Innovation Networks between Germany and Turkey in the Renewable Energy Sector: A Social Network Analysis Outline PART I: German & Turkish Innovation Networks in the Renewable Energy Sector 1. Introduction 2. The Survey & The Findings 3. The Rooster Analysis PART II: Social Network Analysis of the Renewable Energy Sector 1. Introduction 2. Graphical Representation of the Social Network 3. Graphmetrics of the Social Network PART III: Joint Analysis of the Social Network & the Innovation Network 2 PART I: German & Turkish Innovation Networks in the Renewable Energy Sector 1.Introduction • We analyze the differences between the 15 renewable energy firms that have a German shareholder and that do not have a German shareholder with respect to knowledge transfer, competitiveness and innovativeness. 3 • Data is collected from surveys through face to face interviews with the representatives of 15 renewable energy firms. • 8 of them have German shareholders. • SPSS and E-views are used for data mining and analysis. • To detect mean differences t-test is used. • To check if the relation between the two coeffients are meaningful or not Pearson Chi Square test is used. • In order to determine the importance given to a specific variable by the two parties we used MannWhitney U Test. 4 2. The Survey & The Findings • The survey consists of four sections which are: i. ii. iii. iv. Introduction Questions on Knowledge Spillovers Innovation Performance Questions Policy Priorities 5 Introduction • In this part, we ask questions of the descriptive kind in order to identify certain characteristics of firms such as; date and place of establishment partnership agreements percentage of shareholders influence of German shareholders on various firm operations such as R&D, innovativeness, marketing, finances etc. number, education level, nationalities of employees and labor sources determinants of competitiveness (R&D, design, process developments etc.) sources of knowledge recommendations on how to intensify future knowledge transfer between Germany and Turkey. 6 i.Introduction: In this part, we ask questions of the descriptive kind in order to identify certain characteristics of firms. • Table 1 shows that while the firms w/o German Shareholders are inclined to operate in both wind and solar energy sectors, the firms with German Shareholders prefer to specialize on one of the sectors. 7 8 • The findings of the study reveal that there is no difference between the education level of employees. • This result is in line with the fact that renewable energy sector (other than production side) necessitates qualified engineers. • By the way, the firms with German shareholders stated that they did not need to employ Germans for their companies located in Turkey, since they could find cheaper labour with the very same qualifications in Turkey. • They also added that this high quality makes it possible to adapt the technology in Turkey. 9 Table 4: Innovative Performance in the last 3 years 10 • But the analysis results reveal that there is no difference between the groups as far as innovation performance in the last three years are compared. • At that point it would be appropriate to mention that the innovative actions of firms with German shareholders are carried out in Germany that is why the innovation seems to be non existent in that firms. 11 • 2 of the firm representatives stated that in fact Turkish workers contribute to the innovation process carried out in the mother company (in Germany) by means of giving feedbacks and knowledge circulation. • They also pinpointed that if there was a more stable and reliable infrastructure in Turkey, they would carry out innovative activities in Turkey. 12 3. The Rooster Analysis • We conducted a rooster analysis to 12 of the firms. 13 PART II: Social Network Analysis of the Renewable Energy Sector 1. Introduction • We measure and visualize the social network relations between 12 renewable energy firms making use of a rooster analysis within the context of Turkish German Innovation Networks Project. • We made use of the findings of the rooster analysis to make a graphical presentation of the social network and derive graphmetrics analysis. • We use SNA to map the structure of the network, the place of actors in the sector and observe their interconnections. 14 Part II: Social Network Analysis of the Renewable Energy Sector 1. INTRODUCTION: • Network data is collected through face to face interviews with the representatives of 12 renewable energy firms. • There are 4 key figures within the network that these 12 firms are in contact with : – other firms, banks, universities, public and private institutions. 15 • The network is built upon the survey question: – “Which agencies (firms, universities, banks, private and public institutions etc.) do you most often work with ?” 16 2. Graphical Representation of the Social Network 17 • Vertices with more connections are located in the inner parts of the network. • Blue squares denote the renewable energy firms under investigation • Green spheres stand for other firms, • White circles represent banks, • Brown diamonds are the public and private institutions. • Red triangles symbolize universities. 18 2. Graphmetrics of the Social Network Vertex (Firms) Graph Metrics IZM001 13 Betweenness Centrality 105.4962 IZM002 12 440.5752 0.0050 (2) 0.0263 0.2121 (3) IZM003 40 (1) 2063.0095 (1) 0.0055 (1) 0.0519 (1) 0.0372 IZM004 12 79.7242 0.0043 0.0265 0.1364 IZM005 12 134.7106 0.0049 (3) 0.0305 0.2879 (1) IST001 40 (1) 1821.0216 (2) 0.0050 (2) 0.0487 (2) 0.0282 IZM006 20 (3) 591.4344 0.0042 0.0327 0.0842 IZM007 22 (2) 849.4460 (3) 0.0047 0.0340 (3) 0.0823 IZM008 13 167.2281 0.0047 0.0323 0.2436 (2) IZM009 8 110.8697 0.0037 0.0120 0.0000 IZM010 8 114.7781 0.0040 0.0164 0.1786 IST002 13 579.2128 0.0044 0.0138 0.0513 Degree Closeness Centrality 0.0042 Eigenvector Centrality 0.0295 Clustering Coefficient 0.1795 19 • The SNA approach also provides statistical information in order to observe about the quantity and structure of connection paths between organizations. • Graph metrics of this network: degree, betweenness centrality, closeness centrality, eigenvector centrality and clustering coefficient. • Degree denotes the number of ties that each vertice has. • Closeness centrality measure demonstrate the length of shortest paths that each vertice has. This measure helps to monitor the information flow in the network. • Betweenness centrality is significant to figure out how influential vertices are within the network and hence control the flow of information. • Clustering coefficient shows which vertices in a network tend to cluster. • Eigenvector centrality shows the effectiveness of the agent through the network. • These statistics are used to summarize and support network graph numerically. 20 • IZM003 is a subcontractor located in Izmir. It has the largest betweenness (2063.0095) and closeness (0.0055) centrality statistics by having the highest degree (most connections )(40) with other vertices. • It has also a significantly high eigenvector centrality (0.0519) with respect to other agents in the network. • IZM003 has a low degree of clustering coefficient (0.0372), which shows that it is not a part of any cluster. 21 Conclusion • Overall, this evaluation demonstrated that the networking of renewable energy sector shows there is no clustering and much versatility through the use of marketing and technical information. • Network partners demonstrated by low degree of fragmentation, and efforts to reach out to new agencies confirmed with the network graph and graph metrics. 22 • The results also reveal that there exists a very limited social innovation network with few directions of knowledge flows between agents operating in the renewable energy sector. • Such a result would imply that, due to the harsh competition in that sector, firms seldom if ever interact with each other, thus spread of knowledge and networks are very rare in the sector. 23 PART III: Joint Analysis of the Social Network & the Innovation Network • In this part we investigate the correlational relationships between two groups of variables that define firms in terms of their location in the social network and the innovation network, which are: S.N.A. VARIABLES (i) degree (ii) betweenness centrality, (iii) closeness centrality (iv) eigenvector centrality (v) clustering coefficient levels in the social network I.N.A. VARIABLES (a) Involvement with German shareholders (b) Various measures of innovative activity (c) Competitiveness (d) Sources of market knowledge (e) Sources of technical knowledge 24 • We find a positive relationship between «degree» in the social network and increasing financial support by German shareholders. • Positive relationship between «closeness centrality» in the social network and firms’ utilization of specialized journals and academic journals as a source of technical knowledge, in alternative to firms’ own investment in R&D research. • Positive relationship between «closeness centrality» and firms’ intense utilization of market research as a source of technical knowledge. • Positive relationship between firms’ level of participation in technical fairs and «closeness centrality» in the network. 25 • We don’t find a significant relationship between firm innovativeness level and position in the cluster, which is possibly because the firms in the Turkish social network are not really innovative in the Schumpeterian sense, but instead they transfer knowledge from Germany mostly. • Also, we find no significant relationship between presence of German shareholders in a firm and variables of position in the social network. 26 • In conclusion, we find that firms who employ more of their time in «R&D-related efforts» hold a stronger position in the social network. • We would like to be able to say the same for «genuine R&D research» but unfortunately such research does not significantly exist in this sector. • Still, the fact that even «R&D-related efforts» such as taking the time to browse through specialized and academic journals for makes a difference in the social network, directs us to conclude that the renewable energy sector in Turkey would benefit from very high marginal returns to genuine innocative activity, if only it was engaging in it. 27 • Currently, the sector is in a «catching up/imitation» stage of its development, which gives us the hope that, in the future it will manage to turn into a stage where it will start to engage in its own R&D and innovative activities, as expectedly is the case with the common «catching up» processes seen in countries like China, Indonesia, Korea, etc in similar high-technology sectors. 28