Suchergebnisse

To carry out a comparative analysis of the EU countries' national innovation systems (NIS), a feature vector has been compiled, covering three modules, namely, science, education, and innovation. The feature vector is a valid multidimensional data set of sixteen official statistics indices and two sub-indices of the Global Innovation Index. The development of a cognitive model for managing the NIS parameters required a preliminary three-stage empirical study to determine its elements. In the first stage, cluster analysis was performed (the k-means, metric – Euclidean distance algorithm was used). As a result, the EU countries were divided into four clusters (following multidimensional scaling estimates). In the second stage, a classification analysis (using decision trees) was carried out, which allowed determining three parameters that distinguish clusters (or classes) optimally. These parameters are recognized as important ones in terms of positioning the countries in the general ranking; that is, they can be considered as a priority for the NIS development and improving the countries' positions in international comparisons. In the third stage, based on the authors' approach, the significance (information content) of each key parameter is estimated. As a result, a cognitive model was compiled, taking into account the parameter significance. The model can be used in managing the NIS parameters, seeking to increase the system performance and improve the international position of a specific country. The model can also be used by partner countries, for example, Ukraine, as it demonstrates the landscape of EU innovative development and outlines the directions for priority development of NIS towards the European progress.

Innovations have become pivotal for the growth and competitiveness of national economies. Generating innovations necessitates a comprehensive ecosystem as a set of conducive conditions. With competition intensifying and focusing on innovation, countries increasingly prioritize the enhancement of their innovation ecosystems. The foundation for this lies in international comparisons, particularly among countries that are global leaders, as it aids in identifying their specific characteristics and advantages. The aim of the study is to differentiate the innovation ecosystems of world-leading countries by highlighting the indicators in which they differ the most.The paper covered the top 15 countries according to the Global Innovation Index, each characterized by 23 indicators in their innovation ecosystems. In the first stage, using mathematical processing (the k-means method), the countries were divided into six clusters. Then, to find the parameters that differentiate the obtained clusters, a classification analysis was conducted (the "decision tree" method), resulting in 11 indicators that, in various pairwise combinations, most differentiate the analyzed countries. These indicators reflect the features and most important advantages (or weaknesses) of each innovation ecosystem and are also priorities for increasing the parameters of these ecosystems to improve the position of countries. It is advisable to use these indicators to form state innovation policy.

Innovation plays a crucial role in ensuring economic growth and competitiveness of national economies, creating conditions for their sustainable development. By focusing on supporting innovation, the EU is particularly helping to accelerate the development of those member states that lag far behind the EU average. This requires the selection of the indicators reflecting the development of innovation that determine the differences between member countries to the greatest extent. Therefore, the aim of the study is to identify the key factors of the innovation gap (FIG) between EU countries based on a comparison of indicators characterizing the national innovation systems (NIS). For this purpose, 22 relative indicators were selected from the indicators included in the Global Innovation Index to form an array of empirical data. At the first stage, the EU countries were divided into four clusters using the k-means method. At the second stage, using the decision tree method, a group of indicators was identified that together distinguish the obtained clusters to the greatest extent and, accordingly, determine the differences between EU countries and can be considered as FIG, namely: "Researchers", "GERD financed by business", "Joint venture/strategic alliance deals", "Software spending", and "High-tech manufacturing". This allows individual member states to prioritize the development of those indicators (i.e. FIG) that most determine their position in the EU and therefore improve their NIS. At the EU level, this will contribute to the complementarity of the NIS, overcome differences between member states and increase the overall level of convergence in innovation.

The increased final consumption exacerbates the problem of the scarcity of natural resources and leads to environmental pollution. The concept of circular economy, which implies the formation of closed-loop chains of production and consumption with maximum regeneration and recycling of materials, is considered as an alternative to the firmly established "linear economy" (take-make-dispose). As a part of sustainable development strategy, the European Union adopted a general policy on the transition to a circular economy. However, for objective reasons, such transition is quite uneven at the level of member countries, which adversely affects the total progress. Therefore, the need arises to assess the positions of individual countries and identify major reasons for the uneven transition to support the countries that are lagging.The goal of the study is to identify the factors of uneven progress of the EU countries towards a circular economy. For that reason, a set of empirical data (20 indicators) has been compiled; cluster, classification, and parametric analyses have been conducted. As a result, three clusters of the EU countries have been obtained and six indicators, included into combinations that make all clusters different, have been identified. These indicators can be interpreted as the key factors contributing to the uneven progress of the EU countries towards a circular economy. The difference in harmonic means by clusters allowed quantitatively estimating a "circular gap". It is of practical value for the EU policy aimed at bridging the gaps between member countries during the transition to a circular economy.