Suchergebnisse

In the context of globalization of the educational services market, competition between universities is becoming more intense. This manifests itself, among other things, in the struggle for positions in international university rankings. Given that universities are evaluated according to many criteria in such rankings, it becomes necessary to identify the most significant factors in determining their positions.This study aims to identify the key factors determining the world's leading universities' leadership in international university rankings. The numerical values of the criteria for compiling the QS World University Rankings (QS) and Times Higher Education (THE) rankings were an empirical basis for the study. The analysis covered the Top 50 universities (according to the QS ranking) and was conducted based on reports for 2020 and 2021.At first, clustering was carried out (method – k-means); the data set was the combination of numerical values of QS and THE criteria (six and five criteria, respectively). The universities were divided into three clusters in 2020 (23, 19, 8 universities) and 2021 (23, 17, 10 universities). This showed the universities' leadership relative to each other for each year.At the second stage, classification processing was performed (method – decision trees). As a result, criteria combinations that give an absolute separation of all clusters (2020 – five combinations; 2021 – eight combinations) were identified. The obtained combinations largely determine universities' affiliation to clusters; their criteria are recognized as key factors of their leadership in the rankings. This study's results can serve as guidelines for improving universities' positions in the rankings.

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.

This study identifies global factors conditioning the global problematics of the direction of social development. Global threats were evaluated and defined as dangerous processes, phenomena, and situations that cause harm to health, safety, well-being, and the lives of all humanity, and require removal. The essence of global risks was defined. These risks were defined as events or conditions that may cause a significant negative effect for several countries or spheres within a strategic period if they occur. Global problems were conceptualized. These problems were defined as phenomena, matters, and situations that are not completely understandable, interesting, actual, require solving and regulation, and in addition do not have unified solutions. Current global challenges were analyzed, defined as strategic guidelines that cause transformational changes and are receiving attention from humanity. Global trends were defined as courses of social development. The composition and role of global actors were described, and defined as international organizations, leading states, regional organizations, powerful corporations, institutional investors, large cities, and well-known personalities. The directions of positive courses of social development were developed by means of using the authors' mechanism of solving the global problematics.

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 disruptive technologies and cyber-physical production systems are important factors that bring transformations to socio-economic formations. The paper aims to formulate the content, key directions, positive and negative effects of additive economy (AE) in the current transition phase to Industry 4.0. The research method is based on the analysis of structural links in socio-economic systems, where the additive economy potential is realized. The additive economy is treated as a new approach to production technological aspect based on the additive principle of manufacturing and aimed at minimizing the use of primary natural resources for dematerialization of social production. AE is the antithesis of the subtractive economy, which dominates today and uses only a tiny proportion of extracted natural resources. Among the positive effects of AE, there are the reduction in energy intensity of products, dematerialization of production, solidarity of society, economic systems sustainability, and intellectualization of technologies and materials. Among the negative expectations of AE, there are increased information vulnerability of production, risk of losing control over cyber-physical systems, expanding the unification of individuals, and increasing psychological stress. The additive economy is more sustainable than the subtractive economy since it does not require extra components to the production spheres, reduces the resource scarcity, and could satisfy more economic agents' needs. Therefore, improved production efficiency due to AE promises economic growth acceleration, environmental burden and social risk reduction.
Acknowledgment The publication was prepared in the framework of the research projects "Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy" (№ 0121U100470); Fundamental bases of the phase transition to an additive economy: from disruptive technologies to institutional sociologization of decisions (No. 0121U109557).

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.

The widespread use of information and communication technologies and subsequent transformations have led to the formation of a digital economy (DE). The European Union, as an international organization, has become the subject of building such an economy, striving to bring member countries closer in the field of digitalization.The aim of this paper is to compare the DE development parameters of the EU countries based on cluster analysis and determine the most significant of them to solve the problems of bridging the digital divide between countries. For clustering, a feature DE vector of 20 indicators was created and the k-means algorithm and the Euclidean distance metric were used. For classification, the decision tree method was applied.Three clusters of EU countries were identified by the level of DE development (leaders, followers and outsiders), which allowed assessing their positions relative to each other. Key parameters that determine countries' positions in the general rating are identified. A parameter chart is generated to control the establishment of DE in the EU countries, which, in addition to key parameters, includes maximum, minimum and harmonic mean values of these parameters by cluster. This characterizes the landscape of DE development in the EU countries, assesses the digital divide and is the basis for decision-making in the area of bridging this divide.

Current local and national demographic trends have deepened the existing and formed new global demographic processes that have received a new historical reasoning that requires deep scientific research taking into account the influence of the multifactorial global dimension of the modern society development. The purpose of the article is to study the development of global demographic processes and to define the causes of their occurrence, manifestations, implications and prospects for implementation in the first half of the 21st century. The authors have identified and characterized four global demographic processes, namely population growth, migration, increase of tourism, and change in population structure. It is projected that in the 30's of the 21st century, the number and growth rates of the world population will reach the objective growth and these dynamics over the next two decades will begin to change in the direction of reducing the growth rates, which will lead to gradual stabilization, and eventually reduce the size of the world population. By the middle of the 21st century, one can observe the preservation of the growth rates of international and domestic migration, the growth of international migration flows from the South to the North and from the East to the West, the strengthening of new economically developed centers of gravity (Canada, Australia and New Zealand), the increase in migration of rural population to cities, as well as urbanization and activation of the metropolises development. The share of international tourists in comparison with the world population will be constantly increasing, and the annual growth rate of the number of international tourists will significantly depend on the world economy and may vary at the several percent level. Permanent change will occur in the age, religious-cultural and socio-economic structure of the population.