Natalia Nebaba

The purpose of the article is to study the innovation levers of developing the intellectual background for economic growth in two groups of post-socialist Central and Eastern European countries (middle-income and lower-middle-income countries). To achieve that, the quantitative effect of the national intellectual capital components (human capital, market capital, structural capital and capital of renewal and development) on the dynamics of the countries’ economic growth was determined.
For both groups, multiple regressions have been constructed that reflect the quantitative relationship between the economic growth rates (in the regressions – the indicator of real gross domestic product per capita) and the components of national intellectual capital in 2010–2018. It has been established that the key innovative indicator of the economic growth of middle-income countries is the national capital of renewal and development, which in general corresponds to the pan-European model of innovation and investment development. Education is the main factor that provides the basis for the economic growth of lower-middle-income countries. Recommendations on improvement of national innovation policy are offered.

Successful crisis resolution of the enterprise depends heavily on its timely detection, which is facilitated by the use of forecasting models. This allows understanding the scale of the problems in a timely manner and developing the appropriate measures, applying various financial mechanisms to prevent it, and in case of occurrence, reducing the amount of losses. In this context, it is important to choose the most optimal informational model that would provide the most objective forecasts, considering the financial activity peculiarities of the analyzed enterprise. Given a wide list of models that predict the financial crisis, there is a need to analyze and select the most accurate model for enterprises in the real economy. Ten Ukrainian machine builders are used to assess the bankruptcy probability using the most popular models; a taxonomic analysis was carried out, which allows systematizing a large amount of data and analyzing their impact on enterprise development. An integral index was determined, which allowed predicting the financial performance dynamics. For each enterprise, ten indicators were used characterizing their financial state for the period 2014–2018. It is substantiated that the selected models differ from each other by the set of initial data and the number of coefficients from four to seven. It is also determined that the efficient use of studied models is quite different; so when choosing a model to predict the bankruptcy probability, it is necessary to consider the peculiarities of the enterprise’s production activity, the accuracy in creating the financial statements and many other factors, including the presence of company’s shares in circulation at the stock market. It is worthwhile to use a taxonomic analysis to make a comprehensive comparison of the enterprise financial state and to substantiate the final choice of the bankruptcy forecasting model.

Escalating competition, technological changes and the struggle for innovation present companies with a knowledge management (KM) challenge. To implement it at the modern level, it is necessary to develop a knowledge management system (KMS). Significant opportunities for this are created by information technologies (IT), qualitatively changing approaches to knowledge management. Therefore, the study aims to clarify the theoretical foundations of shaping the company’s KMS and conceptualize information tools for its formation. Within the theoretical foundations of KM, its essence (as a systematic management activity and a set of measures to ensure the business processes of obtaining, storing, disseminating and using knowledge in the company), the subject (the aforementioned processes and various types of knowledge), and links with other types of management (innovation, information, personnel management, etc.) are specified. Given the main goals, principles and tasks of KM, its main approaches, key processes and control elements are summarized. The conceptual foundations of KMS development are formulated and its subsystems (methodological, planning, information, and functional subsystems for ensuring business processes for obtaining, distributing and using knowledge) are highlighted. Given the importance of IT, the following concepts have been formulated: a portal for R&D management, innovation management platforms, and a tool for formalizing knowledge and corporate knowledge base. Their purpose, functionality, and the role of ensuring work with knowledge and KM implementation are described. The problem of their implementation, operation and improvement is emphasized. The research results allow creating a new technological basis for the introduction of knowledge management.

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.

The effects of the economic recession and the COVID-19 crisis call for more active support for the tourism industry. To pursue a supranational tourism policy and create a favorable marketing environment at the national level, it is necessary to consider the objective differences between member states and their characteristics in the field of tourism. This study aims to highlight the main factors that characterize the asymmetry of the tourism industry in the EU countries, which allows ensuring the competitiveness of national tourism companies through the formation of an appropriate marketing strategy. The research methodology includes calculation of the asymmetry coefficient and cluster and classification analysis based on Eurostat data.
At the first stage, 27 indicators were selected that characterize the structural proportions of the tourism industry and the intensity of tourism in the EU countries. Based on the calculation of the asymmetry coefficient, a high level of heterogeneity of the tourism industry parameters in the EU countries for each of the indicators was demonstrated. At the second stage, clustering (algorithm – k-means, metric – Euclidean distance) of the EU countries was carried out according to the selected indicators. As a result, eight clusters were obtained, which showed asymmetry in developing national tourism sectors in the EU. At the third stage, as a result of classification (method – decision trees), seven combinations of indicators were identified, which completely distinguish the resulting clusters of the EU countries. The parameters included in these combinations are, in fact, the main factors of the asymmetry in the development of the EU tourism industry.
Based on the analysis of the asymmetric development of the tourism industry by country, it is possible to determine its growth points and competitiveness drivers in the EU internal market and identify marketing strategies.