Mechanism for developing an adaptive strategy in cognitive management of the it companies’ competitiveness
-
DOIhttp://dx.doi.org/10.21511/dm.18(2).2020.03
-
Article InfoVolume 18 2020, Issue #2, pp. 23-32
- Cited by
- 468 Views
-
97 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
In the context of the sectoral policy of Ukraine’s economic development, experts identify the IT sector as one of the drivers of economic growth. The sector is characterized by higher than the global aver¬age economic growth rates, growth rates of tax deductions to local and state budgets, and growing growth rates in the share of exports in the overall structure of Ukraine’s exports. It was revealed that, along with positive trends, the development model of the IT sector in Ukraine remains extremely vulnerable to external “shocks”, since about 98% of orders are generated from the external market. In addition, outsourcing rather than product specialization is inherent in the IT sector of Ukraine. And in this case, the level of operational and financial efficiency, as shown by global statistics, is the lowest. This model of development of the IT sector is due to the low level of competitiveness of IT companies, a decrease in competitiveness in the global market, which necessitates the development of adequate mechanisms for managing the competitiveness of companies in the Ukrainian IT sector. The mechanism of forming an adaptive strategy in cognitive management of IT companies’ competitiveness is offered. This mechanism, in contrast to the existing ones, takes into account the contour of anticipation (warning), which allows determining the stability of competitive positions of companies in Ukraine’s IT sector and developing a proactive adaptive strategy aimed at maintaining a high level of competitiveness of IT companies in both local and global markets, increasing their business value. The blocks structure of the mechanism, the range of problems of each block and methods of their solution are determined.
- Keywords
-
JEL Classification (Paper profile tab)C54, L86, M19
-
References20
-
Tables0
-
Figures1
-
- Figure 1. Схема взаємозв’язку блоків механізму формування адаптивної стратегії в когнітивному управлінні конкурентоспроможністю IT-компанії
-
- Abdikeev, N. (2014). Cognitive management. Management sciences in Russia, 3, 71-78. (In Russian).
- Fabus, M., Dubrovina, N., Guryanova, L., Chernova, N., Zyma, O. (2019). Strengthening financial decentralization: driver or risk factor for sustainable socio-economic development of territories. Entrepreneurship and Sustainability Issues, 7(2), 875-890.
- Fernández-Gamez, M., Gil-Corral, A., & Galan-Valdivieso, F. (2016). Corporate reputation and market value: Evidence with generalized regression neural networks. Expert Systems with Applications, 46, 69-76.
- Finance.ua (2019). UNIT.City predstavyv dopovid shchodo IT-sektoru Ukrainy: holovni tsyfry i pokaznyky [UNIT.City presented a report on the IT sector of Ukraine: main figures and indicators]. (In Ukrainian).
- Kara, N. (2016). Strategy kinds and estimation of external environment factors influence activity of enterprise. Journal of Lviv Polytechnic National University. Series of Economics and Management, 847, 97-102. (In Ukrainian).
- Kizim, N., & Klebanova, T. (Ed.). (2007). Adaptivnyye modeli v sistemakh prinyatiya resheniy [Adaptive models in decision-making systems] (368 p.). Kharkiv: INZHEK. (In Russian).
- Kizim, N., & Klebanova, T. (Ed.) (2010). Modeli otsenki, analiza i prognozirovaniya sotsialno-ekonomicheskikh sistem [Models for assessment, analysis and forecasting of socio-economic systems]. Kharkiv: INZHEK. (In Russian).
- Klebanova, T., Guryanova, L., & Sergienko, E. (2007). Otsenka finansovoy konkurentosposobnosti predpriyatiy na osnove ispolzovaniya panelnykh dannykh [Assessment of the financial competitiveness of enterprises based on the use of panel data]. In M. Kizim (Ed.), Competitiveness: problems of science and practice (pp. 193-214). Kharkiv: INZHEK. (In Russian).
- Ko, Yu-Ch., Fujita, H., & Li, T. (2017). An evidential analysis of Altman Z-score for financial predictions; Case study on solar energy companies. Applied Soft Computing, 52, 748-759.
- Kudryavceva, E. (2013). Cognitive management: Conceptualization of managerial performance (224 p.). Petrozavodsk: PetrGU. (In Russian).
- Li, S., & Wang, Sh. (2014). Financial early warning logit model and its efficiency verification approach. Knowledge-Based Systems, 70, 78-87.
- Lviv IT research (n.d.). Official website.
- McKinsey Global Institute (2019). Operezhayushchaya dinamika: bystro razvivayushchiyesya strany i korporativnyye lokomotivy ikh ekonomiki [Superior dynamics: fast-growing countries and the corporate locomotives of their economies].
- Ministry of economic development, trade and agriculture of Ukraine (2020). 40% GDP growth, USD 50 bln in investment by 2024: economic growth strategy presented.
- Mutviychuk, A. (2010). Bankruptcy Prediction in Transformational Economy: Discriminant and Fuzzy Logic Approaches. Fuzzy Economic Review, 15(1), 21-38.
- Qu, Y., Quan, P., Lei, M. & Shi, Y. (2019) Review of bankruptcy prediction using machine learning and deep learning techniques. Procedia Computer Science, 162, 895-899.
- Sidorin, A., & Sidorin, V. (2016). The process approach to development of adaptive strategy of an organization based on analysis of its external and internal environment. Organizer of productions, 70(3), 28-42. (In Russian).
- Stolbov, M., & Shchepeleva, M. (2020). Systemic risk, economic policy uncertainty and firm bankruptcies: Evidence from multivariate causal inference.
- Tihanov, E. (2017). Teoretiko-metodicheskiye osnovy otsenki i obespecheniya konkurentosposobnosti predpriyatiy-rezidentov industrial’nykh parkov [Theoretical and methodological foundations for assessing and ensuring the competitiveness of enterprises-residents of industrial parks] (237 p.) (Ph.D. Thesis). Yekaterinburg: Uralskiy federalnyy universitet im. pervogo Prezidenta Rossii B. Yeltsina. (In Russian).
- TradingView (2020). S&P 500 information technology.