DOI: 10.33917/es-6.204.2025.50-55

The article examines the role of digital factors in enhancing labor productivity as one of the key elements of technological and economic development. Using the example of the Kyrgyz Republic, it demonstrates that the implementation of digital technologies and the development of digital infrastructure contribute to improving efficiency; however, without the simultaneous modernization of fixed assets and the training of qualified personnel, the effect of digitalization remains limited. The article also proposes the creation of an integrated model of digital factors influencing labor productivity at the macro, meso, and micro levels, which would enable a more comprehensive assessment and management of digital transformation processes.

References:

1. Telyatnikova T.V., Kipervar E.A., Trunkina L.V. Vyyavlenie faktorov, vliyayushchikh na uroven’ proizvoditel’nosti truda, kak osnova formirovaniya ustoychivogo razvitiya predpriyatiya [Identifying Factors Influencing Labor Productivity as a Basis for the Formation of Sustainable Enterprise Development]. Ekonomika truda, 2024, vol. 11, no 3, pp. 361–374, DOI 10.18334/et.11.3.120672

2. Mkrtychan Z.V. Klassifikatsiya faktorov, okazyvayushchikh vliyanie na proizvoditel’nost’ truda [Classification of Factors Influencing Labor Productivity]. Sibirskaya finansovaya shkola, 2020, no 3(139), pp. 23–30.

3. Kontseptsiya tsifrovoy transformatsii Kyrgyzskoy Respubliki na 2024–2028 gody [Concept of Digital Transformation of the Kyrgyz Republic for 2024– 2028]. Ministerstvo yustitsii KR, available at: https://cbd.minjust.gov.kg/30-164/edition/6414/ru?ysclid=mgi8iwipwl116850581

4. Tsifrovoy kodeks Kyrgyzskoy Respubliki ot 31 iyulya 2025 g. N 178 [Digital Code of the Kyrgyz Republic of July 31, 2025, No. 178]. Ministerstvo yustitsii KR, available at: https://cbd.minjust.gov.kg/3-48/edition/35412/ru

5. Rasporyazhenie Kabineta ministrov KR ot 23 iyulya 2024 g. N 4 [Order of the Cabinet of Ministers of the Kyrgyz Republic dated July 23, 2024 No. 4]. Ministerstvo yustitsii KR, available at: https://cbd.minjust.gov.kg/57-19271/edition/14721/ru?ysclid=mgjn8uo4yg604258729

6. Indeks razvitiya elektronnogo pravitel’stva (srednevzveshennyy): Kirgiziya: Po dannym OON [E-Government Development Index (weighted average): Kyrgyzstan: According to UN data]. Statbase, available at: https://statbase.ru/data/kgz-e-government-development-index/

7. Tsifrovizatsiya v Kyrgyzstane: byudzhet v 600 mln somov na 2024 god [Digitalization in Kyrgyzstan: A 600 Million Som Budget for 2024]. News KG, available at: https://news.kg/2024/04/03/cifrovizacija-v-kyrgyzstane-bjudzhet-v-600/

8. Kyrgyzstan v tsifrakh — 2025: Natsional’nyy statisticheskiy komitet [Kyrgyzstan in Figures — 2025: National Statistical Committee], available at: https:// stat.gov.kg/media/publicationarchive/4943edd9-da6d-4075-88f9-8b667e64c8cb.pdf

9. Brovko N.A., Borisenko N.A. Ekonomicheskiy rost v Kyrgyzskoy Respublike cherez prizmu proizvoditel’nosti truda [Economic Growth in the Kyrgyz Republic through the Prism of Labor Productivity]. Ufimskiy gumanitarnyy nauchnyy forum, 2023, no 4(16), pp. 40–55.

DOI: 10.33917/es-6.204.2025.42-49

The objective criterion of management are not ratings and assessments, but results. Since “any modeling is carried out by humans and for humans” [1], we are talking about increasing the quality of people’s life in the relevant public legal entity (hereinafter PLE), including their safety, and about profits of the relevant corporations. For this, an information model of power is important, and it is important for managers to move from thinking in words to thinking in equations [1]. “Less pompous phrases, more simple everyday work and concern for a pood of bread and a pood of coal” [2].

References:

1. Glushkov V.M. Kibernetika. Voprosy truda i praktika [Cybernetics: Issues of Work and Practice]. Moscow, Nauka, 1986. 488 s.

2. Lenin V.I. Polnoe sobranie sochineniy [Complete works]. Vol. 39. Moscow, Izd-vo politicheskoy literatury, 1972, p. 23.

3. Shumpeter Y. Teoriya ekonomicheskogo razvitiya: Issledovanie predprinimatel’skoy pribyli, kapitala, kredita, protsenta i tsikla kon”yunktury [Theory of Economic Development: An Investigation of Entrepreneurial Profit, Capital, Credit, Interest, and the Business Cycle]. Moscow, URSS, 2024, 400 p.

4. Lange O. Vvedenie v ekonomicheskuyu kibernetiku [Introduction to Economic Cybernetics]. Moscow, Progress, 1968, 207 p.

5. Vanchurin V., Wolf Yu.I., Katsnelson M.I., Kooni E.V. Toward a theory of evolution as multilevel learning. PNAS, February 4, 2022, DOI: https://doi.org/10.1073/pnas.2120037119

6. Bychkova N.Yu., Kiselev V.G. Ustoychivoe razvitie upravlyaemoy sistemy kak rezul’tat evolyutsii upravlencheskogo kapitala [Sustainable Development of a Managed System as a Result of the Evolution of Management Capital]. Innovatsii i investitsii, 2025, no 7, pp. 180–184, DOI: 10.24412/2307-180X-2025-7-180-184

7. Glaz’ev S.Yu. Za gorizontom kontsa istorii [Beyond the Horizon of the End of History]. Moscow, Prospekt, 2021, 416 p.

8. Gusakov V.G. Doklad na Mezhdunarodnoy nauchno-prakticheskoy konferentsii “Strategiya razvitiya ekonomiki Belarusi: vyzovy, instrumenty realizatsii i perspektivy”, 16 noyabrya 2023 g. [Presentation at the International Scientific and Practical Conference «Strategy for the Development of the Belarusian Economy: Challenges, Implementation Tools, and Prospects,» November 16, 2023], available at: https://youtu.be/tDQ5x6v1t-0

9. Bychkova N.Yu. Soznanie kak ekonomicheskaya kategoriya [Consciousness as an Economic Category]. Innovatsii i investitsii, 2022, no 12, pp. 22–29, DOI: 10.24412/2307-180X-2022-12-22-29

10. Zhukov N.I. Filosofskie osnovy kibernetiki [Philosophical Foundations of Cybernetics]. Minsk, Izd-vo BGU, 1976, 224 p.

11. Druker P.F. Effektivnyy rukovoditel’ [Effective Leader]. Moscow, Mann, Ivanov i Ferber (MIF), 2012, 240 p.

12. Bychkova N.Yu. Samodostatochnost’ cheloveka kak osnova samodostatochnosti sotsial’no-ekonomicheskikh sistem i mnogopolyarnogo mira [Human Self-Sufficiency as the Basis for Self-Sufficiency of Socio-Economic Systems and a Multipolar World]. Ekonomicheskie strategii, 2024, no 1(193), pp. 93–99, DOI: 10.33917/es-1.193.2024.93-99

DOI: 10.33917/es-6.204.2025.34-41

The article focuses on three key issues that play a special role in studying and regulating the development of the “big data” digital technology: 1) the content and characteristics of big data as a digital technology both as large volumes of information, which predetermine methodological approaches to studying the trajectories of their development, as well as their legal basis, 2) global trends and challenges of transitioning to a data-driven economy; 3) analysis and assessment of Russian trends in the global landscape.

References:

1. Big Data. Definition. Gartner, available at https://www.gartner.com/en/information-technology/glossary/big-data

2. Gaponenko N.V. Tsifrovye tekhnologii za granitsami khaypa: global’nyy landshaft [Digital Technologies Beyond Hype: The Global Landscape]. Ekonomicheskie strategii, 2022, no 6, pp. 104–110, DOI: https://doi.org/10.33917/es-6.186.2022.104-110

3. Going Digital: Shaping Policies, Improving Lives. Paris: OECD Publishing, 2019.

4. Navin Kumar. Statistika bol’shikh dannykh za 2025 god (dannye o roste i rynke) [Big Data Statistics 2025 (Growth and Market Data)]. DemandSage, 2025, 24 iyunya, available at: https://www.demandsage.com/big-data-statistics/

5. Data and AI Leadership Executive Survey. Wavestone, 2024.

6. Data, BI and Analytics Trend. BARC, 2025.

7. Bol’shie dannye: vygodnoe prilozhenie ili dorogostoyashchiy eksperiment? [Big Data: Profitable Application or Expensive Experiment?]. K2 Cloud i Arenadata, 2025.

8. Gotovnost’ rossiyskogo biznesa k ekonomike dannykh: Monitoring tsifrovoy transformatsii biznesa [Readiness of Russian Business for the Data Economy: Monitoring Digital Transformation of Business]. Vyp. 2. Moscow, NIU VShE, 2023.

DOI: 10.33917/es-6.204.2025.26-33

Conceptual approaches to modeling trends in information technology are discussed. It is shown that modeling IT trends solely based on technology interest data, such as Gartner’s Hype Cycle, is insufficient to predict the emergence of new technologies. Various IT examples demonstrate the need to analyze the connections between technologies, as success in some areas of economic digitalization creates the conditions for technological development in others. Furthermore, due to the high knowledge intensity of information technology, the depth of fundamental and applied scientific research must be taken into account. When modeling trends, the demand for technologies in specific economic sectors must also be considered. Simulations based on empirical data must identify simple and explainable relationships, and high-quality predictions must be achieved by synthesizing all the relationships of the technology under study. The proposed conceptual approach can also be applied to modeling other socioeconomic processes based on the synthesis of empirical data.

References:

1. Almalawi A., Soh B., Li A., Samra H. Predictive Models for Educational Purposes. A Systematic Review. Big Data Cogn. Comput., 2024, no 8, pp. 187, DOI: https://doi.org/10.3390/ bdcc8120187

2. Hassan M., Awan F.M., Naz A., Andrés-Galiana, de E.J., Alvarez O., Cernea A.; Fernández-Brillet L., Fernández-Martínez J.L., Kloczkowski A. Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care. A Review. Int. J. Mol. Sci., 2022, no 23, 4645, DOI: https://doi.org/10.3390/ ijms23094645

3. Ribeiro F.L., Rybski D. Mathematical models to explain the origin of urban scaling laws. Physics Reports, 2023, vol. 1012, 23 April, pp. 1–39.

4. Mokhov V., Aliukov S., Alabugin A., Osintsev K. A Review of Mathematical Models of Macroeconomics, Microeconomics, and Government Regulation of the Economy. Mathematics, 2023, no 11, 3246, DOI: https://doi.org/10.3390/ math11143246

5. Helbing D. Pluralistic Modeling of Complex System. Science and Culture, 2010, vol. 76, no 9/10, pp. 399–417.

6. Zhu Z., Xie H., Chen L. ICT industry innovation: Knowledge structure and research agenda. Technological Forecasting and Social Change, 2023, vol. 189, 122361.

7. Kleyner G.B. Dokazatel’noe modelirovanie kak perspektivnyy instrument nauchnogo issledovaniya sotsial’no-ekonomicheskikh protsessov [Evidence-based Modeling as a Promising Tool for Scientific Research of Socio-economic Processes]. Ekonomika i upravlenie: problemy, resheniya, 2023, no 6, vol. 2, pp. 5–16, DOI: https://doi.org/10.36871/ek.up.p.r.2023.06.02.001

8. O’Leary D.E. Gartner’s hype cycle and information system research issues. International Journal of Accounting Information Systems, 2008, no 9, pp. 240–252.

9. Chen X., Han T. Disruptive Technology Forecasting based on Gartner Hype Cycle. 2019 IEEE Technology & Engineering Management Conference (TEMSCON), Atlanta, GA, USA, 2019, pp. 1–6, DOI: 10.1109/TEMSCON.2019.8813649.

DOI: 10.33917/es-6.204.2025.16-25

In the present work the authors practically present a requiem for cybernetics — a classical management science that has fulfilled its historical role, but is now giving way to new approaches emerging at the junction of the philosophy of knowledge, complexity theory, the cognitive and social sciences. The article gives a historical and philosophical overview of cybernetics — from Plato, Ampere and Wiener to the present day. It is demonstrated that classical cybernetic paradigm — a mechanistic control model with feedback loops and representation of the world as a set of “finite state machines” — has proven insufficient in the era of dynamically changing, weakly deterministic, self-organizing living systems. Analysis of the causes of the methodological crisis in cybernetics at the turn of the 20th and 21st centuries is provided and it is argued that its resources are exhausted — further progress requires transition to new conceptual ideas. The authors propose a new engineering approach to understanding the World (K3-Engineering) and an original concept for Genesis of the digital — “Digital Genesis” — as a promising replacement.

References:

1. Wiener N. Cybernetics: or Control and Communication in the Animal and the Machine. 1948.

2. Beer S. Brain of the Firm. London, Allen Lane, 1972. 320 p.

3. Sobolev S.L., Kitov A.I., Lyapunov A.A. Osnovnye cherty kibernetiki [The Main Features of Cybernetics]. Voprosy filosofii, 1955, no 4, pp. 136–148.

4. Thiel P., Douthat R. Peter Thiel and the Antichrist. The New York Times, June 26, 2025.

5. Karp A., Zamiska N. The technological republic: hard power, soft belief, and the future of the West. Crown Publishing Group, 2025.

6. Budanov V.G. Metodologiya sinergetiki v postneklassicheskoy nauke i v obrazovanii [Methodology of Synergetics in Post-Non-Classical Science and Education]. Izd. 4-e dop. Moscow, Lenand, 2017, 272 p.

7. Budanov V.G., Kutin V.N., Khokhlova M.N. Kognitivnyy kollektivnyy konvergentnyy inzhiniring. Chast’ I [Cognitive Collective Convergent Engineering. Part I]. Ekonomicheskie strategii, 2023, no 5, pp. 100–109, DOI: https://doi.org/10.33917/es-5.191.2023.100-109

8. Budanov V.G., Kutin V.N., Khokhlova M.N. Kognitivnyy kollektivnyy konvergentnyy inzhiniring. Chast’ II [Cognitive Collective Convergent Engineering. Part II]. Ekonomicheskie strategii, 2023, no 6, pp. 52–61, DOI: https://doi.org/10.33917/es-6.192.2023.52-61

9. Kutin V.N., Khokhlova M.N. Kak aytishniki “obuvayut” promyshlennikov. A u vas kakaya kollektsiya PPO? O problemakh tsifrovoy transformatsii na primere sistem upravleniya proizvodstvom i promyshlennogo programmnogo obespecheniya (PPO) [How IT Professionals “Shoe” Industrialists. What’s Your Software Collection Like? On the Challenges of Digital Transformation Using Production Management Systems and Industrial Software (IS) as Examples]. OOO “GiperGrafGrupp”, 2022, available at: https://www.gipergraf.ru/kak-ajtishniki-obuvayut-promyshlenniko

DOI: 10.33917/es-6.204.2025.6-15

Despite the diversity of scientific disciplines dealing with management issues, practice very often shows examples of ineffective management. The article identifies and briefly discusses seventeen key barriers to successful management — from complexity issues to implications of the 1990s. These barriers are conditionally divided into three areas related to systemic, instrumental and social aspects of management. At the end of the article the author provides dependence graphs of the barriers to management and an expert assessment of the possibilities to eliminate these barriers.

References:

1. Eshbi U.R. Vvedenie v kibernetiku [Introduction to Cybernetics]. Per. s angl. 4-e izd. Moscow, URSS, 2009, 432 p.

2. Ansoff I. Novaya korporativnaya strategiya [New Corporate Strategy]. Per. s angl. Saint Petersburg, Piter Kom, 1999, 414 p.

3. Ekonomicheskaya strategiya firmy [Economic Strategy of the Firm]. Pod red. A.P. Gradova. 2-e izd. Saint Petersburg, Spets.Lit, 2000, 588 p.

4. Knyazeva E.N., Kurdyumov S.P. Osnovaniya sinergetiki [Foundations of Synergetics]. Moscow, URSS, 2005, 240 p.

5. Solomatin A.N. Entropic Approach to Sustainable Development Issues: Thirteenth International Conference “Management of Large-Scale System Development” (MLSD). Moscow (September 2020). IEEE Conference Publications, IEEE Xplore Digital Library, pp. 1–5, available at: https://doi.org/10.1109/ MLSD49919.2020.9247737

6. Viner N. Kibernetika i obshchestvo [Cybernetics and Society]. Per. s angl. Moscow, AST, 2019, 285 p.

7. Solomatin A.N. Postroenie dopustimykh krupnomasshtabnykh sistem kak uslovie ikh upravlyaemosti i samoorganizatsii: Upravlenie razvitiem krupnomasshtabnykh sistem (MLSD’2010): Trudy Chetvertoy mezhdunarodnoy konferentsii [Construction of Admissible Large-Scale Systems as a Condition of Their Controllability and Self-Organization: Management of Large-Scale Systems Development (MLSD’2010): Proceedings of the Fourth International Conference]. Vol. 1. Moscow, IPU RAN, 2010, pp. 18–26.

8. Prangishvili I.V. Sistemnyy podkhod i obshchesistemnye zakonomernosti [Systems Approach and General System Regularities]. Moscow, SINTEG, 2000, 528 p.

9. Rumyantsev V.Yu., Shokhov A.S. Konveyer platformizatsii ekonomiki Rossii [The Conveyor Belt of Platformization of the Russian Economy], pp. 1–12, available at: https://spkurdyumov.ru/uploads/2025/04/konvejer-platformizacii-ekonomiki-rossii.pdf.

10. Oleskin A.V. Detsentralizovannaya setevaya organizatsiya nauchnogo soobshchestva: perspektivy i problemy [Decentralized Network Organization of the Scientific Community: Prospects and Challenges]. Moscow, URSS, LENAND, 2021, 142 p.

11. Zub A.T. Strategicheskiy menedzhment: teoriya i praktika [Strategic Management: Theory and Practice]. 4-e izd., dop. Moscow, Yurayt, 2014, 375 p.

12. Federal’nyy zakon ot 28 iyunya 2014 g. N 172-FZ “O strategicheskom planirovanii v Rossiyskoy Federatsii” [Federal Law of June 28, 2014 No. 172-FZ “On Strategic Planning in the Russian Federation”]. Konsul’tantPlyus, available at: https://www.consultant.ru/document/cons_doc_LAW_164841

DOI: 10.33917/es-4.202.2025.78-85

The article analyzes socio-economic situation of workers in the mining industry (from 2019 to 2025) and identifies trends in transition from traditional to innovative tools in the personnel’s work activities. It was also discovered that introduction of artificial intelligence technologies helps to minimize the human factor and ensures maximum production ef ficiency.

The present article also examines transformation of the qualitative and quantitative characteristics of the mining industry representatives, their social consequences, in particular, the impact of mining work on psychophysical health.

The paper also studies development of a nonlinear algorithm with the help of ar tificial intelligence methods, including a neural network and cognitive modules, as a factor in transition from traditional to innovative tools in the work of mining personnel, having direct significance for humans.

References:

1. Zinov’eva O.M., Merkulova A.M., Smirnova N.A., Shcherbakova E.A. K voprosu upravleniya psikhosotsial’nymi riskami v gornom dele [Psychosocial Risk Management Issue in Mining]. Gornyy informatsionno-analiticheskiy byulleten’, 2022, no 1, pp. 20–33.

2. Timofeeva S.S., Boboev A.A., Drozdova I.V. Identifikatsiya opasnostey pri dobyche rudnogo zolota v Rossii i Uzbekistane [Hazards Identification in Gold Mining in Russia and Uzbekistan]. Journal of Advances in Engineering Technology, vol. 1(1), Sept. 2020, pp. 1–7.

3. Chebotarev A.G., Leskina L.M., Golovkova N.P. Usloviya truda i professional’nyy risk narusheniya zdorov’ya rabochikh rudnykh kar’erov [Working Conditions and Quarry Workers Occupational Health Risks]. Gornaya promyshlennost’, 2020, no 5, pp. 115–119.

4. Berezovskaya A.V., Fomin A.I. Faktory opasnosti zdorov’yu shakhterov i mery protivodeystviya etim riskam [Health Hazards to Miners and Measures to Counteract These Risks]. Mezhdunarodnyy nauchno-issledovatel’skiy zhurnal, 2023, no 5(131), pp. 1–8.

5. Zykova N.V. Silikoz [Silicosis]. Krasota i Meditsina, 2020, available at: https://www.krasotaimedicina.ru/diseases/zabolevanija_pulmonology/silicosis

6. Poletaeva A.V., Seryy A.V. Psikhologicheskie faktory perezhivaniya neschastnykh sluchaev na proizvodstve rabotnikami ugledobyvayushchikh predpriyatiy [Psychological Factors of Experiencing Industrial Accidents by Coal Mining Enterprises Workers]. Vestnik KRUNTs, 2011, no 2(18), pp. 143–152.

7. Tsymbal A.V. Issledovanie rasprostranennosti proyavleniy psikhologicheskoy dezadaptatsii u shakhterov, perezhivshikh vital’nuyu ugrozu v avariynoy situatsii [A study of the Psychological Maladjustment Manifestation Prevalence in Miners Who Survived a Vital Threat in an Emergency Situation]. Psikhologiya. Psikhofiziologiya, 2012, no 19, pp. 115–118.

8. Voznesenskiy N.K., Paramonova S.V., Sedinin A.L. Psikhovegetativnyy status podzemnykh gornorabochikh [Underground Miners Psychovegetative Status]. Meditsina truda i promyshlennaya ekologiya, 2019. № 9. S. 589.

DOI: 10.33917/es-4.202.2025.70-77

The article dwells on the essence of artificial intelligence as a mathematical model used to automate human cognitive activity. The author identifies practical tasks that can be solved with the help of the above technology, such as automation of routine business processes, marketing offers personalization, fraud detection, management decision-making support and others. Industries where artificial intelligence is actively used are studied, including healthcare, finance, retail, manufacturing, energy and telecommunications. Special attention is paid to specific examples of applying ar tificial intelligence in auctions, in particular in real estate, energy, online advertising and distribution of computing resources. The author reveals auction models, their essence and areas of application, such as online auctions, programmatic adver tising, spot electricity markets, financial markets and government procurement.

The article analyzes the prospects for introducing artificial intelligence-based tools into auctions in the context of fur ther digitalization of the Russian economy and potential benefits of their application, such as increased profits, optimized use of resources, reduced labour costs and other operating costs.

References:

1. Smith C. Introduction. The History of Artificial Intelligence, available at: https://courses.cs.washington.edu/ courses/csep590/06au/projects/history-ai.pdf

2. Timofeeva A.Yu., Koroleva E.N. Iskusstvennyy intellekt v upravlenii ekonomikoy [Artificial Intelligence in Economic Management]. Nauka XXI veka: aktual’nye napravleniya razvitiya, 2021, no 1-2, pp. 184–188.

3. Reutov R.V. Vliyanie innovatsiy tsifrovoy ekonomiki na mekhanizm neytralizatsii defektov bankovskogo rynka: Ekonomiko-pravovye problem obespecheniya ekonomicheskoy bezopasnosti [The Impact of Digital Economy Innovations on the Mechanism for Neutralizing Defects in the Banking Market:

Economic and Legal Problems of Ensuring Economic Security]. 2021, pp. 99–102.

4. Talapina E.V. Ispol’zovanie iskusstvennogo intellekta v gosudarstvennom upravlenii [Use of Artificial Intelligence in Public Administration]. Informatsionnoe obshchestvo, 2021, no 3, pp. 16–22.

5. Nikitaeva A.Y., Salem A.B.M. Institutional framework for the development of artificial intelligence in the industry. Journal of Institutional Studies, 2022, vol. 14, no 1, pp. 108–126.

6. Zhang C., Lu Y. Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 2021, available at:

https://www.sciencedirect.com/science/article/abs/pii/S2452414X21000248

7. Fiala P., Flusserova L. Modelirovanie i reshenie dvustoronnikh auktsionov [Modeling and Solving Two-sided Auctions]. Khronoekonomika, 2022, no 4(38), pp. 4–8.

8. Kapusto A.V., Banshchikov D.O. Auktsiony i ikh modelirovanie: Tendentsii ekonomicheskogo razvitiya v XXI veke [Auctions and Their Modeling: Trends of Economic Development in the 21st Century]. 2021, pp. 428–431.

DOI: 10.33917/es-3.201.2025.98-101

The article examines the concept of an enterprise digital twin (EDT) as an organizational and managerial mechanism based on business capabilities. Business capability is a holistic system for carrying out a specific type of activity, whose behaviour is strategically predetermined in a unique and unambiguous way within an ecosystem and its implementation is variable, including processes, organization and all types of resources. The author proposes a methodology for forming a business capability map using a federalistic approach to integrating data and artificial intelligence (AI) technologies, which ensures high adaptability and ecosystems transparency. Based on theoretical analysis and testing in real companies, practical significance of EDT for vertical and horizontal integration of corporations and, accordingly, management of multi-industry ecosystems is shown.

References:

1. Grieves M. Digital Twin: Manufacturing Excellence through Virtual Factory Replication. 2014.

2. Tao F., Zhang H., Liu A., Nee A.Y.C. Digital Twin in Industry: State-of-the-Art. 2019.

3. Ulrich D., Smallwood N. Capitalizing on Capabilities. Harvard Business Review, 2004.

4. Ross J.W., Weill P., Robertson D. Enterprise Architecture as Strategy: Creating a Foundation for Business Execution. 2006.

5. Kairouz P., McMahan H.B., Avent B., Bellet A., Bennis M., Bhagoji A.N., et al. Advances and Open Problems in Federated Learning. 2021.

DOI: 10.33917/es-3.201.2025.92-97

The article explores digital money characteristics of both central bank and private sector in both advanced and emerging market economies. Newly emerging forms of the private sector electronic money (digital coins, or so-called cryptoassets), such as Bitcoin, have drawn much attention because the underlying distributed ledger technology used therein enables decentralized verification while maintaining cash-like functions.

Meanwhile, some central banks have explored potential application of the distributed ledger technology and issuing their own digital coins for general public or financial institutions – so-called the central bank digital currency initiatives. Over the past 12 months, central bank digital currencies (CBDCs) have transformed from a distant concept into the core of the global economic policy agenda. Debates on CBDC have progressed towards adopting CBDC and its implications for the financial system. Such innovations evidently have serious consequences for the monetary system at both national and global levels.

References:

1. Central bank digital currencies: system design and interoperability. BIS, available at: https://www.bis.org/publ/othp42_system_design.pdf

2. Gross J., Schiller J. A model for central bank digital currencies: Do CBDCs disrupt the financial sector? SSRN Electronic Journal, 2020.

3. Schilling L. Risks Involved with CBDCs: On Cash, Privacy, and Information Centralization, 2019, available at: https://papers.ssrn.com/sol3/papers. cfm?abstract_id=3479035

4. Tercero-Lucas D. Central Bank Digital Currencies and Financial Stability in a Modern Monetary System, 2022.

5. Davoodalhosseini S. Central Bank Digital Currency and Monetary Policy. Journal of Economic Dynamics and Control, 2021, 142, 104150. 10.1016/j. jedc.2021.104150

6. Náñez A., Jorge-Vázquez J., Forradellas R. Central Banks Digital Currency: Detection of Optimal Countries for the Implementation of a CBDC and the Implication for Payment Industry Open Innovation. Journal of Open Innovation Technology Market and Complexity, 2021. 7. 10.3390/joitmc7010072

7. Fernandez-Villaverde J., Sanches D., Schilling L., Uhlig H. Central bank digital currency: Central banking for all? Review of Economic Dynamics, 2020, 41. 10.1016/j.red.2020.12.004

8. Andolfatto D. Assessing the Impact of Central Bank Digital Currency on Private Banks. The Economic Journal, 2020, 131. 10.1093/ej/ueaa073

9. Auer R., Cornelli G., Frost J. Rise of the Central Bank Digital Currencies: Drivers, Approaches and Technologies. SSRN Electronic Journal, 2020, 10.2139/ ssrn.3724070

10. Bindseil U. Tiered CBDC and the Financial System. SSRN Electronic Journal, 2020, 10.2139/ssrn.3513422