a typology study on science parks in europe · web survey eu park managers population: members of...

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

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

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


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

Innovation

Research

Cooperation

[email protected] www.knowledgesharing.space +31 40 247 2937

•  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






(N=25)



0,59






0,18









L x W

4a. Overview ‘innovative locations’






(N=25)



0,59






0,18









L x W

4b. Overview ‘research locations’






(N=25)



0,59






0,18









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

a typology study on science parks in europe · web survey eu park managers population: members of...

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