The purpose of the current study is to analyze the impact of higher education and universities on the dynamics of the digital economy. The authors hypothesized to distinguish three components (educational, research, innovation) in the digital economy development. Within this article, the results of using index and cluster analysis methods to determine the impact of the educational component on the processes of digital economy development in Ukraine at the macroeconomic and meso-economic levels are presented. The special attention is put on the educational component because the higher educational institutions concentrate intellectual capital of the country, as well as prepare future specialists for the needs of digital economy. Moreover the universities’ scientists make an impact on digital economy development by conducting research and transferring their results technological innovations, information and communicational technologies, etc.) into the real economy. During the research, main problems of digital economy development, determined by the poor quality of educational services, insufficient commercialization of university research results in the real economy, are identified. The authors conclude that solving the identified problems requires synchronization of interests and establishing a long-term partnership between universities, business, the state and the public. Importance of optimizing the state regulatory influence on economic entities in the context of digitalization of the national economy is emphasized. In particular, it is proposed to group the set of measures of state regulation into three vectors, namely: neutral-encouraging (support of positive dynamics of intensive development), incentive-providing (resource and information support of development processes) and initiative-mentoring (motivation and coordination of development processes).
How to Cite
digital economy, digitalization, higher education, university, k-means algorithm, index method
Arthur, D., & Vassilvitskii, S. (2006). How slow is the k-means method? In SCG ’06: Proceedings of the twenty-second annual symposium on computational geometry. ACM Press. DOI: https://doi.org/10.1145/1137856.1137880
Artuhur, D., & Vassilvitskii, S. (2007). k-means++: The Advantages of Careful Seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–1035. DOI: https://doi.org/10.1145/1283383.1283494
Bekkers, R., & Bodas Freitas, I. (2008). Analyzing knowledge transfer channels between universities and industry: To what degree do sectors also matter? Research Policy, 37(10), 1837–1853. DOI: https://doi.org/10.1016/j.respol.2008.07.007
Bell, D. (1973). The Coming of Post-industrial Society: A Venture of Social Forecasting. New York: Basic Books, 507 р. E-source: https://www.os3.nl/_media/2011-2012/daniel_bell_-_the_coming_of_post-industrial_society.pdf
Bottou, L., & Bengio, Y. (1995). Convergence properties of the k-means algorithm. Advances in Neural Information Processing Systems. E-source: https://pdfs.semanticscholar.org/2352/d9105de31032538900dfb2ce7c95f6402963.pdf
Carayannis, E., & Grigoroudis, E. (2016). Quadruple Innovation Helix and Smart Specialization: Knowledge Production and National Competitiveness. Foresight and STI Governance, 10/1, 31–42. DOI: https://doi.org/10.17323/1995-459x.2016.1.31.42
Castells, M. (1997). The Information Age: Economy, Society and Culture: The Power of Identity. Oxford: Blackwell. DOI: https://doi.org/10.1177/0739456X9901900212
Cosmulese, C. G., Grosu, V., Hlaciuc, E., & Zhavoronok, A. (2019). The Influences of the Digital Revolution on the Educational System of the EU Countries. Marketing and Management of Innovations, 3, 242–254. DOI: http://doi.org/10.21272/mmi.2019.3-18
Dubrov, A. M., Mxytaryan, V. S., & Troshyn, L. Y. (1998). Mnogomernуe statystycheskye metody [Multidimensional statistical methods]. Мoskva: Fynansy i statystyka. (in Russian)
Elkan, C. (2003). Using the triangle inequality to accelerate k-means. Proceedings of the Twentieth International Conference on Machine Learning, 3, 147–153. E-source: https://dl.acm.org/doi/10.5555/3041838.3041857
Har-Peled, S., & Sadri, B. (2005). How fast is the k-means method? SODA’05: Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms, 877–885, Philadelphia, PA, USA. DOI: https://doi.org/10.1007/s00453-004-1127-9
Hartigan, J. A., & Wong, M. A. (1979). A k-means clustering algorithm. Applied Statistics, 28, 100–108. DOI: https://doi.org/10.2307/2346830
Ivanov, Yu., & Tyshchenko, V. (2015). Public-private partnership potential in knowledge economy: regional aspect. Economic Annals-XXI, 3–4(1), 28–31. E-source: http://soskin.info/userfiles/file/2015/3-4_1_2015/Ivanov,%20Tyshchenko.pdf
Jain, A. J., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys, 31/3, 264–323. DOI: https://doi.org/10.1145/331499.331504
Kanungo, Т., Mount, D. M., Netanyahu, N. S., Piatko C. D., Silverman R., & Wu A. Y. (2004). A local search approximation algorithm for k-means clustering. Comput. Geom, 28(2–3), 89–112. E-source: https://www.cs.umd.edu/~mount/Projects/KMeans/kmlocal-cgta.pdf
Kym, Dzh.-O. (1989). Faktornyj, dyskrymynantnyj y klasternyj analyz. [Factorial, discriminant and cluster analysis]. Мoskva: Finances and statistics.
Masuda, Y. (1983). The Information Society as Postindustrial Society. Washington: Word Future Soc., 45.
Pankaj, K. Agarwal, & Nabil H. Mustafa (2004). К-means projective clustering. PODS ’04: Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. 155–165, New York, NY, USA. ACM Press. DOI: https://doi.org/10.1145/1055558.1055581
Porat, Mark U. (1977). The Digital economy. Nine volumes. Office of Telecommunication, US Department of Commerce. Washington.
Prokopenko, I. F., & Ganin, V. I. (2008). Metodyka i metodologiya ekonomichnogo analizu [Methodology and methodology of economic analysis]. Kyiv: Center for Educational Literature. (in Ukrainian)
Saaty, T. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill. E-source: https://www.scirp.org/(S(lz5mqp453edsnp55rrgjct55))/reference/ReferencesPapers.aspx?ReferenceID=1943982
Sculley, D. (2010). Web Scale K-Means Clustering. Proceedings of the 19th International Conference on World Wide Web. 1177–1178. DOI: https://doi.org/10.1145/1772690.1772862
Wu, X., & Kumar, V. (2009). The Top Ten Algorithms in Data Mining. Chapman & Hall. CRC. DOI: https://doi.org/10.1007/s10115-007-0114-2