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 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.
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 war in Ukraine dealt a crushing blow to the country’s economy. The relevance of the topic is due to the marketing ability to be an effective tool for restoring and developing business in Ukraine. The paper aims to define the state and prospects for developing business and its marketing component during the war in Ukraine. The research analysis demonstrates that the share of business representatives who completely or partially ceased their activities during the first three months of the war decreased from 75.3% in March to 49.0% in May (compared to February 24, 2022), which is indicative of the gradual resumption of business in Ukraine. At the same time, it was found that in May 2022, the food retail, non-food retail, household appliances, and electronics sectors partially resumed their work. The best renewal rates are observed in the jewelry sector, and the worst – in the entertainment sector. A study of marketing activities in Ukraine shows that the most positive changes regarding gradual renewal are observed in digital marketing. The paper highlights the key consequences of hostilities for Ukrainian business and its marketing activities: supply chain disruption, reduced purchasing power, changes in consumer demand, stockpiling, and a state of uncertainty among business representatives. In addition, the study offers general approaches to adapting marketing and SMM during the war to preserve, restore, and further develop business in Ukraine.
Higher education institutions train professional and scientific personnel. Therefore, the quality of higher education and its funding are vital for training highly qualified specialists. This study analyzes the annual volume of expenses (investments) per student in groups of countries, divided according to their socio-economic development, and competitiveness of higher education. The division of countries into groups is based on simultaneous compliance with the criteria for the quality of higher education and the level of social and economic development. The Ward’s clustering method was applied. The analysis was conducted based on data from 32 OECD countries and partner countries. The paper found a significant direct correlation between the level of competitiveness of higher education and the amount of its funding per student (R = 0.895). At the same time, a significant direct correlation was revealed between the level of competitiveness of higher education and the human development index (R = 0.787) and the global competitiveness index (R = 0.888). Finally, a significant direct correlation between the amount of expenditures and the level of competitiveness of higher education was found only in the cluster with the highest indicators of socio-economic development (Rs = 0.707). In other clusters, the correlation is weak or weakly inverse.
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