate School of UNIST
The importance of collaboration and technology boundary spanning has been emphasized in other inquiries into technological innovation. Therefore, this research project first tried to investigate the effect of collaboration on technology boundary spanning. Then, we investigated the effect of collaboration and technology boundary spanning on technological innovation within a firm by using a multilayer network to analyze patent data. The aim of this paper is to provide new insight into the process of analyzing patent data using multilayer networks. This empirical study is based on a sample of 408 firms within the printer industry from 1996 to 2005. Starting with a theoretical discussion of R&D collaboration, technology boundary spanning and innovation performance, the importance of a firm’s collaboration and technology boundary spanning in its technology innovation performance was empirically analyzed using patent data. We followed changes in collaboration networks, technology class networks and the connection between them and tried to find the meaning of those changes in firms’ technology innovation performances. We used degree centrality within the collaboration network and the ratio of collaborated patents to the total number of patents in order to measure a firm’s collaboration and formulated technology boundary spanning represented by exploitation and exploration by using edges of the multilayer network. As dependent variables, we used the number of patents and the average number of citations received over three, five, and 10 years to measure the firm’s quantitative and qualitative innovation performance respectively. The results of the analysis can be summarized as follows: a firm’s collaboration has positive effects on both exploitation and exploration. Firms with more collaborations show higher quantitative innovation performances while firms with more collaborations exhibit lower qualitative innovation performance. Exploitation has a positive impact on a firm’s quantitative innovation performance while exploration has negative effects on a firm’s quantitative innovation performance. The relationship between a firm’s exploration activities and a firm’s qualitative innovation performance manifests as an inverted U-shape. On the other hand, a firm’s exploitation activities have a U-shape relationship with the firm’s qualitative innovation performance. The implication of this study is that multilayer networks can be used to analyze patent data. This study used multilayer networks to formulate the exploitation and exploration only. However, in further research it can be utilized to find the hub firms that fuse technologies.
Department of Management Engineering
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