Publications

Prior research has emphasized the importance of bringing together quadruple helix (QH) actors (academia, industry, government and civil society) to strengthen regional innovation. The QH model forms an integral part of European innovation policy, which aims to create sustainable and inclusive growth in Europe. As part of this policy, European Union (EU) regions are to design and implement research and innovation strategies for smart specialization (RIS3) through the participatory entrepreneurial discovery process (EDP). Despite the strong emphasis on the QH model, the model is still far from a well-established concept in innovation research and policy, and civil society participation in RIS3 has remained low. Our paper aims to support regional governments to engage with and facilitate the participation of civil society in a territorial EDP based on two case studies from Finland and Sweden. It contributes to the literature on regional innovation systems through identifying mechanisms to foster the QH model and suggests lessons learnt for the operationalization of the QH model as part of RIS3.

The article compares the process of designing and implementing EU research and innovation (R&I) strategies for smart specialisation (RIS3) in eight less developed European Union (EU) member states: Croatia, Czech Republic, Estonia, Latvia, Lithuania, Slovakia, Slovenia and Romania. The study additionally explores regional-national differences in governance structures and practices of the RIS3 by focusing on two regions: South Moravia and West Romania. It is argued that RIS3 processes can improve governance of the R&I systems in spite of the baseline quality of governance in the given country or region. An entrepreneurial discovery process (EDP) that is continuous and includes a broad range of actors and is closer to a multi-stakeholder approach can enable a learning trajectory and foster R&I governance. The case studies address i) whether the EDP resulted in engagement with a broad range of stakeholders, ii) whether it encouraged a process of creative co-design and iii) whether it continued into the policy implementation phase. The article offers insight into how learning can be fostered and how broader stakeholder engagement can be beneficial for improving the RIS3 policy framework.

The effect of digital transformation towards more efficient, place-based and bottom-up innovation policies at different spatial scales has proven significant, as digital technologies modify existing policy-design routines in cities and regions. Smart places (cities, districts, neighbourhoods, ecosystems) depend on the way digitalisation disrupts systems of innovation in cities, making it more open, global, participatory and experimental. We argue that the rise and interconnection of various types of intelligence (artificial, human, collective) could bring profound changes in the way smart places are being created and evolve. In this context, cyber-physical systems of innovation are deployed through multiple nodes acquiring digital companions, collaboration is deployed over physical, social, and digital spaces, and actors can use complex methods guided by software and get insights from data and analytics. The paper also presents the case study of OnlineS3, a two-year Horizon 2020 project, which developed and tested a digital platform composed of applications, datasets and roadmaps, which altogether create a digital environment for empowering the design of smart specialisation strategies for local and regional systems of innovation. The results indicate that digital transformation allows the operationalisation of multiple methodologies which have not been used earlier by policy makers, due to lack of capabilities. It can also increase the scalability of indicators facilitating decision making at different spatial scales and, therefore, better respond to the complexity of innovation systems providing dynamic and scale-diverse information.

This report provides a basis for transition to low-carbon (zero-carbon) economy based on smart specialisation. It outlines a number of indicators that should guide the regions to measure the transition towards a low-carbon economy, using the smart specialisation methodology. Furthermore, it provides a smart specialisation zero-carbon transition roadmap as well as comments on necessary modifications of the already existing smart specialisation tools that would help support the process.

A number of challenges arise when interpreting the European and Regional Innovation Scoreboards’ (EIS/RIS) results in specific national or regional policy contexts. Namely, that, “qualitative data on policy developments needs to be taken into consideration, combined with an analysis of time lags as regards their impact on measurable results and spill-over effects, in order to evaluate progress and arrive at a comprehensive assessment of an innovation system”. This note explores the integration of quantitative EIS/RIS indicators with other relevant data (both qualitative and quantitative). It proposes options for more integrated reporting so as to further increase the usefulness of the EIS/RIS results for national and regional policy-makers as well as in the European Semester process.

This report explores the extent to which differences in the scores of a country in the European Innovation Scoreboard (EIS) or a region in the Regional Innovation Scoreboard (RIS) can be explained by various socio-economic, demographic, cultural, etc. factors. The term ‘structural indicators’ is used (e.g. by Eurostat) to refer to statistical indicators used for a quantitative comparison of performances of territories in selected fields1. Such indicators can be both ‘perception-based’ and ‘fact-based’. Both types of indicators have specific advantages and disadvantages. For the purposes of this report, we define structural indicators as independent variables that may influence or determine the behaviour (current values or trends) of innovation indicators used in the EIS (or RIS). These indicators can be thought of as parameters that may influence the medium-to-long run performance of all or parts of a national or regional innovation system.

A smart specialisation strategy for research and innovation (S3) aims to concentrate public funds and leverage private finance to foster territorial economic transformation. Agreeing on strategic priorities is only the first step in a policy cycle and we explore how S3s are translated into operational initiatives notably in terms of the types of instruments applied. We assess whether the entrepreneurial discovery process is extended beyond priority setting and into implementation. We examine the cases of Finland, Scotland, Poland, and Greece and assess how existing policy frameworks and governance arrangements have been adapted to the S3 concept. We find that there are promising EDP processes in all four countries but that implementation has proven harder. The two more advanced countries have experimented with multi-actor, multi-instrument ‘open innovation platforms’. In contrast, the EU Structural Fund programming procedures have hindered the alignment between S3 vertical priorities and horizontal instruments in Greece and Poland.

Over the last decade, southern European countries faced their gradual decline of competitiveness on international market by artificially sustaining demand through borrowing. Government budgets and the banking system offered liquidity, loans beyond the refund capacity of recipients, and supported growth by expanding domestic demand for consumer products. This erroneous reading of the origins of the crisis resulted in the adoption of economic policy recipes that led to spiralling public debt, the collapse of the banking system, and the near bankruptcy of Greece, Slovenia, and Cyprus.

The regional innovation paradox is the greater need of lagging regions to invest in innovation and their relatively lower capacity to absorb funding compared to more advanced regions. Using data on regional public spending, industry composition and economic performance, we test empirically whether there is a differential impact of European funding on regional economic growth between Eastern and Western European regions. We conclude that the paradox is proven and consider the extent to which smart specialisation strategies may help to improve the quality of governance of regional innovation systems.