2.1 Spatial Aspects of Innovation

Innovation is considered to be highly localized process. It does not appear in space uniformly, but is predominantly spatially concentrated (Crevoisier, 2004). A number of diverse theoretical and empirical frameworks have been developed to analyze spatial dimension of innovation. The theoretical approach to the relationship between innovation and local spaces was initially demonstrated in the concepts of ‘new industrial districts’ and ‘innovative milieu’. The first of them, inspired by the Marshall’s industrial district, was introduced by Becattini (Sforzi, 2015) to emphasize the dynamic linkages between the socio-cultural features of a productive community and the rate of growth of both its productivity and innovativeness (Becattini, 2002). Many theoretical considerations and empirical contributions reveal the impact of belonging to industrial districts on innovation performance (Boix et al., 2018; Boix-Domenech et al., 2019; Cainelli, 2008; Cainelli & De Liso, 2005; Muscio, 2006; Parra-Requena et al., 2020). The existence of dynamic efficiency in industrial districts in the form of positive innovation differentials with regard to the economy average, assigned to the existence of Marshallian external economies (economies of localization) is described by Boix and Galletto (2009) as an ‘I-district effect’.

The second—‘innovative milieu’ approach, is considered to be a dynamic counterpart of the ‘industrial district’ concept, developed in the framework of the endogenous growth theory, providing more dynamic spatial elements related to synergies and collective learning, which explain innovation processes at the spatial level (Capello, 1998). In this concept, economic space is defined as a ‘relational space’ of cooperation, interpersonal synergies, and social collective actions that determine the innovation and economic performance of a given area (Camagni & Capello, 2002). The nature of these relationships should be both competitive and cooperative so that enterprises could act together in the common interest. The networks of synergy-producing interrelationships foster processes of cooperative learning that help to reduce the uncertainty during technological breakthroughs and induce innovation locally (Simmie, 2005).

Spatial dimension of innovation is also presented in the learning region concept (Florida, 1995; Morgan, 2007). In line with this approach learning regions, as their name implies, are a central space for knowledge creation and provide an infrastructure which can facilitate the flows of knowledge, ideas, and learning (Florida, 1995). Boekema et al. (2000) suggest that distinction between the learning region (institutional networks that develop and implement a regional innovation strategy) and regional learning (mainly company-initiated cooperation between actors in a region through which they learn) should be considered. Learning through networking and interacting is seen as the main force that encourages firms to cluster in a given space and as the essential determinant of success of an innovative cluster (Breschi & Malerba, 2005). A substantial body of empirical research has convincingly shown that innovative activities tend to be spatially clustered. Audretsch and Feldman (1996) reveal that innovation has a strong tendency to cluster locally in regions where knowledge inputs are available and that the differences in spatial clustering depend on the stage of the industry life cycle and the importance of tacit knowledge. Also in Porter’s (1990) ‘competitive diamond’ concept, the interactions between four sets of factors are more effective when the firms are clustered in space. The level of innovativeness of companies in a cluster is higher as they can take advantage of agglomeration economies, observe the competitors directly, benefit from collective knowledge and network-based effects as well as strengthened social interactions (Bell, 2005). Hassink (2005) proposed the concept of the learning cluster, that is able to bridge the gap between regional learning, that combines the strengths of both the learning region and clusters concept in tackling the problem of ‘lock-ins’ in regional economies.

The other paradigm in which space and innovation co-evolve is constituted by Regional Innovation System (RIS), a counterpart of National Innovation System (NIS) at the regional level (Cooke, 2008). In this concept, innovation is seen as dynamic and interactive learning process between companies and other organizations whose activities lead to initiation, diffusion, modification of new technologies, and determine the innovative performance of national firms (Freeman, 1995). In the triple-helix model, innovation is considered as the outcome of the interaction of three main groups of local actors: firms, government, and research institutions (Leydesdorff & Etzkowitz, 1998), whereas the quadruple-helix model, regarded as an enhancement of the triple-helix perspective, also includes a fourth component of the users and civil society (Höglund & Linton, 2018; Leydesdorff, 2012).

Spatial proximity and relatedness of the actors of innovation process matter for its effectiveness due to knowledge externalities appearance. Griliches (1992) defines knowledge spillovers as ‘working on similar things and hence benefiting much from each other’s research’. Knowledge externalities occur when the knowledge flows are not fully compensated and in situations where the protection of proprietary knowledge is incomplete (Karlsson & Gråsjö, 2014). That limited appropriability is considered to have, on the one hand, negative consequences in terms of missing incentives for entrepreneurs to generate knowledge, but on the other hand, also positive ones in terms of reduced knowledge costs (Antonelli & Colombelli, 2017). It could be thus stated that local knowledge may have the character of a (semi) public good, with properties of non-rivalry. It means that its use by one economic agent does not preclude the use by another economic actor (Roper et al., 2017). The empirical results indicate that knowledge externalities across space impact innovation performance (Bottazzi & Peri, 2003). Roper et al. (2013) reveal that knowledge externalities of openness are positively associated with firms’ innovation performance by either increasing knowledge diffusion or strengthening competition. According to them, such externalities arise not simply from the (semi) public good nature of local knowledge but from the open innovation process itself. Positive social externalities resulting from openness in innovation may extend the sum of the achieved private benefits (Roper et al., 2013).

Co-location enables to establish contacts with potential cooperation partners and to exchange knowledge easier. A fundamental aspect of geographical proximity is the face-to-face contact between actors of innovation process, as it contributes to effective exchange of ideas and to spreading of knowledge as an externality. Face-to-face interactions have four main features: they are an efficient communication technology, they can help solve incentive problems, they can facilitate socialization and learning, and they provide psychological motivation (Storper & Venables, 2004).

The spatial dimension of innovation processes matters particularly for the flows of tacit knowledge. This type of knowledge is highly contextual and difficult to codify, and therefore is more easily transmitted through face-to-face contacts and personal relationships due to geographical proximity (Breschi & Lissoni, 2001). This type of knowledge is the cumulative output of long periods of learning, specific to a particular setting, and cannot easily be written down (Karlsson & Gråsjö, 2014). As Audretsch (1998) points out, the propensity for innovative activity to cluster spatially is highest in industries where tacit knowledge plays an important role. Successful innovation processes involve a mix of contextual and codified knowledge. Tacit knowledge is relatively immobile, whereas codified, freely available knowledge can be transferred independently of its location without any additional costs (Brenner, 2007). Flows of codified knowledge are easier due to ICT development.

As the result of the highly contextual features and the nature of its transmission mechanisms, knowledge is considered to be spatially sticky and its flows are presumed to appear mostly amongst members of a co-located community (Miguelez et al., 2013). Knowledge spillovers are localized and tend to decay rapidly with transmission across geographic space (Audretsch, 2003). However, what is worth to point out, empirical analyses reveal that knowledge externalities unfold within 300 km or comparable distance ranges, thus it indicates a much larger distance than the face-to-face impact of localized externalities (Bottazzi & Peri, 2003; Greunz, 2003; Moreno et al., 2005).

Spatial proximity does not unambiguously mean that knowledge spillovers would appear as they do not have automatic nature (Boschma & Iammarino, 2009). Boschma (2005) suggests that besides the spatial closeness, other forms of proximity facilitate knowledge spillovers. In line with his considerations, geographical proximity may not be determinative in itself but it has a reinforcing power which triggers the other types of proximity: cognitive, organizational, social, and institutional. Empirical results provide evidence on the fact that simultaneously present, different kinds of proximities generate synergic effects on growth (Basile et al., 2012). Moreno et al. (2005), amongst others, have exploited the concept of technological proximity between regions and revealed that cross-regional knowledge externalities flow easily amongst scientists and technicians in highly specialized technological fields, irrespective of their geographical location, due to the fact that they share a specific knowledge background, common jargon, and codes.

Excessively close actors may have little to exchange after a certain number of interactions (Boschma & Frenken, 2010). Innovation processes require the combination of different, although related, complementary pieces of knowledge to be most effective. Hence, combining and recombining local knowledge could make it eventually redundant and less valuable (Bergman & Maier, 2009).

If the internal networks are strong, whereas external connections to other sources of knowledge are weak, the risk of localism might occur and may lead to ‘lock-in’ processes (Arthur, 1989). Regional economy that is unable to acquire external knowledge is likely to be less innovative (Fratesi & Senn, 2009). The balance between proximity and heterogeneity is a major challenge of innovation processes (Mattes, 2012). As Neuländtner and Scherngell (2022) reveal, embedding in inter-regional networks is in general a significant driver for exploitative and explorative modes of knowledge creation.

According to Grillitsch et al. (2018), competitive advantage of a given economy depends not only on local knowledge resources but also on linkages between related entities, which accelerate learning and innovation processes. Moreno and Miguélez (2012) distinguish two patterns of knowledge interactions: an informal, nonintentional, and serendipitous pattern that takes place between agents located in spatial proximity and a formal, intentional, and conscious pattern of linkage formation between actors, irrespective of their geographical location. The second ones are crucial to access external knowledge that would otherwise not be available for a local cluster.

The effective transfer of knowledge and innovation is significantly determined by the absorptive capacity of a given area (Boschma & Frenken, 2010). As discussed by Arrow (1962), absorptive capacity captures the idea that economies may differ regarding their abilities to identify, interpret, and exploit the new knowledge and to adopt new technologies. It is argued that regional innovation potential and knowledge infrastructure, perceived mainly as a complex of universities, research institutes, R&D expenditures and employees, and regional technology policy, is crucial for the innovative performance and growth of the regional economy (Beugelsdijk, 2007). Meeting some preconditions is necessary for a region to benefit from knowledge externalities and to translate knowledge spillovers into innovation and growth (Abreu et al., 2008).

Knowledge is characterized with spatial specificity as its resources in one region differ from that available elsewhere (Roper et al., 2017). According to the innovative milieus approach, a territory is understood as an organization that links companies, institutions, and local populations within a process of economic development. The territorial paradigm takes the differences in innovation potential into account and shows that a territory, as an organization, can generate resources (e.g. know-how, competencies, and capital) and the actors (e.g. companies, innovators, and support institutions) that are necessary for innovation (Crevoisier, 2004). There appears to be an agreement in the economic literature that localized factors shape the rate and direction of knowledge creation, the spatial diffusion of knowledge spillovers, and regional innovation process (Feldman & Kogler, 2010).

The benefits derived from being located close to other economics actors are defined as agglomeration externalities (Rosenthal & Strange, 2004). Agglomeration in one region accelerates growth because it reduces the cost of innovation in that region through externalities due to lower transaction costs. This implies that innovation processes take place in the core region (Martin & Ottaviano, 2001). Agglomeration effects are connected with industrial concentration and specialization leading to intra-industrial externalities (defined as Marshall-Arrow-Romer (MAR) externalities, originating from (Marshall, 1920) contribution and followed by subsequent works by Arrow (1962) and Romer (1986), economic and social diversity leading to cross-sectoral, horizontal spillovers (defined as Jacobs externalities, after Jacobs (1969)), and the intensity of competition (defined as Porter externalities (Porter, 1990; Glaeser et al., 1992)). Additionally, according to Antonelli and Gehringer (2015), the benefits that can be achieved from vertical knowledge externalities add to intra-industrial knowledge externalities. Many empirical studies underline the importance of agglomeration externalities—specifically specialization, diversity, and competition effects that may contribute to innovation, productivity, and regional development (Cortinovis & van Oort, 2015; de Groot et al., 2016; Neffke et al., 2011).

Different types of agglomeration externalities can create various types of benefits for innovation performance. Intra-industrial externalities are expected to induce incremental innovation and process innovation, as the knowledge transfers occur between similar firms producing similar products, and thus they contribute primarily to productivity increases. Jacobs externalities instead, are expected to facilitate particularly radical innovation and product innovation as knowledge flows from different sectors are recombined leading to complete new products or technological processes and thus they contribute to the creation of new markets and employment, rather than productivity increases (Frenken et al., 2007).

2.2 Knowledge-Based Foundations of Regional Development

The capacity to generate and implement advances in knowledge and innovation is regarded as the crucial force driving regional development. Recognition of the importance of knowledge in shaping economic development has its origins in the Schumpeterian theories with reference to ‘new combinations of knowledge’ as the drivers of innovation and entrepreneurship (Schumpeter, 1934). Innovative output is viewed as the product of knowledge inputs in a knowledge production function framework (Griliches, 1979). In Romer’s (1990) long-term growth model, an increase in the stock of knowledge results in a proportional increase in the productivity of the knowledge sector. In the knowledge production function the production of new ideas for each region depends upon the stock of knowledge and the level of human resources engaged in innovative activities. As regions are not ‘isolated islands’, the spatial interaction effects that arise from spatial spillovers of technology should be considered in the regional growth models (Quah, 1996). As knowledge is not easily accessible and its resources are not uniformly distributed across the space, the location of knowledge production and the characteristics of knowledge flows become critical issues in understanding economic growth. The models of knowledge production are considered to hold better for regional units of observation than for enterprises in isolation of spatial context (Audretsch & Feldman, 2004). Region has been found to provide a platform upon which new economic knowledge can be created and commercialized into innovations.

Pivotal role of knowledge diffusion in development processes was initially recognized in the Marshallian externalities approach. Knowledge spillovers are central in endogenous growth models (Grossman & Helpman, 1991; Lucas, 1988; Romer, 1990), in which positive externalities are a common feature of processes of knowledge accumulation. It is considered that the social benefit of knowledge creation is higher than the private benefit of such activity as knowledge is generally non-excludable and imitators have generally no incentives to compensate the innovators for the gained benefits.

Also concepts of the geography of innovation (Audretsch & Feldman, 2004; Feldman & Kogler, 2010; Malecki, 2021) focused on the localized pattern of knowledge spillovers and their role in explaining both the high spatial concentration of economic activity and spatial differences in economic growth. Within the same theoretical framework, new economic geography models (Krugman, 1991) provide the view that the spatial distribution of economic activity is determined by the tension between agglomeration and dispersion forces in the form of immobile factors of production (Redding, 2010). In line with evolutionary thinking, the spatial processes of knowledge creation and distribution are understood as a cumulative, path-dependent, and interactive, whereas new knowledge is expected to be based on related, former sources of knowledge (Balland, 2016).

In regional growth theories, a great emphasis has been put on knowledge as a driving force of development and on the endogenous self-reinforcing mechanisms of knowledge creation. Development is fundamentally dependent on a concentrated organization of the territory, in which a socio-economic and cultural system is embedded (Capello, 2009). Persistence of regional differences in knowledge bases implies that not only innovation is cumulative in nature, as it results from the recombination of existing ideas and localized character of its processes, but also that knowledge developed in one location is often difficult to imitate elsewhere (Balland & Rigby, 2017).

It is argued that dynamics of scientific knowledge is path and place dependent (Heimeriks & Boschma, 2014), and the current research portfolio of a region influences its further capacity to produce knowledge. From evolutionary perspective, the path dependence of knowledge production means that existing scientific knowledge provides the building blocks for further knowledge production (Arthur, 2009). Knowledge production is also place dependent as it is differentiated among locations (Heimeriks & Boschma, 2014). The processes of creation and diffusion of knowledge and innovation are very complex and have a spatial character (Guastella & Timpano, 2016). Uneven spatial distribution of innovation activity is considered to be relevant for emergence and persistence of regional development disparities (Geppert & Stephan, 2008; Meliciani, 2015).

As knowledge is cumulative, characterized by (dynamic) increasing returns, and inevitable in producing new knowledge itself, regions with comparative advantage in generating technological change for growth, are likely to retain a good position (Dosi, 1988). Regions that are less prone to generate knowledge develop the culture of dependency on external sources of knowledge that consequently discourages regional entrepreneurship and innovativeness (Petrov, 2011). It is consistent with the concept of path dependence that is intended to capture the way in which regions set off the mechanisms of self-reinforcement that ‘lock-in’ particular structures and pathways of development (Martin & Sunley, 2006). According to Vergne and Durand (2010), path dependence can be defined as a property of a stochastic process which occurs under two conditions (contingency and self-reinforcement) and causes ‘lock-in’ in the absence of exogenous shock. In the relevant literature, three interrelated versions of this concept could be distinguished: path dependence as a technological ‘lock-in’ (the tendency for particular technological fields to become locked onto a trajectory, even though alternative (and possibly more efficient) technologies are available), as dynamic increasing returns (the development of many phenomena is driven by a process of increasing returns, in which various externalities and learning mechanisms operate to produce positive feedback effects, thereby reinforcing the existing development paths), and as institutional hysteresis (the tendency for formal and informal institutions, social arrangements, and cultural forms to be self-reproducing over time, in part through the very systems of socio-economic action they engender and serve to support) (Martin & Sunley, 2006). In the institutional-evolutionary approach, regions with efficient institutions, formal or informal, are more capable of generating and diffusing knowledge, and consequently achieving faster economic growth (Cortinovis et al., 2017).

Regional growth depends on the amount of innovation activity which is carried out locally, and on the ability to take advantage of external technological achievements (Martin & Ottaviano, 2001). Knowledge spillovers have an important spatial component, as it has been argued that spillovers do not travel easily, so that the performance of an individual region is influenced by its geographical location. The existing evidence reveals that convergence is often confined to groups of geographically contiguous regions (Magrini, 2004) and the ability to receive knowledge spillovers is influenced by distance from the knowledge source (Audretsch & Feldman, 1996). The existence of localized spillovers of technological knowledge plays a significant role in the regional convergence process as the propensity to innovate of each region does depend on that of the surrounding areas and the intensity of the growth spillovers fades significantly with distance (Boschma, 2005; Paci & Pigliaru, 2002). It is widely accepted that spatial effects have an impact on the process of regional growth as contiguous regions tend to grow at similar speeds (Fingleton, 2003; Paci & Pigliaru, 2002). What is worth to point out, the results of prior studies suggest the existence of spatial dependence and positive impact of the knowledge resources in a given region on the growth of other regions, conditional on belonging to the same functional regions (Andersson & Karlsson, 2007).

It is considered that not all knowledge is equally valuable for productivity and economic development. The productivity and growth of a given economy depend on the diversity of its available capabilities, and therefore, development disparities can be explained by differences in economic complexity (Hidalgo & Hausmann, 2009). Complexity is an important qualitative dimension of knowledge that determines the cost and time of knowledge imitation. As empirical results provided by Mewes and Broekel (2020), knowledge complexity has crucial effects on knowledge creation in an economy and determines the regional economic growth.

As revealed by Kijek and Matras-Bolibok (2020) regional TFP is directly affected by knowledge-intensive specialization of the given region (in high-tech manufacturing and knowledge-intensive services). Benefits from specialization and clustering are essential to knowledge-intensive and innovation activities. The New Economic Geography (NEG) paradigm (Krugman, 1998) states that geographical concentration and localized knowledge spillovers shape regional productivity and growth (Ottaviano & Thisse, 2004). According to Kemeny and Storper (2015) regional specialization should positively impact productivity through the three main mechanisms assumptive in the NEG models: sharing of input suppliers, matching of specialized labour demand and supply, and occurrence of technological learning or spillovers effects, especially where innovation involves many different types of actors spread across different organizations. Spatial concentration of economic activities and growth are mutually self-reinforcing processes. The effects of agglomeration externalities according to the product life cycle and the maturity stage of a given area are hypothesized to differentiate the dynamics of regional productivity (Marrocu et al., 2013). It is considered that agglomeration externalities favour regional specialization as economic activities tend to cluster in areas with a strong functional specialization in knowledge-intensive and high-skilled activities (Meliciani & Savona, 2015). Highly specialized and complex outputs are usually produced at relatively few locations and often provide long-run competitive advantage (Hidalgo & Hausmann, 2009; Kogler et al., 2018).

It is worth to point out that the literature concerning regional diversification and specialization is characterized by dichotomy. The question which of them is the main driver of economic growth and innovation has gained the attention of many researchers since the edition of papers by Glaeser et al. (1992) and Henderson et al. (1995) who advocate sectoral diversity and specialization, respectively. However, empirical analysis indicates that the specialization-diversity issue is not an ‘either–or’ question, as both specialization and diversity matter for innovation and regional economic performance on different geographical levels, for different time periods, over the industry lifecycle, and in different institutional settings. To overcome the impasse in the specialization-diversity debate, the related variety concept was introduced (van Oort et al., 2015) that could serve for newly defined cohesion policies, smart specialization policies, or place-based development strategies.

The vision of knowledge-based regional development is the core of the smart specialization concept that was recommended by the Knowledge for Growth Expert Group commissioned by the EU. It is based on the technology-driven model of place-based strategies that can be pursued with advantage both by regions that are at the scientific and technological frontier, and by those that are less advanced (Foray et al., 2009). Smart specialization strategies adapt bottom-up approach and they are focused on both public and private ‘enabling knowledge-based assets’, not on particular economic sectors (OECD, 2013). What is worth to point out, smart specialization is diversified specialization and not the same as specialization as known from previous regional development strategies. The goal of smart specialization is not to make the economic structure of regions more specialized (i.e. less diversified), but instead to leverage the existing and identify the hidden opportunities and to create new areas of high value-added activities that will be critical in building regional competitive advantage (Balland & Boschma, 2021). To achieve diversified specialization a region needs to promote new path development basing on technologically more advanced activities that move up the ladder of higher knowledge complexity (Asheim, 2019).

Smart specialization concept is focused on building competitive advantage in research domains and sectors where regions possessed existing strengths and improving those capabilities through diversification into related technologies and industrial sectors (European Commission. Directorate General for Regional Policy, 2012). Aiming at identification of technological assets that comprise the knowledge cores within regions and extension of innovative place-based capabilities, smart specialiszations should contribute to both reduction of competitive overlap with competing regions and to increase in regional synergies (Rigby et al., 2022). Concentration of public investments in the smart specializations platforms is particularly important for regions that are not leaders in any of the major science or technology domains.

2.3 Policy Framework of Innovation-Driven Regional Development in the European Union

The European Union (EU) introduced a structural policy, known as Cohesion Policy, to tackle with the economic and social disparities. The main purpose of Cohesion Policy is to reduce differences and provide a harmonized development among regions. The European Regional Development Policy (ERDP) is a part of Cohesion Policy, which is focused on regional development. It should be noted that regional development regarded as regional convergence has been a political objective of EC/EU from the beginning of the integration process (Ares, 2020). One of the main tools of The European Regional Development Policy is the European Regional Development Fund (ERDF). Support for innovation is a key priority for ERDF, since the reduction of innovation gap between regional innovation leaders and moderate innovators should lead to lower productivity disparities.

Since 2000 a transition has been observed from the ‘old’ to the ‘new development paradigm’, as reflected in the Structural Funds programming. This coincided with a history-making moment of the preparation for the accession of new Member States, mainly from Central and Eastern Europe. Structural Funds in the 2000–2006 programming period focused on the stimulation of competitiveness by tapping endogenous potentials of regions in the form of intangibles, social capital, and learning capacities. In this period regional development policy, regional research and technological development and innovation (RTDI) strategies and regional innovation system (RIS) approach became synonymous (Pellegrin, 2007). As defined by Autio (1998, p. 135), regional innovation systems are ‘essentially social systems, composed of interacting sub-systems; the knowledge application and exploitation subsystem and the knowledge generation and diffusion sub-system’. RIS was expected to contribute to the Lisbon strategy by leveraging both regional and Community competitiveness (De Bruijn & Lagendijk, 2005). To stimulate the development of regional innovation systems (a ‘learning’ regional economy) and innovation capacities in the less favoured regions, the principle policy tool, known as regional innovation strategy, was financed under the innovative actions of the European Regional Development Fund in the period 2000–2006.Footnote 1

Regional innovation strategies are based on the assumption of giving an impulse to collective social learning and knowledge mobilization by providing regions with a flexible methodological approach to design effective RTDI strategies. Key methodological principles of regional innovation strategy reflect a network perspective of heterogeneous actors instead of a top-down decision-making approach. This means that regional innovation strategy should be integrated and multidisciplinary, demand-led, action-oriented, incremental and cyclical and should promote inter-regional cooperation and benchmarking (Landabaso et al., 2003). From the normative perspective, the perception of regional innovation strategy as a one-size-fits-all model, i.e. applicable to all regions, including the less advanced ones, is the subject of lively scientific debate (Tödtling & Trippl, 2005). It seems clear that a regional innovation strategy and related policy responses should be tailored to the type of regions (e.g. rural or metropolitan regions) and their specific characteristics (Nauwelaers & Wintjes, 2002), but this strategy does not offer universally practicable indications for policy-makers.

After the Lisbon Agenda was relaunched in 2005, stronger pressure in Cohesion Policy was put on innovation and knowledge as key drivers of competitiveness during the 2007–2013 programming period. Although a targeting of Structural Funds to improve competitiveness may at first glance seem to be contrary to the main objective of Cohesion Policy in terms of the reduction of regional disparities in the European Union, the objectives of competitiveness and cohesion should be regarded as complementary, since they both focus on the effective exploitation of endogenous potentials of regions (Pellegrin, 2007). Lagendijk and Varró (2013) point out that the increasing role of innovation in the Lisbon Agenda and EU policies, including Cohesion Policy, resulted in three distinct trends of policy integration. First, innovation-oriented programmes received higher funding. Second, ‘place-based’ cluster approach (Barca, 2009) became increasingly important and therefore deserved close attention from the interconnected industrial and regional policies. Third, ‘place-based’ innovation approaches were incorporated into research policy (Soete, 2009).

According to Foray et al. (2011) some limitations of regional innovation policy during the 2007–2013 programming period were linked to the policy dogma that not favouring any particular sector or technology based on certain priorities is the best choice for policy-makers. Moreover, regional innovation policies are affected by the innovation paradox, which is that less advanced regions have a significantly lower capacity than core regions to use, in an effective way, policy tools designed for improving their innovation potential (Oughton et al., 2002). It results in a further widening of the gap between lagging regions and regions at the frontier of research and innovation. In response to this situation, regional innovation policy in the 2014–2020 programming period was based on the concept of smart specialization, which situated the place-based approach, related variety, revealed competitive advantage and entrepreneurial discovery as four key priority-setting rationales (Hassink, 2020). Research and innovation strategy for smart specialization strategy (RIS3) tries to bring together a sectoral perspective with a spatial context, linking the EU’s Innovation Union strategy that forms part of Europe 2020 strategy for smart, sustainable, and inclusive growth with Cohesion Policy (McCann & Ortega-Argilés, 2015). Over the programming period 2014–2020, developing a RIS3 was a requirement to obtain funding from the European Regional Development Fund.

Table 2.1 presents the evolution of rationale of the European Regional Development Policy towards innovation-driven regional development during three programming periods. From 2000 onwards, the Lisbon Agenda, effectively succeeded by the Europe 2020 strategy, oriented the ERDP towards productivity and economic growth by stimulating innovation activities, in particular within the scope of the ERDF. Over the 2000–2006 programming period, ERDF funds for innovation and R&D were equally divided between three initiatives, i.e. (1) research projects located at universities and research institutes, (2) innovation means, such as knowledge and technology transfer, and (3) RTDI infrastructure in the form of buildings, laboratories, and business incubators. Almost two-thirds of innovation-oriented funds were targeted at direct aid, divided nearly in half between research projects and infrastructure investment (Holm-Pedersen et al., 2009). It is worth noting that the overall support for research and technological development and innovation in the 2007–2013 period amounted on average to 17% of the ERDF and Cohesion Fund in line with the Lisbon Strategy and later Europe 2020. Most of the funding going to innovation went to SMEs for the implementation of more technologically advanced methods of production as well as for the introduction of new products, while only 6% of the overall amount of ERDF support available was allocated to research centres or universities (Ciffolilli et al., 2016). To support regional innovation in the 2014–2020 period, the key focus of the ERDF fund was on research and innovation policy for smart specialization strategies used to establish priorities for research and innovation investments. This is reflected in the allocation of more than EUR 40 billion to these priorities within the ERDF fund (Schmidt, 2019).

Table 2.1 Evolution of rationale of the European Regional Development Policy (Ares, 2020, p. 95)
Full size table

As regards the effectiveness of the European regional innovation policy, Alecke et al. (2010) sought to estimate the effects of ERDF and federal subsidies for enterprise R&D in East Germany in the period 2000–2006. They found that R&D grants led to additional investments, which supports the legitimacy of public R&D intervention. These findings are partially confirmed by Ferrara et al. (2017) who evaluated the effects of RTDI over the period 1999–2010. Their results suggest that there was a strong and statistically significant impact of the research and innovation policy expenditures on Objective 1 regions (i.e. the least economically developed regions in the EU, which came closer to the levels of innovation-related activities (patent applications per million) performed by economically stronger regions. The findings also suggest that the effect was stronger in the earlier years. This tendency is broadly in line with some studies on convergence in innovation activity. For example Mulas-Granados and Sanz (2008) found both R&D expenditure and patents convergence among European regions in 1990–2002 period. However, more recent studies conducted by Kijek et al. (2022) and Barrios et al. (2019) reveal the existence of club convergence in innovation activity within European regions, which to some extent may reflect a change in the approach to regional innovation policy in terms of tailoring its measures and instruments to specific regional capacities and needs.