ASSESSMENT OF REGIONAL INNOVATION SYSTEMS AS AN ASSUMPTION FOR INNOVATION POLICY ADJUSTMENT.

• Author: Zitek, Vladimir

1. Introduction

   The concept of innovation systems was designed in the 1980s and its purpose was to clarify the disparities within the innovation performance of industrial countries. Its adherents state that the differences among countries in the economic and technological performance are due to the combination of the existing institutions and their interactions, as they have an effect on the accumulation of capital, technologies and knowledge. According to them, the innovation performance of countries depends on the institutional differences in the ways of the implementation, improvement, development and dissemination of new technologies, products and processes (Metcalfe and Ramlogan, 2008).

   Originally, the concept of innovation systems only focused on the national level, and it was pointed out that a specific research environment, system of education, finances, and regulations shape the innovation processes of some countries to a considerable extent (Todtling and Kaufmann, 1999). Shortly, many authors applied it also to the multinational level, but the regional level became the primary field. This development was, among others, based on the idea that industries concentrate in certain areas and the existing decentralized policy can be applied to the regional level (Buesa et al., 2006).

   The concept of innovation system is a practical tool for the analysis of dependencies in the innovation process (Todtling and Kaufmann, 1999) or a tool to propose an innovation policy (Lundvall, 2007). This tool is used to determine which alternative of the institutional arrangement supports the strong dynamic performance (national/ regional) of the economy or a sector (Lundvall et al., 2009). There are also differences in the individual authors' approaches. Taking into account two significant representatives, Lundvall and Nelson, Nelson's approach is rather based on empirical case studies, while Lundvall's approach is more theory-oriented and seeks to create alternatives to the traditional neoclassical economics by placing an emphasis on interactive learning, relationships between manufacturers and customers, and innovation in the center of analyses (Edquist, 2005). Empirical approaches are also used in the description and understanding of the structure, dynamics and performance of innovation systems (Bergek et al., 2008).

   The innovation system can be understood in a broader or a narrower sense (e.g., Freeman, 2002 or Asheim and Gertler, 2005). The narrower definition focuses on the institutions that promote the acquisition and dissemination of knowledge and are the main sources of innovation. This primarily includes companies and the research sector and adopts the top-down approach as well as the linear model of the innovation process. An example can be the triple-helix model (Leydesdorff, 2006; Etekowite, 2008). The broader concept of innovation systems encompasses all components and aspects of the economic structure and the institutional arrangement affecting learning, as well as exploration and discoveries. This broader definition includes elements of the bottom-up approach and the interactive model of the innovation process (Asheim and Gertler, 2005).

   Just as states vary in their innovation performance, the ability of regions to innovate and develop their innovation system varies as well. This is due to the fact that regions differ in the sector specialization and in the functional and organizational characteristics. Additionally, their ability of interaction, which depends on the presence of clusters and the approach to cooperation, differs. The problem of some regions can be an inadequate capacity to build relevant institutions as well as the absence of an effective management model (Todtling and Kaufmann, 1999).

   Doloreux (2002) stated that the regional innovation systems consist of four basic interrelated elements, which are the businesses, institutions, knowledge infrastructure, and the policy. Businesses must be understood as learning organizations which are in interaction with other companies and institutions forming their environment. Institutions are, for example, the governments and other institutions which are key players in the creation and transfer of knowledge. Institutions reduce uncertainty, coordinate the use of knowledge, settle conflicts, and provide incentives. They can be formal or informal and they are significantly affected by the national innovation system. The knowledge infrastructure represents the physical and organizational infrastructure for the promotion of innovation. It also includes research institutes, laboratories, and universities. A policy focused on regional innovation is a policy oriented to the improvement of the interactions among the knowledge infrastructure, businesses, and institutions. Policies are to develop the endogenous potential of regions by encouraging the spread of technologies at the regional level. General regional policy is influenced by the idea that innovations play a crucial role within competitiveness enhancement. Due to that the regional policy is often connected with the innovation policy (Klimova and Zitek, 2015).

   Successful economies can be characterized by a compact and integrated system for the conversion of new knowledge and innovation into profitable (productive) economic values. A successful economic development is thus closely linked to the ability of the country to acquire, absorb, spread and apply modern technologies, i.e., the ability represented by the national innovation system (Metcalfe and Ramlogan, 2008). Successful regional innovation systems have several features in common (Skokan, 2005):

   --economic activities (high GDP, export, high representation of businesses, presence of knowledge-intensive industries, skilled workers);

   --research activities (private R&D expenditures, emergence of new technologies in the region);

   --research infrastructure (strong and diversified R&D institutions meeting the requirements of businesses);

   --policy (political awareness, relevant objectives, appropriate strategy); and

   --social networks (interactions between entities, relations between businesses and research representatives, cooperation of the businesses).

   However, it is also necessary to pay attention to the regions with system failures and dysfunctions, because it is the only way to understand better the factors forming the regional innovation performance (Bathelt, 2003; Asheim, Smith and Oughton, 2011).

2. Evaluation of territorial innovation performance

   The literature contains a wide range of studies that deal with the issue of regional competitiveness using various methodologies. Incomparably fewer authors elaborate more specifically on the evaluation of the regional innovation potential or directly regional innovation systems. The following text briefly summarizes the selected studies which evaluate the Czech environment.

   The European Commission evaluates the innovation performance of countries using the Summary Innovation Index (IUS). It consists of 25 indicators representing three main areas (enablers, firm activities, outputs). This study investigates, for example, human resources, expenditures on research and development, research publications, intellectual property rights, innovative companies, cooperation in innovation and economic effects. Based on the composite index, the countries are divided into four groups: innovation leaders, innovation followers, moderate innovators, and modest innovators. The Czech Republic is a moderate innovator (European Union, 2014a). On the regional level (NUTS2), the European Commission evaluates the innovation performance in the same way (RIUS) at two-year intervals. However, not all data are available at the regional level so the index only uses 11 out of the 25 indicators. Furthermore, not all of these 11 indicators are available for each region. In our opinion, this significantly reduces the information value of the innovation index. As a result, all the Czech regions fall among moderate innovators, which is not correct, because we can find big differences among them (European Union, 2014b).

   Polednikova and Kashi (2014) in their evaluation of the innovation performance of the Czech NUTS2 regions in 2011 used the European Commission methodology (European Union, 2014a), but with a smaller number of indicators (5 indicators--employees with a university degree, public and private expenditures on R&D, EPO patents, and employment in high-tech sector) complemented by two more (the use of structural funds and technically innovative businesses). Their calculation methodology was based on the analytic hierarchy process method AHP and the optimization method VIKOR. The South-East cohesion region (i.e., South Moravian region + Vysocina region) was evaluated as the most innovative region. It was followed by Prague. The Northwest cohesion region (Usti region + Karlovy Vary region) was marked as the least innovative region.

   Quite a different approach to the evaluation of the innovation performance of NUTS2 regions in the European Union was adopted by Capello and Lenzi (2013). They used 25 indicators divided into four areas. Some of these indicators were taken from various statistical resources; some of them were calculated by the authors (for the list of indicators see Capello and Lenzi, 2013, pp. 130-134). Based on the cluster analysis (method of k-means) they created the taxonomy of five groups of European regions. The Northwest cohesion region and the Central Bohemia cohesion region belong to the forth cluster (smart and creative diversification area) and the rest of them belong to the third cluster (smart technological application area). In our opinion, the Northwest and Central Bohemia cohesion regions should not fall within the same cluster, because they differ significantly as regards to the structure of economy, economic performance, standard of living and others.

   Drahosova and Bednar (2014) evaluated the...