Suchergebnisse

Employment is one of key parameters of the economy, which characterizes its efficiency, possibility of using the labour potential and growth of population's well-being. The level of employment is the most important indicator of the effectiveness of socio-economic policy of the state. A high level of employment in high-tech and science-intensive business areas is a driver of sustainable economic development of countries, increasing labour productivity, ensuring leadership in the market, and reducing the productions costs. Thus, the assessment of the effectiveness of population employment in high-tech and science-intensive service areas is significant today, as it is a comprehensive assessment of the country's development, its current state in high technologies and further prospects for working with them. The research purpose consists in determining the maximum, most effective value of the population employment efficiency index in high-tech and science-intensive service spheres based on cross-country cluster analysis. The sample of countries all over the world were divided into 3 clusters, taking into account the rating value of the following indices: employment in high- and medium-high-tech production sectors and science-intensive business service spheres; enterprises that conducted training to develop / improve the ICT skills of their personnel; new registered enterprises. During the research there were statistical data analysis, cluster analysis using Ward's method and software Statgraphics, optimization method using Frontier Analyst software. As a result, the efficiency of population employment in high-tech and science-intensive business service sectors of 36 countries in 2021 was determined, and accordingly reference countries with high population employment in this research sphere were identified. The potential reserves for increasing the targeted value of the population employment index in high-tech and science-intensive sectors were also characterized. The obtained results can be useful for business managers, they can adopt the experience of doing business in countries with more effective indicators, with the aim of developing employees, providing them with new training and knowledge that will facilitate doing business in the future.

The purpose of this article is to characterise challenges and opportunities in the 'business-education-science' system in the context of innovation development based on cross-country cluster analysis. The article examines the relationship between science, education and business and their mutual influence on modern society. The main challenges facing science, education and business are considered, and opportunities for cooperation between these fields to overcome them are identified. Key factors that influence the effectiveness of scientific research, the quality of education, and the success of business are also determined, with an emphasis on the important role of cooperation in the 'business-education-science' system in ensuring their impact. To reach article's purpose, a complex methodology was applied, which includes the following stages: collection of information about the current state of science, education, and business; a review of expert opinions and analytical reports on the problems of the interaction of science, education and business; methods of cross-country cluster analysis using STATISTICA 10 software (the k-Means method, the square of the Euclidean metric, etc. for a comparative analysis between 19 countries from a sample to find out which of them have better indicators in the respective fields). The statistical base is formed from the European Union and the WIPO data, which cover 10 key indicators in the context of the development of science, education and business and their affect the country's competitiveness in the global world. As a result, there are two formed clusters: the first includes the USA and China that have the highest level of education-science-business development and the second cluster includes other countries from the sample with a less developed education-science-business sector. For these countries the recommendations have been developed to strengthen their education-science-business sector, in particular: creating favourable conditions for investing in science and business, attracting talented scientists, and supporting their activities, increasing allocations for education, and improving the quality of education, strengthening partnerships between universities and enterprises to create innovative projects and other activities. The obtained results can be useful for further research and for making managerial decisions at different levels of government in the context of innovation development, including through the strengthening of coopetition between business, education, and science.

The article's purpose is to analyse the issue of implementation of knowledge economy and innovation through business education based on cluster analysis. The role of knowledge economy, innovation transfer, entrepreneurship and business-education coopetition are grounded to achieve economic growth and sustainable development. Input data withing the distribution of the knowledge economy through business education include a data of 23 countries for the following indicators: new registered enterprises, labour force, employment in industry, proportion of population studying 'Business, Administration and Law', proportion of population studying 'Services' and proportion of population studying 'Economics'. Using data normalization, Ward and Sturges methods and Statgraphics Centurion 19 soft five clusters were determined to show hidden dependencies and structure in countries sample in this research context. The first cluster includes 2 countries (Austria and the United Kingdom), the second – 11 countries (Belgium, Portugal, Denmark, Italy, Lithuania, Latvia, Poland, Ukraine, Croatia, Norway, and the Netherlands), the third – 5 countries (Bulgaria, Spain, France, Switzerland, and Finland), the fourth – 3 countries (Estonia, Germany and Sweden), and the fifth – 2 countries (the Czech Republic and Hungary). Due to building dendrogram of distribution on clusters and graph of agglomeration distance the quality of countries distribution into clusters was confirmed. Obtained results can be useful for further research and improving the state innovation, information and educational policy based on positive experience of neighbour countries within certain formed cluster.

This study aims to examine whether the labor productivity of the US population directly depends on public or private insurance coverage of people, employment level, life expectancies, spending on the public health system as a percentage of GDP, and spending on the public health system in natural terms. Empirical testing was carried out on the USА statistical data for 1987–2021 using a regression model with the fitting procedure backward stepwise selection (in Statgraphics software), and a multivariate adaptive regression spline MARS (using Salford Predictive Modeler software). The research hypothesis was confirmed for only two indicators: life expectancies and spending on the public health system in natural terms. Their impact on labor productivity appeared to be directly proportional. As an indicator, spending on the public health system has a greater impact on the change in productivity (0.0058%), whereas life expectancy has a lesser effect (0.0047%). The study showed that the MARS model provides more objective and accurate results compared to the regression model with the fitting procedure – backward stepwise selection. This conclusion is based on a comparison of real data modeled by both methods. The study proved that labor productivity in the USA grew yearly from 1987 to 2021 (the constant term in the MARS model's regression equation is +0.48428). To calculate the specific values of labor productivity for each year, a model was developed depending on the optimal basic functions (automatically generated by the MARS model depending on the current values of life expectancies and spending on the public health system in natural terms).
AcknowledgmentThis study is funded by Department of Applied Social Sciences of the Faculty of Organization and Management of the Silesian University of Technology for the year 2023 (grant number 13/020/BK_23/0081).