Interregional collaboration for innovation. Rationale, empirical results and policy.

Transcription

1 Interregional collaboration for innovation. Rationale, empirical results and policy. Agnieszka Olechnicka S3 Platform peer Review Workshop Transborder cooperation Baiona, Spain, 6 th November 2014

2 Presentation outline 1. Why interregional collaboration for innovation is important? 2. How to measure collaboration for innovation? 3. What is the evidence of collaboration on the regional level within Poland? Close friend or distant partner dilemma? 4. The overview of Polish RIS- the outward looking dimension. 5. Beyond science-business relations. Bibliometrics analysis for support developing smart specialisations strategies.

3 Technologicaly advanced regions Sectoral approach presence of high-technology sectors Reykjavik Specialization in high-tech manufacturing Canarias Oslo Stockholm Helsinki Tallinn Guadeloupe Guyane Martinique Réunion HT manufacturing regions Technologicall- Advanced Regions (TAR) Dublin København Riga Vilnius Minsk Madeira EU average Specialization in high-tech services Acores London Paris Amsterdam Bruxelles/Brussel Luxembourg Berlin Praha Warszawa Kyiv This map does not necessarily reflect the opinion of the ESPON Monitoring Committee Low-tech regions HT services regions Bern Vaduz WienBratislava Budapest Kishinev Ljubljana Zagreb Beograd Bucuresti Sarajevo Lisboa Madrid Roma Podgorica Tirana Skopje Sofiya Ankara Athinai Ar Ribat El-Jazair Politecnico di Milano, Project KIT, 2011 Tounis Valletta Nicosia km Technologically-advanced regions 2007 NA Low tech regions Advanced manufacturing regions Advanced services regions Technologically-advanced regions Regional level: NUTS2 Source: Politecnico di Milano, 2011 Origin of data: EUROSTAT employment in high-tech sectors EuroGeographics Association for administrative boundaries KIT Project, ESPON,

4 Scientific regions Functional approach presence of functions like R&D and high education KIT Project, ESPON,

5 Networking regions Relation-based approach presence of interactive and collective learning processes KIT Project, ESPON, Formal links measured by external R&D expenditures, external patents applications, and funds from 5 FP EU. Informal links measured by copatenting, patent citations (interregional) and migrations of inventors.

6 Kishinev Budapest Bern Vaduz Ar Ribat Uneven distribution of knowledge and innovation, two stages taking Ljubljana Zagreb Beograd Bucuresti place in different regions, knowledge suppliers and recipients WienBratislava Sarajevo El-Jazair Politecnico di Milano, Project KIT, 20 Lisboa Ar Ribat Madrid El-Jazair Politecnico di Milano, Project KIT, 2011 Technologically-advanced regions Knowledge economy regions NA None TAR only Scientific regions only Networking regions only TAR and scientific regions TAR and networking regions Scientific and networking regions Integrated knowledge economy regions Tounis Roma Valletta Sofiya Podgorica Technologically-advanced Skopje regions Tirana Knowledge economy regions NA None Athinai TAR only Scientific regions only Networking regions only TAR and scientific regions TAR and networking regions Scientific and networking regions Ankara Nicosia km Regional level: NUTS2 Source: Politecnico di Milano, 2011 Integrated knowledge Origin of economy data: EUROSTATand regions RegPat EuroGeographics Association for administrative boundaries Typology Numerosity TAR only 9 Scientific only 11 Networking only 43 TAR and scientific 3 TAR and networking 19 Scientific and networking 29 TAR, scientific and networking 31 None 135 KIT Project, ESPON,

7 Polish articles with business affiliation in Web of Science bibliographic database; Polish articles with at least 1 business affiliation; 0,64% of all collection, 87% are written together with scientific institutions, Characteristics: smaller number of co-authors, national co-operation more important. Source: own calculations, based on WoS

8 dolnośląskie kujawsko-pomorskie lubelskie lubuskie łódzkie małopolskie mazowieckie opolskie podkarpackie podlaskie pomorskie śląskie świętokrzyskie warmińsko-mazurskie wielkopolskie zachodniopomorskie Share of relation generated by Firms Share of relations inside the region Matrix of business science relations based on publications, (Web of Science, ) R&D INSTITUTIONS from REGION F I R M S f r o m dolnośląskie % 71,1% kujawsko-pomorskie % 28,6% lubelskie % 42,1% lubuskie % 20,0% łódzkie % 40,7% małopolskie % 63,8% mazowieckie % 60,2% opolskie % 7,7% podkarpackie % 12,5% podlaskie % 57,1% pomorskie % 68,8% śląskie % 60,4% świętokrzyskie % 0,0% warmińsko-mazurskie % 25,0% wielkopolskie % 41,0% zachodniopomorskie % 25,0% Share of relation generated by R&D 13% 4% 7% 0% 4% 12% 29% 0% 1% 2% 8% 9% 0% 2% 8% 1% 54,5% 508 Source: own calculations, based on WoS

9 Foreign cooperation of Polish firms (WoS, ) Source: own calculations, based on WoS

10 Source: own elaboration based on interviews in firms Determinant of the geographical proximity role in science-business relations Personal contacts Temporary proximity Enterprise s needs Project phase Communication Geographical proximity

11 Outward looking direction in Polish RISs Revision of RISs made in 2006, and 2012; now repeated for selected regions. The role of regional collaboration for innovation is underestimated. New strategies identify the need for interregional collaboration, but often it is in diagnostic part but not in strategic part of the document or it is stated very generally with no examples. WIELKOPOLSKIE: To enable enterprises to be innovative and competitive internationally it is necessary to stimulate cooperation between business and research institutions, with no necessity to use solutions and technologies from abroad. (RIS web site) OPOLSKIE: For the purpose of the identification of smart specialisation in the region it was assumed that SS occurs only in the situation when the given technology, or its product is present in all three phases of the regional knowledge transfer (research, development and dissemination). (RIS)

12 Regional Innovation Strategy of the Westpomeranian Region for , Szczecin 2011 The Westpomeranian Region recognised its border location and neighbourhood with the two federal states of Germany: Brandenburg and Mecklenburg/West Pomerania as an developmental opportunity; SS: wind farms and devices for the biomass production; already several projects implemented by entities from the Westpomeranian Region together with the German partners within European Territorial Cross-border Cooperation Programme Mecklenburg - West Pomerania / Branderburg/the Westpomeranian Region.

13 Regional Innovation Strategy of the Śląskie Voivodeship for the years , Katowice 2012 Collaboration with the neighbouring regions: Małopolskie, Opolskie, Moravian-Śląskie Region in the Czech Republic and Žilina Region in Slovakia; Metameasure is focusing at increasing the amount of large, strategic projects in the European Horizon 2020 in the area of fundamental and applied research; SS: materials technologies, conventional and renewable power industry and IT; The mechanism of implementing the metameasure is based on two institutional pillars: European Grouping of Territorial Cooperation (TRITIA) and on cooperation strategy implementation system of the Śląskie and Małopolskie Voivodeships. Also, the agreement of 11 universities from the Polish-Czech- Slovakian borderland for innovative activities (PROGRES3) and the cooperation between Polish and Czech universities (Śląskie Universities Rectors Conference).

14 Beyond science-bussiness relations 1. Work in progress, exploratory study. 2. Aims: Deepen knowledge about the structure of the scientific networks of EU regions. Identification of changes in spatial patterns of scientific collaboration networks in Europe. 3. Source of data: Web of Science, Thomson Reuters 4. Time series: Spatial coverage: regions (NUTS2) of 27 European countries (262 regions) 6. Main characteristics of the dataset: circa 4 milion articles circa 6 milion affiliations

15 Source: A.Olechnicka and A.Płoszaj calculations, based on WoS Scientific specialisation of regions; 2010 LQ is a ratio that compares a region to a larger reference region (29 European countries) according to the share of publications in medicine in the overall publication output. It can reveal what makes a particular region unique in comparison to the European average. Location quotient (medicine) 2010

16 Source: A.Olechnicka and A.Płoszaj calculations, based on WoS International collaboration; UE 15 CEE 0.0 Share of international articles 2010

17 Network of relations among EU NUTS 2 regions; WoS, 2010 AT11 AT12 AT13 AT21 AT22 AT31 AT32 AT33 AT34 BE10 BE21 BE22 BE23 BE24 BE25 BE31 BE32 BE33 BE34 BE35 BG31 BG32 BG33 BG34 BG41 BG42 CY00 CZ01 CZ02 CZ03 CZ04 CZ05 CZ06 CZ07 CZ08 DE11 DE12 DE13 DE14 DE21 DE22 DE23 DE24 DE25 DE26 DE27 DE30 DE41 DE42 DE50 DE60 DE71 DE72 DE73 DE80 DE91 DE92 DE93 DE94 DEA1 DEA2 DEA3 DEA4 DEA5 DEB1 DEB2 DEB3 DEC0 DED2 DED4 DED5 DEE0 DEF0 DEG0 DK01 DK02 DK03 DK04 DK05 EE00 ES11 ES12 ES13 ES21 ES22 ES23 ES24 ES30 ES41 ES42 ES43 ES51 ES52 ES53 ES61 ES62 ES70 FI13 FI18 FI19 FI1A FI20 FR10 FR21 FR22 FR23 FR24 FR25 FR26 FR30 FR41 FR42 FR43 FR51 FR52 FR53 FR61 FR62 FR63 FR71 FR72 FR81 FR82 FR83 GR11 GR12 GR13 GR14 GR21 GR22 GR23 GR24 GR25 GR30 GR41 GR42 GR43 HU10 HU21 HU22 HU23 HU31 HU32 HU33 IE01 IE02 ITC1 ITC2 ITC3 ITC4 ITD1 ITD2 ITD3 ITD4 ITD5 ITE1 ITE2 ITE3 ITE4 ITF1 ITF2 ITF3 ITF4 ITF5 ITF6 ITG1 ITG2 LT00 LU00 LV00 MT00 NL11 NL12 NL13 NL21 NL22 NL23 NL31 NL32 NL33 NL34 NL41 NL42 NO01 NO02 NO03 NO04 NO05 NO06 NO07 PL11 PL12 PL21 PL22 PL31 PL32 PL33 PL34 PL41 PL42 PL43 PL51 PL52 PL61 PL62 PL63 PT11 PT15 PT16 PT17 PT18 PT20 PT30 RO11 RO12 RO21 RO22 RO31 RO32 RO41 RO42 SE11 SE12 SE21 SE22 SE23 SE31 SE32 SE33 SI01 SI02 SK01 SK02 SK03 SK04 CH01 CH02 CH03 CH04 CH05 CH06 CH07 UKC1 UKC2 UKD1 UKD3 UKD4 UKD6 UKD7 UKE1 UKE2 UKE3 UKE4 UKF1 UKF2 UKF3 UKG1 UKG2 UKG3 UKH1 UKH2 UKH3 UKI1 UKI2 UKJ1 UKJ2 UKJ3 UKJ4 UKK1 UKK2 UKK3 UKK4 UKL1 UKL2 UKM2 UKM3 UKM5 UKM6 UKN0 Source: A.Olechnicka and A.Płoszaj calculations, based on WoS

18 10 the most important regions in the scientific collaboration; Web of Science, % 12% 10% 8% 6% 4% 2% 0% 12,5% 9,2% 7,4% Galicia 4,8% 3,3% 2,8% 2,4% 2,4% 2,3% 2,0% 14% 12% 10% 8% 6% 4% 2% 0% 13,3% 10,8% Regiao Norte 3,5% 2,8% 2,7% 2,3% 2,3% 1,4% 1,2% 1,2% 25% 20% 15% 10% 5% 0% 24,4% 12,1% Burgundy 6,7% 5,4% 5,3% 4,7% 4,6% 4,5% 4,2% 4,2% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 45,5% 10,4% 10,2% West Finland 3,3% 2,1% 2,1% 1,8% 1,8% 1,6% 1,3% Source: A.Olechnicka and A.Płoszaj calculations, based on WoS

19 Conclusions 1. There is a need for interregional collaboration: Research results: KBE is unevenly distributed within European space: suppliers and receivers of knowledge; Theoretical concepts: local buzz vs. global pipelines, strength of weak ties, geographical and cognitive proximity; 2. The Polish example confirms that geographical proximity seems to be the additional not the crucial factor in building innovative relations between firms and science; 3. External links in science-business relations are underestimate in the Polish regional innovation policies (RIS); positive tendency and some promising examples; 4. Each region should be investigated separately, the successful business- science relation needs not only science sphere with good products and entrepreneurial attitude but also absorption capacity of business sector, which is sometimes lacking as well as efficient sector of intermediate institutions facilitating knowledge transfer. 5. Bibliometric analyses could serve as an useful instrument for recognising endogenous potential, collaboration patterns for smart specialisation strategies building.

20 Thank you for your attention