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.
The transformation processes taking place in the global economy and the expansion of global business ties increase the overall vulnerability of the international banking system. One of the problems related to money laundering is the process of evaluating the efficiency of financial monitoring measures. The article discusses the issues of assessing the effectiveness of financial monitoring in the banking system of the country. For Ukraine, this problem is especially relevant, because there is a bank-centric model of the financial market (about 90% of assets go through the banking system) in the country. According to official data, 50% of economic activity in Ukraine ends with money laundering. The article presents the improved method that quantifies the level of financial monitoring system effectiveness at commercial banks of Ukraine based on calculations of the integral index. The index indicates the dynamics of the financial system protection degree from the money laundering threat based on the expediency and efficiency of financial monitoring in the banking system. As a result, more comprehensive conclusions about the level of financial security of the country are made. According to assessments, in 2017–2018 the efficiency of financial monitoring of the banking system of Ukraine was at the middle level (about 64%). The proposed method can be applied to evaluate the effectiveness of the financial monitoring system in any country and become the basis for improving the anti-money laundering system through the banking system. AcknowledgmentThe study was conducted as part of state budget research of Sumy State University – Formation of a Public Finance Transparency System as a Prerequisite for Combating Corruption in Ukraine (0118U003585) (in the context of evaluating the effectiveness of financial monitoring of the Ukrainian banking system) and Formation of Tools for the Ukrainian Economy Unshadowing Based on Causal Modeling of Interaction Trajectories of Financial Intermediaries (0120U100473) (in the context of substantiating the need and directions for improving the financial monitoring system in Ukrainian banks).
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 world practice has proven that universities can become a source of innovations and the centers of innovative movement. However, there is almost no research into the development of innovative ecosystems of universities under conditions of limited funding and permanent crisis phenomena in the economy. This is a characteristic feature of many countries in the world that are developing or being transformed. The tool for reducing the action of negative factors is to increase the level of organization of innovative ecosystems of universities. It was found that the consequences of creating an innovative ecosystem of the University are an increase in the degree of commercialization of knowledge of university scientists and students, improvement of human capital, and intensifying cooperation between science, education, business and government agencies in the field of research and innovation. The main actors in the university’s innovation ecosystem were separated and their main functional roles were described. In particular, functional roles include education (innovative and entrepreneurial educational programs), basic science (scientific parks, scientific and research laboratories), applied science (startup accelerators, business incubators, startup clubs), and commercialization (centers for knowledge and technology transfer). The experience of creating separate elements of innovative ecosystems of universities of the world was considered and positive results were given. The essence of the concepts of commercialization of knowledge, technology transfer was studied and basic methodological approaches to the creation of the Center for knowledge and technologies transfer on the university base were explored. The purpose, objectives, directions, and functions of the Center for Knowledge and Technology Transfer were highlighted. It was proposed to focus on working at specific requests at any stage of the development of the idea on the principles of the individual approach of targeted assistance, openness to communications, and support throughout all stages of an innovative project.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.