Krisztina Polónyi-Andor (p), Ugo Fratesi , Attila Varga

The smart specialization strategy (S3) concept became a key element of the cohesion policy, and as such, it has become rapidly widespread among EU Member States. Nevertheless, the implementation of the strategy has raised several questions and caused difficulties in practice mainly in the lagging regions. The aim of the strategy is to enhance regional development due to innovation-based economic transformation. S3 should be based on detailed analysis of the regional environment and needs to be developed by participatory methods and an entrepreneurial discovery process. S3 focuses the available resources on selected priority areas. However, in the phase when priority areas are already chosen it is not evident which policy measurements (e.g., human capital development, entrepreneurial support, R&D or investment subsidies) need to be utilized in order to achieve the knowledge-based transformation of the area and, in particular, how the different measures and interventions work as a system.
Our research aims to support the implementation of smart specialization strategy by developing a system dynamics model to help compare the effects of different policy mixes and to estimate the expected paths of innovation-based economic transformation. Therefore the policy simulations aim to uncover the different development paths of the selected area due to various policy mixes. The model can be supportive during the monitoring phase too, as it gives the opportunity to compare the realized transformation to the expected changes.
The methodology used in the paper is system dynamics modelling, which is able to capture the dynamics of the development and transformation of a system modelling its feed-backs and loops. The model is built according to the literature of industrial dynamics, innovation systems and smart specialization.
Empirically, the model of the research is calibrated on the case of a traditional manufacturing sector, namely the machinery industry in a catching-up Hungarian region, South-Transdanubia. The model is adapted to fit actual data which are collected by a survey among firms of the machinery sector and interviews with the main actors of the regional innovation system.

Mathieu Doussineau (p), Julia Bachtrogler , Arnault Morisson

Between 2014 and 2020, the European Union has dedicated over €120bn to support research and innovation through its two main components: The Horizon 2020 programme, excellence based and space blind and the Structural and Investment Funds (ESIF) innovation oriented and place based. Through the design of smart specialisation strategies, EU regions were encouraged to develop synergies between these two main sources of funding in order to strengthen the impact on their respective economies. The objective of this paper is to assess the existence of an impact of the implementation of those synergies on regional innovation ecosystems. We define as synergy between funding the alignment of the distribution of H2020 and ERDF among a set of technological and policy areas. In order to set a common and generic analytical framework, we consider as specialisation areas the Horizon 2020 key enabling technologies (technological areas) and the societal grand challenges (policy areas), together representing approximately 60% of the overall horizon 2020 budget. The information comes from two distinct datasets related to the allocation of EU funding. On one side, the Cordis database gathering the projects funded by horizon 2020 and a dataset gathering the R&I related operations funded by ERDF on the other. Text analysis methods applied on ERDF operations’ titles and descriptions allow us to assign each operations a technological and a policy area to design a common analytical framework. To assess the implementation of synergies we measure the concentration of funding within areas in each of the two funding sources using the location quotient (LQ) method. The location quotient (LQ) is a way of quantifying how concentrated a particular area is in a region (at nuts level 2) as compared to a larger geographical area. It reveals a specialisation profile in comparison to the European average. There is a synergy between funding when the indicator show a concentration of funding for both sources of funding within a same area. The analysis of the correlation between synergy patterns and regional innovation performances (using the information coming from the EU regional innovation scoreboard ) shows whether a positive impact on performance is observed and in which type of regional ecosystem. The paper concludes with a discussion on both advantages and drawbacks of the alignment of EU policies in the context of the new generation of smart specialisation strategies.

Diletta Pegoraro (p), Alessandro Rossi

This paper offers an illustrative example of how the Digital Innovation Hub Belluno Dolomiti (DIH-BD) is a pivotal element in the innovative ecosystem of Belluno, a county in the region of Veneto.

The term ‘ecosystem’ in relation to business and strategic management discipline was proposed by Moore (1996) as ‘An economic community supported by a foundation of interacting organizations and individuals’ (p.26). The ‘economic community’ creates customer value through core nodes (e.g., lead businesses, suppliers, customers, and competitors) and peripheral nodes (e.g., community, local government, educational institutions). Drawing on Moore’s definition of business ecosystem, Adner (2006) presented the concept of ‘innovation ecosystem’ defined as ‘the collaborative arrangements through which firms combine their individual offerings into a coherent, customer-facing solution’ (p.2). In this definition, the role of collaboration is stronger than competition, as actors can now contribute to co-create the innovation. As new technological solutions have permitted to reduce the coordination costs, the exchange of information within the ecosystem has become easier allowing a flourishing environment where start-ups can thrive in the premise of knowledge hubs.

In line with the ‘innovative ecosystem’ proposed by Adner (2006), this paper will shows how the central nodes of a network (e.g. firms and entrepreneurs) have to collaborate with the peripheral nodes of the same network to co-create innovative solutions, and to increase the value of the territory, hence of the region.

The main objective of this paper is to provide best-practice useful for other regions in developing an innovative ecosystem. To reach this goal, we in-depth interviewed CEOs, Policy Makers, Head Teachers and Industrial Stakeholders. They gave us insightful information on the role of the DIH-BD in fostering innovative capabilities inside the firms and private organizations, driving place-based industrial policies, influencing updated curricula in high-school and universities for the new working force, and designing better services to the associate members of the industrial organization. Preliminary results show the central role of knowledge and learning in fostering a regional innovative ecosystems and the importance of the individual as champion of this evolution.


Adner, R., 2006. Match your innovation strategy to your innovation ecosystem. Harvard business review, 84(4), p.98.

Moore, J. (1996). The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems. New York: Harper Business School.