Hanna Yarovenko

Department of Economic Cybernetics, 

Sumy State University,

Ukraine

h.yarovenko@biem.sumdu.edu.ua

ORCID 0000-0002-8760-6835

Digital transformation and economic development in Europe: Classical and machine-oriented approaches

Dmytro Ohol

Department of Monetary Policy and Economic Analysis, 

National Bank of Ukraine,

Ukraine

dmytro.ohol@gmail.com 

ORCID 0009-0000-7195-9348

Laura Ashirbekova

Al-Farabi Kazakh National University Almaty, 

Kazakhstan

laura.ashyrbekova@kaznu.edu.kz,

 ORCID 0000-0003-0377-7854

József Popp

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

jpopp@wsb.edu.pl

ORCID 0000-0003-0848-4591

Abstract. 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.

Received: December, 2024

1st Revision: October, 2025

Accepted: December, 2025

DOI: 10.14254/2071-8330.2025/18-4/13

JEL Classification: C63, F63, O33

Keywords: digital transformation, digital indicators, economic development, machine learning, regression modelling