Strengthening the Knowledge Base for Innovation in the European Union

Strengthening the knowledge base for innovation in the European Union

Edited by Marzenna Anna WERESA

Authors: Adam KARBOWSKI $UNDGLXV]0LFKDï KOWALSKI Marek LACHOWICZ 0DïJRU]DWD6WHIDQLDLEWANDOWSKA Marta MACKIEWICZ Tomasz M. NAPIÓRKOWSKI 0DïJRU]DWDRÓSZKIEWICZ Marzenna Anna WERESA Reviewer Prof. dr hab. Tadeusz Baczko Institute of Economics, Polish Academy of Sciences

Cover design and title pages Przemysław Spiechowski

Photo on the cover by Omelchenko/Shutterstock

Publisher Magdalena Ścibor

Production coordinator Mariola Iwona Keppel

Typesetting Krzysztof Świstak

The “I3U– Investigating the Impact of the Innovation Union” project leading to this publication has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 645884.

This publication reflects only the author’s view and that the Agency/European Commission is not responsible for any use that may be made of the information it contains.

ISBN: 978-83-01-20215-6

Polish Scientific Publishers PWN G. Daimlera 2 02-676 Warsaw Poland tel. +48 22 69 54 321; fax +48 22 69 54 031 e-mail: [email protected]; www.pwn.pl Contents

List of abbreviations ...... 9

Preface ...... 12

Marzenna Anna Weresa 1. Innovation Union initiative – an overview ...... 15 1.1. Introduction ...... 15 1.2. Innovation policy as an element of the innovation system ...... 15 1.3. History of the innovation policy in the European Union ...... 17 1.4. The Europe 2020 strategy and its Innovation Union initiative ...... 20 1.5. The European Union’s innovation performance in the years 2010-2017 in a comparative perspective ...... 23 1.6. Summary and conclusions ...... 27 References ...... 28

Małgorzata Stefania Lewandowska, Małgorzata Rószkiewicz, Marzenna Anna Weresa 2. Additionality from public support to R&D and innovation in the European Union ...... 31 2.1. Introduction ...... 31 2.2. Public financial support for innovation in the EU ...... 32 2.3. Theoretical background and hypothesis development ...... 33 2.4. Sample description and research method ...... 42 2.5. Results ...... 45 2.6. Discussion and conclusions ...... 47 References ...... 54

Tomasz M. Napiórkowski 3. The impact of Framework Programs on innovativeness in the European Union ...... 59 3.1. Introduction ...... 59 3.2. The FP7, CIP and H2020 programs in a nutshell ...... 60 3.3. Positioning of the FP7, CIP and H2020 key elements in the literature on innovation . . 62 3.4. The impact and efficiency of the FP7, CIP and H2020 ...... 68 3.4.1. The impact and efficiency of the FP7 ...... 69 6 Contents

3.4.2. The impact and efficiency of the CIP ...... 71 3.4.3. The impact and efficiency of the H2020 ...... 72 3.5. The relationship between innovation input and innovation output from the innovation actors’ perspective ...... 73 3.5.1. The role of actors in the FP7 ...... 74 3.5.2. The role of actors in the H2020 ...... 75 3.6. Conclusions ...... 76 References ...... 78 Appendix ...... 82

Arkadiusz Michał Kowalski 4. Supporting the research and innovation base through priority European research infrastructures ...... 100 4.1. Introduction ...... 100 4.2. Definition, typology and basic features of research infrastructures ...... 101 4.2.1. Definition, basic features and benefits of research infrastructures...... 101 4.2.2. Types of European research infrastructures ...... 103 4.2.3. Monitoring and evaluating the economic impact of research infrastructures, with a focus on potential indicators and data ...... 105 4.3. Theoretical background for analyzing research infrastructures ...... 108 4.3.1. Social capital theory, innovative milieu, and the concept of creative class . . . . . 108 4.3.2. Innovation systems theory ...... 110 4.3.3. Economic network theory ...... 112 4.4. Why the European Union needs to support the development of research infrastructures ...... 113 4.4.1. Fragmentation of European investments in research infrastructure ...... 113 4.4.2. The high complexity (scale and costs) of European research infrastructures . . . 114 4.4.3. The complexity of projects in partnerships ...... 115 4.4.4. The inherent technical complexity of projects ...... 116 4.4.5. The need to solve key societal challenges ...... 117 4.5. EU policy measures to support priority research infrastructures ...... 118 4.5.1. Solution to the identified problems – European Roadmap for the ESFRI...... 118 4.5.2. Geographical distribution of FP7 (part INFRA) spending ...... 118 4.5.3. FP7 and Horizon 2020 program (part INFRA) investments by main groups of innovation systems ...... 121 4.5.4. Interactions between countries and their organizations engaged in Horizon 2020, part INFRA projects ...... 124 4.5.5. Analysis of FP7 and Horizon 2020 program (part INFRA) investments in research infrastructures by types of actors ...... 126 4.6. The impact of research infrastructures on European innovation – findings from empirical research, including the survey research ...... 128 4.6.1. Results of the survey research conducted on coordinators of research infrastructures ...... 128 Contents 7

4.6.2. The results of the survey research conducted on users of research infrastructures ...... 137 4.7. Conclusions ...... 143 References ...... 148

Małgorzata Stefania Lewandowska 5. The role of Global Research Infrastructures as a tool of innovation policy ...... 153 5.1. Introduction ...... 153 5.2. Global Research Infrastructures as an innovation policy tool ...... 154 5.3. Theoretical perspective ...... 158 5.3.1. Common-Pool Resources Approach ...... 158 5.3.2. The concept of Critical Mass ...... 160 5.3.3. Economics of Network Theory ...... 162 5.4. Implementation of Global Research Infrastructures ...... 163 5.4.1. Legal framework for Global Research Infrastructures ...... 163 5.4.2. Participation framework for Global Research Infrastructures ...... 164 5.4.3. Financial framework for Global Research Infrastructures ...... 165 5.5. Impact of Global Research Infrastructures ...... 169 5.5.1. Effects of financial and institutional leverage derived from non-EU countries ...... 169 5.5.2. Global Research Infrastructure performance indicators ...... 170 5.5.3. Results of the survey on European Research Infrastructure Coordinators . . . . . 170 5.5.4. Results of the survey on participants of projects financed by the FP7 INFRA and H2020 INFRA ...... 172 5.6. Conclusions ...... 173 References ...... 175

Marta Mackiewicz 6. Boosting public sector and social innovation in Europe ...... 181 6.1. Introduction ...... 181 6.2. Public sector and social innovation: the concept ...... 182 6.2.1. Understanding the role of public sector and social innovation ...... 183 6.2.2. Financing research on social innovation and its role in society and the economy ...... 184 6.2.3. The European Public Sector Innovation Scoreboard as a basis for further work to benchmark public sector innovation ...... 185 6.3. Theoretical background for analyzing the impact of financing research on public sector and social innovation ...... 186 6.4. Impact of the research program on public sector and social innovation ...... 188 6.5. Impact assessment of the pilot European Public Sector Innovation Scoreboard (EPSIS) ...... 192 8 Contents

6.6. Actors crucial to fostering public sector and social innovations ...... 193 6.7. Conclusions and policy recommendations ...... 199 References ...... 200

Adam Karbowski and Marek Lachowicz 7. European Institute of Innovation and Technology (EIT): towards the excellence of European science ...... 205 7.1. Introduction ...... 205 7.2. Theoretical perspectives on the EIT contribution to innovation ...... 209 7.2.1. The knowledge triangle concept ...... 209 7.2.2. The I-U collaboration approach ...... 210 7.2.3. The networked innovation approach ...... 211 7.3. The Strategic Innovation Agenda of the EIT ...... 212 7.4. Actors involved in the EIT policy ...... 214 7.5. Impact of the EIT and KIC actions ...... 221 7.6. Conclusions ...... 227 References ...... 230 Appendices ...... 234

Marzenna Anna Weresa 8. Implementing evidence-based policies: lessons learned from Joint Research Centre (JRC) activity ...... 240 8.1. Introduction ...... 240 8.2. Evidence-based policy and its impact on innovation: a literature review ...... 241 8.3. The Joint Research Centre (JRC) as a science support for European policy makers . . . 246 8.3.1. Strengthening the European science base for policy making through the JRC ...... 247 8.3.2. Quality of JRC scientific papers containing evidence for policy making ...... 252 8.4. The impact of science-based policy on innovation: the case of the Joint Research Centre ...... 257 8.5. Conclusions ...... 266 References ...... 267

Marzenna Anna Weresa Final conclusions ...... 270

List of tables ...... 274

List of figures and graphs ...... 277

Editor and author bios ...... 279 List of abbreviations

AEG Assessment Expert Group CATI Computer-Assisted Telephone Interview CAWI Computer-Assisted Web Interview CEE Central and Eastern Europe CEO Chief executive officer CERN European Organization for Nuclear Research CIP Competitiveness and Innovation Framework Program CIS Community Innovation Survey CORDA COmmon Research DAta Warehouse CPR Common Pool Resources DG-RTD Directorate-General for Research EBP Evidence-based policy E-CORDA External COmmon Research DAta Warehouse EC European Commission EEN Enterprise Europe Network EFTA European Free Trade Association EIP Entrepreneurship and Innovation Program EPSIS European Public Sector Innovation Scoreboard EIS European Innovation Scoreboard EIT RIS EIT Regional Innovation Scheme EIT European Institute of Innovation and Technology ELIXIR European Life-Science Infrastructure for Biological Information EMBL European Molecular Biology Laboratory EMMA European Mouse Mutant Archive EPOS European Plate Observing System EPSIS European Public Sector Innovation Scoreboard ERA European Research Area ERC European Research Council ERDF European Regional Development Fund ERIC European Research Infrastructure Consortium ESF European Social Fund ESFRI European Strategy Forum on Research Infrastructures ESIF European Structural and Investment Funds ESO European Southern Observatory ESS The European Spallation Neutron Source 10 List of abbreviations

EU European Union EUR Euro FET Future and Emerging Technologies FP6 Sixth Framework Program FP7 Seventh Framework Program FP7 INFRA – part of the FP7 budget devoted to projects related to research infrastructures FTE Full-time Equivalent GDP Gross Domestic Product GEOSS Global Earth Observation System of Systems GOVERD Government Expenditure on R&D G8 Group of eight of the most influential world economies GRI Global Research Infrastructure GRIs Global Research Infrastructures GSO Group of Senior Officials on Global Research Infrastructures H2020 Horizon 2020 H2020 INFRA – part of the Horizon 2020 budget devoted to projects related to research infrastructures HERD Higher Education Expenditure on R&D HES Secondary and Higher Education Establishments ICGC International Cancer Genome Consortium ICT Information and Communications Technology ICT-PSP Information Communication Technologies Policy Support Program IDRIS International Distributed Research Infrastructure IEE Intelligent Energy Europe Program IMPC International Mouse Phenotyping Consortium IPERION Integrated Platform for the European Research Infrastructure on Cultural Heritage IPR Intellectual Property Right ISI International Scientific Indexing ISIS World-leading center for research in the physical and life sciences at the STFC Rutherford Appleton Laboratory I-U Industry – University JRC Joint Research Centre KIC Knowledge and Innovation Communities KPIs Key Performance Indicators LEIT Leadership in Enabling and Industrial Technologies LNGS Laboratori Nazionali del Gran Sasso MERIL Mapping of the European Research Infrastructure Landscape List of abbreviations 11

MS Member State NA Not available NGO Non-Government Organization OECD Organization for Economic Cooperation and Development OSI Open Social Innovation OTH Other Entities PA Priority Area PETRA III – PETRA III at DESY (Deutsches Elektronen-Synchrotron) PRC Private for Profit Companies PUB Public Bodies R&D Research and Development REC Research Organizations RI Research Infrastructure SIA Strategic Innovation Agenda SII Summary Innovation Index SIS Science in Society SKA The Square Kilometer Array SME Small and Medium-Sized Enterprises SNA Social Network Analysis SPIRAL 2 Système de Producti on d‘Ions Radioactifs et Ligne de 2éme generation SSH Socio-economic Sciences and Humanities SWAFS Science with and for Society WIPO World Intellectual Research Organization WoS Web of Science

Preface

The main objective of the book is to evaluate the effects of EU policy instruments that promote a stronger European dimension of the R&D base in the European Union. This objective can be split into a few specific objectives, which are defined as follows: • Evaluation of the impact of public R&D support on innovation in selected European countries; • Identifying whether EU research and innovation programs have industry- driven priorities and address societal challenges, how they impact innovation and to what extent SMEs are involved in these programs; • Evaluation of the advancement in the construction of priority European research infrastructures and their contribution to the innovation potential of the EU; • Investigating the state of implementation and assess the direct impact of global research infrastructures (GRIs) (which owing to cost can only be developed on a global scale) on the innovation potential of the EU; • Discussing how public sector innovation and social innovation are supported in the EU and identifying their impact on the EU economies; • Evaluation of the role of the European Institute of Innovation and Technology in stimulating innovation in Europe by integrating education, research and innovation, and contributing to excellence of European science through the introduction of the “EIT Degree”. • Assessment of the contribution of the Joint Research Centre and European Forum on Forward Looking Activities to devising comprehensive and proactive European research and innovation policies. These objectives are addressed in the subsequent chapters of this monograph. The book consists of eight chapters that discuss different instruments of innovation policy designed at the European Union level and included in the Europe 2020 Strategy, in particular in one of its flagship initiatives known as “the Innovation Union” (European Commission, 2010). Under this initiative the EU Member States agreed to implement 34 Commitments that are in fact policy instruments aimed at creating an innovation union in Europe. This initiative and all its 34 commitments are discussed in detail in the first chapter of this book. The second chapter focuses on the effects of public support given to research and development (R&D). Using the microdata for 13 EU countries collected in the Community Innovation Survey (CIS) in 2012-2014, it measures the impact of public Preface 13 financial support from the European Union on: 1) innovation output (measured as a logarithm of the fraction of turnover from innovative products introduced in 2010-2012 in the total turnover); 2) input for innovation (measured by additional internal R&D or by the acquisition of advanced machinery, equipment to produce new or significantly improved products and processes); 3) the behavior of innovative enterprises (measured by the cooperation with suppliers, customers, competitors, research institutes, universities). Chapter 3 examines the impact of EU Framework Programs on innovation in the European Union. The three following programs have been analyzed in depth: The Seventh Framework Program (FP7), the Competitiveness and Innovation Framework Program (CIP) and Horizon 2020 (H2020). This analysis links the determinants of innovation distinguished in theoretical literature to the design of these three programs and their implementation. Furthermore, the chapter shows the interactions among the key actors involved in the implementation of EU framework programs. It constitutes a basis for concluding how these programs have been translated into innovation output. Chapters 4 to 8 of this monograph offer a more detailed insight into the role of selected elements of the European Framework Programs in the process of strengthening the knowledge base for innovation in the EU. The aim of Chapter 4 is to study the importance of the European research infrastructures financed from the budget of the FP7 and H2020. In particular, it shows how these infrastructures can facilitate international scientific cooperation and build a critical mass of knowledge, investment and talented people, thus, contributing to the development of the European Research Area. Chapter 5 extends this analysis by adding a global dimension to the construction of research infrastructures. It shows that research infrastructures that operate on a global scale can be a policy tool for boosting international research collaboration and examines whether this is effective. Furthermore, the role of non-European partners in this collaboration is assessed. The empirical part of both chapters that are devoted to European and global infrastructures is based on the data collected during surveys conducted among the coordinators and users of these infrastructures. The EU research programs also support public sector and social innovation. Under the FP7 two programs (i.e. SOCIETY and the SWAFS) were dedicated to these themes. New forms of innovation, including social innovation are also part of the H2020. The effects of the activities undertaken and financed under this social innovation umbrella are studied in Chapter 6. Furthermore, the actors involved in the appraisal of public and social innovations, and their interactions have been described. This study allows to derive recommendations for innovation policy at the European and national level. 14 Preface

In Chapter 7 innovation is presented through the lens of the European Institute of Innovation and Technology (EIT). The contribution of the EIT to the European innovation system is analyzed within the framework of a knowledge triangle, i.e. innovation, research and higher education. The chapter analyzes Knowledge and Innovation Communities (KICs) and the way how they combine education, research and business capacity to bring innovative solutions to the market. The focus of Chapter 8 is on scientific evidence and its impact on the policy- making process and through the policies on innovation in the EU Member States. The analysis covers the knowledge and evidence produced at the EU level by the Joint Research Centre (JRC), which is a scientific unit of the European Commission organized as a Directorate-General. It provides policy makers in the EU with in-house science support. First, the rationale for evidence-based policies is explained. This is followed by the assessment of the JRC research activity seen in the context of its impact on the European science base for policy making. The last part of this monograph summarizes the main findings of the research, showing how the knowledge base for innovation has been developing in the European Union. It also provides policy recommendations based on the research results. This book is a result of the research project “Investigating the Impact of the Innovation Union” funded from the European Union’s Horizon 2020 Research and Innovation Program under grant agreement number 645884. The Authors would like to acknowledge the inspiration and feedback received from all project partners and reviewers at all stages of the project implementation. Their comments and thoughts expressed in the numerous discussions contributed to both the conceptual and empirical parts of this research.

Marzenna Anna Weresa Marzenna Anna Weresa

Chapter 1 Innovation Union initiative – an overview

1.1. Introduction

The foundations of modern innovation policy were developed in the last decade of the twentieth century. The contemporary innovation policy (in the 21st century called the STI policy) is of a horizontal nature and it is based on a broad understanding of innovation, which assumes that innovation is a result of an interactive process. Therefore, the role of the state is to facilitate and coordinate the interactions among actors in innovation systems. The change in the interpretation of the innovation policy functions was a gradual process, and corresponded with the evolution of innovation models, from a linear model of innovation, through a chain-linked model and a network model to the innovation systems approach. Different models of innovation constitute a base for distinguishing a few generations of innovation policy (European Commission, 2002, pp. 49-50; Smith, 2010, p. 366). The evolution of innovation policy observed during the past two decades includes its priorities, instruments, and the relationship of this policy with other types of state interventions in the economy. This evolution has also been taking place in the European Union. The latest initiative, which shapes innovation policy in the European Union, was launched in 2010 as one of the flagship projects of the Europe 2020 strategy entitled “the Innovation union” (European Commission, 2010c). The aim of this chapter is to present this initiative in the context of the innovation performance of the EU in the world economy.

1.2. Innovation policy as an element of the innovation system

There are different approaches explaining innovation, which are reflected in the scope of innovation policy. Some scholars understand innovation in a narrow sense that focuses on inventions, others use a systemic perspective and look at the entire 16 1. Innovation Union initiative – an overview innovation process from the creation of new ideas to their implementation and diffusion. Therefore, the question is, how to define innovation policy, in a narrow or broad view? Edler and Fagerberg (2017, p. 5) identified three main types of innovation policy: • Mission-oriented policy, which focuses on selected priority areas. This term was originally used by Ergas (1987) and has been re-interpreted nowadays (ESIR, 2017; Mazzucato, 2016; 2018). • Invention-oriented policy, which focuses on research and development and how it is translated into inventions. This type of policy is not very much concerned with the implementation and diffusion of new ideas. • System-oriented policy, which focuses on system-level elements (capabilities of actors, interactions among them, etc.). In fact, innovation policy can be considered as part of the innovation system (national, regional, sectoral, technological). The impact of the policy makers on the decisions and behavior of entities operating within the innovation system can be direct or indirect. Direct impact can be seen in regulations of research and entrepreneurial activity. Furthermore, regulations that are applied to other spheres (e.g. new competition rules or regulations of the functioning of the labor market) may indirectly affect innovation activity as well as the transfer and diffusion of innovation in the economy. Regulatory changes determine the norms and values of society. At the same time, the need for growth and change can set the pace and direction of regulatory adjustments. Innovation, however, is determined not only by changes in regulations, but also by the structure of the economy (or sector), society and the market (Rothwell, Zegveld 1982, p. 116). Furthermore, globalization and the development of ICT have led to changes in institutional and cultural relations, which facilitate the transfer of technological and educational solutions or institutions from one country to another (Kleer, 2016, p. 152). Such changes in culture and education supported by innovation policy may also speed up innovation processes. Innovation policy interacts with the entire innovation system, it refers to both the private and the public sector. These interdependencies are illustrated by the triple helix model developed in the 1990s by H. Etzkowitz and L. Leydersorff (1996). This model describes innovation as a spiral, which includes reciprocal links and feedbacks occurring in the institutional environment between the three groups of actors involved in the development of innovations that represent science, business and government (Etzkowitz, 1998). The role of innovation policy is emphasized in the evolutionary approach to the triple helix model, in which the government sector plays a key role 1.3. History of the innovation policy in the European Union 17 setting the normative framework for the other two spheres (science and business), and their interactions (Leydesdorff, Meyer 2006; Leydesdorff et. al., 2017). Some research on the national innovation systems draws more attention to the role of individual actors in influencing politics of development, suggesting that the whole concept of the innovation system can be used as a policy tool (Watkins et al., 2015, p.1417). It should also be pointed out that the evolution of innovation policy observed during the past decades was accompanied by new developments in measuring scientific, technological and innovation activities, which has been reflected in the methodology of the European Innovation Scoreboard (EIS, 2018) as well as in the methodological guidelines of the OECD presented in the latest edition of the Frascati Manual (OECD, 2015).

1.3. History of the innovation policy in the European Union

The concept of innovation policy emerged in the literature in the second half of the twentieth century as a combination of science and technology policy, and industrial policy (Rothwell, Zegveld 1982, p. 1). Nowadays, innovation policy is regarded as a part of economic policy, and its main functions are: • strengthening linkages in the national innovation system; • creating favorable conditions for the implementation of new solutions (innovation); • fostering structural changes in the industry (e.g. changes in technology, quality improvements); • enabling companies to reap the benefits of globalization and international cooperation. Modern innovation policy integrates some selected elements of the policy toward science and technology as well as industrial policy in order to promote development, through an effective use of new products, services and processes by enterprises, public and private organizations as well as individual people. The ‘traditional’ innovation policy, which corresponds to a linear model of innovation, is called the first-generation innovation policy (European Commission 2002, p. 50). Public support was focused primarily on the development of science and on technological progress, and was directed at universities and other academic institutions engaged in R&D. Simplifying this approach, it can be said that it was closer to R&D policy, or science and technology policy, rather than to innovation policy. 18 1. Innovation Union initiative – an overview

The second-generation innovation policy addressed the non-linearity of innovation, feedback loops, interdependencies and interactions in the innovation process. Public support was focused on the relationship between science and business, as well as the application of scientific achievements in practice. This change in the policy objectives was reflected in the transition from a linear understanding of innovation to interactive models, including the innovation systems approach. As a result, the policy focus shifted to the development of innovation systems and clusters. Policy interventions were aimed at improving the functioning of innovation networks. Therefore, this policy promoted the development of intermediary organizations, linking science and business, and dealing with the transfer of knowledge. Thus, the development of innovation systems, flexible enough to adapt easily to a changing environment, was among the main policy concerns. This aim comprises three interrelated issues: • stimulating educational institutions and businesses; • developing an integrated and coordinated vision for the future; designing and implementing corresponding instruments that boost innovation; • developing policies that could allow continuous improvements and adjustments to the new requirements of the economy (Lundvall, Borrás 1997, p. 61). In addition, the role of regional and local administrations in shaping the innovation strategy and the ensuing innovation policy increased at that time. Innovation policy was co-created by central and regional administration, and encompassed a variety of entities involved in the innovation process: research institutions, enterprises and intermediary organizations in knowledge transfer, including the networks created by all these institutions. Furthermore, a support system expanded in order to indirectly influence research institutions through public investments in research infrastructure. The new tools implemented at that time also included programs directed at enterprises that implemented new solutions and new technologies. The further development of the knowledge-based economy and the emergence of new forms of innovation (social innovation, inclusive innovation) in the twenty- first century created new challenges. In order to respond to them the third generation of innovation policy was introduced (European Commission, 2002, p. 50). What were the most important features that characterize this generation of innovation policy? First, its focus was on supporting innovations, regardless of the place where they arise (i.e. research organizations, business sector, public administration or the whole society). Second, innovation was supported in many different areas, which belong to the range of interest of other policies (e.g. competition policy, educational policy). Third, there was a further decentralization in the design and implementation of innovation policies, with regional and local administration playing an increasing 1.3. History of the innovation policy in the European Union 19 role in these processes. An even stronger emphasis was put on entrepreneurship, the commercialization of knowledge and supporting the interactions within the innovation system. Therefore, the third generation of innovation policy was related to many legislative areas, and in addition, greater attention was paid to the process of policy development and management of its implementation, monitoring and the evaluation of its effectiveness (European Commission, 2002, p. 51). The contemporary innovation policy that emerged in the second decade of the twenty-first century has been shaped by the rapid development of information and communication technologies, changing the nature of innovations as well as introducing their new forms (social innovation, institutional innovation, eco- innovation). The EU approach to innovation policy evolving from linear to that focusing on networks and clusters as well as mainstreaming innovation into sectorial policies was finally framed as an Innovation Union strategy. The key document that set the scene for innovation policy in the EU was published in 1995. It was a Green Paper on Innovation. It identified the main challenges of innovation in Europe, in particular, it pointed out that “One of Europe’s major weaknesses lies in its inferiority in terms of transforming the results of technological research and skills into innovations and competitive advantages.” (European Commission, 1995, p. 5). The “European paradox” still exists in the 21st century despite many different policy initiatives that have been implemented since the 1990s. The Lisbon Strategy signed in 2000 was another attempt undertaken at the EU level to strengthen the innovation performance of EU Member States. It had the target to make the EU “[...] the most competitive and dynamic knowledge- based economy in the world capable of sustainable economic growth with more and better jobs and greater social cohesion” (European Parliament, 2000). The strategy represented a ten-year reform program, which was designed as a response to global challenges. It focused on strengthening the EU’s research capacity and entrepreneurship, promoted the development of the information society as well as the modernization of employment policy and social protection systems. The main goal regarding research and innovation was to increase the level of expenditure on R&D to 3% of GDP with 2/3 financed by business. After a mid-term review many problems with implementation were revealed (Kok, 2004) and the strategy was re-launched. However, evaluations of the Lisbon Strategy showed some criticism regarding the strategy’s achievements. These evaluation studies pointed out that the strategy did not reduce the innovation gap between the best and the worst performing countries and its influence on reforms in EU Member States was modest (Tilford, Whyte, 2010, p. 3; European Commission, 2010a, p. 4). The European Commission 20 1. Innovation Union initiative – an overview assessed the overall results of the Lisbon Strategy as positive, although the target related to R&D expenditures (3% of GDP spent on R&D) was not reached at the EU level. Nevertheless, according to Eurostat data, R&D expenditures in the whole EU grew from 1.77% of GDP in 2000 to 1.93% in 2010. Sweden and Finland were the leaders in this respect as their ratios of R&D spending to GDP were higher than 3% already in 2000, and these high levels were maintained throughout the whole decade. Denmark, Germany and Austria were very close to the target set for 2010, but many EU countries (e.g. Greece, Slovakia, Lithuania, Latvia, Poland) did not make much progress. Nevertheless, the Lisbon Strategy brought some growth in R&D and employment, and it also induced a more dynamic business environment and reduced bureaucracy. In a way it helped to define future priority areas and contributed to a “broad consensus on the reforms that the EU needs” (European Commission, 2010a, p. 3). The Lisbon Strategy also brought an intensification of policy learning and exchange of good practices, which was a good base for the formulation of a strategy for the second decade of the 21st century, i.e. the Europe 2020 strategy. The main lessons from the Lisbon Strategy taken into account in the design and implementation of the strategy for the next decade can be summarized as follows (European Commission, 2010a; Tilford, Whyte, 2010): • clear and measurable objectives can improve the effectiveness of a strategy; • national targets can strengthen the motivation for strategy implementation; • strengthening the external dimension can help to prepare the EU for globalization and help shaping it; • communicating both the benefits of the strategy and the necessity to reform the EU can increase citizen support and involvement; • better coordination between the EU level and the national level contributes to successful implementation.

1.4. The Europe 2020 strategy and its Innovation Union initiative

In 2010 the Lisbon Strategy for growth and jobs was replaced by the Europe 2020 strategy. It defined five specific objectives related to: (1) R&D and innovation, (2) education, (3) employment, (4) poverty and social inclusion, and (5) climate change along with energy policy (European Commission, 2010b). The main goals in these five areas are presented in Table 1.1. 1.4. The Europe 2020 strategy and its Innovation Union initiative 21

Table 1.1. Headline targets of the Europe 2020 strategy – an overview

Key strategy areas Targets that should be achieved by the year 2020 R&D and innovation – 3% of GDP should be invested in R&D; – A new indicator to track innovation should be developed. Education – Th e share of early school leavers should be reduced to 10%; – Th e share of the population aged 30-34 having completed tertiary education should increase to at least 40%. Employment – Th e employment rate of the working-age population (i.e. aged 20-64) should reach at least 75%. Poverty and social – Th e number of Europeans living below national poverty lines should inclusion decrease by 25%; – At least 20 million fewer people should be at risk of poverty and social exclusion. Climate change and – Greenhouse gas emissions should be reduced by at least 20% energy compared to 1990 levels; – Th e share of renewable energy in our fi nal energy consumption should reach at least 20%; – Energy effi ciency should increase by 20%. Source: Own elaboration based on the European Commission (2010b), p. 30.

The strategy was supported by detailed ‘national action plans’, agreed between Member States and the European Commission. In each of these five fields Member States developed their ‘national reform plans’ and their own national targets. Policies based on this strategy were supposed to be guided by the ‘open method of coordination’ using benchmarking and leading to the exchange of best practices among individual Member States. The Europe 2020 strategy was divided into the following seven flagship initiatives: • Innovation Union • Youth on the move • A Digital Agenda for Europe • Resource efficient Europe • An industrial policy for the globalization era • An Agenda for new skills and jobs • European Platform against Poverty Innovation constituted the core of the Europe 2020 strategy as it was regarded to be the main component of smart, sustainable and inclusive growth. Direct goals related to research and innovation were included in the Innovation Union initiative (European Commission, 2010c, p. 7). It addressed Europe’s innovation system, and it was aimed at fostering capacity to produce knowledge and turn it into innovation. The Innovation Union was launched in 2010 in order to tackle societal challenges, 22 1. Innovation Union initiative – an overview create more jobs, boost economic growth and social progress, and thus, improve Europe’s competitiveness. The Innovation Union initiative was spilt into the following six blocks, some of them subdivided into a few areas: 1. Strengthening the knowledge base and reducing fragmentation • Promoting excellence in education and skills development • Delivering the European Research Area • Focusing EU funding instruments on Innovation Union priorities • Promoting the European Institute of Innovation and Technology (EIT) as a model of innovation governance in Europe 2. Getting ideas to market • Enhancing access to finance for innovative companies • Creating a single innovation market • Promoting openness and capitalizing on Europe's creative potential 3. Maximizing social and territorial cohesion • Spreading the benefits of innovation across the Union • Increasing social benefits 4. Pooling forces to achieve breakthroughs: The European Innovation Partnerships 5. Leveraging policies externally 6. Making it happen • Reforming research and innovation systems • Measuring Progress In each of these areas the main challenges that the EU faced in the beginning of the second decade of the 21st century were identified and 34 commitments needed to create the Innovation Union in the EU were made by the European Commission and all EU Member States (for a description of these commitments see: European Commission, 2011). It was assumed that the implementation of these 34 commitments should be completed by the year 2020. The public sector was supposed to play a key role in this process by setting the right framework conditions for boosting innovation. In fact, the 34 commitments can be treated as policy initiatives or policy instruments leading to the creation of the Innovation Union by 2020. They address a wide range of elements that impact the innovation eco-system in the EU. The progress made in the implementation of the Innovation Union was assessed by the European Commission four years after its launch. The main conclusions were as follows: “The Innovation Union is succeeding in building momentum around innovation, mobilizing stakeholders and mainstreaming innovation in key European, national and regional policies” (European Commission, 2014, p. 8). One year later in the assessment of the Innovation Union based on the 1.5. The European Union’s innovation performance in the years 2010-2017... 23 stakeholder survey it was observed that: “Decisive actions have been taken on all commitments, but the response has been uneven throughout the Member States. Moreover, while the last steps towards full implementation are within reach, it is not certain that all legislative actions will be implemented or that they will deliver the intended impact (e.g. the Unitary Patent and the revised Public Procurement Directives). The commitments that require greater involvement of Member States appear to have progressed to a lesser extent, either because of the long legislative processes (e.g. directives ratification), or because they are less binding in nature.” (European Commission, 2015, p. 6). However, the results of the stakeholder survey did not show huge differences in the progress in the implementation of the six Innovation Union blocks (Figure 1.1).

Figure 1.1. Perceived success of the Innovation Union blocks according to the stakeholder survey 10 Strengthening the knowledge base 9 8 and reducing fragmentation 7 6 Making it happen 5 Getting good ideas to market 4 3 2 1

Leveraging our policies externally Maximising social and territorial cohesion Pooling forces to achieve breakthroughs: European Innovation Partnerships

Source: European Commission, 2015, p. 6.

The smallest progress seems to be in the block “Making it happen”, therefore the next section of this chapter will look at the innovation performance of the European Union in the world economy compared to its peers since the year that the Innovation Union initiative was launched, i.e. since 2010.

1.5. The European Union’s innovation performance in the years 2010-2017 in a comparative perspective

The aim of this section is to compare the EU innovation performance to its main global economic competitors, such as the United States, the BRICS countries (Brazil, Russia, India, China, and South Africa), Japan, South Korea, Australia and Canada, and identify how the EU innovation performance changed in 2010-2017. The analysis is based on the data published in the European Innovation Scoreboard (editions 2010-2018). The assumption is that the implementation of the Innovation Union initiative should be reflected in speeding up innovation processes in Europe and thus, 24 1. Innovation Union initiative – an overview reducing the innovation gap towards innovation leaders in the world economy, such as South Korea, Japan and the United States. It is also worth considering whether the EU was able to maintain an innovation advantage over emerging economies, the BRICS countries in particular. Innovation performance is measured using the Summary Innovation Index (SII) and its main components. The first step is to measure the progress in EU innovation performance using the Summary Innovation Index values over the period 2010-2017. The index is a composite indicator of the innovation performance and it consists of 27 indices representing four groups of factors that shape innovation performance (i.e.: framework conditions, investments, innovation activities and impacts), that are further split in ten innovation dimensions (EIS, 2018, p. 8). Figure 1.2 shows how the SII in the EU taken as a whole (calculated as the weighted average of the performance of the innovation systems of all 28 Member States) evolved over the 2010-2017 period. Figure 1.3 decomposes the SII into its ten dimensions and shows how they changed over the same period.

Figure 1.2. Summary Innovation Index in the EU, 2010-2017 0.510 108

0.500 106

104 0.490 102 0.480 100 SII value 0.470 98 SII in 2010=100

0.460 96

0.450 94 2010 2011 2012 2013 2014 2015 2016 2017

SII value SII relative to 2010 Source: Own elaboration based on the EIS 2018 database, https://ec.europa.eu/docsroom/documents/30083 and data taken from: EIS (2018), p. 38.

An analysis of the data presented on Figure 1.2 suggests that EU innovation performance measured by the SII value was fluctuating in the years 2010-2014, however, without any significant progress as the SII in 2014 was at the same level as in 2010. Since 2015 an increase in overall EU innovation performance can be observed. In 2017 the SII value was by 5.8 percentage points higher than in 2010. However, not all components of the SII showed a positive change in the 2010- 2017 period (Figure 1.3; EIS, 2018, p. 18). Compared to 2010, EU performance has 1.5. The European Union’s innovation performance in the years 2010-2017... 25 improved the most for ‘Innovation friendly environment’ thanks to a significant increase in broadband penetration. A huge improvement of EU performance can also be seen in ‘Human resources’ with an increase of doctoral graduates contributing the most to this positive change. The following dimensions that also strengthen their performance in 2010-2017 include: • ‘Finance and support’ (mainly thanks to an increase in the indicator of venture capital expenditures); • ‘Research system’ (with the biggest growth of the indicator of international co-publications); • ‘Firm investment’ (to which contributed similar increases in all three indicators, i.e. business R&D, non-R&D expenditures and upgrading ICT skills); • ‘Sales impact’ (which improved mainly due to growth in exports of both high- tech goods and knowledge-intensive services). However, for some dimensions performance has not changed or even slightly deteriorated since 2010. These were (Figure 1.3; EIS, 2018, p. 18): • ‘Employment impacts’ (where the increase in employment in knowledge- intensive activities observed in 2010-2017 was offset by a decrease in employment in fast-growing firms); • ‘Intellectual assets’ (with some increase in trademark application and a decrease in patent and design applications); • ‘Linkages’ (where all three indictors contributing to this dimension did not show any significant changes in 2010-2017). The innovation position of the EU suffered the most in 2010-2017 in the dimension ‘Innovators’ (Figure 1.3; EIS, 2018, p. 18). Here EU performance was worse in 2017 compared to 2010 for all indictors included in this dimension, such as percentage of SMEs introducing product, process, marketing and organizational innovations as well as SMEs innovating in-house measured as a percentage of all SMEs (EIS, 2018, p. 18). Summing up, the analysis conducted above shows that the changes in different dimensions of EU innovation performance may indicate that not all the Innovation Union objectives have been sufficiently addressed. In particular, the declining performance in ‘Innovators’ (a dimension covering innovative SMEs) may be a sign that the contribution of SMEs to the progress in the Innovation Union block named ‘Getting ideas to market’ is rather limited. This is a tentative conclusion based on this preliminary analysis, therefore confirming such hypothesis will require further quantitative research. 26 1. Innovation Union initiative – an overview

Figure 1.3. Change in EU innovation performance in 2010-2017 measured by the 10 main dimensions of the SII

Sales impacts Employment impacts Intellectual assets Linkages Innovators Firm investments Finance and support Innovation-friendly environment Research systems Human resources

-20-15-10-50 5 101520253035 Change in performace (in percentage points)

Source: Own elaboration based on the EIS 2018 database, https://ec.europa.eu/docsroom/documents/30083.

Having analyzed the changes in EU performance in the period of 2010-2017 it is worth taking a closer look at it from the comparative perspective. How fast has the EU been changing compared to its global competitors? To answer this question again the SII will be compared, however this index is not that broad as the previous one calculated for the EU. Due to a lack of data for some indicators it has less components than the index calculated for detailed assessment of the EU Member States. It consists of 16 components (for the methodology see: EIS, 2018, pp. 31-32). Taking into account the performance of these countries measured relative to the EU (EU=100), it appears that in 2017 the average EU innovation performance was lower that of South Korea, Canada, Australia, Japan and the United States. Since 2010 the EU has not been able to surpass any of these countries (Figure 1.4). However, some progress in catching up with global innovation leaders can be observed. Except for South Korea and Australia, for other countries that performed better than the EU, their performance has increased at a lower rate than that of the EU (EIS, 2018, p. 30). This means that the EU managed to speed up its catching up process with the majority of innovation leaders. The gap dividing EU innovation performance from that of the US narrowed from 2 p.p. to 1 p.p., and from that of Japan from 5 p.p. to 3 p.p. (EIS, 2018, pp. 34-35). The catching up process was the fastest with respect to Canada as the innovation gap decreased by 8 p.p. (Figure 1.4). When it comes to innovation performance of the BRICS countries relative to the EU, all these countries were well behind the EU in 2010 and their relative positions compared to the EU did not change much over the period of 2010-2017. China was the only country from the BRICS group that managed to significantly reduce the innovation gap towards the EU. Its summary innovation index measured in relative 1.6. Summary and conclusions 27 terms rose by 10 p.p., from 66% of the EU average in 2010 to 76% in 2017. The innovation performance of other BRICS countries relative to the EU level was low and rather stable in 2010-2017, ranging from 42% (India) to 54% of the EU average (Brazil) (Figure 1.4).

Figure 1.4. Overall innovation performance (SII) of selected countries compared to the EU (EU=100); changes in relative performance over the 2010-2017 period

South Korea Canada Australia Japan United States China Brazil South Africa Russia India

-20 0 20 40 60 80 100 120 140

Change in relative performance, 2010-2017 Relative performance in 2017 (EU2017=100) Relative performance in 2010 (EU2010=100)

Source: Own elaboration based on the EIS, 2018, pp. 30-37.

How can these changes in innovation performance of the EU and its global competitors be interpreted from the Innovation Union perspective? The data show that the EU managed to maintain its position in the world economy being a strong innovator. Since the launch of the Innovation Union initiative in 2010, the EU made some progress in reducing the innovation gap towards the US and Japan, however, it was not possible to start the catching up process towards South Korea. Furthermore, China has become a new player in the global innovation game, significantly reducing the innovation divide with the world leaders, including the EU.

1.6. Summary and conclusions

Innovation strategy and policy combine many different elements related to technology, education, infrastructure, etc. Innovation policy in the EU has over 20 years of history. The evolution of the innovation concept, from the linear model, which assumes that innovation is mainly a result of research and development, to a systemic model, in which innovation is the result of interactions between people, organizations and institutions, caused significant adjustments in innovation strategy and policy concepts and their implementation. This has been reflected in the 28 1. Innovation Union initiative – an overview latest policy initiative, i.e. the Innovation Union, which was launched in 2010 as a flagship project of the Europe 2020 strategy. It addressed a wide range of factors that impact Europe’s innovation eco-system, such as investment in the knowledge base, framework programs architecture, the European Research Area or access to finance for innovative companies. This chapter provided an overview of the Innovation Union objectives supplementing it with an analysis of the changes in EU innovation performance since the launch of the Innovation Union in 2010. The analyses conducted in this chapter show that the Innovation Union brought a new approach to innovation policy shifting its focus from pure research to research and innovation, which address societal challenges. Innovation has been regarded as a key factor to achieve smart, sustainable and inclusive growth. These goals have been reflected in the design of the Innovation Union initiative and its 34 commitments. The commitments of the Innovation Union can be treated as policy objectives for the second decade of the 21st century. The implementation and impact of the selected commitments are discussed in the consecutive chapters of this book. Measuring the impact and benefits of the innovation policy is complicated, and the results may be ambiguous and difficult to interpret. Innovation systems are very complex, and their development is a continuous process, being the result of interventions of many policies. Therefore, to find out if there has been any progress in the development of the European innovation eco-system since the launch of the Innovation Union, in this chapter basic innovation indicators have been compared for the EU and its global competitors. This comparative analysis confirmed that in the period of 2010- 2017 the EU maintained its strong innovative position in the world economy and diminished the innovation gap towards the US and Japan, but was not able to reduce the distance towards South Korea. At the same time China started to catch up with innovation leaders, including the EU. Other emerging BRICS economies were not that successful in closing the innovation gap dividing them from the EU, however, some progress can be seen in the case of Russia and South Africa. These new trends need to be taken into account when setting new objectives for a future innovation strategy of the EU.

References

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Leydesdorff, L., Meyer, M. (2006), Triple Helix indicators of knowledge-based innovation systems: Introduction to the special issue, Research Policy No. 35, pp. 1441-1449. Lundvall, B.-A., Borrás, S. (1997), The Globalising Learning Economy: Implications for Innovation Policy, DG XII, Commission of the European Union, Brussels. Mazzucato, M. (2016), From Market Fixing to Market-Creating: A new framework for innovation policy, Special Issue of Industry and Innovation: Innovation Policy – can it make a difference?, Vol. 23, No. 2. Mazzucato, M. (2018), Mission-Oriented Research & Innovation in the European Union. A problem-solving approach to fuel innovation-led growth, European Commission, Brussels. Ergas, H. (1987), The Importance of Technology Policy, [in:] Dasgupta, P., Stoneman, P. (Eds.), Economic Policy and Technological Performance, Cambridge University Press, Cambridge. OECD (2015), Frascati Manual 2015 Guidelines for Collecting and Reporting Data on Research and Experimental Development, OECD Publishing, Paris, DOI:http://dx.doi. org/10.1787/9789264239012-en. Rothwell, R., Zegveld, W. (1982), Industrial innovation and public policy: preparing for the 1980s and the 1990s, Pinter, London. Tilford, S., Whyte, P. (2010), The Lisbon Scorecard X: The Road to 2020, Center for European Reform, London. Watkins, A., Papaioannou, T., Mugwagwa, J., Kale, D. (2015), National innovation systems and the intermediary role of industry associations in building institutional capacities for innovation in developing countries: A critical review of the literature, Research Policy, Vol. 44, Issue 8, pp. 1407-1418. Weresa, M.A. (2012), Systemy innowacyjne we współczesnej gospodarce światowej, Wydawnictwo Naukowe PWN, Warsaw. Małgorzata Stefania Lewandowska, Małgorzata Rószkiewicz, Marzenna Anna Weresa

Chapter 2 Additionality from public support to R&D and innovation in the European Union

2.1. Introduction

In the economic literature there has been a long-lasting debate on factors that stimulate innovation. Determinants of innovation are analyzed, among others in the concept of national innovation capacity (Stern, Porter, Furman, 2002). Their evaluation is also included in the Innovation Union Scoreboard, which compares the innovative position of EU Member States (European Commission, 2017). The Global Innovation Index is another attempt to measure the innovation of various countries and to identify the causative factors of innovative processes (Cornell University, INSEAD and WIPO, 2015). All theoretical and empirical studies indicate that innovation is based on many pillars, such as resources, talent, capital, infrastructure and network connections. There is no doubt that public financing is one of the important factors that can stimulate innovation, but the effectiveness of its application may vary depending on where the public aid is directed, how it is distributed, etc. Therefore, there is a need to compare the effects of financial public support for innovations within selected EU Member States. In particular, it seems justified to investigate how the role of EU funds supporting research and development (R&D) as well as innovation has changed since the EU has been enlarged. This chapter is aimed at identifying the effects of financial support from the EU budget earmarked for innovation in selected 13 EU Member States (incl. the majority of CEE transition economies). Access to the CIS database covering these issues enables us to fill this gap. Thus, the comparative analysis is carried out based on firm level data from the Community Innovation Survey 2010-2012 from countries such as: Bulgaria, Croatia, Cyprus, the Czech Republic, Estonia, Germany, Hungary, Lithuania, Portugal, Romania, Slovakia, Slovenia and Spain. 32 2. Additionality from public support to R&D and innovation in the European Union

The chapter is structured as follows. The first part presents the statistics derived from the Community Innovation Survey (CIS), concerning public financial support coming from the local, central as well as EU level among European countries. The research is based on data derived from three waves of CIS, namely 2006-2008; 2008- 2010 and 2010-2012. The aim is to show the intensity as well as the structure of public financial support across European countries. The second part of this chapter presents the discussion on the theoretical concept of additionality of public financial support for innovative activities and explains its different dimensions as well as posits the hypotheses to be tested empirically. The third part of the research brings the description of sample and research methods, whereas the fourth part presents the empirical results of the additionality effects of European Union funds. The detailed analysis investigates whether public financial support for innovation boosts firms’ investments in innovation activity (input additionality). The research also shows if EU financial support for innovation influences the intensity of innovation cooperation and personnel training (external behavioral additionality). And finally, the link between EU financial support for innovation and firms’ innovation performance (output additionality) is examined. We use path analysis that was carried out separately for each sample from the above-mentioned countries in order to assess the relationships between variables. Thus, this chapter brings some insight into the deeper evaluation of the effectiveness of programs financed by the EU, especially those directed towards Central European countries. The last part of the chapter includes a discussion and conclusions.

2.2. Public financial support for innovation in the EU

In the CIS questionnaire there is a section where enterprises are asked if they receive any public financial support from local or regional authorities, the central government and/or the European Union. The results of the CIS questionnaire show that in 2010- 2012 the share of innovative enterprises that got any public funds supporting R&D in the total number of enterprises varied from 16% (in Sweden and Latvia) to nearly 60% (in France and in the Netherlands)1. During the same period the percentage of innovative firms that reported to get some support only from the EU budget ranged from less than 3% (in Croatia and Spain) to over 30% (in Hungary). Therefore, on the basis of these two criteria (i.e. intensity of innovative enterprises that got any public support and got some support from the EU budget) EU Member States can

1 The data for the CIS questionnaire is not available for all 28 EU countries, only 24 Member States reveal such data on the micro level. 2.3. Theoretical background and hypothesis development 33 be divided into nine groups with different performance regarding the use of national and EU public funds for their R&D activity (Table 2.1).

Table 2.1. EU countries broken down by the share of innovative enterprises that received any public funds supporting R&D and public funding from the EU budget in 2006-2012

Enterprises that Any public funding supporting R&D received High (above 40%) Medium (20%-40%) Low (below 20%) High Hungary the Czech Republic, - (above 20%) Lithuania, Poland Medium Austria Bulgaria, Slovakia, Malta, Romania, Latvia (10%-20%) Estonia, Slovenia, Portugal Low Finland, the Netherlands, Luxembourg, Germany, Sweden (below 10%) Cyprus, France Croatia, Italy, Belgium, Funds from the from Funds European Union European Spain Source: Own elaboration based on Eurostat data from CIS 2006-2008; CIS 2008-2010, CIS 2010-2012.

Thirteen EU countries selected for our comparative analyses belong to very different groups according to the percentage of innovative enterprises that in 2010- 2012 received any public funding related to their innovation activity. Hungary is the only country in the group of high-high – which means a high level of general funding support for R&D (from local, central EU sources all together) and at the same time high EU funds. Cyprus represents the group high-low – which means a high general level of funding and low funding from the EU. The Czech Republic and Lithuania represent the group medium-high – which means medium general financing, high financing from the EU, whereas Bulgaria, Slovakia, Estonia, Slovenia and Portugal form the group medium-medium. Romania is in the group low-medium and Germany and Spain in the group medium-low – with general public support on the medium level and low public financing from the European Union.

2.3. Theoretical background and hypothesis development

Innovation policy, being a part of the government economic policy, is a system of public administration activities (from various levels – national, regional, local) stimulating the creation of new solutions, as well as their dissemination and implementation (Weresa, 2014, p. 87). The most important argument for policy is the role that innovation plays in increasing the efficiency of enterprises and the country's economic growth (Crépon, Duguet, Mairesse, 1998; Van Leeuwen, Klomp, 2006). An important element is also the effect of knowledge spillover, benefitting enterprises that do not invest in 34 2. Additionality from public support to R&D and innovation in the European Union innovation, but are able to capture the effects of innovations introduced by other market participants. Government actions aimed at supporting innovative activity of enterprises are to compensate for this effect responding to the thesis adopted in economic theory (Nelson 1959; Arrow, 1962), in which it is stated that the enterprise is not interested in investing in innovative activities if it fails to capture and use all its potential benefits (Luukkonen, 2000). As a consequence, innovation policy affects more new areas of activity of enterprises and new groups of enterprises (OECD, 2006). This, in turn, naturally results in increased diversity of impact tools used by decision makers, which can be divided into: regulations (legal regulations, standards, prohibitions, limits); system instruments (financial incentives regulated by law); government programs and projects (including public procurement) and instruments supporting intermediary organizations in innovation processes (Jasiński, 2014, p. 76). Currently, the support system for innovative activities can be defined from the point of view of potential beneficiaries as very complicated, taking into account both the various levels from which the support comes and the procedure for obtaining it. The rules for granting support vary between instruments and levels of origin, which increases bureaucracy and may discourage potential beneficiaries, especially SMEs (European Commission, 2010, p. 10). Innovation policy should therefore be considered taking into account its two main dimensions: the diversity of areas of impact and the diversity of impact levels. Among the decision makers, there is a widespread belief that increasing public support for R&D means increased R&D investment in the enterprise, and thus – raising its level of innovation. To test the truth of this assumption, i.e. the additional stimulus, it is necessary to estimate to what extent the use of a given support program contributes to undertaking additional investments in R&D in an enterprise receiving support. Investigating the phenomenon of the effect of support concerning the change in the level of expenditure of companies on R&D, which is the result of obtaining public funds (input additionality), is to assess to what extent the specific program participates in the additional investment expenditure of the company – the beneficiary directed at R&D. The issue of whether public R&D support stimulates additional efforts in the field of R&D is the subject of broad scientific discussion (David, Hall & Toole, 2000). The additional effect that stimulates inputs means that for each monetary unit transferred, for example, in the form of a subsidy or other support tool, the company will spend at least one of its own monetary units targeted at a given project (Georghiou, 2004). 2.3. Theoretical background and hypothesis development 35

In this context, it should be remembered that numerous studies sought to answer the question of whether public R&D support is complementary and stimulates the undertaking of additional efforts, i.e. the so-called additionality effect, or whether it is substitutive for undertaking this activity, i.e. the crowding out effect. In the latter case, public support means that the enterprise reduces its expenses and does not take planned actions that would have occurred if the support did not occur (Edquist et al., 2004). This effect is observed on average in 30% of commercial projects that are financed from public funds, which in turn intensifies the discussion about the need to support them by government agencies (Busom, 2000). In the analysis of the effect of additional stimulating expenditures, some assumptions should be made, which are not always present in the practice of innovative enterprises, namely that there is a direct relationship between expenditure on innovation and subsequent innovation activity, there is a scale effect in the case of expenditure on innovation and there is no difference between the effect of public support and the effect that arises as a result of private investment (Bach, Matt, 2002). Wallsten (2000), examining the impact of grants on the operations of American enterprises, proves the full crowding effect of the “dollar for the dollar”. However, he also indicates that the program has a different effect, in that in many cases the recipient enterprises, thanks to additional support, were able to maintain R&D spending at a level which in the long run would not be possible for them to achieve. In German enterprises there was no “crowding out effect" and the “additional effect” of public support was confirmed (Hussinger, 2003), while in other studies on Spanish enterprises a “crowding out effect” was observed in the case of R&D financing in large enterprises, and an “additional effect” of support in small companies (Serrano-Velarde, 2008). Gonzalez, Jaumandreau and Pazo (2005), examining a panel of over two thousand enterprises from Spain, using the Tobit model, proved that subsidies stimulate spending on R&D, but projects would continue even without public support. On this basis, researchers reject the full “crowding out effect”, while they do not confirm that subsidies stimulate private R&D spending for all enterprises. Although the results are inconclusive, there is little disagreement about the need for direct government support for commercial R&D projects (Klette, Moen & Griliches, 2000). For the purpose of this research, in addition to the analysis of the impact of public support on investments in the purchase of R&D activities, we will also examine additionality in the case of investments in technology-advanced machinery that aims at the creation of new or significantly improved products and processes. 36 2. Additionality from public support to R&D and innovation in the European Union

This leads to the following hypothesis:

H1: Input additionality Public financial support for innovation boosts firms’ investments in innovation activity, in particular investments in gaining external knowledge and/or in buying machinery or equipment, which in turn enhances its innovation performance.

Public support may also be direct, referred to as “output additionality”, which concerns and enables the identification of the leverage effect on the level of enterprise innovation (Luukkonen, 1998). This raises the question of what the results are, because these can be a direct result of projects supported by public funds (such as new products, new processes, patents, publications), as well as indirect results, such as better financial results of the company obtained thanks to the introduction of a new product or process (Georghiou, 2002). An example of estimating this effect is Halperna's (2010) work, which, examining Hungarian companies, stated the relationship between subsidies and the intensity of introducing innovations (output additionality). Albors-Gorrigos and Barrera (2011), analyzing CIS3 data for enterprises from Spain, proved a positive relationship between subsidies and the level of enterprise innovation measured by the number of innovations introduced and the mediating role of the company's openness to cooperation, cooperation skills and enterprise size (only for enterprises from the high- tech industry) in the studied relationship. In turn, Schneider and Veugelers (2010), examining companies from Germany participating in CIS4, proved the existence of a positive relationship between support and the level of innovation, but not in the case of young, small, more than average innovative enterprises, while Un and Montoro- Sanchez (2010) argued that public funds increase the willingness of enterprises to innovate, but only if they have enough resources to do so. There are also studies on the impact of public aid on patenting in supported enterprises. Studies of Alecke et al. (2012) indicate that the probability of patenting in companies from the SME sector from eastern Germany is higher by 20% if they are the beneficiaries of innovation policy. Similar results, indicating increased patent activity, were obtained by Czarnitzki and Licht (2006) for German enterprises and Herrera and Bravo Ibarra (2010) for Spanish enterprises. The research of Bronzini and Piselli (2016), conducted among Italian companies that received government grants, proves that a subsidy increases the probability of applying for patents, but only among small enterprises. On this basis they conclude that the government's support should apply to smaller entities, for which patent procedures are often too expensive. They also argue that regional policy provides better results than policy pursued at the central level. 2.3. Theoretical background and hypothesis development 37

Freitas et al. (2017), examining enterprises from France, Norway and Italy participating in CIS for 2002-2004, 2004-2006, 2006-2008, indicate that both the additional effect stimulating inputs (input additionality) and the additional effect stimulating results (output additionality) were higher in industries with a higher level of technology. The results obtained, however, differed between countries. Given the empirical literature, the following research hypothesis is formulated:

H2: Output additionality Public financial support for innovation directly improves firms’ innovation performance.

It is worth mentioning that the effects of government intervention do not have to result solely in increased expenditures incurred on research and development activities. As Norman and Klofsten (2010) note, governmental interventions also have an indirect impact on improving the level of knowledge of companies and improving their relationship with the environment. Observation of these phenomena was the basis for the concept of the additional behavior-stimulating effect (behavioral additionality), first formulated by Buisseret, Cameron and Georghiou (1995). Researchers, including Georghiou (2002), argue that the state's financial support does not stimulate the decision to implement or suspend a project, but is expressed in the modification of the behaviors associated with the project. Enterprise behavior can cover many areas of activity. While assessing the public financing effects, Falk (2007) proposes taking into account the fact of extending the scope of implemented innovative projects (scope additionality), entering new research fields, which will increase the potential operating risk (area additionality), undertaking projects with a larger scale than before (scale additionality), increasing the scope of cooperation bringing the effect of increased perceptive ability (cognitive capacity additionality). Clarysse et al. (2004) examined 192 enterprises from the Flanders region, which received government subsidies for innovative activities and compared their results with 84 enterprises that did not receive such support. The study proved the existence of an additional stimulating effect on the behavior of subsidized companies (behavior additionality), which decreased with the increase of subsidized innovation projects that were implemented. One of the areas of behavior additionality is the situation in which public support obtained by the company on R&D affects the shape and status of its cooperation (cooperation additionality) (Wanzenböck, Scherngell, Fischer, 2013). Garcia and Mohnen (2010) found a positive relationship between public support and innovation cooperation. Similar conclusions were given by Teirlinck and Spithoven (2010), although only in the case of cooperation with research 38 2. Additionality from public support to R&D and innovation in the European Union institutes. In turn, Kang and Park (2012), based on the research of South Korean companies from the biotechnology industry, showed a strong positive relationship between government support and cooperation with domestic suppliers and clients. Wanzenböck et al. (2013), examining 155 transport companies from Austria, showed a change in the behavior of subsidized enterprises, which concerned the scale of implemented projects and the scope of cooperation in innovation, but only in the case of small, technologically specialized enterprises with lower than average investments in R&D. This leads to the last hypothesis:

H3: External behavioral additionality Public financial support for innovation influences the intensity of firms’ innovation cooperation and/or investment in personnel training, which in turn enhances their innovation performance.

The conceptualization of our research is presented in Figure 2.1.

Figure 2.1. Conceptual model of the impact of public financial support for R&D on innovation performance

KNOWLEGDE AND MACHINERY ACQUISITION

PUBLIC FINANCIAL H2 INNOVATION SUPPORT FROM THE EU PERFORMANCE

H3 INNOVATION COOPERATION AND TRAINING

Where: Public financial support – PubSuppEU Knowledge acquisition – KnowMaAcq Innovation cooperation and training – InnoCoopTr Innovation performance – InnoPerf Source: own elaboration. 2.3. Theoretical background and hypothesis development 39

The analysis of the impact of innovation policy (see Kotowicz-Jawor, 2012), also using the “additionality effect”, is also carried out in the countries of Central and Eastern Europe (CEE region). Grabowski et al. (2013), assessing the effectiveness of public support in Turkey and Poland based on CIS data for the period 2008-2010, stated that in Turkey government support contributes to the increase of innovation expenditures incurred by enterprises (input additionality), which as a result increases their chances to introduce product innovations, although the support from local authorities proves to be less effective than the support from the central government or support obtained from the European Union. In turn, Weresa and Lewandowska (2014), based on data from CIS 2010 for Poland, showed the existence of “input additionality” in relation to expenditure on the purchase of machinery and equipment and “cooperation additionality” with institutional partners, both as a result of European Union funds. However, no direct relationship was found between the European Union funds and the increase in the innovation of enterprises measured by the share of sales of innovative products in total sales (output additionality). Also Lewandowska and Kowalski (2015), examining large Polish enterprises concentrated in clusters, proved (based on the results of the structural equation model, with an additional estimation of errors made using multiple random sampling) the existence of the impact of public intervention from the level of the European Union on the tendency of enterprises to cooperate in clusters (cluster cooperation additionality), with the lack of such an impact on cooperation in innovations with partners from outside the cluster. In a simplified way, it can be assumed that behavioral changes may concern both the strategic level of the company – the long-term strategy and the operational level. Thus, the scope of behavior and time can be considered in a narrow perspective and refer to behavior during project implementation, while changes in the broader approach concern behaviors that affect the processes within the entire organization. All effects can bring short- or long-term effects for the firm. Although the problem of the effects of public financial support for innovative activity is the subject of many studies, there are still few papers that address the complementarity or substitutability of such support from various levels – local, central and supranational. Garcia and Mohnen (2010) showed that gaining support from the central government level increases the intensity of activities in the area of R&D and increases the share of innovative products in total sales. However, with the simultaneous support from the central level and from the European Union, this second type of support is losing its importance. Czarnitzki and Lopes-Bento (2014) analyzed the impact of public support from the level of the European Union and the level of the government on expenditures and effects related to innovative activities among German enterprises. In the case of “input additionality” it was proven that support from various levels 40 2. Additionality from public support to R&D and innovation in the European Union is complementary to each other. In turn, in the case of “output additionality” it was proved that enterprises receiving support are more active in patenting their innovative solutions. At the same time, these patents in terms of the level of citation exceeded the patents of enterprises that did not receive support, which may indicate that public support promotes more valuable projects, and support from different levels complements each other. Summing up, the financial support of government agencies may exert an effect in the form of increased expenditure on R&D (input additionality), the effect of increased company results (output additionality), such as the introduction of a new product in the market, new patents, increased market share or an improvement of the company's financial results, achieved as a result of new products and effects in the form of changing the behavior of the company (behavioral additionality). While taking into account the three dimensions of the influence of innovation policy mentioned above, we can determine both the strength of impact on each of the three dimensions and the degree of complexity of impact. The effect of public financial support for innovative activities may take the following forms (Marzucchi, Montresor, 2012): 1. Multidimensional support. This is the case in which support takes place both in the case of expenditure, results and the behavior of the supported entity. In this case, you can talk about system support. 2. Bi-dimensional support. This is a situation in which the additional effect occurs in two of three dimensions. 3. Mono-dimensional support. This is the case in which the additional effect can be observed in only one dimension. 4. Partial crowding out effect. This is a situation in which we deal with the effect of crowding in one or two dimensions with a positive effect in the case of others. 5. Total crowding out effect. A situation in which, for all three dimensions, there is a displacement effect. As already mentioned, innovation policy can be carried out at several levels: local/ regional, national and supranational. The coordination of policies at so many levels is a big challenge. It is important that support from different levels does not overlap one another and, more importantly, does not produce contradictory effects. In the case of three dimensions of impact of innovation policy and additional consideration of support from different levels, we obtain the following alternative scenarios: 1. The full additionality effect – this is the case when all policy levels affect all three dimensions (inputs, output, behavioral) and are complementary to each other. 2.3. Theoretical background and hypothesis development 41

2. A partial additionality effect – this is a situation in which the effect of support occurs only for one level of policy – regional / national / supranational. 3. Partial crowding out effect – this is a situation in which one of the levels of support produces positive effects, while support from another level causes a crowding out effect. 4. The total crowding out effect – this is the case when support from all levels causes a crowding out effect. The strength of such an approach is the systematic reflection on the effects of innovation policy in the three dimensions of the additional effect. However, in the context of considering the impact of innovation policy, it is important to take into account the fact that it interacts within different levels (regional, national, supranational), different entities and also offers diversified support tools that interact with other tools, can be compared to self-complementary, they can also be substitutive or compensate each other (Chaminade, Edquist, 2010). An example of a work in which the authors face this complex problem is Magro and Wilson’s (2013) article, in which the authors examine enterprises from the Basque region, which is affected by pro-innovation policy tools from the European Union, the country, the Basque region and the three provinces of the region. They argue that such a complex, multi-level system of co-occurring instruments makes it very difficult to assess their impact reliably, especially as they have a strong interaction. In this context, they suggest the construction of multidimensional tools that would enable the measurement of policy impacts in a more complex way. Another work, in which the whole system of innovative policy tools was analyzed, is the work of Guerzoni and Raiteri (2015). The researchers, after analyzing a group of over 5,000 companies from various countries of the Union, Norway and Switzerland, have proved that innovation policy works best when it shares its various tools: those that stimulate supply – subsidies, tax exemptions; and those that stimulate demand – public procurement. The authors of Nest's report (2013) point to the problems related to measuring the impact of innovation policy on the inputs, results and behavior of enterprises. After analyzing many studies, they prove that this influence is characterized by the “skewness” of the distribution: the success of the program depends on a relatively small number of enterprises, while the "tail" of less spectacular cases is much larger. The researchers also point out that most of the works focus on one observation period, which is their great weakness. They also prove that most works focus on the role of policy in stimulating R&D spending, while the expectations of the decision makers evaluating the programs largely focus on the final results – an increased number of innovations or registered patents. 42 2. Additionality from public support to R&D and innovation in the European Union

2.4. Sample description and research method

The analysis is based on the anonymized microdata on firms from the Community Innovation Survey: CIS 2012 (covering the 2010-2012 period), obtained from the Eurostat since 20172. The Community Innovation Survey (CIS) is a survey on innovation activity in enterprises covering EU Member States and candidate countries, Iceland and Norway. The CIS 2012 was based on a common survey questionnaire and methodology, with reference to the Oslo Manual, ed. 2005, as to get comparable, harmonized and high-quality statistical results. The CIS is designed to obtain information on firms’ innovation activities, as well as their expenditures for process and product innovations, public financial support for innovation activities, sources of information and cooperation within innovation projects, innovation objectives. The CIS also contains data on the introduction of organizational and marketing innovations.

Table 2.2. Initial sample description

Country Abbr. Initial sample Percent Level of innovativeness 2016 Bulgaria BG 14,296 14.9 Modest innovator Romania RO 7,670 8.0 Modest innovator Czech Rep. CZ 5,449 5.7 Moderate innovator Cyprus CY 1,205 1.3 Moderate innovator Estonia EE 1,723 1.8 Moderate innovator Spain ES 32,120 33.4 Moderate innovator Croatia HR 3,193 3.3 Moderate innovator Hungary HU 5,152 5.4 Moderate innovator Lithuania LT 2,231 2.3 Moderate innovator Portugal PT 6,840 7.1 Moderate innovator Slovakia SK 2,897 3.0 Moderate innovator Norway NO 5,083 5.3 Strong innovator Slovenia SI 1,869 1.9 Strong innovator Germany DE 5,328 5.6 Innovation leader Total EU 96,056 100.0 Total Source: own calculations based on microdata from CIS 2010-2012.

The target populations of the CIS 2012 were small (from 10 to 49 employees), medium (50 to 249 employees) and large (250 and more employees) enterprises in all sectors of the national economy. In most countries the survey was carried out on the entire population (census). In order to extrapolate the results to the whole target

2 CIS 2010-2012 microdata obtained based on the “Contract on the use of Community Innova- tion Survey (CIS) micro data for research purposes – CIS/2012/13” signed with European Commis- sion Eurostat, Unit B1 – Quality, methodology and research. 2.4. Sample description and research method 43 population, weighting factors were calculated, based on the proportion between the number of enterprises or the number of employees in the realized sample and the total number of enterprises or employees in each layer of the frame population. The division of the initial sample is presented in Table 2.2. In the first step, in order to obtain samples of enterprises similar in their characteristics, we extracted only manufacturing enterprises (NACE sections B-E). In the following step, due to the CIS questionnaire construction, where the majority of questions refer to innovative enterprises, we assumed, like other researchers (Veugelers, Cassiman 2004, Mothe et al., 2010), the indication of whether the company introduced new or significantly improved products or processes in 2010- 2012 as the filter variable. Furthermore, we assumed that only companies that in the researched period received any public support for innovative activity will be analyzed. Ultimately, out of the initial sample of 96,056 small, medium and large firms from 14 EU countries 16,865 enterprises from 13 countries3 were selected (it should be noted that 801 records had missing data). The selected samples covered N=1,545 firms from Bulgaria; N=621 from Croatia; N=271 from Cyprus; N=1797 from the Czech Republic; N=439 from Estonia; N=1,763 from Germany; N=769 from Hungary; N=401 from Lithuania; N=2,256 from Portugal; N=147 from Romania; N=224 from Slovakia; N=258 from Slovenia and N=6,374 firms from Spain. The profile of CIS data determines the operationalization of selected variables (see Table 2.3 for a detailed description).

Table 2.3. Variables operationalization

Variable Description and construction of variables InnoActComp Filter variable – “Innovation activity” InnoActCompPr “1” if the firm introduced product innovation; “0” otherwise and or InnoActCompProc “1” if the firm introduced product innovation; “0” otherwise IPubFundEU Variable – “Financial support from the EU” “2” if the firm received funding from EU FP7; “1” if the firm received public financial support for innovation activity from the EU; “0” otherwise. InnoPerf Dependent variable – „Innovation performance” Log of fraction (from 0 to 100) of turnover from innovative products introduced in 2010-2012 in the total turnover in 2012. KnowMachAcq Variable – “Expenditures on innovation activities” KnowAcq “1” if the firm declared acquisition of external R&D (purchase or licensing of patents and non-patented inventions, know-how and other types of knowledge for the development of new products and processes), “0” otherwise; or

3 We had to exclude Norway due to the big number of missing data and the low number of enterprises receiving EU financial support. 44 2. Additionality from public support to R&D and innovation in the European Union

Variable Description and construction of variables MachAcq “1” if the firm declared acquisition of advanced machinery, equipment (including computer hardware) or software to produce new or significantly improved products and processes; “0” otherwise. “2” if both of them coexist. InnoCoopTr Variable – “Cooperation and training as innovative activities” InnoCoop “1” if the firm declared cooperation with suppliers, customers, competitors, research institutes, universities, both domestic and from the EU; “2” if the firm declared cooperation with suppliers, customers, competitors, research institutes, universities from third countries (China, India, USA, other countries); “0” otherwise; or InnoTrain “1” if the firm conducted internal or external training for its personnel for the development and/or introduction of new products and processes; “0” otherwise. “2” if both of them coexist. Source: own compilation based on the CIS 2010-2012 questionnaire.

The relationship between the research variables was tested with the use of Path Analysis (Wright, 1921; 1934), which can be viewed as a special case of structural equation modelling (SEM) – one in which only single indicators are employed for each of the variables in the causal model. This method employs simple bivariate correlations to estimate the relationship in the system of structural equations. The method is based on specifying the relationships in a series of regression-like equations (portrayed graphically in a path diagram) that can then be estimated by determining the amount of correlation attributable to each effect in each equation simultaneously. On the path diagram, which graphically portrays the complete set of relationships among the model’s variables, causal relationships are depicted by straight arrows, with the arrow emanating from the predictor variable and the arrowhead pointing to the dependent variable (Hox, 2002). The result is SEM with a structural model, but no measurement model. Path Analysis is acknowledged as a statistical technique, but also as an approach towards building theory in social sciences (Konarski, 2009). It guides exploratory and confirmatory research in a manner combining self-insight and modelling skills with theory. It often suggests novel hypotheses that were not considered (Kline, 2011). Additionally, Path Analysis in contrast to other methods estimates different theories simultaneously (Henseler, 2011). Next, bootstrapping – a method for assigning measures of accuracy to sample estimates (Efron, 1979) – followed by the correction Bootstrap for Goodness-of-Fit Measures (Bollen-Stine, 1992), were applied. 2.5. Results 45

2.5. Results

The statistical approach to testing the hypotheses employed path analysis, the Generalized Least Squares (GLS) method, with the module AMOS 23, and the PS IMAGO program. Because of the fact that the number of distinct sample moments is equal to the number of distinct parameters to be estimated, the model is saturated and the quality of the fitted model to the data is untestable. The model was bootstrapped (10,000 repetitions), which additionally supported the obtained results. An examination of the standardized estimates for path analysis shows that for the group of units being a part of the capital group all of the paths are statistically significant at least at the level of p <0.05. For each country sample a separate model has been constructed. The results for Bulgarian enterprises show that there is a positive relation between public financial support from the EU and knowledge acquisition as well as innovation cooperation. There is no direct impact of public financial support on innovation performance. Thus, hypotheses H1 and H3 were confirmed and H2 was rejected. Additionally, a positive relation between cooperation and knowledge acquisition was confirmed. The results for Croatian enterprises show that there is no relation between public financial support from the EU and knowledge acquisition, innovation cooperation and innovation performance. Thus, hypotheses H1, H2 and H3 were rejected. A positive relation between cooperation and knowledge acquisition was confirmed. The results for Cyprus enterprises also show that there is no relation between public financial support from the EU and knowledge acquisition, innovation cooperation and innovation performance. Thus, hypotheses H1, H2 and H3 were rejected. A positive relation between cooperation and knowledge acquisition was confirmed. The results for Czech Republic enterprises show that there is a positive relation between public financial support from the EU and knowledge acquisition as well as innovation cooperation. There is no direct impact of public financial support on innovation performance. Thus, hypotheses H1 and H3 were confirmed, whereas H2 was rejected. Additionally, positive relations between cooperation and knowledge acquisition as well as cooperation and innovation performance were confirmed. The results for Estonian enterprises show that there is a positive relation between public financial support from the EU and innovation cooperation. There is no direct impact of public financial support on innovation cooperation and innovation performance. Thus, hypothesis H3 was confirmed, whereas H1 and H2 were rejected. 46 2. Additionality from public support to R&D and innovation in the European Union

Additionally, a positive relation between cooperation and knowledge acquisition was confirmed. The results for German enterprises show that there is a positive relation between public financial support from the EU and innovation cooperation. There is no direct impact of public financial support on innovation cooperation and innovation performance. Thus, hypothesis H3 was confirmed, whereas H1 and H2 were rejected. Additionally, a positive relation between cooperation and knowledge acquisition was confirmed. The results for Hungarian enterprises show that there a is positive relation between public financial support from the EU and knowledge acquisition as well as innovation cooperation. There is no direct impact of public financial support on innovation performance. Thus, hypotheses H1 and H3 were confirmed, whereas H2 was rejected. Additionally, positive relations between cooperation and knowledge acquisition as well as cooperation and innovation performance were confirmed. The results for Lithuanian enterprises show that there is a positive relation between public financial support from the EU and innovation cooperation. There is no direct impact of public financial support on innovation cooperation and innovation performance. Thus, hypothesis H3 was confirmed, whereas H1 and H2 were rejected. The results for Portuguese enterprises show that there is a positive relation between public financial support from the EU and knowledge acquisition as well as innovation cooperation. There is no direct impact of public financial support on innovation performance. Thus, hypotheses H1 and H3 were confirmed, whereas H2 was rejected. Additionally, positive relations between cooperation and knowledge acquisition as well as cooperation and innovation performance were confirmed. The results for Romanian enterprises show that there is no relation between public financial support from the EU and knowledge acquisition, innovation cooperation and innovation performance. Thus, hypotheses H1, H2 and H3 were rejected. The results for Slovak enterprises show that there is no relation between public financial support from the EU and knowledge acquisition, innovation cooperation and innovation performance. Thus, hypotheses H1, H2 and H3 were rejected. Additionally, a positive relation between cooperation and knowledge acquisition was confirmed. The results for Slovenian enterprises show that there is a positive relation between public financial support from the EU and innovation cooperation. There is no direct impact of public financial support on innovation cooperation and innovation performance. Thus, hypothesis H3 was confirmed, whereas H1 and H2 2.6. Discussion and conclusions 47 were rejected. Additionally, a positive relation between cooperation and knowledge acquisition was confirmed. The results for Spanish enterprises show that there is a positive relation between public financial support from the EU and knowledge acquisition as well as innovation cooperation. There is no direct impact of public financial support on innovation performance. Thus, hypotheses H1 and H3 were confirmed, whereas H2 was rejected. Additionally, positive relations between cooperation and knowledge acquisition as well as knowledge acquisition and innovation performance were confirmed. Further details are presented in Table 2.4, and a brief description of the results is presented in Table 2.5.

2.6. Discussion and conclusions

The role of EU funds supporting R&D and innovation has been increasing in countries from the CEE region since these countries joined the EU. Using the concept of additionality, this study examined the efficiency of public support for innovation. A comparison of results for 13 selected countries, namely Bulgaria, Croatia, Cyprus, the Czech Republic, Estonia, Germany, Hungary, Lithuania, Portugal, Romania, Slovakia, Slovenia and Spain, revealed that there have been huge differences among these countries with regard to the effects of EU financial support for innovation. The results of the path analysis revealed that there is no multi-dimensionality of EU public support due to the fact that output additionality was not proven for any of the surveyed countries. Bi-dimensionality (impact on knowledge acquisition and innovation cooperation) was found for Bulgaria, the Czech Republic, Hungary, Portugal and Spain, but the strength of public financial impact differed within this group of enterprises. The highest impact for both dimensions was found among enterprises from Bulgaria, the Czech Republic and Hungary, whereas in Spain and Portugal the impact on knowledge acquisition was significantly lower. Mono-dimensionality for the impact of public financial support on innovation cooperation and personnel training was revealed for German, Slovenian, Lithuanian and Estonian enterprises. No impact of financial support was observed among enterprises from Croatia, Cyprus, Romania and Slovakia (for details see Figure 2.2). Summing up, it has to be pointed out that the results of the above study may lead to the conclusion that the potential of EU support for R&D and innovation is still not fully exploited. However, this differs across EU Member States and there is still room for improvement with regard to policy design and implementation. It should also be underlined that no direct impact between public financial support and innovation 48 2. Additionality from public support to R&D and innovation in the European Union performance was revealed. This shows that the simple evaluation of the role of public financial support should be broadened to much more advanced measurement tools. This study is not without limitations. First, it must be noted that it is positioned within the Science, Technology and Innovation approach, the main emphasis of which is on promoting R&D and creating access to explicit codified knowledge, and does not discuss the Doing, Using, and Interacting approach (Jensen et al, 2007), according to which innovation strategies are mainly based on learning and interacting. Another limitation is that despite the representativeness of the initial sample of firms, the extracted number of innovative firms is relatively small. It should also be noted that, especially while examining the impact of public financial support, CIS data should be used cautiously, as they are anonymous and therefore it is not possible to conduct a follow-up survey based on more than one period of observations, which would be beneficial especially in the case of output additionality, where the effects may be postponed. Finally, the presented analysis is limited to manufacturing firms from selected countries, so the results may be influenced by environmental factors that limit the general validity of the findings to the reality of CEE economies. Our study is consistent with more general evidence of the positive role of cooperation for the innovation performance of enterprises. A deeper understanding of the effects of innovation cooperation at firm-level and its underlying mechanisms is a prerequisite for a future design of policies fostering a cooperation-friendly environment. The constructed model, which highlights the importance of external behavioral additionality, may help policy authorities develop a better understanding of how policy contributes to supporting innovation cooperation within transition economies. The analysis points to the possible fruitfulness of further research on the connections between innovation performance, cooperation and public financial support. This paper provides interesting evidence for the managers of innovative firms about the additional effects of public R&D funds, especially in the context of innovation cooperation, which is still limited in many European countries. 2.6. Discussion and conclusions 49 Results of the path analysis for selected European Union Member States Member Union selected European for analysis the path of Results Results of the path analysis for Croatia (HR) Croatia for analysis path the of Results InnoCoopTrKnowMachAcq <--- <---KnowMachAcq <---InnoPerfInnoPerfInnoPerf <--- PublSuppEU PublSuppEU <--- InnoCoopTr .534 .175 <--- .311 KnowMachAcq .176 InnoCoopTr .164 .120 PublSuppEU -.055 .029 .338 .059 3.251 1.457 .046 10.713 .176 2.979 .001 .145 *** -1.173 1.917 .003 .129 .054 .241 .396 .055 .130 -.051 .077 Variable (BG) Bulgaria for analysis the path of Results InnoCoopTr directionKnowMachAcq Impact Variable <--- <---KnowMachAcq <---InnoPerfInnoPerf EstimateInnoPerf <--- PublSuppEU PublSuppEU <--- InnoCoopTr S.E. .493 .257 <--- .292 KnowMachAcq .114 InnoCoopTr C.R. .065 .045 PublSuppEU .024 .017 (CY) Cyprus for analysis the path of Results -.051InnoCoopTr .048KnowMachAcq P 7.612 <--- 5.690 <---KnowMachAcq .035 <--- 16.783InnoPerf .085InnoPerfInnoPerf 2.406 *** *** <--- PublSuppEU PublSuppEU Standardized *** (CZ) Republic the Czech for analysis the path of Results <--- .684 InnoCoopTrInnoCoopTr -.596 .050 .260 <---KnowMachAcq .016 .244 <--- <---KnowMachAcq .190 .133 <--- KnowMachAcqInnoPerf .391 .494 .029 InnoCoopTrInnoPerf .551 .216 .144 PublSuppEUInnoPerf .068 .064 .041 <--- PublSuppEU PublSuppEU .057 <--- InnoCoopTr .490 .019 .221 <--- .070 -.016 .229 .231 1.805 .050 6.018 KnowMachAcq .167 .139 InnoCoopTr .049 .034 PublSuppEU .421 .071 .062 .818 .016 .091 *** 1.286 .342 .037 10.034 .674 6.441 .102 .014 .026 14.245 .198 .054 .341 .732 3.803 *** *** .027 *** 2.369 .083 1.689 .021 *** .230 .144 .018 .320 .091 .096 .060 .041 Table 2.4. Table 50 2. Additionality from public support to R&D and innovation in the European Union Results of the path analysis for Estonia (EE) Estonia for analysis the path of Results InnoCoopTrKnowMachAcq <--- <---KnowMachAcq <---InnoPerfInnoPerfInnoPerf <--- PublSuppEU PublSuppEU (DE) Germany for analysis the path of Results <--- InnoCoopTrInnoCoopTr .359 .119 <---KnowMachAcq .275 <--- <---KnowMachAcq <--- KnowMachAcqInnoPerf .057 InnoCoopTrInnoPerf .098 .082 PublSuppEUInnoPerf .031 .039 <--- PublSuppEU PublSuppEU (HU) Hungary for analysis the path of Results .226 <--- InnoCoopTrInnoCoopTr .516 .070 <--- .072KnowMachAcq .381 3.672 <--- 1.448 <---KnowMachAcq .063 <--- 6.982 KnowMachAcqInnoPerf .124 .099 InnoCoopTrInnoPerf .039 .035 PublSuppEUInnoPerf .787 *** .147 .090 .020 <--- PublSuppEU PublSuppEU (LT) Lithuania for analysis the path of Results .067 *** <--- .490 InnoCoopTrInnoCoopTr .244 .337 1.828 <--- .031KnowMachAcq .237 13.353 <--- .431 2.000 <---KnowMachAcq .066 .173 .029 <--- 18.606 KnowMachAcqInnoPerf .046 .624 .155 .319 InnoCoopTrInnoPerf .068 .074 .048 PublSuppEUInnoPerf 3.176 *** .046 .073 .024 <--- .040 PublSuppEU PublSuppEU *** .138 <--- 3.061 InnoCoopTr .403 .025 .167 1.469 <--- .066 .088 .001 .280 3.316 6.979 .303 .045 .046 10.097 KnowMachAcq .002 .419 .092 .187 InnoCoopTr .142 .098 .072 PublSuppEU 2.340 *** .083 *** .036 .036 *** -.074 1.581 .083 1.502 .075 .036 .019 4.097 2.320 .229 .058 .229 7.843 .109 .114 .332 .133 2.484 *** .092 .020 *** .623 .060 -.677 .056 .013 .108 .201 .533 .366 .498 .134 .034 -.034 2.6. Discussion and conclusions 51 Results of the path analysis for Portugal (PO) Portugal for analysis the path of Results InnoCoopTrKnowMachAcq <--- <---KnowMachAcq <---InnoPerfInnoPerfInnoPerf <--- PublSuppEU PublSuppEU (RO) Romania for analysis the path of Results <--- InnoCoopTrInnoCoopTr .561 .170 <---KnowMachAcq .309 <--- <---KnowMachAcq <--- KnowMachAcqInnoPerf .050 InnoCoopTrInnoPerf .046 .037 PublSuppEUInnoPerf .082 .016 <--- PublSuppEU PublSuppEU (SK) Slovakia for analysis the path of Results -.066 <--- InnoCoopTrInnoCoopTr .408 .213 <--- .025KnowMachAcq .117 12.266 <--- 4.591 <---KnowMachAcq .021 <--- 18.759 KnowMachAcqInnoPerf .044 .055 InnoCoopTrInnoPerf .194 .111 PublSuppEUInnoPerf 2.001 *** *** -.074 .047 <--- PublSuppEU PublSuppEU *** (SI) Slovenia for analysis the path of Results .215 <--- 3.941 InnoCoopTrInnoCoopTr -1.502 .183 -.013 <--- .125KnowMachAcq .045 .255 2.103 <--- 1.909 <---KnowMachAcq .250 .072 .091 <--- 2.501 KnowMachAcqInnoPerf *** .373 .171 .113 .133 InnoCoopTrInnoPerf .106 .166 PublSuppEUInnoPerf .436 .035 .046 .056 .074 .043 <--- PublSuppEU PublSuppEU .142 .012 -1.021 <--- InnoCoopTr .568 .092 .027 -.033 1.258 <--- .108 -.120 .239 1.099 .663 .171 .154 .074 .307 5.981 KnowMachAcq .201 .172 -.032 InnoCoopTr .208 .121 .070 PublSuppEU 1.047 .904 .272 .106 .035 .037 -.058 *** -.087 1.001 .093 .830 .106 .295 4.696 .391 -.007 .073 .056 6.877 .104 .317 .372 .407 -.349 *** .075 .696 *** 1.882 .072 -.555 .727 .055 .281 .023 .060 .406 .579 -.024 .132 -.036 52 2. Additionality from public support to R&D and innovation in the European Union -+ ++++--++ +--++ +--+ - - + - +-- - + 2--- - -H2------H3 - - H1 Taxonomy of European Union innovation activity support within the surveyed countries – verification of hypotheses of – verification the surveyed within countries support activity innovation Union European of Taxonomy Policy typePolicy BID NI NI BID MOND MOND BID MOND BID NI NI MOND BID Output additionality External behavioral additionality Results of the path analysis for Spain (ES) Spain for analysis the path of Results InnoCoopTrKnowMachAcq <--- <---KnowMachAcq <---InnoPerfInnoPerfInnoPerf <--- PublSuppEU PublSuppEU <--- InnoCoopTr .579 .091 <--- .201 KnowMachAcq typeAdditionality .127 InnoCoopTr R&D) (external additionality Input .027 .018 PublSuppEU .011 .008 .128 .030 21.543 5.079 .020 24.839 H .043 BG 4.220 *** HR *** *** CY .554 CZ 2.947 *** EE .261 .062 .305 DE .580 .003 HU .056 LT .008 PO .038 RO SK SI ES Source: own elaboration based on the results of the path analysis. the path of the basedresults on elaboration own Source: 2.5. Table NI – no impact. – mono-; MOND BID – bi-dimensionality; elaboration. own Source:

2.6. Discussion and conclusions 53

additionalitty Impact of EU funds on cooperation and training - external behaviural external - training and cooperation on funds EU of Impact Hungary; 0,229; 0,299 High knowledge aquisition High cooperation training and High knowledge aquisition Low cooperation and training 0,23 Czech Republic;Czech 0,144; Bulgaria; 0,133; 0,19 Impact on knowledge of EU funds aquisition - input additionality Portugal; 0,091; 0,25 Spain; 0,062; 0,261 Croatia; 0; 0 Cyprus ; 0; 0 Low knowledge aquisition Low cooperation and training Low knowledge aquisition High cooperation training and Germany; 0; 0,303 Romania; 0; 0 Estonia; 0; 0,173 Results of the path analysis for selected EU countries – input additionality and behavioral external additionality external behavioral and additionality – input selected EU countries for analysis the path of Results Slovenia; 0; 0,281 Lithuania; 0; 0,201 Slovakia ; 0; 0 0 0,05 0,1 0,15 0,2 0,25 0 0,3 0,2 0,1 0,35 0,25 0,15 0,05 Figure 2.2. Figure 54 2. Additionality from public support to R&D and innovation in the European Union

References

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Chapter 3 The impact of Framework Programs on innovativeness in the European Union

3.1. Introduction

Innovation is a crucial part of today’s economic growth and the overall advancement of society. At the same time, this accelerator contributes to the divergence in growth between the economies that can and those that cannot afford to have innovation as a corner stone of their economy. Lack of innovation can have many sources that range from a lack of the innovative spirit of society to a lack of resources (e.g. funding, equipment, human capital). Regardless of the source, it is found that often the private sector’s push is not enough to close the gap between the underdeveloped and developed economies, and it is not enough to achieve innovation-related societal goals as the private sector’s orientation tends to be short term and unit-focused, while societal innovation, by design, needs to be a long-term solution for the masses. As a result, public officials design schemes that aim to both, increase involvement in innovation activities and (via the first aim) close the innovation gap. On the European Union level, the key programs that strive to attain the two perspectives are dubbed “framework programs”. The most recent completed program was the Seventh Framework Program (which was complemented with the Competitiveness and Innovation Framework Program). At the time this text is written the Horizon 2020 is the chief innovation-stimulating program from the European Union, while the ninth framework program is being constructed. Opinions on the general programs are mixed; therefore, the overall aim of this chapter is to examine the impact that framework programs have on the innovation output in the European Union. The sample of programs for this study is comprised of the three programs mentioned earlier: The Seventh Framework Program (FP7), the Competitiveness and Innovation Framework Program (CIP) and the on currently ongoing Horizon 2020 (H2020). The first sub-aim of this chapter is to present the three programs from the perspective of various source documents. This will allow for an identification of the 60 3. The impact of Framework Programs on innovativeness in the European Union key components of the innovation process, its key determinants that the analyzed programs aim to stimulate. The second sub-aim of this chapter is to examine the established components of the three programs from the perspective of existing studies on such topics as the determinants of innovation. Such an analysis will allow for a comment on the design of the FP7, CIP and H2020. The third sub-aim of this chapter – which could not exist without the prior two – is related to the overall goal of analyzing the impact of the three programs on innovativeness in the European Union. In addition to an attempt to calculate the impact and efficiency of each of the programs – a process highly guided by the type and overall quality of data availability at the time this study is conducted – the empirical part of the chapter is extended to include the role that each of the five key innovation actors plays from the perspective of FP7 and H2020 funding and how this translates into innovation output. Unfortunately, data for CIP was not available for the latter part of the study. The chapter ends with concluding remarks and some food for further thoughts.

3.2. The FP7, CIP and H2020 programs in a nutshell

The purpose of the Seventh Framework Program (2007-2013) was to “contribute to the Union becoming the world’s leading research area ... [therefore] ... require[ed] ... [FP7] to be strongly focused on promoting and investing in the world-class state- of-the-art research, based primarily upon the principle of excellence in research” (Official Journal of the European Union, 2006a, p. L 412/1). The FP7 consisted of five aggregates of programs: a) Cooperation; b) Ideas c) People; d) Capacities; and e) Euratom. The key objective of the Cooperation program was to promote and facilitate “the coherent and strategic development of EU International policy in research and innovation” and it did so by acting as a “‘knowledge centre’ [that] organis[ed] and supervis[ed] policy dialogues with key third countries and regions, works on ‘Framework’ conditions by identifying barriers and obstacles to increased international cooperation and measures to tackle them. It coordinat[ed] international cooperation activities within the thematic areas in order to achieve ‘scale and scope’ and ensure a ‘win-win situation’ with the aim of substantially increasing international cooperation in key strategic areas” (European Union, 2015a). The aim behind the Ideas program was to “reinforce the dynamism, creativity and excellence of European research at the frontier of knowledge and to improve the attractiveness of Europe for the best researchers from European and third countries, as well as for industrial research investment”. It also “aim[ed] to support activities in 'frontier research', carried out by individual teams competing at European level, within and across all fields of research” 3.2. The FP7, CIP and H2020 programs in a nutshell 61

(European Union, 2015b). The only component of Ideas was the European Research Council initiative. The People program aimed at “strengthening, quantitatively and qualitatively, the human potential in R&D in Europe” and at “increasing participation of women researchers” (European Union, 2015c). The Capacities program strived to “enhance research and innovation capacities throughout Europe and unlock the full research potential of European regions, especially convergence regions” (European Union, 2015d). Under the European Atomic Energy Community there were two sets of actions; namely, indirect actions (managed by the Directorate-General for Research, i.e. DG-RTD; consisting of fusion energy research, nuclear fission and radiation protection; European Union, 2015e) and direct actions (undertaken by the Joint Research Centre, i.e. JRC; including nuclear waste management, environment impact and basic knowledge, nuclear safety and nuclear security; European Union, 2015f) (European Union, 2015g). The Competitiveness and Innovation Framework Program (2007-2013; Official Journal of the European Union, 2006b) was created with the objective of achieving the following goals: a) “to foster the competitiveness of enterprises, in particular Small and Medium-sized Enterprises”; b) “to promote all forms of innovation, including eco-innovation”; c) “to accelerate the development of a sustainable, competitive, innovative and inclusion Information Society”; and d) “to promote energy efficiency and new and renewable energy sources in all sectors, including transport” (European Commission, 2008). The CIP included three programs: a) The Entrepreneurship and Innovation Program (EIP); b) The Information Communication Technologies Policy Support Program (ICT-PSP); and c) The Intelligent Energy Europe Program (IEE). The aims of the EIP were to support enterprises (especially SMEs), entrepreneurship, innovation (including eco-innovation) and industrial competitiveness (Official Journal of the European Union, 2006b). Actions of ICT (according to Article 26 of the source) encompassed the development of the single European information space and strengthening the internal market for ICT products and services and ICT-based products and services, the stimulation of innovation through the wider adoption of investment in ICT and the development of an inclusive information society and more efficient and effective services in areas of public interest, and an improvement of the quality of life (Official Journal of the European Union, 2006b). The IEE program aimed to “contribute to ensuring secure, sustainable energy for Europe, while enhancing European competitiveness” (as listed in Article 37, Official Journal of the European Union, 2006b, p. L 310/10). “Horizon 2020 [2014-2020] is the financial instrument implementing the Innovation Union, a Europe 2020 flagship initiative aimed at securing Europe’s global competitiveness” (European Union, 2015h). Similar to the FP7, it consists of a set of individual sections/pillars, which are: Excellent Science, Industrial Leadership, 62 3. The impact of Framework Programs on innovativeness in the European Union

Societal Challenges, Fast Track to Innovation Pilot (2015-2016), Spreading Excellence and Widening Participation, Science with and for Society, EIT, Euratom4. The general aim of the programs under the Excellent Science pillar is to “reinforce and extend the excellence of the Union’s science base and to consolidate the European Research Area in order to make the Union’s research and innovation system more competitive on a global scale” (European Union, 2015i). The Industrial Leadership’s goal is to “speed up development of the technologies and innovations that will underpin tomorrow's businesses and help innovative European SMEs to grow into world-leading companies” (European Union, 2015j). Initiatives under the Societal Challenges umbrella are: a) Health, Demographic Changes and Wellbeing; b) Food Security, Sustainable Agriculture and Forestry, Marine, Maritime and Inland Water Research, and the Bioeconomy; c) Secure, Clean and Efficient Energy; d) Smart, Green and Integrated Transport; e) Climate Action, Environment, Resource Efficiency and Raw Materials; f) Europe in a changing world – Inclusive, innovative and reflective societies; and g) Secure societies – Protecting the freedom and security of Europe and its citizens (European Union, 2015k). The Fast Track to Innovation is a thematically open initiative that aims to: a) fast-forward time from an idea to the market; b) stimulate first-time EU research funding applications; and c) increase private sector investment in R&I (European Union, 2015l). The collective of actions included in the Spreading Excellence and Widening Participation program is a response to the fact that “the European Union sees significant internal disparities in terms of research and innovation performance” (European Union, 2015m). Through the Responsible Research and Innovation approach, the Science with and for Society program aims to “build effective cooperation between science and society, to recruit new talent for science and to pair scientific excellence with social awareness and responsibility” (European Union, 2015n). As is evident from the above presentation of the three programs, they – albeit via various tools – aim to stimulate innovativeness in the European Union through the stimulation of two key innovation factors; namely, human capital and R&D activity.

3.3. Positioning of the FP7, CIP and H2020 key elements in the literature on innovation

A good introductory study is one conducted by Stern et al. (2000), where the authors search for the determinants of country-level R&D productivity for 17 OECD economies for the years 1973-1996. In addition to highlighting the role of human capital and investment in research activities, the authors state that national

4 EIT and Euratom as described elsewhere. 3.3. Positioning of the FP7, CIP and H2020 key elements in the literature on innovation 63 innovation systems are highly shaped by public policy, which (in addition to just increasing R&D funding) “plays an important role in shaping human capital and investment and innovation incentives” (Stern et al., 2000, p. 33). They support this by noticing that increasing estimated levels of innovative capacity are seen in those economies that have previously undertaken actions that aim at higher human capital investment in the areas of science and engineering, among other things. Tavassoli in his 2015 work on data on firms from Sweden distinguishes between two levels of innovation: “innovation prosperity” (the probability of being innovative) and “innovation intensity” (sales of innovation). Right at the start, Tavassoli refers to neo-Schumpeterian studies and names human capital saying that it is “mostly responsible for the generation of new knowledge within firms and the more new knowledge, the more innovation prosperity for firms” (Tavassoli, 2015, p. 19), and therefore more economic growth and development. Next, the author refers to the size of the firm, saying that “large firms probably have competitive advantages over small ones within mature industries [...] hence, the probability to innovate is expected to be higher for larger firms in these industries” (Tavassoli, 2015, p. 20). This (as also highlighted within the studied work) suggests that cooperation between firms (e.g. in terms of capital consolidation) should have a positive impact on innovation prosperity. Another vote for cooperation given by Tavassoli is international trade as it is “assumed to act as a conduit for flow of knowledge for firms” (Tavassoli, 2015, p. 20), with its biggest impact being on firms in declining industries. Shifting to innovation intensity, Tavassoli states that the “classical determinant (input) of innovation intensity has been recognized as R&D investment” (Tavassoli, 2015, p. 20), which he (according to the Oslo Manual) further decomposes into: internal and external R&D, acquisition of machinery, training for employees (i.e. investment in human capital), engagement in introducing innovation to the market and engagement in other external knowledge5. The author concludes that: a) “the determinants of firms’ innovation propensity and intensity [...] differ based on the stages of [industry life cycle]” (Tavassoli, 2015, p. 26); b) for the probability of being innovative: size and human capital have the highest impact for firms in mature industries and participation in an international trade network is of key importance for firms in declining industries; and c) for innovation sales: innovation inputs play a key role as the determinants of this variable for firms in growing industries. Buesa et al. (2010), in their research with the aim of studying the possible explanatory variables of regional innovation in Europe, sum up their study of key theoretical and empirical literature by stating that “innovatory output depends in the

5 Tavassoli also uses a set of control variables, which can be found in Appendix C of the studied work. 64 3. The impact of Framework Programs on innovativeness in the European Union first place on the effort made in allocating resources, regardless of whether the latter is measured via expenditure or staff employed in R&D” (Buesa et al., 2010, 724). The authors add that those determinants impacting innovation, which are “worth of mention” are: human capital; the size and distribution of innovatory firms; the role of universities and public administration that act as R&D agents; the degree of sophistication of demand; the financial system (i.e. access to investment capital); national and regional cultures; structure of the markets; R&D policies; the extent of intellectual property protection; and the presence of cooperation networks. It is clear at this point that the authors provide backing for nearly all aspects of the studied initiatives and find that the key factors explaining innovation in the EU (as measured by patents and patents per capita) have to deal with innovative firms and their environment. The authors also conclude that as much as aid towards cooperation is important, it must not serve as a substitute for initiatives aiming to promote R&D and alike activities “of a neo-classical nature and inspiration” (Buesa et al., 2010, 733). Schneider (2005) models the innovation rate (“the number of U.S. patent applications by residents of a given country each year as reported by the U.S. Patent and Trademark Office” [Schneider, 2005, p. 536]) as a function of: human capital stock; real level of import of high-tech goods from developed economies; the level of R&D expenditure in a given economy; GDP scaled by population; intellectual property rights; foreign direct investment inflows; and the country’s infrastructure. The author finds that all of the mentioned factors (except for foreign investments) are of importance when explaining the rate of innovation. Furthermore, Schneider finds that in developing countries it is the size of the market and infrastructure that play the key role in explaining their innovation, while for developed economies these factors are: high-tech imports, human capital and R&D expenditures. Mariz-Pérez et al. (2012) highlight the importance of human capital in the process of innovation by recognizing that “human capital, understood as both the individual and group knowledge of company employees, is especially important in determining the innovation capacity of firms” (Mariz-Pérez et al., 2012, p. 32). The authors also highlight that as much as investment in R&D is needed, it is, as such, not sufficient to yield innovation capacity. Cabrilo et al. (2014) add to this by firmly stating that human capital and innovation “should be viewed as closely related concepts, which jointly create a reinforcing loop of value creation. As [... human capital ...] is one of the main sources of innovation, innovation strategies need to encompass a wide range of relevant initiatives to stimulate [... human capital]” (Cabrilo et al., 2014, p. 423). Great praise is given to human capital as a determinant of innovation and growth by McGuirk et al. (2015), who study Innovative Human Capital (“employee- managers’ human capital to create a new concept” [McGuirk et al., 2015, p. 965]) and 3.3. Positioning of the FP7, CIP and H2020 key elements in the literature on innovation 65 conclude that it is “a significant concept to consider when creating public support programs for small firms” (McGuirk et al., 2015, p. 965). An interesting take on the role of human capital as a determinant of propensity to patent (after highlighting its importance in enhancing innovation capabilities) was undertaken by Huang and Cheng (2015) when studying 165 Taiwanese information and communication technology firms. The researchers distinguish between firms that employ a “higher proportion of full-time scientists and engineers” and firms that employ a “higher proportion of employed with postgraduate degrees” (Huang and Cheng, 2015, p. 57). Interestingly, only the prior has shown to have an impact on the propensity to patent. This puts forward the hypothesis that it is not simply the number of postgraduate workers that are important to innovation, but the number of those who specifically are scientists and engineers. It is obvious that there is no one strategy for human capital that would fit all possible economies. To aid this issue, Lu et al. (2014) have created a matrix described by two factors: a) economic in/efficiency; and b) R&D in/efficiency. Depending on the location of the economy on the said matrix, it should employ a specific human capital strategy. And so, economies that are efficient in both factors (quadrant A in Figure 3.1), should continue to promote high-technology innovation and symbiotic relationships between industries and government. Those economies that lack R&D efficiency, but have economic efficiency (quadrant B), ought to focus on training and rewarding persons employed in science and technology as well as promoting cooperation between industries and universities with the aim of increasing R&D. When there is R&D efficiency, but no economic efficiency (quadrant C), there is a need for improved education and training systems, especially: applied science, management of technology projects and innovative service quality. Lastly, where there is no R&D nor economic efficiency (quadrant D), education and training systems need to be improved by such means as cooperation, strategic alliances, incentive for innovation studies and incorporation of technology diffusion.

Figure 3.1. Decision matrix per Lu et al. (2014)

Efficiency Economic Efficient B A Inefficient D C Inefficient Efficient R&D Efficiency

The role of human capital and investment in R&D is also highlighted by Ghazal and Zulkhibri (2015). In reference to the prior, the authors find the need for a match 66 3. The impact of Framework Programs on innovativeness in the European Union between the education system’s output (i.e. human capital) and the skills required by firms and industries. Through their results, Ghazal and Zulkhibri challenge the notion that increasing expenditures on R&D will always yield a higher growth, which is stimulated by innovation. They see this factor as “one ingredient of a complex recipe” (Ghazal and Zulkhibri, 2015, p. 253) that needs complementary factors, such as proper governance that can be of key importance in stimulating innovation in developing economies. In their 2015 study of the determinants of high-technology firms in the EU, Baesu et al. (2015) use the following explanatory variables: various measures of expenditures on future human capital (e.g. on education as a percentage of GDP); expenditures on R&D activity (e.g. as a percentage of total government expenditure); current human capital available for research and innovation (e.g. human resources in science and technology as a percentage of active population); and other factors (e.g. exports as a percentage of GDP). The authors find that there is a positive causal relationship from the number of R&D personnel and researchers to patent applications and patents granted but have shown that R&D expenditures have an (unexpected) negative impact. However, in determining trademark application both R&D expenditures and human capital in science and technology are important explanatory variables. Similar results (i.e. increasing access to national-level subsidies having a negative impact on innovation output) were achieved by Zemplinerová and Hromádková (2012) when studying the determinants of innovation decisions and innovation investment of Czech firms for the years 2004-2007, with the researchers adding that there is no one-sided evidence that would confirm the efficiency of subsidies in terms of non- public R&D6. Such subsidies meet further criticism as they “distort price signals”, “can alter the behavior of firms”, firms receiving such money “may be cushioned and suffer by soft budget constraints” and “large companies have better chances to succeed in getting subsidies due to their political power” (Zemplinerová and Hromádková, 2012, p. 501). However, the researchers also highlight the fact that there is a positive causal relationship from access to subsidies (but only at governmental or EU level, not local) to firms’ innovation expenditures. The authors state that the innovation activity of a firm depends on its age, size and available strategic features (e.g. being part of a network, international involvement), problems with financing of innovation, the extent of market competition, the general economic condition of the country and R&D subsidies. The authors explain that since the private returns on R&D investment are smaller than, as a result of positive spillovers, social returns and because R&D

6 Similar conclusions are drawn by Čadil and Petkovová (2014) who state that “the impact of ... [R&D expenditures and human capital] on growth is really inconclusive and a debate regarding the effectiveness of current related policies should be opened” (Čadil and Petkovová, 2014, p. 308). 3.3. Positioning of the FP7, CIP and H2020 key elements in the literature on innovation 67 investment in private firms is less than “socially desirable”, government subsidies for firms’ R&D have a valid backing. The researchers also conclude that the bigger the firm, the less efficient it is in obtaining innovation output from given innovation inputs (supporting the notion of increasing involvement of SMEs in creating the innovation system). Opposite to the general idea of concentration of R&D activities and cross-firm cooperation in order to e.g. achieve economies of scale, Zemplinerová and Hromádková state that these reasons “cannot be unanimously confirmed by economic literature” and that the “risk that R&D cooperation may start collusion in the product market is high” (Zemplinerová and Hromádková, 2012, p. 501). Lastly, it is important to note that Zemplinerová and Hromádková make the observation that the “variables that are expected to determine different components of the innovation process are so numerous that the selection (and omission) of variables is very likely to influence the results of empirical studies” (Zemplinerová and Hromádková, 2012, p. 489). In addition to the results being sensitive to the determinants used, Ghisetti and Pononi (2015), when studying environmental innovations, find that “cross-section analysis has lower chances to find R&D as a statistically significant determinant of ... [environmental innovation], which shows that primary studies cannot avoid taking into account a temporal perspective when analyzing R&D: time is a crucial component of the innovation process” (Ghisetti and Pononi, 2015, p. 64). In the 2005 study, González et al. (2005) come to similar conclusions as Zemplinerová and Hromádková when studying 2000 Spanish manufacturing firms. For example, they start out by saying that there is no definitive proof of the effects of subsidies and if there is one it is “relatively modest and controversial” (González et al., 2005, p. 930). González et al. were trying to see if the presence of a government R&D subsidy has a significant impact on a firm’s decision to carry out R&D projects and on whether these projects would be initiated in the absence of the said support. The first finding is that “nonperformance of innovative activities can effectively be traced back to the presence of optimal efforts below the profitability thresholds (that is, negative profitability gaps)” (González et al., 2005, p. 946) and that, as much as negative profitability gaps can be observed across the spectrum of firms, its extent is highest in small firms. The second finding is that subsidies do indeed impact the decision of undertaking a research activity and this impact is the biggest in small firms that would otherwise not have initiated the project. However, the third finding is that R&D aid is chiefly given to firms that would have undertaken the project regardless of receiving the subsidy. The authors criticize the process of assigning such funds by saying that it has too much of a “risk-aversion” and it “suggests that public policy tends to neglect the inducing dimension of public support” (González et al., 2005, p. 946). 68 3. The impact of Framework Programs on innovativeness in the European Union

However, Sandu and Ciocanel (2014) conclude that “under current European national policies for increasing the intensity of R&D funding, raising the average EU level of R&D expenditures to the target of 3% of GDP, and particularly the EU average of private R&D expenditures to 2% of GDP, may significantly boost exports and competitiveness” (Sandu and Ciocanel, 2014, p. 80), both of which translate into higher economic growth. Similar conclusions are reached by Vogel (2015). As the examined literature has shown, the two elements (i.e. human capital and R&D expenditures and therefore activity) find strong backing in scientific literature.

3.4. The impact and efficiency of the FP7, CIP and H2020

To discuss the impact of the examined programs, the assumption needs to be made that if their objectives have been addressed correctly, the after state (t+n) of innovation should be greater than the before state (t). In other words, ceteris paribus:

Innovationt < Innovationt+n. The question that subsequently arises concerns the possibility of other factors than the FP7, CIP and H2020 impacting Innovationt+n (X; Equation 1).

Equation 1

Innovationt = βx Xt

Innovationt+n = βx Xt+n + ( βFP7 FP7t+n + βCIP CIPt+n + βH2020 H2020t+n );

HA : βFP7 > 0, HA : βCIP > 0, HA : βH2020 > 0

Fortunately, the direct output from the FP7, CIP and H2020 has (to a degree) been reported in annual progress reports; hence, it is possible to confirm all three hypotheses: HA : βFP7 > 0, HA : βCIP > 0 and HA : βH2020 > 0. However, the exact values of the parameters are impossible to establish econometrically due to the lack of appropriate data (as well as due to the fact that the H2020 is an ongoing program). Therefore, the evaluation of the effectiveness of the FP7, CIP and H2020 is based on their direct outputs reported. For the FP7, the data (Table 3.1) clearly shows its contribution to the ex post (t+n) level of innovation, where innovation output is expressed as: 1) new IPRs; 2) new base for further study; and 3) decimation of knowledge and new creation, advancement and exploitation of knowledge and information. Similarly, but from a different perspective (i.e. more on the input side), CIP has proven to be successful due to its involvement of SMEs via providing funds and networking, promotion and information initiatives (Table 3.2). For the H2020, the data collected for the years 3.4. The impact and efficiency of the FP7, CIP and H2020 69

2014-2015 (Table 3.3) shows that the program has positively contributed to the state of innovation on (at least) three fields: 1) publications in peer-reviewed journals; 2) patent applications; and 3) patents awarded. The variables used to analyze the efficiency are as follows7. For FP7 (Table 3.4), the direct output measures are: Group I – Quantitative measure (Final Reports / Number of grant agreements); Group II – IPRs (No. of projects with at least one IPR reported, No. of reported IPRs, IPR reported as patent application); Group III – Knowledge creation and decimation (No. of projects with at least one publication, Total publications, Publications in high-impact journals); and Group IV – Foregrounds establishment and commercialization (Reported foregrounds, Commercial exploitation of R&D results, General advancement of knowledge, Exploitation of R&D results via standards, Exploitation of results through social innovation, Exploitation of results through EU policies). For the CIP (Table 3.2), the output measures are: SME loans (stock, i.e. from 2007); SME loans (flows, i.e. in a given year); EEN partners; Newsletter points of contact (in million); SME in local events; IEE Total number of proposals funded and projects resulting from the call for proposals in a given year. For the FP7, the input measures are: No. of participants; EU co-financing; Project budget. For the CIP, the input measures are budgets for: EIP; Eco-innovation (part of EIP bud.); EIP – committed; ICT-PSP; ICT-PSP – committed; IEE, IEE – committed; Total and Total – committed. The variables used to examine the progress of the H2020 are the 238 key performance indicators listed by the European Commission (2015b) – Table 3.5.

3.4.1. The impact and efficiency of the FP7 Given finite resources, division of funds (i.e. focus of investments) should be driven by the intended form of innovation outcome. And so (Table 3.6), in terms of efficiency of achieving a pre-specified innovation output: 1) if the goal is to stimulate new IPRs, then the investment should be allotted to PEOPLE- and CAPACITY-like programs; 2) if the goal is to stimulate the base for further study and decimation of knowledge (i.e. publications), then the investment should be allotted to PEOPLE-like programs;

7 For proper evaluation, it would be beneficial to compare the established costs of innovation output with other data points; however, no such data is available at the time this analysis is being conducted. 8 Due to the cumulative nature of some of the indicators (e.g. indicator number 6 “LEIT – Patent applications and patents awarded in the different enabling and industrial technologies”) the final number of indicators taken under consideration is extended in order to account for all the possible measures (in the stated example both patent application and patents awarded). Also, given that the establishment of some of the thresholds was not completed at the time that this study was being conducted and the same goes for some data collection, this study focuses only on these indicators for which both the thresholds and data were available. 70 3. The impact of Framework Programs on innovativeness in the European Union and 3) if the goal is to stimulate the creation, advancement and exploitation of knowledge and information (i.e. foregrounds), then the investment should be allotted to CAPACITY-like programs. A very interesting observation is that as much as PEOPLE and CAPACITIES programs do have their areas of focus, COOPERATION appears to be the least efficient in producing innovation output (Table 3.6). This leads to the conclusion that the FP7 has been efficient in stimulating innovation by funding actions that aim to improve training, career development and the mobility of researchers between sectors and countries as well as by funding actions designed to improve Europe’s infrastructure and the research capacity of SMEs (the latter also being heavily stimulated by the CIP). However, providing funding for collaboration and transnational research proved to be a relatively weak means of stimulating any of the measured innovation outputs. These conclusions are further supported when the same analysis is conducted on individual projects within each of the four programs (Table 3.7). And so, in terms of IPR stimulation and knowledge creation and decimation, the Research Potential project (CAPACITIES group) is the most efficient across all measures of innovation output. Shifting to foregrounds establishment and commercialization, the lead is taken by the Research for the Benefit of SMEs (CAPACITIES); at times it is again the Research Potential or the Regions of Knowledge (CAPACITIES) projects, which are the most efficient. Not far (relatively to other projects) behind are the PEOPLE programs. The results of the correlation analysis (Table 3.8) show that the budget of the program is not statistically significantly correlated with any of the measures of innovation output (i.e. H0 : r = 0 could not be rejected at α = 10%). The opposite is the case for the share of co-financing by the EU. This input is very highly, positively and statistically significantly (H0 : r = 0 is rejected at α = 10%) correlated with the number of publications (0.867) and the number of publications in high- impact journals (0.869). Next in strength is the relationship with IPRs reported as patents (0.835) and the general number of IPRs (0.801). The opposite is true for the correlation between the EU co-financing share and the exploitation of results through social innovation (0.467), exploitation of results through EU policies (0.377) and the general advancement of knowledge (0.371) – despite being positive and statistically significant, it is relatively low. Lastly, the number of participants9 is first highly, positively and statistically significantly correlated with the number of publications (0.804) and the number of IPRs reported as patents (0.795), the general

9 Because part of the FP7 programs is devoted to staff assignment, the number of participants in each program is also taken under consideration as an input. 3.4. The impact and efficiency of the FP7, CIP and H2020 71 number of IPRs (0.794) and the number of publications in high-impact journals (0.780). Next, the relationship between the number of participants is highest with the general advancement of knowledge (0.600) and the exploitation of results through social innovation (0.589), followed by the exploitation of results through EU policies (0.489) and the number of reported foregrounds (0.472), and finally with exploitation of R&D results via standards (0.374). The correlation analysis leads to the following observations. First and very interestingly, there appears to be no connection between the total budget and innovation output. This suggests that there is a set of other factors responsible for the creation of the examined output (e.g. the stock of human capital as outlined in the literature review). Second, none of the measured inputs is statistically significantly correlated with the commercial exploitation of R&D results. Third, as much as there are strong relationships of examined inputs with outputs related to IPRs and knowledge creation and decimation, their relationship with tangible / applicable measures of innovation output is significantly smaller. The last two observations lead to the conclusion that the FP7 is (intentionally or not) aimed at creating theoretical advancements in knowledge and not at creating applicable / marketable innovations.

3.4.2. The impact and efficiency of the CIP Evaluation of the CIP in terms of its impact on innovation is harder due to its design, i.e. in the process of innovation creation it employs its resources with engagement and financing (i.e. inputs) rather than the final product, which for obvious reasons is not immediate and would require a series of follow-up studies. Also, it is impossible to use the output/input ratio as it would provide useless (from the perspective of this study) information, e.g. the average cost / value of a proposal funded. Despite these difficulties, it is possible to make the following observations. One, each of the years the output created by EIP (SME loans, EEN partners, newsletter points of contact and SME participation in local events) has been constantly producing positive results with an overall impressive average growth rate of 24.01%; especially when compared with the average growth rate of the EIP budget (5.76%; 5.51% for budget committed). Two, the budget for IEE has been growing on average at 17.66% (17.68% for budget committed), but the average growth rate for its output as measured by the total number of proposals funded equals 2.27%. This may sound discouraging, but when looking at the fact that on average each year 64 proposals were funded the picture becomes more positive and this is the framework through which the IEE should be examined. Three, similar observations can be made for projects resulting from the calls within ICT-PSP; on average 39 projects per year were retained, with an average growth rate of 32.29% and the average budget growth rate of 28.55% (27.88% 72 3. The impact of Framework Programs on innovativeness in the European Union for budget committed). Lastly, it needs to be noted that the annual review reports highlight that “the CIP instruments have successfully reached final beneficiaries and that the actions implemented so far have delivered encouraging results" (European Commission, 2012, p. 3).

3.4.3. The impact and efficiency of the H2020 The H2020 is an ongoing initiative; therefore, its evaluation should be seen as an interim one. In the Excellent Science pillar there are three areas (ERC – Percentage of publications from ERC funded projects which are among the top 1% highly cited; Marie Skłodowska-Curie actions – Cross-sector and cross-country circulation of researchers including PhD candidates; and Research Infrastructures – Number of researchers who have access to research infrastructures through support from Horizon 2020) that are meeting or are on their way to reach the targets set for the end of the H2020. However, two components (FET – Publications in peer-reviewed high-impact journals; and FET – Patent applications and patents awarded in Future and Emerging Technologies) are significantly lagging. Risk finance (specifically Risk Finance – Total investments mobilized via debt financing, in million EUR) is the strong performer of the Intellectual Leadership pillar, while the targets associated with LEIT (i.e. LEIT – Patent applications in the different enabling and industrial technologies; and LEIT – Patents awarded in the different enabling and industrial technologies) significantly underperform. Similarly, the three elements of the Societal Challenges pillar (Societal Challenges – Publications in peer-reviewed high-impact journals in the area of the different Societal Challenges10; Societal Challenges – Patent applications in the area of the different Societal Challenges; and Societal Challenges – Patents awarded in the area of the different Societal Challenges) underperform significantly – for a visual representation see Figure 3.2 to Figure 3.12. Further in the topic of efficiency (Table 3.9), i.e. output per million Euro, Euratom is the best performer in terms of publications in peer-reviewed journals (with 4.89 publications per million Euro), while Science with and for Society (0.03) is at the other side of the spectrum. From the three key pillars of the H2020 Industrial Leadership (0.11) is followed by Excellent Science (0.1) and Societal Challenges (0.05). In terms of patent applications per million Euro, Industrial Leadership is the most efficient (0.01), followed by Societal Challenges (0.009) and Excellent Science (0.001). A slightly different hierarchy is seen when examining patents awarded per million Euro (0.003 – Industrial Leadership, 0.003 – Societal Challenges and 0.0002 – Excellent Science).

10 Data based on “Number of publications in peer-reviewed high-impact journals" as the original indicator was not reported on. 3.5. The relationship between innovation input and innovation output from the innovation... 73

Interestingly, there is some degree of heterogeneity within the pillars themselves. For example, within Excellent Science ERC is the most efficient program in producing patent applications (0.003) and patents awarded (0.0003), while other programs (Future and emerging technologies, Marie Skłodowska-Curie actions and Research infrastructure) produce no output connected with patents. In Societal Challenges, Health, demographic change and wellbeing, Food security, sustainable agriculture and forestry, marine and maritime and inland water research and the bioeconomy are the most efficient in producing publications in peer-reviewed journals (0.095 and 0.108, respectively). However, some of them are relatively inefficient in output measures connected with patents where Secure, clean and efficient energy leads in the efficiency in patent applications (0.018) and Health, demographic change and wellbeing in patents awarded (0.007). Concluding, as much as on an aggregate basis the FP7 does produce innovation output, efficiency with which this is done is heterogeneous across the elements of the FP7 as well as the measures of the output in question. As for the CIP, the output of this project was the input into the innovation process; however, due to no data, it is impossible to link inputs into the CIP with innovation output coming from them. An early assessment of the H2020 initiative shows that the program is successful in producing innovation (invention) output; however, the efficiency of this success is heterogeneous across the individual programs (an issue also mentioned by the European Commission, 2017).

3.5. The relationship between innovation input and innovation output from the innovation actors’ perspective

The aim of this section is to use the cluster analysis to group the examined countries according to the amount of funds each group of actors in these economies receives. This will allow for a comment on the connection between funding for actor groups and innovation output. There are five actors taken under consideration from the perspective of this study: higher or secondary education institutions (HES), research organizations (REC), private companies (PRC), public bodies (PUB) and other (OTH). This structure is a derivative of the data collection method design. Data on funds (amount co-financed by the European Commission, expressed in EUR) and the number of participants has been obtained from the National Contact Point for Research Programs of the European Union Institute of Fundamental Technological Research, Polish Academy of Sciences (2016, 2018). Data on innovation output that is represented by total patent applications (direct and PCT national phase entries), total design applications 74 3. The impact of Framework Programs on innovativeness in the European Union

(direct and via the Hague system) and total trademark applications (direct and via the Madrid system) has been obtained from the WIPO (2017). Innovation output is often expressed as patent applications (Stern et al., 2000; Furman et al., 2002; Furman and Hayes, 2004; Hu and Mathews, 2005). However, in this study industrial designs as well as trademarks are also included. Given that there is a delay between the impulse (in this case represented by funding) and the response (e.g. patent application), and that this impulse can take the form of 3 (Stern et al., 2000; Furman et al., 2002; Hu and Mathews, 2005) or 2 years (Furman and Hayes, 2004; Sandu and Ciocanel, 2014), four delays (d = 0, 1, 2, 3) were taken under consideration for the FP7. The number of maximum delays was dictated by data availability. Due to the fact that the data for the funds is an aggregate value for the entire duration of the program, a parallel approach was used to create innovation output variables. And so, the PAT_07-13i/DES_07-13i/TM_07-13i variable stands for the sum of patent/design/trademark applications from 2007 to 2013 (i.e. d = 0) in country i. By analogy, variables for d = 1-3 were created for periods 2008-2014, 2009-2015 and 2010-2016, accordingly. Due to the fact that the H2020 is an ongoing program, it was only possible to measure innovation output for the years 2014- 2016.

3.5.1. The role of actors in the FP7 The dendrogram resulting from the hierarchical clustering procedure with the Ward clustering method and Squared Euclidean distances suggested a series of possible solutions; namely, from 3 to 6 (Figure 3.13). All clustering options were tested with the k-means method of clustering and proved to be stable solutions as any differences between the final clusters from the k-means method differ less than 0.00% from the initial centroids that were the results of the hierarchical method. From the visual analysis of the dendrogram, the 5-cluster solution appears to be the most suitable. The disadvantage of this solution is that it creates three clusters with only one member; however, this was also the case in the 6-cluster solution. Also, the separation of the United Kingdom has been present in all cluster solutions, while the separation of Germany was also present in the 4- and 6-cluster solutions. The continuous separation of these two economies highlights their overwhelming relative involvement in FP7 funds across all groups of actors. Comparing across clusters, cluster number 1 (n = 22) is characterized by a relatively very low level of involvement (again, represented by the value of received funds) in the FP7 across all actor groups (Table 3.10, Table 3.11). The second cluster (n = 1) has a relatively low involvement in HER, PRC and PUB, but the highest one in REC and OTH. Members of cluster number 3 (n = 3) tend to have an overall low to medium involvement in the FP7. Cluster number 4 (n = 1) has the highest 3.5. The relationship between innovation input and innovation output from the innovation... 75 involvement in PRC and REC, which is opposite from cluster 5 (n = 1) where the highest (relative to other clusters) involvement is found in HES and PUB – with the level of involvement in all other categories of actors being medium or low. Overlapping cluster membership on innovation output shows that Germany (representing cluster number 4), which has a very high involvement in PRC and a high involvement in REC, excels in all measures of innovation output regardless of d (Table 3.12). In terms of patents, the second place is taken by cluster 2 (France), which has a very high involvement in REC and OTH. The third spot is occupied by the United Kingdom (cluster 5) with very high HES and PUB, and the last position is taken by cluster 1, which has a relatively very low involvement across all actor categories. This hierarchy stays unchanged when designs are used as a measure of innovation output. In case of trademarks, the only difference observed is that the second place is taken by cluster 5 (the United Kingdom), while France (cluster 2) takes the third spot.

3.5.2. The role of actors in the H2020 A parallel cluster analysis has been carried out for the H2020. Based on the dendrogram (Figure 3.14), 4- and 5-cluster solutions are probable. Both were tested with k-means and were shown to be stable as differences in cluster centroids for both methods do not differ by more than 0.00%. Given that there was no difference in validity between the two solutions, to keep consistency a 5-cluster solution was selected. Comparing across clusters, members of the first cluster (n = 6) have a generally low involvement across all five groups of actors, while members of the second cluster (n = 17) are characterized by a very low level of involvement across all cross- sections (Table 3.13, Table 3.14). Cluster number 3 (n = 1) is seen to have a very high involvement in HES, REC and OTH, and a high involvement in PRC and PUB. The fourth cluster (n = 3) members have a very high involvement in PRC and PUB, a high involvement in REC, and a medium one in the other categories. Lastly, cluster 5 (n = 1) is seen to have a high involvement in HES and OTH, while in PRC, PUB and REC this level is classified as medium. Overlapping cluster membership on innovation output shows that members of clusters 1 and 2 continuously underperform in innovation output generation (Table 3.15). In terms of patent applications, France (cluster 3) slightly outperforms cluster 4 and the United Kingdom (cluster 5) takes the third spot. When looking at industrial designs, cluster 4 is the top performer, followed by cluster 3 and cluster 5. Lastly, in case of trademarks cluster 4 is again the leader, but it is closely followed by cluster 5 and then by cluster 3. In comparison to the FP7, it can be seen that the issue is not as clear cut, as there is no dominance of one cluster across all measures of innovation output. At the same time, the highest performing cluster in two out of three categories 76 3. The impact of Framework Programs on innovativeness in the European Union is clearly characterized by a relatively very high involvement of PRC and PUB, and a high involvement of REC.

3.6. Conclusions

The chief aim of this chapter was to try and establish the impact of the three studied programs on innovativeness in the European Union. This aggregate goal had three sub-goal components: to present the three programs from the perspective of various source documents; to examine the established components of the three programs from the perspective of existing studies on such topics as the determinants of innovation; and to analyze the impact of the three programs on innovativeness in the European Union, as well as to include the role each of the five key innovation actors plays from the perspective of FP7 and H2020 funding and how this translates into innovation output. Interestingly, as much as support for human capital development and interaction between various agents in the innovation eco-system appears to be unquestioned, the support for public R&D investments or R&D subsidies appears to be mixed. What appears to be the key solution to the arguments undertaken in this issue is to ensure a correct allocation of funds by e.g. focusing on small firms and looking for a project that (if successful) would bring the biggest social benefit, as opposed to allocating funds in big firms (some not needing it) and minimizing the risk of the investment. It needs to be said that these are some of the elements addressed by the CIP, for example. Overall, it can be said that the examined programs are well-based in economic theory and (chiefly) find backing in empirical literature on innovation. The second main conclusion is that all three programs, i.e. the FP7, CIP and H2020, have been implemented successfully on a “macro” level, which has contributed to an increase in the level of innovativeness in the EU. On the other hand, shifting to efficiency, it has been found that not all programs are equally efficient in achieving the final goal of a given innovation output. Reflecting on the impact of the studied policies, economies with a low level of involvement in the FP7 and H2020 significantly underperform overall in comparison to economies that have a very high or high relative level of involvement in at least two actor categories simultaneously. Compared to other groups of economies, the group that outperforms them all has a relatively high or very high involvement in private companies, higher or secondary education institutions and research organizations for the FP7, and in private companies, public bodies and research organizations in the H2020. Therefore, it can be concluded that the common elements of high performing clusters in both programs are: private companies and research organizations. 3.6. Conclusions 77

The above results show that the FP7 is a set of programs, which result in a stimulation of the creation of IPRs and knowledge creation and decimation, but weaker in terms of commercial implications. As much as the CIP is not aimed specifically at directly creating innovation output, but rather innovation inputs (e.g. involvement of SMEs), it is clear by its design that it is oriented at commercial implications rather than IPRs, knowledge creation and decimation; all this makes it a very good complementary program to the FP7. As a result, it is recommended that any further innovation-based initiatives encompass all measures of output, i.e. the creation of new intellectual property, new knowledge and its dissemination (which is crucial to avoid knowledge monopolies), as well as the commercial aspect, i.e. the commercial exploitation of knowledge, the creation of new standards etc. To a certain degree the H2020 follows this suggestion; however, from the set indicators it is clear that a good share of output is directed at the invention (publications, patents) and not the commercialization stage of the innovation process. Therefore, asking whether the aim is to publish and patent to a drawer or to a market is a very valid one (the seriousness of this issue is also highlighted by Zygierewicz, 2017). At the same time it needs to be noted that “the large share of attention devoted to commercialization provides confidence that Horizon 2020 will have important innovation outcomes in the short and medium term” (Grimpe et al., 2017, p. 34). The next suggestion refers to progress tracking and the design of objectives. Starting from the latter, it is better to design specific quantitative objectives, which can be put on a measurable scale, rather than unmeasurable qualitative objectives, the evaluation of the completion of which is significantly prone to bias resulting from the meter and the research method selected. The proposed implementation will lead to the design of operational record tracking tools, which will result in richer data sets available for analysis. The H2020 addresses these issues to some degree, but as can be seen in the data gathered for annual review reports, not all of the 23 key indicators are reported on directly in a comparable manner and some of the indicators will not be available for evaluation until the end or some advanced stage of the project – the problem of a consistent performance definition and monitoring was one of the key conclusions reached by Zygierewicz (2017). Therefore, the recommendation would be to, in addition to the end indicators, have interim evaluation indicators for all of the planned programs. An important note has to be made on the dangers of the parametrization of such intangible concepts as “innovation”. This can be best explained with an example question: is it better to have 100 patents and one commercialization, or 10 patents and 10 commercializations? This again requires significant foresight from the designers of the initiatives / goals / commitments. 78 3. The impact of Framework Programs on innovativeness in the European Union

Last but definitely not least, it is paramount that the projects designed to increase innovation focus on both aspects of innovation, i.e. invention (again, publications and patents) and commercialization. These two should be treated as perfect complementary goods. That is, in order to gain the full benefit, both of them need to be present (in this case invention can exist without commercialization, but its usefulness is extremely limited from the impact’s perspective).

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Exploitation of results through EU policies

Exploitation of results through (social innovation)

Exploitation of R&D results via standards

General advancement of the of Programs r Research knowledge

Commercial exploitation of R&D results

Reported foregrounds

Average publications per project

Publications in high impact per reviewed journals

Total publications

Projects with at least one publication

IPR ported as patent application (%)

IPR ported as patent application (No.)

No. of reported IPRs

No. of projects with at least one IPR reported

Final Reports

Total cost (Euro million) FP7 output Specific program Cooperation 44,342Ideas 1,989People 376Capacities 985Euratom 7,677 5,227 5,600 822 NA 878 4,361 662 0.83 NA 156 125 60 1,273 268 NA 319 24,706 12,100 227 NA 7 12.42 228 663 0.85 NA 0.71 14 184 2,824 NA 314 12,892 308 6,219 14 NA 5,182 2.96 10 1,925 NA 1 676 5.90 41 NA 1,975 95 37 1,019 NA 90 802 469 608 NA 78 21 117 NA 30 11 10.13 NA 25 NA 33 27 5 NA 3 10 1 3 Appendix 3.1. Table was unavailable. when data is listed NA Note: fo Point Contact the National from data (2015a) and Commission the European from based data on elaboration own Author’s Source: European Union Institute of Fundamental Technological Research, Polish Academy of Sciences of (2016, 2018). Academy Polish Research, Technological Fundamental of Institute Union European Appendix 83 ;;; NA 20 49 41 44 41 CIP output SME loans (stock, i.e. from 2007) from i.e. (stock, loans SME year) in a given i.e. (flows, loans SME EEN partners (in million) contact of points Newsletter in local eventsSME NA NA in a Proposals the Call for from resulting Projects year given NA NA NA NA 58,767 58,767 2.2 NA 31,522 90,289 NA 99,849 190,138 2.5 NA NA 210,000 19,862 2.4 570 457,000 600,000 2.0 590 850,000 600 600 EIP EIP bud.) (part of Eco-innovation EIP – committedICT-PSP – committedICT-PSP IEEIEE – committed 23,000,000Total 53,850,000 – committedTotal 61,153,000 67,020,000 75,610,000 42,560,000 271,139,268 288,870,000 67,665,289 312,111,000 44,480,000 269,000,000 311,194,000 291,320,000 351,176,000 113,311,000 315,127,000 352,703,000 116,000,000 65,500,000 314,706,000 125,000,000 353,508,000 58,900,000 136,000,000 44,580,000 353,960,000 70,220,000 114,479,000 116,000,000 398,279,352 90,871,000 125,000,000 403,570,000 58,900,000 136,000,000 103,560,000 516,293,000 117,950,000 530,754,000 70,930,000 132,000,000 594,126,000 393,500,000 90,871,000 620,703,000 406,830,000 103,560,000 520,477,000 117,950,000 534,266,000 132,000,000 596,458,000 621,960,000 OutputsEIP IEE funded proposals of number Total ICT- PSP 74 2007 57 2008 62 2009 48 2010 2011 79 2012 66 Inputs (Budget)Inputs EIP ICT- PSP IEE have the CIP instruments that shows "The report the actions final beneficiaries that reached successfully and results." encouraging delivered so have far implemented 2007 2008 2009 2010 2011 2012 Table 3.2. Table (2008-2013a-d). Commission, the European from based data on elaboration own Author’s Source: 84 3. The impact of Framework Programs on innovativeness in the European Union Patents Patents Awarded Patent Applications Publications Publications journals in peer-reviewed 748.7 81392 19 5 1 2 0 Budget Budget (EUR million) H2020 output Excellent ScienceExcellent ERC technologies emerging and Future actions Skłodowska-Curie Marie infrastructureResearch LeadershipIndustrial Technologies Industrial and in Enabling Leadership risk to financeAccess in SMEInnovation Societal Challenges wellbeing and change demographic Health, maritime and marine forestry, and agriculture sustainable Food security, the bioeconomy and research water inland and efficient energy and Secure, clean 3154.9 transport integrated and green Smart, 478.7 efficiency materials raw resource and environment, action, Climate societies reflective and innovative – inclusive, world in a changing Europe 1648.5 404 256.9 its and Europe security of and the freedom Secure societies – protecting 726.3 6030.8citizens 146 participation widening and excellence Spreading 258 Society for Science and 1267.3 with 612.2 21Euratom 589 11 3738.8 (since 2015) Innovation to Fast-track Pilot: 3291.4 120 22 NA 47 404 583.9 0 1032 1330.8 163 5754 0 117.4 13 0 NA 9 5 NA 38 7 0 297 14 26 0 0 88.8 0 47 1 1 9 9 105.4 NA 0 0 13 NA 24 3 53 1 3 0 NA NA 2 15 90.1 2 0 0 441 0 0 0 0 0 Program Table 3.3. Table (2016). Commission the European from based data on elaboration own Author’s Source: Appendix 85 PRO_BUD (Euro) PRO_BUD ADV (General advancement (General advancement ADV r Research Programs of the of Programs r Research otal publications), PUB_HIGH PUB_HIGH publications), otal reported), IPR_NO (No. of of (No. IPR_NO reported), xploitation of results through EU through results of xploitation EU_COFUND EU_COFUND (Euro) PART_ NO

POL jects with at least one IPR least one at jects with SI

STDR

ADV

COMM

FOR agreements), PR_1_IPR (No. of pro of PR_1_IPR (No. agreements),

PUB_ (E POL social through innovation), results of (Exploitation SI HIGH n No.), PR_1_PUB (Projects with at least one publication), PUB_NO (T PUB_NO publication), least one at with PR_1_PUB (Projects n No.), PUB_ NO PR_1_ PUB IPR_ PAT IPR_ NO PR_1_ IPR

FR_NR Output and input data for FP7 (in no. unless specified otherwise) FP7 (in no. for data input and Output

PA HealthKBBE 400NMP 98 185Energy 350 34ENV 284 105TPT 254 139 89 33SSH 216 344 368 76Space 92 296 280 15 11,662 6,620Security 252 150 131 89 32 176 21 111 79GA 4,046 2,836 0 71 42 75SP1-JTI 7 17 1,982 1,220 9People 182 8 121 55 0 791 105 11 151 20INFRA 79 8 4,361 21 1 108 2,896SME 339 1 156 0 1 88 11 1,273 15 11 80 549Regions 268 12 78 80 60 6 7 86REGPOT 3 46 471 227 0 35 6 166 107 10 19SiS 2 11,431 2,824 105 693 4,786,076,182.95 688 0 2 34 82 12 3 12,892 244 15 1COH 91,374,431,069.30 6,219 232 57 221INCO 11 676 30 30 161 49 3 152 29 39 0 1 49 90 16 10,350Fusion 0 1 95 127 31 3,232,337,559.21 2 52 46 60 0Fission 75,225,184,695.90 32 22 0 0 0 3 0 22 352 252 8 469 7,967 81 3 0 3,024 1 57 0 2 9 1,850,804,918.73 21 8 1,303 0 89 35 57 0 0 0 44,294,369,741.86 1,489 2 30 11 7 4,430 4 7,202 3 0 470 27 1,873 1 3 1,830,876,071.38 0 1,719,304,186.58 16 0 13 0 0 0 1,011 44,635,337,836.51 42,128,778,214.24 19,727 14 27 0 0 8,985 0 755 4,789,751,887.47 0 35 26,224,489,105.35 0 2,271,151,459.10 1 0 4 78 14 6 0 0 6 22 82,523,513,842.67 10 5 24 169 13 35 1 2 0 0 4 10 0 38 3 125 2,798 13 40 2,644 2 472 9,201 579,553,417.81 0 3,987 0 2 17 2 713,287,662.32 136 0 1,246,096,697.15 1,331,371,745.06 4 6 10,936,586,050.68 0 102 16,195,911,374.98 0 34,841,155,753.70 19,551,023,492.19 3 15 0 9 5,292 1 25 6,337 3 4 0 1,528,321,723.55 2,221,857,202.70 0 57,571,899,715.68 0 199,881,932,112.31 0 5 184 308 0 3 1 0 312,687,984.13 377,734,055.86 0 3 0 21 1,217,868,177.75 0 541,734,231.60 1,013 0 0 10 0 0 126,689,333.80 1 0 0 0 0 2,035,674,702.05 3 0 0 1,847 0 2,060 288,397,372.24 1,420 0 131 352,824,122.66 173,417,304.21 4,991,787,821.25 67 14,673,716,876.31 28,213,462.65 3,809,880,069.72 5,248,981.00 132,530,838.77 182,827,568.22 Table 3.4. Table grant of FR_NR (Final Reports / Number area), (Priority PA Where: reported IPRs), IPR_PAT (IPRs reported as patent applicatio as patent (IPRs reported IPRs), IPR_PAT reported R&D results), of exploitation (Commercial COMM (Reported foregrounds), FOR per journals), in high reviewed impact (Publications via standards), R&D results of (Exploitation STDR knowledge), of policies), PART_NO (Participants number), EU_COFUND (EU co-funding), PRO_BUD (Project’s funding). (Project’s PRO_BUD (EU co-funding), EU_COFUND number), (Participants policies), PART_NO zero. to is equal it reported was not if a value that spaces under the assumption empty inserted into were in ref Values Note: fo Point Contact the National from data (2015a) and Commission the European from based data on elaboration own Author’s Source: Sciences of (2016, 2018). Academy Polish Research, Technological Fundamental of Institute Union European 86 3. The impact of Framework Programs on innovativeness in the European Union 146000 09,00013.5%/8.7% 9,000 14.3%/9.1% 146 13.9%/8.9% 28,559 18,000 40 33,74111 33,741 7NA 2NA NA 47 NA 13 NA NA State of implementation of State 1 patent application per €10 application 1 patent funding million which of (out 65,000 researchers 25,000 PhD candidates) researchers 20,000 additional 2020 Horizon during the basis of be on developed To 2020 results Horizon first the basis of be on developed To 2020 results Horizon first both actions for €25 billion 13,015 4,181 17,196 1.80% per €10 million 25 publications funding 7% per €10 applications 7%3 patent funding million 7% H2020 key performance indicators H2020 key performance FET – Patent applications and patents awarded in awarded patents and applications FET – Patent Technologies Emerging and Future and – Cross-sector actions Skłodowska-Curie Marie PhD including researchers, of circulation cross-country candidates between undertaking mobility researchers of Number sector (Private sectors. non-academic academic and participation) participation/SME who researchers of – Number Infrastructures Research infrastructures support through research access to have 2020 Horizon from and enabling in the different awarded LEIT – Patents technologies industrial firms introducing participating of LEIT – Percentage the market to or new the company to innovations years) three plus the period the project of (covering publications public-private joint of LEIT – Number via debt mobilized investments – Total Risk Finance EUR) (in million financing ERC – Percentage of publications from ERC funded ERC from publications of – Percentage ERC cited 1% highly the top among are that projects and enabling in the different applications LEIT – Patent technologies industrial FET – Publications in peer-reviewed high-impact high-impact in peer-reviewed FET – Publications journals Key performance indicator performance Key H2020 the end of at Target 2014 2015 Total Excellent Science International Leadership Table 3.5. Table Appendix 87 Both KPIs are reported by Horizon 2020 Horizon by reported are Both KPIs and a project beneficiaries the end of after mass the critical after only will be available Their has been reached. finished projects of in this available not is therefore value current Report. Monitoring 2020 Horizon by reported are Both KPIs and a project beneficiaries the end of after mass the critical after only will be available Their has been reached. finished projects of in this available not is therefore value current Report. Monitoring 2020 Horizon by reported are Both KPIs and a project beneficiaries the end of after mass the critical after only will be available Their has been reached. finished projects of in this available not is therefore value current Report. Monitoring 2894112 8NA 12 297 3 53 NA 15 NA The instrument has been implemented since has beenThe instrument implemented is not this indicator for 2015. The value Report 2015. in the Monitoring available State of implementation of State 5,000 organizations funded 5,000 organizations funds private of €35 billion and leveraged 50% be based FP7 developed on To first /or and ex-post evaluation results 2020 project Horizon 2 per €10 million On average, (2014 – 2020) funding 2 per €10 million On average, (2014 – 2020) funding the basis of be on developed To 2020 results Horizon first On average, 20 publications per 20 publications On average, all societal (for funding €10 million challenges) SME – Percentage of participating SMEs introducing introducing SMEs participating of – Percentage SME the market or new the to company innovations years) three plus the period the project of (covering SMEs in participating job creation and – Growth SME of in the area applications Societal – Patent Challenges Societal Challenges the different the of in the area awarded Societal – Patents Challenges Societal Challenges different and prototypes of Societal – Number Challenges activities testing Risk Finance – Number of organizations funded and funded and organizations of – Number Risk Finance funds leveraged private of amount Risk Finance – Total investments mobilized via Venture via Venture mobilized investments – Total Risk Finance Capital in peer-reviewed Societal – Publications Challenges the different of in the area journals high-impact of "Number basedSocietal on (data Challenges journals") high-impact in peer-reviewed publications Key performance indicator performance Key H2020 the end of at Target 2014 2015 Total International Leadership cd. Societal Challenges 88 3. The impact of Framework Programs on innovativeness in the European Union ission, (2015d), ission, s listed in the ISI Science Citation Science in the ISI Citation s listed NANA funds Energy challenge the overall of Share activities: non-fossil-fuel-related to allocated NA93% (2014), 94.7% (2015), 92.6% (total) funds Energy challenge the overall of Share NA sustainable of market-uptake to allocated NA 13.9% (2014), 14.5% energy solutions: (2015), 14.2% (total) NA 2020 ben- Horizon by reported are The KPIs will and a project the end of eficiaries after fin- the mass of critical after only be available relevant First has been reached. ished projects 2018. expected as from are available data by will be made available This information the end of 2020 beneficiaries at only Horizon this stage hence at projects; their respective be reported. cannot the indicator 56.9*689** 58.1* 115* 987** 1,676** State of implementation of State To be developed on the basis of the basis of be on developed To 2020 results Horizon first 85% 2017:480, 2020:500 (both values PS) from To be developed on the basis of the basis of be on developed To 2020 results Horizon first the occasion be determined at To evaluation 2020 interim Horizon of in 2017 the occasion be determined at To evaluation 2020 interim Horizon of in 2017 2017:220, 2020:230 (both values PS) from dex, article contribution to other periodicals. other to contribution dex, article New products, processes, and methods launched into into methods launched and processes, products, New the market funds Energy challenge the overall of Percentage renewable activities: research the following to allocated smart grids and end user energy efficiency, energy, activities energy storage in high peer publications of reviewed – Number JRC journals impact Societal Challenges – Number of joint public-private public-private joint of Societal – Number Challenges publications – Participation Widening and Excellence Spreading in journals in high-impact publications of Evolution field research the given institutional of Society for – Number Science and with the program by promoted actions change specific occurrences tangible im- of of – Number JRC and technical from policies resulting European pacts on Centre Research the Joint by provided support scientific Key performance indicator performance Key H2020 the end of at Target 2014 2015 Total Societal Challenges cd. SWEP SWAFS JRC * Support to Commission services. Commission to EUR). signed (in million Contracts * Support periodical peer-reviewed to contribution article a monograph, to contribution article editorship, JRC with ** Books, monographs in Social Science and/or Citation Expanded Index Comm (2015c), European Commission (2015b), European Commission the European from based data on elaboration own Author’s Source: European Commission (2016). Commission European Appendix 89

Exploitation of results through EU policies

Exploitation of results through (social innovation)

Exploitation of R&D results via standards r Research Programs of the of Programs r Research

General advancement of knowledge

Commercial exploitation of R&D results

Reported foregrounds

Publications in high-impact peer-reviewed journals

Total publications

Projects with at least one publication

IPR reported as patent application (No.)

No. of reported IPRs

No. of projects with at least one IPR reported

Final Reports Output/input ratios for FP7 program collections FP7 program for ratios Output/input Specific program NANANANANANANANANANANANANA CooperationIdeas People 0.0449Capacities 0.0085Euratom 0.0222 0.0185 0.0287 0.1680 0.7788 0.5572 0.0239 0.0279 0.0965 0.2729 0.0610 0.0479 0.0113 0.0150 0.0436 0.0405 0.0225 0.0041 0.0601 0.5043 0.0225 0.0069 0.9914 2.3021 0.0595 0.0002 0.3683 1.1105 0.9775 0.0009 0.3778 0.1207 0.1881 0.0020 0.1949 0.0170 0.0402 0.1534 0.0838 0.0080 0.0149 0.0038 0.0048 0.0057 0.0020 0.0161 0.0063 0.0048 0.0016 0.0048 Table 3.6. Table was unavailable. when data is listed NA Note: fo Point Contact the National from data (2015a) and Commission the European from based data on elaboration own Author’s Source: Sciences of (2016, 2018). Academy Polish Research, Technological Fundamental of Institute Union European 90 3. The impact of Framework Programs on innovativeness in the European Union

POL

STDR r Research Programs of the of Programs r Research

ADV

COMM

FOR

PUB_ HIGH

PUB_ NO

PR_1_ PUB

IPR_ PAT

IPR_ NO

PR_1_ IPR

FR_NR

PA Output/input ratios for FP7 individual programs FP7 individual for ratios Output/input HealthKBBE 4.4E-09NMP 1.1E-09 3.1E-09 4.2E-09Energy 2.8E-09 7.7E-10ENV 3.8E-09 4.7E-09 2.0E-09 1.3E-07 2.4E-09 1.8E-09 1.7E-09TPT 7.2E-08 7.4E-10 4.9E-09 3.4E-09 1.9E-09 2.1E-09 3.9E-09 6.4E-08SSH 5.1E-09 4.6E-10 2.0E-09 3.3E-09 2.8E-08 3.6E-10 1.1E-09 1.6E-09Space 5.4E-08 1.8E-10 3.4E-09 5.0E-10 1.1E-11 1.8E-08 2.6E-08 2.3E-11Security 3.9E-10 4.0E-10 1.6E-10 7.6E-09 2.4E-09 0 1.2E-08 9.1E-10 3.6E-09 6.6E-11 1.7E-09 1.1E-09GA 5.7E-09 0 6.7E-10 6.9E-08 4.3E-10 2.3E-09 1.1E-09 3.6E-10 1.3E-09 3.0E-08 1.3E-09 2.6E-10 8.0E-11SP1-JTI 1.0E-09 6.7E-09 1.9E-09 0 6.0E-10 2.7E-11 2.3E-11 5.6E-10 2.0E-09 4.7E-11 3.2E-10 1.5E-10 6.8E-11 3.1E-09 6.1E-10 9.9E-10 7.1E-10 0 1.0E-09People 9.0E-09 0 3.5E-08 4.0E-11 3.5E-10 4.7E-11 7.0E-09 8.2E-10 1.1E-08 6.0E-11 3.9E-10 1.9E-10 1.1E-09 2.5E-09 0 5.1E-11 5.0E-11 0 8.3E-10 6.3E-10INFRA 2.5E-09 1.7E-07 0 7.5E-11 2.6E-10 8.2E-10 5.9E-09 0 2.5E-10SME 2.9E-11 2.1E-07 1.0E-08 1.1E-10 1.5E-09 0 4.7E-08 8.7E-09Regions 0 4.5E-11 1.2E-10 1.1E-07 2.5E-09 2.4E-11 5.9E-10 0 0 4.9E-07 1.9E-10 REGPOT 7.9E-09 5.2E-10 2.9E-08 2.3E-08 2.4E-07 6.3E-08 2.0E-07 9.0E-10 6.5E-09 0SiS 2.6E-08 1.4E-10 1.5E-08 2.2E-08 5.3E-08 1.4E-08 0 3.6E-09 8.6E-11 COH 2.8E-09 9.0E-08 2.3E-08 9.4E-09 1.8E-08 0 0 2.7E-10 8.5E-08 5.2E-10 7.8E-09 8.0E-10INCO 2.7E-10 1.5E-07 0 2.2E-08 4.2E-10 0 0 2.7E-06 5.5E-09 1.8E-08 1.0E-09 0 1.2E-07Fusion 8.7E-07 1.2E-07 0 0 1.6E-08 0 5.0E-08 6.2E-08 0 2.6E-10 1.1E-08 4.7E-08 3.8E-10Fission 0 1.6E-08 1.0E-09 2.4E-08 4.8E-09 5.5E-10 0 0 0 0 0 1.5E-09 1.2E-09 0 0 2.5E-10 5.1E-11 0 3.9E-09 4.8E-10 3.9E-09 9.5E-10 3.5E-11 6.4E-09 0 2.6E-09 0 5.5E-09 9.5E-10 6.9E-11 0 2.0E-09 3.3E-08 0 7.4E-09 2.4E-09 2.5E-09 1.9E-08 4.5E-09 3.2E-08 9.8E-10 0 7.0E-09 4.4E-09 0 1.7E-09 0 0 7.0E-09 3.4E-10 7.5E-09 3.4E-08 2.0E-10 7.5E-08 8.0E-09 6.8E-10 0 1.5E-08 1.2E-09 6.8E-11 7.5E-09 1.1E-08 0 4.9E-10 2.0E-10 0 7.4E-07 1.0E-08 8.2E-08 0 0 6.0E-10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Cooperation People Capacities Euratom Table 3.7. Table fo Point Contact the National from data (2015a) and Commission the European from based data on elaboration own Author’s Source: Sciences of (2016, 2018). Academy Polish Research, Technological Fundamental of Institute Union European Appendix 91

POL -0.001

SI 0.467 0.377 0.026

STDR the of Programs r Research -0.107

ADV 0.6 0.3740.371 0.589 0.125 0.489 -0.058

COMM 0.072 -0.075

FOR -0.070

PUB_HIGH 0.221

PUB_NO 0.197

IPR_PAT 0.273

IPR_NO Pearson CorrelationPearson 0.794 0.795 0.804 0.78 0.472 0.288 Sig. (2-tailed)Sig. N 0.263 0.232 21212121212121212121 0.392 0.336 0.762 0.746 0.802 0.643 0.910 0.996 Sig. (2-tailed)Sig. N CorrelationPearson 0.801 0.000 0.835 0.000 21212121212121212121 0.867 0.000 0.869 0.000 0.239 0.031 0.205 0.004 0.095 0.005 0.025 Sig. (2-tailed)Sig. N CorrelationPearson 0.256 0.000 0.000 21212121212121212121 0.000 0.000 0.297 0.758 0.097 0.588 0.033 0.092 Pearson Correlation Coefficient matrix Correlation Pearson PART_NO EU_COFUND PRO_BUD Table 3.8. Table α = 10%. at significant statistically not are strikethrough with Variables Note: fo Point Contact the National from data (2015a) and Commission the European from based data on elaboration own Author’s Source: Sciences of (2016, 2018). Academy Polish Research, Technological Fundamental of Institute Union European 92 3. The impact of Framework Programs on innovativeness in the European Union

Figure 3.2. ERC – Percentage of publications from ERC funded projects which are among the top 1% highly cited 8% 7% 7%

highly cited 3%

1% Percentage of publications in the top 1% 0% ERC - Percentage of publications from ERC funded projects which are among the top 1 % highly cited

Target at the end of H2020 State as of the end of 2015

Source: Author’s own elaboration based on data from the European Commission (2016).

Figure 3.3. Marie Skłodowska-Curie actions – Cross-sector and cross-country circulation of researchers, including PhD candidates 70 000

60 000

50 000

40 000

30 000 18 000 20 000 Number of researchers 10 000

0 Marie Skłodowska-Curie actions - Crosssector and crosscountry circulation of researchers, including PhD candidates

Target at the end of H2020 State as of the end of 2015

Source: Author’s own elaboration based on data from the European Commission (2016).

Figure 3.4. Research Infrastructures – Number of researchers who have access to research infrastructures through support from Horizon 2020

40 000 33 741 35 000 30 000 25 000 20 000 15 000 10 000 5 000 0 Additional researchers Research Infrastructures - Number of researchers who have access to research infrastructures through support from Horizon 2020

Target at the end of H2020 State as of the end of 2015

Source: Author’s own elaboration based on data from the European Commission (2016). Appendix 93

Figure 3.5. FET – Publications in peer-reviewed high-impact journals 30 25 20 15 10 5 0,24 0 Number of publications per €10 million funding FET - Publications in peer-reviewed high impact journals

Target at the end of H2020 State as of the end of 2015

Source: Author’s own elaboration based on data from the European Commission (2016).

Figure 3.6. FET – Patent applications and patents awarded in Future and Emerging Technologies 1.2 1 0.8 0.6 0.4

million funding 0.2 0 0 Patent application per €10 FET - Patent applications and patents awarded in Future and Emerging Technologies

Target at the end of H2020 State as of the end of 2015

Source: Author’s own elaboration based on data from the European Commission (2016).

Figure 3.7. LEIT – Patent applications in the different enabling and industrial technologies 3.5 3 2.5 2 1.5 1 0.5 0.126 Patent applications 0 LEIT7 - Patent applications in the different enabling and industrial technologies

Target at the end of H2020 State as of the end of 2015

Source: Author’s own elaboration based on data from the European Commission (2016).

Figure 3.8. LEIT – Patents awarded in the different enabling and industrial technologies 3.5 3 2.5 2 1.5 1

Patents awarded 0.5 0.035 0 LEIT7 - Patents awarded in the different enabling and industrial technologies

Target at the end of H2020 State as of the end of 2015

Source: Author’s own elaboration based on data from the European Commission (2016). 94 3. The impact of Framework Programs on innovativeness in the European Union

Figure 3.9. Risk Finance – Total investments mobilized via debt financing (in million EUR) 30000 25000 20000 17 196 15000

Mln Euro 10000 5000 0 Risk Finance - Total investments mobilised via debt ﬁnancing (mln EUR)

Target at the end of H2020 State as of the end of 2015

Source: Author’s own elaboration based on data from the European Commission (2016).

Figure 3.10. Societal Challenges – Publications in peer-reviewed high-impact journals in the area of the different Societal Challenges (data based on "Number of publications in peer-reviewed high-impact journals") 25

10 funding

5 0.516 0 Publications per €10 million Societal Challenges - Publications in peer-reviewed high impact journals in the area of the different Societal Challenges (data based on "Number of publications in peer-reviewed high impact journals")

Target at the end of H2020 State as of the end of 2015

Source: Author’s own elaboration based on data from the European Commission (2016).

Figure 3.11. Societal Challenges – Patent applications in the area of the different Societal Challenges 2.5

1.5

funding 1

0.5 0.092 Publications per €10 million 0 Societal Challenges - Patent applications in the area of the different Societal Challenges

Target at the end of H2020 State as of the end of 2015

Source: Author’s own elaboration based on data from the European Commission (2016). Appendix 95

Figure 3.12. Societal Challenges – Patents awarded in the area of the different Societal Challenges 2.5 2 1.5 1

funding 0.5 0.026 0 Societal Challenges - Patents awarded in the area of the different Societal Challenges Publications per €10 million Target at the end of H2020 State as of the end of 2015

Source: Author’s own elaboration based on data from the European Commission (2016).

Table 3.9. Output/input ratios for H2020 programs collections

Publications in peer- Patent Patents Program reviewed journals Applications Awarded Excellent Science 0.0977 0.0015 0.0002 ERC 0.0495 0.0027 0.0003 Future and emerging technologies 0.3050 0.0000 0.0000 Marie Skłodowska-Curie actions 0.1565 0.0000 0.0000 Research infrastructure 0.0359 0.0000 0.0000 Industrial Leadership 0.1081 0.0126 0.0035 Leadership in Enabling and Industrial 0.1281 0.0149 0.0041 Technologies Access to risk finance NA NA NA Innovation in SME NA NA NA Societal Challenges 0.0516 0.0092 0.0026 Health, demographic change and wellbeing 0.0947 0.0110 0.0071 Food security, sustainable agriculture and forestry, marine and maritime and inland water 0.1082 0.0067 0.0013 research and the bioeconomy Secure, clean and efficient energy 0.0286 0.0180 0.0015 Smart, green and integrated transport 0.0068 0.0029 0.0019 Climate action, environment, resource 0.0151 0.0069 0.0014 efficiency and raw materials Europe in a changing world – inclusive, 0.0817 0.0000 0.0000 innovative and reflective societies Secure societies – protecting freedom and 0.0485 0.0051 0.0000 security of Europe and its citizens Spreading excellence and widening 0.2215 0.0000 0.0000 participation Science with and for Society 0.0285 0.0000 0.0000 Euratom 4.8946 0.0000 0.0000 Pilot: Fast-track to Innovation (since 2015) 0.0000 0.0000 0.0000 Source: Author’s own elaboration based on data from the European Commission (2016). 96 3. The impact of Framework Programs on innovativeness in the European Union

Figure 3.13. Dendrogram for FP7

Source: Own figure based on data from the National Contact Point for Research Programs of the European Union Institute of Fundamental Technological Research, Polish Academy of Sciences (2016, 2018).

Table 3.10. Results of the cluster analysis for FP7 with centroids for each cluster across each clustering variable

Cluster no: 1 2 3 4 5 Actor / n 22 1 3 1 1 HES_EUR 2.18E+08 7.12E+08 1.25E+09 2.64E+09 4.87E+09 PRC_EUR 1.22E+08 1.42E+09 9.57E+08 1.88E+09 1.16E+09 PUB_EUR 1.85E+07 8.97E+07 8.99E+07 7.98E+07 1.29E+08 REC_EUR 1.07E+08 2.42E+09 9.78E+08 2.18E+09 5.20E+08 OTH_EUR 1.28E+07 3.59E+08 4.80E+07 6.67E+07 5.59E+07 Source: Own table based on data from the National Contact Point for Research Programs of the European Union Institute of Fundamental Technological Research, Polish Academy of Sciences (2016, 2018) and WIPO (2017). Appendix 97

Figure 3.14. Dendrogram for H2020

Source: Own figure based on data from the National Contact Point for Research Programs of the European Union Institute of Fundamental Technological Research, Polish Academy of Sciences (2016, 2018).

Table 3.11. Assignment of EU28 economies to each of five clusters based on FP7

Cluster Countries Austria (SI), Belgium (SI), Denmark (IL), Finland (IL), Greece (MI), Ireland (SI), Sweden (IL), Bulgaria (MsI), Croatia (MI), Cyprus (MI), Czech Republic (MI), 1 Estonia (MI), Hungary (MI), Latvia (MI), Lithuania (MI), Luxembourg (SI), Malta (MI), Poland (MI), Portugal (MI), Romania (MsI), Slovakia (MI), Slovenia (MI) 2 France (SI) 3 Italy (MI), Netherlands (IL), Spain (MI) 4 Germany (IL) 5 UK (IL) Source: Own table based on data from the National Contact Point for Research Programs of the European Union Institute of Fundamental Technological Research, Polish Academy of Sciences (2016, 2018). 98 3. The impact of Framework Programs on innovativeness in the European Union

Table 3.12. Innovation output averages according to FP7 clusters

Innovation output / Cluster no:12345 PAT_07_13 28,268 455,182 169,944 1,214,712 354,649 PAT_08_14 29,128 469,132 171,245 1,230,439 357,173 PAT_09_15 29,871 479,624 169,652 1,234,027 359,253 PAT_10_16 30,723 490,276 173,191 1,248,768 363,453 DES_07_13 37,882 428,334 254,030 773,828 347,858 DES_08_14 40,454 441,971 259,946 786,923 349,572 DES_09_15 42,875 447,700 261,598 796,015 357,851 DES_10_16 45,159 454,498 271,847 813,711 373,128 TM_07_13 203,338 2,191,918 1,621,141 4,318,740 2,240,198 TM_08_14 214,442 2,252,461 1,673,519 4,400,375 2,361,741 TM_09_15 227,680 2,327,467 1,732,139 4,524,975 2,512,735 TM_10_16 245,282 2,392,984 1,825,565 4,688,426 2,677,294 Source: Own table based on data from WIPO (2017).

Table 3.13. Results of the cluster analysis for H2020 with centroids for each cluster across each clustering variable

Cluster no: 1 2 3 4 5 Actor / n 6 17 1 3 1 HES_EUR 7.18E+09 1.82E+09 2.54E+10 1.68E+10 2.34E+10 PRC_EUR 6.45E+09 1.19E+09 1.89E+10 2.25E+10 1.67E+10 PUB_EUR 9.84E+08 4.40E+08 2.44E+09 2.56E+09 2.19E+09 REC_EUR 4.16E+09 1.27E+09 1.76E+10 1.59E+10 4.95E+09 OTH_EUR 1.30E+09 2.08E+08 2.18E+09 1.85E+09 2.07E+09 Source: Own table based on data from the National Contact Point for Research Programs of the European Union Institute of Fundamental Technological Research, Polish Academy of Sciences (2016, 2018).

Table 3.14. Assignment of EU28 economies to each of five clusters for H2020

Cluster Countries 1 Austria (SI), Belgium, (SI), Finland (IL), Greece (MI), Netherlands (IL), Sweden (IL) Bulgaria (MsI), Croatia (MI), Cyprus (MI), Czech Republic (MI), Denmark (IL), Estonia (MI), Hungary (MI), Ireland (SI), Latvia (MI), Lithuania (MI), Luxembourg 2 (SI), Malta (MI), Poland (MI), Portugal (MI), Romania (MsI), Slovakia (MI), Slovenia (SI) 3 France (SI) 4 Germany (IL), Italy (MI), Spain (MI) 5 UK (IL) Source: Own table based on data from the National Contact Point for Research Programs of the European Union Institute of Fundamental Technological Research, Polish Academy of Sciences (2016, 2018). Appendix 99

Table 3.15. Innovation output averages according to H2020 clusters

Innovation output / Cluster no:12345 PAT_14_16 51,535 6,482 216,346 215,573 158,907 DES_14_16 42,333 17,624 196,776 213,585 168,427 TM_14_16 265,193 92,996 1,065,398 1,359,554 1,299,670 Source: Own table based on data from WIPO (2017). Arkadiusz Michał Kowalski

Chapter 4 Supporting the research and innovation base through priority European research infrastructures

4.1. Introduction

The aim of this study is to investigate the rationale, activities and theoretical background for analyzing the main areas of impact of priority research infrastructures in the European Union on innovation. Research infrastructures are at the core of the knowledge triangle of research, education and innovation. By offering access to high quality services to researchers from different countries, they assemble a critical mass of people, knowledge and investment, facilitating international cooperation in science. Therefore, constructing the priority European research infrastructures is one of the most important steps in realizing the European Research Area. Internationalized research infrastructures provide platforms, which bring together knowledge, human and other resources, from wherever they are located, to address research issues that cannot be tackled by a single country or region alone. The importance of research infrastructures for European innovativeness and competitiveness was highlighted in the Innovation Union (European Commission 2010), one of the flagship initiatives of the Europe 2020 strategy, especially in Commitment 5, stating that “by 2015, Member States together with the Commission should have completed or launched the construction of 60% of the priority European research infrastructures currently identified by the European Strategy Forum for Research Infrastructures (ESFRI). The potential for innovation of these (and ICT and other) infrastructures should be increased” (European Commission 2010). This study is structured as follows. In the first part, the definition of research infrastructures, their basic futures, benefits and typology are presented. Moreover, a review is given of possible (at least theoretically) indicators that may be used in measuring the impacts of research infrastructures. Next, the most important economic theories that help to explain the main impact areas of research infrastructures are analyzed, such as: the social capital theory; the concepts of innovative milieu and creative class; the innovation systems theory; and the economics of network theory. 4.2. Definition, typology and basic features of research infrastructures 101

Then, the question why the European Union needs to invest in research infrastructures is raised, with an analysis of related problems, such as: the fragmentation of European investments in research infrastructure; the high complexity of research infrastructures connected with e.g. increasing capital-intensity and technical sophistication of modern research; as well as the need to solve key societal challenges, which in many cases are addressed by research infrastructures. The solution to this problem is provided by the European Strategy Forum on Research Infrastructures (ESFRI) Roadmap, which identifies new research infrastructures corresponding to the long- term needs of the European research communities. The next part of the chapter provides an empirical analysis of EU investments in research infrastructures (RIs), and their importance for the innovativeness of the economy. Hence, a statistical analysis of the data on financial allocations of European funds in RIs is performed. Another part of the chapter is based on self-designed questionnaires, carried out in the period November-December 2016 on a sample of 150 coordinators and 400 users of RIs, with the application of the Computer-Assisted Telephone Interview (CATI) and Computer-Assisted Web Interview (CAWI) methods. Next, the method of statistical analysis of the results of this survey research is applied, which forms a basis to conduct an evaluation of how RIs enhance cooperation and stimulate the innovation environment in Europe. It is important to note that the survey research, based on self-designed questionnaires, was conducted also for the needs of the analyses of global research infrastructures in the next chapter.

4.2. Definition, typology and basic features of research infrastructures

4.2.1. Definition, basic features and benefits of research infrastructures There is no single definition of research infrastructure in the literature. One of the definitions is given in the Community legal framework for a European Research Infrastructure Consortium (ERIC) regulation (Council Regulation (EC) No 723/2009 of 25 June 2009), which says that research infrastructure “means facilities, resources and related services that are used by the scientific community to conduct top-level research in their respective fields and covers major scientific equipment or sets of instruments; knowledge-based resources such as collections, archives or structures for scientific information; enabling Information and Communications Technology- based infrastructures such as Grid computing, software and communication, or any other entity of a unique nature essential to achieve excellence in research”. However, in a press release to celebrate the 10th anniversary of the ESFRI Roadmap, the European Commission (2012) stated that “the term ‘research infrastructures’ 102 4. Supporting the research and innovation base through priority European research... refers to facilities, resources and related services used by the scientific community to conduct top-level research in their respective fields, ranging from social sciences to astronomy, genomics to nanotechnologies”. In the European Parliament and the Council of the European Union Regulation on Horizon 2020, research infrastructures were defined as “facilities, resources and services that are used by the research communities to conduct research and foster innovation in their fields. They include: major scientific equipment (or sets of instruments), knowledge-based resources such as collections, archives and scientific data, e-infrastructures, such as data and computing systems and communication networks and any other tools that are essential to achieve excellence in research and innovation” (Regulation (EU) No 1291/2013 of the European Parliament and of the Council, 2013). Considering the existing definitions, a shorter description is provided by the MERIL (Mapping of the European Research Infrastructure Landscape) team, according to which “a European Research Infrastructure is a facility or (virtual) platform that provides the scientific community with resources and services to conduct top-level research in their respective fields” (MERIL 2011). The above definitions of research infrastructures mostly underline their material nature and physical component, however, S. Anderson (2013) postulates that we should move away from the long-view focus on infrastructure as a ‘thing’ to be ‘built’ towards a perception of research infrastructure as “part of a process of change, collaboration, and engagement”. Consequently, if infrastructure is a basically relational concept, it may be the right question to ask: “when” – not “what” – is an infrastructure (Star, Ruhleder 1996). Hence, we may say that there is a real research infrastructure when it becomes dynamic not static, and operates as an innovation ecosystem, in which different elements interact and move in a continuous process of engagement, adjustment and readjustment. The significance of research infrastructures has been increasing. In almost all areas of science they have become indispensable for solving scientific problems and looking for answers to research questions (The German Council of Science and Humanities [Wissenschaftsrat], 2013). According to the MERIL (Mapping of the European Research Infrastructure Landscape) team, a research infrastructure should (European Science Foundation 2013): • offer access to scientific users from Europe and beyond through a transparent selection process on the basis of excellence; • offer top quality scientific and technological performance, that should be recognized as being of European relevance; • have stable and effective management. 4.2. Definition, typology and basic features of research infrastructures 103

At the macro-level, a number of broad categories of offerings of research infrastructures can be identified, like access to: data and physical/analogue objects, services, expertise, and laboratory facilities (European Science Foundation 2011). In practice, research infrastructures may fulfil different functions. For example, some of them aim to create a critical mass for actual research activities in certain areas, whereas others aim to provide unique research services to users from different countries (Stahlecker, Kroll 2013). According to the OECD, there are various possible types of added value from research infrastructures, notably (OECD 2014): • scientific (e.g. developing new equipment, setting common standards and formats); • operational (e.g. speeding up the research, avoiding duplication of actions); • educational (e.g. Ph.D. programs); • economic (in the form of technology transfer to the public or private sectors); • political (e.g. strengthening regional integration, providing the scientific underpinning to an international treaty).

4.2.2. Types of European research infrastructures The most common types of research infrastructures are: 1. ‘Single-sited’ research infrastructures – a single resource at a single location. Single-sited research infrastructures are usually large-scale facilities, the creation of which is strongly associated with construction works, large buildings and extremely expensive equipment. The location of new single-sited research infrastructures is often a compromise between scientific, economic and political factors, as they require long-term planning and can be established only with high-level political and financial support. However, the high visibility of large facilities to the general public makes it also attractive for politicians to support them. In many cases, institutional, centralized funding allows large single-sited research infrastructures to offer access and service to external users free of charge. This type of facilities is mostly known in the field of physics, including astronomy (ERA-Instruments 2010). One of the most famous examples is the European Organization for Nuclear Research (CERN), the world's largest particle physics laboratory. 2. ‘Distributed’ research infrastructures – a network of distributed resources. This type of infrastructure is referred to by the OECD (2014, p. 7) as an International Distributed Research Infrastructure (IDRIS), defined as “a multi-national association of geographically-separated distinct entities that jointly perform, facilitate or sponsor basic or applied scientific research”. Distributed research infrastructures are usually smaller than single-sited ones, they are large research facilities, and may have a “lighter” 104 4. Supporting the research and innovation base through priority European research... administrative structure. The location for the central facility and headquarters is selected based on scientific, financial and political considerations. Due to its inherently distributed nature, efficiency of co-ordination is a crucial requirement. The central staff may be located in a single location (central facility), several locations (when there is shared central responsibility between different partners) or the staff may be distributed among all of the different partners (in case of highly distributed research activities). An example of distributed research infrastructures are: the European Mouse Mutant Archive (EMMA), the Global Earth Observation system of systems (GEOSS) or the International Cancer Genome Consortium (ICGC). 3. ‘Virtual’ research infrastructures – the service is provided electronically Virtual research infrastructures are the most commonly found in social sciences, computer and data treatment, as well as humanities. Their examples are: databases, archives, etc. that can be used by researches from their own workstations. This allows for new forms of cooperation among scientists, who may work together, regardless of their location (Federal Ministry of Education and Research 2013, p. 3). An example of virtual research infrastructure is the GÉANT high-speed network (e-Infrastructure initiative launched to facilitate cooperation among researchers). The view on how common in practice the above-mentioned types of research infrastructures occur is given by the results of a survey research conducted from March 2006 to March 2007 on 598 organizations by the European Commission (The Research Infrastructures unit within DG Research) and European Science Foundation (European Commission/European Science Foundation 2007). It should be noted that the sample is much broader than European priority infrastructures only; the aim of presenting these statistics is to provide a general overview on the prevalence of different types of research infrastructures (that may vary in scale, starting e.g. from a simple digital library) in different areas of science. As it can be seen from Table 4.1, most of the research infrastructures (63%) were single-sited, 25% were working as distributed facilities, and only 12% were virtual. However, the nature of the research infrastructures differed strongly depending on the field of research in question. For example, in the area of energy a large majority of research infrastructures remain single-site laboratories (96%), whereas the opposite was true for social sciences, where virtual networks (42%) dominate. 4.2. Definition, typology and basic features of research infrastructures 105

Table 4.1. Scientific fields and organizational features of research infrastructures distributed/ The field of research single-site virtual cooperative Social Sciences 32% 26% 42% Computer and Data Treatment 48% 24% 28% Humanities 52% 23% 25% Biomedical and Life Sciences 60% 27% 13% Environmental, Marine and Earth Sciences 50% 38% 12% Nuclear and Particle Physics, Astronomy, Astrophysics 74% 18% 8% Engineering 68% 26% 6% Energy 96% none 4% Material Sciences 89% 10% 1% Total 63% 25% 12% Source: European Commission/European Science Foundation (2007).

4.2.3. Monitoring and evaluating the economic impact of research infrastructures, with a focus on potential indicators and data This part of the study reviews the methods and indicators that were specified in the literature as suitable for the measurement of the impacts of research infrastructures. Generally, it is difficult to identify and quantify impact in conventional commercial terms, as this type of investment brings a broad range of social and economic benefits that are not captured by official statistics. In fact, there has been no unified framework for the impact assessment of research infrastructures developed so far. However, there are various conceptual frameworks, which aim to capture some direct or indirect impacts and longer-term effects of such investments. One of the sources of potential indicators that may be used in assessing research infrastructures are publications of the European Commission. The Key Figures report (European Commission 2008, p. 12-13) used data on structural funds and expenditures on research infrastructures to determine the creation of new large-scale research infrastructures at the European level. The publication from 2013 (European Commission 2013) states that indicators related to research infrastructures also include the most active research universities, funding models for universities (types of funding) and additional economic indicators, like the share of GOVERD in total public sector expenditure on R&D (GOVERD + HERD). Moreover, research infrastructures data may be used in the analyses of regional specialization, as they enable building a critical mass in specialized domains of knowledge by establishing networks and partnerships, creating cooperative research organizations and supporting technology transfers. Sharing specialized research infrastructures may allow regions to build strong clusters or a cluster cooperation and facilitate cross- border knowledge sharing and research cooperation (Europe INNOVA/PRO INNO 106 4. Supporting the research and innovation base through priority European research...

Europe 2008, p. 37). Therefore, there are important synergies between research infrastructures, regional specialization and clusters, which may also be exploited in statistical analyses. Another theoretical methodology for evaluating the economic impact of research infrastructures is presented in the Technopolis Group publication (Griniece, Reid, Angelis 2015). It distinguishes two main periods in the lifecycle of a research infrastructure, both of which require different methods and indicators when various direct and indirect benefits are monitored and evaluated: 1) the design and construction phase; 2) the operational phase of research infrastructure. Focus on these two separate periods may be strongly relevant for the analysis of European research infrastructures, as many of them are still in the design and construction phase. Although they are not yet operational, they may bring a lot of benefits: economic and for innovation. Economic advantages are mainly the result of all necessary physical investments in buildings etc. engaging many companies, which create job places and additional revenue in the economy. Innovation benefits are connected to a wide range of technical and scientific knowledge applied to set up the required facilities. Requirements for different design solutions and building functionalities (e.g. laboratories) that are adjusted to the specificity of particular scientific disciplines foster cooperation between scientific units, suppliers and facility mangers, resulting in a higher technology transfer, for example. To conclude, examples of indicators that measure economic and innovation indicators of research infrastructure in the design and construction phase are presented in Table 4.2.

Table 4.2. Indicators of the economic and innovation impact of research infrastructures in the construction phase

Area of impact No Indicators Economic 1 Number of commercial suppliers for RI design and the construction phase 2 Scale of commercial suppliers’ turnover increase due to RI 3 Scale of commercial suppliers’ employment increase due to RI Innovation 4 Number of joint development activities with suppliers 5 Number of contracts concluded for high-tech or specialist services that require development, or calibration of designs/equipment to meet specific requirements Source: Griniece, Reid, Angelis (2015), p. 6-7.

There is a much wider range of benefits that may be identified in the operational phase of research infrastructure. In general, the benefits may stem either from routine operation, the maintenance of and upgrading an infrastructure, or from the use of research facilities. There are five areas of impact of research infrastructures in the operational phase, as presented in Table 4.3. 4.2. Definition, typology and basic features of research infrastructures 107

Table 4.3. Indicators for impacts of research infrastructures in the operational phase

Area of impact No Indicators Economic 1 Number of scientists, students, state-owned or private enterprises that benefited from RI services 2 Total amount of funding generated from services, grants and joint projects 3 Number of new directly and indirectly created jobs 4 Total amount of expenditure on personnel, operations, maintenance 5 Total RI capacity utilization (measured by access hours used as % of the total available access time) 6 RI capacity utilization by external business users 7 Financial sustainability of RI (measured as % of the total costs funded from the provided services, received grants and realized joint projects) Innovation 8 Number of collaborative research projects and volume of funding 9 Number of R&D projects commissioned by companies and the volume of their funding 10 Number of technologies, prototypes, industrial designs developed and transferred 11 Number of start-ups and spin-offs created with the support from RI services – growth in turnover/value added and employment 12 Number of feasibility or market studies for industrial investment and application of technologies 13 Procurement contracts signed for the development and upgrade of research equipment Human 14 Number of new jobs for research and technical staff attracted from abroad resource as % of the total number of staff employed on RI capacity 15 Number of Master theses defended, where knowledge and skills gained on RI were exploited 16 Number of graduates trained in RI 17 Number of foreign students as % of all students trained in RI 18 Data on the post-diploma employment path of those graduates trained in RI Scientific 19 Number of articles published in ISI level international scientific journals activity as a direct result of research using RI 20 Number of methodologies/designs developed 21 International patents granted and published patent applications (all types) 22 Number of PhD dissertations completed 23 Number of scientific events organized on research topics directly relating to RI services Society 24 Number of organized RI open days for a wider public and any available data on participant satisfaction with the events 25 Number of press articles on the investment in research infrastructure 26 Number of new or improved products, services, solutions as a result of research using RI that are diffused in society 27 Account of improved local infrastructure, community services, increase in local cultural/recreational activities due to RI Source: E. Griniece, A. Reid, J. Angelis (2015), p. 8-13. 108 4. Supporting the research and innovation base through priority European research...

Data for research infrastructures and estimates for the construction and operation costs for different infrastructures may be given in a breakdown by six categories of science: 1) Social Sciences and Humanities; 2) Environmental Sciences; 3) Energy; 4) Biological and Medical Sciences; 5) Materials and Analytical Facilities; 6) Physical Sciences and Engineering. Although there are different possible indicators that may be potentially used for the evaluation of the impact of research infrastructures, there are not relevant statistical data.

4.3. Theoretical background for analyzing research infrastructures

4.3.1. Social capital theory, innovative milieu, and the concept of creative class One of the definitions says that social capital is the sum of the resources, actual or virtual, that accrue to an individual or a group by virtue of possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition (Bourdieu, Wacquant 1992, p. 119). The importance of this concept for research infrastructures is connected with the observation made by Putnam (2000) and Adler and Kwon (2002) that the social capital associated with the connections between external players lead to positive effects in raising resources (which are needed for capital intensive investments in RI) and building trust (which stimulates interactions) in the organization. Therefore, social capital provides links that facilitate the discovery of opportunities and the identification, collection and allocation of scarce resources within the organization (Greene, Brown 1997), like research infrastructure. Similarly, J.S. Coleman (1988) associates social capital also with ties between heterogeneous actors or different homogeneous networks that allow “the resources of one relationship to be appropriated for use in others”, which has strong implications for European research infrastructures grouping international researchers in cross-disciplinary teams. In the context of research activities, social capital refers to the stock of relationships, context, trust and norms that encourage suitable behavior for knowledge sharing (Anklam 2002), which includes cognitive and communication skills in a specific context (Widén-Wulff, Ginman 2004). Looking at research infrastructures through a prism of network organizations, social capital may be considered as “the 4.3. Theoretical background for analyzing research infrastructures 109 resources gained from participating in relationships networks which are relatively institutionalized” (Landry, Amara, Lamari 2001, p. 74). This identifies the economic or other advantages of people’s socially embedded interaction that may take place in knowledge organizations. However, social capital may be considered as the fundamental basis for the functioning of research infrastructures, as according to F. Fukuyama (2000) it refers to underlying forces, which trigger interactions: “social capital is an instantiated informal norm that promotes cooperation between two or more individuals”. An analysis of the socio-economic impact of African-European research infrastructure cooperation (Promoting African European Research Infrastructure Partnerships 2012) showed that a major benefit of many research infrastructures is the build-up of social capital. It is created through meaningful interactions between people and therefore facilitates sustainable learning and the use of skills and knowledge. Broadly understood as the institutions, relationships, attitudes, and values that govern interactions among people and contribute to economic and social development, it refers to the benefits that arise from networks, relations and mutual trust. Examples of the positive impact of research infrastructures on social capital include benefits from the inflow of highly skilled professionals through visits of foreign scientists and knowledge exchange, which stimulates diversity and creativity and leads to innovative and creative ideas. Moreover, research infrastructures provide an entry point for young scientists into networks of knowledge, expertise and practice. The notion of social capital shares some similarities (main focus on the importance of socially embedded collaboration for innovation-driven regional development) with the concept of innovative milieu (Fromhold-Eisebith 2004), defined as “the set, or the complex network of mainly informal social relationships on a limited geographical area, often determining a specific external 'image' and a specific internal 'representation' and sense of belonging, which enhance the local innovative capability through synergetic and collective learning processes” (Camagni 1991, p. 3). The milieu approach mainly tries to analyze and explain how a good regional institutional endowment in terms of networked universities, research laboratories, public support institutions and firms, can lead to a higher innovativeness of the regional economy. However, it seems that the concept of innovative milieu is somehow restricted mainly to the local and regional context and therefore has limited applications in the analysis of international cooperation within the framework of European research infrastructures. Another concept strongly related to social capital is the concept of creative class, which consists of two subgroups (Florida 2002): 110 4. Supporting the research and innovation base through priority European research...

1) super-creative core (including occupations in e.g. science, engineering, education, computer programming, research), which is fully engaged not only in problem solving, but also problem finding; 2) creative professionals, who work in a wide spectrum of knowledge-intensive industries, like the high-tech sector or business management. An important finding of R. Florida is that members of the creative class tend to settle in certain places, called creative regions, which according to 3T’s model are characterized by high levels of: technology, talent and tolerance (Florida 2003, p. 8). This concept has its implications for research infrastructures, as they are the centers in which the creative class is deeply rooted. Individuals working in research infrastructures fully engage in the creative process and develop new concepts, resulting in new products and economic growth. In particular, the creative class tends to settle down around big cities, which play an important role in the process of developing high-quality human capital on the one hand, and which create demand for skilled workers on the other hand (Kowalski 2018).

4.3.2. Innovation systems theory Simple linear models of the innovation process, which were the first attempt to illustrate how innovation is created, do not fully cover the entire spectrum of factors influencing the development and implementation of technology. Their weakness is that they do not explain the differences in innovation processes in different countries and regions, some of which are innovation leaders while others remain followers. Numerous comparative analyses of innovation processes in individual economies and sectors led to a perception of the innovation system (Weresa 2012, p. 14). This is a non-linear perspective emphasizing the interdependencies and interactions between the elements of the system, as well as interactive learning (Edquist 1997). The concept of innovation system was initiated by C. Freeman (1982) and further developed by other economists, who were focusing on different dimensions, distinguishing for example: national systems of innovation (B.Å. Lundvall 1992, C. Freeman 1995); regional and local systems of innovation (Cook, 2001); sectoral systems of innovation (F. Malerba 2004); or technological systems of innovation (B. Carlsson, R. Stankiewicz 1991). A study on European research infrastructures shows that they can play a special role in another, new type of innovation system, which may be called the supra-national system of innovation, represented by the emerging European Research Area (Stahlecker, Kroll 2013, p. 2). A special contribution to the conceptual foundation of the systems of innovation was made by K. Smith (1997), who explored the problem of defining infrastructures and their effects on the economic performance of innovation systems, with a focus on the role of public policy in developing and maintaining such infrastructures. 4.3. Theoretical background for analyzing research infrastructures 111

In this study, infrastructures are defined as large-scale indivisible capital goods producing products and services, which become inputs in most or all economic activities on a multi-user basis. Special emphasis is put on knowledge infrastructures, such as universities, research laboratories, training systems, organizations related to standardization and the managing of intellectual property rights, publicly supported technical institutes, libraries and databases, etc. According to K. Smith, knowledge infrastructure performs several important roles in innovation systems, especially: • production and diffusion of scientific and technological knowledge, enabled mainly by intense public R&D funding; • production of skills, connected with the educational and training activities of R&D infrastructure institutions, in which there are processes of personnel movement, with most of the entry into the institute sector coming from the university system, and most of the exit going to industry. This turnover process plays an important but difficult to measure economic role as a process of technology transfer; • establishing technical norms and standards, either implicitly by a specific research infrastructure, or through the coordination of different stakeholders; • creation of enterprises, as publicly supported infrastructures very often act as sources of new firms, which become the 'bearers' of new technology, translating it into economic results; • access and dissemination functions, as a range of infrastructural organizations can be involved in the maintenance of the existing stock of knowledge, in terms of storage, access, availability, dissemination and so on. In the sectoral perspective, research infrastructures play an important role in the development of some areas, for example emerging industries. M. Pero (2015) understands research infrastructure as an entity, which provides access to cutting- edge scientific equipment and research services to the scientific community, thus promoting the exchange and diffusion of knowledge and know-how. In particular, he focuses on one of the research areas where research infrastructures are particularly important, namely materials sciences, characterized by a very wide application scope, ranging from metallurgy to nanotechnology. Another important economic role of knowledge infrastructures is that they can produce results that may not only be directly applicable to industrial production, but can also be used as inputs for the further production of knowledge. In this way, infrastructure-based R&D can open up opportunities that encourage enterprises to perform more R&D (additionality effect). As productivity growth is positively associated with R&D, the economic outcomes are therefore indirect but positive. To 112 4. Supporting the research and innovation base through priority European research... sum up, according to K. Smith (1997) knowledge infrastructure has been identified as a central component of the national innovation system.

4.3.3. Economic network theory The theory of economic network emphasizes the importance of external resource mobilization, for example in research and innovation activity (Oerlemans, Meeus, Boekema 1998). In a general understanding, network describes a collection of nodes and the links between them. However, there is some degree of network dynamics resulting from the micro-decisions of actors. This means that network structures can change because their members may develop strategies to create ties with other actors, based on their awareness of the network configuration. Nonetheless, examining the structure of any given network is a difficult task that requires defining and measuring the links or relationships (Jackson 2008). The network approach helps us understand how social interactions impact economic outcomes (Goyal 2007). In the context of research and innovation activity, networks contribute to the innovative capabilities of organizations exposing them to novel sources of ideas, enabling fast access to resources, and enhancing the transfer of knowledge. Formal collaborations may also allow for a division of innovative labor that makes it possible to accomplish goals that a single actor could not pursue alone. An important challenge to networks of innovation is developing the capacity to simultaneously enhance the flow of information among current members and be open to new entrants (Powell, Grodal 2005). An interesting contribution to this area is a network model of public goods (Bramoullé, Kranton 2007) analyzed based on the example of innovation and information, which are often non-excludable in certain dimensions, and thus maintain the public goods nature. The network approach provides important insights into analyzing research infrastructures. The direction of development of these types of organizations is that they should not only be seen as regionally or nationally available infrastructure, but should rather be taken into consideration as part of an international research network. Research infrastructures can involve major network externalities, and they are often the place within a system where scale and scope economies are very significant. This implies that their existence or non-existence can significantly shape the fates of competing technologies, and thus the evolution of overall techno-economic systems (Smith 1997). The OECD (2014) case study on CERN’s network shows how institutional and personal contacts played a critical role in catalyzing R&D. In fact, CERN is one of the central nodes in a world-wide network of research-oriented organizations, which share and exchange knowledge, research tools and professionals. A well-developed network of links to national research agencies, institutions and laboratories in Europe and beyond brings a lot of numerous benefits. CERN employs some of the 4.4. Why the European Union needs to support the development of research... 113 top experts in accelerator design, who apart from advanced knowledge, have access to extensive international networks. The key point is that they have the capacity to develop long-range plans besides their primary work assignment. The presented case study indicates the open nature of the working environment of CERN as one of the reasons for the richness and productivity of the network. Hence, openness, which is a prerequisite for networking, should get a prominent role in the functioning of European research infrastructures. Another aspect of scientific and technological infrastructure is its potential participation in so-called collective industrial research, which may be defined as “all establishments and activities designed to promote technical progress in a branch of a particular industry sector or in a particular scientific or technical discipline which is being developed in industry” (Rothwell, Zegveld 1981). Collective research may be organized and financed in a variety of ways. Analysis (Rothgang, Lageman, Peistrup 2011) showed that this type of research network provides a unique framework for research on high-tech applications, by enabling collaboration across different sectors and technology fields. Therefore, collective industrial research proved to be successful in achieving the balancing act between the objectives of the network and in some cases the diverging interests of the respective actors.

4.4. Why the European Union needs to support the development of research infrastructures

4.4.1. Fragmentation of European investments in research infrastructure One of the biggest obstacles for restoring the EU leadership position in science and technology is the fragmentation of research policy, which is carried out by the European Commission and 28 Member States. This explains the heterogeneous innovation performances across Europe and the difficulties in organizing holistic approaches at the European level (Sartori, Berlinguer 2013). T. Stahlecker and H. Kroll (2013) confirm that one of the main reasons why the continuously developed European Research Area is still far from acting as a supra-national innovation system (in which research infrastructures would constitute important elements integrating citizens from different Member States) is the fragmented political landscape in research policy, with national governments reluctant to yield more control and budget to the European level. Another problem is that the networks of cooperation and human capital exchange are pre-defined by national boundaries, as according to K. Pavitt, P. Patel (1999) innovative activities are greatly influenced by national systems of innovation in terms of: the quality of basic research, workforce skills, the degree of competitive rivalry, systems of corporate governance, and local inducement mechanisms. 114 4. Supporting the research and innovation base through priority European research...

The tremendous fragmentation of European research infrastructures leads to a lack of transparency and duplication of their objectives and actions (Committee for Research Structure of the Royal Swedish Academy of Sciences 2012). T. Stahlecker and H. Kroll (2013) noticed that although many European scientific units have a long tradition of excellence, when driving progress in numerous key areas of science, the persistent national fragmentation of investments prevents the created infrastructures from reaching a certain critical mass. The understanding of the need for a certain critical mass should not be limited only to a technical level. Fragmentation of European research infrastructures also creates problems with sufficient financing, as the cost of implementation of single investment projects often exceeds the funding capacity of individual countries. The problem is even bigger when taking into account insufficient transnational cooperation between the existing research units of sub- critical size. Moreover, as learning is a cumulative process, all research teams benefit from the increase in diversity and the broadening of the knowledge base (Cooke 2002), and this is lacking in research infrastructures that are fragmented across member countries.

4.4.2. The high complexity (scale and costs) of European research infrastructures In the Innovation Union (European Commission 2010) it was stated that one of the rationales for pooling the resources across Europe to build and operate research infrastructures is their increasing complexity, scale and costs. In the context of scarce public resources, it is an important step in European research and innovation policy to catalyze investments in major infrastructures, which are given political priority and for which new funding mechanisms are being developed (European Commission 2010). In fact, the increasing complexity and costs of research infrastructures makes international collaboration and coordination in that area a necessity. Therefore, transnational cooperation is seen as essential to reach and maintain a competitive level in research. The increasing capital-intensity of modern research is connected with the rapid evolution of science, where more and more sophisticated and powerful experimental instruments need to be designed and constructed in order to back such a progress and push forward the frontiers of knowledge. Large scale scientific projects tend to entail substantial investment costs related to the design and construction of research infrastructures, which often rise considerably from the ex-ante estimates. The growing number of financed research infrastructures and their increasing average cost call for responsible decision making when deciding if to spend considerable amounts of public money (Florio, Sirtori 2014). Setting-up or modernizing research 4.4. Why the European Union needs to support the development of research... 115 infrastructures usually requires a substantial level of financial investment and a long- term investment and operation strategy. This strategy entails careful planning of the operation phase and possible future reinvestments. This involves the purchase of technologically advanced equipment, clustering specific skills and devising appropriate governance structures (Griniece, Reid, Angelis 2015). In order to resolve the complexity and resulting weaknesses of European research infrastructures, the report of the Committee for Research Structure of the Royal Swedish Academy of Sciences (2012) suggested that all these infrastructures should be restructured and reorganized by using an objective evaluation, including impact analysis and cost-benefit studies. In the evaluation, a clear and transparent definition of efficiency needs to be elaborated, covering two aspects: efficiency in producing knowledge and efficiency in its commercialization. Moreover, research infrastructures should be listed and divided into different areas, defined by their scientific or technological objectives, in order to identify and eliminate fragmentations and duplications. Research infrastructures represent important supporting pillar of the research system. Along with their scientific importance also the challenges grow: financially, because they become more and more resource-intensive, and in terms of the organization, because the degree of complexity of the institutions or the networks increases (The German Council of Science and Humanities [Wissenschaftsrat] 2013). A critical ingredient of any infrastructure is high capital intensity. According to M. Florio and E. Sirtori (2014, p. 5), capital fixed expenditure overcomes operating costs and is a large fraction of the total present value of project cost. This is particularly true in so-called Big Science, which is performed using some of the most expensive equipment. However, ERA-Instruments (2010) states that two necessary key factors for research infrastructures are operation costs and personnel. This view is confirmed by facility managers who in many cases do not consider the purchase of equipment as the major bottleneck for research infrastructures. The real limiting factors are: the costs of operation, maintenance and upgrades, and the costs of personnel running equipment and increasingly the costs of processing data.

4.4.3. The complexity of projects in partnerships Complexity generally refers to an emergent property of systems made of large numbers of self-organizing agents that interact in a dynamic and non-linear fashion and share a path-dependent history (Cilliers 1998). The increasing complexity of scientific processes makes partnerships a key success factor for research infrastructures, which need to pool people and resources in order to achieve a technological critical mass. However, usually research teams face different types of difficulties in pooling and sharing knowledge. The complexity of carrying out projects in partnerships 116 4. Supporting the research and innovation base through priority European research... is particularly high in case of interdisciplinary or international collaboration. Interdisciplinary teams face particular challenges around their lack of redundancy in disciplinary coverage and their lack of a shared base of domain and procedural knowledge. Such groups depend on their members, who represent different sub- disciplines and bring knowledge to the team’s work. Consequently, those from other disciplines must be open to hearing and incorporating such knowledge. To integrate and synthesize knowledge, the research team must be ready to engage together in contributing knowledge and learning from others. To bridge differences in disciplinary practice, research groups need infrastructures to support their joint work (Haythornthwaite et. al., 2006). Despite the difficulties inherent in working in international and multilingual teams, EU funding for collaborative research, including research infrastructures, can stimulate the development of an international science teamwork model. In this model individual researchers often serve as native informants, offering insider knowledge about the phenomena under study in their own countries. A single researcher can thus become part of a larger network, pooling information and broadening disciplinary perspectives. In this way, collaborative work produces synergy and helps to make sense of complexity and diversity (Hantrais 2005).

4.4.4. The inherent technical complexity of projects Together with the growing complexity of science and technology, the research infrastructure landscape also becomes increasingly more complex. In order to achieve a technological critical mass, many large-scale facilities in Europe are financed jointly by the European Union and its Member States. The increasing inherent technical complexity of the projects also changes the nature of research infrastructures, which become more digital and distributed. Modern science becomes extremely complex, and the research infrastructure landscape in Europe is diverse and multi-layered. Paradoxically, in some cases efforts to pool resources have added to the complexity, as large-scale EU-funded investments can combine several types of activities under one umbrella, networking different partner facilities and sub-projects. This growing complexity prevents research infrastructures in Europe from being exploited to their full potential. There is a high risk of both duplication of effort and neglect of gaps across the European Union (European Science Foundation 2013). The technical complexity of research projects is also growing together with the rise of so-called data-intensive science, also referred to as Linked Data, Big Data, or the 4th Paradigm (Bizer, Heath, Berners-Lee 2009), and other similar concepts. This new approach highlights the importance of investments into collecting and preparing massive amounts of data. Therefore, providing a digital collection and preservation of research data is one of the key services that has to be provided by research 4.4. Why the European Union needs to support the development of research... 117 infrastructures. Together with the increasing complexity of research projects, the need for preservation goes beyond just maintaining data accessible. Capturing and documenting the context of its creation and use requires sophisticated information networks. It is a massive task that requires pooling resources if European research infrastructures are to be able to capture and maintain usable series of data processing routines and modules. This is needed in order to establish the validity of scientific analyses, to repeat earlier computations on new data, and in general to make full use of the potential enrooted in data-intensive science (Rauber 2012).

4.4.5. The need to solve key societal challenges In recent years, there has been an increasing interest in solving the problems of modern societies. These problems are associated with so-called Grand Challenges, such as: the ageing of society, mass urbanization, the growth of social inequalities, poverty, the tightening supply of energy, greenhouse gas emissions, environmental problems, migration, lifestyle diseases, etc. Priority European Research Infrastructures may play an important role in finding innovative solutions to today’s key societal challenges, as they constitute more permeable organizational structures, characterized by a greater absorption capacity and engagement of various international actors. This modern approach to innovation, based on interactions and taking into account social needs, is reflected in the Open Social Innovation (OSI) paradigm, proposed by H. Chesbrough and A. Di Minin (2014). Open Social Innovation is defined by these economists as “the application of either inbound or outbound Open Innovation strategies, along with innovations in the associated business model of the organization, to social challenges”. This paradigm is composed of two concepts: open innovation and social innovation, which in fact are not the same, but both ultimately strive for a user- focused collaborative process (Kowalski 2015). D. Chalmers (2013) expects that a full implementation of the Open Innovation model will help building more porous organizational structures, characterized by greater absorption capacity and engaging various stakeholders in the social innovation processes. That is why priority European Research Infrastructures, which enable cross-disciplinary, frontier research and innovation, are indispensable in tackling the Grand Challenges. By attracting and bringing together researchers, funding agencies, politicians and industry to act together and tackle the interdisciplinary scientific and technical issues of critical importance for society, investments in infrastructures enable excellent research that cannot be realized without access to these facilities due to a lack of capacities (The European Strategy Forum for Research Infrastructures (ESFRI) (2011)). As part of the European Research Area, many of the priority European Research Infrastructures identified by the ESFRI are already providing an environment supporting research to address the Grand Challenges in science, industry and society. 118 4. Supporting the research and innovation base through priority European research...

4.5. EU policy measures to support priority research infrastructures

4.5.1. Solution to the identified problems – European Roadmap for the ESFRI An important phase in the realization of large research infrastructures is roadmapping, which may be understood as systematic strategic planning. The term “roadmap” was adopted by the OECD Global Science Forum (GSF) (2008), which identified two principal actors/stakeholders in the roadmapping process: the scientific community and governmental authorities (especially funding agency officials). The key role of scientists is restricted to scientific arguments, aimed at defining the research questions, and identifying a corresponding optimal set of high-priority research infrastructures. However, policy makers must often also introduce non-scientific issues and priorities into the roadmapping process, including social, political and economic priorities, for example linking research infrastructures to innovation, economic competitiveness, and job creation. The European Strategy Forum on Research Infrastructures (ESFRI) Roadmap identifies new research infrastructures corresponding to the long-term needs of European research communities, covering all scientific areas, regardless of possible location. The ESFRI got the mandate from the Competitiveness Council in 2004 to develop a roadmap for research infrastructures in Europe. The first roadmap from 2006 included 35 new research infrastructures or a major upgrade of existing ones. Since then, there were two ESFRI roadmap updates in 2008 and 2010, and the third one is planned to take place in 2016. The identified research infrastructures are characterized by different degrees of maturity, but they are usually supported by a relevant European partnership or intergovernmental research organization. A growing number of countries have prepared national roadmaps that establish the prioritization of national and pan-European research infrastructures, using the ESFRI Roadmap as a reference. This is taken into account when defining national budgets, which ensures long-term financial commitment (Warneck 2014).

4.5.2. Geographical distribution of FP7 (part INFRA) spending This section of the chapter focuses on the analysis of statistical data concerning the projects implemented by RIs within the framework of the FP7 and Horizon 2020 programs (part INFRA), and the related financial allocations of funds (data on actual spending are not available). These data, received from the National Contact Point for Research Programs of the European Union in Warsaw, are in fact the key data available for research infrastructures in Europe. The primary source of these data is the E-Corda database, used by the National Contact Point for Research Programs of the European Union. 4.5. EU policy measures to support priority research infrastructures 119

The geographical distribution of EU funds spending on RIs allows to draw some conclusions on their impact on economic cohesion in the European Union, in particular building the European Research Area (ERA). Data on EU Member States’ share in the FP7 (part INFRA) allocations are presented in Table 4.4.

Table 4.4. Financial data concerning the distribution of the FP7, part INFRA, in EU countries

Total number Average EU Total R&D Total financing Total financing of financed financing for personnel, Country from FP7 (in from FP7 per national the participant average EUR) R&D employee participants (in EUR) 2007-2013 Germany 273,838,510 679 403,297 552,661 495 UK 272,716,645 629 433,572 353,468 772 France 204,083,016 479 426,061 396,896 514 Italy 139,968,448 431 324,753 228,098 614 Netherlands 115,407,117 365 316,184 105,500 1,094 Spain 73,040,835 328 222,685 212,399 344 Sweden 44,620,552 167 267,189 78,617 568 Greece 37,222,145 163 228,357 37,998 980 Denmark 34,670,705 92 376,855 55,870 621 Finland 30,203,445 119 253,810 55,207 547 Poland 25,644,694 142 180,596 82,145 312 Belgium 23,323,429 134 174,055 62,010 376 Austria 19,895,795 106 187,696 60,010 332 Ireland 15,669,242 73 214,647 20,847 752 Hungary 14,396,594 106 135,817 31,784 453 Czech Republic 12,285,024 80 153,563 54,465 226 Portugal 10,779,978 87 123,908 45,970 235 Bulgaria 5,497,782 59 93,183 17,179 320 Romania 5,252,836 64 82,076 29,618 177 Cyprus 4,715,628 25 188,625 1,256 3,754 Slovenia 4,463,986 39 114,461 13,255 337 Estonia 2,479,385 22 112,699 5,462 454 Croatia 2,342,746 19 123,302 10,574 222 Lithuania 2,047,114 22 93,051 11,701 175 Slovakia 1,985,713 26 76,374 16,935 117 Latvia 1,912,685 22 86,940 5,742 333 Luxemburg 1,399,792 10 139,979 4,836 289 Malta 407,440 15 27,163 1,142 357 EU15 1,296,839,654 3,862 335,795 2,270,387 571 EU13 83,431,627 641 130,159 281,258 297 Total EU 1,380,271,281 4,503 306,523 2,551,645 541 Source: own elaboration based on data from the EU Contact Point, Warsaw, and data from the Eurostat Statistical Database [file: rd_p_persocc] for the number of researchers (full-time equivalent – FTE). 120 4. Supporting the research and innovation base through priority European research...

Total FP7, part INFRA, allocations in the EU amounted to 1,380,271,281 EUR, allowing to finance 4,503 project participations, with an average funding of 306,523 EUR per single organization. However, strong differentials in the financial allocations between the 15 old EU Member States (often referred to as Western Europe) and the 13 countries that joined the EU in the 21st century (often referred to as Central and Eastern Europe – CEE) are observed. The total funding from the FP7 in the EU15 amounted to 1,296,839,654 EUR, and was about 15.5 times higher than FP7 spending in the EU13, which amounted to 83,431,627 EUR. The contributing factors were: an about 6 times higher number of participants (3,862 in the EU15 versus 641 in the EU13) in FP7 co-financed projects, and an about 2.6 higher average funding for individual participant (335,795 EUR in the EU15 versus 130,159 EUR in the EU13). The differences between WE and CEE are smaller, however still considerable, when we control financial allocations per number of R&D personnel (full-time equivalent – FTE) employed in particular countries. In this case, financing from the FP7 (part INFRA) per R&D employee is about 1.9 higher in old EU Member States (571 EUR in the EU15 versus 297 EUR in the EU13). The above identified trends show strong discrepancies in the EU framework programs’ financial allocations between Western European and CEE countries, which may contribute to increasing the innovation gap between EU Member States. From this perspective, there is a great challenge for future EU research and innovation policy to address the European Research Area (ERA) as a “priority objective for facilitating growth and economic, social and cultural development in the EU, as well as scientific excellence and cohesion between the Member States, regions and societies” (European Commission 2013). It is worth mentioning, however, that an important role in stimulating innovation capabilities in CEE countries is played by international technology transfer in the framework of global value chains, as observed for example in the aviation sector in Poland (Baczko 2011). The adaptation of technology, or process or product innovations related to the presence of an enterprise in global value-added chains results in spillover effects as well as productivity gains (National Bank of Poland 2016). It is worth adding that also organizations from non-EU countries participate in EU framework programs, the most noticeable example being the EFTA countries (Switzerland, Iceland and Norway). However, as it may be expected, most of the investments take place in EU Member States. As the total financial allocations in the FP7 (part INFRA) amounted to 1,528,321,724 EUR (with 5292 financed participations), the EU member countries with 1,380,271,281 EUR had a 90.3% share in the total budget. 4.5. EU policy measures to support priority research infrastructures 121

4.5.3. FP7 and Horizon 2020 program (part INFRA) investments by main groups of innovation systems The analysis of European investments in research infrastructures by main groups of innovation systems that are found in European Union Member States is based on the findings from previous papers prepared within the framework of the I3U project (Deliverable 9.1: Verspagen et al., 2016; and Deliverable 9.3: Verspagen et al., 2018), in which four main groups of innovation systems were identified: • Group 1 – Strongly developed innovation systems: Austria, Belgium, Denmark, Finland, Germany, the Netherlands, Slovenia, Sweden, and the United Kingdom; • Group 2 – Public policy-led innovation systems: France, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, and Portugal; • Group 3 – Developing innovation systems: Bulgaria, Croatia, Cyprus, Czech Republic, Hungary, Romania, Slovakia, and Spain; • Group 4 – Lagging behind group: Estonia, Greece, and Poland. The statistical analysis concerning the projects implemented by RIs within the framework of the FP7 and Horizon 2020 programs (part INFRA), and the related financial allocations of funds (data on actual spending are not available) in the four main groups of innovation systems that are found in European Union Member States, is based on data received from the National Contact Point for Research Programs of the European Union in Warsaw. The primary source of these data is the E-Corda database. Data on the European Union Member States’ share in the FP7 (part INFRA) allocations, are presented in Table 4.5.

Table 4.5. FP7 (part INFRA) investments by the four main groups of innovation systems found in EU Member States

Total Total number Average EU Total R&D Total financing financing Type of innovation of financed financing for personnel, from FP7 from FP7 system / country national the participant average (in EUR) per R&D participants (in EUR) 2007-2013 employee Strongly developed, 819,140,184 2,330 351,562 1,336,598 613 out of which: Austria 19,895,795 106 187,696 60010 332 Belgium 23,323,429 134 174,055 62010 376 Denmark 34,670,705 92 376,855 55870 621 Finland 30,203,445 119 253,810 55207 547 Germany 273,838,510 679 403,297 552661 495 Netherlands 115,407,117 365 316,184 105500 1,094 Slovenia 4,463,986 39 114,461 13255 337 Sweden 44,620,552 167 267,189 78617 568 122 4. Supporting the research and innovation base through priority European research...

Total Total number Average EU Total R&D Total financing financing Type of innovation of financed financing for personnel, from FP7 from FP7 system / country national the participant average (in EUR) per R&D participants (in EUR) 2007-2013 employee UK 272,716,645 629 433,572 353468 772 Public policy-led, 376,267,715 1,139 330,349 715232 526 out of which: France 204,083,016 479 426,061 396,896 514 Ireland 15,669,242 73 214,647 20,847 752 Italy 139,968,448 431 324,753 228,098 614 Latvia 1,912,685 22 86,940 5,742 333 Lithuania 2,047,114 22 93,051 11,701 175 Luxemburg 1,399,792 10 139,979 4,836 289 Malta 407,440 15 27,163 1,142 357 Portugal 10,779,978 87 123,908 45,970 235 Developing, 119,517,158 707 169,048 374,210 319 out of which: Bulgaria 5,497,782 59 93,183 17,179 320 Croatia 2,342,746 19 123,302 10,574 222 Cyprus 4,715,628 25 188,625 1,256 3,754 Czech Republic 12,285,024 80 153,563 54,465 226 Hungary 14,396,594 106 135,817 31,784 453 Romania 5,252,836 64 82,076 29,618 177 Slovakia 1,985,713 26 76,374 16,935 117 Spain 73,040,835 328 222,685 212,399 344 Lagging behind, 65,346,224 327 199,836 125,605 520 out of which: Estonia 2,479,385 22 112,699 5,462 454 Greece 37,222,145 163 22,8357 37,998 980 Poland 25,644,694 142 180,596 82,145 312 Total EU 1,380,271,281 4,503 306,523 2,551,645 541 Source: own elaboration based on data from the EU Contact Point, Warsaw, Poland and data from the Eurostat Statistics Database [file: rd_p_persocc] for the number of researchers (full-time equivalent – FTE).

There are strong discrepancies in the FP7 (part INFRA) financial allocations between the main groups of innovation systems found in EU Member States. The biggest share of investments is identified in the countries with strongly developed innovation systems (819 million EUR), followed by public policy-led innovation systems (376 million EUR), developing innovation systems (119 million EUR), and lagging behind innovation systems (65 million EUR). This is mostly due to average EU financing for the participant, which was higher in the countries representing strongly developed innovation systems (351,562 EUR) and public policy-led innovation systems (330,349 EUR) than in countries with developing (169,048 EUR) 4.5. EU policy measures to support priority research infrastructures 123 and lagging behind (199,836 EUR) innovation systems. The differences in financial allocations between the group of countries with strongly developed innovation systems and public policy-led innovation systems, and the group of countries with developing and lagging behind innovation systems may contribute to increasing the innovation gap between EU Member States. The analysis of Horizon2020 (part INFRA) allocations in EU countries is presented in Table 4.6 (covering the period until 31.03.2018).

Table 4.6. Horizon 2020 (part INFRA) investments by the four main groups of innovation systems found in EU Member States (data until 31.03.2018)

Total Total number Average EU Total R&D Total financing financing Type of innovation of financed financing for personnel, from H2020 from H2020 system / country national the participant average (in EUR) per R&D participants (in EUR) 2007-2013 employee Strongly developed, 13,781,597,369 1,636 8,423,959 1,493,637 9,227 out of which: Austria 612,216,014 65 9,418,708 68,710 8,910 Belgium 806,586,333 103 7,830,935 73,283 11,006 Denmark 653,707,857 61 10,716,522 59,748 10,941 Finland 817,331,802 101 8,092,394 51,249 15,948 Germany 3,442,206,375 441 7,805,457 609,496 5,648 Netherlands 2,604,045,929 293 8,887,529 126,197 20,635 Slovenia 406,891,423 34 11,967,395 14,546 27,973 Sweden 1,134,541,020 129 8,794,892 83,998 13,507 UK 3,304,070,616 409 8,078,412 406,410 8,130 Public policy-led, 8,423,613,955 994 8,474,461 767,285 10,978 out of which: France 3,566,594,392 435 8,199,068 418,243 8,528 Ireland 545,869,690 66 8,270,753 28,912 18,880 Italy 2,828,313,822 358 7,900,318 248,804 11,368 Latvia 177,521,634 14 12,680,117 5,655 31,392 Lithuania 164,411,450 12 13,700,954 11,157 14,736 Luxemburg 137,620,978 8 17,202,622 5,418 25,401 Malta 151,948,060 9 16,883,118 1,418 107,157 Portugal 851,333,929 92 9,253,630 47,678 17,856 Developing, 4,735,734,165 523 9,054,941 384,502 12,317 out of which: Bulgaria 271,510,947 28 9,696,820 20,878 13,005 Croatia 246,608,287 20 12,330,414 10,336 23,859 Cyprus 193,601,190 16 12,100,074 1,257 154,018 Czech Republic 646,385,468 80 8,079,818 65,439 9,878 Hungary 432,281,027 45 9,606,245 37,088 11,656 Romania 319,833,342 35 9,138,095 31,361 10,198 124 4. Supporting the research and innovation base through priority European research...

Similar to the FP7, the analysis of financial investments (until 31.03.2018) from Horizon 2020 (part INFRA) indicates strong differentials between allocations in different groups of innovation systems found in EU Member States. Countries with strongly developed innovation systems had the biggest share in total financing from Horizon 2020, part INFRA (13.78 billion EUR), followed by countries representing public policy-led innovation systems (8.42 billion EUR), developing innovation systems (4.74 billion EUR), and lagging behind innovation systems (2.27 billion EUR). This was mostly due the difference in the total number of financed national participants (1,636 in countries with strongly developed innovation systems, 994 – public policy-led, 523 – developing, and 259 – lagging behind innovation systems), because, contrary to the FP7, average EU financing for the participant was fairly similar in all analyzed groups, ranging from 8,423,959 EUR in the countries representing strongly developed innovation systems to 9,054,941 EUR in the Member States with developing innovation systems.

4.5.4. Interactions between countries and their organizations engaged in Horizon 2020, part INFRA projects Interactions between actors engaged in the projects co-financed from Horizon 2020, part INFRA, located in different countries, are illustrated in Graphs 4.1 and 4.2. These graphs present the strength of financial relations between individual EU countries. While not ideal, this is one of the few ways to present data in a comprehensible way. The dataset was cleared of countries outside the EU and was grouped based on participation in unique projects. Projects that had fewer than two participants (no connection) were removed. Next, vertices were created by establishing participation between countries in a project. For example, if a Bulgarian university cooperated in 4.5. EU policy measures to support priority research infrastructures 125

Graph 4.1. The strength of financial relations between partners implementing Horizon 2020, part INFRA projects, in different EU Member States (for the strongest connections)

Source: Calculations and graphics taken by Marek Lachowicz, SGH-WERI, on data delivered by the National Contact Point for Research Programs of the European Union in Warsaw.

Graph 4.2. The strength of financial relations between partners implementing Horizon 2020, part INFRA projects in different EU Member States (for all the connections)

Source: Calculations and graphics taken by Marek Lachowicz, SGH-WERI, on data delivered by the National Contact Point for Research Programs of the European Union in Warsaw. a project with institutions from Poland and Sweden, two pairs: Bulgaria-Poland and Bulgaria-Sweden, were created. Vertex size depended on EU financing for the given country, calculated as a sum of EU financing for individual institutions. Edge width depended on a country weight, calculated as an average of two aggregates. The first one encompassed the overall value of a country by adding budgets of all projects a 126 4. Supporting the research and innovation base through priority European research... country was involved in, and the second summed up EU financing for all projects a country participated in. For each pairing, these weights were then summed. Parallel edges were then collapsed with edge weights added to each other and final graphs were produced using Kamada – Kawai’s layout. The first graph is for countries where the weight for each connection initially exceeded 0.1 and was created to make the analysis more legible by removing the least important pairings. The second graph presents all the connections. Both graphs show the superior role of Germany and the United Kingdom as the centers of European investments in research infrastructures, and the counties exhibiting the highest strength of interactions with other countries. The strongest relations exist between Western European countries, namely: Germany, Italy, Spain, France, the Netherlands, and the United Kingdom. On the other hand, countries from Central and Eastern Europe, which joined the European Union after 2000, while already integrated into the European innovation network, have not yet established strong connections, neither with one another nor with the countries from Western Europe.

4.5.5. Analysis of FP7 and Horizon 2020 program (part INFRA) investments in research infrastructures by types of actors The classification of different actors in the innovation system is based on the typology used in different EU studies on framework programs, e.g. the European Commission (2018, p. 62), where the following categories of organizations, together with their definitions, are provided: • Secondary and higher education establishments (HES): a legal entity that is recognized by its national education system as a University or Higher or Secondary Education Establishment. It can be a public or a private body. • Research organizations (excluding education) (REC): a legal entity that is established as a non-profit organization and whose main objective is carrying out research or technological development. • Private for profit companies (PRC): private, for-profit entities, including small or medium-sized enterprises and excluding Universities and Higher or Secondary Education Establishments. • Public bodies (excluding research and education) (PUB): any legal entity established as a public body by national law or an international organization. Excludes Research Organizations and Higher or Secondary Education Establishments. • Other entities (OTH): any entity not falling into one of the other four categories. 4.5. EU policy measures to support priority research infrastructures 127

Table 4.7 presents FP7, part INFRA investments by the above identified categories of organizations participating in the projects.

Table 4.7. FP7 (part INFRA) investments by the five categories of organizations participating in the projects

Total number of Average EU EU financing Categories of Total budget financed national financing for the for the projects organizations of the projects participants participant (in EUR) (in million EUR) HEC 1,956 223,276 436,727,813 21,614,359,696 REC 2,291 354,497 812,152,952 23,226,108,208 PRC 428 384,130 164,407,809 4,710,793,216 PUB 402 128,563 51,682,283 4,640,218,014 OTH 216 297,795 64,323,642 3,438,721,339 Total EU 5,293 288,928 1,529,294,499 57,630,200,473 Source: own elaboration based on data from the EU Contact Point, Warsaw.

The biggest group of actors engaged in FP7, part INFRA projects are research organizations (2,291 participants), followed by secondary and higher education establishments (1,956 participants). These two groups of actors also have the biggest share in EU financial investments. This proves that the scientific sector holds the key type of actors of innovation systems using research infrastructures. The analysis of Horizon 2020 (part INFRA) financial allocations (covering the period until 31.03.2018) by the five categories of organizations is presented in Table 4.8.

Table 4.8. Horizon 2020 (part INFRA) investments by the five categories of organizations participating in the projects (data until 31.03.2018)

Total number of Average EU EU financing Categories of Total budget of financed national financing for the for the projects organizations the projects participants participant (in EUR) (in million EUR) HEC 1,438 8,382,083 12,053,435,298 13,233,803,962 REC 1,694 8,434,370 14,287,823,372 16,168,979,722 PRC 319 8,819,886 2,813,543,550 3,168,425,137 PUB 181 11,037,032 1,997,702,773 2,602,838,283 OTH 238 10,249,381 2,439,352,885 3,077,387,641 Total EU 3,870 8,680,066 33,591,857,878 38,251,434,745 Source: own elaboration based on data from the EU Contact Point, Warsaw.

Similar to FP7, the biggest group of actors engaged in Horizon 2020, part INFRA projects are research organizations (1,694 participants), followed by secondary and higher education establishments (1,438 participants). However, there are also private for profit companies, public bodies and other entities, proving that research infrastructures are not only about knowledge generation, but also technology transfer 128 4. Supporting the research and innovation base through priority European research... to industry, and that they are an important element of the innovation system. This verifies the findings from the literature review, in which research infrastructures where perceived as large-scale indivisible capital goods producing products and services, which become inputs in most or all economic activities on a multi-user basis (Smith 1997). As it was put down by ESFRI (2018, p. 15), “research Infrastructures are privileged places where research meets innovation and industry – in the form of industrial applications, technologies and business. They bring together highly skilled scientists, engineers, technicians and managers, funding agencies, public authorities, policy decision makers and industry, including SMEs. RIs are characterized by their scientific and technical multi- and cross- disciplinarity and a mix of a very broad range of interactions with their economic and societal surrounding environments. RIs are major drivers of – industrial – innovation: in their construction and major upgrade phases – design, engineering, commissioning – as sources of (pre-)commercial procurements and purchasers of new high-tech components, instruments and related services; in their operation phases, as facilities serving industrial research and innovation, offering opportunities to remove technological barriers leading to further innovation and to generate knowledge transfer”.

4.6. The impact of research infrastructures on European innovation – findings from empirical research, including the survey research

This Section contains the results of two surveys, based on self-designed questionnaires, conducted with the application of Computer Assisted Telephone Interviewing (CATI) and Computer Assisted Web Interview (CAWI) methods in the period November – December 2016 by the market research institute Indicator. The first survey research addressed the coordinators of European RIs [N=150 RI coordinators], registered in the MERIL database, as well as European RIs and global research infrastructures (GRIs) indicated by the European Strategy Forum on Research Infrastructures (ESFRI) and a Group of Senior Officials (GSO) on Global Research Infrastructures. The second survey was conducted among participants taking part in projects financed from FP7 INFRA and H2020 INFRA budgets, referred to as users of RIs [N=400 RIs users].

4.6.1. Results of the survey research conducted on coordinators of research infrastructures The survey research was conducted on a group of 150 coordinators of RIs, according to the self-designed questionnaire. Information on the types of surveyed RIs is provided in Table 4.9, with the application of the following typology of RIs: 4.6. The impact of research infrastructures on European innovation – findings from... 129

1. Single-sited RIs, which are infrastructures with a single resource at a single location. These are usually large-scale facilities, the creation of which is strongly associated with construction works, large buildings and extremely expensive equipment. 2. Distributed RIs, functioning as a network of distributed resources, which typically are multi-national associations of geographically-separated distinct entities that jointly perform, facilitate or sponsor basic or applied scientific research. 3. Virtual research RIs, which provide services electronically and allow for new forms of cooperation among scientists, who may work together, regardless of their geographical location.

Table 4.9. Type and number of RI coordinators that took part in the survey research [N=150 RI coordinators]

Type Number Percent Single-sited 96 64% Distributed 33 22% Virtual 19 13% Hybrid/mixed model 2 1% Total 150 100% Source: data derived from a survey on 150 RI coordinators. Statistical analysis conducted on SPSS by Grzegorz Michalski, SGH-WERI.

Most of the respondents coordinate single-sited RIs (96 RIs or 64%), followed by distributed RIs (33 RIs or 22%) and virtual RIs (19 RIs or 13%), with the remaining 2 coordinators (1%) representing RIs characterized by the hybrid/mixed model. These findings are consistent with the results of the survey research conducted from March 2006 to March 2007 on 598 organizations by the European Commission (The Research Infrastructures unit within DG Research) and European Science Foundation (2007), according to which most of the analyzed research infrastructures (63%) were single- sited, while 25% were working as distributed facilities, and 12% were virtual. As found out in the theoretical review, one of the biggest research challenges in evaluating the impact of RIs on the economy is to select the indicators that would be suitable for the measurement of the effects of research infrastructures, and to collect relevant statistical data, which are usually not publicly available. Generally, it is difficult to identify and quantify RI impact in conventional commercial terms, as this type of investment brings a broad range of long-term social and economic benefits that are not captured by official statistics. Information on the specific measures that are considered by RI coordinators to be important for evaluating RI research performance and/or productivity is indicated in Table 4.10. 130 4. Supporting the research and innovation base through priority European research...

Table 4.10. Specific measures considered to be important for RI research performance and/or productivity evaluation, by type of RI (in %) [N=150 RI coordinators]

Measure Single-sited Distributed Virtual Hybrid/mixed model Total Number of publications 85% 91% 84% 100% 87% Number of publication citations 69% 70% 63% 50% 68% Number of PhD degrees 59% 61% 32% 50% 56% Number of consulting contracts 50% 45% 47% 50% 49% Number of licenses sold 30% 36% 42% 50% 33% Number of patents 21% 21% 21% 50% 21% Number of patent citations 10% 9% 11% 0% 10% Other 0% 6% 0% 0% 1% Source: data derived from a survey on 150 RI coordinators. Statistical analysis conducted on SPSS by Grzegorz Michalski, SGH-WERI.

Table 4.10 shows that there is no a clear consensus between RI coordinators on how to evaluate research performance and productivity of infrastructures. The measures that were indicated by more than half of the respondents were: number of publications (87%), number of publication citations (68%), and PhD degrees granted by RIs (56%). However, it must be noted that although these categories of indicators are potentially appropriate for measuring the direct impact of infrastructures on innovation, corresponding statistical data are very fragmented and in case of many RIs – missing. This finding is supported by the results of the conducted survey research, in which very few RI coordinators were able to deliver data related to RI performance for the years 2007-2015 (in fact the only question in the survey posing great difficulties for the respondents and was mostly left unanswered). In fact, the rate of responses to the question connected with delivering statistical data related to particular indicators is presented in Table 4.11. Table 4.11 confirms the general scarcity of data related to the functioning and performance of RIs. The most well-known information is related to the indicator on the number of publications, as in this case altogether 36 RI coordinators (or 24%) were able to deliver annual data for at least some years in the 2007-2015 period. However, it seems that there is no practice in RIs to use bibliometric methods to measure research performance, as no respondent was familiar with the number of RI-related publication or patent citations. The general recommendation from this survey research is that the European Union should focus more on monitoring the results of financial spending on RIs, in particular demand the delivery of annual statistical data on selected groups of indicators from coordinators of RIs that are co-financed from European funds. It should also be underlined that a long-term perspective should be adopted in the monitoring model as the development of RIs is a long-lasting process and in many cases the impact will be observed in decades rather than years. 4.6. The impact of research infrastructures on European innovation – findings from... 131

Table 4.11. The number and share of RIs, which delivered statistical data related to the functioning and performance of RIs [N=150 RI coordinators]

RIs, which delivered RIs, which Total: RIs, which complete data for the delivered data only delivered any Measure whole 2007-2015 period for some years data Number Share Number Share Number Share Number of publications 15 10% 21 14% 36 24% Number of publication 0 0% 0 0% 0 0% citations Number of patents 0 0% 9 6% 9 6% Number of patent citations 0 0% 0 0% 0 0% Number of PhD degrees 2 1% 3 2% 5 3% Number of users 0 0% 2 1% 2 1% Number of R&D projects 0 0% 0 0% 0 0% implemented R&D spending 0 0% 0 0% 0 0% Private co-financing of the 0 0% 0 0% 0 0% R&D projects implemented Operational costs 0 0% 0 0% 0 0% Source: data derived from a survey on 150 RI coordinators. Statistical analysis conducted on SPSS by Grzegorz Michalski, SGH-WERI.

The findings presented above are supported by Mayernik et al. (2017), who state that:

“the central methodological challenge for research infrastructure metrics is the lack of consistent and sustainable ways to gather the underlying data. Nontextual research resources are, at best, cited and referenced inconsistently within the scientific literature. Typically, however, infrastructural resources, such as data sets, software, and facilities, have not been cited or referenced at all in past scientific studies. The robust tools that have supported the development of the bibliometric and informetric disciplines – such as Dialog, the WoS, and Scopus – do not yet exist for the collection and analysis of metrics for most research infrastructures. (...) No single tool yet provides indexing that includes citations to software, facilities, or other types of research infrastructures. The WoS's ‘Cited Reference Search’ capability has limited utility when looking for references to research infrastructure, because such references, if provided at all, have traditionally been given in the methods or acknowledgments sections of published literature, not in reference lists. Studies of research infrastructure metrics thus face similar, if not greater, challenges as studies of acknowledgments. Acknowledgments require notoriously labor-intensive data- gathering procedures given their nature as unstructured textual statements. (...) Thus, citations and acknowledgments for research infrastructures do show up 132 4. Supporting the research and innovation base through priority European research...

in the scholarly literature, but in inconsistent and hard-to-analyze ways. (...) Without citations to uniquely identify data, it is difficult, if not impossible, to develop tools to measure the impact such resources and infrastructures have within the communities they belong to, or to understand the spread of that impact to broader scientific communities.” (Mayernik, et al., p. 1342-1343).

A similar problem was reported by D. Bailo et al. (2017), who, focusing on the Research Infrastructure “European Plate Observing System (EPOS)”, which was included in the European Strategy Forum on Research Infrastructures (ESFRI) Roadmap, analyze the mapping of solid earth data and research infrastructures to Current Research Information Systems (CERIF). The authors try to answer the question how to integrate multiple sources that provide access to data and services at different levels (e.g. national, regional or local), presenting the following data characteristics (Bailo et al., 2017, p. 113): a) they are usually scattered over Europe; b) they often use community specific standards; c) data, services and results of a discovery action are seldom standard, and depend upon the local technologies. Finally, the authors conclude that “CERIF related software developed by the many initiatives at European level is hard to find in some cases. Software is scattered across different public repositories like GitHub, google code, and others and there is no central place to go to search for software” (Bailo et al., 2017, p. 120). Additionally, several studies (Chabbi, Loescher 2017; Chabbi, Loescher, Dillon 2017; Loescher, Kelly, Lea 2017) showed that Environmental Research Infrastructures remain a largely untapped scientific resource, as the management of the data, data products and services they provide lacks the coordination among stakeholder groups, and this creates inefficiencies in data collection, funding and management. On the other hand, Buddenbohm et al. (2017) focus specifically on digital humanities and related ESFRI projects, stating that despite the documentation and reusability of software being recognized as good scientific practice, the infrastructure and services necessary for software are still in their infancy. This paper provides some recommendations on how to archive software and make it available for future use. Information on the opinions of RI coordinators on the effect of EU funding from framework programs is provided in Table 4.12. The data from Table 4.12 shows that according to the opinions of RI coordinators, the most important effect of EU funding from framework programs is a stronger integration of European researchers, from both academia and industry, ensuring their optimal use and joint development. This finding is important in the light of the 4.6. The impact of research infrastructures on European innovation – findings from... 133

Table 4.12. The effects of EU funding from framework programs, by type of RIs (%) [N=150 RI coordinators]

Hybrid/ Single- Distri- Effect Virtual mixed Total sited buted model Integration of European researchers, from both 91% 85% 100% 100% 91% academia and industry, ensuring their optimal use and joint development Increased efficiency and productivity of researchers 90% 88% 95% 100% 90% Defining workflows and ensuring coordination, 88% 82% 89% 50% 86% harmonization, integration and interoperability of data, applications and other services with other research infrastructure initiatives The use of open standards and interoperability in 85% 88% 84% 50% 85% data and computing services Addressing societal challenges with a global 74% 79% 74% 50% 75% dimension, for example climate change or the ageing of society Better managing, preserving and computing with 70% 73% 95% 100% 74% big research data Enhancement of the technical architecture of RIs 74% 79% 68% 50% 74% Focus on policies, models and solutions for data and 69% 73% 84% 100% 72% knowledge handling, including access, preservation and management, and the protection of sensitive data and samples Wider interaction with end-users (especially 73% 58% 89% 50% 71% industry and SMEs) Developing synergies and complementarities 72% 76% 53% 50% 70% between different actions undertaken by RIs Wider access provision to RIs for potential users 72% 61% 42% 50% 65% Change in data management, including possible 60% 64% 58% 50% 61% open access to data The development of Regional Partner Facilities 43% 82% 89% 100% 58% Fostering the potential for innovation by reinforcing 54% 48% 79% . 55% partnership with industry Enlargement of the number of member/partner 39% 85% 68% 50% 53% organizations Human capital development, e.g. through trainings 44% 55% 47% 50% 47% Faster adoption of best practices 44% 30% 53% 50% 42% Economies of scale and saving of resources due to 28% 36% 21% . 29% the optimization of implementation and operation through the common development of components and solutions Source: data derived from a survey on 150 RI coordinators. Statistical analysis conducted on SPSS by Grzegorz Michalski, SGH-WERI. 134 4. Supporting the research and innovation base through priority European research... outcomes of the theoretical review, according to which enhancing partnerships and cooperation plays a crucial role in stimulating innovation processes. The positive role of RIs in this area is especially valuable taking into account the complexity of realizing the projects in partnership, which generally refers to an emergent property of systems made of large numbers of self-organizing agents that interact in a dynamic and non-linear fashion and share a path-dependent history (Cilliers 1998). The increasing complexity of scientific processes makes partnership a key success factor for research infrastructures, which need to pool people and resources in order to achieve a technological critical mass. However, usually research teams face different types of difficulties in pooling and sharing knowledge. Additionally, the increasing inherent technical complexity of the projects also changes the nature of research infrastructures, which come to be more digital and distributed. Modern science becomes extremely complex, and the research infrastructure landscape in Europe is diverse and multi-layered. Paradoxically, in some cases efforts to pool resources have added to the complexity, as large-scale EU-funded investments can combine several types of activities under one umbrella, networking different partner facilities and sub-projects. The technical complexity of the research projects is also growing together with the rise of so-called data-intensive science, also referred to as Linked Data, Big Data, or the 4th Paradigm (Bizer, Heath, Berners-Lee 2009), and other similar concepts. This new approach highlights the importance of investment into collecting and preparing massive amounts of data. Thus, providing a digital collection and the preservation of research data is one of the key services that has to be provided by research infrastructures. Together with the increasing complexity of research projects, the needs for preservation goes beyond just maintaining data accessible. Capturing and documenting the context of its creation and use requires sophisticated information networks. This is a massive task requiring to pool resources if European research infrastructures are to be able to capture and maintain usable series of data processing routines and modules. This is needed in order to establish the validity of scientific analysis, to repeat earlier computations on new data, and in general to make full use of the potential enrooted in data-intensive science (Rauber 2012). As it was written in the European Commission (2017, p. 37):

“technological developments are dramatically increasing the capacity of research infrastructures to collect and produce data and the developments in distributed computing, overall computer power and high-volume data transmission have combined to produce an explosion of data-driven science, giving scientists in many disciplines inter-operable access to research data of a hitherto-unimagined scale and diversity”. 4.6. The impact of research infrastructures on European innovation – findings from... 135

An example of developing protocols that may help to create databases with comparable data from multiple research infrastructures is presented by Firbank et al. (2017). This refers to large-scale ecological research, which requires data that can be linked across sites in order to better understand earth system processes (Guo, Lin 2016). The need for developing protocols is connected with the fact that the study of ecosystem processes over multiple scales of space and time is often best achieved using comparable data from multiple sites. Traditionally, long-term ecological observatories have often developed their own data collection protocols. This problem was addressed by Firbank et al. (2017) by proposing a set of ecological protocols suitable for widespread adoption by the ecological community, which can benefit from a more consistent approach to data collection within the resources available at most long-term ecological observatories. Pooling resources leads to an increased efficiency and productivity of researchers, which is pointed out by 90% of the respondents. Other key effects of EU funding from framework programs are: defining workflows and ensuring coordination, harmonization, integration and interoperability of data, applications and other services with other research infrastructure initiatives (indicated by 86% of RI coordinators), the use of open standards and interoperability in data and computing services (85%), and addressing societal challenges with a global dimension, for example climate change or the ageing of society (75%). An important challenge for the EU research and innovation policy is to endow Europe with world-class RIs that are accessible to all researchers in Europe and fully exploit their potential for scientific advancement and innovation. The opinions of RI coordinators on the level of their actual exploitation are presented in Table 4.13.

Table 4.13. State of European RI exploitation, by type of RIs (%) [N=150 RI coordinators]

Single- Hybrid/ mixed Exploited Distributed Virtual Total sited model Exploited in 75% to 100 % of its capacity 61% 79% 63% 50% 65% Exploited in 50% to 75 % of its capacity 25% 15% 32% 0% 23% Exploited in less than 50% of its capacity 4% 3% 0% 0% 3% Hard to say/do not know 9% 3% 5% 50% 8% Source: data derived from a survey on 150 RI coordinators. Statistical analysis conducted on SPSS by Grzegorz Michalski, SGH-WERI.

The results of the survey research show that RI potential for fostering innovation in Europe is not yet fully exploited. Only 65% of the respondents claim that their RIs are exploited in 75% to 100 % of their capacity, with the best result for distributed RIs (79% coordinators). This means that the opportunities provided by the development of components, instruments, services and knowledge for the implementation and upgrade of RIs could be better exploited to push the limits of existing technology. 136 4. Supporting the research and innovation base through priority European research...

As stated by the ESFRI (2018, p.14), various access regimes determine the use of facilities by different actors. The European Commission (2016) developed the Charter for Access to Research Infrastructures, in order to promote the harmonization of access procedures and improve the transparency of access policies. The charter promotes access to RIs and interaction with a wide range of social and economic activities, including business, industry and public services, in order to maximize the return on investment in RIs and to drive innovation, competitiveness and efficiency. According to the analyzed document, access to RIs may be provided according to three different access modes, or any combination of them: • excellence-driven access, which is exclusively dependent on the scientific excellence, originality and quality, as well as technical and ethical feasibility of an application evaluated through peer review conducted by internal or external experts; • market-driven access, which is defined through an agreement between the user and the research infrastructure that will lead to a fee for the access that may remain confidential; • wide access, which guarantees the broadest possible access to scientific data and digital services provided by the research infrastructure to users, wherever they are based. The results of survey research related to different types of access modes to RIs are presented in Table 4.14.

Table 4.14. Type of access to data of RIs, by type of RIs (%) [N=150 RI coordinators]

Type of access Single-sited Distributed Virtual Hybrid Total Excellence-driven Access 66% 82% 95% . 72% Market-driven Access 41% 15% 26% 50% 33% Wide Access 63% 52% 68% 100% 61% Source: data derived from a survey on 150 RI coordinators.

As indicated in Table 4.14, the most common type of access mode to RIs is excellence-driven access (72%), which enables collaborative R&D efforts across geographical and disciplinary boundaries. This is especially important in case of virtual RIs (95%), and distributed RIs (82%). The least common type of access mode to RIs is market-driven access. One of the reasons is that in the practice of some countries providing solely market-driven access is not permitted for RIs listed on the National Roadmaps. Modern research and innovation activity is increasingly more dependent on external factors and sources that remain outside the organization, for example by finding and combining ideas that are complementary to existing R&D projects, and 4.6. The impact of research infrastructures on European innovation – findings from... 137 the creation of cooperative relations with other people and units. The survey results on the RI role in the formation of an adequate mix of knowledge, skills and activities of various actors are presented in Table 4.15.

Table 4.15. The role of cooperation within the framework of RIs in reaching a necessary critical mass for breakthrough research activities, by type of RIs (%) [N=150 RI coordinators]

Critical mass for breakthrough Single-sited Distributed Virtual Hybrid Total research activities Yes 34% 24% 37% 0% 32%

Source: data derived from a survey on 150 RI coordinators.

As presented in Table 4.15, 32% of RI coordinators claim that cooperation within the framework of RIs is successful in reaching a necessary critical mass for breakthrough research activities, with the best result for virtual RIs (37%), followed by single-sited RIs (34%), and distributed RIs (24%). This confirms that RIs play a positive role in increasing the innovation capabilities of the European economy, but their potential is still not fully exploited.

4.6.2. The results of the survey research conducted on users of research infrastructures The survey research was conducted on a group of 400 RI entities that benefited from the financing of the FP7 INFRA or H2020 INFRA, referred to as RI Users, according to the self-designed questionnaire. The respondents were representing different types of organizations, as evidenced in Table 4.16.

Table 4.16. Types of organizations taking part in the survey research [N=400 RIs Users]

Types of organizations Number Share Higher or secondary education 154 39% Research organization 147 37% Private for profit 51 13% Public body 35 9% Other 13 3% Total 400 100% Source: data derived from a survey on 400 RI entities that benefited from the financing of FP7 INFRA or H2020 INFRA (referred to as RI Users). Statistical analysis conducted on SPSS by Grzegorz Michalski, SGH-WERI.

As indicated in Table 4.16, the most commonly surveyed organizations represent the higher or secondary education sector (154 or 39%), followed by research organizations (147 or 37%) and firms (51 or 13%). This shows that the prevailing 138 4. Supporting the research and innovation base through priority European research... category of organizations cooperating with RIs are research and educational units, which in the conducted survey count together for 75% of the respondents. The results of the survey research on different access modes to RIs (described in the previous subchapter) of particular types of organizations are presented in Table 4.17.

Table 4.17. Types of organizations taking part in the survey research, and the type of access that it has to RI data [N=400 RIs users]

Type of access Category of RI users Average / type of innovation system HES REC PRC PUB OTH Excellence-driven access, out of which countries 74% 73% 65% 63% 62% 71% representing a specific innovation system: Strongly developed 76% 78% 64% 73% 63% 74% Public policy-led 80% 57% 67% 50% 67% 68% Developing 75% 81% 67% 57% 0% 74% Lagging behind 88% 86% 0% 100% 0% 82% Market-driven access, out of which countries 28% 22% 20% 17% 23% 24% representing a specific innovation system: Strongly developed 25% 22% 17% 9% 25% 22% Public policy-led 33% 35% 25% 16% 0% 30% Developing 25% 10% 0% 14% 100% 17% Lagging behind 38% 14% 0% 0% 0% 24% Wide access, out of which countries representing 60% 63% 67% 74% 54% 63% a specific innovation system: Strongly developed 55% 56% 61% 55% 50% 56% Public policy-led 53% 52% 67% 100% 67% 59% Developing 56% 74% 67% 71% 100% 69% Lagging behind 75% 71% 100% 100% 0% 76% Source: data derived from a survey on 400 RI entities that benefited from the financing of FP7 INFRA or H2020 INFRA (referred to as RI Users).

As indicated in Table 4.17, the most common type of access mode to RIs is excellence-driven access (71%), which enables users to get access to the best facilities, resources and services wherever located. However, for public bodies a more important role is played by wide access, which maximizes availability and visibility of the data and services provided. For all types of organizations, the least important is market-driven access. It is worth noticing that these outcomes are consistent with the results of the survey presented in the previous subchapter, as 72% of RI coordinators declared excellence-driven access as the most common type of access mode to their RIs, 61% – wide access, and 33% – market-driven access. One of the rationales for public spending on RIs is the expectation that it will result in additionality, which can be defined as a change in financed organizational R&D spending, behavior or performance that would not have occurred without the 4.6. The impact of research infrastructures on European innovation – findings from... 139 public program or subsidy (Buisseret, Cameron, Georghiou 1995). For the purpose of this study, different types of additionalities were examined (Table 4.18), as adopted in the questionnaire: • acceleration additionality – when public funding speeds up the course of the project; • challenge additionality – when public funding helps to take more risk in projects; • cognitive capacity additionality – when public funding has a positive impact on competencies and expertise; • follow-up additionality – when public funding helps to establish follow-up projects; • input additionality – when public funding allows for additional investment in R&D; • management additionality – when public funding improves company management routines; • network additionality – when public funding helps to create networks; • output additionality – when public funding has a direct effect on a firm’s innovation performance; • scale additionality – when public funding allows the project to be conducted on a larger scale; • scope additionality – when public funding allows the coverage of an activity to a wider range of markets.

Table 4.18. Different types of additionalities of the European Union funds that occurred as a consequence of being a user of RIs [N=400 RIs users]

Higher or secondary Research Private Public Type of additionality Others Total education organization for profit body Network additionality 98% 97% 100% 97% 100% 98% Cognitive capacity 84% 84% 88% 89% 85% 85% additionality Follow-up additionality 83% 78% 86% 86% 85% 82% Input additionality 75% 78% 75% 74% 77% 76% Scale additionality 72% 65% 71% 74% 77% 70% Acceleration 72% 63% 75% 71% 77% 69% additionality Output additionality 61% 56% 61% 60% 54% 59% Scope additionality 58% 53% 55% 57% 62% 56% Challenge additionality 59% 50% 59% 43% 46% 54% Management 40% 39% 43% 54% 23% 41% additionality Source: data derived from a survey on 400 RI entities that benefited from the financing of FP7 INFRA or H2020 INFRA (referred to as RI Users). 140 4. Supporting the research and innovation base through priority European research...

The most common type of additionality experienced by all types of organizations that used RIs is network additionality (98% of all RIs users), which shows that investments in RIs are successful in external resource mobilization, and linking different actors of the innovation system in Europe. These findings are very important taking into account the outcomes of the theoretical study form, i.e. applying the economic network theory, which emphasizees the importance of external resource mobilization (Oerlemans Meeus, Boekema 1998). They also enabled identifying that research infrastructures can involve major network externalities, as they are often the place within a system where scale and scope economies are very significant. Moreover, research infrastructures viewed as networks contribute to the innovative capabilities of organizations, by exposing them to novel sources of ideas, enabling fast access to resources, and enhancing the transfer of knowledge. Despite some difficulties inherent in working in international and multilingual teams, EU funding for collaborative research, including research infrastructures, can stimulate the development of an international science teamwork model. In this model, individual researchers often serve as native informants, offering insider knowledge about the phenomena under study in their own countries. The single researcher can thus become part of a larger network, pooling information and broadening disciplinary perspectives. In this way, collaborative work produces synergy and helps to make sense of complexity and diversity (Hantrais 2005). However, with reference to European Environmental Research Infrastructures (RI), Huber et al. (2017, p. 15166) find out that: “network operation is usually a cumbersome aspect of these RIs facing specific technological problems related to operations in remote areas, maintenance of the network, transmission of observation values, etc. Robust inter-connection within and across these networks is still at infancy level and the burden increases with remoteness of the station, harshness of environmental conditions, and unavailability of classic communication systems, which is a common feature here. Despite existing RIs having developed ad-hoc solutions to overcome specific problems and innovative technologies becoming available, no common approach yet exists”. According to the conducted survey research, other most important types of additionality are: cognitive capacity additionality (85% of all analyzed users of RIs), follow-up additionality (82% of all users), and input additionality (76% of all users). Being a user of RIs brings different types of added value for particular types of organizations, as presented in Table 4.19. 4.6. The impact of research infrastructures on European innovation – findings from... 141

Table 4.19. Different types of added values from being a user of RIs [N=400 RIs users]

Networking New Long lasting Knowledge Solid Types of organization with other knowledge contacts exchange infrastructure scientists acquisition Higher or secondary 95% 96% 95% 95% 68% education Research organization 94% 90% 90% 92% 62% Private for profit 96% 94% 94% 86% 50% Public body 94% 91% 97% 80% 71% Others 100% 100% 100% 92% 67% Total 95% 94% 93% 91% 62% Source: data derived from a survey on 400 RI entities that benefited from the financing of FP7 INFRA or H2020 INFRA (referred to as RI Users).

According to data from Table 4.19, the most common type of added value experienced by users of RIs results from the possibilities of networking with other scientists (95% of all surveyed RI users). This finding is consistent with the conclusion from Table 4.18, where the most common type of additionality pointed out by respondents is network additionality. This is additional evidence that RIs in Europe play a positive role in networking researchers and organizations engaged in innovation activity, as well as in pooling people and resources in order to achieve a technological critical mass, thus reducing the fragmentation of the European innovation system. Other important types of added value resulting from using RIs are: new knowledge acquisition (94% of all surveyed RI users), long lasting contacts (93%), and knowledge exchange (91%). An interesting observation is that these rather soft forms of added value are much more prevalent than the value added resulting from the possibility of having access to solid infrastructures (laboratories, etc.), which is experienced by 62% of all RI users. The users’ opinions on the importance of the services and functions provided by the RIs are presented in Table 4.20. Among the different types of services and functions provided by RIs, the most important role is played by the quality of facilities (96% of respondents), followed by the quality of laboratories (89%) and organization and support of the cooperation between RI users and international partners (88%). Table 4.21 presents the importance of possible barriers experienced by users in the process of accessing RIs. 142 4. Supporting the research and innovation base through priority European research...

Table 4.20. The users’ evaluation of the importance of the services and functions provided by the RIs [N=400 RIs users]

Higher or Private Research Public Services and functions secondary for Others Total organization body education profit The quality of facilities 97% 96% 94% 97% 85% 96% The quality of laboratories 89% 89% 86% 91% 77% 89% Organization and support of the 86% 91% 90% 86% 77% 88% cooperation between the RI users and international partners Data storage 84% 84% 90% 89% 62% 85% Organization and support of the 86% 85% 88% 86% 69% 85% cooperation between the RI users from EU countries Organization and support of the 82% 84% 80% 94% 77% 84% cooperation with other RIs Training opportunities 78% 76% 75% 71% 69% 76% Organization and support of 73% 68% 75% 71% 62% 71% cooperation with the entities outside the RI Source: data derived from a survey on 400 RI entities that benefited from the financing of FP7 INFRA or H2020 INFRA (referred to as RI Users).

Table 4.21. The importance of different barriers in the process of accessing RIs [N=400 RIs users]

Higher or Private Research Public Barrier secondary for Others Total organization body education profit Open data access 90% 87% 100% 89% 62% 89% Data ownership 88% 88% 92% 86% 77% 88% Cost of the access 87% 88% 78% 94% 77% 87% Additional costs (travel, hotels, etc.) 26% 25% 24% 37% 38% 27% Source: data derived from a survey on 400 RI entities that benefited from the financing of FP7 INFRA or H2020 INFRA (referred to as RI Users).

The most important barriers in the process of accessing RIs are connected with Intellectual Property Rights (IPR) related issues, in particular open data access policy (89% of all users of RIs), and data ownership regulations (88%). Whereas financial barriers do not play as much of a significant role, especially additional costs related to the use of RIs, like the costs of travel, hotels, etc. The conducted research survey together with the results of other empirical research (European Commission 2017, p. 37) show that research infrastructures supported by EU framework programs already contribute to Europe's excellent science with tools, materials and data accessible from across Europe and by supporting the 4.7. Conclusions 143 mobility and training of researchers. Specifically, the Horizon 2020 key performance indicators related to the reinforcement of European research infrastructures, together with target values and progress achieved so far, are presented in Table 4.22.

Table 4.22. Horizon 2020 key performance indicators related to the reinforcement of European RIs

Key Performance Indicators (KPIs) Target Progress so far Number of national research The target by the National RIs networked thanks infrastructures networked (in the sense of end of Horizon to Horizon 2020 support by the being made accessible to all researchers 2020 is 900. end of 2015 were 363. in Europe and beyond through Union support). Number of researchers who have access to Target: 20,000 33,741 (approximate value research infrastructures through support additional calculated on FP7 grants until from Horizon 2020. researchers during 2015 as data from Horizon Horizon 2020. 2020 grants is not yet available). Source: European Commission (2017, p. 83) based on the Corda database.

Data from Table 4.22 show that thanks to Horizon 2020 support, a total of 363 national research infrastructures have been made accessible to all researchers in Europe and beyond, out of a target of 900 by the end of Horizon 2020. According to the thematic assessment of Research Infrastructures: “the development of EU research infrastructures has raised awareness of the burgeoning potential and stimulated scientific communities across the EU. In close conjunction with ESFRI, it has enabled the EU to be effective in conceiving and delivering large research infrastructure projects at the European and global scale. These would not otherwise have been realized because of their large size, cost and complexity, which has required an EU-wide common vision and the combined efforts of several Member States to initiate them” (European Commission 2017, p. 84).

4.7. Conclusions

According to the conducted analysis, the most important problems that constitute a rationale for increased investments in priority RIs are resulting from the increasing complexity and capital-intensity of modern science, which makes large research infrastructures an important pillar of the research system. The examination of the European research system shows, however, that investments in this area are very fragmented, which prevents the created infrastructures from reaching a certain critical mass. The ESFRI identifies this problem and provides a solution in the form of pooling resources across Europe to build and operate major research infrastructures, which are given political priority and for which new funding mechanisms are being 144 4. Supporting the research and innovation base through priority European research... developed. An important motivation for international cooperation in this area is also the need for partnership and collaborative work, which produces synergy and helps to make sense of complexity and diversity. The problems connected with the so-called Grand Challenges constitute another rationale for the construction of priority European Research Infrastructures, which enable interdisciplinary, frontier research and innovation. The analysis of different definitions of a research infrastructure shows that they focus mostly on its material nature and physical component. However, a very important element of research infrastructures is also the intangible sphere, as they are basically a relational concept, constituting a part of a dynamic process of change, collaboration and engagement. The three most common types of research infrastructures are: 'single-sited', 'virtual' and 'distributed'. The most common research infrastructures are 'single-sited'. The form of research infrastructures strongly depends on the field of science, with a stronger role of single-sited laboratories in the field of energy and material sciences, distributed and cooperative facilities in environmental sciences, and virtual networks in the social sciences. In this study, roadmapping (understood as systematic strategic planning) was indicated as an important phase in the realization of large research infrastructures. This task was introduced by the European Strategy Forum on Research Infrastructures (ESFRI) Roadmap, which indicated that research infrastructures correspond to the long-term needs of European research communities, covering different scientific areas. The literature review conducted in this chapter shows the most important economic theories, which form the conceptual background for analyzing the main areas of European research infrastructure impact. The social capital theory stresses the importance of interactions and connections between external players leading to positive effects in raising resources, building trust in the organization, and knowledge sharing, which is of high relevance for research infrastructures. Applying the R. Florida concept, we may find research infrastructures as the creative centers, in which the so-called super-creative core of the creative class is deeply rooted and engaged in problem finding and problem solving, subsequently resulting in the creation of innovation and economic growth. Another theoretical foundation for the study is the concept of innovation systems, in which several important roles are performed by knowledge infrastructure, mainly: the production of scientific and technological knowledge, developing skills, establishing technical norms and standards, the creation of new enterprises, and access and dissemination functions. In a sectoral perspective, research infrastructures play an important role in the development of some areas, for example emerging industries. Finally, applying the economics of network theory, which emphasizes the importance of external resource mobilization, enables identifying that research infrastructures can involve major network externalities, as 4.7. Conclusions 145 they are often the place within a system where scale and scope economies are very significant. The study reviews possible indicators that were identified in the literature as suitable for the measurement of impacts of research infrastructures. The finding is that the set of possible measures differs for research infrastructures in the design and construction phase, and those that are in the operational phase. The problem is that there has been no unified framework for the impact assessment of research infrastructures developed so far. Although there are different examples of indicators that may be potentially used for analyzing the impact of research infrastructures, the relevant statistical data are not publicly available. Most of the data may be collected only through a direct research on the impact of the analyzed infrastructures (separately for each of them), which, if at all possible, would be a very costly and time-consuming process. As evidenced in the study, the EU has been successful in pooling financial resources across Europe to build and operate RIs. This plays a key role in reducing the negative impact of fragmentation of the European innovation system, and reaching a certain critical mass of investments in RIs, which is needed in the face of the increasing complexity and capital-intensity of research activities. The study also focuses on the analysis of the geographical distribution of EU framework program (part INFRA) allocations, allowing to draw some conclusions on their impact on the economic cohesion in the European Union, and building the European Research Area (ERA). In this respect, the study shows that there is a strong discrepancy in EU framework program (part INFRA) spending between Western Europe (EU15) and the CEE (EU13), which may increase the innovation gap between Member States. From this perspective, EU spending on RIs does not contribute to building the European Research Area, which would be characterized by a homogeneous and balanced development of innovation potential across the whole European territory, thus reducing the actual gaps in terms of research capacity between Member States. The analysis of interactions between actors engaged in projects co-financed from Horizon 2020, part INFRA, located in different countries, shows that the strongest relations exist between Western European countries, like: Germany, Italy, Spain, France, the Netherlands, and the United Kingdom. Whereas countries from Central and Eastern Europe, which joined the European Union after 2000, while already integrated into the European innovation network, have not yet established strong connections, neither with one another or with the countries from Western Europe. Moreover, the analysis of financial investments from EU framework programs indicates strong differentials between allocations in different groups of innovation systems found in EU Member States in FP7, part INFRA, but this discrepancy has diminished in Horizon 2020. Ensuring a strong participation of users from the 146 4. Supporting the research and innovation base through priority European research... countries representing all types of innovation systems is an important challenge for European innovation policy, especially in terms of developing the European Research Area (ERA). In order to achieve this, different obstacles in the system of building, accessing and using RIs should be removed. The research shows that the most important barriers in the process of accessing RIs are connected with Intellectual Property Rights (IPR) related issues, in particular open data access policy, and data ownership regulations. The analysis of the FP7 and Horizon 2020 program (part INFRA) investments in research infrastructures shows that the biggest group of actors engaged in co-financed projects are research organizations, followed by secondary and higher education establishments, proving that the scientific sector is the key type of actor of innovation systems in using research infrastructures. However, there are also private for profit companies, public bodies and other entities, demonstrating that research infrastructures are not only about knowledge generation, but also technology transfer to industry. This is consistent with the findings from the literature review, in which research infrastructures where perceived as network structures where research meets innovation and industry, contributing to the innovative capabilities of organizations, by exposing them to novel sources of ideas, enabling fast access to resources, and enhancing the transfer of knowledge. The other group of findings on the role of RIs in the European innovation system and their impact on innovation is formulated on the basis of the results of a survey research conducted on 150 coordinators and 400 users of RIs. According to both surveys, the most common type of access mode to RIs is excellence-driven access, which allows organizations to get access to the best facilities, resources and services wherever located, and which enables collaborative R&D efforts across geographical and disciplinary boundaries. In most of the cases, the surveyed coordinators represent single-sited RIs (64%), followed by distributed (22%) and virtual (13%). One of the biggest challenges for measuring the impact of RIs on innovation is the scarcity of statistical data on the performance of RIs. This problem is confirmed by the results of the survey research, as most of the RI coordinators, while hypothetically recognizing the importance of different indicators (like number of publications, publication citations or number of PhD degrees) in evaluating the RI research performance and productivity, in practice were unable to deliver related statistical data for their RIs. For example, it turns out that there is no general practice in RIs to use bibliometric methods to measure the research performance, as no respondent delivered data on the number of publication or patent citations. This reveals that there are no standardized databases for research infrastructures available. Statistical 4.7. Conclusions 147 data, especially these concerning the output of the research infrastructures, are very fragmented and in most cases – missing. Moreover, research infrastructures in particular disciplines of science differ significantly in terms of size and costs or the way of functioning and reporting, making them incomparable to each other. The international nature of many research infrastructures makes it impossible to assign any data (other than some financial data connected to EU framework programs) to a particular country, as these infrastructures act as supranational networks of many entities. The involvement of a huge number of actors, with the application of different rules, practices, and organizational solutions (regarding for example access to the research infrastructures, using the research results or reporting) also makes it impossible for most of the infrastructures to collect output data (like number of patents, publications and citations) that would allow to measure such variables like research productivity, etc. These findings lead to the recommendation for the European Commission to put more stress on monitoring the results of investments in RIs, in particular to demand the delivery of annual statistical data on a selected group of indicators from RIs that implement projects co-financed from EU funds. It is also important to adopt a long-term perspective in this monitoring model as the development of RIs is a long-lasting process, and usually the impact will be observed in decades rather than years. An important outcome from the survey research is related to the impact of RIs on networking and cooperation. This is evidenced by the fact that the most common type of additionality experienced by all RI users is network additionality (98% of all RI users). Moreover, the most common type of added value experienced by users of RIs results from the possibilities of networking with other scientists (95% of all surveyed RI users). This shows that investments in RIs were successful in stimulating cooperation, and pooling people and resources in order to achieve a technological critical mass, thus reducing the fragmentation of the European innovation system. This conclusion is additionally supported by the opinion of 91% of RI coordinators, according to which the most important effect of EU funding from framework programs is related to a stronger integration of European researchers, from both academia and industry, ensuring their optimal use and joint development. In general, 32% of RI coordinators claim that cooperation within the framework of RIs is successful in reaching a necessary critical mass for breakthrough research activities. This confirms that RIs play a positive role in increasing the innovation capabilities of the European economy, however, their potential is still not fully exploited, as only 65% of the respondents claim that their RIs are exploited in 75% to 100 % of the capacity. 148 4. Supporting the research and innovation base through priority European research...

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Chapter 5 The role of Global Research Infrastructures as a tool of innovation policy

5.1. Introduction

In 1986, the Single European Act included a chapter on research, which put the emphasis on applied research aiming at supporting the competitiveness of European industry. The ratification of the Single Act in 1987 gave the Community a particular competence in research and technological development. Title VI “Research and technological development” defines the activities of the members of the European Communities in this field (art.130g): “(...) the Community shall carry out the following activities, complementing the activities of the Member States: (a) implementation of research, technological development and demonstration programmes, by promoting cooperation with undertakings, research centres and universities; (b) promotion of cooperation in the field of Community research, technological development and demonstration with third countries and international organizations; (c) dissemination and optimization of the results of activities in Community research, technological development, and demonstration”11. Article 130i defines the tool for implementing these activities: “(...) The Community shall adopt a multiannual framework programme setting out all its activities. The framework programme shall lay down the scientific and technical objectives, define their respective priorities, set out the main lines of the activities envisaged ...”12. Horizon 2020, the European Framework for Research and Innovation from 2014- 2020, gave its definition in the section concerning research infrastructure: “Research infrastructures are facilities, resources and services that are used by the research

11 Treaties establishing the European Communities, available at: http://europa.eu/eu-law/decision-making/treaties/pdf/treaties_establishing_the_european_communities_single_european_ act/treaties_establishing_the_european_communities_single_european_act_en.pdf, (accessed on: 16.06.2015). 12 op. cit. 154 5. The role of Global Research Infrastructures as a tool of innovation policy communities to conduct research and foster innovation in their fields. Where relevant, they may be used beyond research, e.g. for education or public services. They include: major scientific equipment (or sets of instruments; knowledge-based resources such as collections, archives or scientific data; e-infrastructures, such as data and computing systems and communication networks; and any other infrastructure of a unique nature essential to achieve excellence in research and innovation”. Such infrastructures may be ‘single-sited’ (a single resource at a single location), ‘virtual’ (the service being provided electronically) or ‘distributed’ (a network of distributed resources). A European Distributed Research Infrastructure, as recognized by the ESFRI, is a Research Infrastructure with a common legal form and a single management board responsible for the whole Research Infrastructure, and with a governance structure including among others a Strategy and Development Plan and a one-access point for users, although its research facilities have multiple sites. It must be of pan- European interest, i.e. shall provide unique laboratories or facilities with user services for the efficient execution of top-level European research, ensuring open access to all interested researchers based on scientific excellence, thus creating a substantial added value with respect to national facilities. A European Distributed Research Infrastructure must bring significant improvement in the relevant scientific and technological fields, addressing a clear integration and convergence of the scientific and technical standards offered to the European users in its specific field of science and technology. In the OCDE report13 the term “research infrastructure” is defined as “large centralised facilities (such as telescopes or research vessels) to include physically distributed resources for research, such as computing networks, and large collections of data or physical objects”, whereas large international research infrastructures are research infrastructures that are “truly international; that is, that are based on formal agreements between governments, agencies, or research institutions from more than one global region” 14.

5.2. Global Research Infrastructures as an innovation policy tool

Research Infrastructures that are globally accessible, characterized by a large set of challenges and opportunities, that emerge when multiple international partners from around the globe come together as equal (or nearly equal) partners, become global

13 OECD Global Science Forum Report on Roadmapping of Large Research Infrastructures December 2008, available at: http://www.oecd.org/science/sci-tech/41929340.pdf, (accessed on: 19.07.2015). 14 http://www.oecd.org/science/sci-tech/47027330.pdf 5.2. Global Research Infrastructures as an innovation policy tool 155 infrastructures. In some cases, their complexity, high construction and operation costs, and the global nature of the scientific challenge addressed, make it impossible for one country or region alone to build and operate these facilities. “(...) In such cases it becomes crucial to make concerted efforts at the international level for the realisation of ‘global research infrastructures’ ”15. The subject of global research infrastructures has been discussed during meetings on science policy since 2007. In 2006 the first roadmap for research infrastructures edited by the European Communities, prepared by the European Strategy Forum on Research Infrastructures, presented a set of projects covering a broad spectrum of scientific fields, and projects planning for a lifespan of several decades, as well as projects of a scale and scope requiring a global approach. In 2008 the G8 ministers and the European Commissioner responsible for science and technology discussed, among other things, solving global issues through international cooperation. They recognized the importance of global earth observation, the importance of the need to reduce greenhouse gas emissions, the importance to reach a consensus to foster science-based solutions for environmental and other challenges at a national and global scale, and the importance of promoting collaboration on all energy alternatives. These are global research infrastructures that provide a unique opportunity to stimulate knowledge accelerating the building of capacities needed in addressing the above-mentioned challenges. The European Commission (EC), together with the ESFRI, has decided to establish the Assessment Expert Group (AEG), to assess the maturity of the research infrastructures on the ESFRI roadmap. The AEG indicated16 8 projects that are likely to be ready for implementation by 2015 and 11 projects that require substantial efforts to be ready for implementation by 2015. Also, the GSO members developed a list of research infrastructures of global interest (GRIs) (“the list”), aimed at exploring or enhancing potential international partnerships. Taking into account the list of prioritized GRIs proposed by the ESFRI 2006; 2008; 2010 as well as the suggestions prepared by the GSO, a list of Global Research Infrastructures is presented (Table 5.1). It covers 12 GRIs that are coordinated by the EU MS, are of global character and are suggested for prioritization by the ESFRI, by the GSO or by both bodies.

15 Framework for a coherent and coordinated world-wide development and operation of global research infrastructures, available at: https://ec.europa.eu/research/infrastructures/pdf/gso_frame- work_for_global_ris.pdf 16 From the 48 projects presented in the overview of the ESFRI projects 13 projects were not assessed; 11 of those projects reached the implementation phase earlier, and two projects are at present supervised by the EUROATOM. 156 5. The role of Global Research Infrastructures as a tool of innovation policy √√ ESFRI Roadmaps ESFRI 2006 2008 2010 Recommended for for Recommended implementation the from support State Member ESFRI’s decision after after decision ESFRI’s assessment Priority √ √√ √√ √ Distributed Distributed global of whose interest governance is fundamentally in international character √ √√ √√√ √√√ Single-sited Single-sited global of whose interest governance is fundamentally in international character √ √ √ √ GSO recommendation, 2015 GSO recommendation, National research research National global infrastructure of national with interest governance with include to possibility members international European Global Research Infrastructures – recommendations of the GSO and ESFRI the GSO and of – recommendations Infrastructures Global Research European Project/topic/scientific domain- GSO/ESFRI GSO/ESFRI domain- Project/topic/scientific classification Social Science Humanities and for Platform CH / Integrated IPERION on Infrastructure Research the European Heritage Cultural Science Environmental Observing Plate System EPOS / European Sciences Medical and Biological Life-Science ELIXIR / European – Information Biological For Infrastructure Upgrade A Major Biology Molecular EMBL / European Laboratory Phenotyping Mouse IMPC / International Consortium Facilities Analytical and Materials Neutron Spallation European ESS / The Source DESY PETRA III / at Science Engineering and Physical Southern Observatory ESO / the European life and in physical research for / center ISIS sciences Sasso del Gran Nazionali / Laboratori LNGS Array Kilometer / The Square SKA 2 SPIRAL Table 5.1. Table Source: own elaboration. own Source: 5.2. Global Research Infrastructures as an innovation policy tool 157 ESFRI 128 (‘06-15) 800’15 785 (‘99-‘16) construction 183’15 130’14 127’13 1731’15. In : [email protected] GSO GSO GSO; ESFRI GSO; 456’14 446’13 419’12 1366 ’13 1370 ‘12 Physical Science Engineering and Physical ESFRI (’96-’15) 860‘15 GSO GSO; n.d 12 005 2000/year 2 000’15 1071 ’14 Materials and and Materials Facilities Analytical GSO; ESFRI 25’12-11 r further investigation – basic data and performance indicators performance and – basic data r further investigation ESFRI IT SE DE DE UK IT650’15 65’15-13 UK FR GSO GSO; FR, IT 695’15 652’14 613’13 3158’15 (beamlines) 23 000 000’15 (websites) further investigation – basic data and performance indicators performance and – basic data further investigation r. Distr. Distr. Distr. S. sited Distr. S. sited S. sited Distr. S. sited GSO; ESFRI Biological and Medical Science Medical and Biological Open science platform month ESFRI Envir. Science Newsletter Social Science Twelve Global Research Infrastructures (GRIs) selected fo (GRIs) Infrastructures Global Research Twelve Abbr. byRecommended GSO GSO; IPERION EPOS ELIXIR EMBL IMPC ESS PETRA III ESO ISIS LNGS SKA 2 SPIRIAL Twelve Global Research Infrastructures (GRIs) selected for selected for (GRIs) Infrastructures Global Research Twelve Feature GRI TypeLocation Distr. 12 EUs Distr. IT Dist UK; DE DE,UK, Life cycle NetworksIntegrated in 2010 participants of Number EU countries of No. YES count. non-EU of No. %Internationalization 0 YES 0 in 2015/2016 participants of Number Constr. 0 EU countries of No. Constr. count. non-EU of No. YES 14%Internationalization 4 12 1 Oper. 22% YES 8%Publications Oper. 10 21 23% 3 4 16% Oper. 15 25% Constr. 16 5 n.d 16% Oper. 3 54% 6 22% 21 EPOS 11% Oper. 7 6 8 35% Oper. 54% 6 1 13% Oper. 13 7% 7 13 Constr. 50% 7 44% 2 13 15 Oper. 18% 53% 1 15 50% 9 50% 14 44% 7 15 3 7 67% 7% YES 17 8 7 18 44% 8 10 14 12 20 1 10 8 Staff personnelTotal CollaboratorsUsers n.d n.d n.d n.d n.d n.d n.d n.d n.d n.d 1615’15 n.d n.d n.d 910’15 160,000 / 346’15 n.d. n.d n.d 346’15 1000’15 n.d 683’15 n.d 404’15 1033’15 n.d 683’15 56’15 404’15 n.d 83’15 n.d 56’15 781’15 n.d n.d n.d Table 5.2. Table Source: own elaboration based on data collected on 12 GRIs in the form of a case study – extended profiles available on request on available profiles – extended a case study of in the form 12 GRIs on collected based data on elaboration own Source: 158 5. The role of Global Research Infrastructures as a tool of innovation policy

5.3. Theoretical perspective

The role of Global Research Infrastructures as a tool of innovation policy can be explained with the help of three streams of theories: the Common-Pool Resources approach, the concept of Critical Mass and Economics of Network Theories.

5.3.1. Common-Pool Resources Approach Common-Pool Resources (CPR) are defined as resources that are rival and not- excludable, which means that it is challenging to eliminate their users through physical or institutional barriers. Additionally, the consumption of resource by one person or group leaves less for another, as they are subtractable (Ostrom et al., 1994). CPR usually consists of a core resource (like water), expressing the stock variable, while offering a limited quantity of extractable fringe units, which defines the flow variable. The core resource is protected in order to allow for its constant exploitation, the fringe units can be consumed (Ostrom, 1990). Thus, CPR, just like “public goods”, are characterized by the difficulty of developing physical or institutional barriers for excluding beneficiaries. Similarly to “private goods”, CPR have the attribute that one person’s consumption subtracts from the quantity that is available to others (Hess, Ostrom, 2003, p. 120). CPR not only include natural resources, such as forests or the atmosphere, but also human-made resources, such as radio frequency spectra, irrigation infrastructure, wastewater treatment facilities and other public infrastructures (Madani, Dinar, 2011a). In the joint usage of CPR, individuals acting autonomously and rationally in accordance with self-interest perform differently compared to the interest of the whole group, which destroys the optimal needed to sustain the system (Gardner et al., 1990). Such non-cooperative behavior results in the “tragedy of the commons” (Hardin, 1968). Thus, common-pool resources can be overused, polluted and destroyed, unless usage limits are enforced (Hess, Ostrom, 2003). Although there are many examples of overexploitation of such resources, the users in many cases overcome incentives to destroy the resources and develop long-enduring institutions, which enable them to utilize these resources more effectively (Ostrom, 1990): In order to govern CPR, E. Ostrom (Ostrom, 2008) proposes adaptive governance: a “range of interactions between actors, networks, organizations, and institutions emerging in pursuit of a desired state for social-ecological systems” (Chaffin et al., 2014). Madani and Dinar (2011b) propose to classify CPR management institutions into three broad categories: 1. Non-cooperative management institutions. Under this solution, beneficiaries impose short term plans, based on their individual rationality, which in 5.3. Theoretical perspective 159

the long run result in the “tragedy of the commons”. Beneficiaries may also develop plans that are not ignorant, but still are conducted on a non- cooperative basis, which may lead to the imposition of external regulations (Madani, Dinar, 2010). 2. Exogenous regulatory institutions. In order to avert overuse, regulators may intervene in the form of external regulations of extraction of CPR or privatization, which offers two solutions – “the private property solution”, which involves dividing the commons into private plots and externalities are internalized or “the market solution”, which requires that transaction costs are minimized and spillover effects across property boundaries is decreased to an optimal level (Sinden, 2007). 3. Cooperative management institutions. Under cooperative management institutions beneficiaries base their activities on group rationality rather than individual rationality and build plans that help to enlarge the continuing gains for all users and grant sustainable benefits (Madani, Dinar, 2011a). The third solution, which requires communication and trust, provides the highest benefits for users, although its complexity may discourage them from entering into this type of arrangement. The inevitable requirement for this type of management solution is perfect information for each of the beneficiaries about the plans and decisions of the other parties. The system of equations known as the “Core of the cooperative game” (Gillies, 1959) is a set of game allocation gains that are not dominated by any of the allocation sets. The Core for cooperative CPR management suggests a range of solutions that are acceptable for each of the beneficiaries (Madani, Dinar, 2011a). The allocation should satisfy the following restrictions:

(1) Individual rationality condition ui* ≥ ui ∀i ∈ N

(2) Group rationality condition ∑ ui* ≥ v (s) ∀s ∈ S, S ⊆ N i∈S

(3) Efficiency condition ∑ ui* = v (N ) i∈N

Equation (1) imposes the “individual rationality condition”, demanding the allocation under cooperation to each of the beneficiaries to be greater than what can be gained individually without cooperation, where: ui* ≥ ui, [ui* and ui are the utility of CPR beneficiary i under cooperation (ui*) and under no cooperation (ui,), respectively]; N = {1,2,...,n} is a coalition (group of collaborating beneficiaries i) and {i} (i=1,2,...,n) are the non-cooperative coalitions with single beneficiaries, and N is 160 5. The role of Global Research Infrastructures as a tool of innovation policy

the grand coalition which includes all the beneficiaries; ∀i ∈ N meaning “for all” i that are included in the group N. Equation (2) fulfils the “group rationale condition”, demanding the sum of cooperative allocations to any group of beneficiaries to be greater than the total available benefits under any coalition that includes the same beneficiaries, where S is the set of feasible coalitions (groups of beneficiaries) in the game; v(s) is the value of coalition s or the total obtainable benefits by the member of coalition s; ∀s ∈ S

– meaning “for all” s that are included in the group S; S ⊆ N – meaning that S is a subgroup of N. Equation (3) – “efficiency condition” demands that the total available benefits under the grand coalition are fully allocated to the members of the coalition, where v (N) is the value of the grand coalition. Satisfying the Core conditions (equations 1 to 3) is necessary for the acceptability of the allocation solution for all the players (Madani, Dinar, 2011a, op. cit). The cooperative solutions should not only fulfil the Core requirements, but should also appear to be stable over time, especially as some solutions may be found unfair for some of the beneficiaries, which leads to instability and a situation where some of the beneficiaries leave the grand coalition to form smaller groups of beneficiaries, thus their implementation requires trust among the users and a high level of information availability.

5.3.2. The concept of Critical Mass The large group problem of joint action has a very simplified logic: the bigger the number of people involved in cooperative good production, the lower the value of single individual contribution (Centola, 2013). The concept of “Critical Mass” suggests that there is a minimum number of early contributors, whose efforts cause a “bandwagon effect”, which has the power to involve the rest of the population (Granovetter, 1978). Therefore, there is a minimum alliance min (n), such that if actors organize into coalitions of size n, at least n people will prefer mutual cooperation to unilateral defection, and this is calculated as follows:  N  ()− ≥ min (n) s.t. ∑ HRii T n  i=1  where n is the overall population and min (n) is the minimum coalition size (DiStefano et al., 2015). The latter hangs on the Heaviside function for the variance between Reward and Temptation payoffs, Ri and Ti respectively, evaluated considering different types of games (Centola, 2013). Based on “self-reinforcing logic of cooperation”, Marwell and Olivier (1993) argue that contributions to collective actions create “positive externalities”, while initial 5.3. Theoretical perspective 161 contributions create incentives for succeeding actions. The authors argue that it is the significant heterogeneity of distribution of resources that creates highly motivated individuals that will contribute enough to generate large positive externalities for others. However, the basic measure of the success of a collective action is the number of participants that can be mobilized (Scheling, 1978), as the models of collective behavior are developed to describe the situations where participants have two alternative solutions and the cost and/or benefit of the choice depends on how many other participants choose which of the alternative solutions. The key concept here is the “threshold” – “the number or proportion of others who must make a decision before a given actor does so” (Granovetter, 1978, p. 1420). Every individual / organization has their own “threshold” in terms of what is the number of other people / organizations connected to them who should join the action before they will do the same (Gonzales et al., 2013). Mutually, the structural and behavioral elements are essential to analyze the origin of the detected social dynamics inside the population (Easley, Kleinberg, 2010). Research underlines the importance of collective identity (shared sense of belonging to a group) (Collins, 1993), solidarity (union or fellowship arising from common responsibilities and interests) (Hechter, 1987) and shared commitment (Heckathorn, 1990) in enhancing collective actions. Thus, the activities of nodes can be influenced by many factors, among them homophily being the one of the most important. Homophily is the principal stating that a “contact between similar people happens more frequently than among dissimilar ones” (McPherson et al., 2001, p. 416). Lanzarsfeld & Merton (1954) distinguish two categories of homophily: “status homophily”, where similarities are based on status, and “value homophily”, which is based on values, attitudes and beliefs. Both types of homophily can effectively enhance cooperation. The results of the research conducted for over 300 international NGOs suggest that these organizations are more willing to collaborate when they have the same status, similar founding dates and when they are headquartered in the same geographic regions (Atouba, Shumate, 2015). However, some studies underline that too much homophily can hamper collective actions and acts as a possible linkage polarizer, as it limits the diversity of the actors involved. Contrary to previous findings, this leads to the conclusion that competition among organizations using similar strategies, of similar size, and in geographical proximity to one another tends to be stronger than competition among dissimilar organizations (Chiang, 2007). Collective actions may therefore prevent local initiatives from spreading across the social space (Centola, 2013). Not only the characteristic of partners, but also the structure of networks, especially those of weak ties (strength of weak ties), which disorder the local structure 162 5. The role of Global Research Infrastructures as a tool of innovation policy of spatial networks, create at the same time connections with remote actors and therefore are beneficial to spreading cooperation (Granovetter, 1985). Opp and Gern (1993) suggest that a homogenous, clustered network where trust is recognized, can play an important role in the mobilization of collective action. The more difficult the collective action problem is, the more the mobilization depends on clustered social networks (Centola, 2013).

5.3.3. Economics of Network Theory Extensions of the resource-based view of firm research on inter-firm networks (defined as “groups of three or more legally autonomous organizations that work together to achieve not only their own goals but also a collective goal” [Provan & Kenis, 2008, p. 231]) emphasize the importance of cooperation in innovation activities and suggest that access to resources of collaborating partners can be a source of firms’ competitive advantage (e.g. Lorenzoni & Lipparini, 1999; Lechner & Dowling, 2003; Lavie, 2006; Gulati, 2007). Research reveals that innovation often results from a firm’s internal knowledge integrated with external knowledge accessible in inter- firm networking (e.g. Chesbrough, 2003; Almeida & Phene, 2012; Cantwell & Zhang, 2012), treated as a “locus of innovation” (Powell et al., 1996). A review of 174 studies on links between innovation and cooperation indicates the following most significant innovation benefits from inter-organizational networks: knowledge exploration and exploitation (Nooteboom, 2000); access to complementary assets (Hagedoorn & Dusters, 2002; Marquardt, 2013), both via vertical integration or achieving economies of scale via horizontal integration (Hennart, 1988); access to new technologies and markets (Powell, 1987; Hagedoorn, 1993); the commercialization speed of new products (Almeida & Kogut, 1999); risk sharing (Grandori, 1997); and protection of property rights (Liebeskind et al., 1996). For a more detailed study see: Pittaway et al., 2004. Additionally, international networks function as ‘pipelines’ through which disembodied, also tacit knowledge is transmitted over long distances (Owen-Smith & Powell, 2004; Torre, 2008; Herstad et. al. 2014). These networks, however, due to their high marginal costs involved in changing the configurations, are likely to experience inertia and the lock-in effect (Narula, 2002). It should be noted that the success of innovation networking depends on the knowledge based-competencies of partners, including absorptive capacity, i.e. the ability to recognize the value of external knowledge, assimilate it and apply it commercially, which in turn stems from firms’ resources and competencies (Cohen & Levinthal, 1989, 1990). Apart from valuing and integrating external knowledge, superior R&D capacity allows a firm to recognize new opportunities in the market 5.4. Implementation of Global Research Infrastructures 163

(Cohen & Levinthal, 1994) and finally allows it to better evaluate opportunities for collaborative R&D projects. Some authors also suggest that a firm’s absorptive capacity moderates the relationship between remote collaboration and the innovative performance of firms, as investment in absorptive capacity simplifies the knowledge transfer with partners who are located far away (Berchicci et al., 2013; Enkel & Heil, 2014; Lewandowska, 2014). Thus, cognitive proximity, which is enhanced by investments in absorptive capacity, may balance the lack of geographical proximity between partners.

5.4. Implementation of Global Research Infrastructures

The implementation of Global Research Infrastructures (GRIs) as a tool of innovation policy can be investigated based on three pillars: • The legal framework (institutional activities and documents related to GRIs); • The participation framework (the level and pace of internal [EU28] participation in GRIs, and the level and pace of non-EU countries’ participation in GRIs, indicating their internationalization); • The financial framework (the level of support for projects related to GRIs from FP7 INFRA and H2020 INFRA).

5.4.1. Legal framework for Global Research Infrastructures An analysis of the legal framework concerning GRIs shows that several important issues are already regulated and implemented. There are established bodies, aimed at the development of GRIs as well as promotion and support of international cooperation in the field of GRIs. These are: the Directorate General for Research and Innovation Research Infrastructure; the European Strategy Forum on Research Infrastructures (ESFRI), set up in 2002; the European Research Infrastructure Consortium (ERIC) entered into force on 28 August 2009; the European Expert Group on Cost Control and Management Issues of Global Research Infrastructures; the Group of Senior Officials for Global Research Infrastructures, established at the G8 Ministerial meeting, (Okinawa, 15 June 2008). The documents that settle the stage for GRIs issued by European Union bodies are: Cost control and management issues of global research infrastructures, issued in October 2010; Prioritization of Support to ESFRI Projects for Implementation – the European Strategy Forum on Research Infrastructures, issued in April 2014; ERIC Practical guidelines: Legal framework for a European Research Infrastructure Consortium, issued in March 2015; European research infrastructures (including e-Infrastructures) Revised (European Commission Decision C (2015)8621 of 4 164 5. The role of Global Research Infrastructures as a tool of innovation policy

December 2015); Annex 4 Horizon 2020 Work Programme 2016-2017, 4. European research infrastructures (including e-Infrastructures), issued in 2015; European Charter for Access to Research Infrastructures. Principles and Guidelines for Access and Related Services, European Commission, issued in 2016; the document “Enabling synergies between European Structural and Investment Funds, Horizon 2020 and other research, innovation and competitiveness-related Union programmes”. Guidance for policy-makers and implementing bodies. 1.9 Research Infrastructures and ESIF p. 90-92. Also, the GSO issued documents that are strongly related to GRIs. These are: The G8 Science and Technology Ministers’ Meeting Chair’s Summary; Towards a Framework for Collaboration at Global RIs – A GSO Initiative; Group of Senior Officials on Global Research Infrastructures. Framework for a coherent and coordinated world-wide development and operation of global research infrastructures; G8 Science Ministers Statement London UK, issued in June 2013; Group of Senior Officials on Global Research Infrastructures Progress Report 2015, Meeting of the G7 Science Ministers, issued in October 2015.

5.4.2. Participation framework for Global Research Infrastructures The analysis of participation17 relates to the involvement of 28 EU and non-EU countries in the selected 12 GRIs between 2010 and 2016. In 2010 there were 123 participations from EU countries and 61 from non-EU countries in the 12 GRIs. The majority of EU countries were coming from the old EU15. Till 2016 there were 163 participations from EU countries (out of which 74% from EU15) in the 12 GRIs and 90 non-EU participations. Although the number of international partners increased, due to the fact that infrastructures themselves were “internally” expanding, the pace of internationalization increased only slightly. In order to classify the 12 GRIs, two strategic maps were invented. The first one shows the internal expansion of GRIs, the second one their international expansion. The first graph is built on the two digestions: number of European Union new Member States (13) engaged in Global Research Infrastructures matched with the number of European Union “old” Member States (15) engaged in Global Research Infrastructures and then divided into four groups. The majority of GRIs are in the group called “EU15 Focused” (more than 7 countries of EU15 and less than 6 EU13). Two GRIs (LNGS and EPOS) are classified as “EU28 Open” (more than 7 countries of EU15 and more than 6 of EU13), and

17 The “participation” means involvement of countries in selected 12 GRIs. One country can be involved in more than one GRI, what means that in the total number countries are counted several times. This rule applies as well to other than EU countries. 5.4. Implementation of Global Research Infrastructures 165 another three (IMPC, ESS, SPIRAL2) as “Internally Closed” (less than 7 of EU15 and less than 6 of EU13). For details see Figure 5.1. The second graph is built on other two digestions: number of all European Union Member States (28) engaged in Global Research Infrastructures matched with the number of non-European Union countries (third countries) engaged in Global Research Infrastructures and then divided into four groups. The analysis of the international involvement of the 12 GRIs shows that the majority of them are classified as “Internally Focused” (involvement of less than 14 countries of the EU28 and less than 10 non-EU). There are three GRIs (EMBL, ELIXIR, EPOS) classified as “Internally Expanded” (more than 14 countries of the EU28 and less than 10 non-EU). SKA is classified as “Born Globals” (less than 14 countries of the EU28 and more than 10 non-EU), whereas LNGS and PETRA III are classified as “Internally Expanded and Intensively Globalized” (more than 14 countries of EU28 and more than 10 non-EU). For details see Figure 5.2.

5.4.3. Financial framework for Global Research Infrastructures The financial analysis covers data from the FP7 INFRA and H2020 INFRA on projects directly related to the 12 GRIs18. There were six projects related to five out of the 12 GRIs financed from the FP7 INFRA and five projects related to five out of the 12 GRIs financed from the H2020 INFRA. As for FP7 INFRA, there were 121 participations19 financed, total financing reached 24,699,983 EUR and the projects attained 1,483,997,562 EUR, which resulted in a high leverage effect of 6120. As for May 30, 2016, there were 133 participations financed from the H2020 INFRA, for a total financing reaching 70,318,253 EUR and a size of financed projects of 2,797,182,022 EUR, which resulted in a leverage effect of 40. The financing to the 12 GRIs constituted 2% of the total FP7 INFRA budget, whereas it already reached 12% of the H2020 INFRA budget. The size of the projects related to the 12 GRIs constituted 3% of the FP7 INFRA budget, and it attained 20% of the H2020 INFRA budget. The changes among the two sources of financing covering data for projects directly related to GRIs and extracted from financial data on projects financed from the FP7 INFRA and H2020 INFRA are presented in Figure 5.3.

18 Only projects that are directly related to the 12 GRIs and that have participants from EU and non-EU countries were taken into account. Project where GRIs were financed, but they acted as direct participants, were not analyzed. 19 A proposal is submitted by one or more applicants. If the proposal is successful and is funded it becomes a project, which is implemented by one or more participants. A participant might be involved in other projects, in which case it has a number of participations (Horizon 2020. First results). 20 Financial leverage – multiplying scarce budgetary resources by attracting private and public funds to support EU policy objectives. 166 5. The role of Global Research Infrastructures as a tool of innovation policy EMBL EMBL ELIXIR EPOS ESO ESO EPOS PETRA III 2013 LNGS PETRA III " 2013 " ISIS 2015 IPERION ESS ELIXIR EU15 Focused " EU28 Open LNGS " SKA Number of EU15 countries engaged in infrastructures, data 2010-2016 for SKA ISIS ESS SPIRAL 2 SPIRAL SPIRAL 2 SPIRAL " " IMPC 2010-2015 Focused EU13 " Internally Closed

Involvement of the EU15 and EU13 in 12 Global Research Infrastructures, changes between 2010-2016 changes Infrastructures, EU13 in 12 Global Research the EU15 and of Involvement

" Social Science Environmental Science Biological and Medical Science Materials and Analytical Facilities Physical Science and Engineering Number of EU13 countries engaged in infrastructures, data for 2010 -2016 2010 for data infrastructures, in engaged countries EU13 of Number 0123456789101112131415 9 8 7 6 5 4 3 2 1 0 Figure 5.1. Figure elaboration. own authors Source: 13 12 11 10 5.4. Implementation of Global Research Infrastructures 167 " " EPOS Internally Expanded and " Intensively Globalised Internally Expanded Recommended paths of paths Recommended internationalization " EMBL LNGS ELIXIR LNGS PETRA III EMBL ESO ESO EPOS 2013 ESS PETRA III 2015 SKA SPIRAL 2 SPIRAL ELIXIR Number of EU 28 countries engaged in the infrastructure, data for 2010-2015 SPIRAL 2 SPIRAL IPERION ISIS 2013 ESS SKA ISIS " " IMPC 2010-2015 Internally Focused Social Science Environmental Science Biological and Medical Science Materials and Analytical Facilities Physical Science and Engineering Born Globals " Pace of internationalization of 12 Global Research Infrastructures, data for 2010-2016 for data Infrastructures, 12 Global Research of internationalization of Pace " 0 1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728

9 8 7 6 5 4 3 2 1 0

15 14 13 12 2010-2015 11 for 10 data 20 19 18 17 16 mbro ohrcutiseggdi h infrastructure, the in engaged countries other of Nmnber Figure 5.2. Figure elaboration. own authors Source: 168 5. The role of Global Research Infrastructures as a tool of innovation policy s H2020 1 427 418 295 € EPOS IP, FP7 Number of financed participants ELIXIR, 193 674 522 € H2020 800 162 244 € ELIXIR-EXCELERATE, FP7 FP7 857 701 525 € PREPSKA, GOSKA, 215 178 974 € SPIRAL2PP, H2020 FP7 195 779 706 € H2020 IPERION CH, IPERION EPOS, 139 457 084 € 358 955 352 € Brightn ESS, FP7 72 737 152 € H2020 FP7 NEUTRONSOURCEESS 14 866 425 € IN-SKA, 6 814 945 €

Projects related to Global Research Infrastructures co-financed by FP7 and H2020 (till 05.2016) funds, changes among two source two among changes H2020 (till 05.2016) funds, FP7 and by co-financed Infrastructures Research Global to related Projects iaaigfo EU EU from from Finanacing Finanacing ELT Prep-ESO, 0 5 10 15 20 25 30 35 40 45 50 0€ 5 000 € Figure 5.3. Figure CP EU Poland. from based data on elaboration own Source: 25 000 € 20 000 € 15 000 € 10 000 € 5.5. Impact of Global Research Infrastructures 169

5.5. Impact of Global Research Infrastructures

The direct and indirect effects of the policies in the context of Global Research Infrastructures is based on four different streams of research: 1. The analysis of financial and institutional leverage derived from cooperation with non-EU countries in projects financed by the FP7 INFRA and H2020 INFRA; 2. The analysis of indicators of the 12 GRIs performance derived from available data collected in the form of case studies, as well as analysis of the 12 GRIs projects in relation to Grand Challenges; 3. The results of a survey conducted on European Research Infrastructure Coordinators (N=150), measuring the potential effects in three dimensions: • The effects of FP7 INFRA and H2020 INFRA financing on European RIs; • The effect of already acquired international partners on European RIs; • The effect of potential international partners incorporated in European RIs resulting in potential cost sharing; 4. The results of a survey conducted on the participants of projects financed by the FP7 INFRA and H2020 INFRA and users of European RIs (N=400), measuring the impact in three dimensions: • The effect of FP7 INFRA and H2020 INFRA financing on users of European RIs resulting in different forms of additionality; • The effect of European RIs on their users resulting in added value; • The effect of incorporating international partners into European RIs resulting in different benefits for its current users.

5.5.1. Effects of financial and institutional leverage derived from non-EU countries Financial leverage in relation to European RIs can be attained by attracting not only EU Member States but also third countries. Financial data from the FP7 INFRA show that total financing granted to non-EU participants reached 142,683,140 EUR, whereas the sum of projects supposed to be financed by non-EU countries attained 9,771,608,362 EUR, which resulted in a leverage effect of 107. Financing for non-EU participants in projects directly related to the 12 GRIs attained 2,392,547 EUR, with a total size of the projects related to non-EU partners of 271,609,306 EUR. Data for the H2020 INFRA on 30.05.2016 show that there were 192 participations of non-EU financed, for a sum of 49 055 459 EUR, and s size of financed projects of 1,498,592,388 EUR, which resulted in a leverage effect of more than 37. Financing for 170 5. The role of Global Research Infrastructures as a tool of innovation policy non-EU participations in projects directly related to the 12 GRIs attained 14,954,761 EUR, with a total size of projects of 358,446,253 EUR. Data for H2020 till March 2018 show that financing for non-EU countries from the H2020 INFRA reached 76,911,760 EUR, which is followed by project sizes of 4,283,038,947 EUR. The institutional leverage (benefiting from the expertise of the entities involved) in relation to the presence of non-EU participants in the 12 GRIs, taking into account the change between FP7 INFRA and H2020 INFRA, is growing but still cannot be perceived as reflecting globalization. Many, although reputable partners come from other European countries – mainly EFTA, ties with Canada, USA, South America are rather limited, although improving.

5.5.2. Global Research Infrastructure performance indicators The second part of the assessment is based on data on performance indicators as well as projects conducted by the 12 GRIs facing the Grand Challenges identified in H2020. All the 12 GRIs conduct cooperation with other GRIs as well as different RIs, so their openness level can be evaluated as high. All of them have Management Scientific Committees, which are responsible for the direction and quality of the research conducted. Data on performance indicators (number of publications, number of conducted projects, size of the personnel, etc.) are very fragmented and do not allow to derive any deeper conclusions. Additionally, the 12 GRIs do cover very different fields of research and are at very different stages of development, so a direct comparison is not possible. Data on selected projects conducted by the 12 GRIs related to the Grand Challenges show that many of the Grand Challenges put forward in H2020 are already covered or would be faced in the future. It should be noted, however, that the nature of the projects conducted in the 12 GRIs as well as the Grand Challenges they face, do not allow to derive conclusions in the short run, thus a direct impact assessment in a short perspective is difficult, if not impossible. For details see Table 5.3.

5.5.3. Results of the survey on European Research Infrastructure Coordinators The third part of the assessment is based on data from the study conducted on a sample of N=150 European Research Infrastructure (RI) Coordinators, conducted with the application of CATI and CAWI in the period November-December 2016 by the market research institute Indicator. It addressed the coordinators of European RIs, registered in the database MERIL, as well as the European RIs and Global Research Infrastructures (GRIs) indicated by the European Strategy Forum on Research Infrastructures (ESFRI), and the Group of Senior Officials (GSO) on Global Research Infrastructures. 5.5. Impact of Global Research Infrastructures 171 Total financingTotal the projects of Size Leverage astructure; Group of Senior Officials on Global Research Senior Officials Global Research of on Group astructure; Average financing financing Average per participation itutional initiatives introduced by the EC and GSO the EC and by introduced initiatives itutional Directorate-General for Research and Innovation Research Infr Research Innovation and Research for Directorate-General Infrastructures (GSO); Expert Group on Cost Control and Management; European Research Infrastructure Consortium Consortium Infrastructure Research European Management; and Cost on Control (GSO); ExpertInfrastructures Group (ERIC) The state of the Commitment 32 implementation –summary 32 implementation the Commitment of The state Institutional initiatives The state of the Commitment 32 implementation –summary 32 implementation the Commitment of The state inst and – documents analysis Legal framework 1. Documents internationalization pace of and – state analysis framework Participation 2. Year the EC by prepared 7 in 2010-2016 in the 12 GRIs (countries)* participants of Number in 2010-2016 in the 12 GRIs EU28 countries of Number in 2010-2016 the EU in the 12 GRIs outside countries of Number internationalization pace of and State H2020 till 30.05.2016 FP7 and from data – financial analysis framework Financial 3. GRIs to Financing 184 FP7 (6/12) GRIs to Financing the GSO by 5 prepared FP7 61FP7 part INFRA 123 INFRA FP7 part to Relation participations financed of No. H2020 (5/12) GRIs to Financing 121H2020H2020 part INFRA INFRA H2020 part to Relation 2% 253 133 33% 292 5 267 603 € 90 8% 163 135 922 24 699 983 € 606 1 789 177 € 2010 93% 288 799 € 1 483 997 562 € 39 993 333 644 € 70 318 253 € 216% 528 321 724 € 1 61 36% 365 756 € 2% 2 797 182 022 € 57 571 899 716 € 45 349 606 045 € 949 384 952 161 € 414 874 € 40 38 587 404 066 € 12% 21 2016 16 592 060 490 € 13 818 664 277 € 3% 292 636 275 624 € 24 18 20% Table 5.3. Table CP EU Poland. from financial data and the 12 GRIs for based data on elaboration own Source: 172 5. The role of Global Research Infrastructures as a tool of innovation policy

The impact of the FP7 INFRA and H2020 INFRA on European RIs is most often reflected in: the integration of European researchers, both from academia and industry, ensuring their optimal use and joint development (91% of the sample), increased efficiency and productivity of researchers (90%), and defining workflows and ensuring coordination, harmonization, integration and interoperability of data, applications and other services with other research infrastructure initiatives in thematic areas (86%). The current state of internationalization of European RIs shows that the majority of partners are coming from the EU (95%), EFTA (75%) or other European countries (45%), with a very low rate of indications for non-European partners. However, the state of globalization is considered as already attained (71% of the sample) or attained with the attempts to look for another partner (19%). Only 10% of the sample considered themselves as “not globalized”. The impact of an acquired international partner on European RIs is mainly reflected in the quality (97% of the sample) and number of publications (93%), their financial involvement (95%), increase in the fields of research (91%) and access to international scientific or technological knowledge (90%) (indications for very highly/highly important). The direct impact of potentially incorporating international partners in European RIs resulting in cost sharing is envisaged as important only in the case of European partners (EU, EFTA, other European). Barriers perceived as most important in incorporating international partners into European RIs are: doubts about their scientific excellence, financial contribution as well as conducted research programs (indications for very highly/highly important).

5.5.4. Results of the survey on participants of projects financed by the FP7 INFRA and H2020 INFRA The fourth part of the analysis is based on data from the study on entities that were financed by the FP7 INFRA or H2020 INFRA (N=400) and are users of research infrastructures. An analysis of the additionality effects (change in financed firms’ R&D spending, behavior or performance that will not occur without the public program or subsidy, derived from FP7 INFRA and H2020 INFRA funds) shows that the network effect is indicated as the most important for both FP7 INFRA and H2020 INFRA funds, followed by cognitive capacity additionality (positive impact on competencies and expertise) as well as follow-up additionality (positive impact on follow-up projects). Output additionality (direct effect on a firm’s innovation performance) and challenge 5.6. Conclusions 173 additionality (taking more risk) as well as management additionality were mentioned far less often. The impact of European RIs on their users resulting in the added value they offer is mainly reflected in: networking with other scientists (96%), new knowledge acquisition (94%), long-lasting contacts (93%), knowledge exchange (91%) and to the least extent in solid infrastructure they offer (62%) (indications for very highly/ highly important). The potential impact of incorporating international partners in European RIs is evaluated by current European RI users as equally highly important for partners from all geographical directions. However, RI users perceive only potential partners from the EU, EFTA and other European countries to provide a wide range of benefits. Surprisingly, cooperation with partners from the USA, Japan, USA, BRIC, Africa, South America, other North American and Asian countries is evaluated as far less beneficial. Barriers perceived by RI users as most important in working with international partners are similar to those from the survey on RI Coordinators and are as follows: doubts about scientific excellence, conducted research programs, financial contribution, open data access as well as data ownership (indications for very highly/ highly important).

5.6. Conclusions

It has to be underlined that Global Research Infrastructures as a policy tool were prepared and implemented, with the following results: • Institutional initiatives as well as twelve important documents set the stage for further management, development and increase of international cooperation within GRIs. • The internationalization level (the percent of third countries in the total number of participating countries) for the 12 GRIs is moderate (36%) but there are GRIs like ISIS, IMPC, SKA where it exceeds 50% (data for 2016). • The internationalization pace between 2010 and 2015 is moderate: the involvement of international partners changed from 61 (33%) in 2010 to 90 (36%) in 2016. It should be remembered, however, that also the total number of participations in the 12 GRIs increased from 184 in 2010 to 253 in 2016. • The total financing from the H2020 INFRA related to selected GRIs already tripled in relation to the FP7 INFRA. The number of financed participations is already higher, the average financing per participation is higher as well (for details see Table 5.3). 174 5. The role of Global Research Infrastructures as a tool of innovation policy

The conclusions related to the effects of the policies are as follows: • The financial leverage effect from non-EU participations financed from the FP7 INFRA and H2020 INFRA is on average higher than the leverage effect for the EU28 entities. • The total financing for non-EU countries is growing, although it is very fragmented among many partners from very different countries. • The total financing from H2020 related to non-EU participants in projects related to the 12 GRIs raised significantly in relation to FP7. The number of financed participations is already higher. This reflects a shift towards deeper international cooperation. • The base of non-EU partners in projects related to the selected the 12 GRIs financed from the FP7 INFRA and H2020 INFRA is still rather limited, which restrains the institutional leverage effect derived from cooperation with international partners. • The performance indicators for the 12 GRIs, gathered based on case studies, are fragmented and do not allow to derive proper conclusions regarding the GRI innovation performance. • The scale of the Grand Challenges and the nature of the projects conducted by the 12 GRIs does not allow to determine the effects in the short run, but definitely leads to the conclusion that in the long term the Grand Challenges are possible to attain, at least to a certain extent. • The assessment of the impact of funds from the FP7 INFRA and H2020 INFRA on European RIs leads to the conclusion that their major effect is reflected in the integration of European researchers, both from academia and industry, as well as an increased efficiency and productivity of researchers. • The impact of international partners in European RIs is reflected in the quality of publications, the number of publications, financial involvement, increase in the field of research and access to international scientific or technological knowledge. • The impact of potentially incorporating international partners in European RIs on cost sharing is envisaged as important only in the case of European partners (EU, EFTA, other European). • The impact of FP7 INFRA and H2020 INFRA financing on users of European RIs is mainly reflected in creating networks and enhancing competences, and the expertise of benefiting organizations. • The impact of European RIs on the innovation performance of their users is best reflected by enabling networking with other scientists, new knowledge acquisition, long-lasting contacts and knowledge exchange. References 175

• The impact of incorporating international partners into European RIs is high in the following areas: access to international scientific or technological knowledge; access to international workforce; financial involvement; increase in the fields of research; increase in the number of PhD students; patents; research programs; responsibilities; scientific excellence; the number and quality of publications. It should be noted, however, that these benefits are evaluated highly only in the case of partners from European countries (EU, EFTA, other European). Although potential partners from other geographical areas are regarded as important partners, the added value they can potentially offer is evaluated as low.

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Chapter 6 Boosting public sector and social innovation in Europe

6.1. Introduction

The European Union (EU) research programs, among other objectives, support public sector and social innovation. The EU research and innovation funding program Horizon 2020 (H2020) includes a dedicated activity line on ‘new forms of innovation, with special emphasis on social innovation and creativity’. The substantial financing from EU programs is due to the fact that social innovation is perceived as a remedy for social challenges and problems which can be solved effectively neither by the market nor by direct public interventions. According to the European Commission (EC), social innovation should address social needs, contribute to addressing societal challenges (e.g. ageing society) and reshape society in the direction of participation, empowerment and learning. Social innovation is understood as a new idea (product, service and model) that simultaneously meets social needs (more effectively than alternatives) and creates new social relationships and collaborations, which is in line with the definition of Murray et al. (2010). The aim of this chapter is to present public support for boosting social and public sector innovations and to provide a snapshot on the effects of the projects financed under the SOCIETY21 and the SWAFS22 programs under the Seventh Framework Program for Research (FP7) and H2020, looking at the following issues: (i) the scope of filling the gap in the market and public sector interventions in responding to social demand; and (ii) accordance of the supported projects to the above-mentioned definition of social innovation. As a case allowing the exemplification of actions undertaken under the EU Flagship Initiative "Innovation Union" the EC Commitment 27 – Support, a research program on public sector and social innovation, the pilot European Public Sector Innovation Scoreboard, was used (hereafter Commitment 27).

21 Social Innovation – Empowering the Young (SOCIETY) for the Common Good, http:// cordis.europa.eu/project/rcn/106760_en.html. 22 Science with and for Society, https://ec.europa.eu/programmes/horizon2020/en/h2020- section/science-and-society. 182 6. Boosting public sector and social innovation in Europe

The chapter starts with a literature review aimed at showing evidence for the causal relationships between supporting research programs on public sector and social innovation and the expected results. Subsequently, the impact on innovation in the public sector as well as on social innovation is examined. In that part the results of two surveys were used23. Next, the actors crucial for public and social innovations development, their roles and interactions are described. Finally, some policy recommendations resulting from the research are suggested.

6.2. Public sector and social innovation: the concept

Social innovation is a relatively new concept and varying definitions are plentiful in the subject literature. As the concept evolves over time, there is no agreement about cause-and-effect relationships or about the specific policies which foster social innovation (OECD, 2005). In effect, social innovation is now used to describe a very broad range of activities. These include: the development of new products, services and programs; social entrepreneurship and the activity of social enterprises; a reconfiguration of social relations and power structures; workplace innovation; new models of local economic development; societal transformation and system change; non-profit management; and enterprise-led sustainable development. From this list some common, basic characteristics of social innovations can be inferred. Social innovations are new solutions that simultaneously meet a social need and lead to new or improved capabilities and relationships, and better use of assets or resources. Murray et al. (2010) define social innovations as new ideas (products, services and models) that simultaneously meet social needs and create new social relationships or collaborations. Not only do they improve the well-being of society but they also enhance society’s capacity to act (Caulier-Grice et al., 2012). Public sector innovation can be defined as a process of generating new ideas, and implementing them to create value for society (Mulgan, 2007). Public sector innovation can be both internal, focusing on improved processes, and external, focusing on public services (Rivera Leon et al., 2012). According to the OECD, the goal of public sector innovation is to use new approaches, from policy design to service delivery, for a high performing, more responsive public sector (OECD, 2011). Public sector innovation involves significant improvements in the services that the government has a responsibility to provide, including those delivered by third parties (OECD, 2012). Innovation in the public sector has often been viewed as

23 One survey was directed at the coordinators of projects financed within the FP7 and H2020 programs (within SOCIETY and SWAFS calls). The second survey was addressed at policy makers dealing with innovation issues at the country and regional levels in all EU Member States. 6.2. Public sector and social innovation: the concept 183 an oxymoron, with many academics showing skepticism about its existence, due to a risk-averse attitude of senior managers in public organizations. The public sector was considered to be responsible for providing a regulatory framework for innovation in the private sector, and a passive recipient of innovations from the private sector (Windrum, 2008). However, large-scale surveys in Europe (Arundel and Hollanders, 2010; Bugge et al., 2011) showed evidence that innovation in the public sector does occur (Arundel, Huber, 2013). Conceptually, social innovations are situated between private and public sector innovations – their origins can be in both sectors – but social innovation as a concept can legitimize a socially-oriented and solidarity-based political economy, and some of public sector innovation falls into the category of social innovation (Fraisse, 2013).

6.2.1. Understanding the role of public sector and social innovation The rationale behind supporting social innovation is the increasing demand for quality of public services and the social challenges resulting from socio-economic development. There is a strong need for efficiency gains in the public sector in order to address the rising demand for services related to social problems and the "silver economy". Governments are under pressure from society to increase their effectiveness and quality with fewer resources (Keating and Weller, 2001; McAdam and Reid, 2000). Moreover, governments are expected to demonstrate greater accountability and transparency. In particular, insufficient information on legislation, barriers and public procurement linked to social innovation and the role of social entrepreneurship need to be addressed. Recently, the pressure on the public sector to increase efficiency and improve performance has shifted towards a more challenging task; to develop and offer ‘personalized’ public services (Alves, 2013; Albury, 2005; Du Gay, 1993). The public sector is expected to offer services that are responsive to individual as well as community needs and aspirations. Innovation in the public sector affects multiple aspects of society and that is why providing more information is fundamentally important (Alves, 2013). While academics focus on the role of entrepreneurs, partnerships with private actors and non-profit organizations and the support of social networks in generating social innovations (e.g. Austin et al., 2006; Mair and Marti, 2006; Shaw and Carter, 2007; Thompson, 2002), research on the role of public policy and governments needs to be further developed. As governments do not have enough evidence, they often choose to promote the “production” of innovation, with funding for research and development, specifically for the technology sectors. Funding technological innovations can be a useful option, but it neglects the role of social innovations in creating the context for technical innovations to arise (Collins, 1997). Therefore, 184 6. Boosting public sector and social innovation in Europe understanding the role of social innovation, on the one hand, and the importance of broad systemic change to support both social and technical innovations on the other hand, is very important for policy makers. The impact of policy instruments on social innovation will vary across stages in the process. Recognition of the distinct phases of social innovation is key to understanding which policy will be most suitable; that is, different policies are appropriate for the generation, selection, adoption, and institutionalization processes that any social innovation will need to undergo.

6.2.2. Financing research on social innovation and its role in society and the economy The economic literature provides a number of reasons for financing research on social innovation. Financing research on social innovation and the dissemination of the research results, together with providing a framework for the measurement of social innovation, including innovation in the public sector, is a good way to improve innovative performance. Evidence exists confirming that the knowledge sharing network upgrades skills and knowledge more quickly (Cooke, 2002). Fast, widespread diffusion of knowledge advances common wealth in society. Public policy is more effective when stakeholders are able to participate effectively (Riege, Lindsay, 2006). In addition, governments face significant challenges in developing effective stakeholder partnerships when there are imbalances in knowledge sharing capability, which are perhaps most evident in marginalized groups that are frequently excluded from public debate on policy issues (Barnes et al., 2003; Deakin, 2002). This is one of the reasons for supporting research on social innovation. The more involved society and various groups of stakeholders and governments are, the more successful the public policy. Financing research on social innovation may serve the need to approach stakeholders in a heuristic manner with a view to learning rather than adopting quick-fix solutions (Adams and Hess, 2001). Another reason for financing research on social innovation is the concept of open innovation. While innovation in the private sector is usually protected from copying by others, for the public sector this may be the opposite. Here diffusion of innovation across the public (and private) sector may ensure a better use of public resources (Moore, 1995; Mulgan and Albury, 2003; Rolfstam et al., 2011). It must be emphasized that the demand from policy makers and practitioners for new research on social innovation is increasing (Jenson, 2013). Research on social innovation is – by nature – mainly empirical and its primary field of development is the local level, where stakeholders can more easily be mobilized on concrete issues (European Commission, 2014). 6.2. Public sector and social innovation: the concept 185

Empirical studies show that one of the most important barriers reducing the number of social innovations is the lack of stable and sustainable funds as well as the tendency for grants to be short-term making long-term planning difficult. Organizations that implement social innovations complain about high costs associated with securing funds – as senior management’s energies are often focused on obtaining funds rather than managing their organizations (Murray, 2010). These organizations also require considerable non-financial support (research, consultancy services, knowledge of methods, including what works best). These conclusions arising from empirical studies also justify the need for financing research on social innovation. By promoting and facilitating social innovation the public sector can address some social needs and challenges, both through its own services and through encouraging various stakeholders to provide or complement public service.

6.2.3. The European Public Sector Innovation Scoreboard as a basis for further work to benchmark public sector innovation The European Commission launched a pilot European Public Sector Innovation Scoreboard (EPSIS) with a view to improving its ability to benchmark the innovation performance of the public sector in Europe. The objective was to present public sector innovation in a similar way to the innovation performance rating of countries in the Innovation Union Scoreboard and thereby encourage and facilitate innovation activity across the public sector. In terms of beneficial outcomes from innovation, it is impossible to predict the types of innovation methods that lead to better outcomes, because there has been very little research on this topic (Arundel et al., 2015). Arundel identifies three different methods that agencies use to innovate: ‘bottom-up’, ‘knowledge-scanning’, and ‘policy-dependent’ methods. The prevalence of the first two methods varies consistently across European countries, while there is no consistent difference for policy-dependent innovation. The results of research indicate that the ‘bottom-up’ and ‘knowledge-scanning’ methods are correlated with better outcomes than the ‘policy-dependent’ approach (Arundel et al., 2015). The European Public Sector Innovation Scoreboard can be considered as a tool of knowledge sharing, presenting good practice and a kind of award for the countries placed on the top of the list (or a stick to those on its end). There is some empirical evidence that using innovation awards results in a higher rate of cooperation among public agencies (Borins, 2001). The subject literature provides a few suggestions that collaboration (Pärna and von Tunzelmann, 2007) and the use of external knowledge 186 6. Boosting public sector and social innovation in Europe sources (Torugsa and Arundel, 2015) have an impact on the number of implemented innovations. A main driver for the adoption of diverse knowledge management initiatives in public services is the change of organizational culture. This can be achieved by “naming and shaming” the public administration of the low-ranked countries. That is why benchmarking and tools like the Public Sector Innovation Scoreboard can be used to improve the performance of public administration, and capitalize on a broader, more integrated and easier accessible knowledge base. It can also be used to improve accountability and mitigate risk by making informed decisions and resolve issues faster, supported by access to integrated, transparent information across all organizational boundaries (West, 2005). Benchmarking is an element of a comprehensive knowledge management strategy. Internal and external benchmarking provide an opportunity for public sector organizations to simultaneously improve productivity, the quality, level and breadth of services to constituents, as well as strategic effectiveness. An improvement in effectiveness is a consequence of a shift from a narrow operational focus to a broader strategic focus. Benchmarking is a tool to identify best practices. Research provides examples that benchmarking led to superior performance (Drew, 1997). It helps to develop an inter-organizational synergy, to increase the ability of organizations to improve their knowledge competencies, offsetting their knowledge deficiencies, enabling new knowledge creation and diffusion processes (Inkpen, 1996). According to Lundvall, processes of ‘learning by comparing’ can be seen as a useful policy-learning tool (Lundvall, Tomlinson, 2002).

6.3. Theoretical background for analyzing the impact of financing research on public sector and social innovation

Literature on the nature of innovation, its sources and drivers, presents various concepts such as the learning economy (Lundvall and Johnson, 1994), national innovation systems (Lundvall, 1992; Nelson, 1993), regional innovation systems (Cooke, 1992; Asheim and Isaksen, 1997), technological innovation systems (Teece,1996), triple helix (Etzkowitz and Leydesdorff, 2000), open innovation (Chesbrough, 2003), and user-driven innovation (von Hippel, 2005). The systems of innovation approach allows for the inclusion not only of economic factors influencing innovation but also institutional, organizational, social and political factors. However, the systems of innovation approach is not considered a formal theory – its development has been influenced by different theories of innovation such 6.3. Theoretical background for analyzing the impact of financing research on public sector... 187 as interactive learning theories and evolutionary theories. The concept of innovation systems is compatible with the notion that processes of innovation are, to a large extent, characterized by interactive learning (Edquist, 1997), so it complements the knowledge management theory. Foray argues that an efficient system of distribution and access to knowledge increases the social value of knowledge that is produced by experience-based learning and by organized research, as well as the knowledge acquired and assimilated from external sources. This is the reason why a system of innovation must be characterized as much by its 'distribution power' as by its capabilities for generating new knowledge – that is by the system's ability to support and improve the efficient functioning of procedures for distributing and utilizing knowledge. The activity of diffusing economically relevant knowledge does not happen by itself. Rather, it is socially constructed through the creation of appropriate institutions and conventions such as open science (Foray, 1997). The innovation systems approach stresses that innovation does not occur in isolation, but depends upon the interplay between many different types of actors that take part and play various roles in an innovation process. It is often in the relations between actors and their respective knowledge bases that innovation occurs, through the combination and adaptation of existing knowledge (Schumpeter, 1934 [1959]; Weitzman, 1998; Johansson, 2004). There are several factors in the institutional surroundings of the innovation system that shape the conditions for innovation within it (North, 1990). These institutions may be either formal or informal and include elements such as laws, regulations, cultural norms, social rules and technical standards (Edquist, 2005). One of such informal elements is a benchmarking tool, which encourages institutions to catch-up with the best performing ones. Due to the growing attention and awareness of the need for public sector innovation, there is a potential for expanding the roles of the public sector in these approaches. More recent theoretical work on the systemic characteristics of innovation in the public sector includes networked governance (Hartley, 2006), community governance (Hessand, Adams, 2007) and collaborative innovation (Bommert, 2010; Sørensen and Torfing, 2011), and is primarily oriented at societal outcomes (Hess and Adams, 2007). Knowledge openness is a critical factor of efficiency in these concepts. For a knowledge system as a whole, the pace of innovation may be sped up if competitors are able to build on other innovators' advances, rather than being allowed to block the progress of others. The innovation system theory leads to the conclusion that financing research on social innovation may result in creating knowledge that produces positive externalities. Financing research and distributing knowledge by for example publishing a Scoreboard on public sector innovation may increase the probability 188 6. Boosting public sector and social innovation in Europe of creating useful new products, processes, and ideas arising from novel and unanticipated combinations, and facilitate replication of findings as well as raise the social value of knowledge by lowering the chance that it will reside with persons and groups who lack the resources and ability to exploit it (Foray, 1995). According to the research carried out by Arundel, the implemented innovations in the public sector resulted in: • simplification of administrative procedures; • cost reduction of providing services; • faster delivery of services; • improvement of employee satisfaction related to working conditions (Arundel et al., 2015). In addition to cost reduction as a result of innovation in the public sector, other benefits such as increased citizen satisfaction, improved image of the public sector and innovation boost in the private sector can follow (Alves, 2013). Summing up – the literature review provides conclusions that benchmarking is a valuable tool in the public sector and may lead to implementing better and more efficient solutions. In the public sector, which in general is risk-averse, an example of an innovation that brings positive effects can encourage organizations to implement new solutions. The concept of innovation system explains the results of financing research on social innovation. Financing research leads to creating new knowledge which can be disseminated (because it is financed with the assumption that the research results will be distributed). Sharing new knowledge and research results increase the probability of creating new ideas that are useful for society as a whole.

6.4. Impact of the research program on public sector and social innovation

The following part of the study focuses on the impact of financing a research program on public sector and social innovation and launching the European Public Sector Innovation Scoreboard (EPSIS). It consists of a description of the results of two surveys that were carried out in order to consult the key stakeholders on the effects of Commitment 27. The first survey was directed at the coordinators of projects financed within the FP7 and H2020 programs (within SOCIETY and SWAFS calls): (i) FP7 coordinators registered at the E-CORDA database24 selected from 489 calls

24 CORDA (Common Research Data Warehouse) and E-CORDA (External Common Research Data Warehouse – the analogue intended for external stakeholders) are databases containing data on applicants/proposals and signed grants/beneficiaries with regards to a specific Framework Program for Research; http://www.moliseineuropa.eu/sites/moliseineuropa.eu/files/ Confidentiality%20rules%20FP%20data%20CORDA.pdf 6.4. Impact of the research program on public sector and social innovation 189 for proposals (November 2015); (ii) H2020 coordinators registered at the E-CORDA database selected from 241 calls for proposals (May 2016). The survey was conducted in the period August to September 2016. The total number of responses was 52 and the number of analyzed projects – 137. The second survey was addressed at policy makers dealing with innovation issues at the country and regional levels in all the EU Member States. The survey was conducted in September – October 2016. The number of responses was 570 and covered all the MS25. Surveys are widely used as one of the methods of impact assessment (European Commission, 2009, OECD, 2011). However, it must be noted that the method has some limitations: the coordinators of research projects might be unable to accurately assess the impacts of the implemented projects, respondents could also have strategic reasons for overstating project impacts and the policy makers may find it difficult to distinguish the results of the European Public Sector Innovation Scoreboard from many other reports. Moreover, both surveys are based on small sample sizes. The results of the surveys served to provide further evidence for the mentioned conclusions based on the literature review. The most frequently reported outcomes of the projects financed within the FP7 and H2020 in the field of social innovation were new ways of collaboration and new publications. The relatively low share of new technologies and new products can be explained by the characteristics of social innovation and its focus on developing new forms of interactions to respond to social issues and creating new relationships rather than new products (Figure 6.1). It should be noted that the share of new technologies and new products increased in H2020 in comparison to FP7, which is a sign that there is more understanding and recognition of the expected results of research projects.

Figure 6.1. The distribution of project results by source of financing

New technology H2020 New product(s) New process(es) New model(s) of collaboration FP7 New publication(s) New quality standard(s) 0% 20% 40% 60% 80% 100% Source: Own elaboration based on survey results.

The respondents of the survey confirmed that the research program on public sector and social innovation facilitated comparing, validating, scaling-up and

25 The sample was based on the following assumptions: the number of responses per country should be 20 and should cover all the levels of the government that deal with innovation policy. The technique used was a mix-mode survey (telephone survey complemented with the on-line tool). 190 6. Boosting public sector and social innovation in Europe monitoring social innovation initiatives or transferring good practices more easily. The declared knowledge transfer is relatively high, which can be explained (again) by the characteristics of social innovation, as mentioned above. The focus is mainly on open solutions, less on new technologies, which usually are protected in the form of IPR. This model promotes learning and participation. In general, the results of the survey confirmed that financing research on social innovation enables guiding by common principles and dissemination of the know- how needed for innovation. The majority of respondents (71%) considered the knowledge transfer effect as "high" or "very high" (Figure 6.2).

Figure 6.2. Respondents’ views on the knowledge transfer effect of projects Very High High Medium Low 0% 20% 40% 60% 80% 100% Hard to say

Source: own elaboration based on the survey results (coordinators of research projects).

The dimension of social innovation related to new collaborations is strongly present in the supported projects. The majority of respondents (93%) declared that they were going to cooperate with at least some partners involved in the project in the future (Figure 6.3). Other studies also show that the partnerships remain – it is easier to apply for a new project with a proven partner, the division of roles is easier and the risks related to project implementation are mitigated (partners know what can be expected from each other, what quality will be delivered and so on).

Figure 6.3. Distribution of answers to the question: Are you planning to sustain the partnerships established for the FP6, FP7 and H2020 research programs? 2% 5% 9% Yes, with all partners Yes, with some partners No

84% Don't know/Not applicable

Source: own elaboration based on the survey results (coordinators of research projects).

The research program was supposed to contribute to innovation in the public sector and 38% of the project coordinators evaluated this contribution as “high” or “very high”, as presented in Figure 6.4. 6.4. Impact of the research program on public sector and social innovation 191

Figure 6.4. Respondents’ views on effecting innovation in public services by their research project

Very Low Low Medium High Very High 0% 20% 40% 60% 80% 100%

Source: own elaboration based on the survey results (coordinators of research projects).

A wide range of indirect effects was identified, e.g. network building, innovation diffusion, involving stakeholders, high knowledge transfer and enhanced awareness of social innovation. The coordinators of research projects in the field of social innovation indicated the outcomes of their projects rating them on a scale from “very low” to “very high” (Figure 6.5).

Figure 6.5. Effects of research projects financed within the H2020 – mean grade (very low = 1; very high = 5)

Network building Collaborative approaches - involving different stakeholders Innovation diffusion

Empowerment of some groups of people

Innovation in public services Adaptive systems - mechanisms which help adapt to changing circumstances Institutional changes. creating the conditions for lasting effects of innovations Open source innovation

Increase of the society’s capacity to innovate

Better use of resources

Behavioural changes

Other

Social entrepreneurship. creating new organizations

Safe environment

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Source: own elaboration based on the survey results. 192 6. Boosting public sector and social innovation in Europe

6.5. Impact assessment of the pilot European Public Sector Innovation Scoreboard (EPSIS)

The European Commission launched the pilot EPSIS with a view to improving its ability to benchmark the innovation performance of the public sector in Europe. The objective was to present public sector innovation in a similar way to the innovation performance rating of countries in the European Innovation Scoreboard (EIS) and thereby encourage and facilitate innovation activity across the public sector. Among the 570 respondents 22% acknowledged they knew their country score in the pilot EPSIS, 54% did not know the score of their country and 24% had never heard of the EPSIS. The majority of respondents (73%) who knew their country's score admitted that EPSIS is helpful in benchmarking and knowledge sharing in public administration (Figure 6.6).

Figure 6.6. Respondents’ views on the EPSIS’s role in benchmarking and knowledge sharing in public administration Very Helpful Somewhat Helpful Neither Helpful nor Not Helpful Not at all Helpful 0% 20% 40% 60% 80% 100% Don’t know|Hard to say

Source: own elaboration based on the survey results (policy makers).

Respondents who knew about the EPSIS and the position of their country in the ranking were asked about the effects of publications / rankings like the EPSIS with regard to public sector innovation. The most frequently mentioned effect was an increased interest in implementing innovations in the public sector (indicated by almost 90% of policy makers). This view was shared mostly by respondents from countries that belong to the group of Strong innovators and Innovation Leaders (in the EIS26) and countries in which the problem-solving model of science-policy interactions is dominant27. An increased interest in sharing best practices in the public sector was mentioned by 61% of the respondents (mostly from countries that belong to the group of Strong innovators and in which the problem-solving model

26 According to the European Innovation Scoreboard the Member States are classified into four groups: Innovation Leaders, Strong Innovators, Moderate Innovators, Modest Innovators. 27 Statistically significant differences between groups of countries have been confirmed by the Kruskal-Wallis H test (Kruskal, Wallis,1952) 6.6. Actors crucial to fostering public sector and social innovations 193 of science-policy interactions is dominant). Increased efficiency in the public sector was also considered as an important effect, indicated by 43% of the respondents (also from the group of Strong innovators and countries in which the interactive model of science-policy interactions is dominant). Those who did not know their score or had never heard of the EPSIS were asked how helpful a tool such as the EPSIS would be for benchmarking and knowledge sharing in public administration. More than half of the respondents (53%) indicated that it would be a useful tool.

Figure 6.7. Distribution of answers to the question: How helpful would a tool such as the EPSIS be for benchmarking and knowledge sharing in public administration? Helpful

Neither Helpful nor Not Helpful

Not Helpful

0% 20% 40% 60% 80% 100% Don’t know|Hard to say

Source: own elaboration based on the survey results (policy makers).

They were also asked about the potential effects of publications like the EPSIS. In this group the increased interest in sharing best practices was the most frequently indicated effect (56%) and an increased interest in implementing innovations in the public sector was indicated by 32%. All the respondents were asked about their opinion on the impact of the EPSIS on their country. The majority of policy makers link the EPSIS to positive indirect effects e.g.: better evaluation, enhanced cooperation and exchange of experience between MS, positive competition, motivation to change, assessment of country's performance, which gives an answer to the question on what still needs to be done. There is also a large group of those who can see some potential effects that may occur in the future, such as: economic progress, adopting innovation in strategic planning, improvement of the situation of the weakest EU MS, enhanced competitiveness, investment from countries outside the Union.

6.6. Actors crucial to fostering public sector and social innovations

The following part describes the actors crucial to fostering public sector and social innovation, their roles and interactions. As before, the analysis is focused on the case of Commitment 27. Parts of the FP7 and H2020 were selected for the analysis (SOCIETY 194 6. Boosting public sector and social innovation in Europe and SWAFS programs under the FP7 and H2020)28. The interactions and linkages between actors were researched using the Social Network Analysis (SNA)29. The analysis of actors and linkages between the organizations participating in the research programs was a base for summarizing the direct and indirect results of actions supposed to foster the innovations in question. The limitation of the analysis of the direct and indirect results is related to an insufficient measurement framework. The number of internationally comparable indicators which could provide information on the development of social innovation and public sector innovation remains too narrow. This was also the reason for discontinuing the publication of the Public Sector Innovation Scoreboard. In the case of social innovation, the measurement framework is also underdeveloped due to the complexity and diversity of social innovation. The most important actors in the process of innovating in the public and social sphere are policy makers and public institutions. Their role is not only to participate in projects financed under the H2020, but also to facilitate the implementation of the results of research and development (or at least this should be their role). Public institutions can also play an important role in creating the right conditions for implementing social innovations, in their implementation and in scaling and dissemination. Passive functions refer to the subject of research under the EPSIS (which unfortunately will not be continued). As follows from the analysis presented in the next section, research institutes and universities are the most important recipient of funds within the areas of the FP7 and H2020, which potentially support social innovations. In most countries universities are the dominant actors in the public research landscape. In some countries public research institutes also play a significant role in the development of new ideas and new technologies conducive to the wellbeing of citizens. Table 6.1 presents the actors involved in the implementation of Commitment 27 based on the classification of actors in the innovation eco-system (see Verspagen, Hollanders and Noben, 2016). There is one additional category involved in the implementation of C27 that was not distinguished in this classification – NGOs. The role of NGOs is small and underestimated. However, these institutions can and should participate more closely in research projects, because they often have direct contact with the groups at which social innovations are aimed. In selected cases they could, at least to some extent, introduce a perspective of ultimate recipients – the targets of social innovations.

28 Data on projects financed from the FP7, registered at the eCORDA database, selected from 489 calls (v. 20 Nov. 2015), and H2020 projects registered at the eCORDA database, selected from 457 calls (v. 10 March 2018). 29 The software Gephi was used to analyze and map the collaboration of organizations participating in the selected calls (actors) based on the eCORDA data provided. 6.6. Actors crucial to fostering public sector and social innovations 195

Table 6.1. Innovation system functions in implementing Commitment 27 by actor category

Actor category Primary functions Derived functions Governments – policy Addressing globalization and Providing information on the makers (PUB) grand societal challenges characteristics of public sector Providing services to address these innovation needs/challenges Supporting and working with Promoting and facilitating social other societal actors innovation Stimulating business innovation Financing research programs which could address societal on public sector and social challenges innovation Education institutions, Undertaking research in the fields Providing scientific knowledge especially higher of the public sector and social and evidence for effective policy education (Higher or innovation making and benchmarking secondary education Developing new forms of Providing a measurement institutions – HES) organization and interactions to framework which would give respond to social issues information on the characteristics of public sector innovation Semi-public and Undertaking research in the fields Providing scientific evidence public research of the public sector and social for effective policy making and institutes (Research innovation benchmarking organizations – REC) Developing new forms of Providing a measurement organization and interactions to framework which would give respond to social issues information on the characteristics of public sector innovation Firms (Private Investing in new technologies Scaling up and transferring companies – PRC) responding to social needs innovation (in particular social Undertaking research innovation) Consumers – Citizens Promoting and facilitating social Validating social innovation innovation Testing, demonstrating and scaling up new solutions Contributing to reshaping society in the direction of participation, empowerment and learning NGOs Promoting and facilitating social Scaling up and transferring innovation innovation (in particular social Complementing existing public innovation) services Providing expertise in the public Developing new forms of sector and social innovation organization and interactions to respond to social issues Source: own elaboration based on the description of Commitment 27. 196 6. Boosting public sector and social innovation in Europe

The basis for the analysis was a concept of connection which is created between each pair of participants of a project, regardless of their role, type of institution, country, etc. That also means that no special role of the project’s coordinator is assumed. For example, in a hypothetical project with four engaged participants a total of six connections is created. In the case of the H2020 a connection’s significance is measured by the amount of EU funding assigned to each partner as a proxy for their engagement in the project (the lower number is taken as we assume the connection is as significant as its less engaged partner). The graphs should be interpreted in the following way. The nodes of the network represent countries or types of institutions. The size and color intensity of each node represents the centrality of a node measured by its weighted degree (the measure of centrality using the connections’ significance as a weight). The bigger and darker a node, the bigger the value and number of relations with partners within projects. Both the thickness and color intensity of the edges (connections between nodes) represent the weight measured by the connections’ significance aggregated as a simple sum. The thicker and darker the connection, the bigger the value of the projects realized by both nodes. The precise location of each node on the graph is the result of an algorithm; the more central the node, the greater its importance in research networks. Graph 6.1 shows that universities are the key nodes of connections between actors participating in Commitment 27. They are also the largest beneficiaries of projects in the analyzed areas of the FP7 and H2020. While other institutions (e.g. NGOs) played a major role in the FP7, besides research institutes, their role in the H2020 is small – smaller than the role of private companies. In the H2020 the role of public institutions in relation to the FP7 has increased, which may be an indirect result of financing research programs on public sector and social innovation – public institutions have more links both with universities and research institutes, as well as with enterprises. A similar analysis was carried out to present the interactions between countries in the H2020 (Graph 6.2). Financing a research program contributes to creating networks of cooperation. There are dominant hubs in the networks: Great Britain, Germany, Italy. The significant role of Greece is surprising. That may be linked to a general European policy towards supporting Greece during and after the crisis (in the FP7 Greece was hardly visible). 6.6. Actors crucial to fostering public sector and social innovations 197

Graph 6.1. Interactions between the main actors in selected FP7 and H2020 areas Interactions between actors in FP7 Interactions between actors in H2020 (SIS, SSH) (SOCIETY, SWAFS)

Source: own elaboration based on the eCordis database.

Graph 6.2. Interactions between countries in the H2020 (SOCIETY and SWAFS)

Source: own elaboration based on the eCORDA database selected from 457 calls (v. 10 March 2018). 198 6. Boosting public sector and social innovation in Europe

It can be noted that those countries that have a relatively low rank in the European Innovation Scoreboard (EIS) are also underrepresented in the EU Research and Innovation program (Figure 6.8). This may result from a lot of factors, first of all a lower ability to participate in the calls for proposals by entities from countries where the level of innovation is lower. This is evident in the case of areas supporting research and development for industry. This does not have to be a rule in the case of research supporting the area of social innovation or innovation in the public sector. It should also be taken into account that both the number of applications submitted and the success rate in a given area may depend on very different conditions, such as: number of entities operating in a given area; number of entities conducting research and development work in a specific field; availability of other sources of R&D funding; participation in international networks; the number and potential of scientific institutions operating in a given field; the strength and potential of industry in a given field. In addition, small countries do not always have an interest in investing in public innovation due to their small scale. Commercial innovations can be easily sold overseas, public innovations may be limited to small populations. Thus, for small countries it may be more cost-effective to adopt someone else's solution, even if it is not perfectly suited to their needs. The amount of funding is also partly determined by the size of the economy.

Figure 6.8. EIS score vs. H2020 centrality

Source: own elaboration based on the eCORDA database selected from 457 calls (v. 10 March 2018) and the European Innovation Scoreboard (http://ec.europa.eu/DocsRoom/documents/24141). 6.7. Conclusions and policy recommendations 199

The mentioned relationship between EIS score and participation in the H2020 can be reduced by encouraging the inclusion of entities from countries weaker in the innovation ranking into consortia. Their role in projects does not have to be large, neither does the share of the budget. Rather, it is about improving the ability to apply and raising the level of knowledge on how to implement projects. This would also strengthen cooperation networks under the European Innovation System. The strategy of including a larger number of entities is also beneficial from the point of view of the development of innovativeness of the European economies. Public spending does not have such an impact on the economy when isolated as when it is borne by many entities, even if the scale is smaller. The more enterprises and research units conduct innovative activity, the more the demand for innovation increases. It also raises the awareness of the need to introduce innovations, which in turn leads to the initiation of cooperation between enterprises and research institutions.

6.7. Conclusions and policy recommendations

The measurable effects of a research program on public sector and social innovation are quite limited. In particular, they result in a relatively small number of implementations in the field of technical solutions, products, services and procedures to solve complex social problems. The most frequently reported outcomes of projects financed within SOCIETY and SWAFS programs are new ways of collaboration and new publications. A stronger focus on getting a clear social impact and the presence of the R&D component would help to achieve the goals indicated in the Innovation Union flagship initiative. A positive effect is a high knowledge transfer within these projects and prospects for a sustainable partnership among institutions implementing projects. In order to enhance the impact of supported projects it may be worth considering the requirements of a practical implementation of their outcomes. In the case of projects where the end result is a model of action or a model of cooperation or a procedure, “practical implementation” should mean a state in which the final results of the project are made available to persons or entities included in the target group and a specific implementation action plan is completed. Particular attention should be paid to ensuring the financing of this process and identifying the specific entities responsible for implementation and at the same time having an impact on target groups. Therefore, a greater participation of actors having a direct link to the target group, in particular NGOs, seems important for the dissemination of social innovation and its effective implementation. 200 6. Boosting public sector and social innovation in Europe

The strategy of including a larger number of entities is also beneficial from the point of view of the development of innovativeness of the European economies. It is understandable that the countries that belong to the group of Innovation Leaders have a higher share of the budget and more participants (or participations)30. However, it would be advisable to encourage the participants from the leading MS to form partnerships with entities from MS that belong to Modest Innovators or Moderate Innovators. Even if the role of these entities remains small, the participation will result in knowledge transfer and increase their ability to apply for grants in a research program. The study indicates that an instrument that allows knowledge transfer in the public sector is needed. In order to support MS in developing policies to foster public sector innovation, a common base of information on innovations and the state of their implementation should be developed. The same could work for social innovation – a database with solutions for various problems could be created along with a search engine allowing thematic search. It would also be beneficial to promote such initiatives as the European Year of Creativity and Innovation or identifying companies with innovative potential. The literature review and the conducted study bring about the conclusion that further research should be focused on: (a) the impact of knowledge dissemination and benchmarking on the performance of institutions (public institutions and other organizations that supply some kind of public services); (b) developing a measurement framework of public sector and social innovation.

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30 One entity can have several projects, so it is more appropriate to use the number of “participations”. References 201

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Chapter 7 European Institute of Innovation and Technology (EIT): towards the excellence of European science

7.1. Introduction

The European Institute of Innovation and Technology (EIT) is contributing to the Innovation Union objectives through integration of the European knowledge triangle (innovation, research and higher education). As the first European initiative to fully integrate all three sides of the knowledge triangle, its mission is to capitalize on the innovation capacity and capability of EU researchers, students and entrepreneurs from the EU and beyond (eit.europa.eu). According to Commitment 9 of the Innovation Union strategy, by mid-2011, the EIT should set out a Strategic Innovation Agenda (SIA) to expand its activities as a showcase for Innovation in Europe. This should map out its long-term development within the Innovation Union, including the creation of new Knowledge and Innovation Communities (KICs), close links with the private sector and a stronger role in entrepreneurship. It should also build on the EIT Foundation set up in 2010 and on the introduction in 2011 of the EIT degree as an internationally recognized label of excellence. The EIT’s mission is carried out by the use of the following instruments: 1. Knowledge and Innovation Communities; 2. EIT-labelled educational programs (both at Master’s and PhD level); 3. EIT awards to recognize the most successful start-ups, innovative projects and young entrepreneurial talents in Europe; 4. EIT roundtable of entrepreneurs; 5. EIT Regional Innovation Scheme (EIT RIS). The Knowledge and Innovation Communities are integrated European innovation platforms that combine research, education and business capacity to address larger societal challenges under central executive governance (Olesen, 2014). In the future, KICs are expected to play a role of platforms which connect various stakeholders of innovation processes and knowledge structure development. This ambitious task can be carried out by creating a proper environment for start-up development and 206 7. European Institute of Innovation and Technology (EIT): towards the excellence... expansion as well as using intellectual property with regard to the type of patentee better (see, e.g. Baczko and Puchała-Krzywina, 2013). Currently six communities exist, with further two planned. Their purpose is to increase Europe’s innovation potential in specific areas, at every step of innovation creation and development. KICs seek to facilitate cooperation between parties such as enterprises, universities, and research centers. The scope of activity of KICs ranges from investing in human capital and training, through lab research to marketing of innovations and incubating businesses. These communities are largely autonomous, with their own legal status, regulations, methods and even CEOs, which is unique for EU-labelled organizations. The first communities were launched in 2010 and these were Climate, Digital and InnoEnergy communities. Two were launched in 2014: Health and RawMaterials, and the final one began its work in 2016: Food (eit.europa.eu). KIC Climate focuses on lowering carbon emission to zero and establishing a climate aware society as well as urban transitions, sustainable production systems and sustainable land use. It comprises 190 entities in 6 geographical regions. Its primary activities include creating products, services and jobs related to climate change, educating businesspeople and facilitating startups, and bringing together companies with their clients. KIC Climate has helped develop 76 startups, 127 knowledge transfers and adoptions, 71 products and services, 96 Master’s graduates and 21 PhD graduates (climate-kic.org). EIT Digital concentrates on boosting economic growth and the quality of life. Its major themes are quality of life, high-performing and secure infrastructures, informed citizens and prevention. EIT Digital consists of 130 entities grouped in 11 broad geographical locations. Despite being an European effort, it is linked to the Silicon Valley. EIT Digital focuses strongly on attracting and training top talent. EIT Digital has supported over 200 scale-ups, aided in the transfer of over 80 technologies, raised nearly 70 million EUR in external investment, helped create 66 companies, supported over 50 products and services, and engaged nearly 1,500 students in various programs (eitdigital.eu). InnoEnergy places particular focus on clean coal and gas, enabling energy storage, renewable energies, efficiency in energy consumption, smart grids, nuclear power and energy from chemicals. InnoEnergy has 24 shareholders and over 360 project partners. Its educational programs are proud of over 200 PhD and 600 Master’s students, with graduates finding well-paid jobs within 6 months of graduation or starting their own businesses. InnoEnergy has supported nearly 200 startups, raised over 75 million EUR in external investment and aided in the creation of 90 companies. It filled 77 patents and supported 90 products and services with estimated sales at 3 billion EUR against 1.4 billion of total project costs, and 170 million investments from InnoEnergy (kic-innoenergy.com). 7.1. Introduction 207

Health aims to improve European healthcare, increase the competitiveness of health industry and improve European quality of life. It seeks to achieve these goals by promoting a healthy and active lifestyle, awareness, mental activity and dealing with chronic diseases as well as bringing fragmented European healthcare systems together. The community consists of over 140 partners, spans over 14 countries coordinated through six regional centers. It helped incubate several important innovations, such as stimulators for movement disorders, artificial pancreases, improved diagnosis and launched various educational programs (eithealth.eu). RawMaterials concentrates on securing a safe supply of increasingly better quality raw materials to European recipients. The community operates over the entire value chain, promoting efficiency, introducing new technologies and modifying underlying economic frameworks. RawMaterials aggregates over 120 partners located in six regional centers. RawMaterials has helped establish companies dealing with recycling contaminated scrap, improved mining techniques, worked on replacing some chemicals and metals with eco-friendly substitutes and secured a domestic source of nickel for the EU (eitrawmaterials.eu). Food focuses on developing a competitive food sector, with particular significance placed on bioeconomy. The community aims to improve nutrition, introduce resource efficiency, educate citizens and serve as a startup hub. It has five regional centers and over 50 partners. EIT Food hopes to launch a public education platform informing on food technology, a trust barometer for companies, introducing and integrating healthy eating procedures into school curricula, reduce food waste and help launch startups and innovations around the EU (eitfood.eu). The EIT-labelled educational programs offered by leading European universities aim at fostering creativity, mobility and entrepreneurship among European students. Both EIT awards and the roundtable of entrepreneurs aim to facilitate and promote innovative entrepreneurship in European economies. The EIT Regional Innovation Scheme supports “the integration of the knowledge triangle and increase the innovation capacity in areas and regions in Europe not directly benefitting from the EIT and its KICs” (eit.europa.eu). In Europe, it is now widely recognized that the relations and synergies between innovation, research and education are the main drivers of the global knowledge economy. European policy makers understood that: (1) progress in innovation, research and education has to be looked for in a synchronized way: the lack of progress in one domain can hinder advances in the other two; (2) attention must be paid to the links between the three domains: the lack of proper links between the elements of the system can render advances in a single domain ineffective. 208 7. European Institute of Innovation and Technology (EIT): towards the excellence...

The European Commission has been expressing the need to better integrate and exploit all the parts of the knowledge policy agenda for some time, in particular stressing the following (European Commission, 2010; eit.europa.eu): • the limited capacity of the EU to convert knowledge into commercial opportunities as the main weakness of the EU in the innovation area; • difficulties in promoting an innovation culture in research and education in the EU; • difficulties in developing critical masses of resources in innovation in the EU; • difficulties in rewarding excellence in research and education in the EU. At the same time, European policy makers have recognized the importance of today’s grand societal challenges (Rhisiart, 2013). Ensuring that the undertaken research is translated into innovative products and services that serve to tackle the key societal challenges faced by Europe is an identified challenge which has to be properly addressed. The EU2020 strategy puts special emphasis on the following major societal concerns (ec.europa.eu): • health, demographic change and well-being; • food security, sustainable agriculture and forestry, marine and maritime and inland water research; • secure, clean and efficient energy; • smart, green and integrated transport; • climate action, environment, resource efficiency and raw materials; • Europe in a changing world – inclusive, innovative and reflective societies; • secure societies – protecting the freedom and security of Europe and its citizens. The highlighted problems and concerns gave rise to the creation of the EIT. The EIT is an EU complex initiative to fully integrate the three sides of the knowledge triangle. Integration of all three sides of the knowledge triangle is expected to (Soriano and Mulatero, 2009, 2010; Maassen and Stensaker, 2011; Serbanica, 2011; Rus Mircea-Iosif, 2013; Veugelers and Del Rey, 2014): • effectively enhance knowledge sharing between firms, universities and research institutes in Europe; • invigorate the exchange of resources between members of the knowledge triangle for value-added innovation purposes; • smooth the process of university knowledge conversion into commercial opportunities; • collaboratively respond to the key societal challenges faced by Europe. Other EIT activities, such as EIT labelled degree programs and EIT awards, are designed to effectively promote an innovation culture in research and education in 7.2. Theoretical perspectives on the EIT contribution to innovation 209 the EU as well as adequately reward excellence in European research and education. The EIT roundtable of entrepreneurs helps to foster innovative entrepreneurship in Europe and to overcome the political impediments and mindset restrictions in this arena. The aim of this chapter is to evaluate the role of the European Institute of Innovation and Technology in stimulating innovation in Europe by integrating education, research and innovation, and contributing to the excellence of European science through the introduction of the “EIT degree”. This chapter proceeds as follows. First, the theoretical perspectives on the EIT contribution to innovation are presented. Next, the implementation of the EIT strategic agenda is assessed, followed by a critical analysis of the actors involved in the EIT policy. The subsequent section is devoted to the evaluation of the impact made by the EIT and KIC actions. And finally, the conclusions are presented.

7.2. Theoretical perspectives on the EIT contribution to innovation

The EIT actions contribute to European innovation, economic growth, competitiveness and employment through: (1) shaping values and attitudes in European societies that trigger innovation (mobility, openness, cooperativeness, trust); (2) introducing European regulations enabling the creation of KICs (Regulation [EC] No. 294/2008, Regulation [EU] No. 1292/2013); (3) fostering new management and control methods in European innovation (KIC as a new business model for the knowledge triangle, EIT RIS, EIT awards, EIT Performance Measurement System); and (4) pooling resources from EU (financial, human, physical) and non-EU countries (mostly human) from both the public and private sector. Selected theoretical frameworks are utilized here to analyze and comprehend the impact of collaboration (coordinated by EIT actions) between higher education and research institutions and industry (business firms) on innovation in Europe.

7.2.1. The knowledge triangle concept The concept of the knowledge triangle refers to the integration of innovation, research (and technology) and (higher) education. These three areas are peculiar from the economic viewpoint due to the existence of externalities – both intrinsic and between the three areas indicated above (Soriano and Mulatero, 2010; Romer, 1990; Grossman and Helpman, 1991; Aghion and Howitt, 1992). The intrinsic externalities of the three areas indicated above are derived from the difference between private and social returns, i.e. the private returns to innovation, research and education are lower than the social ones. This usually leads to underinvestment in the three discussed 210 7. European Institute of Innovation and Technology (EIT): towards the excellence... areas, resulting in market failure and the call for public intervention (Hendrikse, 2003). Intrinsic externalities associated with the three analyzed areas justify the public policy focus (to correct the market failure). However, policy makers also have to take into account the positive externalities arising between innovation, research and education, and manage those interactions in a systemic way (Soriano and Mulatero, 2010). It should be stressed that the EIT strategic goal to integrate the knowledge triangle in the EU corresponds to the major weaknesses of the European knowledge policy. As we can see from the cited literature, a fully-fledged and efficiently managed knowledge triangle exploits synergies and cross-fertilization between research and innovation (Harryson, 2006; Harryson et al., 2007; Soriano and Mulatero, 2009, 2010; Maassen and Stensaker, 2011; Serbanica, 2011; Rus Mircea-Iosif, 2013; Veugelers and Del Rey, 2014). A properly working knowledge triangle links universities to industry (businesses), allowing to effectively convert knowledge into commercial use and speedily gather the resources necessary to innovation processes (Harryson, 2006; Harryson et al., 2007; Soriano and Mulatero, 2009, 2010). Higher education institutions participating in the knowledge triangle remodel its routines and culture by imposing more mobility, openness and creativity as well as fair competition among students and faculty members.

7.2.2. The I-U collaboration approach According to Freeman (1991) and Okubo and Sjoberg (2000), R&D departments of business firms develop links to external sources of knowledge in order to facilitate successful innovation (Harryson et al., 2007). Research on industry-university collaboration shows that I-U relationships emerge not as a substitute of internal corporate R&D, but as a complementary activity (Adams et al., 2001; Callon et al., 1992; Gibbons et al., 1994; Harryson et al., 2007). As a result, we observe the emergence of new, interactive models of knowledge generation (Etzkowitz, 2003b; Kruecken, 2003). This reasoning is supported by the Triple Helix literature of university- industry-government relations (Leydesdorff and Etzkowitz, 1998; Etzkowitz and Leydesdorff, 2000; Etzkowitz, 2003b). The organizing principle of the Triple Helix is that the university plays a greater role in society as an entrepreneur (in this concept the entrepreneurial university takes over the central role of the Schumpeterian entrepreneur in industrial dynamics; Etzkowitz, 2003a; Schumpeter, 1934; Andersen, 2011). In the form of the Triple Helix we observe specific and unique I-U collaboration that presumes taking the role of the other (universities and firms assume some of the capabilities of the other: the entrepreneurial university takes a proactive stance in 7.2. Theoretical perspectives on the EIT contribution to innovation 211 putting knowledge to commercial use and firms move closer to an academic model, involving in high levels of training and sharing of knowledge; Etzkowitz, 2003b). On the basis of a review (Harryson et al., 2007) of several important publications in the field of I-U collaboration, the main advantages and benefits that companies expect in this context are: • gaining access to and acquiring new knowledge in specialized fields (e.g. energy, health, raw materials, ICT, green technologies); • creating a forum for networking by obtaining access to researchers, facilities and infrastructures; • getting access to complementary skills and resources; • enhanced R&D productivity by sharing R&D costs – sometimes also through access to government support; • improved appropriability conditions and accelerated commercialization of R&D outputs. The benefits of university partners who team up with companies are also manifold: improving the ability to conduct excellence-driven research and commercially exploit its results (Howells et al., 1998; Lee, 2000; Rogers et al., 1998); assuring proper protection, marketing and diffusion of the academic intellectual property and accelerating the rate of development of new products (Poyago-Theotoky et al., 2002; Rogers et al., 1998); gaining knowledge about practical problems for better alignment with industry and consumer needs (Lee, 2000; Lee and Win, 2004); as well as earning royalties – usually through IPR-licensing (for details, see Harryson et al., 2007).

7.2.3. The networked innovation approach It is also worth noticing that European KICs can be perceived as networks consisting of diverse stakeholders coming from industry, academia and policy – networks targeted at solving grand societal problems faced by Europe. The networked innovation approach can be traced back to the sociological theory of (strong and weak) ties (Granovetter, 1973; Boase et al., 2003; Van Wijk et al., 2004). In the context of interorganizational cooperation (such as KICs), qualities of weak ties determine network creativity and innovation (Granovetter, 1973; Harryson et al., 2007). According to Granovetter (1973), new ideas more often emanate through weak ties (from the margins of a specific network) rather than through strong ties (from the core of a specific network). This means that weak ties instead of strong ones are efficient for new knowledge acquisition and sharing (Harryson et al., 2007). Networked innovation brings about a lot of advantages to network members. As stated in the literature (Pittaway et al., 2004, p. 137), “principal benefits of networking [...] are risk sharing, obtaining access to new markets and technologies, speeding 212 7. European Institute of Innovation and Technology (EIT): towards the excellence... products to market, pooling complementary skills, safeguarding property rights when complete or contingent contracts are not possible”. The networked innovation paradigm stresses the role of science partners in the context of network formation. Industry-university collaboration may galvanize the process of network formation and inject an impetus into the venture innovation project. Some authors (Pittaway et al., 2004, p. 154) say that university partners “play an important role as independent network brokers and intermediaries within business networks”. University allies usually support networked innovation through informal and personal networks (Ahuja, 2000; Pittaway et al., 2004). University researchers tend to be most important for radical innovations (Fritsch, 2001). Laursen and Salter (2004) refer to the supporting role of university allies in search processes for new product ideas, new forms of organization and solutions to existing problems. Kaufmann and Tödtling (2001, p. 791) claim that “crossing the border to science increases the diversity of firms’ innovation partners and respective innovation stimuli which, in turn, improves the capability of firms to introduce more advanced innovations”. From the networked innovation literature we can infer that KICs (as interorganizational networks) can generate radically new knowledge (through the exploitation of numerous weak ties) at a relatively high rate, which usually leads to the creation of break-through innovations (Harryson, 2006), which, in turn, may constitute answers to the grand societal challenges faced by Europe.

7.3. The Strategic Innovation Agenda of the EIT

The European Commission proposal for a Strategic Innovation Agenda of the EIT was submitted to the Council of the European Union and the European Parliament on November 30, 2011. This proposal has built on the draft SIA version presented by the EIT Governing Board on June 15, 2011 and also took into account the recommendations of the external evaluation report and the results of wide and extensive consultations with stakeholders. On December 11, 2013 the European Parliament and the Council of the European Union, (1) having regard to the Treaty on the Functioning of the European Union, (2) having regard to Regulation (EC) No. 294/2008 of the European Parliament and of the Council, (3) having regard to the proposal from the European Commission, (4) after transmission of the draft legislative act to the national parliaments, (5) having regard to the opinion of the European Economic and Social Committee, and (6) acting in accordance with the ordinary legislative procedure, adopted the Strategic Innovation Agenda of the European Institute of Innovation and Technology for the period from 2014 to 2020 7.3. The Strategic Innovation Agenda of the EIT 213

(cf. Decision No. 1312/2013/EU published in the Official Journal of the European Union on December 20, 2013, L 347/892 EN). The decision of the Parliament and the Council on the adoption of the SIA of the EIT entered into force on December 23, 2013. The adopted Strategic Innovation Agenda of the EIT constitutes a fully-fledged strategic plan for innovation in Europe and allows the EIT to serve as a leading European initiative to fully integrate the three sides of the knowledge triangle with the mission to capitalize on the innovation capacity and capability of EU researchers, students and entrepreneurs from the EU and beyond (eit.europa.eu). The SIA set out by the EIT was expected to expand EIT activities as a showcase for innovation in Europe. Specifically, the SIA was expected to map out long-term development of the EIT within the Innovation Union, including the creation of new KICs, close links with the private sector and a stronger role in entrepreneurship. The SIA was also expected to be built on the EIT Foundation set up in 2010 and on the introduction in 2011 of the EIT degree as an internationally recognized label of excellence. In terms of long-term development of the EIT, the adopted SIA maps out: • the process of consolidating and fostering growth and impact of the existing KICs (Climate-KIC, EIT Digital and KIC InnoEnergy); • the process of creating new KICs (EIT Health and EIT RawMaterials in 2014, two further calls in 2016 in the themes of “food for future” and “added-value manufacturing”, and a call in 2018 in the theme of “urban mobility”); • the process of setting up close links with the private sector and emphasizing the role of entrepreneurship – the main drivers of development at EIT level are listed in Section 2.2. of the EIT Strategic Innovation Agenda: a. “innovation-driven excellent research for the creation of new businesses and new business models, including the possibility for SMEs and public institutions to participate more actively in innovation, management of IP portfolios and new approaches to IP sharing, entrepreneurship and new integrated forms of multi-disciplinary education; b. innovative governance and financial models based on the concept of open innovation or involving public authorities” (page 14 of the EIT SIA). The adopted SIA succinctly refers to the EIT Foundation and defines it as a “legally independent organization dedicated to promoting and supporting the work and activities of the EIT, and to enhancing the EIT’s societal impact” (page 5 of the EIT SIA). In the latter part of the document (the part focusing on the long- term development of the EIT) no single reference is made to the EIT Foundation. Therefore, it cannot be concluded that the EIT SIA builds on the EIT Foundation. 214 7. European Institute of Innovation and Technology (EIT): towards the excellence...

The EIT Foundation was the first foundation ever set up by a body of the European Union as a not-for-profit organization. Formally, the foundation was established as a charity under Dutch law in September 2010, in Rotterdam. As a philanthropic organization in its nature, the EIT Foundation aimed at attracting and channeling funding for initiatives complementing both the EIT and KICs, thus enhancing and broadening the EIT’s impact in promoting and stimulating innovation across Europe. Unfortunately, the last actions and initiatives taken by the EIT Foundation date back to 2013. After that, the foundation was closed. The significant role of the EIT degree as an internationally recognized label of excellence has been carefully stressed in the EIT SIA. New, trans- and interdisciplinary EIT-labelled degrees are expected to enable the EIT to be a leader of “a collaborative effort towards education for innovation with clear linkage to the broader European agenda for the modernization of higher education institutions thereby promoting the European Higher Education Area” (page 8 of the EIT SIA). The EIT is supposed to actively promote EIT-labelled degrees by monitoring its quality and consistent implementation across KICs (by the extensive use of peer and expert evaluations, as well as a dialogue with national and international quality assurance bodies). These steps are supposed to “enhance the national and international recognition and reputation of the EIT-labelled qualifications and raise their attractiveness globally, thereby enhancing the employability of graduates while providing a platform for collaboration at international level” (page 11 of the EIT SIA). On the basis of the above analysis, we may then conclude that Commitment 9 of the Innovation Union has been implemented in at least 83% (5 out of 6 components of the commitment have been successfully completed, the components are equally weighted here).

7.4. Actors involved in the EIT policy

Based on the classification of different actors in the innovation eco-system (Verspagen, Hollanders and Noben, 2016), we distinguish four main actors involved in the EIT and KIC actions, i.e. higher education institutes/universities, research institutes, business firms, and governments (policy makers). KICs supervised by the EIT are socio- technological networks consisting of the four above-mentioned types of innovation actors. The actor-based structure of each KIC is given below in Table 7.1. As we can infer from Table 7.1, the most numerous KIC partners are business firms (336), followed by universities/higher education institutes (241), research institutes (92) and government agencies/municipalities (26) (see also Figure 7.1). 7.4. Actors involved in the EIT policy 215

Table 7.1. The actor-based structure of KICs supervised by the EIT. The cells contain the numbers of actors and their share (in brackets) in the total number of given KIC partners

KIC University/ Research Business Government/ Total Education Institutes Firms Municipality (rows) Climate 39 13 65 12 129 (30%) (10%) (50%) (9%) EIT Digital 33 (23%) 11 (8%) 100 (69%) - 144 InnoEnergy 28 (39%) 15 (21%) 28 (39%) - 71 RawMaterials 47 (39%) 27 (22%) 45 (37%) 2 (2%) 121 EIT Health 81 (45%) 21 (12%) 68 (37%) 12 (7%) 182 EIT Food 13 (27%) 5 (10%) 30 (63%) - 48 Total (columns) 241 92 336 26 695 Source: own calculations based on KIC websites and official reports.

Figure 7.1. The shares of different innovation actors in all KICs Govern./Muni.

University

Business Firms

Research Institutes

Source: own calculations based on KIC websites and official reports.

Business firms dominate as KIC partners for Climate KIC (50% of all partners), EIT Digital (69% of all partners) and EIT Food (63% of all partners). This structure justifies the highest numbers of knowledge transfers/adoptions for Climate KIC and EIT Digital in the whole KIC family. Thus, Climate KIC, EIT Digital and EIT Food seem to be the most industry-oriented KICs in the entire family. Higher education institutes/universities dominate as KIC partners for EIT RawMaterials (39% of all partners) and EIT Health (45% of all partners). Thus, those KICs seem to be profiled towards innovative educational programs and radical rather than incremental innovations, since a high share of university partners in the innovation network is positively correlated with breakthrough rather than incremental innovations (Fritsch, 2001; Harryson, 2006). The structure of partners for the EIT Health justifies its extremely high impact on students. The actor-based structure of KIC InnoEnergy is the most balanced or ambidextrous. 39% of all partners for this KIC are higher education institutes/ 216 7. European Institute of Innovation and Technology (EIT): towards the excellence... universities, and 39% of all partners for this KIC are business firms. It seems that this KIC is both education- and industry-driven. It should also be observed that from the first-wave KICs (established in 2010), KIC InnoEnergy has developed the smallest number of partnerships (71 in total). Figure 7.2 below shows the sizes of partnership networks for all KICs. Observe that EIT Health is particularly active in initiating partnerships, since this KIC developed 182 partnerships (the highest number among all communities), even though this is the second-wave KIC (set up in 2014).

Figure 7.2. The sizes of partnership networks for all KICs supervised by the EIT EIT Food

Climate EIT Health EIT Digital

RawMaterials InnoEnergy

Source: own calculations based on KIC websites and official reports.

Based on the typology developed by Verspagen, Hollanders and Noben (2016), we can next identify the capabilities of actors involved in the EIT policy. A shortlist of those capabilities is given below in Table 7.2.

Table 7.2. Actor capabilities for participants of KIC innovation networks

Actor category Actor main capability Actor additional capabilities Higher education Providing education, undertaking Assimilating external knowledge institutes/universities fundamental research Research institutes Undertaking fundamental and Assimilating external knowledge, applied research addressing societal needs (also grand societal challenges faced by Europe) Business Firms Undertaking applied research, Generating economic value introduction of innovations, marketing of innovations Governments/ Providing regulation and Leveraging innovation for the Municipalities legislation, organizing and funding public good the public education and research system Source: Verspagen, Hollanders and Noben (2016) as well as KIC websites and official reports. 7.4. Actors involved in the EIT policy 217

However, the capabilities of all KICs together are far more complex than the simple sum of capabilities specified in Table 7.2. Synergistic relationships occur between capabilities of single actors listed in Table 7.2 leading to the unique capabilities of KICs perceived as innovation networks (see Table 7.3). These emergent capabilities of KICs are derived from the properly managed interactions between members of European innovation communities.

Table 7.3. Emergent capabilities of KICs dependent on the institutional level of analysis (Williamson, 2000)

Institutional level of analysis KIC unique (interaction-based) capabilities Social foundations (embeddedness) Shaping values and attitudes such as openness, mobility, cooperativeness and trust in European societies Institutional environment Creating formal rules of cooperation enabling actors to make innovations further Governance Developing heterogeneous, flexible and network-type forms of governance and organization Resource allocation Sourcing, pooling, mobilizing and allocating resources on a very large scale Source: Williamson (2000) and conducted interviews.

The economic theory allows to identify and explain specific interactions between different KIC partners. Education institutes produce skills that are indispensable inputs to research activities (performed both in research institutes and business firms). Research activities conversely exert pressure on education institutes and universities hopefully leading to education improvement (Soriano and Mulatero, 2010). Education institutes play a key role in fostering innovation in the economy. Proper education is required for consumers to fully benefit from new technologies as well as easily adapt to innovative marketing solutions (Soriano and Mulatero, 2010). Education institutes create the effective demand for innovation – well-educated consumers are often early adopters of new goods and services offered by business firms (Soriano and Mulatero, 2010). Research activity (both fundamental and applied) delivers the knowledge base that is embodied in numerous inventions that later are transformed to innovations. Innovations conversely stimulate the new waves of research undertaken by research- oriented universities, research institutes and business firms (Soriano and Mulatero, 2010). Innovations also have an impact on public education systems and government agencies, not only by providing new techniques and media to support the teaching process, but also by improving the learning and knowledge-sharing environment, for example through digital platforms aimed at dealing with the needs of students as well as faculty and public administration (Soriano and Mulatero, 2010). 218 7. European Institute of Innovation and Technology (EIT): towards the excellence...

Qualitative research interviews conducted with the representatives of KICs and the EIT allowed to identify several obstacles or barriers to the dynamic development of KIC innovation networks. The identified obstacles and the corresponding quotes from the interviews are given below in Table 7.4.

Table 7.4. Obstacles identified in KIC innovation networks on the basis of conducted interviews

Interviewee Identified obstacle Quotes Dr. Karen Hanghøj Conservative attitudes of “I will say that, actually as it’s turning up, (Chief Executive Officer, professors and teaching they are actually quite conservative. They Managing Director of faculties participating have a curriculum that they like, they have the EIT RawMaterials) in the EIT labelled classes that they like to teach. And I think programs actually the raw materials sector is actually quite conservative. They will not like me to say that, but I think actually speaking to a professor, teachers, mining engineers and telling him or her that entrepreneurship is important, is something that is new to them”. Dr. Karen Hanghøj Lack of human capital “I think again for our field, this is the (Chief Executive Officer, in certain European greatest challenge that the raw materials Managing Director of industries sector has, is the lack of human capital. the EIT RawMaterials) And again, this is different for different KICs, but mining and economic geology and all those kinds of things have been shut down in Europe over the last couple of decades. So there are very few places left that actually have excellence. This is so why we need the labelling to give, you could say, new ideas and innovation in our education. We also just need to expand education in general. But at the same time, we also need to expand other aspects, but I think education is extremely important for the long-term impact”. Dr. Karen Hanghøj No clear regulations and “And actually having to spend resources, (Chief Executive Officer, guidelines given by the developing that, in 5 different ways, Managing Director of EIT when it comes to in 5 different KICs is ridiculous in my the EIT RawMaterials) KIC management opinion. And you have people like myself with technical PhDs and a lot of smart people who should be out there making innovation in our sector, being knocked down by deciding where we will go or whether we can take a taxi from the airport, because there is no guidelines. So I think that it also will nicely go into your report, because if it’s read by the EIT, as constructive feedback as well, then you can do better”. 7.4. Actors involved in the EIT policy 219

Interviewee Identified obstacle Quotes Michał Górzyński Different nature and “OK, I can come back to the examples, (Innovation Officer and behavioral patterns of but actually the issue of a completely new Head of Monitoring actors as possible sources approach, distant approach, which is about Section, European of tensions bringing together different actors from Institute of Innovation different disciplines”. and Technology) Michał Górzyński Overlapping grand Question: “So each KIC is created so to (Innovation Officer and societal challenges faced address the given grand societal challenge Head of Monitoring by KICs in Europe, right?” Section, European Answer: “More or less, but when you look Institute of Innovation at raw materials, it addresses directly the and Technology) societal challenge “restart efficiency in raw materials”, but also goes for the security and efficient energy for example”. Dr. Jakub Miler (Chief Possible clashes between “The role of industry knowledge should Executive Officer, KIC the Strategic Innovation be underlined; the knowledge specific to InnoEnergy Poland Plus) Agenda (SIA) of the one sector cannot be translated to another EIT and sector specific sector; sector regulations are far more regulations important than the SIA”. Dr. Jakub Miler (Chief Different models of “Each KIC is a totally different institution, Executive Officer, KIC organization of KICs and each KIC is differently organized. InnoEnergy Poland Plus) (lack of appropriate Each KIC exhibits a totally different model common guidelines) of operations”. Source: conducted interviews.

The identified obstacles boil down to: (1) human capital issues (specific attitudes of the teaching faculties at the universities and education institutes participating in the KICs, lack of appropriately qualified employees in certain European industries addressed by KIC actions); and (2) too much heterogeneity of KICs (lack of common guidelines in certain areas, behavioral and institutional diversity of actors, different models of organization, overlapping societal challenges addressed by KICs, different sector regulations that affect KIC actions). The first obstacle is in general difficult to be dealt with, at least in the short term, even with a strong policy focus, while the second obstacle calls for a detailed and immediate discussion between KIC managers and the EIT representatives. Both KIC managers and the EIT employees should be aware of possible tensions between education institutes, business firms and research institutes (Maassen and Stensaker, 2011). First, there might be tensions between education institutes and business firms. The increasingly strong pressure for external control over academic program development, as materialized through the introduction of new national and supranational accreditation schemes, stands in contrast to the political ambitions concerning creativity so stressed within the area of industry innovation. Since accreditations and guidelines all have a strong influence on academic standards, the 220 7. European Institute of Innovation and Technology (EIT): towards the excellence... two logics (logic of standards in higher education and logic of creativity in industry innovation) seems to collide in this (Maassen and Stensaker, 2011). A possible implication could be that knowledge transfer from universities to industry is slowed down as innovations are decoupled from practical and educational application (Maassen and Stensaker, 2011). Second, potential tensions between education and research institutes can occur. On the one hand, the leading universities in Europe insist on offering research-based curricula. On the other hand, we observe significant political pressures in the EU to concentrate research activity (in particular, fundamental research) in relatively few universities seen as the key actors (Maassen and Stensaker, 2011). As a result, education, especially at the undergraduate level, runs the serious risk of becoming a separate activity. Various scenarios of this separation might materialize, e.g. within the university a stronger division of labor might develop between “teaching staff” and “researchers” as one of the consequences (Dill and Soo, 2005). Third, possible tensions between research and innovative business firms should be taken into consideration. A number of problems can be identified. For example, concentration of resources in research may actually weaken the strategic ability of research institutes and universities (Geuna and Martin, 2003; Geiger, 2004; Maassen and Stensaker, 2011) in developing external links. General incentives linked to research activities may actually be negatively related to innovation and technology transfer (Marksman et al., 2004; Maassen and Stensaker, 2011). Gilsing and colleagues (2011) elaborate upon the frequent case of conflict of interests between business firms and researchers: business firms are focused on the appropriation of research results, whereas researchers look for the dissemination of research results to gain a wide scientific reputation. Based on the typology developed by Verspagen, Hollanders and Noben (2016), we can now identify the behavioral roles of actors involved in the EIT policy. A shortlist of those roles is given below in Table 7.5.

Table 7.5. Behavioral roles of actors involved in EIT policy

Actor category Main behavioral role Additional behavioral roles Higher education institutes/ Education services Fundamental research and universities industry services Research institutes Fundamental and applied Industry services research Business Firms Science-based innovation Applied research and education services Governments/Municipalities Organizing role with specific Public-interest driven policy instruments education and consultancy Source: Verspagen, Hollanders and Noben (2016) as well as KIC websites and official reports. 7.5. Impact of the EIT and KIC actions 221

Please observe that the identified behavioral roles of actors involved in EIT policy strongly correspond to the roles elaborated upon in the Triple Helix literature of university-industry-government relations (Leydesdorff and Etzkowitz, 1998; Etzkowitz and Leydesdorff, 2000; Etzkowitz, 2003a; 2003b). The organizing principle of the Triple Helix is that the university/education institute plays a greater role in society than an entrepreneur (Etzkowitz, 2003a; Schumpeter, 1934; Andersen, 2011). In the Triple Helix we observe specific and unique I-U collaboration that presumes taking the role of the other (universities and business firms assume some of the capabilities of the other: the entrepreneurial university takes a proactive stance in putting knowledge to commercial use and business firms move closer to an academic model, involving in high levels of training and sharing of knowledge; Etzkowitz, 2003b).

7.5. Impact of the EIT and KIC actions

The collected data show the continuous increase in: (i) the number of innovations coming from the “first wave” KICs (number of new or improved products/services/ processes); (ii) the number of start-ups created (2015 seems to be an exception); (iii) the number of business ideas incubated; (iv) the number of new graduates from EIT labelled programs; and (v) the sum of knowledge transfers/adoptions that are the direct output of KIC activity. The attractiveness of EIT labelled degree programs, measured as a ratio of the number of eligible applicants divided by the number of available seats for eligible EIT labelled Master’s and PhDs degrees, significantly rose (doubled) from about 1.5 in the years 2010-2012 to more than 3.0 in the years 2013-2015. Based on the EIT independent evaluation report 2017, one may also compare the achieved results with the targeted key performance indicators of the three first- wave KICs for the years 2010-2015 (see Table 7.6). As we can observe, 22 out 36 targets have been met for the EIT Climate-KIC, 12 out of 36 for EIT Digital, and 20 out of 36 for EIT InnoEnergy. Thus, the average result for all three KICs is 54 out of 108 targets, which is exactly 50% of the plan. This result could certainly be improved, but generally remains at a satisfactory level.

Table 7.6. Key Performance Indicators for KICs (comparison of targets and results)

2010-2012 2013 2014 2015 EIT Climate KIC Achieved Target Achieved Target Achieved Target Achieved Attractiveness of educational 0.8 0 20.1 0 4.2 1.4 3.9 programs New graduates 17 20 42 50 46 123 117 Business ideas incubated 72 100 133 98 216 225 276 Start-ups/spin-offs created 1 45 33 71 48 83 38 222 7. European Institute of Innovation and Technology (EIT): towards the excellence...

2010-2012 2013 2014 2015 EIT Climate KIC Achieved Target Achieved Target Achieved Target Achieved Knowledge transfers/ 15 15 67 70 82 109 82 adoptions New/improved products/ 6 30 44 20 39 118 52 services/processes EIT Digital Attractiveness of educational 0 3 3.1 2.8 2.7 5.9 4.1 programs New graduates 0 0 0 70 74 165 146 Business ideas incubated 32 90 93 218 169 134 174 Start-ups/spin-offs created 9 18 10 35 21 14 8 Knowledge transfers/ 24 75 48 163 123 123 193 adoptions New/improved products/ 6 30 2 34 20 26 24 services/processes EIT InnoEnergy Attractiveness of educational 6.7 0 2.1 0 3.4 7.4 6.1 programs New graduates 28 120 98 149 121 145 132 Business ideas incubated 76 59 39 98 58 54 91 Start-ups/spin-offs created 8 10 14 15 21 19 23 Knowledge transfers/ 2 5 9 10801653 adoptions New/improved products/ 0931512816 services/processes EIT Health forecast for 2016- 2018 Business ideas incubated 340 Start-ups/spin-offs created 165 New/improved products/ 160 services/processes Number of students (online 1 000 000 courses) EIT RawMaterials forecast for 2016- 2019 New graduates 300-600 New jobs created 500-1500 Start-ups/spin-offs created 10-20 New/improved products/ 50-80 services/processes 7.5. Impact of the EIT and KIC actions 223

2010-2012 2013 2014 2015 EIT Climate KIC Achieved Target Achieved Target Achieved Target Achieved Number of patents 120-180 EIT Food forecast for 2018- 2024 New graduates 400 Business ideas incubated 533 Start-ups/spin-offs created 86 New/improved products/ 398 services/processes Number of stakeholders 147 000 Source: EIT independent evaluation report 2017, data received from EC Policy Officer – EIT Strategy, Economic and Societal Impact.

Table 7.7. Key Performance Indicators for KICs

Key Performance Definition 2015 2014 2013 2012 2011 2010 Indicator Attractiveness Ratio of the number of eligible 4.70 3.13 5.68 1.42 1.65 1.16 of educational applicants divided by number programs of available seats for eligible EIT labelled Master’s and PhD degrees Number of new Number of new graduates from EIT 395 241 131 36 9 30 graduates labelled PhD and Master’s programs Number of Number of formalized 509 443 207 101 79 0 business ideas commitments established between incubated KICs and entrepreneurs Number of start- Number of start-ups or spin-offs 66 90 28 14 4 1 ups created that are a direct output of a KIC activity Knowledge Sum of knowledge transfers (from 314 285 75 35 5 0 transfer/adoption one KIC partner to another KIC partner or to third parties) and adoptions (by KIC partners) that are a direct output of a KIC activity New or improved Number of new or improved 92 71 19 9 3 0 products/ products/services/processes that services/processes area direct output of a KIC activity launched into the market Source: EIT Annual Activity Reports. 224 7. European Institute of Innovation and Technology (EIT): towards the excellence...

Table 7.8. Outcomes of collaboration in KIC networks reported by KIC partners

New New New models New Publi- Field Patents technology product of collaboration process cations Climate change and 11.67 3.33 11.67 13.33 1.67 5 Environment Energy 15 3.33 1.67 23.33 0 15 Food security 1.67 6.67 5 6.67 0 3.33 Health 25 25 26.67 23.33 10 25 ICT 18.33 20 10 16.67 5 8.33 Manufacturing 8.33 18.33 6.67 33.33 6.67 8.33 Materials 6.67 11.67 13.33 15 0 10 Urban mobility and 3.33 5 0 5 0 5 Transport Source: own survey.

Table 7.9. Impact made by EIT and KIC actions – indirect impact assessments based on interviews

Area of impact Interview 1 Interview 2 Interview 3 Economic Increased quality Facilitating SMEs Facilitating growth and competition; Creation of growth; Increased investment; Creation of new SMEs; Development innovation new SMEs; Increased of specific sectors and and research; sectoral competitiveness; regions in Europe; Increased sectoral Increased innovation and Stimulation of R&D competitiveness; research activities of firms; Energy independence Increase in employment Social Increased social Positive changes Increased social cooperation; Enhanced in the educational cooperation; Enhanced equality treatment; systems in Europe equality treatment; Positive changes in the Increased health and educational systems in safety levels; Positive Europe changes in the educational systems in Europe Environmental More efficient energy use Fostering the efficient Fighting climate change; use of resources; Fostering the efficient Reducing and use of resources; managing waste Reducing and managing waste; Minimizing environmental risks Source: own elaboration based on the interviews.

Based on Dobrinsky (2016), three areas of indirect policy impact – economic, social and environmental – have been distinguished here (cf. Table 6.9). In terms of economic impact made by the EIT, all interviewees pointed to the creation or growth facilitation of SMEs, increase in innovation and research taken up by business firms or 7.5. Impact of the EIT and KIC actions 225 universities, and boosting sectoral competitiveness or developing specific industrial sectors. In terms of social impact, all interviewees stressed the observed positive changes in the educational systems in Europe and also two interviews allowed to report the observed increased social cooperation resulting from KIC actions. In terms of environmental impact, the efficiency gains in using energy or resources have been identified by all interviewees. Two of them also pointed to the observed reduction and better management of waste. European policy makers put special emphasis on the following major societal concerns (ec.europa.eu): • health, demographic change and well-being; • food security, sustainable agriculture and forestry, marine and maritime and inland; • water research; • secure, clean and efficient energy; • smart, green and integrated transport; • climate action, environment, resource efficiency and raw materials; • Europe in a changing world – inclusive, innovative and reflective societies; • secure societies – protecting the freedom and security of Europe and its citizens. At least some of the grand societal challenges faced by Europe and mentioned above are addressed by the KIC actions (see Table 7.10). Table 7.10, based on the survey results (see appendix B), shows the numbers of KIC partners (and their countries of residence) involved in tackling the European grand societal challenges. Certainly, such measures are not the direct effects of the EIT and KIC policy, but can be treated as indicators of the indirect impact of EIT policy on the major concerns faced by Europe. As we can infer from the table below, most KIC partners (over 70% of the sample) strive to contribute to the project of inclusive, innovative and reflective European societies. Lastly, it is necessary to know how the EIT and KIC actions aimed at exerting both a direct and indirect impact are efficient, effective, relevant, coherent and value generating. The effectiveness analysis considers how successful the given action has been in achieving or progressing towards its objectives. Efficiency considers the relationship between the resources used by an action and the changes produced by this action. Relevance looks at the relationship between the needs and problems in society and the objectives of the particular action. The coherence criterion looks at how well the action works (i) internally and (ii) with other EU actions/interventions. The EU added value criterion considers arguments about the value resulting from EU actions that is additional to the value that would have resulted from actions initiated at regional or national levels by both public authorities and the private sector. 226 7. European Institute of Innovation and Technology (EIT): towards the excellence...

Table 7.10. Grand societal challenges addressed by KIC partners – survey results Demographic change sustainable Food security, agriculture and forestry Secure, clean and efficient energy Smart, green and integrated transport efficiencyResource and raw materials world in a changing Europe and innovative – inclusive, reflective societies Secure societies – protecting the freedom and security of and its citizensEurope Denmark0110032 United Kingdom1221063 Finland0012351 France1010053 Germany3221151 Ireland0000010 Spain1131200 Netherlands0110010 Portugal0010021 Austria0000010 Croatia0000010 Estonia0000010 Poland0021320 Sweden2211342 Belgium1100030 Switzerland 1 1 1 2 1 1 1 Italy0011121 All10111710144315 All (percentage) 16.67% 18.33% 28.33% 16.67% 23.33% 71.67% 25.00% Source: own survey.

Based on the online survey targeted at KIC partners (see Table 6.11), we may say that in general the functioning of KICs is assessed well. KICs work most efficiently in the opinion of business firms, followed by universities and non-university research institutes. KICs are most effective in the opinion of business firms, followed by research institutes and universities. The actions taken by KICs are the most relevant in the opinion of business firms, followed by research institutes and universities. The same sequence goes for the coherence criterion. Business firms perceive KIC actions as the most value generating in comparison to the assessment given by universities and research institutes. As we can see, the functioning of KICs is best assessed by business 7.6. Conclusions 227 firms, perhaps because business firms benefit the most from the collaboration in KIC innovation networks and firms’ benefits seem to be the most direct and visible in a relatively short term. The benefits of the education and research sector are much more spanned in time, and often these are only indirect.

Table 7.11. How do you assess the functioning of the Knowledge and Innovation Community that is your partner (please use a 1-6 scale, where 1 is the lowest grade and 6 is the highest)?

University/higher education institution Mean Standard deviation efficiency (costs to benefits relation) 3.74 1.05 effectiveness (extent to which objectives are achieved) 4.21 0.85 relevance (correspondence between implemented actions and KIC 4.26 0.56 goals) coherence (consistency of actions taken by the given KIC) 4.32 0.58 European Union added value (value additional to the value that would have resulted from actions initiated at national level by 4.32 0.58 public or private sector) Non-university research institute efficiency (costs to benefits relation) 3.71 0.91 effectiveness (extent to which objectives are achieved) 4.29 0.83 relevance (correspondence between implemented actions and KIC 4.29 0.73 goals) coherence (consistency of actions taken by the given KIC) 4.43 0.51 European Union added value (value additional to the value that would have resulted from actions initiated at national level by 4.14 0.53 public or private sector) Business Firm efficiency (costs to benefits relation) 3.96 0.90 effectiveness (extent to which objectives are achieved) 4.59 0.84 relevance (correspondence between implemented actions and KIC 4.52 0.64 goals) coherence (consistency of actions taken by the given KIC) 4.56 0.70 European Union added value (value additional to the value that would have resulted from actions initiated at national level by 4.56 0.58 public or private sector) Source: own survey.

7.6. Conclusions

A review of relevant literature suggests that collaboration (coordinated by EIT actions) between higher education and research institutions and industry (business firms) enhances innovation performance in Europe. EIT actions are expected to appropriately address European knowledge policy concerns, i.e.: 228 7. European Institute of Innovation and Technology (EIT): towards the excellence...

• limited capacity to convert knowledge into commercial opportunities; • difficulties in promoting an innovation culture in research and education; • difficulties in developing critical masses of resources in innovation; • difficulties in rewarding excellence in research and education. We may conclude that Commitment 9 of the Innovation Union has been implemented in at least 83% (5 out of 6 components of the commitment have been completed successfully and in a timely manner). The adopted SIA refers to the EIT Foundation in a very limited manner. In the main part of the SIA (the part focusing on the long-term development of the EIT) no single reference is made to the EIT Foundation. Thus, it is difficult to conclude that the EIT SIA builds on the EIT Foundation. The implementation of the SIA and the subsequent taking up of various KIC actions led to expected policy results in terms of stimulating innovation in Europe. Qualitatively, KIC actions facilitated the creation and growth of SMEs, brought increase in European innovation and research taken up by business firms and universities, enhanced the competitiveness of key industrial sectors (economic impact), brought positive changes in European educational systems, increased social cooperation (social impact) as well as allowed to achieve efficiency gains in managing energy, resources and waste (environmental impact). What is especially worth mentioning is that KIC actions have been almost perfectly executed in terms of the effectiveness, efficiency, relevance, coherence and EU added value evaluation criteria. We distinguished four main actors involved in the EIT and KIC actions, i.e. higher education institutes/universities, research institutes, business firms, and governments (policy makers). Business firms dominate as KIC partners for Climate KIC (50% of all partners), EIT Digital (69% of all partners) and EIT Food (63% of all partners). Higher education institutes/universities dominate as KIC partners for EIT RawMaterials (39% of all partners) and EIT Health (45% of all partners). The actor- based structure of KIC InnoEnergy is the most balanced or ambidextrous. 39% of all partners for this KIC are higher education institutes/universities, and 39% of all partners for this KIC are business firms. When it comes to the identification of actor capabilities, we conclude that the typical capabilities of single actors (providing education, undertaking fundamental and/or applied research, assimilating external knowledge, addressing societal needs, introducing innovations, marketing innovations, generating economic value, providing regulations and legislation, organizing and funding the public education and research system, leveraging innovation for the public good) undergo a synergistic transformation in KIC networks leading to the creation of emergent, unique 7.6. Conclusions 229 capabilities (shaping values and attitudes such as openness, mobility, cooperativeness and trust in European societies, creating formal rules of cooperation enabling actors to make innovations further, developing heterogeneous, flexible and network-type forms of governance and organization, sourcing, pooling, mobilizing and allocating resources on a very large scale) of European innovation communities treated as systems. As regards the roles of actors, we identified the following roles in the functioning of KIC networks: education services, fundamental research, applied research, industry services, science-based innovation, organizing role with specific policy instruments, and public-interest driven education and consultancy. In terms of the indicators of the impact made by the SIA, a continuous increase in: (i) the number of innovations (number of new or improved products/services/ processes); (ii) the number of start-ups created; (iii) the number of business ideas incubated; (iv) the number of new graduates from EIT labelled programs; and (v) the sum of knowledge transfers/adoptions, can be observed. Also, the direct outcomes reported by the KIC partners are promising. In the fields – climate change and environment, energy, food security, manufacturing, materials and urban mobility and transport – the most frequently reported direct outcome were process innovations. New technologies were relatively frequent in the health and ICT fields. New products were relatively frequent in health and ICT sectors. Process innovations were especially frequent in the manufacturing sector. Patents have been registered in four sectors, i.e. climate change and environment, health, ICT and manufacturing. In general, however, patents are not very frequent as a direct outcome of collaboration under the KIC umbrella. Publications were quite frequent, but only in the health field. Lastly, based on the online survey targeted at KIC partners, we may conclude that in general the functioning of KICs is assessed well. KICs work most efficiently in the opinion of business firms, followed by universities and non-university research institutes. KICs are the most effective in the opinion of business firms, followed by research institutes and universities. The actions taken by KICs are the most relevant in the opinion of business firms, followed by research institutes and universities. The same sequence goes for the coherence criterion. Business firms perceive KIC actions as the most value generating in comparison to the assessment given by universities and research institutes. As we can see, the functioning of KICs is best assessed by business firms, perhaps because business firms benefit the most from the collaboration in KIC innovation networks and firms’ benefits seem to be the most direct and visible in a relatively short term. The benefits of the education and research sector are much more spanned in time, and often these are only indirect. 230 7. European Institute of Innovation and Technology (EIT): towards the excellence...

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Appendices

Appendix A – Scenario of the qualitative research interviews (list of questions prepared) 1. The integration of all three sides of the knowledge triangle is expected to: • effectively enhance the knowledge and resource sharing between firms, universities and research institutes in Europe; • smooth the process of university knowledge conversion into commercial opportunities; • collaboratively respond to the key societal challenges faced by Europe. Based on your professional experience, do you think that KICs fulfil these expectations?

2. How (by what actions) does KIC EIT RawMaterials/KIC InnoEnergy/do KICs enhance knowledge and resource sharing between firms, universities and research institutes in Europe? How effective are those actions? How efficient?

3. How (by what actions) does KIC EIT RawMaterials/KIC InnoEnergy/do KICs smooth the process of university knowledge conversion into commercial opportunities? Could you give some examples? How effective are those actions? How efficient?

4. Do you think that the actions taken by KICs are relevant, given the needs and objectives specified in the Innovation Union Strategy?

5. Do you think that the actions taken by EIT RawMaterials/KIC InnoEnergy/KICs respond to the key societal challenges faced by Europe (demographic change and well-being, food security, sustainable agriculture and forestry, secure, clean and efficient energy, smart, green and integrated transport, resource efficiency and raw materials, Europe in a changing world – inclusive, innovative and reflective societies, secure societies – protecting the freedom and security of Europe and its citizens)? How do you assess the effectiveness and efficiency of such actions?

6. Other EIT activities, such as EIT labelled degree programs are designed to: effectively promote an innovation culture in research and education in the EU as well as adequately reward excellence in European research and education. Based on your professional experience, do you think that EIT labelled degree programs effectively promote an innovation culture in research and education? What positive changes do Appendices 235 you observe as direct results of EIT labelled educational programs? Could you give some examples?

7. Do you think that EIT labelled degree programs are successful at fostering entrepreneurial spirit among European students and academics? Do you strive to build and support a functional and strong network of graduates from KIC educational and training activities? Have you already taken some actions to achieve this goal?

8. On the one hand we have KICs and on the other EIT labelled educational programs. Do you feel that these actions are coherent? Do you see some complementarities between them? Or maybe some clashes or a wasteful duplication of resources? Are the actions taken by the EIT and KICs consistent with other Innovation Union initiatives, such as the construction of priority European research infrastructures, stronger involvement of SMEs in research and innovation programs (CIP, FP7, Horizon 2020) or strengthening the scientific base for policy making through the Joint Research Center?

9. Based on your experience, do you think that the actions taken by KICs influence pro-innovative values and attitudes, such as openness, mobility, cooperativeness or trust, in European societies? How durable can these changes be? What long-term effects of these behavioral changes do you foresee?

10. Do you take the Strategic Innovation Agenda of the European Institute of Innovation and Technology into account while making strategic decisions in the KIC you manage? Do you think that the Strategic Innovation Agenda of the European Institute of Innovation and Technology has an impact on innovation in Europe? How do you assess this impact? Do you see a need to introduce some changes in the SIA or other formal documents regulating the functioning of EIT and KICs? Maybe you see some impediments in the SIA?

11. Have you heard about the actions taken by the EIT Foundation? How do you assess them? Did you build on experiences of the EIT Foundation while creating your own projects and programs?

12. Do you find the network-type organization of KICs innovative? What can you tell about the governance structure of EIT RawMaterials/KIC InnoEnergy, do you find some innovative solutions in it? Does it allow to appropriately respond to market and societal challenges? 236 7. European Institute of Innovation and Technology (EIT): towards the excellence...

13. What resources can be acquired by entrepreneurs thanks to EIT RawMaterials/ KIC InnoEnergy? Could you briefly elaborate upon “validation and acceleration” (networks of infrastructure and up-scaling projects) and “business creation and support” (entrepreneurship support services, start-up boosters and funding instruments) activities? Could you provide some examples?

14. How do you assess the economic impact of the actions taken by the EIT RawMaterials/KIC InnoEnergy/KICs? Do they e.g. lead to: a. increased quality competition in the European market? b. creation of new SMEs? c. development of specific sectors and regions in Europe? What sectors and regions? d. stimulation of R&D activities of firms? e. labor market changes (employment increase)?

15. How do you assess the social impact of the actions taken by the EIT RawMaterials/ KIC InnoEnergy/KICs? Do they e.g. lead to: a. increased social cooperation? b. enhanced equality treatment? c. positive changes in the educational systems?

16. How do you assess the environmental impact of the actions taken by the EIT RawMaterials/KIC InnoEnergy/KICs? Do they e.g. lead to: a. more efficient energy use? b. increased adaptability to climate change?

17. Do you monitor (by using a set of indicators) the impact made by the EIT RawMaterials/KIC InnoEnergy/KICs on innovation in Europe? Do you have data on e.g. the number of business creations, patents, the number of graduates resulting from the EIT RawMaterials/KIC InnoEnergy/KIC actions?

18. Qualitatively, how do you assess the impact of EIT RawMaterials/KIC InnoEnergy/ KICs on innovation in Europe, both in the short and long term?

19. Do you think that actions of the EIT RawMaterials/KIC InnoEnergy/KICs allowed to achieve EU added-value, that is value additional to the value that would have resulted from actions initiated at regional or national levels by public authorities or the private sector? Appendices 237

20. What long-term effects of KIC operations do you foresee in terms of European innovation, competitiveness and employment?

Appendix B – Online survey directed at KIC partners (questionnaire) 1. What type of KIC partner are you? a. university/higher education institution b. non-university research institute c. business firm d. other

2. What is the name of the Knowledge and Innovation Community that is your partner? a. Climate b. EIT Digital c. InnoEnergy d. RawMaterials e. EIT Health

3. What is the scientific/industrial profile of your organization? Please specify (e.g. Life Sciences, Material Science, etc.). What is your country of residence? Please specify.

...

4. What grand societal challenges are addressed by your actions? Please choose from the list below. • demographic change and well-being • food security, sustainable agriculture and forestry • secure, clean and efficient energy • smart, green and integrated transport • resource efficiency and raw materials • Europe in a changing world – inclusive, innovative and reflective societies • secure societies – protecting the freedom and security of Europe and its citizens

5. How do you assess the functioning of the Knowledge and Innovation Community that is your partner in terms of (please use a 1-6 scale, where 1 is the lowest grade and 6 is the highest): a. efficiency (costs to benefits relation) b. effectiveness (extent to which objectives are achieved) 238 7. European Institute of Innovation and Technology (EIT): towards the excellence... c. relevance (correspondence between implemented actions and KIC goals) d. coherence (consistency of actions taken by the given KIC) e. European Union added value (value additional to the value that would have resulted from actions initiated at national level by public or private sector)

6. Based on your experience, do you think that actions taken by KICs influence pro-innovative values and attitudes, such as openness, mobility, cooperativeness or trust, in European societies? a. definitely yes b. yes c. no d. definitely no e. hard to say

7. Do you think that the Strategic Innovation Agenda of the European Institute of Innovation and Technology has a positive impact on innovation in Europe? a. definitely yes b. yes c. no d. definitely no e. hard to say

8. How do you assess the governance mode of Knowledge and Innovation Communities, i.e. the so-called knowledge triangle? a. extremely effective in boosting European innovation b. somewhat effective in boosting European innovation c. no impact on boosting European innovation d. somewhat effective in hindering European innovation e. extremely effective in hindering European innovation

9. Do Knowledge and Innovation Communities allow to effectively enhance knowledge and resource sharing between firms, universities and research institutes in Europe? a. definitely yes b. yes c. no d. definitely no e. hard to say Appendices 239

10. What are the direct outcomes of your cooperation under the KIC umbrella? Tick the appropriate

New New New models New Publi- Field Patents technology product of collaboration process cations Health ICT Materials Urban mobility and Transport Climate change and Environment Energy Food security Manufacturing Marzenna Anna Weresa

Chapter 8 Implementing evidence-based policies: lessons learned from Joint Research Centre (JRC) activity

8.1. Introduction

The aim of this chapter is to identify whether and how scientific evidence impacts the policy-making process in the Member States of the European Union and through these policies further impacts innovation in the EU. The focus is on the knowledge and evidence produced at the EU level by the Joint Research Centre (JRC), which is a scientific unit of the European Commission organized as a Directorate-General of the European Commission serving policy makers in the EU as in-house science support. This chapter addresses the following research questions: • What is a rationale for evidence-based policies? • To what extent has JRC research activity strengthened the European science base for policy making since the launch of the Innovation Union initiative in 2010? • How can evidence-based policies shape the policy-making process in the EU? • To what extent do evidence-based policies impact innovation in the EU? The starting point is a literature review showing the potential impact channels through which scientific evidence impacts policy making. This is followed by an empirical analysis, which examines how scientific evidence delivered by the Joint Research Centre of the European Commission is used in the policy process by the national and regional administration in EU Member States. The theoretical considerations and empirical analysis enable forming conclusions about the role of scientific evidence produced at the EU level in Member States’ policy making. 8.2. Evidence-based policy and its impact on innovation: a literature review 241

8.2. Evidence-based policy and its impact on innovation: a literature review

The idea that scientific evidence should underpin policy decisions is not new. It can be found in the work of Plato and Aristotle or Descartes, among others (Sutcliffe and Court, 2005, p. 1). The relationship between science and policy has been a subject of debate for many years and there is no doubt that policy should be based on the best available scientific and technical information (see for instance: Marston and Watts, 2003; Pollard and Court, 2005; Wilsdon, 2014; European Commission, 2015; Newman, 2017; Parkhurst, 2017). However, it is not easy to provide a clear definition of evidence -based policy (EBP). Young et al. (2002, p. 216) indicate two dimensions of evidence- based policy. One is related to the ways of policy-making, and the other is embodied in the nature of social sciences. The theory of public policy defines this policy as a set of actors, institutions, decision-making processes and outcomes assuming that the latter is a result of policy interventions that use rigorous evidence in the design, implementation and refinement of policy to meet designated policy objectives. There is a consensus among public policy theorists that a causal relationship exists among these elements constituting public policy. The outcomes are determined by both effectiveness and efficiency with which the results are generated, and by the unintended side effects and legitimacy of a policy (Kitschelt, 1986, p. 67). The European Commission (2015, p. 3) defines EBP indirectly providing the following requirements to scientific advice: “Scientific advice needs to be independent of political or institutional interests, bring together evidence and insights from different disciplines and approaches, and ensure adequate transparency”. Science can be included in the policy process not only in order to provide robust evidence or to conduct impact assessment, but also to ensure adequate monitoring and evaluation of policies. As Newman stated in a review of recent literature on evidence-based policy “[...] there is a basic gap between those who produce evidence and those who use it for making policy decisions” (Newman, 2017, p. 1108). The arguments justifying a stronger involvement of science in policy making have been adequately summarized by Andrews (2007, p.161), who pointed out that new knowledge is a key factor enabling progress. This general explanation why the scientific component is needed in the policy cycle can be flavored by adding the European perspective. The rationale behind strengthening the science base for policy making in EU Member States has been presented in the Innovation Union initiative, which is one of the flagship initiatives 242 8. Implementing evidence-based policies: lessons learned from Joint Research Centre... of the Europe 2020 strategy31. Using scientific evidence as a base for policies in the EU can help to address societal challenges more effectively by appropriate policy instruments, tackle unfavorable framework conditions and reduce fragmentation of efforts by creating stronger linkages among all actors of the innovation systems. Strengthening the science base for policy making allows to design, implement and evaluate policies as well as coordinate them in a more effective way (European Commission, 2010, p. 7). When considering the role of science in policy making one should first examine the policy process. According to Jann and Wegrich (2007), there are four stages of a policy cycle: 1. Agenda setting, which is a stage for problem recognition; 2. Policy formulation and decision making; 3. Implementation of the policies; 4. Evaluation of policy effectiveness and termination. The majority of scholars agree that the evidence is important in the whole policy cycle (Sutclife and Court, 2005; Fischer et. al, 2007; Cairney, 2016). However, the science-policy interface can be organized in many different ways. There are five different models of science-policy interactions distinguished in the public policy literature (Young et al., 2002, p. 216): • Knowledge-driven model, which assumes that policy choices have been determined by research (policy is led by research); • Problem-solving model under which policy shapes research priorities (research agendas are adjusted to policy priorities); • Interactive model, which assumes broad interactions between research and policy; • Political/tactical model where policy is shaped in a political process and research is commissioned by the government to justify policy choices; • Enlightenment model, which does not directly involve scholars in solving policy problems. The role of science is limited to informing policy makers about evidence. How can a sustainable flow of high-quality knowledge and scientific evidence in political decision-making processes be ensured? Gathering adequate scientific evidence requires different methodological approaches, such as meta-analysis, systematic review, quantitative studies, qualitative research and observational studies, experimental research or randomized policy trials. The evidence for policy should reflect on what works and how it works.

31 The characteristics of this initiative are provided in Chapter 1. 8.2. Evidence-based policy and its impact on innovation: a literature review 243

The organizational aspects of the policy advice process also matter for the impact of evidence-based policies on innovation. The institutional set up of advisory process depends, to a high extent, on the traditions and cultures of countries (Bijker et al., 2009). According to the OECD, scientific advice can be organized in the form of: • advisory councils or committees; they can be independent structures that have a governmental mandate or embedded in the government’s structures; • permanent or ad hoc scientific advisory bodies, such as in-house research organizations or bodies independent from the government; • national academies, scientific societies and other research organizations; • individual scientific advisors appointed formally or advising using informal connections (OECD, 2015, p. 13). The impact of scientific evidence depends on the advisory structure and mandate as well as type of output and its quality produced by advisors (Bijker et al., 2009). What are the potential channels through which science-based policies can impact innovation in the EU and what are the main types of this impact? The impact of science-based policies on the outcome can be analyzed at four levels of social analysis (Williamson, 2000, p. 596): 1. Embeddedness: informal institutions, customs, religion; 2. Institutional environment: formal rules of the game; 3. Governance: play of the game; 4. Resource allocation and employment (prices, quantities, incentive alignment). These levels of analysis can be applied to the impact assessment by confronting them with the functions of science-based policy. On the basis of a comprehensive review of public policy theories as well as findings of numerous empirical studies (see for instance: Shaxson, 2005; Fischer et. al, 2007; Wilsdon 2014; Cairney, 2016) the following functions of science in the policy-making process can be identified: • Informing policy about available evidence. In particular, analysis of various policy options, their impact on outcomes can be provided. New ideas based on trends can be created and transmitted as an input to policy conceptualization and design. • Facilitating implementation of policy solutions. The role of scientific advisors is to build awareness of current performance and future challenges of sectors and countries through ‘science diplomacy’, collaboration and networking. • Facilitating participation in policy-making. This function of scientific evidence is related to shaping society’s opinion and increasing the involvement of different stakeholders in the policy-making process. This can bring some improvements of transparency and legitimacy. 244 8. Implementing evidence-based policies: lessons learned from Joint Research Centre...

• Evaluating policy efficiency. Science can be involved in impact assessment in many ways, in particular by providing methodologies and techniques for assessing policy effectiveness and efficiency, conducting evaluation studies or disseminating the evaluation culture. • Reshaping science and policy agendas and deciding about R&D budgets. Scientific advisers, policy makers and other stakeholders work together and share ideas about different aspects of policy making including setting priorities, which should speed up learning in the innovation eco-system. • Reconfiguring the policy process. This function is related to the invention and application of new scientific methods to policy analysis in order to better address challenges. • Symbolic science function, which means that science is involved in shaping society’s values, building trust and creating confidence in policy rationale by communicating the evidence base to the public. Having identified the basic functions of science in the policy process, we can analyze them using the four levels of social science analysis defined by Williamson (2000). With regard to embeddedness it should be noted that in the economic literature there is a consensus that informal institutions change very slowly (centuries or millennia) (North, 1990; Williamson, 2000). Therefore, at this level the impact of science-based policies on innovation can be expected only in the long run. Although, the potential impact can be identified and characterized (ex ante), it would be extremely difficult to measure it. The main impact channels of science-based policies on innovation correspond mainly with the three science functions mentioned above: informing policy, facilitating participation and the symbolic function of shaping values and creating innovation culture. Science-based policies can change institutional conditions through their functions related to evaluating policy efficiency and reshaping future science as well as policy agendas and R&D budgets. Governance structures can be affected by science-based policies because science can lead to the reconfiguration of the policy process and facilitate the participation of the different groups of stakeholders in policy-making. Science-based policy can also impact resource allocation (such as finance, human resources, technology) directly through the funds that have been spent on strengthening the science base for policy making, and indirectly through reshaping future science and policy agendas and shaping R&D budgets in all sectors where science-based policy is applied. 8.2. Evidence-based policy and its impact on innovation: a literature review 245

When it comes to the European perspective on the impact analysis of science- based policies, it seems that it is worth structuring it within the framework of sustainable development and competitiveness, which corresponds with the EU Europe 2020 strategy (European Commission, 2010). Sustainable development is often defined as being based on three interdependent and mutually supporting pillars: 1. Social, with the focus on people; 2. Environmental, with the focus on planet; 3. Economic, with the focus on prosperity. These pillars are linked to each other and equally important. Furthermore, changes in one pillar reinforce adjustments in the others. Therefore, taking into account the sustainability context of the impact analysis, the following types of impact can be identified: • economic impact; • social impact; • environmental impact. However, science-based policies might bring benefits for society as a whole, but at the same time the influence might be positive or negative on some social or economic groups. Furthermore, benefits might vary over time. Summing up the literature review, we have to recognize that the growing role of science in public policy analysis is confirmed by researchers (see for instance: Ehrenberg, 1999; Pielke Jr., 2007; Wilsdon, 2014; Newman, 2017; Parkhurst, 2017) and policy makers (Wilsdon, Allen and Paulavets, 2014; Stiftung Mercator, 2015). The potential impact of science-based policies on innovation is determined by: • the content of scientific input (quality of evidence and its relevance) (Nutley, 2003; Shaxson, 2005; Newman et. al., 2012); • the appropriate use of scientific evidence and the rationality of its application – different types of evidence are needed in different parts of the policy cycle (Sutclife and Court, 2005; Fischer et. al, 2007; Sutherland et. al, 2012; Cairney, 2016); • the organizational aspects of the policy advice process, i.e. the model of the policy-science interface, the size, type and power of the scientific advisory body and the nature of the advisory body mandate (OECD, 2015; Bijker, et al., 2009; Wilsdon, 2014). On the basis of the literature review the following impact types of science-based policies on innovation performance can be distinguished: economic, social and environmental. The issues discussed above will be covered in further empirical analysis. 246 8. Implementing evidence-based policies: lessons learned from Joint Research Centre...

8.3. The Joint Research Centre (JRC) as a science support for European policy makers

The Joint Research Centre is a Directorate-General of the European Commission serving EC policy makers as in-house science support. Its mission is “to provide EU policies with independent, evidence-based scientific and technical support throughout the whole policy cycle”. There are seven JRC institutes located in five different EU countries, namely in Belgium, Germany, Italy, the Netherlands and Spain. The Institutes carry our research in different fields, ranging from environment, energy, health care and food safety to technology and radioactive materials. The Institutes and their locations are as follows: • The Institute for Environment and Sustainability (IES) located in Ispra (Italy) • The Institute for Energy and Transport (IET) in Petten (the Netherlands) and in Ispra (Italy) • The Institute for Health and Consumer Protection (IHCP) in Ispra (Italy) • The Institute for the Protection and Security of the Citizen (IPSC) in Ispra (Italy) • The Institute for Prospective Technological Studies (IPTS) in Seville (Spain) • The Institute for Transuranium Elements (ITU) located in two sites, in Ispra (Italy) and in Karlsruhe (Germany) • The Institute for Reference Materials and Measurements (IRMM) in Geel (Belgium) Brussels is the headquarters of the JRC. There is also the Ispra Site Management, which is responsible for different services delivered to 5 institutes located in Ispra. All JRC Institutes work in partnership with a network of scientific institutions and international organizations. The range of activities of the JRC Institutes includes scientific expertise, foresight studies, work on standards and infrastructure (including e-infrastructure) as well as nuclear safety and security. JRC’s research mainly serves the policy Directorates-General of the European Commission, but many research results are open access resources and know-how is also provided to the Member States, as well as international partners. The focus of the science areas of the JRC Institutes includes: • Agriculture and food safety; • Economic and Monetary Union; • Energy and transport; • Environment and climate change; • Health and consumer protection; • Information society; 8.3. The Joint Research Centre (JRC) as a science support for European policy makers 247

• Innovation and growth; • Nuclear safety and security; • Safety and security; • Standards. In particular, JRC research activities cover the following fields: the protection of the environment, sustainable management of natural resources, renewable energies (solar, photovoltaics, biomass), bioenergy, nuclear energy, energy infrastructures, sustainable transport, fuels, energy efficiency, global security, crisis management, engineering and information technologies, satellite image processing and analysis, open source information analysis, structural mechanics, agriculture, food security, digitalization, low-carbon economy, health diagnostics, advanced materials, aviation security, nuclear industry, and nuclear safety. Another field of research includes harmonization and standardization activities, development of new methods, tools and standards. The research carried out by the JRC Institutes is aimed at supporting the development and implementation of policies in the EU. The JRC collaborates with over a thousand organizations worldwide having over 200 collaboration agreements. In order to strengthen the science base of the JRC the first JRC Work Program under Horizon 2020 was adopted in 2013 (European Commission, 2013). The program has been adjusted for the period 2015-2016 (European Commission, 2015a; European Commission 2015b) and is aligned to the EU policy priorities (Junker, 2014). It states that the JRC will contribute to designing new policies, improving and evaluating existing policies. Therefore, the JRC contribution to policy making will include recommendations both ex ante and ex post (European Commission, 2015b, p. 2).

8.3.1. Strengthening the European science base for policy making through the JRC In order to find out if the science base for policy making has been strengthened through JRC activity, the following changes will be analyzed in the period of 2010- 2016: • Changes in inputs to JRC scientific activity (resources dedicated to R&D, i.e. JRC budget and the changes in human resources); • Changes in the JRC outputs, such as publications and conference contributions. As far as input to JRC activity is concerned, the key questions are: (1) How has the JRC budget been changing since 2010 when the Innovation Union came into force?; (2) How have the human resources of the JRC been changing since 2010? 248 8. Implementing evidence-based policies: lessons learned from Joint Research Centre...

The JRC’s budget is voted on by the European Council and the European Parliament. It is a so-called institutional budget (European Commission (2015, p. 58). The JRC is mainly funded by the EU Framework Program for research and innovation, Horizon 2020, and the EURATOM research and training program. Additional income is gained through additional work for the European Commission as a result of services and contract work for third parties (JRC European Commission 2015, p. 25). Figure 8.1. shows the JRC institutional budgets in 2001-2015 allocated to different types of expenditures, i.e. (1) staff expenses, (2) means of execution (such as maintenance of buildings, equipment, electricity, insurances), and (3) operational expenses (direct scientific procurement, lab equipment etc.). It also presents the annual changes in overall budgets in 2001-2015 (right axe on Figure 8.1). The data show that in 2010 the budget grew significantly, by 5.4% compared to the preceding year, in 2011-2013 the growth rate of the JRC budget was relatively stable amounting to 3.1-3.5% annually. However, since 2014 there has been a decrease of JRC expenditure, by 4.8% in 2014 and 2.4% in 2015. The highest decrease was observed in staff expenditures and this declining trend started already in 2013 and deepened in the next consecutive years. Staff expenditures declined by 1.4% in 2013 (but the overall JRC budget was still growing – see Figure 8.1). In 2014 staff expenditures declined by 1.5% and in 2015 by 5.4%. As a result, the share of staff expenses in the overall budget decreased by 3 percentage points, from 67% in 2010 to 64% in 2015, while the share of means of execution (such as maintenance of buildings, equipment, electricity) constituted 26.6% in the total institutional budget increasing from 19.7% in 2010.

Figure 8.1. JRC budget (left axe in million EUR) and its changes in 2001-2015 by type of expenses (right axe in %) 450 14.0% 400 12.0% 350 10.0% 8.0% 300 6.0% 250 4.0% 200 2.0%

150 change in % 0.0% 100 -2.0% expenses in million EUR 50 -4.0% 0 -6.0% 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

staff expenses means of execution operational appropriations change yoy (%)

Source: own elaboration based on JRC annual reports (data collected as a Deliverable 2.1.).

Comparing the period of the implementation of the Innovation Union (2010- 2015) to the previous period (2001-2009), it can be observed that over the whole first 8.3. The Joint Research Centre (JRC) as a science support for European policy makers 249 decade of the 21st century the total institutional budget of the JRC has been steadily growing with the annual growth rate ranging from 2.4% to 12.9%, and no declines were observed in 2001-2009, even during the global financial crisis. Therefore, the JRC budget declines in 2014-2015 cannot be explained by the crisis shocks, and they are probably related to some changes in JRC activities under the Horizon 2020 program. Apart from the institutional budget the JRC generates an additional income conducting some other research work on the basis of the service contracts commissioned by the European Commission and by third parties. These budgets have been fluctuating in 2010-2015, and after significant increases in 2012-2013 they decreased sharply, nearly by 30 percent in 2014 and remained at this low level also in 2015 (Figure 8.2).

Figure 8.2. JRC budget: value of contracts (left axe in million EUR) and its changes in 2001- 2015 by contract type (right axe in %) 100 60%

90 40% 80 20% 70 60 0% 50 -20%

40 -40%

30 change yoy in % -60% 20 -80% value of contracts in million10 EUR 0 -100% 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Indirect Actions Competitive activities Third party work change yoy

Source: own elaboration based on JRC annual reports.

Summing up the above analysis, it can be concluded that since the implementation of the Innovation Union in 2010, the overall JRC budget (both its components, i.e. institutional budget and value of contracts) have been increasing till 2013 and declined significantly in 2014-2015. Looking from this perspective at the state of implementation of Commitment 8 of the Innovation Union, it seems that since 2014 less financial resources have been devoted to strengthening the science base for policy making. A similar conclusion can be drawn with regard to human resources of JRC institutes (Figure 8.3). In the period of 2002-2010 the overall number of personnel employed in JRC institutes has been growing, with the highest growth rate in 2008- 2010, while in 2011 core staff employment was cut from 2,822 to 1,791 employees 250 8. Implementing evidence-based policies: lessons learned from Joint Research Centre... and remained at this reduced level in 2012-2015. The number of visiting staff, after a constant growth in the years 2002-2006, was relatively stable since 2006, fluctuating around 1,000 temporary employees. However, it dropped significantly in 2015 to the level of 785 visiting employees (Figure 8.3). This decline was a result of the reduced number of post-doctoral grant holders. This visiting staff category has become very important since 2011, constituting nearly half of the total visiting staff personnel. However, after a peak in 2013 (703 post-doctoral grant holders), this number decreased to 324 persons in 2015.

Figure 8.3. JRC human resources: overall number of employed personnel in both visiting and core staff segments in 2004-2015 4500 4000 3500 3000

2500 2822 2683 2732 2000 1836 1803 1708 1717 1733 1791 1785 1631 1803 1500 1642 1572 1000 1252 500 947 1073 1037 925 961 1019 1037 1037 1187 591 694 744 785 0 JRC staff by category, number of persons 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Visiting Staff Overall Core Staff Overall

Source: own elaboration based on JRC annual reports.

Thus, looking at the JRC input side, both the budgets and staff started to decline over the last few years. The lower number of resources devoted to JRC activity can be tentatively interpreted as some slowdown in strengthening the science base through JRC. As far as the JRC output side is concerned, there has been some growth in the number of JRC scientific publications since 2005, except two dips, which occurred in 2011 and 2014 (Table 8.1). The most rapid growth, in particular in 2014 and 2015, was noted in two types of publications, namely: “JRC contributions to policy documents” and “Scientific, policy and technical reports” (Figure 8.4). Such publications are directly related to Commitment 8 and can be treated as a sign of its implementation. This shift towards supporting the Commission’s policy priorities with scientific advice is included in the new JRC Work Program for 2014-15, which identifies how JRC science supports different EU policies (European Commission, Directorate-General for Research and Innovation, 2014, p. 30). 8.3. The Joint Research Centre (JRC) as a science support for European policy makers 251

Table 8.1. JRC publications in 2005-2015

Type of 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 publications Books and articles 437 612 635 748 740 837 763 684 744 689 987 in peer reviewed journals Scientific, policy 179 314 394 426 442 415 347 577 677 615 773 and technical reports JRC contributions n/an/a33519645893 to policy documents PhD theses1114182022121198811 Other 1687475000000 Total number of 795 947 1,054 1,204 1,214 1,265 1,130 1,276 1,433 1,370 1,864 JRC publications* *Publications in conference proceedings are not included. Source: own elaboration based on JRC annual reports for the years 2005-2015.

However, it should be pointed out that more than half of the JRC publications are books and scientific articles. These are important as scientific output, but probably not commonly accessed by policy makers as some of them are not published in open access journals.

Figure 8.4. JRC publications by type: years 2010 and 2015 compared 2010 2015 PhD JRC PhD JRC theses contributions theses; contributions 0.6% to policy 0.9% to policy documents; documents 0.1% 5.0% Books and Books and articles in peer Scientific. articles in Scientific. reviewed policy and peer policy and journals technical reviewed technical 53.0% reports; journals reports 32.8% 66.2% 41.5%

Source: own elaboration based on JRC annual reports.

A comparison of JRC input indictors (human resources) with JRC output (publications) reveals that since 2005 there have been only small changes in JRC output per unit of input (Figure 8.5). When it comes to the number of the JRC publications per employee it fluctuated around 0.3 in 2005-2010, increasing slightly since 2010 when the Innovation Union initiative was launched to 0.5 in 2012-2013 and 0.7 in 2015. 252 8. Implementing evidence-based policies: lessons learned from Joint Research Centre...

Figure 8.5. JRC input-output comparison in 2005-2015: number of publications per JRC employee 0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10 number of JRC publications per employee 0.00 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Source: own elaboration based on JRC annual reports.

The analysis of the developments of JRC activities shows that despite some reductions of the JRC budget and human resources (in particular in the years 2014- 2015), the JRC output measured by the number of publications fluctuated up and down since 2010, increasing significantly by 25% in 2015 compared to the preceding year. What should be stressed is the growing role of scientific, policy and technical reports in the total number of JRC publications, and it can be assumed that such publications have the strongest direct impact on policy making. Since 2010 the share of scientific, policy and technical reports and JRC contribution to policy documents in overall JRC publications increased by 13 percentage points, i.e. from 33% in 2010 to 46% in 2015. This restructuring process of the publication type towards more policy-oriented papers may indicate that the process of strengthening the science base for policy making through JRC has been initiated.

8.3.2. Quality of JRC scientific papers containing evidence for policy making As it was pointed out above in the literature review section, the impact of scientific evidence on policy and thus on the economy is shaped by the quality of evidence for policy making. This section will look at the citations of JRC papers assuming that the more they are cited, the better quality they represent. This analysis takes into account all JRC publications that have been included in the Web of Science repository as well as citations of these publications. Table 8.2 summarizes the results. Since 2010 the number of JRC publications has been fluctuating, ranging from 845 in 2010 to 1,132 in 2015 and going slightly 8.3. The Joint Research Centre (JRC) as a science support for European policy makers 253 down in 2016 to 923. Citations of JRC papers have been growing rapidly over the 2010-2016 period, increasing more than 10-fold for JRC papers that were published in the period of 2010-2016. The same growing trend can be observed for JRC papers published since 2007 till 2016. The citations of these papers published since 2007 grew from 6,331 in 2010 to 28,624 in 2016. This may indicate an increasing quality of JRC papers, as well as their stronger impact.

Table 8.2. Publications and citations of JRC papers in 2010-2016

2010 2011 2012 2013 2014 2015 2016 Total 2010-2016 Number of JRC 845 902 893 984 1,089 1,132 923 6,768 publications (WoS) Number of citations (WoS; 511 2,867 6,0634 10,492 15,799 21,027 23,077 134,407 JRC publications dated 2010 onwards) Number of citations (WoS; 6,331 9,401 12,784 17,456 22,568 27,516 28,624 141,175 JRC publications dated 2007 onwards) Sum of the times cited without self-citations in 2010-2016 (JRC publications dated 7,1637 2010 onwards) Average Citations per item (JRC publications dated 2010 onwards) 11.85 Average citations per year (JRC publications dated 2010 onwards) 8,914.67 Source: Own elaboration based on Web of Science data accessed on December 30, 2016.

A report prepared by Thomson Reuters allows taking a closer look at the JRC publishing quality in the period of 2007-2013 (Thomson Reuters, 2014). This report covers top-cited papers32 that the JRC has produced and compares the results to those achieved by similar institutions in Europe and worldwide, such as the National Institute of Standards & Technology (USA), the VTT Technical Research Center (Finland), the Max Planck Society (Germany), the Netherlands Organization for Applied Scientific Research, the Consiglio Nazionale Delle Ricerche (Italy). There has been an overall growth in the highly-cited publication output of the JRC from 77 in 2007 to 97 in 2010 and 217 in 2013 (the last year of the Thomson Reuther analysis) (Table 8.3). The percentage of highly-cited publications out of the total JRC publication output per year has also been increasing since 2009 (with the exception of the results achieved in 2011). The share of highly-cited JRC publications in 2013 constituted 30.18% of the total JRC publications and was higher by 15 percentage points compared to 2010 (Thomson Reuters, 2014, p. 12).

32 Thomson Reuters defined highly-cited publications as those that have been cited “in the top 10% of world papers by citation impact taking into account field and year of publication” (Thom- son Reuters, 2014, p. 10). 254 8. Implementing evidence-based policies: lessons learned from Joint Research Centre...

Table 8.3 compares the JRC citation impact for its papers in 2007-2013 with five peer scientific organizations. The number of JRC highly-cited papers was lower than the number noted for the National Institute of Standards & Technology (USA) or the Consiglio Nazionale Delle Ricerche (Italy), but higher than that of the peer research institutions in the Netherlands or Finland. However, the percentage of highly-cited JRC publications in the analyzed period is comparable to the peer institutions in the EU and the US.

Table 8.3. Highly-cited publications: JRC and other scientific institutions compared, 2007 to 2013

Percent of Average Number of highly-cited publications highly-cited impact from total factor number of per year 2007 2008 2009 2010 2011 2012 2013 publications in 2007- in 2007-2013 2013 JRC 77 42 62 97 82 143 217 16.23% 2.54 VTT Technical 22 21 21 38 71 37 116 12.07% 2.89 Research Center (Finland) Netherlands 58 37 50 43 40 71 130 11.78% 3.03 Organization for Applied Scientific Research Consiglio 311 269 292 299 280 369 991 9.06% 3.58 Nazionale Delle Ricerche (Italy) Max Planck 1,028 1,024 1,213 1,278 1,457 1,799 2,997 16.89% 5.25 Society (Germany) National Institute 139 168 139 143 194 216 440 16.11% 4.06 of Standards & Technology (USA) Source: own elaboration based on Thomson Reuters (2014), p.13 and p. 29.

Moreover, the average impact factor for JRC publications increased for the period of 2007-2013, but the total times cited for JRC publications decreased (Thomson Reuther, 2014, p. 31). In 2007-2013 the European Commission, with 2,517 citations as the top citer of the JRC papers, followed by the Chinese Academy of Sciences with a 1,091 citing publication count and the Wageningen University and Research Center (the Netherlands) with 621 citations of JRC publications. The majority of the top 10 citing institutions are public sector research organizations from Europe and the US (except the Wageningen University and ETH Zurich). The ranking list of top citing institutions by country is not the same as the ranking of top citing countries. Among countries that have cited JRC publications in 2007-2013, the first place is taken by 8.3. The Joint Research Centre (JRC) as a science support for European policy makersn 255 the United States with 8,244 citing publications, followed by Germany (4,855 citing publications) and the UK (4,523 citing publications), Italy (4,136) and China (3,706) (Thomson Reuther, 2014, p. 35). When the average impact factor of JRC publications is compared to some peer research organizations, it can be noted that for JRC it was similar to peer organizations such as the VTT Technical Research Center (Finland), but much lower than the impact factor for the remaining four institutions selected for this analysis (Table 8.3). The JRC key research competencies can be identified by analyzing the 20 Web of Science (WoS) Journal categories for which these publications have the highest citation impact. Such an analysis reveals that in 2007-2013 the JRC published the most in the Environmental Sciences category of the WoS and the highest impact was identified in the field of Biodiversity Conservation (Thomson Reuther, 2014, p. 53). Similar results, in terms of the most popular fields of science of JRC publications, have been found for the period of 2010-2016. Figure 8.6 shows the shares of the top 15 JRC publications in 2010-2016 by different science categories according to the Web of Science classification of research fields. It appears that Environmental science dominates as a field of JRC publications with a share of 17%. Next is Nuclear science with a share of 10%, followed by Meteorology and Atmospheric science (7%).

Figure 8.6. JRC publications in 2010-2016 by science fields according to Web of Science categories of research fields

OTHER; 11% ENVIRONMENTAL CHEMISTRY SCIENCES; 17% PHYSICAL; 3%

MARINE FRESHWATER BIOLOGY; 3% NUCLEAR SCIENCE TECHNOLOGY; 10% ECONOMICS; 3%

ENVIRONMENTAL STUDIES; 3% METEOROLOGY ATMOSPHERIC SCIENCES; 7% ECOLOGY; 4%

REMOTE SENSING; 6%

CHEMISTRY TOXICOLOGY; 5% ANALYTICAL; 5% ENERGY FUELS; 6% ENGINEERING ELECTRICAL GEOSCIENCES MATERIALS SCIENCE ELECTRONIC; 5% MULTIDISCIPLINARY; MULTIDISCIPLINARY; 5% 6%

Source: own elaboration based on the Web of Science database, assessed on December 30, 2016.

It is also worth identifying JRC papers that have been related to collaboration with industry. Such an analysis may shed some light on the impact of JRC papers on innovation. According to the Thomson Reuters data (Thomson Reuther, 2014, 256 8. Implementing evidence-based policies: lessons learned from Joint Research Centre... pp. 74-76), JRC collaboration with industry is less common than with universities or non-profit organizations. In the period of 2007-2013 there were 211 collaborative publications with private sector authors and the number has been relatively stable over time fluctuating around 30, with a peak in the year 2010 (Figure 8.7). This may indicate that since the implementation of the Innovation Union there has been no significant change in the impact of JRC activity on industry.

Figure 8.7. JRC publications with private authors in 2007-2013 40 35 35 32 32 30 29 29 30

24 25

JRC publication count 10

0 2007 2008 2009 2010 2011 2012 2013

Source: own graph based on Thomson Reuther, 2014, p. 74.

The top five countries where companies collaborating with JRC come from are: Germany, the UK, the USA, Italy and Switzerland (Thomson Reuther, 2014, p. 75).

Figure 8.8. Top 15 JRC publications with private authors in 2007-2013 by research field

INSTRUMENTS & INSTRUMENTATIONS 8 IMAGING SCIENCE PHOTOGRAPHIC… 8 GEOSCIENCES MULTIDISCIPLINARY 8 MATERIALS SCIENCE… 10 REMOTE SENSING 11 BIOCHEMICAL RESEACH METHODS 14 FOOD SCIENCE & TECHNOLOGY 15 PHARMACOLOGY & PHARMACY 16 NUCLEAR SCIENCE TECHNOLOGY 17 CHEMISTRY, ANALYTICAL 18 METEOROLOGY ATMOSPHERIC… 20 GENETICS & HEREDITY 38 ENVIRONMENTAL SCIENCES 40 BIOTECHNOLOGY & APPLIED… 41 TOXICOLOGY 77 0 102030405060708090 publication count

Source: own elaboration based on data from Thomson Reuther, 2014, p. 76. 8.4. The impact of science-based policy on innovation: the case of the Joint Research... 257

The top three research fields of JRC-industry collaboration in 2007-2013 are: Toxicology, Biotechnology & Applied Microbiology and Environmental Sciences (Figure 8.8). A simple comparison of these most popular research fields (Figure 8.8) in which JRC collaborates with industry with the fields with the highest share of JRC publications (Figure 8.6) shows that only a few fields with the most intense JRC publication record appear in the list of top 15 fields in which JRC collaborates with industry. The fields that are not included in the top 15 research areas of JRC publications but have a relatively strong collaboration with industry are for instance: Biotechnology and Applied Microbiology, Genetics and Heredity, Pharmacology and Pharmacy, and Food Science & Technology. However, it is worth noting that Environmental Science is in the top 3 fields on both ranking lists, so it can be assumed that the impact of the knowledge produced by JRC in this field on innovation may be stronger than in others. This will be further investigated and discussed in the next section of this chapter.

8.4. The impact of science-based policy on innovation: the case of the Joint Research Centre

The aim of this section is to show how the research conducted by the Joint Research Centre impacts the policy-making process in EU Member States and through these policies further impacts the innovation performance of the EU. The analysis is based on the assessment of JRC activity made by policy makers and collected during the survey conducted in September-October 2016. The main aim of the survey was to find out what evidence is used in the policy process in EU Member States and how evidence-based policies impact innovation. In particular, the results of the survey allow to find out how these three types of impact (economic, social, environmental) are related to the importance of scientific advice, model of policy-science interactions and the stage of the policy cycle in which scientific advice is used. The survey was conducted among 570 policy makers from all EU Member States representing either the national or regional/local government. Tables 8.4 and 8.5 show the characteristics of the respondents by the type of institution they represent, and by country. The respondents are spread quite proportionally among EU Member States, the percentage of respondents from individual countries ranges from 1.75% (Cyprus) to 4.39% (both Poland and Hungary). Half of the respondents represent regional or local governments, only 4 respondents (0.7%) classified themselves to the “Other” category, the remaining 48.8% of respondents represent national level of policy making. 258 8. Implementing evidence-based policies: lessons learned from Joint Research Centre...

Table 8.4. Distribution of respondents by type of their institution

Type of institution Number of respondents Percent Central Government 278 48.8 Regional / Local Government 288 50.5 Other 4 0.7 Source: elaboration by G. Michalski based on the survey results.

Table 8.5. Distribution of respondents by country

Country Number of respondents Percent Austria 20 3.51 Belgium 20 3.51 Bulgaria 20 3.51 Croatia 20 3.51 Cyprus 10 1.75 Czech Republic 21 3.68 Denmark 20 3.51 Estonia 19 3.33 Finland 24 4.21 France 20 3.51 Germany 23 4.04 Greece 20 3.51 Hungary 25 4.39 Ireland 20 3.51 Italy 20 3.51 Latvia 20 3.51 Lithuania 15 2.63 Luxembourg 20 3.51 Malta 20 3.51 Netherlands 21 3.68 Poland 25 4.39 Portugal 20 3.51 Romania 20 3.51 Slovakia 20 3.51 Slovenia 23 4.04 Spain 23 4.04 Sweden 21 3.68 United Kingdom 20 3.51 TOTAL 570 100.00 Source: elaboration by G. Michalski based on the survey results.

The questionnaire for this survey research was designed with the use of the results of the literature review on the role of evidence-based policy in shaping policies 8.4. The impact of science-based policy on innovation: the case of the Joint Research... 259 and innovation performance. It contained 32 questions of which 20 were closed- ended questions and the remaining 12 were open-ended questions. The open-ended responses were coded before they were processed for the analysis. The answers were collected using a combination of the Computer-Assisted Web Interviewing (CAWI) and Computer Assisted Telephone Interviewing (CATI) Methods in the period of September 15, 2016 – October 20, 2016 by the Center for Marketing Research “INDICATOR”. The responses have been scaled using the Likert-type scale. The key questions in the survey conducted among policy makers from EU Member States were about the impact of science-based policies on innovation in the country represented by the survey respondents. There was a general question “In which area can you identify the impact of science-based policy on innovation in your country?”, and three types of impact identified in the literature review have been taken into account: economic, social and environmental impact. The respondents were asked to assess each type of impact separately. The majority of respondents were not sure if any impact of science-based policies on innovation occurs. The answer ‘Don’t know/hard to say’ was chosen by the majority of the respondents: 60.2% of them were not able to assess the environmental impact, 58.6% had no opinion about the social impact and 48.1% did not assess the economic impact. However, when it comes to the assessment of the economic impact the remaining 43.5% of the respondents declared that it is high, 7.5% assessed it as moderate and only 0.9% of the respondents saw it as low. The environmental impact was assessed as high by 27% of the respondents, as moderate by 8.8% and low by 4%. A relatively small percentage assessed the social impact as high, this opinion was supported by 17.4% of the respondents, while 18.4% of them saw the social impact as moderate and 5.6% as low (Figure 8.9).

Figure 8.9. Impact of science-based policy on innovation according to the survey respondents (N=570)

Environmental impact 60.2 4.0 8.8 27.0

Don't know / Hard to say Social impact 58.6 5.6 18.4 17.4 Low Moderate Economic impact 48.1 0.97.5 43.5 High 0% 20% 40% 60% 80% 100%

Source: own elaboration based on the survey results.

An attempt to cluster EU Member States according to their assessment of the impact of science-based policies on innovation was undertaken, however due to the high concentration of answers regarding impact assessment around medium levels 260 8. Implementing evidence-based policies: lessons learned from Joint Research Centre...

(medium, high), it was not possible to determine the appropriate number of clusters. Both the Cubic Clustering Criterion and pseudo-F statistic increase constantly as the number of clusters increases. These two statistics performed the best as methods to estimate the number of clusters (Milligan and Cooper, 1985). However, it is not possible to distinguish country clusters according to impact assessment. Therefore, impact assessment was broken down by four groups of EU Member States distinguished according to their innovation performance in the European Innovation Scoreboard 2016 (EIS, 2016, p. 12). The survey responses assessing the impact have been analyzed separately for each type of impact (Table 8.6). There are some differences in the responses by these country groups, but the Kruskal-Wallis test performed to test the significance of these differences in distributions of impact assessment shows that they appear to be not statistically significant.

Table 8.6. The impact of science-based policies on innovation broken down by impact types and by EU Member States innovation performance groups

Economic impact Social impact Environmental impact (assessment in % of (assessment in % of (assessment in % of Performance responses of each all responses of each responses of each group performance group) performance group) performance group) Mode- Mode- Mode- Low High Low High Low High rate rate rate Innovation 0.00 26.23 73.77 8.51 50.00 34.83 3.03 36.36 60.61 Leaders Strong 2.38 30.95 66.67 9.09 59.09 31.82 3.57 51.19 45.24 Innovators Moderate 2.67 25.33 72.00 15.75 49.61 34.65 4.29 43.56 52.15 Innovators Modest 4.17 20.83 75.00 8.33 50.00 41.67 0.00 63.16 36.84 Innovators Note: The responses (low, moderate, high) assessing each type of impact sum up to 100% for each performance group. Source: elaboration by G. Michalski based on the survey results; the country groups are distinguished according to the European Innovation Scoreboard 2016, European Commission, http://ec.europa.eu/growth/ industry/innovation/facts-figures/scoreboards/index_en.htm, p. 12.

Nevertheless, it is still worth discussing the differences in the assessment of each type of impact by these four groups of EU countries distinguished according to their innovation performance. The economic impact has been assessed as high by all country groups regardless of their innovation performance. Furthermore, the social and environmental impact were assessed lower than the economic impact by respondents representing all country groups. 8.4. The impact of science-based policy on innovation: the case of the Joint Research... 261

When it comes to social impact assessment the majority of the respondents in all country groups assessed it as moderate. There are some differences among the respondents’ opinions in the assessment of the environmental impact. The majority of the respondents representing ‘Modest innovators’ assessed this type of impact as moderate. Similar results were observed for ‘Strong innovators’. Over half of the respondents representing the two other groups of EU Member States, i.e. ‘Innovation leaders’ and ‘Moderate innovators’, assessed the environmental impact of science- based polices as high (Table 8.6). It is also worth noting that there are differences in the assessment of the impact of evidence-based policies on innovation when the responses of national and regional/ local governments are compared. A relatively higher percentage of representatives of national government compared to respondents from regional administration assessed the economic impact and environmental impact as high (35.5% versus 27.4% in the case of economic impact, and as for environmental impact the figures were 27% versus 20.1%). The opposite result was found for social impact. Only 13.1% of national government respondents declared that this impact was high, while in the case of regional administration the percentage of respondents assessing this impact as high was 21.5% (Table 8.7). These differences among national and regional respondents regarding the impact assessment are also statistically significant (p≤0.05) according to the Mann-Whitney U test procedure.

Table 8.7. Intra group differences: impact assessment of science-based policies by policy makers in EU Member States by type of impact and type of institutions represented by the respondents (national vs. regional/local government) (in percentage; N=570)

Answers Economic impact Social impact Environmental impact Country Regional/ Country Regional/ Country Regional/ level local level level local level level local level Low impact 0.4% 1.4% 8.2% 3.1% 0.7% 1.0% Medium 27.7% 11.8% 24.5% 12.5% 21.3% 9.7% High impact 35.5% 27.4% 13.1% 21.5% 27.0% 20.1% Don't know/ 36.5% 59.4% 54.3% 62.8% 51.1% 69.1% Hard to say TOTAL 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Source: elaborated by G. Michalski based on the survey results.

As shown in Table 8.8, the higher the decision level (EU / Country / Region) the more important the scientific evidence for policy making. In the respondents’ opinion scientific evidence is highly important for policy making at the EU level and country levels, but only moderately important at regional level. 262 8. Implementing evidence-based policies: lessons learned from Joint Research Centre...

Table 8.8. The assessment of the importance of scientific evidence for policy making at the level of the EU, country and region (N=570)

EU level Country level Regional level Importance of science Number of Number of Number of base for policy making Percent Percent Percent responses responses responses Very Low Importance 1 0.18 1 0.18 5 1.74 Low Importance 1 0.18 15 2.63 95 32.98 Moderate Importance 24 4.21 42 7.37 163 56.60 High Importance 276 48.42 363 63.68 25 8.68 Very High Importance 265 46.49 149 26.14 0 0.00 Don't Know / 3 0.53 0 0 0 0.00 Not Applicable TOTAL 570 100.00 570 100.00 288 100.00 Source: elaborated by G. Michalski based on the survey results.

To assess how the importance of scientific evidence is related to the impact assessed by the survey respondents the average value of the respondents’ answers was calculated. The answers have been combined into 2 groups: (1)‘Very Low’ ‘Low’ and ‘Moderate’; (2) ‘very High’ and ‘High’. The value ‘Don’t Know / Not Applicable’ has not been taken into account. The contingency table for distributions of impacts and the importance of science base is presented below (Table 8.9). The respondents that found scientific evidence to be more important for policy making were more eager to assess the economic impact higher and this can be confirmed by the Kruskal-Wallis test (p-value: 0.0005). The importance assessment does not differentiate significantly with regard to social and environmental impacts (the p-values were, respectively: 0.3222 and 0.3019).

Table 8.9. Relationship between the importance of scientific base and different types of impact (N=570)

Importance of Economic impact Social impact Environmental impact scientific evidence Mode- Mode- Mode- Low High Low High Low High for policy making rate rate rate Low or moderately 15.38 53.85 30.77 0.00 54.55 45.45 7.69 53.85 38.46 important Highly important 1.63 25.49 72.88 12.86 50.62 36.51 3.45 44.83 51.72 Note: The responses (low, moderate, high) assessing each type of impact sum up to 100% for each performance group. Source: elaborated by G. Michalski based on the survey results.

Although scientific evidence was considered to be important in the policy- making process, the publications prepared by the JRC are used only rarely (Table 8.10). 8.4. The impact of science-based policy on innovation: the case of the Joint Research...n 263

Table 8.10. The frequency of using scientific advice given in policy papers or briefs issued by the JRC (N=566; 4 respondents from the sample that classified themselves in the ‘Other’ category, i.e. neither national nor regional government, have not been taken into account).

Frequency of using scientific advice JRC publications given in policy papers or briefs Number of responses Percent Very Rarely 126 22.26 Not Often 134 23.67 Neither Often nor Not Often 196 34.63 Often 52 9.19 Very Often 35 6.18 Don't Know / Hard to Say 18 3.18 Never heard of the JRC 5 0.88 Source: own elaboration based on the survey results.

Table 8.11. Relationship between frequency of using scientific advice from JRC papers and the assessment of different types of impact on innovation (N=566; The ‘Other’ category of respondents has not been taken into account)

Importance of Economic impact Social impact Environmental impact scientific evidence Low Mode- High Low Mode- High Low Mode- High for policy making rate rate rate Low or moderate 2.16 28.02 69.83 12.30 53.48 34.22 3.92 49.41 46.67 High 2.13 25.53 72.34 11.43 37.14 51.43 2.27 36.36 61.36 Kruskal-Wallis test 0.7374 0.1038 0.0714 p-value Note: The responses (low, moderate, high) assessing each type of impact sum up to 100% for each performance group. Source: elaborated by G. Michalski based on the survey results.

Table 8.11 presents how the use of scientific advice given in papers issued by the JRC is related to the assessment of the impact of science-based policies on innovation. The frequency of using scientific advice from JRC publications does not differentiate the assessment of economic and social impact, although it does when the environmental impact is concerned, which is confirmed by the Kruskal-Wallis test. The majority of the respondents stated that scientific advice is used quite often in every stage of policy making. According to the survey about 33% of the respondents often use scientific advice at the stage of problem recognition, 26% of the respondents need it for policy formulation, 23% of the respondents use it at the implementation stage, and 42% at the evaluation stage.33

33 The percentages do not sum up to 100% as the respondents could indicate more than one stage of the policy process. 264 8. Implementing evidence-based policies: lessons learned from Joint Research Centre...

To assess how the frequency of using scientific advice is related to the impact on innovation, the frequencies have been combined into two groups (‘Rarely or moderately often’ and ‘Often’). The ‘Don’t Know / Not Applicable’ category has been omitted (Table 8.12).

Table 8.12. Relationship between frequency of using scientific advice at various stages of policy making and the impact on innovation (N=566; The ‘Other’ category of respondents has not been taken into account)

Environmental Economic impact Social impact impact Policy stage Frequency Mode- Mode- Mode- Low High Low High Low High rate rate rate Problem Rarely or 7.35 30.88 61.76 17.50 50.00 32.50 5.48 41.10 53.42 recognition Moderately and issue Often selection Often 0.80 25.60 73.60 11.37 51.18 37.44 3.10 46.51 50.39 Policy Rarely or 5.56 26.39 68.06 16.00 54.00 30.00 5.10 42.86 52.04 formulation Moderately and decision Often making Often 1.22 26.83 71.95 11.00 50.50 38.50 3.00 46.35 50.64 Implemen- Rarely or 4.11 31.51 64.38 16.07 50 33.93 5.13 42.31 52.56 tation Moderately Often Often 1.63 25.2 73.17 11.28 51.28 37.44 3.15 46.06 50.79 Evaluation Rarely or 19.23 57.69 23.08 26.09 52.17 21.74 11.11 55.56 33.33 Moderately Often Often 7.35 30.88 61.76 17.50 50.00 32.50 5.48 41.10 53.42 Note: The responses (low, moderate, high) assessing each type of impact sum up to 100% for each performance group. Source: elaboration by G. Michalski based on the survey results.

As shown in Table 8.12, generally the frequency does not differentiate the respondents’ assessment of the impact on innovation, with the following exceptions: 1) Using scientific advice more often during the problem-recognition stage is related to a higher assessment of the economic impact of innovation (p-value: 0.0308). 2) Using scientific advice more often during the evaluation stage is related to a higher assessment of the economic, social and environmental impact of innovation (p-values, respectively: <0.0001, 0.0361, 0.0732) Furthermore, the importance of the different functions of science in the policy- making process is related to the respondents’ assessment of the impact of science- 8.4. The impact of science-based policy on innovation: the case of the Joint Research... 265 based policy on innovation, which was confirmed by the Kruskal-Wallis test (Table 8.13). In particular, the results lead to the following conclusions: 1) The higher importance of the informing function is related to a higher assessment of the economic impact on innovation (p-value: 0.0065). 2) The higher importance of the function to ‘facilitate policy implementation’ is related to a higher assessment of the economic and social impact on innovation (p-values, respectively: 0.0002; 0.0298). 3) The higher importance of the science function ‘reshaping future policy agendas’ is related to a higher assessment of the economic impact (p-value: 0.0485).

Table 8.13. Relationship between the science function in policy making and the assessment of the impact of science-based policies on innovation (N=566; The ‘Other’ category of respondents has not been taken into account)

Science function in the Economic impact Social impact Environmental policy-making process impact Mode- Mode- Mode- Low High Low High Low High rate rate rate Informing Low or 3.01 32.33 64.66 15.00 43.00 42.00 5.44 40.82 53.74 policy moderate High 1.61 22.58 75.81 10.53 55.92 33.55 2.16 48.65 49.19 Facilitating Low or 3.61 33.73 62.65 12.98 58.02 29.01 5.71 45.14 49.14 policy imple- moderate mentation High 0.65 18.95 80.39 11.57 42.98 45.45 1.27 45.22 53.50 Facilitating Low or 2.72 26.63 70.65 12.06 49.65 38.30 3.85 43.75 52.40 participation moderate in policy High 1.49 26.87 71.64 11.82 52.73 35.45 3.25 47.97 48.78 making Evaluating Low or 3.09 26.8 70.1 13.86 49.4 36.75 4.13 43.12 52.75 policy moderate efficiency High 0.81 26.61 72.58 9.41 54.12 36.47 2.65 49.56 47.79 Reshaping Low or 2.55 28.57 68.88 10.53 52.63 36.84 3.85 47.12 49.04 future science moderate and policy High 1.64 22.95 75.41 15.15 48.48 36.36 3.23 41.94 54.84 agendas Reconfiguring Low or 2.88 25.48 71.63 2.88 25.48 71.63 3.54 45.58 50.88 the policy moderate process High 0.92 29.36 69.72 0.92 29.36 69.72 3.85 45.19 50.96 Symbolic Low or 2.38 30.95 66.67 10.91 55.76 33.33 3.17 43.89 52.94 science moderate function High 1.87 18.69 79.44 15.48 42.86 41.67 4.59 48.62 46.79 Note: The responses (low, moderate, high) assessing each type of impact sum up to 100% for each performance group. Source: elaborated by G. Michalski based on the survey results. 266 8. Implementing evidence-based policies: lessons learned from Joint Research Centre...

Last, but not least, according to the literature the type of advisory structure might matter for the impact of scientific advice for policy makers on innovation. However, the results of the survey show that the impact assessment does not differ significantly between the respondents that represent countries with different advisory structures. This can be explained by the fact that nearly in all countries there have been many different scientific advisory bodies (Figure 8.10). The potential policy impact of scientific evidence and advice can be diverse, depending on the nature of the advisory body mandate (OECD, 2015, p. 23).

Figure 8.10. Types of scientific advisory structures according to the survey respondents (N=570) 90% 77.21% 79.86% 80% 73.32% 71.20% 70% 60% 50% 40% 30% 20%

10% 0.88% 0% Advisory councils Permanent or ad hoc National academies Individual scientific Other structures advisors

Note: The percentages do not sum up to 100% as the respondents could indicate more than one type of advisory structure to cover all structures existing in their countries. Source: own elaboration based on the survey results.

8.5. Conclusions

The analysis of the developments of JRC activities shows that despite a decrease in JRC budget and human resources (in particular in the years 2014-2015), the JRC output measured by the number of publications as well as citations of these publications has been increasing. It should also be noted that JRC publications become more policy-oriented, which may be evidence that the process of strengthening the science base for policy making through the JRC has been initiated. Nevertheless, the results of this process are still weak as the “science productivity” of the JRC measured by the number of publications per JRC employee remained relatively stable over time, growing only slightly in recent years. This conclusion is also confirmed by the results of the survey conducted among policy makers. The interactions between the JRC and policy makers assessed by the frequency of using JRC publications for policy making do not seem to be very intensive. Only 15% of the respondents declared that they use JRC publications in shaping policies. References 267

Therefore, building awareness about the importance of scientific advice and how and where this advice can be accessed and used in the policy process seems to be an important issue. As wider dissemination of JRC policy papers prepared at the EU level is indispensable, a discussion on and elaboration of a new strategy in terms of how and where to disseminate the findings of the JRC and expert groups among all actors of the innovation system should be recommended. The complexity of stakeholder preferences should be taken into account. Therefore, there is a need to create appropriate mechanisms for the science-policy interface. An important point is that this process has already been initiated at the EU level through reviewing existing practice and setting up a European Science Advice Mechanism (European Commission, 2015c). A continuation of these efforts seems to be important and necessary.

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Marzenna Anna Weresa

Final conclusions

We are facing an era of change, where technological advancements transform industries, enable new business models and disrupt the structures of economies and societies. Innovation, which is a broad phenomenon that covers new developments in the field of technology, marketing, organization of production processes etc. curves a path to modernity. Technological change needs to be supported by institutional adjustments that allow to address new societal challenges in a better way. These challenges have become a focus of European innovation policy, which evolved from a linear approach in the twentieth century to a more holistic, systemic approach nowadays. In 2010 the EU launched a flagship policy initiative “Innovation Union” aimed at increasing the innovation rate and translating it into smart, sustainable and inclusive growth. Strengthening the knowledge base is one of its six pillars. Therefore, the question arises about the progress made in this area since the launch of the Innovation Union initiative. This monograph presents the main findings of the research on this important topic. The main objective of the research presented in this monograph is to evaluate the effects of the EU policy instruments that promote a stronger European dimension of R&D base in the European Union necessary for boosting innovation activity. This research, being part of the EU Horizon 2020 project34, covers the impact of selected areas of the Innovation Union initiative on innovation in Europe. These areas are: EU framework programs (including those focused on public sector innovation and social innovation); European and global research infrastructures; the creation and functioning of the European Institute of Innovation and Technology; and direct scientific support for policy making offered by the Joint Research Centre. This detailed analysis of the six commitments of the Innovation Union is supplemented by a general assessment of the role of public support to R&D and innovation seen as input, output and behavioral additionalities. The questions that the book attempts to answer are the following: What are the effects of these diverse policy initiatives implemented at the EU level? To what extent

34 “Investigating the Impact of the Innovation Union” project funded from the European Union’s Horizon 2020 research and innovation program under grant agreement number 645884. Final conclusions 271 have they been implemented after seven years since the Innovation Union launch? What are the policy implications and lessons learned? The research findings are discussed in detail in eight chapters. These are based on a variety of unique data collected during desk and field research, including surveys and interviews, which reflect the opinions of all innovation system actors and stakeholders affected by these policy instruments. The key messages resulting from the research are as follows: • Since the launch of the Innovation Union initiative in 2010, the EU made some progress in reducing the innovation gap towards the US and Japan. However, it was not possible to diminish the innovation divide towards South Korea, and to stop the catching up process by China. • The potential of EU support for R&D and innovation is still not fully exploited. The impact of public financial support on innovation performance is mainly indirect. This research confirmed that there has been a positive impact of EU funding that supports cooperation in innovation activities on the innovation performance of European enterprises. However, the effectiveness of public support for innovation measured by R&D additionality differs across EU Member States. • EU countries with a low level of involvement in the FP7 and H2020 significantly underperform in comparison to economies that have a very high relative level of involvement in these programs. The common elements of high performing country clusters in the FP7 and H2020 programs are the high activity of private companies and research organizations. • All three EU framework programs, i.e. the FP7, CIP and H2020, have been implemented successfully contributing to the increase in the level of innovativeness in the EU. However, they have not been equally efficient in achieving the final goal of a given innovation output. The FP7 has been relatively more successful in stimulating knowledge creation, while the CIP has brought relatively better results in the commercial exploitation of knowledge, the creation of new standards, etc. Early assessment of the H2020 initiative shows that it has been successful in delivering innovation (invention) output; however, the results are heterogeneous across the individual sub-programs. • The measurable effects of the EU research programs on public sector and social innovation are still quite limited as they delivered a relatively small number of innovations or procedures to solve complex social problems. The major outcomes of projects financed within the SOCIETY and SWAFS programs are new ways of collaboration and publications. • The Innovation Union initiative strengthened the science base through pooling financial resources across Europe to build and operate priority 272 Final conclusions

research infrastructures. The impact of RIs on networking and cooperation has led to a stronger integration of European researchers, from both academia and industry. However, there is a strong discrepancy within the EU framework program (part INFRA) spending between EU countries from Western Europe and Central and Eastern Europe, the latter having absorbed significantly less funding for constructing European infrastructures, which may result in an increase of the innovation divide between Member States. • Global research infrastructures as an innovation policy tool have helped the EU to leverage its research and innovation activity externally. Since the launch of the Innovation Union initiative the number of non-EU countries involved in GRIs increased from 184 to 253. Nevertheless, the internationalization level measured as a percentage of third countries in the total number of participating countries is still moderate but growing slightly. Incorporating international partners into European RIs has improved access to international scientific and technological knowledge as well as contributed to increasing financial resources assigned for the creation of these infrastructures. This, in turn, has brought an increase in the number of PhD students, patents, the number and quality of publications and consequently has contributed to scientific excellence. • The contribution of the European Institute of Innovation and Technology to the Innovation Union objectives has been positively assessed by all actors involved in EIT activity. Its role in the European innovation system has been growing as its Knowledge and Innovation Communities have been integrating various partners from the European knowledge triangle (innovation, research, and higher education) representing different EU Member States. • The research activity of the EU Joint Research Centre in shaping the policy- making process in EU countries is still not sufficient. Evidence produced by JRC research has more often been used in policy making at the EU level than at national or regional levels. The interactions between the JRC and policy makers assessed by the frequency of using JRC publications for policy making do not seem to be very intensive yet. The results of this research on the implementation and impact of the selected commitments of the Innovation Union also have certain policy implications. It is widely recognized that the development of relationships between emerging and incumbent enterprises, collaboration between business and academia as well as dynamic European and global linkages and networks are crucial for boosting the growth of future innovative markets and upgrading the competitiveness and welfare of nations. Governments at European, national and regional levels can play a key role in boosting innovation and competitiveness, and the Innovation Union initiative Final conclusions 273 with its 34 commitments has been an attempt to strengthen Europe’s potential for research and innovation. However, there is still room for improvement in terms of policy design, implementation and evaluation. Innovation needs to be supported by institutional adjustments. Institutions understood as rules that organize economic, social and political relations, also include innovation policy regulations. Some institutional innovations in the area of policy making are needed to cope with current societal challenges. The new approach to designing an innovation strategy and policy should be based on a wider involvement of all stakeholders in setting policy agendas and co-creating policy objectives. Furthermore, it seems to be necessary to strengthen the role of scientific evidence in policy making. Designing evidence-based policies can be facilitated by broader interactions between research and policy. Therefore, a new platform of science-policy interface and revisiting the way how scientific advice is organized seem to be important. Furthermore, it is worth considering how the objectives can be quantified as the progress in the implementation of immeasurable qualitative objectives is difficult to monitor and evaluate. Also, to increase the impact of EU-funded programs and projects, it may be worth considering that all further innovation-related initiatives are encompassing all measures of output, i.e. the creation of new intellectual property, new knowledge and its dissemination as well as the commercial aspect, i.e. the commercial exploitation of knowledge, creation of new standards, etc. The key element that can contribute to a better implementation of policy interventions is also easy access to policy instruments and simple formal requirements, as bureaucracy has been indicated by innovators as one of the barriers in the implementation of new solutions. Focus on linkages and synergies between different policy interventions and a proper coordination between them can help to create more effective support systems for both researchers and innovative companies. Another policy recommendation refers to the need to better track the impact of different policy interventions. This implies that evaluation methodologies should be developed that capture the direct and indirect effects of intervention in the short, medium and long run. Last, but not least, not only strengthening the knowledge base for policy making, but also coherence among different policy levels (local, regional, national, European) is indispensable in order to increase policy impact.

List of tables

Table 1.1. Headline targets of the Europe 2020 strategy – an overview ...... 21 Table 2.1. EU countries broken down by the share of innovative enterprises that received any public funds supporting R&D and public funding from the EU budget in 2006-2012 ...... 33 Table 2.2. Initial sample description ...... 42 Table 2.3. Variables operationalization ...... 43 Table 2.4. Results of the path analysis for selected European Union Member States . . . . . 49 Table 2.5. Taxonomy of European Union innovation activity support within the surveyed countries – verification of hypotheses ...... 52 Table 3.1. FP7 output ...... 82 Table 3.2. CIP output ...... 83 Table 3.3. H2020 output ...... 84 Table 3.4. Output and input data for FP7 (in no. unless specified otherwise) ...... 85 Table 3.5. H2020 key performance indicators ...... 86 Table 3.6. Output/input ratios for FP7 program collections ...... 89 Table 3.7. Output/input ratios for FP7 individual programs ...... 90 Table 3.8. Pearson Correlation Coefficient matrix ...... 91 Table 3.9. Output/input ratios for H2020 programs collections ...... 95 Table 3.10. Results of the cluster analysis for FP7 with centroids for each cluster across each clustering variable ...... 96 Table 3.11. Assignment of EU28 economies to each of five clusters based on FP7 ...... 97 Table 3.12. Innovation output averages according to FP7 clusters ...... 98 Table 3.13. Results of the cluster analysis for H2020 with centroids for each cluster across each clustering variable ...... 98 Table 3.14. Assignment of EU28 economies to each of five clusters for H2020 ...... 98 Table 3.15. Innovation output averages according to H2020 clusters ...... 99 Table 4.1. Scientific fields and organizational features of research infrastructures ...... 105 Table 4.2. Indicators of the economic and innovation impact of research infrastructures in the construction phase ...... 106 Table 4.3. Indicators for impacts of research infrastructures in the operational phase . . . 107 Table 4.4. Financial data concerning the distribution of the FP7, part INFRA, in EU countries ...... 119 Table 4.5. FP7 (part INFRA) investments by the four main groups of innovation systems found in EU Member States ...... 121 Table 4.6. Horizon 2020 (part INFRA) investments by the four main groups of innovation systems found in EU Member States (data until 31.03.2018) ...... 123 Table 4.7. FP7 (part INFRA) investments by the five categories of organizations participating in the projects ...... 127 Table 4.8. Horizon 2020 (part INFRA) investments by the five categories of organizations participating in the projects (data until 31.03.2018) ...... 127 List of tables 275

Table 4.9. Type and number of RI coordinators that took part in the survey research [N=150 RI coordinators] ...... 129 Table 4.10. Specific measures considered to be important for RI research performance and/or productivity evaluation, by type of RI (in %) [N=150 RI coordinators] ...... 130 Table 4.11. The number and share of RIs, which delivered statistical data related to the functioning and performance of RIs [N=150 RI coordinators] ...... 131 Table 4.12. The effects of EU funding from framework programs, by type of RIs (%) [N=150 RI coordinators] ...... 133 Table 4.13. State of European RI exploitation, by type of RIs (%) [N=150 RI coordinators] ...... 135 Table 4.14. Type of access to data of RIs, by type of RIs (%) [N=150 RI coordinators] . . . . 136 Table 4.15. The role of cooperation within the framework of RIs in reaching a necessary critical mass for breakthrough research activities, by type of RIs (%) [N=150 RI coordinators] ...... 137 Table 4.16. Types of organizations taking part in the survey research [N=400 RIs Users] . 137 Table 4.17. Types of organizations taking part in the survey research, and the type of access that it has to RI data [N=400 RIs users] ...... 138 Table 4.18. Different types of additionalities of the European Union funds that occurred as a consequence of being a user of RIs [N=400 RIs users] ...... 139 Table 4.19. Different types of added values from being a user of RIs [N=400 RIs users] . . 141 Table 4.20. The users’ evaluation of the importance of the services and functions provided by the RIs [N=400 RIs users] ...... 142 Table 4.21. The importance of different barriers in the process of accessing RIs [N=400 RIs users] ...... 142 Table 4.22. Horizon 2020 key performance indicators related to the reinforcement of European RIs ...... 143 Table 5.1. European Global Research Infrastructures – recommendations of the GSO and ESFRI ...... 156 Table 5.2. Twelve Global Research Infrastructures (GRIs) selected for further investigation – basic data and performance indicators ...... 157 Table 5.3. The state of the Commitment 32 implementation –summary ...... 171 Table 6.1. Innovation system functions in implementing Commitment 27 by actor category ...... 195 Table 7.1. The actor-based structure of KICs supervised by the EIT. The cells contain the numbers of actors and their share (in brackets) in the total number of given KIC partners...... 215 Table 7.2. Actor capabilities for participants of KIC innovation networks ...... 216 Table 7.3. Emergent capabilities of KICs dependent on the institutional level of analysis (Williamson, 2000) ...... 217 Table 7.4. Obstacles identified in KIC innovation networks on the basis of conducted interviews ...... 218 Table 7.5. Behavioral roles of actors involved in EIT policy ...... 220 Table 7.6. Key Performance Indicators for KICs (comparison of targets and results) . . . . 221 Table 7.7. Key Performance Indicators for KICs ...... 223 276 List of tables

Table 7.8. Outcomes of collaboration in KIC networks reported by KIC partners ...... 224 Table 7.9. Impact made by EIT and KIC actions – indirect impact assessments based on interviews ...... 224 Table 7.10. Grand societal challenges addressed by KIC partners – survey results ...... 226 Table 7.11. How do you assess the functioning of the Knowledge and Innovation Community that is your partner (please use a 1-6 scale, where 1 is the lowest grade and 6 is the highest)? ...... 227 Table 8.1. JRC publications in 2005-2015 ...... 251 Table 8.2. Publications and citations of JRC papers in 2010-2016 ...... 253 Table 8.3. Highly-cited publications: JRC and other scientific institutions compared, 2007 to 2013 ...... 254 Table 8.4. Distribution of respondents by type of their institution ...... 258 Table 8.5. Distribution of respondents by country ...... 258 Table 8.6. The impact of science-based policies on innovation broken down by impact types and by EU Member States innovation performance groups ...... 260 Table 8.7. Intra group differences: impact assessment of science-based policies by policy makers in EU Member States by type of impact and type of institutions represented by the respondents (national vs. regional/local government) (in percentage; N=570) ...... 261 Table 8.8. The assessment of the importance of scientific evidence for policy making at the level of the EU, country and region (N=570) ...... 262 Table 8.9. Relationship between the importance of scientific base and different types of impact (N=570) ...... 262 Table 8.10. The frequency of using scientific advice given in policy papers or briefs issued by the JRC (N=566; 4 respondents from the sample that classified themselves in the ‘Other’ category, i.e. neither national nor regional government, have not been taken into account)...... 263 Table 8.11. Relationship between frequency of using scientific advice from JRC papers and the assessment of different types of impact on innovation (N=566; The ‘Other’ category of respondents has not been taken into account) ...... 263 Table 8.12. Relationship between frequency of using scientific advice at various stages of policy making and the impact on innovation (N=566; The ‘Other’ category of respondents has not been taken into account) ...... 264 Table 8.13. Relationship between the science function in policy making and the assessment of the impact of science-based policies on innovation (N=566; The ‘Other’ category of respondents has not been taken into account) ...... 265 List of figures and graphs

Figure 1.1. Perceived success of the Innovation Union blocks according to the stakeholder survey ...... 23 Figure 1.2. Summary Innovation Index in the EU, 2010-2017 ...... 24 Figure 1.3. Change in EU innovation performance in 2010-2017 measured by the 10 main dimensions of the SII ...... 26 Figure 1.4. Overall innovation performance (SII) of selected countries compared to the EU (EU=100); changes in relative performance over the 2010-2017 period ...... 27 Figure 2.1. Conceptual model of the impact of public financial support for R&D on innovation performance ...... 38 Figure 2.2. Results of the path analysis for selected EU countries – input additionality and behavioral external additionality ...... 53 Figure 3.1. Decision matrix per Lu, et al. (2014) ...... 65 Figure 3.2. ERC – Percentage of publications from ERC funded projects which are among the top 1% highly cited ...... 92 Figure 3.3. Marie Skłodowska-Curie actions – Cross-sector and cross-country circulation of researchers, including PhD candidates ...... 92 Figure 3.4. Research Infrastructures – Number of researchers who have access to research infrastructures through support from Horizon 2020 ...... 92 Figure 3.5. FET – Publications in peer-reviewed high-impact journals ...... 93 Figure 3.6. FET – Patent applications and patents awarded in Future and Emerging Technologies ...... 93 Figure 3.7. LEIT – Patent applications in the different enabling and industrial technologies ...... 93 Figure 3.8. LEIT – Patents awarded in the different enabling and industrial technologies ...... 93 Figure 3.9. Risk Finance – Total investments mobilized via debt financing (in million EUR) ...... 94 Figure 3.10. Societal Challenges – Publications in peer-reviewed high-impact journals in the area of the different Societal Challenges (data based on "Number of publications in peer-reviewed high-impact journals") ...... 94 Figure 3.11. Societal Challenges – Patent applications in the area of the different Societal Challenges ...... 94 Figure 3.12. Societal Challenges – Patents awarded in the area of the different Societal Challenges ...... 95 Figure 3.13. Dendrogram for FP7 ...... 96 Figure 3.14. Dendrogram for H2020 ...... 97 Graph 4.1. The strength of financial relations between partners implementing Horizon 2020, part INFRA projects, in different EU Member States (for the strongest connections) ...... 125 278 List of figures and graphs

Graph 4.2. The strength of financial relations between partners implementing Horizon 2020, part INFRA projects in different EU Member States (for all the connections) ...... 125 Figure 5.1. Involvement of the EU15 and EU13 in 12 Global Research Infrastructures, changes between 2010-2016 ...... 166 Figure 5.2. Pace of internationalization of 12 Global Research Infrastructures, data for 2010-2016 ...... 167 Figure 5.3. Projects related to Global Research Infrastructures co-financed by FP7 and H2020 (till 05.2016) funds, changes among two sources ...... 168 Figure 6.1. The distribution of project results by source of financing ...... 189 Figure 6.2. Respondents’ views on the knowledge transfer effect of projects ...... 190 Figure 6.3. Distribution of answers to the question: Are you planning to sustain the partnerships established for the FP6, FP7 and H2020 research programs?. . . 190 Figure 6.4. Respondents’ views on effecting innovation in public services by their research project ...... 191 Figure 6.5. Effects of research projects financed within the H2020 – mean grade (very low = 1; very high = 5) ...... 191 Figure 6.6. Respondents’ views on the EPSIS’s role in benchmarking and knowledge sharing in public administration ...... 192 Figure 6.7. Distribution of answers to the question: How helpful would a tool such as the EPSIS be for benchmarking and knowledge sharing in public administration? ...... 193 Graph 6.1. Interactions between the main actors in selected FP7 and H2020 areas . . . . 197 Graph 6.2. Interactions between countries in the H2020 (SOCIETY and SWAFS) . . . . . 197 Figure 6.8. EIS score vs. H2020 centrality ...... 198 Figure 7.1. The shares of different innovation actors in all KICs ...... 215 Figure 7.2. The sizes of partnership networks for all KICs supervised by the EIT ...... 216 Figure 8.1. JRC budget (left axe in million EUR) and its changes in 2001-2015 by type of expenses (right axe in %) ...... 248 Figure 8.2. JRC budget: value of contracts (left axe in million EUR) and its changes in 2001-2015 by contract type (right axe in %) ...... 249 Figure 8.3. JRC human resources: overall number of employed personnel in both visiting and core staff segments in 2004-2015 ...... 250 Figure 8.4. JRC publications by type: years 2010 and 2015 compared ...... 251 Figure 8.5. JRC input-output comparison in 2005-2015: number of publications per JRC employee ...... 252 Figure 8.6. JRC publications in 2010-2016 by science fields according to Web of Science categories of research fields ...... 255 Figure 8.7. JRC publications with private authors in 2007-2013 ...... 256 Figure 8.8. Top 15 JRC publications with private authors in 2007-2013 by research field . . 256 Figure 8.9. Impact of science-based policy on innovation according to the survey respondents (N=570) ...... 259 Figure 8.10. Types of scientific advisory structures according to the survey respondents (N=570) ...... 266 Editor and author bios

Marzenna Anna Weresa, Professor of Economics, since 2005 director of the World Economy Research Institute; in 2016 elected as Dean of the Collegium of World Economy at the Warsaw School of Economics for the period of 2016-2020. She holds a PhD degree in Economics (1995) and a habilitation degree (D.Sc.) in Economics (2002). In 1999-2000 she worked as a research fellow at the University College London, UK. Her research and academic teaching focus on international economics, and economics of innovation in particular, issues relating to FDI, technology transfer, innovation systems as well as the effects of FDI and foreign trade on competitiveness. She authored and co-authored over 100 books and scientific articles. She has carried out many advisory projects for enterprises and governmental organizations in the field of internationalization strategies, R&D and innovation. Since 2012 she works as an expert of the European Commission providing advice on policies for research and innovation. In 2012-2015 she was a member of the High-Level Economic Policy Expert Groups (“Innovation for Growth – I4G” and “RISE”). In 2017 she joined the European Commission expert group “Economic and Societal Impact of Research and innovation (ESIR)” providing economic analyses and recommendations in view of supporting policy implementation. She was also appointed chair of the EC expert panel working on “Mutual Learning Exercise – The evaluation of business R&D grant schemes”.

Adam Karbowski, PhD in Economics, M.A. in Psychology, P.G. Dip. in Philosophy. He published in Personality and Individual Differences, Behavioural Processes, Frontiers in Psychology, PLOS ONE, Economics and Sociology, among others. His research is interdisciplinary and combines recent advances in behavioral science, economics and sociology.

Arkadiusz Michał Kowalski, PhD, is an Associate Professor and a Head of the Department of East Asian Economic Studies at the World Economy Research Institute at the SGH Warsaw School of Economics. He holds a habilitation degree in Economics (2013) from the Collegium of World Economy, and a PhD in Economics (2006) from the Collegium of Economic Analysis. His research and academic teaching focus on innovation policy, clusters, international competitiveness, and the internationalization of firms. He has been involved in different European or domestic research projects in 280 Editor and author bios these fields, which resulted in more than 60 publications, including books, chapters, articles in scientific journals, and expert reports.

Marek Lachowicz, research analyst at the World Economy Research Institute and Warsaw School of Economics PhD student.

Małgorzata Stefania Lewandowska, PhD, is a lecturer and researcher in the Institute of International Management and Marketing, Warsaw School of Economics (SGH), Poland. She holds a PhD degree in Economics (2006) from the Collegium of World Economy SGH and an MBA (2002 – graduation with honors) from the Universite du Quebec a Montreal, Canada. She carried out many research projects on international competitiveness of enterprises, cooperation in innovation processes and innovation strategies.

Marta Mackiewicz, PhD in Economics. She is a researcher at the World Economic Research Institute, Warsaw School of Economics. She has been involved in economic research for 18 years, focusing on innovation and competitiveness issues as well as on public policies. She is the author and co-author of numerous publications and reports.

Tomasz M. Napiórkowski, Assistant Professor at the Warsaw School of Economics in Poland. Since he returned to Poland in 2011 from the U.S. (where he studied and worked at the Old Dominion University), he authored/co-authored various scientific texts (published both in Polish and English), conducted a series of scientific projects (including grants from the Polish National Science Center, INSO 3 for the European Commission, DG Enterprise) and continues to implement economic theory through consultancy projects (for Master Card, The Conference of Financial Companies in Poland and the Polish National Debt Register [Krajowy Rejestr Długów]). He focuses on macroeconomic conditions, especially foreign direct investment, as well as on forward-looking topics like innovation’s role in economic growth.

Małgorzata Rószkiewicz, Professor and researcher at the Institute of Statistics and Demography, Warsaw School of Economics. Her scientific activity concentrates on field works, sampling methods and applied multivariate quantitative analysis.