Abstract
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. AcknowledgmentThis study has been supported by the Technology Agency of the Czech Republic under project No TL01000349.
Highlights
When running a business, it is often necessary to make decisions in very complex processes (Synek, 2011)
The paper is focused on SME business performance assessment based on Economic Value Added, calculated using the INFA build-up model
Company performance is visualized by comparing Principal Component Analysis and Kohonen neural networks
Summary
It is often necessary to make decisions in very complex processes (Synek, 2011). Assessing the influence of individual predictors is often difficult and time-consuming, especially in the case of a dimensional decision problem when individual predictors influence the result (Oo & Thein, 2019). The visualization of data can be very useful for the interpretation of the results supporting the decision-making process (Marakas, 1999). A comparison of these methods (Brosse et al, 2001) has already been analyzed in technical fields (Blayo & Demartines, 1991). These methods are used to analyze economic factors predicting the performance of small and medium-sized enterprises in the Czech Republic. The paper is aimed at assessing SMEs’ business performance based on Economic Value Added, calculated using the INFA build-up model
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
