# 80 398-403 View full article

УДК 005.52:339.138

DOI: https://doi.org/10.36887/2415-8453-2025-4-80

Lagodiienko Nataliia,
Doctor of Science in Economics, Professor, Professor of the Department of Finance, Accounting and Taxation,
International University of Business and Law,
https://orcid.org/0000-0002-8472-1395
Nemchenko Valerii,
Doctor of Sciences in Economics, Professor, Professor of the Department of Finance, Accounting and Auditing,
Odesa National University of Technology,
https://orcid.org/0000-0002-2984-3357
Melnikov Artem,
Postgraduate student of the Department of Journalism and Advertising,
State University of Trade and Economics,
https://orcid.org/0009-0008-1280-4029
Nemchyninov Yevgen,
Postgraduate student of the Department of Trade Entrepreneurship, Commodity Studies and Business Management,
Odesa National University of Technology,
https://orcid.org/0009-0004-8261-5435
Moroz Oleksandr,
Postgraduate Student of the Department of Marketing, Entrepreneurship, and Trade,
Odesa National University of Technology,
https://orcid.org/0009-0002-0858-6555

JEL classification: D 81, M 15, C 55, O 32

Published: 19.12.2025

The article examines the functioning of managerial decision-making mechanisms on digital marketing platforms in the context of the digital transformation of the economy. The relevance of the research topic is determined by the growing role of data, big data analytics, artificial intelligence, and digital platforms in enterprise management processes, which require the development of theoretical foundations for managerial decision-making and the identification of modern tools to support them. The study aims to analyze, generalize, and develop the theoretical foundations of managerial decision-making in the context of economic digitalization, and to substantiate the role of digital tools in the formation of modern mechanisms to support managerial decisions. The article examines the evolution of managerial decision-making theory from classical rational models to behavioral, systemic, and digital approaches. The main decision-making concepts are identified, including bounded rationality, behavioral models, normative mathematical approaches, and modern data-driven management. It is substantiated that modern managerial decision-making mechanisms integrate digital marketing platforms, big data analytics, decision support systems, and artificial intelligence algorithms. The study analyzes practical cases of digital tool use by leading companies, demonstrating how big data analytics informs strategic and marketing decisions, forecasts demand, personalizes customer interactions, and optimizes management processes. It is determined that digital marketing platforms enable the transition from retrospective analysis to predictive and prescriptive management, thereby increasing the validity of managerial decisions and reducing uncertainty. The study of the practical use of BI systems, CRM platforms, Big Data tools, predictive analytics systems, and marketing automation platforms identified their significant role in shaping modern managerial decision-making mechanisms in enterprises. The study also examines potential risks associated with the use of digital technologies in managerial decision-making processes. The following risks were identified: algorithmic bias, opacity of artificial intelligence models, excessive automation of management, discriminatory pricing, and inaccurate forecasts based on big data. To minimize these threats, it is necessary to establish a balanced decision-making mechanism that integrates the analytical capabilities of digital platforms with expert managerial experience.

Keywords: managerial decision-making process, digital marketing platforms, marketing tools, marketing, decision support systems, digital transformation.

Rеferences

  1. Korzachenko, O.V. (2020). «Evolution of decision-making models: from classical political economy to behavioral economics». Modeliuvannia ta informatsijni systemy v ekonomitsi. Issue 100. рр. 103–112.
  2. Parra,, Tort-Martorell, X., Alvarez-Gomez, F. (2023). «Chronological Evolution of the Information-Driven Decision-Making Process (1950–2020)». J Knowl Econ. Vol. 14. рр. 2363–2394. DOI:https://doi.org/10.1007/s13132-022-00917-y.
  3. Simon,A. (1947). Administrative behavior; a study of decision-making processes in administrative organization. Macmillan.
  4. Lindblom,E. (1959). «The Science of “Muddling Through”». Public Administration Review. Vol. 19(2). рр. 79–88. DOI:https://doi.org/10.2307/973677.
  5. Cyert,M., March, J.G. (1963). A Behavioral Theory of the Firm. Englewood Cliffs. Prentice-Hall. NJ. USA.
  6. March,G. (1994). A Primer on Decision Making: How Decisions Happen. Free Press. New York. USA.
  7. Cohen,D., March, J.G., Olsen, J.P. (1972). «A Garbage Can Model of Organizational Choice». Administrative Science Quarterly. Vol. 17. рр. 1-25. DOI: http://dx.doi.org/10.2307/2392088.
  8. Mintzberg,, Raisinghani, D., Theoret, A. (1976). «The Structure of “Unstructured” Decision Processes». Administrative Science Quarterly. Vol. 21(2). рр. 246–275. DOI: https://doi.org/10.2307/2392045.
  9. Mintzberg, (1994). The Rise and Fall of Strategic Planning. Free Press.
  10. Saaty,L. (1980). The Analytic Hierarchy Process. McGraw-Hill. New York. USA.
  11. Kahneman,, Tversky, A. (1979). «Prospect Theory: An Analysis of Decision under Risk». Econometrica. Vol. 47(2). рр.263–291. DOI: https://doi.org/10.2307/1914185.
  12. Tversky,, Kahneman, D. (1992). «Advances in prospect theory: Cumulative representation of uncertainty». J Risk Uncertainty. Vol. 5. рр. 297–323. DOI: https://doi.org/10.1007/BF00122574.
  13. Thaler,H., Sunstein, C.R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press.
  14. Brynjolfsson,, McElheran, K. (2016). «The Rapid Adoption of Data-Driven Decision Making». American Economic Review. Vol. 106(5). рр. 133–139. DOI: https://doi.org/10.1257/aer.p20161016.
  15. Turban,, Sharda R., Delen, D. (2021). Business Intelligence, Analytics, and Data Science: A Managerial Perspective. Pearson.
  16. (2016). Making Smarter Business Decisions with Big Data: A Netflix Case Study. EDC-Data Science. 2016. Available at: https://oceansofdata.org/making-smarter-business-decisions-big-data-netflix-case-study.
  17. van Es, K (2023). «Netflix & Big Data: The Strategic Ambivalence of an Entertainment Company». Television & New Media. 24(6). рр. 656-672. DOI: https://doi.org/10.1177/15274764221125.
  18. Bitter, (2024). How Amazon is using AI to make your shopping better. Business Insider. Available at: https://www.businessinsider.com/how-amazon-is-using-ai-from-rufus-to-movie-recommendations-2024-7.
  19. Haag,, Hopf, K., Menelau, V.P., Staake T. (2022). «Augmented Cross-Selling Through Explainable AI – A Case From Energy Retailing». ECIS 2022 Research Papers. Vol. 129. DOI: https://doi.org/10.48550/arXiv.2208.11404.
  20. Yang,, Ongpin, M., Nikolenko, S., Huang, A., Farseev, A. (2023). «Against Opacity: Explainable AI and Large Language Models for Effective Digital Advertising. The 31st ACM International Conference on Multimedia. DOI:https://doi.org/10.1145/3581783.3612817.
  21. Belenguer, (2022). «AI bias: exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical industry». AI Ethics. Vol. 2(4). рр. 771-787. DOI:https://doi.org/10.1007/s43681-022-00138-8.
  22. Dolata,, Schwabe, G. (2024). «Towards the Socio-Algorithmic Construction of Fairness: The Case of Automatic Price-Surging in Ride-Hailing». International Journal of Human–Computer Interaction. Vol. 40(1). рр. 55–65. DOI:https://doi.org/10.1080/10447318.2023.2210887.
  23. Herzlich,, Fickenscher, L., Barrabi, T. (2025). Dynamic pricing’ used to raise costs of everything from food and Uber rides to museum visits. NYP Holdings. Available at: http://nypost.com/2025/12/11/business/dynamic-pricing-used-to-raise-costs-of-everything-from-food-and-uber-rides-to-museum-visits/.
  24. (2025). Why you shouldn’t count on humans to prevent AI hiring bias. The Washington Post. Available at: https://www.washingtonpost.com/business/2025/11/25/biased-ai-hiring-research-university-of-washington-study/.

Quote article, APA style

Lagodiienko N. , Nemchenko V. , Melnikov A. , Nemchyninov Y. , Moroz O. Mechanisms of managerial decision-making using digital marketing platforms. Ukrainian Journal of Applied Economics and Technology. 2025. №4. 398-403 pp. https://doi.org/10.36887/2415-8453-2025-4-80

Quote article, MLA style

Lagodiienko N. , Nemchenko V. , Melnikov A. , Nemchyninov Y. , Moroz O. Mechanisms of managerial decision-making using digital marketing platforms. Ukrainian Journal of Applied Economics and Technology. https://doi.org/10.36887/2415-8453-2025-4-80