"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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  BENEFIT SEGMENTATION OF BULGARIAN TOURIST

  The peculiarities of consumer behaviour is of key importance for the successful customer centric approach implementation in tourism marketing. The majority of existing studies focused on Bulgarian tourist market groups customers on the basis of the tourist nationality or the type of tourism practiced. Insufficient researched remains attitudes and ... The peculiarities of consumer behaviour is of key importance for the successful customer centric approach implementation in tourism marketing. The majority of existing studies focused on Bulgarian tourist market groups customers on the basis of the tourist nationality or the type of tourism practiced. Insufficient researched remains attitudes and expectations. The study aims to segment Bulgarian tourists by using benefit segmentation approach. Benefits are described via 16 binary variables. Data is collected via online survey with 858 valid respon¬ses. The segmentation was performed by cluster analysis. Segments identification is made both by hierarchical and non-hierarchical clustering algorithms. The existence of five segments has been proven. They are profiled on the base of socio-demographic and behavioural characteristics. Recommendations for using the information from the study in business practice and scientific researches are formulated.

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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.