Summary Report of the Focus Group on ‘Organisational Mapping’
A. RATIONALE
It is now common ground among economists active in innovation research that firms do not
innovate on their own but that innovative firms have become actors in networks of clients,
suppliers, (potential) competitors and other knowledge generating and diffusing organisations. These networks increasingly transcend sectoral, technological and national borders.
On the theoretical level, with the shift from the traditional linear model of innovation to a
more systemic approach, the analysis focuses now on the interactions between the main actors of national innovation systems and on aspects of technology diffusion and absorption. In
OECD (1997) collaboration between firms is regarded to result in highly significant knowledge flows in national innovation systems.
In the framework of the OECD activities concerning national innovation systems the Belgian
participants proposed a graphtheoretical analysis to map the interactions between firms, universities and private or public research institutions as they emerge in subsidised and private
technological collaboration. This mapping can be interpreted as an institutional mapping
projected from the angle of R&D collaboration between the different national and foreign
actors on the national and international scene. The projection from this particular angle offers
the advantage of quantifiability and therefore also of inter-country comparability.
The subject of this Focus Group is indeed the international comparison of the characteristics
of the country-graphs obtained by considering the network of (subsidised) joint R&D projects
between firms, research laboratories, universities and government agencies, and of
(unsubsidised) R&D agreements between firms. The philosophy behind the methodological
approach which is chosen is based on the fact that, when it comes to studying institutional
mappings of national innovation systems, the problems that one has when wanting to make
international comparisons are even more severe than for other aspects of the NIS.
Instead of searching for quantifiable and therefore measurable indicators that can be
compared, a possible strategy could therefore be to impose comparability at the outset
through the chosen design of the data-gathering process, and obtain measurability as a result.
Comparability followed by measurement, rather than measurability followed by comparison.
Comparability and quantifiability through the cross-national uniformity of the data-gathering
process is what this Focus Group is about.
The proposed approach allows for an analysis at different levels of aggregation. Data are
collected at the micro level of interfirm collaboration and collaboration between firms and
other types of organisations. But the analysis can also be performed on the sectoral level, revealing patterns of intra- and intersectoral knowledge flows. Finally further aggregation can
result in the analysis of international knowledge flows.
When the creation of focus groups was proposed by the NIS working group Belgium offered
to be leading country of a group in which network analysis is undertaken in an international
comparative analysis of ‘institutional’ mapping. Italy, the Netherlands, Spain and Switzerland
agreed to participate in this work.
1
In what follows we will present interim results of the work in this focus group.
As the focus group started with some delay the analysis may not be regarded as concluded,
and a not unconsiderable part of the envisaged analysis is left for future research.
B. METHODOLOGY
The graphtheoretical approach that is proposed for the analysis of some aspects of the NIS
fits within the paradigm of social network analysis. Data on the relations between different
entities can be used to create a graph that enables to detect linkages in different types of networks and the impact these network relations may have on the behaviour of its members
(Sprenger and Stokman, 1992). As such it seems an appropriate analytic tool to trace interactions and (tacit) knowledge flows between firms, universities, research institutions and other
actors of the NIS.
For the analysis data were gathered on participation in RTD projects falling within the
Framework Programmes (FWP) of the EU. These projects are defined as ‘pre-competitive’ as
they are supposed to foster collaboration in the early stage of basic R&D.
Secondly, we gathered data on Eureka projects, labelled as ‘near-market’. Eureka is an intergovernmental initiative, proposed by the French government in 1985. The EU promotes the
follow-up of its own RTD projects by more near-market Eureka projects. There seems however to be little evidence that the ‘pipeline’ relation between FWP and Eureka is significant
(Peterson, 1993; EC, 1994, Dumont and Meeusen, 1999).
The third data source was the MERIT-CATI databank which contains data on some 13000
private co-operative agreements, established up to 1996, that relate to technology transfer or
joint research.
The network criterion chosen is whether two actors collaborate in a R&D project (FWP,
Eureka) or have established a co-operative agreement. All actors are defined in a pointfile to
which additional data can be added (organisation type, nationality, industrial sector, balance
sheet data, etc.). All pairs of linked actors appear in a tail-head combination in a linefile in
which data on the specific project or agreement can be added (programme ident, starting date,
ending data, number of partners, etc.). Lines can be defined as ‘directed’ if they run from the
prime contractor to a partner and as ‘undirected’ if they run between partners of equal status.
The graph consisting of nodes and lines, together with the additional information is defined
as a network. The graph is a multigraph as two actors can be linked by different lines.
The software package that is used for the graphical analysis, GRADAP 2.0, has a size limit of
6000 nodes and 60000 lines. Some additional constraints hold with respect to specific procedures in GRADAP.
For all 3 sources we retained data from 1990 onwards. Furthermore only links between actors
of a given country and its (foreign) partners were considered. Links between the foreign partners of the country were disregarded for reasons of manageability. With regard to the ‘precompetitive’ RTD projects only those projects in which at least one firm of the given country
participates were used for the analysis. Projects that only contained non-firm partners of the
country were not considered.
Even with these constraints the created graphs are too large to perform some procedures that
are available in GRADAP.
2
In GRADAP a considerable number of procedures and analyses can be carried out on the
complete graph or on subgraphs that consist of the nodes and lines that meet some specific
point- or line-criterion. We will discuss some of these procedures that are relevant to our
analysis.
Components
A component C of a graph can be defined as a maximally connected subgraph, i.e. every
node of C is connected with at least one other node of C , and there exists no node outside
C which is connected to at least one node of C. The number and size of components in a
graph reveals the degree of interdependence of the different actors. The density of a component relates the number of actual links to the number of potential links within the group and
thus measures its degree of connectivity.
Cliques
N-Cliques can be defined as subgraphs of which all points are linked with one another
through a path with maximal length equal to n in a way that no point outside the subgraph
has the same quality. In our analysis cliques, defined at a certain level of multiplicity, can be
seen as clusters of actors that collaborate frequently with one another.
By merging the detected cliques the core of the R&D network can be detected and its density
can be revealed.
Centrality
Centrality can relate to the graph as a whole or to specific points in the graph.
Freeman (1979) distinguishes between three types of point-centrality indicators based on degree, closeness and ‘betweenness’, respectively.
A node is considered to be more central than others if more nodes are adjacent to it. This
leads to the following expression for the degree-centrality of a node :
n
C = ∑ a ( i, j )
D
i
j =1
j ≠i
where a(i,j) is 0 or 1 ; 0 if i and j are not connected, 1 if they are.
Closeness is a related concept, but looks at distances d(i,j) , i.e. the length of the shortest path
from i to j (‘geodesics’). ‘Betweenness’ or ‘rush’ is an indicator for the strategic position of
a node in a graph and is based on the number of ‘geodesics’ between two nodes j and k that
pass through i.
Memory constraints often do not permit - given the size of the graphs - to compute ‘closeness’ and ‘betweenness’ centrality indicators.
C. RESULTS
As the work in the Focus Group started with some delay not all country graphs have yet been
created. In this section we will discuss some of the results for the Belgian, Spanish and Swiss
graphs. In a later stage the analysis will be extended to the Dutch and the Italian graphs.
3
General description of the graphs
In table 1 the main characteristics of the graphs are listed.
It can be seen that both in the Belgian and Spanish graph the FWP projects provide a large
number of all lines. This may result in a ‘pre-competitive’ bias in the network as well as a
bias of those disciplines that get the most support from the EU like ICT, biotechnology and
new materials.
It is interesting to see that Switzerland, as the smallest country, has the largest number in absolute terms both of ‘near-market’ Eureka lines as well as of lines related to private strategic
alliances (p.s.a.). The relatively low number of FWP lines is of course explained by the fact
that Switzerland does not belong to the EU. Swiss firms can participate in almost any EUprogramme although they can not initiate nor lead a project. Swiss technology and innovation
policy is generally not directed to promoting R&D partnerships as the Swiss authorities regard business as a private matter. The high participation of Switzerland in private alliances
can be attributed to a considerable number of important Swiss multinationals (Sandoz,
Hoffman-La Roche, ABB, ...).
Belgium and Spain on the contrary have few nation-based multinationals. The fact that foreign multinationals located in Belgium and Spain actively participate in FWP and Eureka, but
not in private alliances might indicate that the results of R&D activities in these countries are
not fully valorised in the countries themselves, but are repatriated to the home countries of
foreign multinationals.
For all graphs the number of components compared with the number of lines is relatively low
for the FWP which reveals a relatively high degree of connectedness in the ‘pre-competitive’
R&D network. However for the Swiss graph there also seems to be a high connectedness of
the Eureka network and the network of private agreements.
Table 1. General features of the graphs
BE
FWP
EUREKA
p.s.a.
# LINES
8243
758
32
# ACTORS
2322
447
35
average
3.55
1.70
0.91
COMPONENTS Largest Density
13
2275
0.00
36
330
0.01
13
5
0.40
CH
FWP
EUREKA
p.s.a.
2447
2614
226
882
695
155
2.77
3.76
1.46
5
17
21
849
633
84
0.01
0.01
0.03
ES
FWP
EUREKA
p.s.a.
10568
1051
76
2808
591
47
3.76
1.78
1.62
33
82
14
2687
336
11
0.00
0.01
0.84
Relative Specialisation
We computed relative specialisation indices (RSI) for different subject codes in the RTD
projects of the EU.
RSI relates the number of projects of a given country in a given discipline to the overall
weight of the given discipline in the whole group of countries.
If a country is relatively specialised in a discipline this will be revealed by a RSI greater than
1.
4
We used the number of prime contractors to compute RSI.1 The results are shown in Table 2.
Belgium appears to be relatively specialised in telecommunications, medicine, (micro-) electronics and IPS. Biotechnology and environmental protection are fields in which Belgian
actors are relatively underrepresented as primary contractors in ongoing projects.
Spanish actors are relatively specialised in (micro-) electronics, information processing and
materials technology and relatively weak in biotechnology and medicine.
Swiss actors have a high RSI for environmental protection, telecommunications and biotechnology and a low RSI for materials technology and energy technology.
Table 2. RSI in ongoing projects of the EU Framework Programmes (1997)
BE
DK
DE
GR
ES
FR
IE
IT
LU
NL
PT
GB
FI
SE
CH
BIO
0.70
1.42
0.66
0.22
0.35
1.04
0.79
0.70
0.00
1.34
0.00
1.25
0.89
2.38
1.81
ELM
1.12
0.78
0.99
1.17
1.33
1.05
1.35
1.30
0.50
0.54
0.72
0.87
0.93
0.41
1.03
ENV
0.72
0.95
0.84
0.86
0.66
0.93
1.04
0.77
0.00
1.45
0.96
1.25
0.75
0.89
1.55
ESV/RSE
0.88
2.53
1.08
2.09
0.82
0.81
0.66
0.80
0.00
1.26
2.56
1.02
1.38
0.56
0.65
IPS
1.12
0.73
0.98
1.33
1.26
1.01
1.52
1.17
1.16
0.74
0.83
0.94
0.80
0.50
0.95
MAT
1.08
0.64
1.29
0.55
1.26
0.93
0.35
1.12
2.96
1.00
1.12
0.85
1.32
1.56
0.46
TEL
1.20
0.56
0.91
1.40
0.79
1.03
2.05
0.84
2.16
1.03
1.02
0.95
0.58
0.70
1.34
MED
1.16
1.61
0.75
0.40
0.48
1.29
0.16
0.70
0.00
1.06
0.34
1.38
1.09
1.97
0.80
AER
0.85
1.15
1.08
0.85
0.92
1.09
0.75
0.90
0.00
1.14
1.39
0.91
1.26
1.08
1.00
Note: BIO: Biotechnology; ELM: (micro-) electronics; ENV: Environmental protection; ESV/RSE: Energy saving/ renewable sources
of energy; IPS: Information processing, MAT: materials technology; TEL: Telecommunications; MED: Medicine; AER: Aerospace.
There are some remarkable differences in the participation in the different networks.
If Belgium has high RSI for electronics in the FWP it has a rather low number of technology
alliances in this field. On the other hand it has a relatively high number of alliances in biotechnology for which it has a RSI below 1 in the FWP. Spain has a RSI below 1 for telecommunications for which it has a high number of alliances compared to the average (for
evidence on the participation of firms in strategic technology alliances see Hagedoorn and
Narula, 1996).
These findings may indicate that there is some kind of mismatch between competencies in
basic and applied research and innovative performance. For Switzerland there seems to be a
better matching of specialisation in FWP and alliance specialisation (e.g. the high RSI for
biotechnology and the high number of private alliances with Swiss partners in this field).
Archibugi and Pianta (1992) find some evidence of a poor link between the scientific and the
technological advantage of countries but rightly claim that a more profound analysis of this
link has to focus on specific aspects of national systems of innovation, the relation between
basic and applied research and development efforts, the role of institutions and the funding
and sectoral patterns of each country (Archibugi and Pianta, 1992, pp. 98-100).
1
As Swiss firms cannot act as a project leader we used overall Swiss participation to compute RSI.
5
Both Spain and Belgium appear in the top 10 of countries with the highest average annual
growth of shares of EPO patens for the period 1986-1995 for electronics. Spain is in fourth
place with average annual growth of 49.6 % and Belgium in eighth place with 12.1 %.
For computers and office machinery Belgium holds second place with an average annual
growth of 89.3 % (EC, 1997: p.161).
These figures indicate a certain shift of specialisation in accordance with specialisation in
FWP.
Point Centrality
In Table 3 we list the 10 most central actors in the subgraphs related to FWP, Eureka and private agreements. The high number of universities in the Belgian top 10 of FWP participation
reflects the relative dominance of HEIs for Belgium. This confirms results from other
sources : 38.9 % of Belgian participation in the fourth FWP was accounted for by HEIs,
compared with a EU15 average of 29.3 % ; large Belgian companies accounted for 15 % and
SMEs for 18.5 %, compared with EU15 averages of respectively 19.3 % and 17.3 % (EC,
1997, p. 551).
For Spain the relatively higher FWP participation of firms is also reflected in the top 10.
In the fourth FWP Spanish firms accounted for 37.6 % of participation compared with an
EU15 average of 36.6% (Ibid.).
If for the three countries some degree of coincidence exists between the top 10 of FWP participation and Eureka participation, this does not hold true when participation in subsidised
pre-competitive (FWP) and near market (Eureka) projects is compared with the pattern of
private alliances. This supports our previous findings of a certain mismatch between scientific and technological competencies although once again the dynamic aspects of the innovation and technological specialisation process probably remain unaccounted for by this kind of
static comparison.
Another explanation, relevant for centrality analysis, lies of course in the fact that subsidised
collaboration is biased by policy preferences for specific technologies like ICT.
Components
In table 4 we report on the number of components and the size and density of the largest
component at different levels of multiplicity.2 Due to the relatively low participation of
Swiss actors in FWP projects the size of the largest component decreases rapidly. For Belgium and Spain, at a multiplicity level of 8 the largest component still contains over 40 actors. This means that there is a group of more than 40 actors of which each actor collaborates
8 times or more with at least one other actor of the component.
These high multiplicity components represent the small groups of actors that frequently participate and collaborate in FWP, as opposed to the large group of organisations that only participate occasionally.
2
For Spain components could only be detected from a multiplicity level of 5 onwards.
6
Table 3. The 10 most central actors in FWP, Eureka and private strategic alliances
Belgium
IMEC
University of Gent
Alcatel Bell
Catholic Univ. of Leuven
Catholic Univ. of Louvain
WTCM/CRIF
University of Liege
Belgacom
Free University Brussels
Alcatel Microelectronics
ROR
EDU
IND
EDU
EDU
ROR
EDU
IND
EDU
IND
FWP
262
242
194
191
190
169
144
134
119
118
Catholic Univ. of Leuven
University of Gent
Catholic Univ. of Louvain
Alcatel Bell
Union Miniere
Radius Engineering
Belgacom
VITO
Solvay
Bosal Benelux
EDU
EDU
EDU
IND
IND
IND
IND
ROR
IND
IND
EUR
57
48
43
37
36
36
34
32
26
16
Solvay
Petrofina
Enimont (It)
DSM (Nl)
Elf-atochem (Fr)
Plant Genetic Systems
UCB
PRB
Beamech (Gb)
Foamex (Us)
IND
IND
IND
IND
IND
IND
IND
IND
IND
IND
p.s.a.
4
4
4
4
4
2
2
2
2
2
IND
EDU
EDU
ROR
IND
IND
IND
IND
IND
IND
FWP
308
268
245
237
194
115
115
114
107
96
Univ. Politecnica de Madrid
Telefonica
Retevision
Univ. Complutense de Madrid
Univ. autonoma de Barcelona
Univ. Barcelona
AIDO Inst. Tec. de Optica
Fagor
Iberdrola
Base Doc. de la Empresa
EDU
IND
IND
EDU
EDU
EDU
ROR
IND
IND
IND
EUR
76
47
33
28
26
25
24
24
24
20
Rhone-Poulenc (Fr)
Halesa-MBT
Lab S.A. (Fr)
Indaver (Be)
SITA (Fr)
Vicarb
Hoogovens (Nl)
Electrocell (Se)
Corning Glass (Us )
CPI (Us)
IND
IND
IND
IND
IND
IND
IND
IND
IND
IND
p.s.a.
10
9
9
9
9
9
9
9
9
9
IND
EDU
EDU
IND
IND
NCL
IND
IND
IND
IND
FWP
122
117
96
85
66
66
61
42
38
37
EPFL
ETH Zurich
Centre CIM de Suisse Occidentale
CSEM
Hochschule ST. Gallen
Eidgenoessische Materialpruefungs
Sulzer
ABB
Schindler Waggon
Hydrel Maschinenfabrik
EDU
EDU
ROR
IND
EDU
ROR
IND
IND
IND
IND
EUR
152
119
88
63
63
52
50
43
43
41
Ciba-Geigy
Hoffman-La Roche
ABB
Sandoz
ETH Zurich
Zeneca
Siemens (De)
GPT (Gb)
British Telecom. (Gb)
Telecom Italia (It)
IND
IND
IND
IND
EDU
IND
IND
IND
IND
IND
p.s.a.
40
28
19
14
6
6
6
6
6
6
Spain
Telefonica
Univ. Politecnica de Catalunya
Univ. Politecnica de Madrid
CSIC
Construcciones Aeronauticas
Tekniker
Alcatel Standard Electrica
Indra
Iberdrola
Fatronik Systems
Switzerland
Ascom
ETH Zurich
EPFL
CSEM
Nestle
Swiss PTT
Sulzer
IBM
Alusuisse
Landis & Gyr
7
Table 4. Number of components, size and density of the largest component at different multiplicity levels
# comp.
Mult
Mult
Mult
Mult
Mult
Mult
Mult
2
3
4
5
6
7
8
12
3
2
1
1
1
1
Belgium
Size
largest
359
144
95
67
53
46
41
Density
0.01
0.03
0.05
0.07
0.08
0.08
0.08
# comp.
N.A.
N.A.
N.A.
2
3
1
1
Spain
Size
largest
99
76
57
47
Density
# comp.
0.03
0.04
0.05
0.06
7
3
4
1
1
1
1
Switzerl.
Size
largest
349
119
62
38
26
17
10
Density
0.01
0.02
0.04
0.07
0.08
0.13
0.20
Table 5. RCP in the fourth FWP (1994-96)
BE
DK
DE
GR
ES
FR
IE
IT
LU
NL
AU
PT
GB
NO
FI
CH
SE
BE
DK
DE
GR
ES
FR
IE
IT
LU
NL
AU
PT
GB
NO
1.00
0.82 1.73
1.04 0.96 0.74
0.86 0.97 0.94 1.38
0.99 0.75 0.91 0.98 1.13
1.13 0.71 1.18 0.89 1.20 0.77
0.91 1.16 0.79 1.10 0.96 0.88 1.54
0.89 0.76 1.05 1.24 1.23 1.13 0.76 0.85
3.18 1.09 1.00 1.02 0.48 1.18 1.19 0.71 19.46
1.24 1.32 1.09 0.80 0.81 0.91 1.07 0.77
0.73 1.07
0.88 0.77 1.42 0.97 0.96 0.89 1.04 1.04
1.11 0.78 2.17
1.06 1.02 0.84 1.22 1.39 0.86 1.22 0.92
0.88 0.86 0.57 2.22
0.96 1.12 1.07 1.04 0.92 1.11 1.32 0.97
0.52 1.20 0.80 0.99 0.73
0.82 1.82 0.83 0.92 0.69 0.87 1.06 0.78
1.21 0.98 0.77 0.84 1.22 2.86
0.90 1.30 0.94 0.93 0.77 0.71 1.24 1.21
1.49 1.03 1.23 0.89 0.91 1.29
1.28 0.84 1.18 0.80 0.76 1.06 0.78 1.03
0.81 1.02 1.18 0.70 0.94 1.21
0.78 1.43 1.07 0.91 0.80 0.90 0.90 0.87
0.51 1.05 0.79 0.97 1.06 1.14
FI
1.51
0.87
1.57
CH
1.55
0.82
SE
1.27
Source: Own calculations based on EC (1997), p.560.
Note : RCPij = (NLij/NL)/ ( Nlj *NLi)
NLij: Number of Collaborative Links between country i and country j
Nlj , Nlj : Number of Collaborative Links of country i respectively country j
NL: Total number of Collaborative Links for the group of 17 countries
8
Cliques
Every project or alliance with three or more partners can be regarded as a clique.
In our analysis, as links between foreign actors are not considered, detected cliques can only
contain at most 1 foreign actor. Even with this constraint cliques, because of limitations of
the software used, can only be detected from a certain level of multiplicity onwards. By combining however the cliques that can be detected at a certain level of multiplicity the core of
the network of the most actively collaborating partners (i.e. the core of the components at
high levels of multiplicity) can be represented. The interlinkage of detected cliques3 is shown
in figure 1.
Figure 1. Cliques in the complete graphs
Belgium (multiplicity = 7)
Alcatel SEL (DE)
Alcatel (FR)
Alcatel Bell (BE)
Siemens (DE)
Philips (NL)
IMEC (BE)
Thomson (FR)
Plessey (GB)
Alcatel Microel. (BE)
Matra (FR)
Spain (multiplicity = 7)
Ascom (CH)
PTT (NL)
BT (GB)
Thomson (FR)
UPC (ES)
Telefonica (ES)
UPM (ES)
Alcatel Bell (BE)
CSELT (IT)
France Telecom (FR)
Switzerland (multiplicity = 4)
Ascom (CH)
ABB (CH)
ETH Zurich (CH)
CIM (CH)
EPFL (CH)
Firm
Higher Education Establishment
Research centres
3
In figure 1 the interlinkage is shown of the cliques detected at the lowest possible level of multiplicity in
GRADAP.
9
Figure 1 reflects the importance of ICT (and the focus on telecommunications in recent years)
in FWP and the dominance of these disciplines by multinationals and national telecom operators. For Belgium the most central actor is the Interuniversity Microelectronics Centre
(IMEC), which was established in 1984 by the government of the Flemish Community to
strengthen the microelectronics industry in Flanders. As mentioned before Belgium has a RSI
greater than 1 for this discipline and for the period 1986-1995 occupied the eighth position of
top countries with regard to average annual growth of EPO patents in electronics.
Despite the fact that Spain has an RSI below 1 in telecommunications there is a rather dense
network centred around Spanish telecom operator Telefonica which matches the relative high
number of private alliances with Spanish partners in this field.
For Switzerland, even at a multiplicity level of 4, only cliques without any foreign partners
can be detected. EPFL and ETHZ are two Swiss polytechnic institutes with a tradition to
collaborate. Ascom and ABB are known to be inclined to form partnerships with education
establishments. The centre CIM is an initiative, initiated 5 years ago and initially sponsored
by federal funds, to form ‘competence’ centres to combine training and research relating to
CIM.
The most central actor in the Swiss FWP graph is not surprisingly Swiss telecom multinational Ascom. Swiss pharmaceutical firms, which are very active in private technological
collaboration, do not occupy a central position in the networks of subsidised collaboration.
Revealed Comparative Preference
In Table 5 we present the Revealed Comparative Preference (RCP) of 17 European countries,
computed by means of the number of collaborative links in the fourth FWP. A RCP greater
than 1 reveals a relative preference of actors of two given countries to collaborate. In general
neighbouring countries seems to have RCPs above 1. Apart from this both small and large
countries seem to prefer collaboration with partners from large countries.
There is a very significant relation between country size and the share of collaborative links
between actors of the same nationality. The correlation between the share of links between
actors of the same country (as listed in table 6) and the 1997 population is 0.91.
Table 6. Relation between the share of national links (FWP, alliances) and country size
DE
FR
GB
IT
ES
NL
GR
BE
PT
SE
AU
CH
DK
FI
NO
IE
LU
%
(FWP)
10.75
11.10
10.38
8.97
8.73
7.04
6.83
4.37
6.71
6.03
4.68
3.04
5.08
5.11
5.65
3.45
3.72
%
(Techn. alliances)
11
12
11
5
7
4
0
4
0
8
N.A.
N.A.
0
7
N.A.
8
N.A.
1997 population
82190
58543
58201
57236
39718
15661
10522
10188
9803
8844
8161
7277
5248
5142
4364
3559
417
Source: Own calculations based on EC (1997), p.560 and p.613.
10
Table 7. RCP in EUREKA (ongoing projects as at 30 June 1996)
BE
BE
DK
DE
GR
ES
FR
IE
IT
LU
NL
AU
PT
GB
NO
FI
CH
SE
DK
0.93
1.04
0.64
1.00
1.59
0.94
1.05
0.00
1.86
0.76
0.76
0.82
0.72
0.68
0.82
0.78
DE
0.92
0.64
0.74
0.82
1.57
1.20
0.00
1.16
0.57
1.21
1.59
1.09
1.46
0.67
1.49
GR
0.61
0.99
1.03
0.74
0.89
2.83
1.32
2.18
0.86
1.12
0.69
0.88
1.77
1.11
ES
1.52
1.23
0.00
1.99
0.00
0.57
0.36
2.86
0.58
1.83
1.84
0.56
0.98
FR
1.49
0.75
1.43
0.00
1.08
0.68
2.28
1.18
0.67
0.85
0.53
1.03
IE
0.76
1.67
0.00
1.10
0.65
1.39
1.21
0.99
1.03
1.15
0.72
IT
0.84
0.00
1.12
0.71
1.13
1.33
0.90
2.53
1.11
0.72
LU
0.00
0.81
1.02
0.94
1.19
0.64
1.04
0.66
0.69
NL
3.55
0.00
0.00
3.62
0.00
0.00
0.00
0.00
AU
0.80
0.72
1.22
0.94
0.64
1.38
0.89
PT
0.51
1.04
0.55
0.77
2.28
1.03
GB
0.92
0.87
1.05
0.40
0.81
NO
0.88
1.00
0.90
0.94
1.53
0.64
3.24
FI
CH
0.86
1.79
0.91
Source: Own calculations based on EC (1997), p.588.
Table 8 RCP in Inter-Enterprise Alliances (1992-1995)
BE
BE
DK
DE
GR
ES
FR
IE
IT
LU
NL
PT
GB
FI
SE
DK
4.91
0.00
0.84
0.00
1.94
0.00
0.00
0.80
0.00
4.00
0.00
0.83
0.00
0.00
DE
0.00
0.93
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.31
8.76
2.79
GR
1.49
0.00
0.66
0.93
0.00
1.09
2.09
1.36
0.00
0.69
0.00
0.38
0.00
0.00
0.00
0.00
12.00
0.00
0.00
0.00
0.00
0.00
0.00
ES
FR
3.06
1.03
0.00
2.53
0.00
0.63
0.00
0.87
0.00
0.00
IE
1.47
0.81
1.27
0.00
0.96
4.88
0.81
0.00
1.11
IT
30.67
0.00
0.00
0.00
0.00
2.08
0.00
0.00
LU
1.30
0.00
0.78
0.00
0.63
0.00
0.55
NL
0.00
0.00
0.00
2.08
0.00
0.00
PT
1.57
0.00
0.63
0.00
0.00
GB
0.00
0.00
0.00
0.00
FI
1.62
1.19
0.94
SE
22.53
7.17
6.84
Source: Own calculations based on EC (1997), Appendix S-109.
11
The results clearly indicate that FWP collaboration with actors of foreign countries is more
important to small countries than to large countries. This also holds true for the share of national alliances in the total of technology alliances of a given country established in the period 1992-1995 (correlation equals 0.65).
In table 7 RCP is calculated for collaboration in ongoing Eureka projects. There is a greater
variance in RCP in Eureka than for collaboration in FWP. Once again RCP on average is
high for neighbouring countries.
In table 8 RCPs are given for private partnerships between countries. Once again the variance
in RCP is greater. Apparently the closer to the market the more outspoken the preference for
specific (mostly neighbouring) countries. In EC (1997) it is shown that the share of intra-EU
alliances, after increasing from 24 % in the period 1984-1995 to 26 % in the period 19881991, decreased to 23 % to the benefit of the share of alliances with all partners from the
same country (EC, 1997, p. 613). So where the period of the first FWPs seems to have coincided with a relative increase of intra-EU private partnering as from 1990 onwards, European
countries folded back on partners within their own country. This is also revealed by the high
intra-country RCP in table 8.
Coincidence
In Table 9 the coincidence of lines in FWP, Eureka and private alliances is reported.
Spain and Belgium have a similar pattern of coincidence with some 3 % of links in FWP coinciding with more than 10 % of Eureka links. As to the sequence, out of 73 coinciding pairs
in the Belgian graph, 41 collaborations took place in FWP prior to collaboration in Eureka,
whereas out of the 97 coinciding pairs in the Spanish graph in only 23 this ‘right’ (i.e. in accordance with the pipeline model) sequence emerges.
For Switzerland this ‘right’ sequence emerges for less than half of the coinciding pairs.
The coincidence between FWP and private alliances is low. The sequence of the 4 coinciding
pairs in the Swiss graph suggests the follow up of pre-competitive R&D collaboration by a
private agreement.
Table 9. Coincidence of lines and pairs of actors in FWP, Eureka and private alliances
FWP-Eureka
Coinciding lines
Belgium
Spain
Switzerland
Coinciding pairs (sequence)
Belgium
Spain
Switzerland
FWP-Private alliances
Eureka-Private alliances
267 (3.2 %) - 80 (10.6 %)
313 (2.9 %) - 112 (10.7 %)
102 (4.1 %) - 26 (1.0 %)
1 (0.01 %) - 1 (3.12 %)
2 (0.02 %) - 1 (1.32 %)
12 (0.49 %) - 7 (3.10 %)
0-0
0-0
1 (0.04 %) - 1 (0.44 %)
73 (41 FWP > Eureka)
97 ( 23 FWP > Eureka)
56 ( 27 FWP > Eureka)
1 (Alliance > FWP)
1 (FWP > Alliance)
4 ( 3 FWP > Alliance)
1 (Alliance > Eureka)
D. POLICY IMPLICATIONS
Although networking has become an apparent feature of the present innovation process and
business strategies, there are important differences among actors, sectors and countries with
regard to the occurrence, magnitude and causes of collaboration in basic and applied research
and the development of new products and processes.
12
For one thing, the present (partial) results seem to confirm that firms of small countries are
significantly more inclined to partner with foreign actors than firms of large countries. This
suggests that they rely on networking to compensate for insufficient resources and appropriate partners in their home country : networking as a substitute for scale.
Countries with few own multinationals, like Spain and Belgium, are relatively active in basic
research but less in near-market collaboration and private technology alliances, indicating insufficient valorisation of the national R&D potential. The high participation in Eureka and
private alliances of a small country with a considerable number of own multinationals, like
Switzerland, supports this view.
A comparison of specialisation patterns reveals a certain mismatch between technological
activities and economic performance, which once again is more significant for Belgium and
Spain than for Switzerland, although there are some indications that from a more dynamic
perspective specialisation in basic and applied research in time tends to improve innovative
performance in the given technological domains (e.g. high average annual growth of EPO
patenting in electronics for Spain and Belgium and in computers and office machinery for
Belgium).
In the FWP of the EU, which aim at fostering pan-European collaboration, countries on average are still somewhat inclined to collaborate with neighbouring or culturally related countries whereas both small and large countries tend to prefer collaboration with partners from
large countries. The preference for other countries is more outspoken the closer the collaboration gets to the development of products or processes.
There is little if any evidence at all to support the pipeline model which suggests the followup of pre-competitive research by more near-market collaboration and the establishment of
strategic private agreements.
E. FUTURE RESEARCH
The analysis presented in this paper will be extended, in the short run, with data on Dutch
and Italian collaboration. Future research will further elaborate the link between basic and
more applied research and between public supported R&D activities and private initiatives
and possible mismatches.
The analysis will complement other NIS analyses by focusing on disembodied knowledge
flows at the level of sectors (matrix of intra- and intersectoral flows) and at the international
level (detecting international knowledge flows and spillovers through collaboration).
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