Comparing a spatial structure of innovation network between Korea and Japan: through the analysis of co-inventors network

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1 Asia-Pac J Reg Sci (2017) 1: DOI /s PERSPECTIVES ON SPATIAL DYNAMICS: CITIES, CULTURE AND ENVIRONMENT Comparing a spatial structure of innovation network between Korea and Japan: through the analysis of co-inventors network Hwajin Lim 1 Tetsuo Kidokoro 2 Received: 24 November 2016 / Accepted: 19 May 2017 / Published online: 2 June 2017 Ó The Japan Section of the Regional Science Association International 2017 Abstract This study clarifies the relationship between spatial proximity and innovation networks by analyzing and comparing Korean and Japanese innovation systems. These systems have their similar backgrounds, but different innovation policies. The structural features of these innovation networks include hubs, sub-groups, and islands with pipeline linkages. The innovation network is formed by mixing of organizational proximity and geographical proximity within urban systems. Therefore, innovation policy should consider these network structure and urban system patterns, with flexible network governance. In addition, the innovation network can be successfully fostered through promotion policies within a single region rather than as distributions across regions. Keywords Social network analysis Joint patents Knowledge networks Innovation policy South Korea Japan 1 Introduction Sustainable innovation can be seen as key to economic development in a rapidly changing modern knowledge economy. Accordingly, Cooke and Morgan (1998) has focused on innovation networks as a determinant of innovation with associated knowledge flows. The network innovation model from collaborative economy & Hwajin Lim hwajin@tcu.ac.jp Tetsuo Kidokoro kidokoro@urban.t.u-tokyo.ac.jp 1 2 Faculty of Urban Life Studies, Department of Urban Life Studies, Tokyo City University, , Todoroki, Setagaya, Tokyo , Japan Faculty of Urban Engineering, Department of Engineering, University of Tokyo, Hongo, Bunkyo-ku, Tokyo , Japan

2 134 Asia-Pac J Reg Sci (2017) 1: studies in sustainable innovation examines the issue of fostering sustainable innovation across a region. However, the situation in a rapidly changing local economy means that innovation change is very complex. Understanding the features of the space in which innovation occurs is a key to enhance regional innovation potential, particularly in modern mega city regions where space is not easily separated from external influences (Hall and Pain 2006). Few empirical analyses regarding spatial innovation model requirements have been conducted. In particular, the influence of space on knowledge flows that lead to innovation have not been analyzed empirically to determine the actual relationship between network space and innovation. On the other hand, there are many policies for encouraging innovation in various aspects of regional development. The OECD (2009) has reviewed methods for transforming spaces within regional innovation clusters while various other actors have implemented these ideas. The Netherlands and Germany have developed the regional innovation system as a central element in their long-term regional spatial planning programs. More specifically Korea has developed a regional innovation system as part of its regional development vision, (APCTT-ESCAP 2011). Similarly, Japan s innovation systems are discussed in terms of regional specific industrial policy and local industrial clusters. This study focuses on the similarities and differences in innovation policies and the spatial innovation models used in Korea and Japan. Korea and Japan are similar in promoting innovation system, but they differ in terms of the method of distribution and promotion of policy. Korea has promoted the theme of centralization and selection while Japan maintains its traditional distribution strategy. Despite this governmental and agency concern in fostering innovation, this top down implementation has not achieved the expected results. This study takes an empirical approach by analyzing both formation factors and the actual situation of the spatial structures of innovation networks in Japan and Korea. Comparing Japan and Korea, who have similar socio-economic conditions but different innovation policy, allows an investigation of networks with structural and spatial differences and their related challenges in regional planning. 2 Theoretical review of regional innovation First part of this review focuses on spatial and territorial innovation models and the role of space plays in generating innovation and knowledge spillover with respect to the learning process. The second part focuses on the actual exchange of knowledge with theory and studies concentration on innovation networks. The third part looks at the relationship of innovation and proximity. At the end, research questions are specified and hypotheses generated. 2.1 Territorial innovation model as spatial innovation theory Research in economic geography has highlighted various knowledge creation practices and the role of innovation as a driving force in economic growth and

3 Asia-Pac J Reg Sci (2017) 1: industry development since the 1990s (Dosi et al. 1988; Lundvall 1992; Cooke and Morgan 1994; Cooke et al. 1997, 2002; Park 2001, 2003; Cooke 2002; Porter 2003). Regional innovation systems belong to territorial innovation models, which include cluster models among others (Moulaert and Sekia 2003). The learning region model in regional perspective can also be included in territorial innovation models (Cooke and Morgan 1993). Social network analysis assumes that a local economy can benefit through innovation by learning and networking, which leads to knowledge sharing. On the other hand, spatial clusters, as highlighted by M. Porter, strengthen the external effect of geographic proximity so that it becomes a source of cluster competitiveness. In recent years, industrial cluster policy has grown to include not only simple industrial agglomerations, but also activation of networks for innovation, placing a priority on the interaction between actors as part of innovation clusters. Regional innovation systems are regarded as opportunities to facilitate easy information exchange between regional actors. 2.2 Innovation networks Innovation networks start from a discussion of economic activity with embedded social networks in a knowledge economy (Granovetter 1985; Uzzi 1997). An innovation network is a network of development products, services, new knowledge, cooperation, and mutual exchange between major economic actors that contributes to innovation (Camagni 1991). In addition, new economies are based on technology and knowledge through a innovation network (Castells 1999). Innovation networks have become increasingly important in overall economic activity not only as consumption, but also as components of production networks. Previous studies examining the role of networks in creating innovation have focused on finding effective network structures (Hong et al. 2009). Other research has explored the requirements for effective innovation networks, identifying a process or series of activities by various innovation actors exchange resources in a mutually complementary manner through communication (Choi 2010). 2.3 Innovation and proximity Proximity is regarded as sufficient to generate innovation (Boschma 2005). The current study examines networks in physical proximity, and considers how similarities or differences in the spatial distribution of sub-groups in a network effects innovation. Moarrocu et al. (2013) examined the relationship of innovation and proximity using a dataset of R&D and human capital across the 276 EU regions. According to the results, geographical proximity is important but less than technological and cognitive proximity. Social and organizational proximity are also significant, but their role is not extraordinary. Broekel and Boschma (2012) found some evidence that cognitive, social, organizational and geographical proximity were crucial for explaining the

4 136 Asia-Pac J Reg Sci (2017) 1: knowledge network. Cromarias and Alexandre (2013) insist that geographic proximity is necessary but not sufficient. Boschma (2005) and Rallet and Torre (1999) show that organizational and cognitive proximity may substitute for geographical proximity. On the other hand, technological evolution has resulted in the increase in mobility of people which in turn facilitates temporary geographical proximity (Torre and Rallet 2005). Lorentzen (2008) disagrees with the basic premise of territorialized innovation theories, which state that a region affects the innovativeness and competitiveness of firms. Rather Lorentzen claims that it is firms who have a hold on their capabilities and network environment, not regional networks and institutions, that drive innovation. 2.4 Research questions Following reviews of previous research, many authors have elaborated on the relationship of spatial factors and innovation with analysis of proximity, networks and regional innovation systems. From these theoretical elaborations, the following research questions were derived. Research questions 1: what is the model of the spatial innovation network? What kinds of features do these models have? Research questions 2: Korea and Japan have similar backgrounds but different regional policies. In addition, they have different situations related to the spatial innovation model such as urban systems. What factors influence spatial innovation models? Research questions 3: an innovation network is formed by both proximity, social and geographical characteristics but its appearance is complicated by physical space. How does each of these characteristics affect the spatial structure of innovation network itself? 3 Current history of innovation policy in Korea and Japan Korea s industrial structure is very similar to that of Japan s, and both countries have experienced shifts in major industrial activities from secondary to tertiary industries. For example, in case of the research funding for science and technology, Japan is about three times larger than Korea and has a longer record of innovation, although both countries exhibit similar GDP per capita. The recent growth rate of Korea s GDP has been faster than that of Japan. There are also many cultural and industrial organization similarities. These similarities and differences make these countries good candidates for a cross-comparison to examine the role of proximity in regional innovation patterns and policies. There are differences between Korea and Japan in terms of territorial innovation policies and regional industrial policies (Fig. 1). The first stage of innovation policies commenced during the high-growth period after World War II; however, these early policies did not incorporate spatial concepts. By the early 1990s, policymakers began to focus on innovation policies established via both science and technology policies and industrial policies. As a result, innovation policies in both

5 Asia-Pac J Reg Sci (2017) 1: Japan and Korea were transformed into policies promoting regional innovation systems. For example, industrial clusters, a trend that has become more prominent in the past decade, is clarified by the different approaches used in the two countries. Currently, Korean innovation policy focuses on spatial units, while policy makers in Japan apply innovation policy across the entire country. At the regional level, the spatial aspect of innovation policies in both countries has been considered as part of the regional planning, particularly in case of the science and technology policy and industrial location policy including projects such as the development of R&D centers. Japan s policies can be categorized into two classes: those promoting industrial clusters or those promoting intellectual clusters. Some studies have asserted that the regional level is simply too vast to effectively create the small-scale networks that innovation requires. However, not all regional innovation strategies are applicable equally across the entire region, but instead have various ranges. Previous regional industrial policies have defined their spatial boundaries to match their objectives. The selected regional unit is dependent on their objective, and a policy was promoted only inside that specific area. However, one consequence of this approach was an unintended separation between companies research institutions and their headquarters. Past president of Korea, Lee Myung-park, encouraged regions to expand greatly the areas covered by these policies. On the other hand, regional planning area in Japan was divided into eight regions. As a result, regional innovation policies were implemented on a wider scale. However, this policy approach risks spreading the inputs to innovation so thin that they lose effectiveness. Fig. 1 Innovation policy of Korea (upper part) and Japan (lower part) on each of the stages

6 138 Asia-Pac J Reg Sci (2017) 1: Overall, the biggest difference between Korea and Japan is their policy promotion systems. Korea prefers selection and concentration, while Japan chooses decentralization and distribution. Korea has a history of driving compression? economic development by Chaebol (large conglomerate) and innovation policy is primarily implemented by Chaebols. The central government also exerts a great deal of control over regional innovation policies. For example, only four regions were selected in the first stage of the regional innovation policy, and each region was selected for a limited range of industries as policy targets. This selection was intended to enhance policy efficiency, but it also presented local authorities with great control over the companies which would receive substantial government support, while other companies, perhaps with just as much potential, were excluded. Moreover, this system enabled the creation of competition between wide area administrations. On the other hand, the innovation policy of Japan used a decentralization and distribution oriented approach. For example, industrial districts and techno-polis policy were considered as part of national territorial planning; despite apparent similarities of regional distribution and industrial variety, these decisions were taken as part of industrial cluster policies coordinated at the national level instead of being devolved to the regions, resulting in lower risk of competing regions or focusing on specific industries. In addition, innovation networks were structured differently in each country. In the early stage of Korean S&T policy, increased funding was given to Korean universities, resulting in the emergence of innovation systems geared toward scientific innovation against the backdrop of growing S&T. However, the economic crisis of Korea encouraged the private sector, facilitating large firms to capture entire product production cycles, investing in research privately to completely own their findings. The innovation networks were therefore largely locked inside these companies. The Korean government strove to enlarge regional innovation capacity by encouraging SMEs and R&D in science and technology. In spite of these efforts, R&D capacity remains concentrated in large companies, and disparities in innovation capacities have not been eliminated. As a STEPI (national institution of Science and technology policy in Korea) has highlighted, the Korean government s path-dependent research policy relies on concentration and selection for effectiveness and ability to produce results in an acceptable frame (Cho et al. 2007). After 2000, President Roh shifted regional innovation policy to encourage SMEled innovation systems. Roh s government expanded programs and increased funding for programs implemented by the previous government. Even this policy encouraged bottom up regional innovation system, the entire economic structure has been focused on major companies. The next government, headed by President Lee, tried deregulating economic sectors to reduce this large company stranglehold on innovation policy resources.

7 Asia-Pac J Reg Sci (2017) 1: Methodology 4.1 Social network analysis Social network analysis integrates graph theories and information science, social studies approaches. Recently, some researchers used large-scale database named Big data processing to examine the various ways in which increasingly complex social networks impact. These originated in a network theory, which assumed that relationships explore not only attributes, but also structural features that consist of links and nodes. Therefore, network analysis is a flexible tool that can overcome the limitations of previous analyses that focused on individual attributes rather than on relationships. Network analysis contributes toward the examination from smallscale onto large systems, and makes it possible to analyze networks constructed by a particular individual or a global company. 4.2 Data collection and network database by joint patent data This research investigated network between patent inventors. Information on joint patents was obtained by gathering patent gazettes from websites of each government [Japanese government is from BTmTopPage, Korean data are from government websites at KIPRIS ( kipris.or.kr/enghome/main.jsp)]. This study focused on patent networks because they are regarded as good markers for innovation, and are rarely the result of original research. While patents have limitations as the unit of measurement, patent information is relatively easily obtainable and seeds of innovation networks can be detected from them (Fig. 2). For using municipality as the spatial unit of analysis of innovation networks in the community analysis, we logged with the inventor s address as spatial information, which we used to recreate the spatial structure of a particular patent network. Patents included two types of actor information: inventor and applicant. Applicant indicates those who own the intellectual property, while inventor recognizes the original invention s branch or research. Using co-inventor patent data as a patent network, we assumed that a relationship exists between inventors named together in same patent. By collecting both these pieces of information together with associated physical location, the database would be able to track areas where the invention is actually performed (Table 1). The concept of centrality is one of the indices most often used in network analysis. Centrality is an index for evaluating and comparing the importance of each Fig. 2 The method of data collection

8 140 Asia-Pac J Reg Sci (2017) 1: vertex in a network (Suzuki 2009). There are four major centrality concepts and indices: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. This study proposes two types of power nodes though centrality concepts: quantitative power nodes and structural power nodes. A quantitative power node has high degree of centrality, while a structural power node has a high level of betweenness centrality. Previous studies have employed the binary network indicator methodology; however, this study developed a weighted network methodology that could more accurately recreate the actual situation. The method of Opsahl et al. (2010) which tried to convert weight was used. They recommend using an alpha of 0.5 for advantage of weaker ties as well as the typical example of a knowledge network application. Degree centrality indicates the extent to which a node can be considered central to a network; it measures a node s number of links or ties, both incoming and outgoing, to the rest of the network. The larger the degree, the more central the node a is the weight aggregate for degree centrality (Opsahl et al. 2010; Freeman 1978). The following equation shows how you can determine the intensity a to aggregate the weight. This measure can be formalized as follows: ki ¼ CDðiÞ ¼ XN j x ij ; ð1þ where i is the focal node, j represents all other nodes, N is the total number of nodes, and x is the adjacency matrix, in which the cell x ij is defined as 1 if node i is connected to node j, and 0 otherwise. This measure has been formalized as follows: s i ¼ C W D ðiþ ¼XN j w ij ; ð2þ where w is the weighted adjacency matrix in which w ij is greater than 0 if node i is connected to node j, and the value represents the weight of the tie. Table 1 Overview of data Japan Korea Annual 2000, 2005, , 2005, 2010 Scale of data (Joint patent number)/(total number of patents) 00: 3879/ : 7451/ : 4624/ : 3245/ : 5039/ : 3251/32, 498 Data source Patent publication (40 45 per year, choosing 13 per year) KIPRIS (published patent) Data summary Undirected weighted network

9 Asia-Pac J Reg Sci (2017) 1: CD wa ðiþ ¼ki S a i ¼ k ð1 aþ i S a i k ; ð3þ i where a is a positive tuning parameter that can be set according to the research setting and data. If this parameter is between 0 and 1, then a high degree is favorable, whereas if it is set above 1, a low degree is favorable. Betweenness centrality, first proposed by Freeman (1978), measures the number of paths that pass a node if the network s shortest distance is followed. If a node s betweenness centrality is significant, the node s position is a broker that connects other nodes. In this study, the betweenness centrality is calculated as a weighted method (Opsahl et al. 2010): C wa B ðiþ ¼gwa g wa jk jk ðiþ : ð4þ Here, betweenness centrality is defined as the binary shortest distance to add clarity to our argument: dði; jþ ¼minðx ih þþx hj Þ; ð5þ where h are intermediary nodes on paths between nodes i and j. We define the length of the shortest path between two nodes, which incorporates the method for identifying it, as follows: d w 1 ði; jþ ¼min þþ 1 ; ð6þ w ih w hj d wa ði; jþ ¼min 1 ð Þ a þþ 1 a!; ð7þ w ih w hj where w is weight and a is a positive tuning parameter. Network community structure analysis is based on modularity. Modularity is the ratio of the number of inner community links in the entire links. Entire modularity Q can be obtained from the following equation. To test whether a particular division is meaningful, we define a quality function or modularity Q following Newman (2001). Q ¼ X i e ii X ijk e ij e ki ¼ X i e ii a 2 i ; k r a i ¼ X r2g i 2M ¼ X j e ij : ð8þ

10 142 Asia-Pac J Reg Sci (2017) 1: Let Q denote the fraction of edges that fall within communities, excluding the expected value of the same quantity if edges fall at random without regard for community structure. If a particular division gives no more within-community edges than would be expected by random chance, then Q = 0. Values other than 0 indicate deviations from randomness, and in practice values greater than approximately 0.3 appear to indicate existence of a significant community structure. 5 Results: features of regional innovation networks and the role of hubs 5.1 Network overview with socio-economic background In this part, we compared network densities of Japan and Korea. At first, Japan s network scale is larger than Korea s in all years. The year 2005 witnessed the highest network growth. Second, the average distance is short, indicating that both networks have a feature of small world network. The change of average degree at time scale, Japanese patent inventors are more interrelated than those in Korea. Both Japanese and Korean networks are multilayered and complex. This structure is related to transportation networks. For example, the Tokaido Corridor, from Osaka to Tokyo in Japan, formed its horizon axis along the Shinkansen line; the Gyeongbu vertical axis runs from Busan to Seoul in Korea. Japanese networks showed no significant change in the past decade, and the number of patents has not greatly changed. In contrast, in 2005, Korea witnessed a significant increase in the number of patents. One of the reasons of unstable values Fig. 3 Major locations of region in Korea and Japan

11 Asia-Pac J Reg Sci (2017) 1: Table 2 Overview of basic network indicators in Japan and Korea Japan Korea Number of nodes 31,464 51,500 29,232 52, ,238 45,328 Number of links 40,807 76,133 44,347 95, ,887 86,789 Average distance Density Average degree of 2005 is the effect of economic shocks and changes in patent policy. After that, the number of patents in 2010 is significantly higher than that in 2000 (Fig. 3; Table 2). 5.2 High dependency on metropolitan area In Korea, regarding concentrations of network distribution, dependency on the capital metropolitan area is high in the whole country. Specifically, the network dependency in early stages was distributed depending on the distance to Seoul, but dependence in 2005 was located throughout the country independent of the distance to Seoul. Regional dependence on Seoul metropolitan areas related to local branch of companies having a head office in Seoul. In addition, Seoul Metropolitan Area, which comprises Seoul and Gyeonggi Province, has 70% of the entire patent network. Accordingly, Seoul Metropolitan Area has been identified as a powerful region in Korea s patent network. On the other hand, Japanese network showed that Osaka, Toyota City, and Hitachi City as having low dependence on the metropolitan area, and Seirei-shitei city s (local metropolitan city) is low. It is because headquarters of large companies are located in geographical proximity to each other and form independent networks. Korea s Daedeok-ku and Yuseong-ku, in which the Daedeok research complex is located, are relatively independent. The growth of joint patents between Yuseongku and wards in Seoul in case of 2000 is significant. This trend indicates increasing of network connections to the metropolitan area. Moreover, Daejeon has a significant relationship between Busan and Gwangju, even those regions are far away. Overall, capital areas such as Tokyo and Seoul have large effects in the patent network, with ratios reaching more than 40% in all the time-series (Table 3). While the ratio of rural core cities metropolitan areas has decreased, Tokyo s ratio is gradually rising. Accordingly, the concentration trend in Tokyo implies that it can be regarded as a network destination from another city. In other words, there is a tendency for interaction to increase between large cities such as Tokyo. In addition, local metropolitan areas are primarily linked to Tokyo except in western Japan, where local cities are linked with Osaka, Kyoto, and Kobe metropolitan areas. This reflects the fact that Osaka, Kyoto, and Kobe have traditionally fulfilled a central function for western areas in aspects other than innovation.

12 144 Asia-Pac J Reg Sci (2017) 1: Table 3 The ratio of the joint patent network coupled with Tokyo and Seoul metropolitan areas Ratio (%) Japan Tokyo only (%) Tokyo Metropolitan Area (%) Korea Seoul only (%) Seoul Metropolitan Area (%) For other areas, a community on the Nagoya Metropolitan Area has formed a network among the Chubu region, and there are also associated networks with the Kansai Metropolitan Area. This pattern follows global trends of networks concentrated in metropolitan areas hosting major R&D institutions. 5.3 Power nodes As mentioned above, evaluating network structure, quantitative power nodes are measured by degree centrality, and the betweenness centrality is measured using structural power nodes. For identifying of the location of inventors, inventors locations were aggregated by administrative units. Next, we aggregated top 100 inventors on each parameters. This ranking indicates top 100 high centrality inventors based on centrality aggregated by administrative units (Table 4). The greater the value of centrality, the greater the centrality parameter acts as a bridge and hubs between groups. As shown in Table 5, the top 100 inventors are mainly located in the southern part of SMA and Daejeon. Across all periods covered in this study, inventors and their institutions located in Daejeon function as hubs. Southern SMA shows a similar status. However, this pattern has become more scattered since Inventors ranked in the top 100 belong to various prefectures. Therefore, an inventor in Kanagawa Prefecture can have the same level as Tokyo. Aichi prefecture is shown to have an increasing influence. In terms of the regional distribution of power node structures, Tokyo Metropolitan Area scored the highest in case of 2000, followed by Aichi and Kanagawa, Ibaraki, and Osaka Prefecture. However, Osaka and Kanagawa prefecture topped this list in 2005, followed by Ibaraki prefecture, Aichi prefecture, and Tokyo. In 2010, Aichi had become the Table 4 Quantitative power node and structural power node in Japan Degree power node Betweenness power node Tokyo 28 Tokyo 34 Tokyo 30 Tokyo 34 Tokyo 31 Tokyo 37 Kanagawa 22 Kanagawa 16 Aichi 16 Kanagawa 21 Kanagawa 13 Aichi 15 Ibaraki 10 Aichi 14 Kanagawa 11 Aichi 11 Osaka 12 Ibaraki 9

13 Asia-Pac J Reg Sci (2017) 1: Table 5 Quantitative power node and structural power node in Korea Degree power node Betweenness power node Yuseong, Daejeon 29 Yongin, Gyeonggi 17 Yuseong, Daejeon 33 Yuseong, Daejeon 23 Suwon, Gyeonggi 30 Yuseong, Daejeon 28 Suwon, Gyeonggi 7 Anyang, Gyeonggi 14 Yongin, Gyeonggi 9 Suwon, Gyeonggi 21 Yuseong, Daejeon 23 Suwon, Gyeonggi 12 Seocho, Seoul 6 Dongjak, Seoul 7 Seongnam, Gyeonggi 9 Icheon, Gyeonggi 7 Yongin, Gyeonggi 12 Yongin, Gyeonggi 8

14 146 Asia-Pac J Reg Sci (2017) 1: second largest region after Osaka. Top 5 regions (Tokyo, Kanagawa, Ibaraki, Osaka, and Aichi) occupied structurally important positions as hubs in their inventor networks. On the other hand, some power nodes entering into the top 100 rankings in the inventor network are located in Shizuoka prefecture, Kyoto, Saitama, and Chiba prefecture. Shiga and Yamaguchi prefectures witnessed a reduction in the number of nodes in the top 100. In addition, the strong relationship between Nagoya and Shizuoka indicates structural concentrations occurring in the Tokaido Corridor. In summary, while the quantitative data indicate that inventors were concentrated in the Tokaido Corridor as a hub, regional inventors continue to occupy important positions in the structure of the network mainly concentrated in metropolitan areas. In Korea, the Daejeon region has the highest concentration of quantitative power nodes, with 29 power inventors in 2000 and 33 power inventors in However, this trend changed by The quantitative power nodes of 2010 moved from Daejeon to the Southern region of SMA. Only few quantitative power nodes are seen in Pohang and across the local region. This pattern implies that inventors in the southern part of the Seoul Metropolitan Area, such as major corporations and research institutions, actively collaborated in relation to innovation. In terms of Japan, results of betweenness centrality and degree centrality indicate regional characteristics. Results of degree centralities indicate that hub inventors are primarily located in Nagoya, Tokyo, and Osaka. Meanwhile, the betweenness centrality indicates that inventors in Toyota are related to those in other metropolitan areas. In addition, Tokyo is strongly rooted as a structural hub. 5.4 Community structure with proximity In this part, the results of community structure analysis were discussed. The purpose of community structure analysis is detecting the sub-group on the co-patent network. This analysis is meaningful to combine the analysis of hub on the network for the spatial model of innovation network. To compare time-series, 30 communities in Japan and 15 communities in Korea were selected as optimal Q-values within the range of maximum values on the condition of a fixed number of communities. The maximum value of Q was detected between 0.4 and 0.5 each time, and the value in 2005 was higher in Japan. On the other hand, the Q-value in case of Korea in 2005 maintained the maximum value steadily from the early stage of clustering. Therefore, the latest maximum value was chosen to detect optical communities. Each step is 794, 896, 769 in Japan, and 170, 203, 180 in Korea (Table 6). Next, we discuss whether geographical proximity affected the spatial structure of communities. In Japan, the overall trend follows that of Tokyo, where communities covered a wide area. However, in Chubu and Kinki regions, community based on geographical proximity can be found. While main distribution in spaces followed metropolitan areas, some communities range beyond these borders. Each of the 30 communities followed either a close physical proximity pattern or a geographically scattered pattern. Communities having geographical proximity emerged as the overall majority trend. These communities were formed around major metropolitan areas (Figs. 4, 5; Tables 7, 8).

15 Asia-Pac J Reg Sci (2017) 1: Table 6 Maximum value and step of modularity Maximum Choice Q Step Q Step Japan Korea Fig. 4 Geographical distribution and structure of communities in Korea (different color indicates different community) Source: revised from Lim (2013) Communities can be found not only in major metropolitan city regions. Members might be physically distant, but sharing a network relationship in case of belonging to the same company. In addition, geographical patterns in metropolitan areas such as Tokyo, Osaka, and Nagoya showed closed groups distributed Kyushu communities on Western Japan. In contrast, network community structures in Korea drastically changed over several decades, and the network community structure of 2005 was particularly different from that seen in other decades for the lowest modularity Q. In 2000 and 2010, communities were formed in local areas with geographical proximity, whereas distant regions were included in the same community in The results of 2005 represent a very complex distribution of community structure in the form of nationwide mosaics. In 2000, communities mainly formed across adjacent areas, for example, Seoul, Chungnam, Ulsan, Busan, and Gyeongnam. In particular, Seoul had links all over the country without forming a cluster. Trends in 2010 and 2000 were similar, and the communities formed in 2010 represented geographical

16 148 Asia-Pac J Reg Sci (2017) 1: Fig. 5 Geographical distribution and structure of communities in Japan (different color indicates different community) Source: revised from Lim and Kidokoro (2013) Table 7 Geographical distribution and structure of communities in Korea (regional distribution of the top three groups of each decades) G3 Daegu-gyeongbuk 20 G1 Gyeonggi 12 G1 Chungchung 7 Other regions 5 Gangwon 12 Other regions 1 Other regions 16 G4 Seoul metropolitan region 44 G6 Jeonnam 9 G4 Jeonnam 19 Other regions 9 Other regions 25 Other regions 20 G10 Busan metropolitan region 36 G8 Busan-gyeongnam 14 G9 Busan-gyeongnam 27 Other regions 0 Other regions 29 Other regions 6 proximity compared with However, linkage space transcends physical space range of metropolitan and economic blocs, even as policy and administrative spaces expanded.

17 Asia-Pac J Reg Sci (2017) 1: Table 8 Geographical distribution and structure of communities in Japan (regional distribution of the top three groups of each decades) G1 Kanagawa, TMR 15 G8 Osaka, Kansai 32 G6 Aichi, Chubu 19 Tokyo, TMR 9 Kyoto, Kansai 14 Gifu, Chubu 10 Saitama, TMR 8 Nara, Kansai 12 Hyogo, Kansai 5 Other regions 41 Other regions 61 Other regions 27 G5 Aichi, Chubu 33 G15 Fukuoka, Kyushu 9 G20 Tokyo, TMR 10 Gifu, Chubu 14 Nagano, Chubu 9 Hyogo, Kansai 7 Mie, Chubu 6 Ōita, Kyushu 8 Saitama, TMR 7 Other regions 29 Other regions 28 Other regions 49 G17 Osaka, Kansai 28 G18 Saitama, TMR 22 G40 Osaka, Kansai 26 Hyogo, Kansai 17 Chiba, TMR 22 Hyogo, Kansai 13 Kyoto, Kansai 12 Ibaraki, TMR 16 Nara, Kansai 13 Other regions 59 Other regions 68 Other regions 70 TMR Tokyo Metropolitan Region 6 Discussion: spatial model of patent network 6.1 Model of network structure: hubs, sub-groups, and islands with pipelines According to the above results, we constructed the spatial innovation model with hubs and sub-groups (community) in both countries. This complex spatial model has two characteristics. One is based on a mixture of organizational and geographical proximities, while the other varies by city regions. The relationship between spatial proximity and features of innovation networks was divided into three: hubs, subgroups, and islands with pipeline (Fig. 6). Fig. 6 Structure of innovation network

18 150 Asia-Pac J Reg Sci (2017) 1: In addition, different temporary communities and network hubs, such as Kyushu and Hokuriku, were revealed in each decade. In terms of the existing hubs, global metropolitan cities function as innovation hubs and spread a certain type of homogeneous space to surrounding areas. Innovation networks have structures in which homogeneous spaces exist even in scattered communities. Furthermore, hierarchical networks can have unique overlapping inter-hub networks that do not reflect intra-hub hierarchies; such networks produce increasingly complex layers of relationships. 6.2 Geographical proximity and other proximity within urban systems Regarding traditional and historical urban systems, the Japanese urban system has changed from a hierarchal system into a complex network system because of modernization and industrialization. On the other hand, the Korean urban system has remained a fixed hierarchal system whose center has been Seoul for 500 years. Reflecting these different backgrounds, the spatial model of Japanese innovation networks forms a complex network that has evolved from a hierarchal system. However, Korean network structure indicates a continuous strong hierarchal network with connection hubs concentrated in Seoul. Communities based on geographical proximity tend to be located in the metropolitan areas of central Tokyo, Osaka, and Nagoya, and are mainly clusters formed by the major companies. Meanwhile, where islands formed outside of metropolitan regions are nevertheless associated with large metropolitan areas, this relationship is considered as a reflection of the influence of non-geographic proximities. In these urban systems, interactions between geographical and organization proximities resulted in networks having a variety of forms. Networks in Tokyo and Seoul metropolitan areas are not affected by geographical proximity only. Daejeon works as a hub in the innovation network by its strong geographical proximity, but it is different to the capital. In contrast, Chaebol in Korea and Zaibatsu (or Keiretsu) in Japan are examples of networks created through organizational proximity. Innovation networks are easy to foster between these affiliated groups under such close relationships. 6.3 Regional innovation policy and innovation networks In Japan, while three metropolitan regions are developing in each stage separately, temporary regional clusters based on geographical proximity have existed in Kyusyu region in 2000, Shikoku region in 2005, and Hokuriku region in Furthermore, a powerful hub located in a major city extended its links from its adjacent regions into another city region, not only as a new hub but also as scattered nodes. This finding indicates that innovation networks in Japan do not experience drastic changes across the entire nation, but show temporary changes reflecting the regional industry promotions of each administration area. In contrast, the network system in Korea is hierarchical, and hubs are linked among themselves. In contrast with Japan,

19 Asia-Pac J Reg Sci (2017) 1: Korea has drastically changed in the last three decades, and this transformation can be considered as the effect of both deliberate policy and socio-economic changes. Specifically, 2000 witnessed the first stage of innovation policy limited to fundamental industries. Therefore, innovation networks did not develop significantly. After 2005, President Roh promoted national growth with innovation output. However, regional innovation capacity remains underdeveloped outside the Seoul Metropolitan Area. This policy led to the quantitative expansion of innovation networks of local city regions. However, this growth was dependent on Seoul due to the shortage of innovation capacity at another region. After this quantitative expansion, government policies changed in 2010 to selected and concentrated industries. This policy has been more successful in promoting innovation network growth as not just the main hub of Seoul, but also the hub of Daejeon. Korea and Japan both sought to increase their innovation networks through policy efforts, and their different network structures indicate that policies do impact network breadth, depth, and number, especially in the Daejeon region. 7 Conclusion This study clarified the relationship between spatial proximity and innovation networks by comparing analysis of Korean and Japanese co-patent networks, due to their similar socio-economy backgrounds. The models of innovation networks were divided into three: hubs, sub-groups, and islands with pipeline. Hubs on networks in Japan are concentrated along the Tokaido Corridor. In addition, structural power nodes were mainly located in the Tokyo Metropolitan Area. Power nodes were distributed across three main metropolitan regions, but despite this dispersion, local city regions have only 1 or 2, and some regions lacked power nodes altogether. No significant change was seen in time-series distribution of power nodes. We therefore conclude that active actors in patent networks in Japan influence consistently the entire structure by their role as ahub. In Korea, majority of hubs are located in the Seoul Metropolitan Area, Daejeon area, Gyongnam Province, and Pohang City. However, structural hubs were mainly researchers and companies from abroad. Therefore, the patent network in Korea was dependent on abroad and had to catch-up. In 2005, networks witnessed particularly robust growth. Half of the country s structural power nodes were located in Daejeon, unlike the result of quantity hubs. The Daejeon region is an extremely important hub in the network structure, and it is useful to promote innovation resources to spread the beneficial effects more widely. This study also examined sub-group structures using community analysis. Basically, communities organized by geographical proximity, but there are communities based on other proximities, mainly an organizational proximity. In Japan, while communities distributed in the Tokyo Metropolitan Area affect the entire nation, the Kinki and Chubu areas are forming community spaces based on geographical proximity not limited to their inner administrative boundaries. Furthermore, with the exception of 23 special Tokyo wards, local city regions

20 152 Asia-Pac J Reg Sci (2017) 1: have been forming communities near each other. Of these local communities, widely scattered communities seem to be aggregated in similar cultural areas. These scattered groups are linked with each hub via pipelines, and geographical proximity is affected only within another proximity, especially organizational proximity. Analyzing changes over time, Japan hosts communities based on both types of proximities. Organizational proximity is more powerful than geographical proximity in Japan in 2005, because communities are scattered widely. To summarize our overall results, this study found scattered clusters located in non-metropolitan areas and rural areas. Geographically closed communities were mainly located in Tokyo, Osaka, and Nagoya metropolitan areas in Japanese innovation network. Meanwhile, in 2005, Korean innovation networks are transformed hierarchal networks which are linked to metropolitan areas. The results of this study have following policy implications. Korea and Japan have used regional policies to boost innovation networks. However, their differing network structures while their similar socio-economic background indicate that innovation policy affected innovation network on regional and national levels. The case of the Daejeon region is a particularly relevant example as the government s strong support for the formation of network hubs was necessary to initiate their formation. Now, the Daejeon region is an extremely important hub in the patent network structure. Therefore, innovation networks can be successfully fostered through promotion policies and input innovation resources with limiting investments to a single region rather than distribution. The innovation network is formed by mainly organizational and geographical proximity, but previous policies that promote regional innovation system are focused on spatial boundary of region. Therefore, we conclude that future innovation policy should be considered about network structure and urban system, with supporting flexible network governance. References APCTT-ESCAP (2011) Strengthening the governance of national innovation systems. In: Proceedings and papers presented at the Asia-Pacific Forum on Strengthening the Governance of National Innovation Systems (NIS) for Senior Policy-Makers, New Delhi, India Boschma RA (2005) Proximity and innovation: a critical assessment. Reg Stud 39:61 74 Broekel T, Boschma R (2012) Knowledge networks in the Dutch aviation industry: the proximity paradox. J Econ Geogr 12(2): Camagni R (1991) Innovation networks: spatial perspectives. Belhaven-Pinter, London Castells M (1999) An introduction to the information age. Sage, London Cho HD et al (2007) The evolution of public research systems of major countries and policy recommendations for Korea. STEPI policy report (in Korean with English abstract) Choi I (2010) An empirical study on the relationship between open innovation network and performance manufacturing industry perspective Department of Information & Industrial Engineering, The Graduate School Yonsei University (in Korean) Cooke P (2002) Metropolitan innovation systems: theory and evidence from three metropolitan regions in Europe. Euro Plan Stud 10: Cooke P, Morgan K (1993) The network paradigm new departure in corporate and regional development. Environ Plan D Soc Space 11:

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