Inter-technology networks to support innovation strategy: An analysis of Korea’s new growth engines
Sungjoo Lee
Department of Industrial & Information Systems Engineering, Ajou University, Kyungki-do, Republic of Korea
Moon-Soo Kim
Professor, School of Industrial & Management Engineering, Hankuk University of Foreign Studies (HUFS), Kyongki-do, Republic of Korea
PP: 88 - 104
Abstract
As the interactions between technologies increase during the innovation process, which is well described in the concept of fusion technology or a multi-technology industry, recent innovation research has given much attention to inter-technology networks. This study reviews two techniques to develop such networks using patent data, a patent interaction network (PIN) and a patent citation network (PCN). This review's purpose is to understand their features and to argue that the two techniques can be complementary. It also tries to explain how the techniques can be used individually and collectively to provide useful information for innovation strategy through a case study. The case study focuses on Korean innovation strategy and the relations between 17 new growth engines announced by the Korean government. The analysis of the results is expected to help understand the hidden dynamics of technology interactions during the innovation process and will ultimately support to develop innovation strategy.
Keywords
Inter-technology network, patent interaction network, patent citation network, innovation strategy, patent citation, competition, Korea
Article Text
Innovation happens everywhere, and organisations need to continue to create innovation in order to survive. At the firm level, companies try to develop their ability to innovate, on which their future success depends (Christensen, 1997; Christensen & Raynor, 2003). At the government level, how to design policies that can stimulate innovation is a hot issue. As a result, a large amount of literature has emerged dealing with various aspects of innovation (Fagerberg, 2004) and many new research units to study innovation have been formed in recent years (Fagerberg & Verspagen, 2009). One of the main streams of this research is using networks to understand the innovation process. A network is described as a specific type of relationship linking a set of persons, objects or events (Knoke & Kuklinski, 1983). Therefore, the types of networks may vary according to the object of research, although the most common type is a network between firms to examine their innovation activities or a network between individuals to understand the diffusion process.
Out of various networks, recent research has focused on inter-technology networks that propose to consider the relations between technologies, as the interactions between technologies are increasing during the innovation process. This trend has been based on the following two reasons. First, the interactions between technologies are outstanding, particularly in fusion technology or converging technologies, most of which are regarded as innovators of new growth for decades to come. For example, nanotechnology is difficult to pin down as one discrete field since it is not basic science and is not confined to one industry. Instead, it is an enabling set of technologies that cuts across versatile industry sectors (Islam & Miyazaki, 2008). To investigate the innovation system in such technologies, inter-technology networks will be greatly helpful. Secondly, as products and services become increasingly multi-technology-based, so do the corporations that produce them. A multi-technology corporation needs to continuously manage and innovate different technologies (Pavitt 1998), each with varied life cycles, and each becoming ever more complex (Granstrand & Sjölander 1990). The high-technology requirements of such industries necessitate high levels of R&D, and thus organisations in such industries need information about the relations between technologies to understand their innovation systems and processes. Using the inter-technology networks, companies will be able to make better strategies for innovation, especially R&D decisions about where to invest among various technologies.
Inter-technology networks have mainly been developed based on patents. Patent documents are an ample source for technical and commercial knowledge of technology progress and innovative activity (Ernst, 2003) and have long been applied to understand the linkages between industries, nations, or technologies in terms of technological innovations and knowledge flow. Several quantitative techniques such as co-classification analysis (Choi et al., 2007), citation analysis (Choi & Park, 2008), and co-word analysis (Lee et al., 2008), have been suggested to develop networks, and the techniques have been applied to diverse industry sectors to find implications from the networks.
Traditionally, citation analysis is a popular technique to examine the relations between technologies. The analysis focuses on knowledge flow measured by patent citations to define the relations between technologies and thus can explain the direct interactions between them. Yet this analysis addresses only positive interactions and is a static model. Hence, using this model, it is difficult to describe dynamic evolutionary processes based on technological interactions that can cover both positive and negative effects. To overcome the limitation, a patent interaction network (PIN) was proposed and applied to information and communications technology (Lee et al., 2009). A PIN is an analysis technique that emphasises competitive interactions between technologies, working as a dynamic model. Nevertheless, it still is subjected to a limitation in that the information a PIN gives is only the possible existence of relations, as a correlation coefficient does, and not the context of the relations, which might be more important in innovation studies.
When used together, however, they can be more powerful tools to analyse inter-technology networks complementing each other, since a PIN was proposed to overcome the limitations of citation analysis while, in turn, the characteristics of citation analysis can make up for the weak points of PIN. The previous studies on inter-technology networks have attempted to suggest new techniques based on graph theory and to apply them individually to understand innovation patterns. On the contrary, few studies have investigated several techniques which can be used to develop inter-technology networks at the same time to compare the strength and weakness of them or to find ways to extract meaningful implications from the combinations of results attained from implementing them.
We believe that the two techniques can provide more useful information to understand the innovation process when used together and suggest ways to use them. First, this research reviews the two network analysis techniques, a PIN and a patent citation network (PCN), and then develops two inter-technology networks to understand their features. Finally, it describes how the two techniques can be used together to be a complementary tool with a real case study. Here, the case study focuses on the Korean innovation strategy, which was announced in January 2009 to promote innovation in Korea. The Korean government has published 17 new growth engines, which will be intensively fostered as strategic industries through policy. With the spirit of exploration, this paper attempts to analyse such innovation strategy by conducting an empirical study on the 17 engines and so to discover how the two techniques can be applied to revitalising the innovation process in Korea. The research output is expected to help understand the hidden dynamics of technology interactions during the innovation process based on inter-technology networks and will ultimately support to develop innovation strategy and to make decisions on R&D investments in open innovation environments.
The remainder of this paper is organised as follows. The basics of PIN and PCN as the theoretical background of this research are briefly reviewed in Section 2. Based on that review the research framework is designed in Section 3, where the inter-technology networks for Korea's new growth engines are developed. The networks are studied in detail to support Korean innovation strategies in Section 4, and Section 5 draws conclusions from our research and suggests directions for future studies.
2. Patent-based innovation networks
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