FORECASTING THE INNOVATIVE AND DIGITAL STRENGTH OF UKRAINE’S ECONOMY ON THE BASIS OF CORRELATION-REGRESS ANALYSIS

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Published: Sep 20, 2024

  Kateryna Kraus

  Nataliia Kraus

  Oleksandr Marchenko

Abstract

The purpose of the research is to present the realised forecasts of Ukraine's economic power in order to find reserves for the recovery of the national economy and opportunities for the formation of digital entrepreneurship on the basis of innovative functioning, which will lead to economic growth in the medium and long term. The object of scientific research is the expenditure on SRD and S&T development by types of work in Ukraine from 2010 to 2022 and the analysis of indicators of GDP the influence of time, employment and inflation factors; expansion of digital capabilities of entrepreneurship due to the conducted R&D, which will become a guarantee of the emergence of breakthrough innovations as one of the key reserves of innovative and digital development of the country during the war and post-war reconstruction of Ukraine. Methodology. A study was conducted utilising dialectical, systematic, mathematical and statistical methods to investigate the R&D expenses by types of work in Ukraine from 2010 to 2022 and the GDP from 2011 to 2023. This analysis determined the prospective existing reserve of economic strength with positive dynamics and highlighted the necessity for a strategic format of digital business work based on innovation. A CRA was conducted to determine the strength of the relationship between SRD expenditures and time (i.e., a set of factors that consistently influence SRD funding and drive its growth trend). Fisher's test was calculated, and an econometric analysis was performed based on GDP indicators over 56 quarters, establishing the dependence of the GDP volume on the time factor and the cyclicality of seasonal fluctuations. Resluts. The conditions for accelerating the digitisation of business processes at domestic enterprises are, in particular, the presence of highly qualified S&R personnel, innovators and researchers in the country, the development of new institutes of innovative and digital development, and the transformation and adaptation of old institutes of development to the existing conditions. The obtained data of CRA show that there is a close relationship between the GDP of Ukraine and the time factor, and the direction of the relationship is direct, i.e., linear, which in this case is a positive fact. It is determined that in pursuit of the goal of restoring the innovative potential of the national economy in the post-war period and further active development of digital entrepreneurship in Ukraine, it is necessary to continue financial support for scientific research and scientific and technical developments carried out in various sectors of the economy. Practical implications. The analysed statistical data had a positive impact on the professionalism of the forecast calculations and allowed to state that in 2027, with a probability of error of 6.29%, the volume of expenditures on research and development is projected to range from 20,202.74 to 29,201.18 million UAH. The results of the CRA show that the multiple correlation coefficient (R) is 0.94, which indicates a close overall relationship between the country's GDP and three independent variables (inflation rate, unemployment rate, time factor). The linear regression equation fits the sample data well and the model is qualitative. The results of the forecast are as follows: Ukraine has the potential for post-war recovery and can develop models for post-war economic reconstruction and changes in its structure. Government officials can develop institutional instruments to attract investment and provide effective mechanisms for the future transformation of the existing labour market and human capital institution. Value / Originality. Having conducted a thorough analysis of the statistical data on the dynamics of spending on S&T development and implementation of the SRD in Ukraine in 2010-2022, the authors managed to determine the relative error of approximation – a criterion for assessing the reliability of the forecast, which amounted to 8.74% and considered the approximation to be qualitative, and the forecast for 2027 is reliable. It is determined that the regression equation is most accurate when R2 approaches its maximum value, that is, 1, and in this case, it is 0.9096, which is a good result and means that the linear regression equation fits the sample data well and the model is of high quality. And for Ukraine's economy, which is under martial law, such positive expectations for GDP and the possibility of increasing R&D spending give hope that Ukraine's economic strength is real, as confirmed by the forecast calculations.

How to Cite

Kraus, K., Kraus, N., & Marchenko, O. (2024). FORECASTING THE INNOVATIVE AND DIGITAL STRENGTH OF UKRAINE’S ECONOMY ON THE BASIS OF CORRELATION-REGRESS ANALYSIS. Baltic Journal of Economic Studies, 10(3), 180-192. https://doi.org/10.30525/2256-0742/2024-10-3-180-192
Article views: 192 | PDF Downloads: 127

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Keywords

economic potential, R&D, expenditure on S&T development, correlation-regression analysis, GDP, econometric analysis, digital entrepreneurship, innovative development, inflation, employment, time factor, economic growth

References

Alam, A., Uddin, M., & Yazdifar, H. (2019). Financing behaviour of R&D investment in the emerging markets: the role of alliance and financial system. R&D Management, Vol. 49, No. 1, p. 21–32. DOI: https://doi.org/10.1111/radm.12303

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin, Vol. 103, No. 3, p. 411–423. DOI: https://doi.org/10.1037/0033-2909.103.3.411

Babukh, I., Lisitsa, V., Kraus, N., Kraus, K., & Novikova, O. (2023). Activities of Digital Platforms on the Basis of Clusterization and Innovative Development Strategies in the Conditions of European Integration. WSEAS Transactions on Environment and Development, No. 19, p. 1179–1195. DOI: https://doi.org/10.37394/232015.2023.19.108

Bagozzi, R. P., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, Vol. 36, No. 3, p. 421–458.

Biletskyi, V. S. (2023). Methodology of scientific research of technical objects and their optimization. Kyiv: FOP Khalikov R.Kh.

Bitektine, A., & Haack, P. (2015). The ‘macro’ and the ‘micro’ of legitimacy: toward a multilevel theory of the legitimacy process. Academy of Management Review, Vol. 40, p. 49–75.

Bliese, P. D. (2000). Within-group agreement, non-independence, and reliability: implications for data aggregation and analysis. Multilevel Theory, Research, and Methods in Organizations. San Francisco, CA: Jossey-Bass, p. 349–381.

Botti, A., Parente, R., & Vesci R. (Ed.) (2021). How to do business in digital era? : a casebook. Salerno-Cracow: Cracow University of Economics.

Bozeman, B. (2000). Technology transfer and public policy: a review of research and theory. Research Policy, Vol. 29, p. 627–655.

Bravo, F., & Reguera-Alvarado, N. (2017). The effect of board of directors on R&D intensity: board tenure and multiple directorships. R&D Management, Vol. 47, No. 5, p. 701–714. DOI: https://doi.org/10.1111/radm.12260

Chen, C. J., Lin, B. W., Lin, J. Y., & Hsiao, Y. C. (2018). Technological diversity, knowledge flow and capacity, and industrial innovation. Technology Analysis & Strategic Management, Vol. 30, No. 12, p. 1365–1377. DOI: https://doi.org/10.1080/09537325.2018.1472759

Cooper, R. G., & Kleinschmidt, E. J. (1987). Success factors in product innovation. Industrial Marketing Management, Vol. 16, No. 3, p. 215–223.

Cuvero, M., Granados, M. L., Pilkington, A., & Evans R. (2023). Start-ups’ use of knowledge spillovers for product innovation: the influence of entrepreneurial ecosystems and virtual platforms. R&D Management, Vol. 53, No. 4, p. 584–602. DOI: https://doi.org/10.1111/radm.12567

Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin B. J. (2016). Common methods variance detection in business research. Journal of Business Research, Vol. 69, No. 8, p. 3192–3198.

Girotra, K., Terwiesch, C., & Ulrich, K. T. (2007). Valuing R&D projects in a portfolio: evidence from the pharmaceutical industry. Management Science, Vol. 53, No. 9, p. 1452–1466.

Gupta, V., Fernandez-Crehuet, J. M., & Hanne, T. (2020). Fostering continuous value proposition innovation through freelancer involvement in software startups: insights from multiple case studies. Sustainability, Vol. 12, No. 21, p. 1–35. DOI: https://doi.org/10.3390/su12218922

Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: a Regression-Based Approach. 3rd edn. New York, NY: The Guilford Press.

Herold, D. M. (Ed.) (2022a). Digital Entrepreneurship: Curriculum. Vienna-Cracow: Cracow University of Economics.

Herold, D. M. (Ed.) (2022b). Teaching notes for Casebook ‘How to do business in digital era?’. Salerno-Cracow: Cracow University of Economics.

Hoisl, K., Gruber, M., & Conti, A. (2017). R&D team diversity and performance in hypercompetitive environments. Strategic Management Journal, Vol. 38, No. 7, p. 1455–1477.

Ishchenko, I., Kraus, K., Kraus, N., Manzhura, O., & Radzikhovska, Y. (2023). Digital Transformation of Business Processes of Enterprises on the Way to Becoming Industry 5.0 in the Gig Economy. WSEAS Transactions on Business and Economics, No. 20, p. 1008–1029. DOI: https://doi.org/10.37394/23207.2023.20.93

Kraus, K., Kraus, N., Hryhorkiv, M., Kuzmuk, I., & Shtepa, O. (2022). Artificial Intelligence in Established of Industry 4.0. WSEAS Transactions on Business and Economics, Vol. 19, p. 1884–1900. DOI: https://doi.org/10.37394/23207.2022.19.170

Lanzolla, G., Pesce, D., & Tucci, C. L. (2021). The digital transformation of search and recombination in the innovation function: tensions and an integrative framework. Journal of Product Innovation Management, Vol. 38, No. 1, p. 90–113. DOI: https://doi.org/10.1111/jpim.12546

Magliocca, P. (Ed.) (2021). Doing business digitally: a textbook. Foggia-Cracow: Małopolska School of Public Administration, Cracow University of Economics.

Manzhura, O., Kraus, K., & Kraus, N. (2021). Digitalization of Business Processes of Enterprises of the Ecosystem of Industry 4.0: Virtual-Real Aspect of Economic Growth Reserves. WSEAS Transactions on Business and Economics, Vol. 18, p. 569–580. DOI: https://doi.org/10.37394/23207.2021.18.57

Nerkar, A. & Paruchuri, S. (2005). Evolution of R&D capabilities: the role of knowledge networks within a firm. Management Science, Vol. 51, No. 5, p. 771–785.

Register of statistical units. UkrStat, 2024a. Available at: https://www.ukrstat.gov.ua/

Tripathi, N., Oivo, M., Liukkunen, K., & Markkula, J. (2019). Startup ecosystem effect on minimum viable product development in software startups. Information and Software Technology, Vol. 114, p. 77–91. DOI: https://doi.org/10.1016/j.infsof.2019.06.008

Gross domestic product in actual prices in 2022–2023. UkrStat, 2024d. Available at: https://www.ukrstat.gov.ua/

Gross domestic product (2010–2021). UkrStat, 2024e. Available at: https://www.ukrstat.gov.ua/

GDP and Expenditure on scientific research and development by types of work for 2010–2022. UkrStat, 2024c. Available at: https://www.ukrstat.gov.ua/

Expenditures on scientific research and development by types of work for 2010–2022. UkrStat, 2024b. Available at: https://www.ukrstat.gov.ua/