Policy Learning and EU Innovation Policy: The Case of the European Institute of Innovation and Technology

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Policy Learning and EU Innovation Policy:

The Case of the European Institute of Innovation

and Technology

MA Thesis in European Studies

Graduate School of Humanities

University of Amsterdam

Author: Annick Zweers Student number: 11338792 Supervisor: dr. J. Shahin

Second Supervisor: dr. A. van Heerikhuizen

Amsterdam June 2017

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Abstract

Since the economic crisis of the 1970s, the European Union has shifted the focus from science and technology to innovation, in order to become the best knowledge economy in the world and create growth and jobs. Throughout the years, the EU has developed a wide range of policy initiatives to enhance innovation in Europe. Still, the EU is economically behind countries like the United States and Japan. In 2008, the EU established the European Institute of Innovation and Technology (EIT), that had to solve the "European Paradox". This is the trouble that the EU is experiencing with translating research into marketable products. Several years later, the EIT is not achieving the ambitious goals that the EU envisioned for it. After a few critical evaluations, it is the question if the EIT still has EU added value, especially when looking at the large variety of EU innovation policy instruments that exist. In order to answer this question, this thesis examines how policy learning takes place for EU innovation policy and the EIT. Therefore, this thesis looks at how policy-makers learn from interaction with innovation theory in the literature, experience, and comparison with policies of other countries. The main finding is that all the three types of policy learning take place for EU innovation policy. The EIT, however, is facing structural problems because of its governance, which hampers effective policy learning. It is therefore likely that the problems that the EIT is facing will persist.

Word count: 17865 words (excluding table of contents, abstract, list of figures, list of abbreviations, bibliography and appendix.

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List of Figures

Figure 1: Directorate-Generals responsible for innovation policy Figure 2: Innovation policy instruments

Table 1: Overview of Knowledge and Innovation Communities Table 2: Policy learning model by Nauwelaers and Wintjes (2008) Table 3: Policy learning model by Kuhlmann et al. (2010)

Table 4: Theoretical Framework

Table 5: EU definitions and objectives for innovation

List of Abbreviations

DG Directorate-General

ECSC European Coal and Steel Community EHEA European Higher Education Area

EIT European Institute of Innovation and Technology EU European Union

ERA European Research Area FP Framework Programme JTI Joint Technology Initiatives

KIC Knowledge and Innovation Community

OECD Organisation for Economic Cooperation and Development MIT Massachussetts Insitute of Technology

NIS National innovation system RIS Regional innovation system

RTO Research and Technology Organisation SME Small and Medium-sized Enterprise

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Introduction

Even though Europe has a broad range of policy initiatives to support research and

innovation, it is economically behind countries like the United States and Japan. Europe has excellent scientists, but is having trouble to translate research into marketable products, which is referred to as “the European Paradox” (Conti and Gaule 2011, 123). In order to tackle this problem, the European Institute of Innovation and Technology (EIT) was set up in 2008. This institute had to enhance Europe’s innovation performance through a so-called knowledge triangle: the integration of business, universities, and research organisations. Since its

establishment, the EIT has received critique for not reaching the goals that the EU envisioned for it. At the moment of writing this thesis, the EIT is being evaluated and the EU is

determining the course for the EIT after 2020. The question arises if the EIT has EU-added value, and if it should remain one of the key players in the European innovation landscape. Does the EIT have the capacity to foster innovation and has the institute learned from its previous policy experiences? This thesis will assess the extent to which policy learning has taken place for the EIT in order to determine if the EIT can improve in the future. The research question of this thesis is therefore: How does policy learning occur for EU

innovation policy and does the European Institute of Innovation and Technology bring added value to this?

Innovation is a key determinant for growth and wellbeing (European Commission 2006). With innovation, living standards can be raised and it can be the solution to societal challenges like climate change and health problems. It is therefore important to analyse how policy can best stimulate innovation. Several studies have been carried out to examine

innovation policy, but it remains hard to define the optimal policy instruments for innovation. Policy-makers therefore constantly learn from innovation theories, practices, and innovation policies in other countries. This thesis examines these learning processes by looking at how EU policy-makers have learned from these three different sources of input. It takes the EIT as a case study to closely examine how learning takes place. These insights are relevant for European Studies, as it explores how EU policy-making and policy learning takes place in this challenging environment.

In the first chapter, the European innovation policy landscape will be presented. The chapter first illustrates the historical context of EU innovation policy, and will then look at the current policy framework and instruments for innovation. The chapter will end with an

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6 theoretical framework for this thesis. It further describes theories behind policy learning from theory, practice and international comparison. Subsequently, chapter three deals with

innovation theories. The main innovation theories and their impact on EU policies are examined and the chapter describes the EU’s definitions and objectives for innovation throughout the past decades. Chapter four looks at policy learning from previous policy experiences. The first part analyses how evaluations take place for EU innovation policy and what actors learn from this process. The second part looks specifically at trans-national learning between the EU Member States through soft methods like benchmarking. In chapter five, I will examine the extent to which the EU learns from international organisations and external countries. More specifically, the chapter compares the differences between learning from the OECD and the United States. In the final chapter of this thesis, the findings of the previous chapters are being tested for the EIT. I will look at the extent to which the EIT has learned from theory, practice, and international comparison.

The main finding of this thesis is that EU policy-makers indeed learn from various sources like theory, practice and comparison, but the EIT’s governance structure hampers effective learning and the improvement of innovation policy. It can therefore be expected that the main problems that the EIT is facing now will continue to play a role, even if the EIT will still be a prominent initiative in the innovation landscape post-2020.

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Chapter 1: The Innovation Policy Landscape

1.1 Introduction

A European Science and Technology Policy was already present at the end of the Second World War; innovation policy came into the picture much later. As a result of the economic crisis in the 1970s and new technological developments, innovation came to be seen as a driving force of economic growth. In this era, an innovation policy started to develop, which began to encompass research, education, and industrial and SME policy. Under Horizon 2020, the 8th _{and current Framework Programme, innovation plays a major} role. Since 2008, the European Institute of Innovation and Technology (EIT) is also an actor in the European innovation landscape. Its main goal is to stimulate Europe’s innovation performance through the knowledge triangle: the interaction between business, universities and research institutes. This chapter analyses how EU innovation policy has developed since the establishment of the European Coal and Steel Community (ECSC), and how it gained a significant place in EU policy. It will provide an overview of the current state of play in the EU innovation landscape, showing the main policy fields involved in the innovation process and the main policy instruments that are used to stimulate innovation. The chapter will end with an analysis of the role of the EIT in this complex policy landscape.

1.2 Historical Overview of EU Innovation Policy

After the Second World War, there was a general consensus that European countries had to be built up with science. Before the war, governments already funded research, but it was limited to fields in which governments exerted direct influence only, and the funds were very low (Tindemans 2011, 4). The governance of science and technology at the EU level started with the creation of the EURATOM and ECSC. These were separate international organisations that were founded by the at that time six European member states. The aim of EURATOM was to produce knowledge in the field of nuclear energy, and was thus motivated by security reasons (Borrás 2003, 1). The European Economic Community, however, had no policy for science and technology, which shows that except for the other two European Communities, science and technology were still absent from the debate in Europe (Tindemans 2011, 5). According to Hsuan Chou and Gornitzka, intergovernmentalism explains best how the EU governed research policy until approximately the 1980s, since there were only meetings between research ministers and limited roles for EU institutions (2014, 12).

The 1970s marked a period when drastic economic change took place. Economics and politics were tightly interwoven, as the economic theories of John Maynard Keynes, also

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8 known as Keynesianism, were very influential for economic policy until the crisis of the 1970s. Because of stagflation, which is high inflation combined with high unemployment, the economy slowed down and governments realized that the current economic system of state interference could not adequately deal with new economic developments. New information technologies brought about a need for new ways to deal with economic growth and social development (European Commission 1979). The focus turned to technology and innovation as the main driver of economic growth, and thus to the ideas of intellectuals like Schumpeter, Smith and Marx. With the creation of the Single Market and the initiatives of the Delors Commission in the 1980s, technological development gained attention in the EU. Jacques Delors initiated the first Framework Programme (FP) which led to increasing EU involvement in scientific and technological matters (Borrás 2003, 2). These Framework Programmes are funding programmes to promote research in Europe. Hsuan Chou and Gornitzka define the EU mode of governance in this period as “programmed”, since it was based on the

Framework Programmes (2014, 13).

The shift from Keynesianism to advanced capitalism led to a crucial role for knowledge instead of labour and capital (Borrás 2003, 2). There was a need for a new economic approach, since technological advances did not yet lead to growth (Mytelka and Smith 2002, 1477). Instead of focusing on science and technology, EU policy from then on centred around innovation. The idea developed that innovation could be best stimulated through collaboration between institutions and firms. These innovation systems could produce growth. Innovation systems can be defined as “national or local environments where

organisational and institutional developments have produced conditions conducive to the growth of interactive mechanisms on which innovation and the diffusion of technology are based” (OECD 1997, 238). The new Lisbon Strategy, an action plan for the EU economy between 2000 and 2010, opened the doors to a far-reaching political agenda in the field of innovation, which now extended beyond security or industrial advance, and had the goal to produce more wealth in Europe. The EU broadened the policy field significantly, as it now focused on many other issues like education and training, organisational change, institutional framework, intellectual property rights and standards (Borrás 2003, 2). Research and

development, education and training, industrial and SME policies, and regional policy thus became crucial policy fields for innovation.

Innovation Policy

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9 objectives for innovation. The first official innovation strategy was created in 2006 with the updated Lisbon Strategy. The EU relaunched the Lisbon Strategy (Lisbon II) in 2005 after a perceived failure of the first Lisbon Strategy. This updated strategy depended on new funds and programmes, experimentation with trans-national learning and coordination of national or European policies (Edler 2012, 168). The result was that there were large new initiatives at the EU level, which were now focused on demand and the market (Edler 2012, 171). Also, structural funds became more widely available for research innovation. In 2006, the first innovation strategy was developed which aimed to intensify cooperation between

stakeholders and to boost funding through a mixture of schemes, like the European Research Council, Joint Technology Initiatives, Competitiveness and Innovation Programme, etc. It also encouraged governments to set an example by using innovative approaches for public administration (European Commission 2006). The discourse that the Commission used to promote this strategy was that it aimed to address societal challenges with this new innovation strategy (Edler 2012, 172). In 2010, the Innovation Union Strategy was presented, that further integrated the European Research Area (ERA) initiatives with EU innovation policies. This is part of the greater ten-year Europe 2020 strategy of 2010, that followed the Lisbon Strategy and aims to achieve “smart, sustainable, inclusive growth with greater coordination of

national and European policy” (European Commission 2010a). Therefore, since Europe 2020 innovation not only focuses on creating growth, but is also oriented towards addressing societal challenges. The Innovation Union Strategy includes the reform of research and innovation systems, measuring progress by two indicators: R&D investment target and a new Innovation indicator, and it calls upon consolidation of the collective efforts of institutions and stakeholders to make the Innovation Union reality (European Commission 2010b).

Research Policy

Further European cooperation in the field of research became crucial for creating knowledge and innovation. The establishment of the ERA was key to the Lisbon strategy; it aimed to create innovation-friendly environments, new businesses (especially small- and medium-sized enterprises), and an information society that was accessible for everyone (Edler 2012, 171). The establishment of the ERA also led to a new mode of European governance, namely a multilevel mode of governance (Hsuan Chou and Gornitzka 2014, 13). Research policy became more complex, with multiple actors and authorities, both on national and international level. Research policy changed after 2000, with growing EU influence and initiatives. Before, the Framework Programmes supported individual projects and a small set of joint research centres. Apart from that, there was little EU involvement in national research policies (Edler

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10 2012, 173). A set of national goals were established with the most important being a 3% Action Plan, that set the objective for Member States to reserve a minimum of three

percentages of their gross domestic product (GDP) for R&D by 2010 (European Commission 2006). Other goals were more networking between research organisations and firms, better instruments and resources for the funding of research, promoting a greater mobility for research in Europe, and create more cohesion between national and European research policies (European Commission 2006). Joint Programming initiatives, for which Member States agreed on common visions and Strategic Research Agendas, were created in 2008, were launched by the Commission to coordinate research activities more effectively. The Lisbon Treaty of 2009 incorporated Article 181, also referred to as the Ljubljana Process: “EU level and Member State level shall better coordinate policies on research and

technological development to ensure consistency.” The latest funding programme that the EU established is the 8th_{Framework Programme that runs from 2014-2020, also known as}

Horizon 2020. This funding programme for research in the European Research Area consists of three pillars: “excellent science”, “industrial leadership”, and “societal challenges”

(Commission 2011).

Education Policy

Another policy field that is considered key to enhancing innovation is education policy. Since the launch of the Lisbon Strategy, education at the EU level is increasingly framed from an economic perspective (Garben 2010, 5). This enhanced the need for a stronger grip of the EU on higher education, since the EU saw education as crucial for creating an EU knowledge economy that is more competitive and leads to economic growth. For many years in European history, there was not much cooperation between Member States in the field of education and training. Education is one of the policy fields that is regarded as sensitive, since Member States see it as part of their national spheres (Olsen 2002, 931). This has changed slightly since the launch of the Lisbon Strategy and the Bologna Process. The Lisbon Strategy and the Bologna Process were two important political processes that enhanced cooperation between Member States in the field of education. During the Bologna Process, different ministerial meetings took place between 47 countries. Even though these meetings also consisted of non-EU countries, it played a great role in the Europeanization of higher education. The process led to the Bologna Accords that established a European Higher Education Area (EHEA). This meant that agreements were made to harmonize higher education: a three cycle system

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11 another country within the EHEA. The Bologna Accords are based on soft law, since no formal, binding treaties were signed. The political processes of the Lisbon Strategy and Bologna differ, but nevertheless there is convergence between the two. Froment argues that “the current tendency at European level is to look at the Bologna process as an element of the Lisbon strategy. This is the result of the European Commission actions, and has important consequences because the Lisbon strategy takes a narrower view of higher education activities” (cited in Veiga and Amaral 2009, 136). Because the Lisbon Strategy turned the focus to growth and employment, it was legitimate for the EU to also promote education and training policies, which resulted into the convergence of the Lisbon Strategy and the Bologna Process into one “policy framework” (Veiga and Amaral 2009, 136).

EU cooperation in the field of science and technology started after World War II, but shifted to a focus on knowledge and innovation. This was due to the economic crisis in the 1970s and the increasing complexity of society as a result of technological advances. The EU sought to address these challenges through making the EU a knowledge economy. Innovation systems became a crucial approach for creating innovation and knowledge, and led to a spill-over of innovation policy to different policy fields. Research and education policy are

significant policy fields for innovation. Research had already been an important policy field to create technological advances, and thus cooperation between Member States was intensified. EU competence in education policy increased, since the EU could justify better coordination at the supranational level for creating jobs and economic growth. The goals that the EU linked to innovation also changed throughout the years, from creating competitiveness, to economic growth, and a greater focus on addressing societal challenges.

1.3 The Policy Landscape

Innovation policy is not a separate policy field, which makes it more difficult to determine how the EU actually tries to stimulate innovation. Rather, innovation policy is an umbrella policy that comes back in many different EU policy fields. This section will present an overview of what the innovation landscape looks like in the EU under Horizon 2020, and introduces the main instruments that the EU uses to promote innovation. Innovation policies can be divided in three categories: key policies, key framework conditions, and sectoral policies (Reillon 2016, 3). Key policies are policies directed at actors in the innovation process, and include research and development (R&D) policy, industrial and SME policy, education and skills policy, and regional and cohesion policy (Reillon 2016, 3). Reillon defines key framework conditions as “covering policies and instruments shaping the

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12 interactions and organising the flows of knowledge, skills and funds between the actors in the innovation process” (2016, 3). A crucial condition for these key framework conditions is the European Single Market. Sectoral policies are also part of the innovation process as they introduce new regulations in the health, environmental, energy, and transport sector and can thus support or conflict with the innovation process (Reillon 2016, 3). Figure 1.1 shows the key policies, key framework conditions and sectoral policies and the Directorate-Generals (DGs) that are involved in the policy-making process.

Policy Instruments for Innovation

Borrás and Edquist divide innovation policy instruments into three categories:

regulatory instruments, economic and financial instruments and soft instruments (2013, 1516). The scheme that they accompany to their three-fold typology of policy instruments shows that regulations are about binding rules that are made with regard to innovation (2013, 1516). Economic and financial instruments are used as means to create incentives and support certain activities (2013, 1516). Policy-makers also increasingly turn to soft, voluntary methods. Policy tools can be divided between the different stages of the innovation process: basic research, commercialization of technology, and diffusion of technology (Feige 2015, 13). The EU innovation policy tools can also be categorised into regulatory instruments, economic and financial instruments, and soft instruments. The following paragraphs will go deeper into EU’s current financial, regulatory, and soft tools.

Figure 1: Reillon (2016) Figure 1: Reillon (2016) Figure 1: Reillon (2016)

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Figure 2: Borrás and Edquist (2013)

Financial Support

Financial tools that can be used are financial and technical assistance, government procurement, and promoting national systems of entrepreneurship. Governments can financially support small businesses, especially to avoid that a start-up will go bankrupt before it can secure a steady stream of revenues (Markham et al. 2010, 402). This phenomenon is known as the metaphorical “valley of the death”. There are multiple EU funding programmes that directly support research and related innovation activities (Reillon 2016, 14).

EU funding programmes

• Horizon 2020: This is the 8th_{Framework Programme with a budget of 74.8 billion} euros, aimed at supporting research and innovation activities and other parts of the innovation process.

• Structural and Investment Funds: The European Regional and Development Fund (ERDF), and the European Social Fund (ESF) are important funds to create more regional cohesion in the field of income, wealth and opportunities between the different Member States. Research and innovation are regarded as crucial policies to create more cohesion.

• Europe’s programme for the Competitiveness of Enterprises and SMEs

(COSME): this programme is aimed to strengthen the position of SMEs and to

stimulate the creation of new SMEs.

• Specific Research Programmes: The EU also has a few separate thematic research programs, which are the Space programmes, and programmes under the EURATOM

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14 Treaty and the ECSC Treaty legacy. These are focused respectively on the themes of space, nuclear energy, and coal and steel.

• Sectoral Programmes: Erasmus+ (for education), Connecting Europe Facility (for transport, telecommunications and energy), Life (for environment and climate action), and Health.

• European Fund for Strategic Investment (EFSI): This is an initiative launched by the European Investment Bank (EIB) and the Commission to overcome market failures through strategic investment, also in research and innovation.

State Aid

Even though state aid is often considered as a distortion of the functioning of the internal market, the Commission already defined state aid as a useful tool to enhance innovation in 1993 (European Commission 1993). The EU adopted a Framework for Research in 2001, which was extended to 2005 (Reillon 2016, 15). In 2005, the Commission presented a new State Aid Action Plan, to modernise state aid policy (European Commission 2005). A key priority of this action plan was to target innovation and R&D to strengthen the knowledge society. The Framework for Research and Development was modified to correspond to the new Barcelona (3% of GDP to R&D) and Lisbon objectives (European Commission 2005). A State Aid Modernisation plan followed in 2012. In 2014, the EU updated the framework, that now provided more possibilities for state aid, namely for: R&D projects, feasibility studies, the construction and update for research infrastructure, innovation activities and innovation clusters (Reillon 2016, 16).

Tax Policy

Tax policy is reserved for the competence of the Member States. Therefore, actions that the EU takes with regard to tax incentives are based on soft tools such the exchange of best practices and recommendations (Reillon 2016, 16). The EU thus recommends the Member States to adopt tax incentives that stimulate innovation, and tries to encourage a more unified tax incentive system among the Member states (Reillon 2016, 16).

Regulations Framework

The diffusion of technology is mostly related to creating regulations and standards. Governments can choose technologies and favour them over others (Feige 2015, 19). Standards and regulations are crucial; a government can have influence on how fast an innovation can be taken up by the market (Feige 2015, 19). Regulations can both stimulate

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15 and hamper the innovation process (Feige 2015, 18). In 2016, the Commission introduced a working document for better regulations for innovation-driven investment at the EU level. This was in line with the Better Regulations Agenda, adopted in 2015, which is aimed at making EU regulations less complex and more transparent (European Commission 2015a). In the document, the Commission stresses the importance of an innovation-friendly regulatory environment and introduces the concept of Innovation Deals. Innovation Deals are aimed at creating openness for innovation, through voluntary cooperation with innovators, the EU and national, regional and local authorities (European Commission 2016a). They can address regulatory obstacles and thus shorten the time for an innovation to actually be taken up by the market (European Commission 2016a).

Standards

Standards can be defined as “voluntary technical specifications defining requirements for products, production processes, services or test-methods” (Reillon 2016, 21). Standards are crucial for the single market, competition and the reducing of costs (Reillon 2016, 21). Every year, the Commission presents a work programme for standardisation.

Intellectual Property Rights

Intellectual property rights (IPRs) can be divided into patents, copyright and

neighbouring rights, design rights, trademarks, and trade secrets (Reillon 2016, 23). IPRs are an important tool to promote or to slow down the diffusion of technology (Feige 2015, 18). When a government has stronger IPR protection, the diffusion process is slower, since there is a temporary monopoly on the technology. The other way around, when the IPR protection is loose, the diffusion process is faster.

Other Initiatives (Soft Instruments)

There are several other initiatives in the field of research and innovations (Reillon 2016): • European Technology Platforms: These are platforms for stakeholders led by the

industry that create roadmaps for action in the field of research and innovation. These platforms are important for the collaboration between different stakeholders so that they can tackle challenges. There are 41 different independent and self-financing European Technology Platforms that focus on themes like ICT, energy, bio-based economy, environment, production, and transport.

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16 • European Institute of Innovation and Technology: The European Institute of

Innovation and Technology (EIT) focuses on fostering innovation through the establishment of Knowledge and Innovation Communities (KICs). The following section will provide more information on the structure and background of the EIT. • Joint Technology Initiatives: These JTIs are long-term public-private partnerships

that are laid down in TFEU Article 187, which promote cross-national and large-scale research activities aimed at tackling major European challenges.

• Contractual Public-Private Partnerships: Industry provides advice on research priorities through contractual public-private partnerships. These contractual partnerships are long term investments in research and innovation.

• European Innovation Partnerships: European Innovation Partnerships are

partnerships especially focused on challenges and bring together relevant actors at the different levels in the innovation process.

Innovation policy under Horizon 2020 can thus be divided into key policies, key framework conditions and sectoral policies. The key policy fields that are important for the innovation process is research policy, industrial and SME policy, education and training policy and regional and cohesion policy. Research and education policy are important for the creation of innovative ideas, industrial and SME policy is aimed at the diffusion of technology to the markets, and regional and cohesion policy addresses the need to strengthen the EU’s overall innovation performance to avoid regional disparities. Key framework conditions can be divided into financial tools, regulatory tools and soft tools. The EU has a wide variety of available instruments to enhance innovation. Financial tools that are used are from EU funds, but the EU also stimulates Member States to use financial incentives. Regulatory instruments are often related to the diffusion of innovation, and EU soft instruments for innovation are aimed at bringing public and private actors together to promote collaboration in the

innovation process. Sectoral policies also have an impact on innovation in the EU, since they also introduce regulations that can hamper or stimulate the innovation process.

1.4 The European Institute of Innovation and Technology

The European Institute of Innovation and Technology (EIT) is an EU body that is also part of Horizon 2020 and addresses the need for a better integration of the so-called

knowledge triangle - the interaction between universities, research organisations, and business - in the EU. This last section will provide the background of this institution by examining its establishment, governing structure, and goals.

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Context

The EIT was developed within the framework of the Lisbon Strategy. It was proposed by the European Commission President José Manuel Barroso in 2006 and officially established on 11 March 2008. Barroso had the idea of establishing a European institute after the example of the Massachusetts Institute of Technology (MIT); the initial proposal was thus that of a physical institute. The EIT had to ensure that Europe could compete economically with countries like the United States and Japan by better translating research into marketable products (Jofre and Andersen 2009, 2). The idea of a physical institute was, however,

controversial and Barroso had to make compromises in order for the EIT to be established. A less drastic form of the EIT survived, which ensured that Member States were not threatened by EU interference of their national spheres, but which could still have an impact on

innovation (Gournitzka and Metz 2014, 127). Analysis by Huisman and De Jong (2014) shows that the EIT was established in a relatively short time, most notably because of the personal efforts of Barroso (371). They argue that the idea for an EIT was not entirely wiped from the table because of the need for new ways to foster innovation, after the Lisbon

Strategy did not bring the results hoped for (Huisman and De Jong 2014, 372). The solution was a focus on a “knowledge triangle”, which is the interaction between education, research and innovation, without a physical institute.

Objectives

The main objectives of the EIT were highlighted in the “Regulation Establishing a European Institute of Innovation and Technology” of 2008: to create sustainable economic growth and competitiveness and to translate research into higher value products and services (European Parliament 2008). The EIT was thus set up to deal with the so-called “European Paradox”; the inability of Europe to transfer research output into marketable outcomes to create economic growth (European Court of Auditors 2016). The objectives of the EIT changed after its initial period (2008-2013). In the 2008 regulation, the EIT’s objectives were to boost

competitiveness, growth, and jobs. In the amended regulation of 2013, however, the EIT’s goals shifted to fostering entrepreneurship in higher education, research and innovation

activities. We can also see a change in the definition that is provided for concept of innovation in the two regulations. In 2008, innovation was defined as “the process, including its outcome, by which new ideas respond to societal or economic demand and generate new products, services or business and organisational models that are successfully introduced into an

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18 the regulation of 2013. In this new definition, it was added that innovation should not only be introduced into an existing market or create a new market, but also provide value to society. The focus of the EIT thus shifted from tackling the European Paradox to addressing societal challenges and promoting entrepreneurship.

Organisation

The EIT headquarters is located in Budapest, and it is being governed by an independent Governing Board. In the Commission, the DG for Education (EAC) is responsible for the EIT. The role of the Governing Board is to select, evaluate and support the Knowledge and

Innovation Communities (KICs). KICs are partnerships between businesses, research centres, and universities, and have a bottom-up approach. Important for the EIT is to transfer

innovation activities to the business sector, which is why the EIT is based on these

autonomous partnerships (European Parliament 2008). These partnerships can exist for 7 to 15 years. With the KICs, the EIT should enhance networking and create synergies in the European innovation landscape (European Parliament 2008). KICs are centred around certain themes, and are organised around physical co-location centres, where various activities take place. There are currently six different type of KICs that all focus on a societal challenge: EIT Climate-KIC, EIT Digital, EIT InnoEnergy, EIT Health, EIT Raw Materials and EIT Food. Also, there are EIT-labelled master degrees and PhD degrees that correspond to the KIC themes, which are mainly focused at promoting entrepreneurship for students. The EIT also offers the EIT Awards to successful entrepreneurial start-ups that are created out of the KICs. In the initial period of the EIT (2008-2013), the EU reserved a budget around the 300 million euros for the EIT. For the second period, the EIT received a budget of 2.7 billion euros. The EIT is different from the initiatives in Horizon 2020, because it is an independent body, a mix of a top-down (EIT) and bottom-up approach (KICs), and because the KICs can exist for a longer period of time, namely 7 to 15 years.

Table 1: KICs and their locations

KIC Locations

EIT Climate-KIC Vienna, Brussels, Paris, Berlin, Frankfurt, Budapest, Bologna, Utrecht, Copenhagen, Helsinki,

Trondheim, Gothenburg, Wroclaw, Valencia, Zurich, London, Birmingham, Dublin, Cork

EIT Digital Brussels, Berlin, Budapest, Eindhoven, Madrid, London, Paris, Stockholm, Trento, Silicon Valley EIT Inno-Energy Benelux, Iberia, Alps Valleys, Sweden, Poland,

Germany (cities not listed)

EIT Health London, Stockholm, Barcelona, Paris, Heidelberg, Rotterdam

EIT Raw Materials Espoo, Metz, Wroclaw, Lulea, Rome, Leuven EIT Food Leuven, London, Madrid, Munich, Warsaw

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1.5 Conclusion

This chapter was designed to present the historical background of EU innovation policy and to provide an overview of the EU innovation policy landscape and to show the role of the EIT in this. The EU turned the focus on creating a knowledge economy as a result of the economic crisis of the 1970s and many new technological advances that made society more complex. Innovation played a major role in this European knowledge economy, as it became to be seen as the driving force of the economy. The objectives of innovation shifted from creating competitiveness to economic growth and addressing societal challenges. Research and education were seen as crucial components of the innovation process; they are also key policy fields for EU innovation in Horizon 2020, together with industrial and SME policy and regional and cohesion policy.

Integrating research, education and innovation is an important objective of the European Institute of Innovation and Technology. The Knowledge and Innovation Communities (KICs); partnerships between businesses, universities and research

organisations had to ensure that the EU would be able to compete with economies like that of the US and Japan. It therefore has to address the EU’s lack of capacity to transfer research outputs into marketable products and services. Through the knowledge triangle, the EIT should also address societal challenges. The EIT is different from other initiatives in Horizon 2020, because of its independence, top-down and bottom-up approach, and long existence of the KICs.

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Chapter 2: Innovation and Policy Learning: A Theoretical Framework

The previous chapter has given an overview of the historical development of EU innovation policy and the current state of play in the EU innovation landscape. In this chapter, I will present the theoretical framework of this thesis. The theoretical framework is based on theories of policy learning. This chapter examines the influence of policy learning for the EU in general and for EU innovation policy. Through analysis of the main theories on policy learning, I will design a specific model that will help to answer the research question of this thesis. First, the chapter gives a brief overview of the main theories on policy learning. Then, I will focus on theories that look at policy learning for innovation policy. With the use of tables, the theoretical framework will be further explained in the final section of this thesis.

2.2 The EU and Policy Learning

Many theories of European integration,like liberal intergovernmentalism and institutionalism, examine macro processes, instead of zooming in on policy-making processes (Zito and Schout 2009, 1103). Theories of policy learning are an example of these micro processes that can account for a change in policy. The main assumption is that learning processes are an important factor of policy change (Borrás 2011, 726). Learning in policy analysis can be deﬁned as “a process of exercising a judgement based on an experience or some other kind of input that leads actors to select a different view of how things happen (‘learning that’) and what courses of action should be taken (‘learning how’)” (May 1992, 331-332).

Many different perspectives on policy learning have emerged in the literature. According to Freeman (2008), studies on government learning emerged in the 1960s, as a reaction to social, economic and technological change (2). The first use of the concept of learning in relation to policy came from Deutsch (1963), who included it in his analysis of rational decision-making (Zito and Schout 2009, 1106). Heclo (1974) further elaborated on learning in public policy; he argued that “policy making is a form of collective puzzlement on society's behalf” (305–6). By the 1990s, conflicting theories had emerged that focused on different aspects of learning: who learns, what is being learned and what effects does learning have on policy (Bennett and Howlett 1992, 278). Several works were published that aimed to create a more coherent theory of policy learning by combining these strands of policy learning literature that had already emerged. Bennett and Howlett (1992), for example, argued that policy learning can be divided into three types of learning with different actors on different levels involved: government learning for state officials, lesson drawing for policy networks,

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21 and social learning for policy communities (289).

Like Bennett and Howlett’s theory on policy learning, EU governance is based on the interaction between actors on different levels. EU governance can be argued to be a multi-level system with three multi-levels: EU, national and sub-national multi-levels (Ştefan 2014, 359). Hooghe and Marks (2001) developed this multi-level governance theory through studying new governance structures. According to Hooghe and Marks, decision-making power is not only reserved for states, but shared with actors at different levels (2001, 3). The different levels in this multi-level system are linked “not by binding decisions but by transfers of information, not by delegates with clearly defined mandates but by representatives who negotiate on goals and not fixed positions” (Benz 2000, 33).

This multi-level governance was accompanied by new modes of governance that were focused on soft methods. The Open Method of Coordination (OMC) is the EU’s main new mode of governance. The EU introduced the OMC at the Lisbon summit in March 2000. Since this summit, the EU increasingly uses non-binding, voluntary modes of governance in which coordination plays a major role. The OMC is aimed at reforming Member States’ policies through mutual learning, and it is applied in many different policy areas (Kerber and Eckardt 2007, 241). Especially for policy fields where EU competence is minimal or Member States’ interests are too diverse, the OMC is used as an instrument to effectively reform national and regional policies (Radaelli 2008, 239). The OMC method is based on broad common objectives that are adopted by the European Council, jointly established measuring instruments, like statistics, indicators, guidelines, and on benchmarking, which is the

comparison of the performances of EU Member States and the exchange of best practices.

2.3 Learning in Innovation Policy

This thesis studies the specific influence of policy learning on EU innovation policy. Innovation policy can be defined as “the public actions that influence innovation processes, i.e. the development and diffusion of (product and process) innovations” (Chaminade and Edquist 2010, 1). The innovation policy-making process is a process of innovation itself, since there is no optimal policy model or set of policy instruments (Nauwelaers and Wintjes 2008, 1). Innovation policy is thus shaped by learning from previous policies and improving them. The OMC is also used as a mode of governance for innovation policy. During Lisbon I, however, OMC instruments were limited for innovation policy (Kaiser and Prange 2004, 249). Policy tools that have been used under the OMC after Lisbon II are peer reviewing, benchmarking and people mobility (Nauwelaers and Wintjes 2008, 7).

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22 Even though theories of policy learning have received interest from scholars in the past decade, the literature on learning in innovation policy is limited (Borrás 2011, 725). Learning processes are difficult to study due to the ambiguity of the innovation concept and innovation policy is often only studied from a normative perspective, instead of taking an analytical approach (Borrás 2011, 725).

There are, however, a few studies on policy learning in EU innovation policy. Borrás (2011) elaborated further on the framework of Bennett and Howlett by creating an analytical model for learning in innovation policy. She also divides learning in three types of actors, but argues that government officials learn about organisational practices, policy networks learn about innovation systems, and socio-economic actors (social learning) learn about “major reflexive and institutional capacity” (Borrás 2011, 730). Nauwelaers and Wintjes (2008) distinguish three other types of learning in innovation policy but study this on the policy-making level: intra-organisation learning, intra-system learning, and inter-system learning (5). Kuhlmann et al. (2010), argue that innovation policy emerges through “interactive learning” between innovation practice, innovation-related public intervention strategies and innovation research and theory (7). They argue that innovation and innovation policy are based on new combinations and an “innovation policy dance” between innovation theory, practice, and policy-making (Kuhlmann et al. 2010, 7).

Interaction between Innovation Theory, Policy and Practice

This thesis also assumes that EU policy-makers learn from the interaction between innovation theory, policy and practice. Theories like the evolutionary approach of Nelson and Winter (1977), innovation systems by Freeman (1987) and Lundvall (1992) were crucial for the development of innovation policy (Kuhlmann et al. 2010, 10). Mytelka and Smith (2002) confirm that there is a link between social science research and innovation policy: “the theory-policy link has been central to the intellectual development of this field, which would have been impossible within the constraints of existing disciplinary structures and university funding systems” (1467). Kuhlmann et al. argue that there are two modes for learning from theory: formal learning, which is learning about theories on user-producer innovation, and learning by interacting: researchers act as consultants for policy-making.

Not only is there a link between theory and policy, but also a link between policy and practice. Nauwelaers and Wintjes (2008) divide the actors that learn from innovation practice in a three-level-system: organisation learning in the policy-making institution, intra-system learning with users and partners in innovation intra-systems, and inter-intra-system learning by

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23 international comparison. Policy-makers can learn about the impact of their policies by

evaluating them, by interacting with actors in the field and assessing their experiences with the given policies, and by comparing and sharing their policy experiences with other countries.

Table 2: Modes of policy learning in innovation (Nauwelaers and Wintjes 2008)

Table 3: The innovation practice, policy and theory dance (Kuhlmann et al. 2010)

Innovation Practice Theory Policy Innovation Practice Learning by searching

(e.g. researchers learn on user-producer relations from real life experiences with sustainable housing

Learning by interacting

(e.g. researchers use experiences of actors as empirical input for user-producer research)

Learning by using

(e.g. policy makers learn from the impact of their policies by evaluation)

(e.g. policymakers learn from the impact of their policies by talking to actors in the field

Policy Learning by using

(e.g. entrepreneurs learn by using policy

measures)

Learning by searching

(e.g. researchers learn on user-producer relations from the impact of policies focusing on sustainable housing)

(e.g. researchers use experiences of policymakers as empirical input for user-producer research)

Theory Formal learning

(e.g. entrepreneurs learn from theories on user-producer innovation and change their mental frame, conceptual use)

(e.g. researchers act as consultants for entrepreneurs

Formal learning

(e.g. policymakers learn from theories on user-producer innovation and change their mental frame, conceptual use)

(e.g. researchers act as consultants for policymakers)

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24 Because policy learning for innovation policy takes place on multiple levels, different types of actors are involved and interact with each other. Innovation theory involves researchers from universities and research organisations, policy involves government, and practice involves both businesses and researchers. The triple helix model by Etzkowitz and Leydesdorff is a well-known innovation model to explain the cooperation between industry, government and universities in the innovation system. Etzkowitz and Leydesdorff argue that collaboration with the three links in the triple helix results into the optimal environment for creating innovation (2008, 1). The free flow of knowledge between these networks is crucial for creating innovation. As the work by Chesbrough (2006) on open innovation demonstrates, there is a new focus on open innovation instead of closed innovation (2). Networks are a significant aspect of this open innovation; through networks (between universities, business, and RTOs) knowledge is being shared between different actors in the innovation process.

2.4 Theoretical Framework

For the theoretical framework of this thesis, I will mainly focus on the works of Nauwelaers and Wintjes (2008) and Kuhmann et al. (2010), illustrated in the two tables on the previous page. The perspective that I will take in this thesis is that policy learning is shaped by the interaction between innovation practice, policy and theory. My perspective will further elaborate on the framework of Kuhlmann et al., but this thesis will only analyse learning on the policy-making level. It will, therefore, focus on the influence that innovation theory and practice have on innovation policy. This is not to neglect that innovation theory and practice also interact and influence each other. This thesis will also draw upon the works of Dolowitz (2009), since international comparison is also a crucial aspect of policy learning which is neglected in the analytical framework of Kuhlmann et al. The following aspects of policy learning for innovation policy are further examined: 1) the influence of innovation theory on innovation policy, 2) the influence of innovation practice on innovation policy by looking at the three levels as described by Nauwelaers and Wintjes (2008): organisation, intra-system, and inter-system. This theoretical framework is first used to empirically prove that these three types of learning take place for EU innovation policy in general. The insights of this research will feed into the case study of the EIT, as they will provide conditions to assess if effective policy learning has taken place for the EIT. This helps determine if the EIT will be able to improve in the future and if it has added value for EU innovation policy.

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Table 4: Theoretical framework (based upon Kuhlmann et al. (2010), Nauwelaers and Wintjes (2008) and Etzkowitz and Leydesdorff (1996))

Theory Practice

Policy Policy-makers learn from theories

of innovation

Researchers can act as consultants for policy-makers

Triple Helix: Academy

Intra-organisation

Policy-makers learn from past experiences with policy

Triple Helix: Government

Intra-system

Policy-makers learn from interaction with actors in the field

Triple Helix: Industry/Academy

Inter-system

Policy makers learn through comparison with other countries’ innovation policies

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Chapter 3: Learning from Theory

The third chapter of this thesis further examines the first type of learning presented in the theoretical framework: learning from innovation theory. The amount of literature on innovation has increased significantly in the past few decades. Many fields of study have incorporated studies on innovation, which makes it difficult for a single domain to encompass all different aspects of innovation (Teixeira and Silva 2013, 472). Studies on innovation can be found in fields like economics and other social sciences, business studies, and management studies. As a result, there is no coherent theoretical framework for science, technology and innovation research (Teixeira and Silva 2013, 472).

This chapter first examines how learning from theory in generally occurs for EU innovation policy by analysing the main literature on innovation. The chapter looks at early theories of innovation, national and regional innovation system theory, and social innovation theory. This will help determine which theories have been an inspiration for the EIT. Then, the chapter analyses key EU innovation policy documents from the 1980s to present, and compares the policy framework for innovation and the objectives that are provided in these documents. This will help assess how definitions and objectives were shaped throughout the years and how theory has influenced that.

3.2 Innovation Theory and Policy

Theories of Innovation

Various innovation theories of influential researchers have been used to inform innovation policy. Among the first authors to use the concept of innovation was Joseph A. Schumpeter (1883-1950). Schumpeter is argued to be one of the main intellectuals in the field of

innovation theories: his neo-classical ideas are still considered as relevant in contemporary society. He argued that structural economic change is the “perennial gale of creative

destruction” (cited in Hospers 2005, 23). He saw technological innovation as the main source of economic growth (Hospers 2005, 23). Schumpeter divided innovation in four stages: invention, innovation, diffusion and imitation (Śledzik 2013, 90).

Early theories of innovation, like those of Schumpeter, analyse the processes related to innovation as linear. Godin (2006) argues that the linear model of innovation is still relevant because of statistics (659). The starting point of this model is that innovation develops in three stages: research to applied research, then experimental development, and lastly production and diffusion. These three stages are also linked to three scientific groups: natural scientists,

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27 scientists from business schools, and economists from businesses (Godin 2006, 659). Godin claims that the model gained its significance because of its simplicity: statistics of the three stages are often the only available data in the innovation process (Godin 2006, 659). Policy makers thus keep turning to the linear model and base their policy on it, because alternative models are too complex and data is not available. The linear model of innovation has received considerable criticism among scholars because of its simplicity, and as a result, a diversity of more complex models have been created in order to better understand how the diffusion innovation works. Examples of these are; the Actor-Network theory by Callon (1986), the social shaping of technology (SST) by Williams and Edge (1996), and social learning (e.g. Tuomi (2002)).

National and Regional Innovation Systems

Another theory that gained influence among policy-makers is the innovation system theory that the first chapter already briefly mentioned. Lundvall defines an innovation system as “the elements and relationships which interact in the production, diffusion and use of new, and economically useful, knowledge ... and are either located within or rooted inside the borders of a nation state” (1992, 2). National or regional innovation systems thus describe how knowledge flows between different actors, institutions, and businesses, which is crucial for the innovation process.

The first scholar that incorporated the idea of a national system of innovation in his work was Friedrich List (1841-1959). He stressed that the government should promote education and training and stimulate industrial development (Lundvall 1992, 17). He thus already highlighted the key elements of a national system of innovation (Lundvall 1992, 17). The first real use of the concept, however, was by Freeman (1988) in his book Japan: a new

national system of innovation. In this work he focuses on the role that the government plays,

the organisation of R&D and inter-firm relations (Lundvall 1992, 18). In the same year, Nelson also published a study on the national innovation system of the US and the public-private cooperation in the field of technology. Many scholars thus focused on specific countries or regions when writing about systems of innovation.

The concept was also taken up by the Organisation for Economic Cooperation and Development (OECD), as the OECD published several reports on innovation in collaboration with scholars like Nelson and Freeman. With the publication of Technology and the

Economy: The Key Relationship in 1992, many key concepts of innovation studies, like

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28 More importantly, the concept of innovation systems was included into the report. This was based upon the works of influential scholars in innovation studies.

For the EU, the concept became relevant somewhat later. When the focus turned to regional development policies after new Member States joined the EU in 2004, the insights provided by national innovation systems (NIS) theories became incorporated into the EU programs (Mytelka and Smith 2002, 1477). The EU aims to decrease the gaps between the innovative performances between the different Member States through regional and cohesion policy and the European Research Area (ERA). The regional innovation systems (RIS) concept thus became a very popular concept for policymakers because it “fits in what is considered to be a new paradigm of regional policy oriented towards self-sustained, supply-side oriented measures aimed at improving regional competitiveness” (De Bruijn and Lagendijk 2005, 1155). RIS is mostly used as a normative concept in EU policymaking circles, and it is used in both regional and innovation policy (De Bruijn and Lagendijk 2005, 1155).

The innovation system theory takes into account that dynamic clusters of businesses and public research organisations are crucial for the ability of a country to be able to attract investment in innovation and international mobility of high-skilled workers (Kuhlmann et al. 2010, 10). This systemic approach gives policy-makers a justification for further public intervention in the innovation system (Kuhlmann et al. 2010, 12). Therefore, governments take up a major role in fostering innovation within these systems. In order to determine when and how governments should intervene, it is important to understand the system in which innovation takes place (Lundvall 1992, 5). This is where the innovation systems theory comes in.

As pointed out in the previous chapter, multilevel governance plays a major role in EU innovation policy. A crucial aspect of national and regional innovation systems is therefore the integration of business, universities and research organisation to stimulate the flow of knowledge. The triple helix theory by Etzkowitz and Leydesdorff (1996) is therefore very relevant for innovation systems, as it analyses academy-industry-government interactions. We can also see this idea of a system that combines universities, research institutes and businesses coming back in the KICs of the EIT.

The variety of actors active in an innovation system make it difficult, however, to develop effective instruments to foster innovation. Because the innovation system theory approaches innovation processes as dependent on the specific country or region, it is harder for EU policy-makers to find the right policy instruments (Mytelka and Smith 2002, 1477).

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29 The translation from innovation system theory into policy instruments and measurement tools is thus a challenge because every innovation system is different (Mytelka and Smith 2002, 1477).

Social innovation

Another trend in the literature on innovation is the concept of social innovation. Cajaiba-Santana defines social innovation as “a result of the exchanges of knowledge and resources by actors mobilised through legitimisation activities. From a structuration perspective, social innovation is socially constructed as individuals collectively engage in purposeful actions and reflexively monitor the outcome of their actions.” (2014, 49). The field of social innovation has gained significant interest among scholars and policy-makers since 2002 (Van der Have and Rubalcaba 2016, 1923). The term is still considered as ambiguous and vague, even though it is very popular among policy makers (Grimm et al. 2013, 437).

Since the economic crisis of 2007/2008, Europe is challenged by social, economic and environmental challenges, like an ageing population, migration, and climate change (Grimm et al. 2013, 436). This has resulted in a growing engagement of citizens and organisations in innovation, and in critique on dominant business models and narrow economic outlooks on development and declines in public spending (Van der Have and Rubalcaba 2016, 1923). Social innovation is thus a type of innovation that policy-makers use to deal with societal challenges that cannot be tackled through traditional welfare systems (Borzaga and Bodini 2012, 411). In EU innovation policy documents, the objective of addressing societal

challenges also increasingly plays a role, as the concept is central to the Europe 2020 strategy (European Commission 2010a). According to the Commission, research is necessary to better understand what kind of social innovations work, and to address the needs of vulnerable groups in society, like the elderly, disabled or unemployed (Grimm et al. 437).

The EU Innovation Policy Framework

With the creation of evolutionary theories and theories on the innovation process, policy-makers developed new ideas about innovation and created new objectives and instruments for policy (Mytelka and Smith 2002, 1467). In EU policy documents on innovation, we can see the different definitions and objectives the EU attached to innovation throughout the past decades. The table on the next page shows the different definitions that the EU has used in different policy documents from 1982 to 2016. In these policy documents, several ideas about innovation are coming back. In the definition of 1995 and 2003, the EU speaks about

“production, assimilation, and exploitation”. This demonstrates that policy-makers were still looking at the innovation process mostly from a linear perspective. With the Updated

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Innovation Policy in 2006, the EU started to look at innovation as a broader concept.

Innovative business models, design and services also increasingly played a role and

innovation is considered a co-creation process, in which multiple different actors are involved that exchange knowledge: open innovation. The broadening of the term also leads to a greater amount of objectives that innovation has to achieve: from creating competitiveness and growth to also creating jobs and address societal challenges.

Table 5: Definitions and objectives for innovation

Year Definition Objective

1982

A plan for the transnational development of the supporting infrastructure for innovation and technology transfer

Not clearly stated Competitiveness and growth

1995

Green Paper on Innovation

“The successful production, assimilation and exploitation of novelty in the economic and social spheres”

Maintaining and strengthening competitiveness and employment

1996

The First Action Plan for Innovation in Europe

Not clearly stated Competitiveness and employment

2000

Innovation in a knowledge-driven economy

Not clearly stated Become the most competitive and dynamic knowledge-based economy in the world

2003

An Updated Innovation Policy

“The successful production, assimilation and exploitation of novelty in the economic and social spheres”

Becoming the most competitive and dynamic knowledge-based economy

2006

A broad-based innovation strategy for the EU

“Innovation comes in many forms other than technological innovation, including organisational innovation and innovation in services”

Enhance Europe’s global economic competitiveness

2009

Reviewing Community innovation policy in a changing world

“The ability to take new ideas and translate them into commercial outcomes by using new processes, products or services in a way that is better and faster than the

competition”

Competitiveness

2010

Innovation Union

“Research-driven innovation and innovation in business models, design, branding and services”

Growth, jobs, and addressing major societal challenges

2016

The Three O’s: Commissioner Moedas’ Strategy for Innovation

“A specific innovation can no longer be seen as the result of predefined and isolated innovation activities but rather as the outcome of a complex co-creation process involving knowledge flows across the entire economic and social environment”

Create new markets and fostering a stronger culture of entrepreneurship

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3.3 Conclusion

The aim of this chapter was to analyse the first type of learning aforementioned in the theoretical framework of this thesis. It demonstrated the link between innovation theory and innovation policy through analysis of influential innovation theories and innovation policy documents. By comparing innovation theory to policy, we can see that in earlier theories, innovation is often considered as a linear process. This is also confirmed in EU policy documents, where innovation was defined as a linear process in 1995 and 2003 as well. With the arrival of national and regional innovation systems theory, policy-makers could justify more intervention in the innovation process. The innovation system theory looks at specific context in which an innovation system operates, however, which makes it more difficult to use these theories for the development of effective innovation policy instruments for the EU. The policy documents also show that the definitions of the concept of innovation became broader and linked to more ambitious and wide-ranging objectives. This is also reflected in the social innovation theory, which attaches more (social) goals to innovation. The translation from innovation theory to policy is therefore often a challenge, since there is no coherent framework for innovation theory and because of the wide range of objectives to be achieved. The findings of this chapter are relevant for the research question of this thesis. It shows that learning from theory can be a way to improve innovation policy but only when it leads to better insight into the right sets of instruments and measurement tools.

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Chapter 4: Learning from Experience

The previous chapter has examined innovation policy learning from theory. The second type of learning of my theoretical framework assumes that policy-makers also learn from previous experiences in policy-making. Nauwelaers and Wintjes (2008) claim that learning from experience takes place on different levels: intra-organisation, intra-system, and inter-system. Learning from experience takes place through internal monitoring, evaluations, national monitoring systems, and international benchmarks. This chapter examines learning from experience on all the three levels that Nauwelaers and Wintjes describe, by looking at learning within the EU institutions (intra-organisation), with actors and partners in the innovation system (intra-system), and learning of the EU Member States (inter-system).

In the first part of this chapter, I will examine the evaluation process of innovation policy through an in-depth analysis of the interim evaluation of Horizon 2020, which also includes the evaluation of the EIT, that is ongoing at the time of writing this thesis. In order to assess how the EIT learns from experience in the last chapter, this chapter provides contextual information on how evaluations and the sharing of best practices take place and how this helps to improve policy. The focus will be on what policy-makers learn from evaluations like these, and how the input and work of actors in the field influences the learning. The second part of the chapter will specifically study the ways in which Member States learn from each other’s innovation policies with soft OMC tools like benchmarking.

4.2 Learning from Evaluations

“Evaluation is the careful retrospective assessment of the merit, worth, and value of administration, output, and outcome of government interventions, which is intended to play a role in future, practical action situations” (Vedung 1997, 2). There are two rationales for evaluations; they are used to increase accountability of policy, and for learning (Højlund 2015, 35). Evaluation can serve as a source of legitimacy for government intervention, but the results of an evaluation can also be used to improve policy. The value that is attached to evaluation is linked to a growing need for evidence-based policy-making (Sanderson 2002, 1). Since 2001, the Commission is formally bound to carry out an impact assessment of all major legislative proposals (Lee and Kilpatrick 2006, 23). Thus, governments are looking for evidence that their policies work (or do not). There are two factors that pressure governments to do so: influence of international organisations that produce data on country performances and the public opinion (Mulgan 2005, 215). This chapter will focus more on the second

Policy Learning and EU Innovation Policy: The Case of the European Institute of Innovation and Technology