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
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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.
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• 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.
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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
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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.
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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.
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• 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.
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Table 4: Innovative Performance in the last 3 years
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• 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.
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• 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.
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3. The Rooster Analysis
• We conducted a rooster analysis to 12 of the
firms.
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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.
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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.
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• The network is built upon the survey question:
– “Which agencies (firms, universities, banks,
private and public institutions etc.) do you most
often work with ?”
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2. Graphical Representation of the Social Network
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• 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.
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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
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• 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.
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• 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.
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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.
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• 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.
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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
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• 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.
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• 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.
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• 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.
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• 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.
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