a typology study on science parks in europe · web survey eu park managers population: members of...
TRANSCRIPT
A typology study on science parks in Europe
Benny Ng, Rianne Appel-Meulenbroek,
Myriam Cloodt & Theo Arentze
Faculty of Built Environment Urban Systems & Real Estate
Technische Universiteit Eindhoven
University of Technology ERES Delft 2017
European Real Estate Society
PAGE 2
A SCIENCE PARK CAMPUS
TECHNOLOGY PARK
RESEARCH PARK
TECHNOPOLE
SCIENCE & TECHNOLOGY
PARK
Are all science parks the same?
SCIENCE PARK
Park firms VS off-park firms
Outcome
Science parks
Case studies
Unit of analysis
Networking
Innovation
Economic output
Science parks
Science parks
A large group of similar science parks…?
…or are the differences in-between large?
Characteristics
Organization Size Knowledge intensiveness Location
Research institutes
Knowledge intensiveness
Small-medium enterprise / multinationals
University / higher education
Organization
Management function Age Ownership
L x W
Surface area site
Size
Single or multiple buildings
Number of resident organisations
Facilities and services
Urban context
Location
R&D facilities Manufacturing space
Are all science parks the same?
Methodology
Characteristics Web survey EU park managers
Population: members of science park associations
Cluster analysis on cases
TwoStep (SPSS)
Respondents - N=82 (12% of population); 16 countries
Spain Sweden
Portugal
Norway
Italy
Germany
France Denmark
The Netherlands
United Kingdom
Switzerland
Selection of clustering variables
3. Ownership
L x W 4. Surface area site
5. Mix of leisure facilities 6. Laboratory 7. Mix of other facilities
1. Research institutes 2. Higher educational institutes
3 Clusters
Cluster comparison 1 (N=37)
2
(N=20)
3 (N=25)
Research institutes
Higher educational institutions
Laboratory
Mix of leisure facilities 0 0 3 Surface area site S M L
Mix of other facilities 2 2 4 Ownership /
L x W
Cluster comparison 1 (N=37)
2
(N=20)
3 (N=25)
Research institutes
Higher educational institutions
Laboratory
Mix of leisure facilities 0 0 3 Surface area site S M L
Mix of other facilities 2 2 4 Ownership /
1. Innovative locations 2. Research locations 3. Cooperative locations
L x W
Are all science parks the same?
3 different types types = labels?
Business Pôle: Sophia ANTIPOLIS, FR ESTER TECHNOPOLE (Limoges), FR
LISPOLIS - Ass. technological pole of Lisboa, PT Polo Innovazione Automotive Metalmeccanica, IT
Portsmouth Technopole, UK Techno-pôle de Sierre, CH
POLE
Bergen Teknologioverforing, NO Catalyst-Inc, UK
La Salle Technova Barcelona, ES NCE Smart Energy Markets. NO
Parc Balear d'Innovació Tecnològica, ES Stevenage Bioscience Catalyst, UK
Total Innovation, NO
INNOVATION & TECHNOLOGY
OTHERS
BioArk Visp, DK ComoNExT, IT CUBEX41, GE
Fondazione Novara Sviluppo, IT PhytoArk, CH Scion DTU, SE
SETsquared Partnership, UK Symbion, DK
CERFITT, IT GTC Gummersbach, GE
MAFINEX-Technologiezentrum,GE MTZ Münchner
Technologiezentrum, GE Queen Mary Bioenterprises
Innovation Centre, UK Techno-Z Salzburger
Technologiezentrum, GE University centre Technicom, SK
CENTRE
Brightlands Chemelot Campus, NL Dairy Campus, NL
Green Chemistry Campus, NL Grow Campus, NL
High Tech Automotive Campus, NL High Tech Campus Eindhoven, NL
Innovatiecampus Kennispark / Novel-T, NL Wageningen Campus, NL
CAMPUS
Gijón Science and Tecnology Park, ES Lakeside Science and Technology Park, AT Parque Científico y Tecnològico Cartuja, ES Nonagon - Parque de Ciência e Tecnologia
de S. Miguel, PT Pomeranian
Science and Technology Park Gdynia, PL Sci-Tech Daresbury, DK
VEGA - Venice gateway for science and technology, IT
S & T PARK
High Tech Systems Park, NL MADAN PARQUE, PT
Pivot Park, NL Sandbacka Park, SE Silverstone Park, UK
PARK
Amsterdam Science Park, NL Exeter Science Park, UK Ideon Science Park, SE
Johanneberg Science Park, SE Kent Science Park, UK
Kilometro Rosso Science Park, IT Leiden Bio Science Park, NL
Loughborough University Science and Enterprise Park, UK Parkurbis, Parque de Ciência e Tecnologia da Covilhã, IT
Pitea Science Park, SE Rennes Atalante Science Park, FR
Science and industrial Park TEMIS, FR Science Park Mjärdevi, SE
Syddanske Forskerparker, DK TU/e science park, NL
University of Iceland Science Park, IS University of Valencia Science Park, ES University of Warwick Science Park, UK
Utrecht Science Park, NL York Science Park, UK
Bio-Technopark Schlieren-Zürich, CH Parque Tecnologico de Andaluciã, ES
Scottish Enterprise Technology Park, UK Tartu Biotechnology Park, EE Technology Park Ljubljana, SI
Technopark Winterthur, CH TECHNOPARK Zurich, CH
TECHNO(LOGY) PARK
Barcelona Biomedical Research Park, ES
Surrey Research Park, UK
RESEARCH PARK
EPFL Innovation Park, CH Innovationspark Wuhlheide, GE
Ipark innovasjonspark Stavanger, NO
Karlstad Innovation Park, SE
INNOVATION PARK
Amsterdam Medical Business Park, NL
BUSINESS PARK
SCIENCE PARK
Innovative locations Research locations
Cooperative locations
Name of cases – clusters
Limitations & Conclusions • Limited number of cases; respondents NL & UK dominate
Cluster solutions based on average values • ‘Labels’ within name not often telling
• Centers & Others à cluster innovative locations • Campus used only in NL • Science park à mix of 3 clusters
• Additional data and cases required to further investigate taxonomy of science park
• Formann, A.K., 1984, Die Latent-Class-Analyse: Einführung in die Theorie und Anwendung. Weinheim: Beltz. • Lamperti, F., Mavilia, R., Castellini, S., 2015, The Role of Science Parks : a Puzzle of Growth , Innovation and R & D
Investments
• Löfsten, H. & Lindelöf, P., 2002, Science Parks and the growth of new technology-based firms—academic-industry links, innovation and markets
• Löfsten, H. & Lindelöf, P., 2003, Science park location and new technology-based firms in Sweden–implications for strategy and performance
• Ramírez-Alesón, M. & Fernández-Olmos, M., 2017, Unravelling the effects of Science Parks on the innovation performance of NTBFs
• Squicciarini, M., 2008, Science Parks' tenants versus out-of-Park firms: Who innovates more? A duration model • Tkaczynski, A., 2017, Segmentation Using Two-Step Cluster Analysis. In Segmentation in Social Marketing, pp. 109 -
125 • Vásquez-Urriago, A., Barge-Gil, A., & Modrego-Rico, A., 2014, Which firms benefit more from being located on a
science and technology park • Vásquez-Urriago, A., Barge-Gil, A., & Modrego-Rico, A., 2016, Science and technology parks and cooperation for
innovation • Yang, C., Motohashi, K. & Chen, J., 2009, Are new technology-based firms located on science parks really more
innovative? Evidence from Taiwan
References
1. Example studies comparing park firms and off-park firms 2. Possible cluster solutions 3. Cluster variant selection: output BIC and AIC 4. Overview cluster solutions
a. Overview ‘innovative locations’ b. Overview ‘research locations’ c. Overview ‘cooperative locations’
5. Test of differences between clusters
Appendix
Networking
Innovation
Economic performance
• Squicciarini, 2008; increased patenting activity à possible due to selection of firms.
• Yang et al., 2009; higher output elasticity of R&D, invest more efficiently
• Vásquez-Urriago et al., 2014; smaller firms experience higher innovation outcomes
• Ramírez-Alesón & Fernández-Olmos, 2017; No effect à but attract new tech firms
• Löfsten & Lindelöf, 2003; new tech firms collaborate lessà no effect innovation
• Vásquez-Urriago et al., 2016; Fosters intangible results, but no evidence for economic results from cooperation
• Löfsten & Lindelöf, 2002; enhanced growth in jobs and networking with university
• Lamperti et al., 2015; No effect growth sales à more patent applications & R&D investments
1. Example studies comparing park firms and off-park firms
2. Possible cluster solutions Knowledge
intensiveness Size Organisation Location
Clu
ster
sol
utio
ns
Clu
ster
coh
esio
n
C1
C2
C3
C4
C5
Hig
her e
duca
tiona
l ins
titut
ions
Rese
arch
inst
itute
s
SMEs
/ m
ultin
atio
nals
Sing
le o
r mul
tiple
bui
ldin
gs
Surfa
ce a
rea
site
# of
resi
dent
org
anis
atio
ns
Ow
ners
hip
stru
ctur
e
Man
agem
ent f
unct
ion
Tech
nolo
gy s
ecto
r foc
us
Age
Urb
an c
onte
xt
Mix
of l
eisu
re fa
cilit
ies
Mix
of o
ther
faci
litie
s
Mix
of s
ervi
ces
Labo
rato
ry
Incu
bato
r
Cle
an ro
om
Pilo
t roo
m
Man
ufac
turin
g sp
ace
1a 0.4 45 37 0.95 1 0.22 0.09 0.25 0.09 0.15
1b 0.3 37 25 20 0.95 1 0.31 0.18 0.33 0.26 0.59
1c 0.2 20 19 19 13 11 0.65 0.83 0.26 0.41 0.42 0.56 1
2 0.4 47 35 0.83 1 0.49 0.20 0.10 0.16 0.02
3 0.4 45 37 0.95 1 0.35 0.22 0.09 0.25 0.15
4 0.4 54 28 1 0.32 0.11 0.10 0.05 0.08 0.06
5 0.4 46 36 0.84 1 0.36 0.20 0.08 0.27 0.15
6 0.4 45 37 0.86 0.91 1 0.41 0.19 0.34 0.11
7a 0.4 45 37 0.95 1 0.22 0.09 0.25 0.09 0.06
7b 0.3 37 30 15 0.95 1 0.18 0.10 0.79 0.40 0.13
8 0.4 45 37 1 0.83 0.19 0.06 0.25 0.11 0.14
9 0.4 45 37 0.95 1 0.22 0.09 0.25 0.09 0.03
10 0.3 24 23 21 14 0.7 0.12 0.34 0.35 0.52 1 0.92
11 0.3 30 28 24 0.13 0.06 0.07 0.21 0.40 1 0.46
12 0.3 68 14 0.15 0.39 0.59 1 0.75 0.99 0.88
13 0.3 68 14 0.13 0.39 0.59 1 0.75 0.99 0.8
14 0.3 67 15 0.12 0.10 0.48 0.84 0.62 1 0.87
15a 0.3 59 23 0.10 0.03 0.18 0.25 0.21 1 0.4
15b 0.2 26 22 18 16 0.97 0.09 1 0.31 0.24 0.99 0.31
16 0.3 62 20 0.46 0.08 0.40 0.56 0.6 0.12 1
17 0.3 46 36 1 0.12 0.13 0.12 0.09 0.17 0.29
18 0.3 42 40 0.21 0.02 0.05 0.09 0.04 0.01 1
19 0.3 48 34 0.31 1 0.22 0.03 0.09 0.23 0.14
20b 0.3 48 34 0.86 0.78 1 0.47 0.15 0.26 0.17
20b 0.3 33 29 20 1 0.65 0.57 0.24 0.22 0.27 0.25
21 0.3 41 41 0.95 1 0.62 0.33 0.24 0.72 0.28
22a 0.3 70 12 0.26 0.12 0.37 1 0.67 0.67 0.57
22b 0.2 27 22 21 12 0.88 0.18 0.28 1 0.79 0.97 0.51
# sub cllusters
Schwarz's Bayesian Criterion (BIC)
BIC Changea
Ratio of BIC Changesb
Akaike's Information Criterion (AIC)
AIC Changea
Ratio of AIC Changesb
Ratio of Distance Measuresc
1 1366,585 1306,417 2 1304,481 -62,104 1,000 1184,145 -122,272 1,000 1,610 3 1307,618 3,137 -,051 1127,115 -57,031 ,466 1,296 4 1335,169 27,551 -,444 1094,497 -32,617 ,267 1,002 5 1362,845 27,676 -,446 1062,005 -32,492 ,266 1,513 6 1418,497 55,652 -,896 1057,489 -4,516 ,037 1,099 7 1479,043 60,546 -,975 1057,867 ,378 -,003 1,127 8 1545,176 66,134 -1,065 1063,833 5,966 -,049 1,100 … … … … … … … … 15 2097,132 87,161 -1,403 1194,612 26,993 -,221 1,043 Better fitting model = lower BIC/AIC value & high ratio of distance measure. 3 cluster solution is selected, because; - Similar low BIC value - Not the lowest AIC value, but has a higher ratio of distance measure than the 4-cluster variant. - Previous slide show relative higher overall predictor importance value than the 2-cluster variant. - With 82 cases in the sample, a 5-cluster variant would result in small sub clusters.
Auto-selected
Auto-selected
Chosen
3. Cluster variant selection: output BIC and AIC
Predictor importance
Cluster variables Total sample
(N=82) Innovative locations
(N=37) Research locations
(N=20) Cooperative locations
(N=25)
1,00 Research institutes presence 53 8 22% 20 100% 25 100%
0,95 Higher educational institutions presence 54 9 24% 20 100% 25 100%
0,59
Laboratory present 42 18 49% 18 90% 6 24%
Shared laboratory present 28 9 24% 0 0% 19 76%
Laboratory absent 12 10 27% 2 10% 0 0% 0,33 Mix of leisure facilitiesa 0 (1,17) 0 1 0 1 3 1
0,31 Surface area site (1.000 m2)b 364 (758) 79 177 288 413 846 1.177
0,26 Mix of other facilitiesa 2 (2,14) 2 1 2 1 4 3
0,18
Ownership - university 12 7 19% 3 15% 2 8%
Ownership - public 21 14 38% 0 0% 7 28%
Ownership - private 12 4 11% 7 35% 1 4%
Ownership - university-public 8 2 5% 2 10% 4 16%
Ownership - triple helix 8 5 14% 0 0% 3 12%
Ownership - university private 1 0 0% 0 0% 1 4%
Ownership - public-private 20 5 14% 8 40% 7 28%
aThevaluesfor'mixofleisureandotherfacili5es'arethemodesthenfollowedbythestandarddevia5on.bThevariable‘surfaceareasite’islistedasmeansandfollowedbythestandarddevia5on.
L x W
4. Overview cluster solutions
Predictor importance
Cluster variables Total sample
(N=82) Innovative locations
(N=37) Research locations
(N=20) Cooperative locations
(N=25)
1,00 Research institutes presence 53 8 22% 20 100% 25 100%
0,95 Higher educational institutions presence 54 9 24% 20 100% 25 100%
0,59
Laboratory present 42 18 49% 18 90% 6 24%
Shared laboratory present 28 9 24% 0 0% 19 76%
Laboratory absent 12 10 27% 2 10% 0 0% 0,33 Mix of leisure facilitiesa 0 (1,17) 0 1 0 1 3 1
0,31 Surface area site (1.000 m2)b 364 (758) 79 177 288 413 846 1.177
0,26 Mix of other facilitiesa 2 (2,14) 2 1 2 1 4 3
0,18
Ownership - university 12 7 19% 3 15% 2 8%
Ownership - public 21 14 38% 0 0% 7 28%
Ownership - private 12 4 11% 7 35% 1 4%
Ownership - university-public 8 2 5% 2 10% 4 16%
Ownership - triple helix 8 5 14% 0 0% 3 12%
Ownership - university private 1 0 0% 0 0% 1 4%
Ownership - public-private 20 5 14% 8 40% 7 28%
aThevaluesfor'mixofleisureandotherfacili5es'arethemodesthenfollowedbythestandarddevia5on.bThevariable‘surfaceareasite’islistedasmeansandfollowedbythestandarddevia5on.
L x W
4a. Overview ‘innovative locations’
Predictor importance
Cluster variables Total sample
(N=82) Innovative locations
(N=37) Research locations
(N=20) Cooperative locations
(N=25)
1,00 Research institutes presence 53 8 22% 20 100% 25 100%
0,95 Higher educational institutions presence 54 9 24% 20 100% 25 100%
0,59
Laboratory present 42 18 49% 18 90% 6 24%
Shared laboratory present 28 9 24% 0 0% 19 76%
Laboratory absent 12 10 27% 2 10% 0 0% 0,33 Mix of leisure facilitiesa 0 (1,17) 0 1 0 1 3 1
0,31 Surface area site (1.000 m2)b 364 (758) 79 177 288 413 846 1.177
0,26 Mix of other facilitiesa 2 (2,14) 2 1 2 1 4 3
0,18
Ownership - university 12 7 19% 3 15% 2 8%
Ownership - public 21 14 38% 0 0% 7 28%
Ownership - private 12 4 11% 7 35% 1 4%
Ownership - university-public 8 2 5% 2 10% 4 16%
Ownership - triple helix 8 5 14% 0 0% 3 12%
Ownership - university private 1 0 0% 0 0% 1 4%
Ownership - public-private 20 5 14% 8 40% 7 28%
aThevaluesfor'mixofleisureandotherfacili5es'arethemodesthenfollowedbythestandarddevia5on.bThevariable‘surfaceareasite’islistedasmeansandfollowedbythestandarddevia5on.
L x W
4b. Overview ‘research locations’
Predictor importance
Cluster variables Total sample
(N=82) Innovative locations
(N=37) Research locations
(N=20) Cooperative locations
(N=25)
1,00 Research institutes presence 53 8 22% 20 100% 25 100%
0,95 Higher educational institutions presence 54 9 24% 20 100% 25 100%
0,59
Laboratory present 42 18 49% 18 90% 6 24%
Shared laboratory present 28 9 24% 0 0% 19 76%
Laboratory absent 12 10 27% 2 10% 0 0% 0,33 Mix of leisure facilitiesa 0 (1,17) 0 1 0 1 3 1
0,31 Surface area site (1.000 m2)b 364 (758) 79 177 288 413 846 1.177
0,26 Mix of other facilitiesa 2 (2,14) 2 1 2 1 4 3
0,18
Ownership - university 12 7 19% 3 15% 2 8%
Ownership - public 21 14 38% 0 0% 7 28%
Ownership - private 12 4 11% 7 35% 1 4%
Ownership - university-public 8 2 5% 2 10% 4 16%
Ownership - triple helix 8 5 14% 0 0% 3 12%
Ownership - university private 1 0 0% 0 0% 1 4%
Ownership - public-private 20 5 14% 8 40% 7 28%
aThevaluesfor'mixofleisureandotherfacili5es'arethemodesthenfollowedbythestandarddevia5on.bThevariable‘surfaceareasite’islistedasmeansandfollowedbythestandarddevia5on.
L x W
4c. Overview ‘cooperative locations’
Significant on p = .05 (yellow) or .001 (purple) level (2-tailed; χ2 or Fisher’s Exact-test). *Part of clustering variables ‘mix of leisure/other facilities’
Knowledge intensiveness Innovative (N=37) Research (N=25) Cooperative (N=37)
Small and medium enterprises presence 27 73% 19 95% 24 96%
Multinational companies presence 19 51% 16 80% 20 80% Size
Multiple building location 14 38% 17 85% 20 80%
Resident organisations - less than 50 21 57% 5 25% 3 12%
Resident organisations - More than 100 11 30% 8 40% 16 64%
Leisure facilities*
Hotel 2 5% 4 20% 10 40%
Sport centres 5 14% 7 35% 17 68%
Sporting grounds 4 11% 3 15% 15 60%
Other facilities*
Banking 2 5% 4 20% 12 48%
Child care 6 16% 5 25% 18 72%
Medical 5 14% 3 15% 11 44%
Residential housing 3 8% 4 20% 8 32%
Shops (food) 7 19% 8 40% 15 60%
Services
Consultancy 33 89% 9 45% 19 76%
Venture capital access 25 68% 9 45% 20 80%
5. Test of differences between clusters