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Публікація A Model of the Impact of Digital Transformation on the Country’s Economic Growth(2024) Revenko, D.; Romanenkov, Yu.; Lebedchenko, V.The transformation of the economy through digitalisation has become one of the biggest challenges of our time. Modern digital technologies are fundamentally changing all aspects of economic activity and social life. These technologies not only change the way we do business and interact socially but also transform approaches to education, communication, and entertainment, opening up new opportunities and creating new challengesПублікація The impact of digitalization on the economic growth of the European Union: an empirical study(2024) Revenko, D.; Romanenkov, Yu.; Polozova, T.; Lebedchenko, V.; Molchanova, K.The object of this study was the process of digitalization of the economy and society of the European Union. The task of researching the impact of digitalization on the economic growth of the European Union has been solved. The relevance of the chosen topic is due to the complexity of digital transformation processes taking place in the world economy and the economy of the European Union, in particular, the need to assess their directions and intensity. Four approaches to modeling the impact of digitalization on economic growth based on the neoclassical production function were proposed. The structure of the model was chosen, which makes it possible to evaluate the influence of various factors of digitalization on the key parameters of economic growth. A regression multivariate model for assessing the impact of digitalization on the economic growth of the European Union has been developed. To this end, a sequence of stages was performed: formation of the information base, grouping of digitalization factors, as well as their selection based on correlation analysis. With the help of regression analysis, the parameters of the production function were expressed through digitalization indicators. At the same time, due to the limitation of input data, the Elastic Net Regression method was used. This made it possible to ensure the quality of the new model, namely, to remove low-impact parameters, reduce the multicollinearity of factors, make the model statistically significant, and ensure the stability of coefficients to data changes. The resulting model is eleven-factor; it demonstrates a high predictive ability (the coefficient of determination is 0.987). It can be used as an analytical tool for assessing the impact of digitalization on the economic growth of the European Union. Practical use of the model will help governments and businesses make informed decisions about digital transformation policies