The EU developed Smart Specialisation policies (S3) due to frustrations with earlier innovation policies. European innovation systems were fragmented and there was a mismatch between research strengths and business needs, lack of strategic alignment of investments in industry and research, and weaknesses in value chains. By leaving innovation policy to nations, through the National Innovation System approach, countries indicated they wanted different types of high tech (albeit some small) clusters.

Instead, S3 is a place-based strategy of regional transformation with an entrepreneurial discovery process (EDP) at its core. EDP is a process of learning what the future might be, based on possible, but as of yet unrealized, combinations of science-based knowledge, economic-industrial practices and market insights. This collective process of learning is expected to lead to discoveries, involving real business entrepreneurs who know markets, researchers who have ideas of new application of scientific knowledge, and regional institutions, providing resources. Initially, S3 was primarily on place-based development, but recently spatial proximity has been extended with transnational thematic networks between regions, clusters and universities.

OK, but how can this mysterious discovery happen?

For the discovery to happen, new combinations of different forms of knowledge are needed. But therein lies a problem. It is difficult to combine knowledge from business, academia and governance with very different values, interests and visions. The challenge is greatest in regions lagging behind in innovation, which are also those in most need of EDP policies.  EDP means that existing knowledge barriers must be overcome. But crossing barriers between different helices (business, academia and governance) requires knowledge translations from one context with one type of knowledge to another.

In the discovery, different types of knowledge needs to interact in hybrid knowledge spaces, where actors learn how to cross borders. How can these hybrid knowledge spaces be formed?

We suggest that the discoveries may be supported by appropriate forms of experimental governance. Based on case studies and policy analysis in the book The Entrepreneurial Discovery Process and Regional Development, we make recommendations for the policy-makers of S3:

Discover new solutions by asking citizens and other stakeholders for guidance. Regional planning is full of difficulties. Powerful stakeholders often have different objectives and interests. They may not relate to each other and they might not even want to discuss common challenges. However, there will always be at least some citizens, firms, entrepreneurs, institutions and other stakeholders, who are open for dialogues and new approaches. By presenting analysis to them, and listening to their suggestions and advice, it might be possible to discover concrete experimental solutions, small projects with local stakeholders in key positions, which may be tested experimentally.

Learn by doing successful experiments. Instead of starting with abstract, long-term priorities start with a concrete, short-term experiment: a project with a start, an end date, and an expected outcome.  By enabling local stakeholders to start small-scale projects, some are likely to work and deliver. This is a process of ‘learning by doing’.  In this way, a regional strategy that is stuck may be restarted through a simple solution.

Take care of and learn from emerging success stories. Once small-scale projects start, there may be many stumbling blocks. It is crucial to follow and nurture some of the best projects in the direction of success. Successes should be made visible, appreciated, talked about, and used as building blocks in new, more advanced projects. If we look back into the history of successful clusters, many of them start with small experiments undertaken in the right place, where they can form roots into existing strengths. This is how small experiments may reshape regional economies and institutions. They can be the beginning of new, institutionalized ways of cooperation, which becomes routine, and generate self-reinforcing loops.

Talk, talk, talk. These small experiments start with “talk jobs”. It is crucial to set up fora for discussions and dialogues, where people involved in the process from different positions regularly meet, exchange experiences, and discuss new, possible approaches. It is crucial to give them a common platform, and share knowledge, analysis and evaluations on a regular basis. This formation of a hybrid knowledge space is the phase before a new discovery. It is a breeding ground for learning from experiments, and a continuation of the search process.

Promote mobility across helices and sectors. Entrepreneurs are able to cross barriers between sectors, helices and different forms of knowledge. They may do that because they have careers where they have learned different things in different places. They may be valuable partners and translators. They should be listened to and involved. It is equally important to promote mobility of clever people between helices and sectors.

Encourage universities to do brokering and bridging. Academic careers are usually structured by institutions that are actively discouraging cross-disciplinary, industry-related research, and careers where professors become industrial leaders or entrepreneurs. Academics can be encouraged to act as brokers and bridges between university research and innovation processes in the economy.

Research inside firms. Whereas emerging economies may still grow through related variety-inspired innovations combining skills from different sectors without R&D input, innovation networks in advanced countries increasingly need science-based knowledge. Advanced economies need to integrate key enabling technologies (KETs) across most sectors. This increasingly demands more in-house R&D in firms. In-house R&D helps firms to combine exploration with exploration of their existing domain, and it drives growth.

Connect place-based knowledge with transnational networks. The regions need to absorb KET, and they can do it in transnational European networks. This strategy relies on potential synergies between leading regions and their institutions providing access to KET, and other regions interested in applying these technologies within their domains. Shared communities of expertise can emerge in places, where experts meet, discuss ideas and monitor progress. They are strengthened by experimental investments in pilots. This is now tested across a wide range of sectors. One of the cases, photonics, has created a framework for co-management of a knowledge commons. This strategy has extended the scope of learning and discovering from place-based to transnational. It combines spatial proximity with temporary proximity (conferences, meetings, regularized seminars) and organizational, through transnational network governance and learning.

Seija Virkkala is professor of regional studies at the School of Management, University of Vaasa, Finland. Her research interests are regional development and innovation, transnational learning, innovation systems in peripheral regions and smart specialisation. She is a leader of the research program Place-based Society and Regional Development Policies at the University of Vaasa. Seija is a co-editor of two books, The Entrepreneurial Discovery Process and Regional Development, Regions and Cities (Routledge, 2019), and Learning Transnational Learning, Routledge Studies in Human Geography (2013).

Åge Mariussen is research manager of regional studies at the University of Vaasa, Finland, and senior researcher, Nordland Research Institute, Norway. He has been working as a researcher for almost four decades on questions relating to regional development, regional restructuring, innovation, transnational learning, and smart specialisation. Åge is a co-editor of two books, The Entrepreneurial Discovery Process and Regional Development, Regions and Cities (Routledge, 2019), and Learning Transnational Learning, Routledge Studies in Human Geography (2013).

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