ANALYSIS OF THE SCIENTIFIC AND METHODOLOGICAL APPROACHES FOR STUDYING THE RELATIONSHIPS BETWEEN DIGITALIZATION, SOCIO-ECONOMIC SPHERE AND SECURITY

Keywords: digitalization, socio-economic sphere, cybersecurity, scientific and methodological approaches, econometric models, artificial intelligence, machine learning, Big Data

Abstract

The invasion of digital technologies into the socio-economic sphere has tangible advantages and significant disadvantages, including security issues related to personal data protection. The corresponding changes also affect research methods applied in this area, including the ability to analyze and predict complicated relationships affected by wide usage of digital tools and technologies by state-owned and private companies, educational institutions, and so on. Traditional econometric approaches have proven to be effective for years, but nowadays they are beginning to give way to the more actual methods that rely on artificial intelligence and machine learning usage, big data analysis, etc. due to their inability to respond dynamically to the economic and social sphere changes, as well as cover the rapid technological breakthroughs caused by the pace of digitalization. This topic is relevant today and is being considered by many domestic and foreign researchers. Scientists are trying to update traditional econometric methods to make them more effective considering the digital age for calculating forecasts and to create models based on them according to research areas. This article aims to analyze the pros and cons of the scientific and methodological approaches that can be used to study the relationship between digitalization and the areas it directly affects: the economy, social sphere, and security. The analysis was carried out by collecting data on the capabilities of traditional and modern econometric models, giving examples of specific models, highlighting their weaknesses and strengths, and outlining the possibilities of updating classical models to use them in the current realities of the digital economy. The results show that it's a misconception to state that some models are superior to others. Each model has its strengths and weaknesses, and it is necessary to find a middle ground between the advantages of classical and modern models for effective work with them. The results of this study can be used by researchers who aim to study the interconnections between digitalization, the socio-economic sphere, and security.

References

Zapata H. O., Mukhopadhyay S. A bibliometric analysis of machine learning econometrics in asset pricing. Journal of risk and financial management. 2022. Vol. 15. No. 11. P. 535. DOI: https://doi.org/10.3390/jrfm15110535 (дата звернення: 15.03.2025).

Wang J., Xu T. Environmental impact of Information Communication Technology: a review of econometric assessment methods, influential mechanism, and influential direction. Environmental impact assessment review. 2021. Vol. 89. P. 106590. DOI: https://doi.org/10.1016/j.eiar.2021.106590 (дата звернення:15.03.2025).

Wang L., Zhao L. Digital economy meets artificial intelligence: forecasting economic conditions based on big data analytics. Mobile information systems. 2022. Vol. 2022. P. 1–9. DOI: https://doi.org/10.1155/2022/7014874 (дата звернення:15.03.2025).

Bello A. The impact of Big Data on economic forecasting and policy making. International journal of development and economic sustainability. 2022. Vol. 10, no. 6. P. 66–89. URL: https://eajournals.org/ijdes/vol10-issue-6-2022/the-impact-of-big-data-on-economic-forecasting-and-policy-making/ (дата звернення:15.03.2025).

Dong C., Gao J., Peng B. Varying-Coefficient panel data models with nonstationarity and partially observed factor structure. Journal of business & economic statistics. 2020. P. 1–12. DOI: https://doi.org/10.1080/07350015.2020.1721294 (дата звернення:15.03.2025).

Bertschinger N., Mozzhorin I. Bayesian estimation and likelihood-based comparison of agent-based volatility models. Journal of economic interaction and coordination. 2020. Vol. 16. P. 173-210. DOI: https://doi.org/10.1007/s11403-020-00289-z (дата звернення:15.03.2025).

Yao J. A fusion method integrated econometrics and deep learning to improve the interpretability of prediction: evidence from Chinese carbon emissions forecast based on OLS-CNN model. Computational economics. 2024. DOI: https://doi.org/10.1007/s10614-024-10793-0 (дата звернення:15.03.2025).

Tkachenko S. Prospects for the development of the digital economy in the global space. Economies' horizons. 2023. No. 2(24). P. 101–109. DOI: https://doi.org/10.31499/2616-5236.2(24).2023.281234 (дата звернення:15.03.2025).

Співак Д. Економіко-математичне моделювання впливу диджиталізації на фінансовий сектор економіки : Бакалаврська робота : 051. Суми, 2021. 35 с. URL: https://essuir.sumdu.edu.ua/handle/123456789/85086 (дата звернення: 15.03.2025).

Бегун С., Хомюк Н., Подзізей О. Економетричні методи та моделі в прийнятті управлінських рішень в умовах цифрової трансформації. Економіка та суспільство. 2024. № 66. DOI: https://doi.org/10.32782/2524-0072/2024-66-16 (дата звернення: 15.03.2025).

Bondarchuk O. Development of the concept “digitalization” and its impact on different spheres of the economy. Scientific bulletin of Odessa national economic university. 2024. Vol. 7-8, no. 320-321. P. 31–40. DOI: https://doi.org/10.32680/2409-9260-2024-7-8-320-321-31-40 (дата звернення: 15.03.2025).

Khaustova V., Kriachko Y., Bondarenko D. Modeling the impact of digitalization factors on the economic development of countries around the world. The problems of economy. 2024. Vol. 2, no. 60. P. 61–73. DOI: https://doi.org/10.32983/2222-0712-2024-2-61-73 (дата звернення: 15.03.2025).

Global digitalization in 10 charts. World Bank. URL: https://www.worldbank.org/en/news/immersive-story/2024/03/05/global-digitalization-in-10-charts (дата звернення: 16.03.2025).

Benefits of AI in data analytics. Devtodev. URL: https://www.devtodev.com/resources/articles/benefits-of-ai-in-data-analytics (дата звернення: 22.03.2025).

Test scores of AI systems on various capabilities relative to human performance. Our world in data. URL: https://ourworldindata.org/grapher/test-scores-ai-capabilities-relative-human-performance?tab=table&time=2006 (дата звернення: 22.03.2025).

Artificial intelligence in economic forecasting and analysis. Maseconomics. URL: https://maseconomics.com/artificial-intelligence-in-economic-forecasting-and-analysis/#elementor-toc__heading-anchor-8 (дата звернення: 22.03.2025).

Zapata, H. O., Mukhopadhyay S. (2022). A bibliometric analysis of machine learning econometrics in asset pricing. Journal of Risk and Financial Management, vol. 15(11), p. 535. DOI: https://doi.org/10.3390/jrfm15110535.

Wang, J., Xu T. (2021). Environmental impact of Information Communication Technology: a review of econometric assessment methods, influential mechanism, and influential direction. Environmental impact assessment review, vol. 89, p. 106590. DOI: https://doi.org/10.1016/j.eiar.2021.106590.

Wang L., Zhao L. (2022). Digital economy meets artificial intelligence: forecasting economic conditions based on big data analytics. Mobile information systems, vol. 2022, pp. 1–9. DOI: https://doi.org/10.1155/2022/7014874.

Bello A. (2022). The impact of Big Data on economic forecasting and policy making. International journal of development and economic sustainability, vol. 10(6), pp. 66–89. Available at: https://eajournals.org/ijdes/vol10-issue-6-2022/the-impact-of-big-data-on-economic-forecasting-and-policy-making/ (accessed March 15, 2025).

Dong C., Gao J., Peng B. (2020). Varying-Coefficient panel data models with nonstationarity and partially observed factor structure. Journal of business & economic statistics, pp. 1–12. DOI: https://doi.org/10.1080/07350015.2020.1721294.

Bertschinger N., Mozzhorin I. (2020). Bayesian estimation and likelihood-based comparison of agent-based volatility models. Journal of economic interaction and coordination, vol. 16, pp. 173-210. DOI: https://doi.org/10.1007/s11403-020-00289-z.

Yao J. (2024). A fusion method integrated econometrics and deep learning to improve the interpretability of prediction: evidence from Chinese carbon emissions forecast based on OLS-CNN model. Computational economics. DOI: https://doi.org/10.1007/s10614-024-10793-0.

Tkachenko S. (2023). Prospects for the development of the digital economy in the global space. Economies' horizons, no. 2(24), pp. 101–109. DOI: https://doi.org/10.31499/2616-5236.2(24).2023.281234.

Spivak D. (2021). Ekonomiko-matematychne modeliuvannia vplyvu dydzhytalizatsii na finansovyi sektor ekonomiky [Economic and mathematical modeling of the impact of digitalization on the financial sector of the economy] (bachelor’s thesis), Sumy. Available at: https://essuir.sumdu.edu.ua/handle/123456789/85086 (accessed March 15, 2025).

Behun S., Khomiuk N., Podzizei O. (2024). Ekonometrychni metody ta modeli v pryiniatti upravlinskykh rishen v umovakh tsyfrovoi transformatsii [Econometric methods and models in management decision-making in the context of digital transformation]. Ekonomika ta suspilstvo – Economy and society, no. 66. DOI: https://doi.org/10.32782/2524-0072/2024-66-16.

Bondarchuk O. (2024). Development of the concept “digitalization” and its impact on different spheres of the economy. Scientific bulletin of Odessa national economic university, vol. 7-8, no. 320-321, pp. 31–40. DOI: https://doi.org/10.32680/2409-9260-2024-7-8-320-321-31-40.

Khaustova V., Kriachko Y., Bondarenko D. (2024) Modeling the impact of digitalization factors on the economic development of countries around the world. The problems of economy, vol. 2, no. 60, pp. 61–73. DOI: https://doi.org/10.32983/2222-0712-2024-2-61-73.

Global digitalization in 10 charts. World Bank. Available at: https://www.worldbank.org/en/news/immersive-story/2024/03/05/global-digitalization-in-10-charts (accessed March 16, 2025).

Benefits of AI in data analytics. Devtodev. Available at: https://www.devtodev.com/resources/articles/benefits-of-ai-in-data-analytics (accessed March 22, 2025).

Test scores of AI systems on various capabilities relative to human performance. Our world in data. Available at: https://ourworldindata.org/grapher/test-scores-ai-capabilities-relative-human-performance?tab=table&time=2006 (accessed March 23, 2025).

Artificial intelligence in economic forecasting and analysis. Maseconomics. Available at: https://maseconomics.com/artificial-intelligence-in-economic-forecasting-and-analysis/#elementor-toc__heading-anchor-8 (accessed March 23, 2025).

Article views: 21
PDF Downloads: 13
Published
2025-01-27
How to Cite
Ostrovskyi, O. (2025). ANALYSIS OF THE SCIENTIFIC AND METHODOLOGICAL APPROACHES FOR STUDYING THE RELATIONSHIPS BETWEEN DIGITALIZATION, SOCIO-ECONOMIC SPHERE AND SECURITY. Digital Есопоmу and Economic Security, (1 (16), 376-382. https://doi.org/10.32782/dees.16-57