Analyzing and Identifying the Latent Factors of Digital Transformation in EU Countries Using Data Science Methods

Abstract

Modern conditions of economic functioning require all subjects of economic, social, and political activity to implement digital solutions. In many countries, digitalization has been set as a strategic medium-term goal, as it becomes a key factor in increasing the competitiveness of countries in almost all areas. It has been proved that digitalization contributes to innovative growth, more efficient resource management, and the formation of new forms of interaction both among citizens and between business and the State, among others. Therefore, it is very important to develop and implement a systematic set of models based on modern methods of economic-mathematical modeling and machine learning, which will ensure sustainable economic growth and reduce the risks of socioeconomic imbalances. The work substantiates that such Data Science methods as principal component analysis, correlation and cluster analysis, etc., make it possible to systematically study the multidimensional structure of digital transformation, identify latent factors of digital development, and determine the trajectories of countries’ movement in the digital space. The combination of factor analysis with the dynamic study of the cluster structure allows for a deeper understanding of the mechanisms of digital evolution of EU countries and for assessing the efficiency of digital policy at the level of the European Union and individual countries. A structured system of digital development indicators relevant for analyzing EU countries has been formed. A correlation analysis of the specified indicators was also carried out, highlighting the structural relationships between them. Based on the principal component method, latent generalized factors of digital transformation were identified, integrating numerous digital indicators into several key areas of development. This allows for reducing the dimensionality of data and identifying the main driving forces of digitalization. The research results confirmed that today digitalization serves as the basis for increasing competitiveness, productivity, and innovation activity of the economy. It was substantiated that digital transformation is a complex multidimensional process that requires systemic analysis and the use of modern Data Science and machine learning methods, which will make it possible to assess the effectiveness of State policy, conduct comparative analysis of countries, and develop recommendations to accelerate digital progress.