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Analysis of labor productivity in the context of technological transformations

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Parent Category: 2026

Category: Content №3 2026

Created on 26 June 2026

Last Updated on 26 June 2026

Published on 30 November -0001

Written by T. V. Martyn, V. S. Nitsenko, V. I. Kyrylenko, O. O. Tkachenko, Yu. V. Stavska, O. M. Kulhanik

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Authors:

T. V. Martyn*, orcid.org/0009-0003-0673-2456, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V. S. Nitsenko, orcid.org/0000-0002-2185-0341, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine; INTI International University, Nilai, Malaysia, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V. I. Kyrylenko, orcid.org/0000-0002-7433-0630, Vadym Hetman Kyiv National University of Economics, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O. O. Tkachenko, orcid.org/0009-0004-0569-6120, Vadym Hetman Kyiv National University of Economics, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Yu. V. Stavska, orcid.org/0000-0003-2799-1556, Vinnytsia National Agrarian University, Vinnytsia, Ukraine; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O. M. Kulhanik, orcid.org/0000-0003-2276-1161, Vinnytsia Trade and Economics Institute of the State Trade and Economics University, Vinnytsia, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* 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. 2026, (3): 225 - 234

https://doi.org/10.33271/nvngu/2026-3/225

Abstract:

Purpose. Conceptual explanation of the productivity paradox when using artificial intelligence (AI) technologies, as well as theoretical and empirical justification for the identification of a new macroeconomic phase determined by the introduction of intelligent systems.

Methodology. The study is based on a comprehensive approach that combines secondary data analysis (aggregated statistical series of the US Bureau of Labor Statistics) and methods of theoretical analysis and generalization. For the mathematical and statistical calculation of long-term average annual rates of productivity change (AAPC), the index method and the geometric mean growth rate formula were used. Comparative analysis was used to retrospectively compare macroeconomic cycles and identify deviations from the long-term trend. In addition, the analysis of corporate cases and empirical reports was used to assess the micro-level effects of AI implementation in organizations.

Findings. Eight distinct macroeconomic cycles of productivity change in the United States from 1947 to 2025 are identified. The advent of the “artificial intelligence phase” is justified. It is demonstrated that the gap between anticipated macroeconomic efficiency gains (expressed through the aggregate labor productivity index) and micro-level outcomes (specifically, the localized reduction in operational task execution time, the enhanced quality of generated solutions, and the accelerated skill acquisition by employees) is attributed to organizational and behavioral factors. The key micro-mechanisms of the labor productivity paradox have been identified: task expansion, the blurring of boundaries between work and non-work time, the intensification of multitasking, and the accumulation of “AI debt” by organizations.

Originality. The theoretical explanation of the labor productivity paradox under conditions of systemic artificial intelligence implementation has been further developed. The periodization of US macroeconomic cycles was refined by calculating AAPC indices and identifying emerging stages, specifically the “pandemic and adaptation phase” and the “artificial intelligence phase”. A conceptual model of the AI productivity paradox has been proposed to explain the underlying causes of efficiency loss. Furthermore, the understanding of micro-mechanisms governing AI’s impact on organizational efficiency has been systematized and deepened, with their specific role in decelerating the growth of aggregate macroeconomic indicators being formalized.

Practical value. A theoretical framework has been established for the development of targeted organizational and managerial measures aimed at overcoming micro-level productivity barriers and ensuring the harmonious integration of innovations into business processes. The study demonstrates the necessity of systemic alignment between the technological potential of artificial intelligence algorithms, organizational support, and the human factor to transform AI-driven benefits into sustainable economic growth.

Keywords: artificial intelligence, productivity paradox, labor productivity

References.

1. Brynjolfsson, E., Rock, D., & Syverson, C. (2017). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. Working Paper No. 24001. National Bureau of Economic Research. https://doi.org/10.3386/w24001

2. Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. https://doi.org/10.1126/science.adh2586

3. Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work. Working Paper No. 31161. National Bureau of Economic Research. https://doi.org/10.3386/w31161

4. Fukumura, Y. E., Gray, J. M., Lucas, G. M., Becerik-Gerber, B., & Roll, S. C. (2021). Worker perspectives on incorporating artificial intelligence into office workspaces: Implications for the future of office work. International Journal of Environmental Research and Public Health, 18(4), 1690. https://doi.org/10.3390/ijerph18041690

5. Acemoglu, D. (2024). The simple macroeconomics of AI. Working Paper No. 32487. National Bureau of Economic Research. https://doi.org/10.3386/w32487

6. Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3-30. https://doi.org/10.1257/jep.33.2.3

7. Agrawal, A., Gans, J. S., & Goldfarb, A. (2019). Artificial intelligence: The ambiguous labor market impact of automating prediction. Journal of Economic Perspectives, 33(2), 31-50. https://doi.org/10.1257/jep.33.2.31

8. Babina, T., Fedyk, A., He, A. X., & Hodson, J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151, 103745. https://doi.org/10.1016/j.jfineco.2023.103745

9. Trivedi, J., Devi, M.S., Vithalani, C., Parmar, K., & Dave, D. (2025). Industrial intelligence for smart cities: the role of AI and IoT in transforming urban mobility and infrastructure. Scientific Journal of Silesian University of Technology. Series Transport, 129, 261-281. https://doi.org/10.20858/sjsutst.2025.129.15

10.      Felten, E. W., Raj, M., & Seamans, R. (2021). Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses. Strategic Management Journal, 42(12), 2195-2217. https://doi.org/10.1002/smj.3286

11.      Ingram, K., Diachenko, O., Halytskyi, O., Nitsenko, V., Romaniuk, M., & Zhumbei, M. (2022). Formalization of the Optimal Choice of the Activities of Agricultural Enterprises for the Implementation of Information and Communication Technologies. Financial and Credit Activity: Problems of Theory and Practice, 3(44), 141-149. https://doi.org/10.55643/fcaptp.3.44.2022.3758

12.      Cruces, G., Fernández Meijide, D., Galiani, S., Gálvez, R. H., & Lombardi, M. (2026). Does generative AI narrow education-based productivity gaps? Evidence from a randomized experiment. Working Paper No. 34851. National Bureau of Economic Research. https://doi.org/10.3386/w34851

13.      Dell’Acqua, F., McFowland, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., …, & Lakhani, K. R. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Technology & Operations Mgt. Unit ­Working Paper No. 24-013. https://doi.org/10.2139/ssrn.4573321

14.      Eisfeldt, A. L., Schubert, G., & Zhang, M. B. (2023). Generative AI and firm values. Working Paper No. 31222. National Bureau of Economic Research. https://doi.org/10.3386/w31222

15.      Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv preprint. https://doi.org/10.48550/arXiv.2303.10130

16.      Webb, M. (2020). The impact of artificial intelligence on the labor market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3482150

17.      Bessen, J., Impink, S. M., Reichensperger, L., & Seamans, R. (2022). The role of data for AI startup growth. Research Policy, 51(5), 104513. https://doi.org/10.1016/j.respol.2022.104513

18.      Gal, U., Jensen, T. B., & Stein, M. K. (2020). Breaking the vicious cycle of algorithmic management: A virtue ethics approach to people analytics. Information and Organization, 30(2), 100301. https://doi.org/10.1016/j.infoandorg.2020.100301

19.      Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410. https://doi.org/10.5465/annals.2018.0174

20.      Schildt, H. (2017). Big data and organizational design–the brave new world of algorithmic management and computer augmented transparency. Innovation, 19(1), 23-30. https://doi.org/10.1080/14479338.2016.1252043

21.      Nitsenko, V., Martyn, T., Hutorov, A., Gutorov, O., Zhemerdieiev, A., & Chekanov, O. (2026). Transformation of business models: Methodology for transition to the “AI-first» paradigm. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (1), 157-164. https://doi.org/10.33271/nvngu/2026-1/157

22.      Akkaya, B., Apostu, S. A., Hysa, E., & Panait, M. (2023). Technostress and burnout of female employees: A review of the healthcare field in perspective of digital transition. Digitalization, Sustainable Development, and Industry 5.0, (pp. 317-335). Emerald Publishing Limited. https://doi.org/10.1108/9781837531905

23.      Marwan, M., & Soleman, M.M. (2025). Analysis of role antecedent variables on innovative work behavior and the impact on business performance of micro and small enterprises. Intellectual Economics, 19(1), 210-233. https://doi.org/10.13165/IE-25-19-1-09

24.      Bregenzer, A., & Jimenez, P. (2021). Risk factors and preventive factors for technostress and burnout in employees. International Journal of Environmental Research and Public Health, 18(11), 5873. https://doi.org/10.3390/ijerph18115873

25.      Figueroa, A., & Garcia, A. (2025). The dual impact of AI on burnout and technostress in manufacturing workplaces. The Proceedings of the 20 ^th European Conference on Innovation and Entrepreneurship, 20(1), 892-897. https://doi.org/10.34190/ecie.20.1.3863

26.      Salo, M., Pirkkalainen, H., & Koskelainen, T. (2019). Technostress and social networking services: Explaining users’ concentration mitigation and network avoidance. Information & Management, 56(4), 514-527. https://doi.org/10.1016/j.im.2018.09.001

27.      Tarafdar, M., Pirkkalainen, H., Salo, M., & Makkonen, M. (2020). Taking on the “dark side” ‒ Coping with technostress. IT Professional, 22(6), 82-89. https://doi.org/10.1109/MITP.2020.2977343

28.      Sfounis, D., Kolovos, D., Kostas, A., Tsoukalidis, I., & Karasavvoglou, A. (2024). Use of an AI-based digital prediction model for the evaluation of urban infrastructure in terms of accessibility and efficient urban movement for people with disabilities. Intellectual Economics, 18(2), 237-260. https://doi.org/10.13165/IE-24-18-2-01

29.      Perevozova, I., Gubernat, T., Hontar, K., Shayban, V., & Bocharova, N. (2024). Using Big Data Analytics to Improve Logistics Processes and Forecast Demand. Pacific Business Review (International), 17(4), 30-39. https://doi.org/10.5281/zenodo.14756955

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