"Scientific research" Almanac

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  STATISTICAL DIMENSIONS OF THE DIGITAL ECONOMY

  The aim of the study is to evaluate the statistical dimensions of the digital economy in terms of cross-section and time-series analysis and assessment of the degree of convergence withinn EU countries. The research thesis is that statistical methods improve the possibilities for statistical assessment of the digital economy and assessment of the ... The aim of the study is to evaluate the statistical dimensions of the digital economy in terms of cross-section and time-series analysis and assessment of the degree of convergence withinn EU countries. The research thesis is that statistical methods improve the possibilities for statistical assessment of the digital economy and assessment of the degree of convergence within the EU countries. A critical analysis of the theoretical foundations of the essence of the digital economy has been made and as a result a definition has been proposed. A system of indicators, deliberately selected by the authors for characterization of the digital economy, as well as their information sources are presented. A methodological framework of the study of the statistical dimensions of the digital economy has been developed. It consists of - cross-section and time-series analysis, classification of countries into groups and assessment of convergence through the approaches of absolute sigma and beta convergence. As a result of the statistical analysis for the period 2010–2020, an upward trend in the levels of the selected indicators for almost all countries has been identified. Based on the level of indicators in 2010 and the speed of change in the period 2010-2020, the EU-27 countries are classified into four groups - running ahead, lagging ahead, catching up and falling behind countries. Convergence in indicators characterizing the digital economy within EU-27 is assessed upon the methodology of absolute beta and sigma convergence.

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  THE TRAINING IN INSURANCE AND SOCIAL AFFAIR IN D. A. TSENOV ACADEMY OF ECONOMICS – SVISHTOV – STATE, PROBLEMS AND PROSPECTS

  The object of study is the organisational and economic issues of insurance and social affair, and the subject – the institutional framework of training in insurance and social affair in D. A. Tsenov Academy of Economics in Svishtov and similar universities in the country. An attempt has been made to argue the thesis that the modern innovative ... The object of study is the organisational and economic issues of insurance and social affair, and the subject – the institutional framework of training in insurance and social affair in D. A. Tsenov Academy of Economics in Svishtov and similar universities in the country. An attempt has been made to argue the thesis that the modern innovative training in insurance and social affair can be a projection only of solid research capacity with theoretical-methodological and practical-applied dimensions, adapting but also incorporating the rich traditions to the dynamics of modernity. The main goal of the research is to outline the market profile of the training in insurance and social affair in D. A. Tsenov Academy of Economics. In this connection the specific tasks are set: - presentation of the traditions and outlining the research foundation of the training in insurance and social affair in D. A. Tsenov Academy of Economics; - carrying out a comparative analysis of the training in insurance and social affair in D. A. Tsenov Academy of Economics and in similar universities in the country; - outlining the market advantages, weaknesses, challenges and deve-lopment guidelines of the training in insurance and social affair in D. A. Tsenov Academy of Economics. The relevance of the study corresponds to the role of D. A. Tsenov Academy of Economics – Svishtov as a participant in the imposed cluster approach in the organisation of economic education. The study of the educational process in the leading economic universities in the country allows to outline the development guidelines of the training in insurance and social affair in D. A. Tsenov Academy of Economics.

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  UNCOVERING SHOPPING PATTERNS IN BIG DATA ENVIRONMENTS

  In this study, we present the feasibility of applying selected algorithms to rapidly detect regular, common, and recurring patterns of shopping behavior for products or services purchased simultaneously and/or in some temporal sequence. Such analyses are often defined by the term „'market basket analysis“. The object of the present study is the ... In this study, we present the feasibility of applying selected algorithms to rapidly detect regular, common, and recurring patterns of shopping behavior for products or services purchased simultaneously and/or in some temporal sequence. Such analyses are often defined by the term „'market basket analysis“. The object of the present study is the analysis of large data sets of sales transactions observed across multiple individual acts of purchase, and the subject matter is the discovery of the capabilities of some numerical algorithms from the field of machine learning to detect hidden patterns in acts of purchase by processing data from individual customer „market baskets“. The goal is to convince the reader of the possibilities of extracting association rules from big data through demonstrations with publicly available open data. The exposition follows a „concept-to-application“ logic and consistently provides answers to the questions of „why“ it is necessary to do it, „what“ it is done with, and „how“ it is done. After a brief introduction to the logic and specifics of the most popular algorithms for discovering patterns by extracting association rules from large datasets, we provide detailed working procedures and instructions for analyzing and interpreting the analytical results. Finally, we provide a synopsis and provide guidelines and recommendations for using the analytical procedures in a marketing context.