Tomsk, Russian Federation
UDC 004.9
The article examines the organizational and economic conditions under which digital information models can influence labor productivity in oil and gas construction. The relevance of the study is determined by the fact that digital information modeling is increasingly used in design and construction processes, while its labor productivity effects remain heterogeneous and insufficiently explained in economic and labor studies. The purpose of the article is to identify and systematize the conditions that transform digital information models from an engineering data tool into a factor of labor productivity growth. The research is based on a process approach, factor analysis, systems thinking, and the principles of labor economics, according to which productivity should be considered through the relationship between labor results and living labor inputs. As a result, several groups of conditions are identified: digital maturity of work processes, integration of information systems, quality of data regulation, digital competencies of employees, adaptation of incentive systems, rational organization of working time, and occupational safety. It is shown that digital information models do not automatically increase productivity. Their effect depends on whether they are embedded into real work procedures, coordination mechanisms, decision-making processes, and personnel development practices. The article concludes that the assessment of digital information models should include not only technical parameters of model use, but also organizational and economic conditions that determine the sustainability of labor productivity growth.
labor productivity, digital information models, oil and gas construction, digital transformation, organizational conditions, digital maturity, human capital, labor processes, incentive system, working time
1. Shcherbakov, A. I. (2023). Measurement of labor productivity. Social and Labor Research, 3, 80–86.
2. Fedchenko, A. A., Veshkurova, A. B. (2023). Drivers of labor productivity growth: variability, degree and methods of impact assessment. Social and Labor Research, 2, 109–118.
3. Konovalova, G. I. (2021). Methodology for managing labor productivity at an industrial enterprise. Organizer of Production, 29(1), 21–29.
4. Mezhevov, A., Arakelyan, A., Vorontsova, Y. (2023). The impact of digitalization on labor productivity. In Proceedings of the 2023 International Conference on Computer Science and Information Technologies (CSIT). Yerevan: IEEE, 306–309. [Electronic source] URL: https://www.researchgate.net/publication/373934608_The_Impact_of_Digitalization_on_Labor_Productivity (Date of access: 10.04.2026).
5. Zhang, X. (2023). How digital transformation of enterprises can improve labor productivity: Evidence from Chinese-listed companies. In Proceedings of the 2023 2nd International Conference on Enterprise Management and Economic Development (ICEMED 2023). Amsterdam: Atlantis Press, 50–61. [Electronic source] URL: https://www.researchgate.net/publication/373417210_How_Digital_Transformation_of_Enterprises_Can_Improve_Labor_Productivity_Evidence_from_Chinese-Listed_Companies (Date of access: 10.04.2026).
6. Karnauhov, A., Kozhubaev, Y., Ilin, A., Ivanov, V. (2023). Controlling of the digital transformation oil and gas industry. E3S Web of Conferences, 431, 05031. [Electronic source] URL: https://www.researchgate.net/publication/374706108_Controlling_of_the_digital_transformation_oil_and_gas_industry (Date of access: 11.04.2026).
7. Shinkevich, A. I., Nadezhdina, M. E. (2021). Methodology for assessing the efficiency of digitalization of production processes at a petrochemical enterprise. Herald of the Bauman Moscow State Technical University. Series Mechanical Engineering, 2(137), 72–84.
8. Wanasinghe, T. R., Trinh, T., Nguyen, T., et al. (2021). Human centric digital transformation and Operator 4.0 for the oil and gas industry. IEEE Access, 9, 113270–113291. [Electronic source] URL: https://www.researchgate.net/publication/353785424_Human_Centric_Digital_Transformation_and_Operator_40_for_the_Oil_and_Gas_Industry (Date of access: 14.04.2026).
9. Cammeraat, E., Samek, L., Squicciarini, M. (2021). The role of innovation and human capital for the productivity of industries. Paris: OECD Publishing, 58. [Electronic source] URL: https://www.researchgate.net/publication/350075658_THE_ROLE_OF_INNOVATION_AND_HUMAN_CAPITAL_FOR_THE_PRODUCTIVITY_OF_INDUSTRIES (Date of access: 14.04.2026).
10. Ayroyan, Z. A., Korkishko, A. N. (2016). Project management of the oil and gas complex based on information modeling technologies (BIM-tekhnologiy). Engineering Journal of Don, 4(43).
11. Nikitin, A. R., Sinenko, S. A. (2025). On the results of analysis of design documentation for construction developed using information modeling technologies. Proceedings of Universities. Investment. Construction. Real Estate, 15(2), 257–265.
12. Khugaev, A. Z., Leiman, E. N. (2024). Business process reengineering in design and survey organizations related to industry digitalization and BIM modeling implementation. Economics: Yesterday, Today and Tomorrow, 14(8-1), 132–142.
13. Rashev, V. S., Astafyeva, N. S., Rogozhkin, L. S., Grigoriev, V. Yu. (2020). Analysis of information modeling technology implementation in Russian construction companies for engineering systems design and construction. The Eurasian Scientific Journal, 12(3).
14. Leitão, J., Pereira, D., Gonçalves, Â. (2021). Quality of work life and contribution to productivity: Assessing the moderator effects of burnout syndrome. International Journal of Environmental Research and Public Health, 18(5), 2425. [Electronic source] URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC7967557/ (Date of access: 14.04.2026).
15. Golovikhin, S. A., Nevolin, V. S. (2021). Main directions for improving the socio-economic efficiency of occupational safety. Bulletin of Chelyabinsk State University, 12(458), 138–146.
16. Pshenichnaya, S. S. (2024). Formation of new competencies of oil and gas industry workers in the era of artificial intelligence technologies. Financial Markets and Banks, 12, 119–125.
17. Al-Hajri, A., Hamouda, A., Abdella, G. (2025). Sustainability-based strategic framework for digital transformation in the oil and gas industry. IEEE Access, 13, 52114–52133. [Electronic source] URL: https://www.researchgate.net/publication/389643255_Sustainability-based_Strategic_Framework_for_Digital_Transformation_in_the_Oil_and_Gas_Industry (Date of access: 18.04.2026).
