Digital transformation and economic development in Europe: Classical and machine-oriented approaches
Yarovenko H. Ohol D. Ashirbekova L. Popp J.
2025Centre of Sociological Research
Journal of International Studies
2025#18Issue 4256 - 284 pp.
The article analyses the influence of digital transformation on the economic development of European countries using a combination of classical econometric approaches and machine learning algorithms. The study uses 85 indicators of digital economy and society for 27 countries during 2017–2022, covering various aspects of digitalisation: human capital, digital infrastructure, broadband coverage, ICT specialisation, business innovation activity, etc. After preliminary data processing, multicollinearity diagnostics, and hierarchical clustering, factor analysis identified four latent components: digital competence and business innovation, digital infrastructure and connectivity, broadband coverage and penetration, ICT human resources and specialisation. To evaluate the relationships between digital factors and GDP per capita, pooled OLS, ridge, lasso regressions, random forest, XGBoost, and support vector regression models were applied. The highest forecasting accuracy was demonstrated by the SVR model, which provided minimal error values and effectively captured nonlinear dependencies in panel data. Feature-importance analysis revealed the leading role of digital competence and business innovation, as well as the considerable cross-country heterogeneity of digital drivers of economic development. The results confirm the need for developing differentiated digital-policy strategies and provide the basis for further advancement of causal and spatial modelling of the digital economy.
digital indicators , digital transformation , economic development , machine learning , regression modelling
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Department of Economic Cybernetics, Sumy State University, Ukraine
Department of Monetary Policy and Economic Analysis, National Bank of Ukraine, Ukraine
Al-Farabi Kazakh National University, Almaty, Kazakhstan
John von Neumann University Doctoral School of Management and Business Administration, Hungary
Faculty of Applied Sciences, WSB University, Poland
College of Business and Economics, University of Johannesburg, Johannesburg, South Africa
Department of Economic Cybernetics
Department of Monetary Policy and Economic Analysis
Al-Farabi Kazakh National University
John von Neumann University Doctoral School of Management and Business Administration
Faculty of Applied Sciences
College of Business and Economics
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