Business performance assessment of small and medium-sized enterprises: Evidence from the Czech Republic
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Author(s)Vojtech StehelLink to ORCID Index: https://orcid.org/0000-0001-6068-6794
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Jakub HorakLink to ORCID Index: https://orcid.org/0000-0001-6364-9745
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Tomas KrulickyLink to ORCID Index: https://orcid.org/0000-0002-0378-2699
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DOIhttp://dx.doi.org/10.21511/ppm.19(3).2021.35
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Article InfoVolume 19 2021, Issue #3, pp. 430-439
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- 602 Views
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183 Downloads
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Creative Commons Attribution 4.0 International License
Business performance assessment is one of the basic tasks of management. Business performance can be assessed using a number of methods. The basic ones include financial analysis, Balanced Scorecard or Economic Value Added (EVA). The paper is focused on SME business performance assessment based on Economic Value Added, calculated using the INFA build-up model. According to this method, companies were divided into four categories. The first category included companies with a positive EVA value. The second category included companies with negative EVA, but with the economic result above the risk-free rate. The third category included companies with a positive economic result above the risk-free rate. The fourth category included companies with a negative economic result. The model did not include companies with negative equity. The input represented 15 predictors based on their financial statements. The data were normalized and all extreme values, likely caused by a data rewriting error, were removed. Company performance is visualized by comparing Principal Component Analysis and Kohonen neural networks. Compared to similar research, the methods are compared using the data that analyzes the performance of companies. Both methods made it possible to visualize the given task. With regard to the purpose of facilitating the interpretation of the results, for the given case, the use of PC seems to be more appropriate.
Acknowledgment
This study has been supported by the Technology Agency of the Czech Republic under project No TL01000349.
- Keywords
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JEL Classification (Paper profile tab)G17, G32, M21
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References25
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Tables2
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Figures8
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- Figure 1. Principle of the PCA method
- Figure 2. One-dimensional space
- Figure 3. Kohonen network
- Figure 4. Importance of individual components
- Figure 5. Individual parts involved in a component
- Figure 6. Position of companies in 2-dimensional space
- Figure 7. Mutual dependence of the individual predictors
- Figure 8. Position of the companies and Kohonen neural networks
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- Table 1. Company size coding
- Table 2. Number of companies in original and modified data set
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- Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433-459.
- Blayo, F., & Demartines, P. (1991). Data analysis: How to compare Kohonen neural networks to other techniques? In A. Prieto (Ed.), Artificial Neural Networks (Lecture Notes in Computer Science) (pp. 469-476). Springer, Berlin, Heidelberg.
- Brosse, S., Giraudel, J. L., & Lek, S. (2001). Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages. Ecological Modelling, 146(1-3), 159-166.
- Buhmann, J., & Kuhnel, H. (1992). Unsupervised and supervised data clustering with competitive neural networks. In IJCNN International Joint Conference on Neural Networks (pp. 796-801).
- Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417-441.
- Kaplan, R. S., & Norton, D. P. (1992). The Balanced Scorecard – Measures That Drive Performance. Harvard Business Review, 70(1), 71-79.
- Kislingerová, E. (2007). Manažerské finance [Managerial Finance] (2nd revised and expanded edition). Prague: C. H. Beck.
- Kohonen, T. (1982). Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics, 43(1), 59-69.
- Kohonen, T. (1989). Self-organizing and associative memory (3rd ed.). Berlin: Springer-Verlag.
- Machová, V., & Vochozka, M. (2019). Using artificial intelligence in analysing and predicting the development of stock prices of a subject company. In S. Ashmarina, & M. Vochozka (Eds.), Sustainable Growth and Development of Economic Systems: Contradictions in the Era of Digitalization and Globalization (pp. 235-245). Springer, Cham.
- Marakas, G. M. (1999). Decision support systems in the twenty-first century. Upper Saddle River, N.J.: Prentice Hall.
- Mařík, M. (2011). Metody oceňování podniku: proces ocenění – základní metody a postupy [Business valuation methods: valuation process – basic methods and procedures] (3rd ed.). Prague: Ekopress.
- Matlabacademy. (2019, December 12). Machine Learning with MATLAB. USA: The MathWorks.
- Ministry of Industry and Trade. (2019, December, 1). Analytical materials.
- Neumaierová, I. (1998). Řízení hodnoty [Value Management]. Prague: University of Economics in Prague.
- Neumaierová, I. (2003). Aplikace řízení hodnoty [Value Management Application]. Prague: University of Economics in Prague.
- Nývltová, R., & Marinič, P. (2010). Finanční řízení podniku: moderní metody a trendy [Financial management: modern methods and trend]. Prague: Grada.
- Oo, M. C. M., & Thein, T. (2019). An efficient predictive analytics system for high dimensional big data. Journal of King Saud University – Computer and Information Sciences.
- Pearson, K. (1901). On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine, 2(11), 559-572.
- Shaw, P. J. A. (2003). Multivariate statistics for the Environmental Sciences. Hodder-Arnold.
- Synek, M. (2011). Manažerská ekonomika [Managerial economy] (5th ed.). Prague: Grada.
- Vochozka, M. (2011). Metody komplexního hodnocení podniku [Enterprise comprehensive evaluation methods] (1st ed.). Prague: Grada Publishing.
- Vojáček, A. (2006, December 11). Samoučící se neuronová síť – SOM, Kohonenovy mapy [Self-learning neural network – SOM, Kohonen maps].
- Vondrák, I. (2000). Umělá inteligence a neuronové sítě [Artificial intelligence and neural networks] (2nd ed.). Ostrava: Technical University of Ostrava.
- Vrbka, J., & Rowland, Z. (2019). Assessing of financial health of companies engaged in mining and extraction using methods of complex evaluation of enterprises. In S. Ashmarina & M. Vochozka, (Eds.), Sustainable Growth and Development of Economic Systems (pp. 321-333). Springer, Cham.
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Iryna Skliar ,
Nataliya Pedchenko ,
Victoria Strilec ,
Victoria Novikova ,
Yevhenii Kozmenko
doi: http://dx.doi.org/10.21511/ppm.18(3).2020.07
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Certain shortcomings of linear models for evaluating the results appeared during the design and implementation of the strategy to manage sustainable economic development. The potential for establishing erroneous targets increases in the formation of strategic objectives for the next forecast period. There is a special need to choose adequate indicators to comprehensively approximate the factors of economic development and evaluation methods that allow more sensitively measuring the results of management decisions in the implementation of the strategy.
The article evaluates the results of the Sustainable Development Strategy “Ukraine – 2020”, employing the potential of the neural network method for a flexible combination of a large number of factors in constructing nonlinear models of impact on the resulting indicator. As a result of applying the neural network model with one hidden layer for evaluation, based on 16 indicators identifying economic, social, and institutional aspects of sustainable development of Ukraine, it was found that institutional transformations contribute most to achieving sustainable development. Reforms in terms of deregulation and support of entrepreneurship, property rights protection, and competitive environment have the most significant positive impact. On the other hand, low efficiency of capital market reforms, implementation of the energy efficiency program, and reform in the field of public procurement determine the need to revise the program of their fulfilment. -
The influence of innovative development in the EU countries and Ukraine on the competitiveness of national economies: A comparative analysis
Viktoriia Strilets ,
Liudmyla Franko ,
Mariia Dykha ,
Maksym Ivanov ,
Larysa Rybina
doi: http://dx.doi.org/10.21511/ppm.22(2).2024.01
Problems and Perspectives in Management Volume 22, 2024 Issue #2 pp. 1-16 Views: 375 Downloads: 94 TO CITE АНОТАЦІЯRussian aggression adversely affected the economy of Ukraine and emphasized the need to adapt the best practices of EU countries to determine steps to restore the country’s competitiveness. This study aims to determine the influence of the innovative development of countries on their competitiveness and identify prospects for Ukraine’s post-war economic recovery. The study constructed neural networks to assess the relationships between the factors of innovative development and the competitiveness of the EU countries and Ukraine. Six main factors of innovative development of countries are identified: “innovations in business (S1),” “intellectual property (S2)”, “innovations in industry (S3),” “eco-innovations (S4),” “innovation management (S5),” and “digital innovations (S5).” Groups of factors are determined by the strength of influence (strong, moderate, or weak). For Ukraine, S1 and S6 have a strong effect (33.3%), S5 shows moderate (16.7%), S2, S3, and S4 show weak effects (50%). For EU countries, S1 and S6 have a strong influence, S2 and S3 – moderate, S5 and S4 – weak. This comparative analysis concluded that EU countries consider intellectual property, green economy, and state innovation policy as key components of their competitiveness. The results discovered a weak relationship between intellectual property protection, innovation in industry, and competitiveness of Ukraine compared to EU countries. However, digital innovations significantly and positively affect Ukraine’s competitiveness.
