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Post-industrial transformations of society and their impact on the employment structure

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Category: Content №5 2025

Last Updated on 25 October 2025

Published on 30 November -0001

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Authors:

E. V. Prushkivska, orcid.org/0000-0002-4227-8305, The National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine

Y. I. Pylypenko*, orcid.org/0000-0002-4772-1492, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A. M. Tkachuk, orcid.org/0009-0005-3061-282X, Dnipro University of Technology, Dnipro, Ukraine

H. M. Pylypenko, orcid.org/0000-0003-2091-4320, Dnipro University of Technology, Dnipro, Ukraine

V. O. Los, orcid.org/0000-0002-7932-5232, Kyiv Metropolitan Institute named after Borys Grinchenko, Kyiv, Ukraine

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2025, (5): 177 - 187

https://doi.org/10.33271/nvngu/2025-5/177

Abstract:

Purpose. To clarify the essence and consequences of digital transformation of employment in the context of post-industrial transformations, to identify key factors influencing the structure, forms, and dynamics of employment in the digital economy, and to subsequently develop a methodological approach to analyzing these changes based on cognitive modeling.

Methodology. The study uses cognitive modeling as an interdisciplinary tool for analyzing the transformation of employment in the digital economy. The methodological basis is a systematic approach to identifying and formalizing complex cause-and-effect relationships between economic, technological, socio-demographic, and institutional factors that determine changes in the structure of the labor market. The modeling process uses an expert analytical knowledge base that provides a representative reflection of current transformation trends, in particular the dynamics of the formation of new forms of employment (gig economy, remote work) and the gradual reduction of traditional forms of labor participation. This approach allows for the effective study of insufficiently structured or poorly formalized processes characteristic of rapid digitalization.

Findings. A cognitive model of employment transformation has been developed, which visualizes the interrelationships between the main factors of digitalization and structural changes in the labor market. The key influences of technological progress on changes in labor demand, professional mobility, the dynamics of the emergence of new professions, and the transformation of labor relations have been identified. The model allows forming scenarios for the development of employment in the digital economy and adapt labor market policies to new challenges.

Originality. The feasibility of using cognitive modeling as a tool for studying employment transformation is substantiated, which provides a deeper understanding of structural changes in the labor market in the context of digitalization. A conceptual model has been developed that combines technological, social and economic aspects of post-industrial changes and allows predicting their impact on employment, identifying hidden relationships and creating a basis for making strategic management decisions.

Practical value. The results of the study can be used to improve employment management strategies in the context of the digital transformation of the economy. The proposed cognitive model of the digital transformation of employment allows us to identify key factors that determine the dynamics of changes in the structure of the labor market. Along with this, it can be used to assess the risks and potential of automation in various sectors of the economy, to model employment development scenarios depending on the level of digitalization. The practical application of the results of cognitive modeling will allow us to form more effective employment policy directions that can ensure flexibility, social sustainability and competitiveness of the national labor market in the long term.

Keywords: digitalization, post-industrial transformation, employment, employment structure, labor market, cognitive modeling

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