Nick Clifton (p), Adi Weidenfeld , Teemu Makkonen

Richard Tuffs

The need to collect systematic evidence about how non-local knowledge influences regional diversification requires a multi-scalar perspective. This is particularly relevant to Smart Specialisation, which offers enhanced opportunities for regions to “bypass” the national state to affect direct collaboration with regions from other nations.

Whereas interregional networks of individual actors have recently received growing attention, networks in terms of collective regional groups of organisations interacting with their counterparts remain largely ignored. This is surprising given the Smart Specialisation’s ‘outward looking’ approach, particularly in less advanced regions. This conceptual paper explores the rationales and dimensions underpinning interregional knowledge exchange, networks and systems including a definitional clarity, a typology of networks and the impacts of different types of proximity. The paper also introduces the concept of inter-regional innovation systems and sets a new agenda for studying their structure, formation and evolutionary path.

This paper aims to address the gap in the literature by exploring the multiple rationales and dimensions underpinning interregional knowledge transfer through formal and informal networks and systems. It argues that networks play an important role in the creation of learning regions and in the creation of RIS. Whereas a region’s present connections provides only a snapshot, there is a need to examine how they evolve along time from an evolutionary perspective. For this purpose, the paper begins by suggesting a definitional clarity in the concepts of networks and systems and identifying the differences between them in the regional context. Although exact definitions remain problematic, policy-networking bodies are instrumental (as a necessary but not sufficient condition) in the development from a network to a system. Second, it identifies five distinct types of interregional knowledge exchange networks, including regions from the same country or different countries. Third, it explores the barriers and incentives for interregional knowledge flows particularly through different proximity types including cognitive, institutional and spatial. These may vary depending on the regions in question, implying a variety of potential outcomes. Fourth, it introduces the concept of Inter-Regional Innovation Systems (IRIS) and discusses the understudied formation and evolution of such innovation systems. In this context it is suggested that the degree of interdependency (i.e. externality) between actors defines a system- or at least a network with systematic traits. The paper sets up a research agenda for future studies by providing a conceptual framework for the study of interregional knowledge transfer and innovation with an emphasis on less advanced regions.

Richard Tuffs (p)

Dimitri Corpakis

Regional innovation ecosystems or place-based strategies for integrated development are gaining currency as key aspects of research, innovation and increasingly industrial policy. However, these systems need the ‘boundary spanners’, the ‘orchestrators’ and ‘conductors’ to develop the ecosystem. We know that these roles should be found in triple or quadruple helix organisations but there is still an issue that there is no career path, often no job title for these key actors in universities or industry and local and regional government. Is smart specialisation the process that can identify and valorise these roles?

Dimitri Corpakis (p)

Richard Tuffs

This paper focuses on the need for mainstreaming the concept of Smart Specialisation in the EU Framework Programme for Research and Innovation to maximise its beneficial impact on European countries and regions, regardless of their degree of development of economic growth. The importance of innovation as the backbone of European growth policies was stressed in particular in the context of the present programming period (2014-20), both in the inception and architecture of the EU Research and Innovation Framework Programme (Horizon 2020) but also and in particular in the context of Regional and Urban Policy (Cohesion Policy funding through the European Structural and Investment Funds). Cohesion Policy made Research and Innovation Strategy for Smart Specialisation (RIS3) a conditionality for Member States and Regions, in order to fund Research and Innovation productive investments through the ESIF. However, except for a few recitals in the institutional texts adopting Horizon 2020, no particular provisions were foreseen in the FP pointing to effective interactions and synergies between Horizon 2020 and Smart Specialisation Strategies, imposed nevertheless to all Member States in the context of the Structural Funds. While this can be explained in institutional terms, essentially because of the conceptual origins of the two programming frameworks, in operational terms this is rather an anomaly: although both Smart Specialisation Strategies and the R&I FP share the objective of innovation based growth, the spatially blind profile of the FP drives it to ignore the importance of a coordinated approach to innovation at European level. Thus the significant potential for synergies between the two (2) frameworks is largely wasted. This paper discusses ways to mainstream Smart Specialisation inside the R&I FP and transform it as key enabler for an effective coordination between Cohesion and Research and Innovation Policies in the EU. To make this process a success, relevant authorities implementing the two policies but also universities and businesses have to engage at national and local level in a process that goes well beyond the so-called Entrepreneurial Discovery Process (EDP) and connects relevant stakeholder experience together.

Thomas Brenner (p)

Richard Tuffs

The concept of smart specialization has been first introduced in 2009 by the Knowledge for Growth Expert Group (Foray et al. 2009) and has ever since received great recognition and approval by policy makers. Despite its success in getting rapid political acknowledgement and experiencing fast application, a systematic operationalization of smart specialization is pending as yet. The two contributions of Balland et al. (2017) and Eichler et al. (2015), to our knowledge, represent the only approaches that offer first explicit methodological and empirical insights to assess and measure regional smart specialization potentials. Our paper seeks to close these research gaps by providing an integrated method to assess opportunities for smart specialization. We combine the measurement of regional capabilities and potentials, with regard to industrial, technological and scientific characteristics. The newly developed approach is applied to Germany in order to evaluate its usefulness.
Our method combines two aspects by defining an indicator combining a measure for regional potentials and possible regional domains for smart specialization and an indicator assessing the potential future relevance of specific economic activities. The indicator integrates economic, technological and scientific characteristics of a region. Furthermore, in order to assess how a specific industry fits, i.e. is embedded into the regional structure, we do not only measure the existing specialization but measure for all potential specializations the fit to the existing specializations. To this end we calculate relationships. All specializations and relationships are calculated by using data on employees, employee characteristics, patent applications and scientific publications. By matching sectoral data to data on patent applications as well as scientific publications we are able to consider regional domains (rather than merely regional industries or technologies alone).
A number of potential indicators are developed and compared in order to identify the best structure and composition of the indicators. The approach is applied to German regions. Using data from 2000 and recent developments allows to check whether the potential activities identified by the approach fostered growth in the past. Different analyses are conducted and the various indicators are tested for their predictive potential for industry growth, increasing diversification and new developments. Data on governmental subsidies and start-up numbers are used in this context as well as employment data.