Outline

1. Innovation Networks between Germany and Turkeyin theRenewable Energy Sector:A Social Network Analysis

2. Outline PART I: German & Turkish Innovation Networks in the Renewable Energy Sector • Introduction • The Survey & The Findings • The Rooster Analysis PART II: Social Network Analysis of the Renewable Energy Sector • Introduction • Graphical Representation of the Social Network • Graphmetrics of the Social Network PART III: Joint Analysis of the Social Network & the Innovation Network

3. PART I: German & Turkish Innovation Networks in the Renewable Energy Sector1.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.

4. 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 Mann-Whitney U Test.

5. 2. The Survey & The Findings • The survey consists of four sections which are: • Introduction • Questions on Knowledge Spillovers • Innovation Performance • Questions Policy Priorities

6. 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.

7. 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.

9. The findings of the studyrevealthatthere is no difference between the educationlevel of employees. • Thisresult is in linewith the factthatrenewableenergysector (otherthanproductionside) necessitatesqualifiedengineers. • By the way, the firmswithGermanshareholdersstatedthattheydid not needtoemployGermansfortheircompanieslocated in Turkey, since theycouldfindcheaperlabourwith the verysamequalifications in Turkey. • Theyalsoaddedthatthishighqualitymakes it possibletoadapt the technology in Turkey.

10. Table 4: Innovative Performance in the last 3 years

11. But the analysisresultsrevealthatthere is no difference between the groups as far as innovationperformance in the lastthreeyearsarecompared. • At thatpoint it would be appropriatetomentionthat the innovativeactions of firmswithGermanshareholdersarecarriedout in Germany that is why the innovationseemsto be nonexistent in thatfirms.

12. 2 of the firmrepresentativesstatedthat in factTurkishworkerscontributeto the innovationprocesscarriedout in the mothercompany (in Germany) bymeans of givingfeedbacks and knowledgecirculation. • Theyalsopinpointedthatiftherewas a morestable and reliableinfrastructure in Turkey, theywouldcarryoutinnovativeactivities in Turkey.

13. 3. The Rooster Analysis • We conducted a rooster analysis to 12 of the firms.

14. PART II: Social Network Analysis of the Renewable Energy Sector1. 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.

15. 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.

16. The network is built upon the survey question: • “Which agencies (firms, universities, banks, private and public institutions etc.) do you most often work with ?”

17. 2. Graphical Representation of the Social Network

18. 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.

19. 2. Graphmetrics of the Social Network

20. 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.

21. 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.

22. 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.

23. 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.

24. PART III: Joint Analysis of the Social Network & the Innovation Network • Inthispartweinvestigate the correlationalrelationships between twogroups of variablesthat define firms in terms of theirlocation in the social network and the innovation network, whichare:

25. We find a positive relationship between «degree» in the social network and increasing financial support byGermanshareholders. • Positive relationship between «closenesscentrality» in the social network and firms’ utilization of specializedjournals and academicjournals as a source of technicalknowledge, in alternativetofirms’ owninvestment in R&D research. • Positive relationship between «closenesscentrality» and firms’ intenseutilization of market research as a source of technicalknowledge. • Positive relationship between firms’ level of participation in technicalfairs and «closenesscentrality» in the network.

26. Wedon’tfind a significant relationship between firminnovativenesslevel and position in the cluster, which is possiblybecause the firms in the Turkish social network are not reallyinnovative in the Schumpeterian sense, but insteadthey transfer knowledgefrom Germany mostly. • Also, wefind no significant relationship between presence of Germanshareholders in a firm and variables of position in the social network.

27. Inconclusion, wefindthatfirmswhoemploymore of their time in «R&D-related efforts» hold a strongerposition in the social network. • Wewouldliketo be ableto say the samefor «genuine R&D research» but unfortunatelysuchresearchdoes not significantly exist in thissector. • Still, the factthateven «R&D-related efforts» such as taking the time tobrowsethroughspecialized and academicjournalsformakes a difference in the social network, directs us toconcludethat the renewableenergysector in Turkeywould benefitfromveryhighmarginalreturnstogenuineinnocativeactivity, ifonly it wasengaging in it.

28. Currently, the sector is in a «catchingup/imitation» stage of itsdevelopment, whichgives us the hopethat, in the future it willmanagetoturninto a stagewhere it will start toengage in itsown R&D and innovativeactivities, as expectedly is the casewith the common «catchingup» processesseen in countrieslikeChina, Indonesia, Korea, etc in similar high-technology sectors.