Source: Икономически изследвания Economic Studies Location: Bulgaria Author(s): Igor Britchenko, Ana Paula Monte, Igor Kryvovyazyuk, Lidiia Kryvoviaziuk Title: The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises Issue: 1/2018 Citation style: Igor Britchenko, Ana Paula Monte, Igor Kryvovyazyuk, Lidiia Kryvoviaziuk. "The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises". Икономически изследвания 1:87-108. https://www.ceeol.com/search/article-detail?id=676014 The Central and Eastern European Online Library The joined archive of hundreds of Central-, Eastand South-East-European publishers, research institutes, and various content providers You have downloaded a document from CEEOL copyright 2018 CEEOL copyright 2018 87 Igor Britchenko1 Ana Paula Monte2 Igor Kryvovyazyuk3 Lidiia Kryvoviaziuk4 Volume 27 (1), 2018 THE COMPARISON OF EFFICIENCY AND PERFORMANCE OF PORTUGUESE AND UKRAINIAN ENTERPRISES This article intends to analyze the performance and the efficiency of companies and to identify the key factors that may explain it. It was selected a sample with 15 enterprises: 7 Portuguese and 8 Ukrainian ones, belonging to several industries. Financial and non-financial data was collected for 6 years, during the period of 2009 to 2014. Research questions that guided this work were: Are the enterprises efficient/profitable? What factors influence enterprises' efficiency/performance? Is there any difference between Ukrainian and Portuguese enterprises' efficiency/performance, which factors have more influence? Which industrial sector is represented by more efficient/profitable enterprises? The main results showed that in average enterprises were efficient with low level of profitability. According to gained results several indicators were highlighted so that companies would pay more attention to them. JEL: D21; D24; D29; D 51; F15; F22 Introduction Nowadays every enterprise set stable development and efficiency as a target to achieve. In order to achieve that use of comprehensive economic and financial analysis considered to be a must. One of the main targets of the research is to compare enterprise efficiency of Portuguese and Ukrainian enterprises while conducting a study of the theoretical basis of enterprise performance and efficiency, factors which influence them, choosing how to conduct comprehensive economic and financial analysis. 1 Igor Britchenko is Professor, Doctor of Economic Science, Head of Finance and Economics Department, Uzhgorod Trade and Economic Institute Kyiv National University of Trade and Economics, Ukraine, phone: +38 095 005 01 02, e-mail: ibritchenko@gmail.com. 2 Ana Paula Monte is Profesor of Economics and Management in Politechnic Institute of Bragança. 3Igor Kryvovyazyuk is Professor of Economics and Business Department in Lutsk National Technical University, Lutsk, e-mail: krivovyazuk-igor@mail.ru. 4Lidiia Kryvoviaziuk is Master in Business Management, Polytechnic Institute of Bragança, Bragança, Portugal. CEEOL copyright 2018 CEEOL copyright 2018 Britchenko, I., Monte, D. P., Kryvovyazyuk, I., Kryvoviaziuk, L. (2018). The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises. 88 The need for such a comparison is a result of the increasing complexity of companies activity in conditions of high competition at the global level. The environment in which they function is constantly getting more complicated, risks are growing, and access to resources is reducing, which leads to deterioration of economic and financial activity results. Additional threats are created by the absence of needed attention to the issues of economic analysis of enterprises activity, which is used from time to time in the process of managing their activities. The following tasks were solved during research: • the essence of the categories "performance", "efficiency" and "effectiveness", was revealed, whose meaning is important for improving the efficiency of enterprise performance; • existing determinants of the performance were studied; • methodical approaches of comprehensive economic analysis of enterprise's economic activity were analyzed and on this basis modern and more substantiated method of its implementation was developed; • the researched methodology was made (objectives, collecting data process, sample, applied methods were chosen); • comprehensive economic analysis of enterprise was made in order to identify the level of efficiency and performance, identify factors impact and presentation of its results was given. The practical object of the study was to conduct economic and financial analysis of enterprise efficiency and profitability via linear regression analysis of comprehensive indicators (Asset Turnover Ratio and Return on Assets), identify factors of influence and their impact on dependable variables; use the results to define average efficiency and profitability levels among the sample in general or separately by its country (Portugal or Ukraine) or industry (paper; building materials; building; steel or engineering (automotive)). The research sample consists of 90 observations in total: 7 enterprises from Portugal and 8 enterprises from Ukraine, which operate in the industrial sector of the economy. The chosen enterprises belong to 5 sectors: paper; building materials; building; steel and engineering (automotive). Each enterprise had been studied during 6 years, for the period of 2009 to 2014. The Literature Review Efficiency is one of the main categories of the economy, which is directly linked to the achievement of the final results of the company. The world is constantly changing and is always characterized by continuous progress; also the market economy does not remain constant. All of those require active steps from the enterprises for improving their activity CEEOL copyright 2018 CEEOL copyright 2018 – Economic Studies (Ikonomicheski Izsledvania), 27 (1), p. 87-108. 89 performance. It obligates development and provision of sustainable performance in order to achieve success. That is why the pre-research has great importance for continued comparison of Portuguese and Ukrainian enterprises. Review of determination of efficiency was carried, in particular, by Adzhavenko (2014), who had determined that efficiency can be defined from different angles, as a set of properties and constituent elements: productivity, operability, economy (a measure of the use of system resources), quality, profitability, quality of working life. As written by Mĺkva (2013), performance is an economic category which is closely linked to the systemic view of its measurement and evaluation. The system whose performance is to be measured and evaluated corresponds to its internal structure. To measure the performance of the enterprise is, therefore, necessary to know which (and also how) subsystems of its internal structure contribute to the overall performance. Efficiency as an economic category is the qualitative and quantitative characteristics of performance management (Krivovyazyuk, 2012). It is typical for the whole reproduction process and all its phases separately including (production, distribution, exchange and consumption); describes the activities of any business section and economic systems at all levels (companies or industrial enterprises, households, industries, region, state economy as a whole). The definition of efficiency found place in a large number of studies, our vision of effectiveness, efficiency and performance according to aspects of economic practice is next: • Effectiveness is a measure characteristic which shows if everything is going according to made plan and if company achieves set targets; • Efficiency is a measure which shows the quality of some activity, the ability not only achieve target but do it with less costs spent; • Performance – characteristic of success connected to a specific activity. One of the central questions in the economy is why some firms succeed and others fail. Enterprise success is influenced by many factors and variables. Determining the firm performance using a set of financial measures has been and still is an interesting and challenging problem. A lot of factors were researched by scientists in the context of a variety of performances' types. Among factors of influence, the literature has established that slack financial resources can play an important role in improving CSP. In particular, Aguilera-Caracuel et al. (2015) analysed whether excess financial resources can lead to better benefits of the multinational enterprises (MNEs) gained from their international cultural diversification and as a result can lead to conducting advanced corporate social responsibility activities, which improve their CSP level (Aguilera-Caracuel et al., 2015). CEEOL copyright 2018 CEEOL copyright 2018 Britchenko, I., Monte, D. P., Kryvovyazyuk, I., Kryvoviaziuk, L. (2018). The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises. 90 Vătavu (2014) in order to highlight determinants of profitability made an analysis based on cross-sectional regressions, where performance indicators were based on the rest of variables and performance was considered as a function of financial and non-financial indicators. Return on Assets (ROA) was set as a performance proxy, the variables (factors) which had influence were debt, asset tangibility, size, liquidity, taxation, risk, inflation and crisis. Regression results indicated that Romanian companies had had higher performance when they have been using limited borrowings. Negative impact on dependent variable had tangibility, business risk and the level of taxation. Though earnings are provided by significant sales turnover, performance is affected by high levels of liquidity. Unstable economic times displayed by high inflation rates and the current financial crisis, which also had a strong negative influence on total corporate performance (Vătavu, 2014). In order to identify indicators that impact corporate financial performance, Ching and Gerab (2012) used principal component and multiple regression analyses of 16 Brazilian listed companies for the period 2005-2009 (Ching & Gerab, 2012). As the result of first analysis five factors that impact financial performance were extracted from 20 variables and ratios, which ones had been used later in multiple regression analysis. The last analysis was used to confirm indicators influence on corporate profitability and define the influence level. The financial performance of companies was influenced by factors such as firm size (the most predominant accounted for 26.9 % of total variance), working capital management, solvency (liquidity), margin, financial debt (the least important, accounted for 9.1 %). The influence of several variables on the financial performance in the context of capital structure was made by Banerjee and De (2014). In their work independent variables such as "business risk", "size of the firm (in sales)", "growth rate", "debt service capacity (interest)", "dividend payout", "financial leverage", "degree of operating leverage", "firm's age" and "size of the firm (in assets)" were researched to find out which might have some impact on the profitability of the Indian iron and steel industry. The study showed that "financial leverage", "debt service capacity (interest)" and "size of the firm (in assets)" are significant factors influencing the profitability of the firms (Banerjee & De, 2014). Another study employed next methodology: the underlying dimensions of the financial ratios were identified by using exploratory factor analysis, which was followed with the discovery of any possible potential relationships between the firm performance and financial ratios using predictive modelling methods (Delen, Kuzey & Uyar, 2013). Results defined next factors: liquidity (the most significant, was explaining 11.48% of the total variance); asset structure (explaining 9.59% of the total variance); asset and equity turnover ratio (9.1%) and showed how efficiently a company used its assets and equity to generate sales revenues; gross profit margin (6.95%); financial debt ratio (6.58%); current assets (5.29%); leverage (4.83%); net profit margin (4.81%); net working capital (NWC) turnover ratio (3.99%); sales & profit growth ratio (3.92%); asset growth ratio (3.89%). In this study decision tree algorithms (like C5.0, Classification and Regression Trees, Chi-squared Automatic Interaction Detector and The Quick, Unbiased, Efficient Statistical Tree) were used to evaluate the financial performance of Turkish companies listed on the Istanbul CEEOL copyright 2018 CEEOL copyright 2018 – Economic Studies (Ikonomicheski Izsledvania), 27 (1), p. 87-108. 91 Stock Exchange. According to findings of conducted prediction models, two profitability ratios (i.e., EBIT ratio and net profit margin) have the biggest impact on company performance. These ratios indicate the potential ability of a company to control their costs and expenses. The leverage and debt ratios had an impact on the company performance as well and the sales growth and Asset Turnover Ratio (ATR) had indicated the ability of a company to generate sales. For improving its overall performance firm must have high sales performance. Finally, findings corroborated the Dupont analysis, which decomposed Return on Equity (ROE) into the three multiplicative ratios of Profit margin, Asset Turnover, and Leverage. Kijewska (2016) identified the determinants of ROE using an original and five-factor version of the DuPont formula was analysed on the example of two Polish companies from mining and metallurgy sector. The last method was used in order to analyse in more detail ROE dependence and possible ways to improve return of the firm. Kotane and Kuzmina-Merlino (2012) for more effective analysis suggested using the system of financial indicators that should have taken into account industry and companies conditions. According to them, the basis for the mentioned system should have included: Current ratio; NWC to Sales ratio; Debt to Equity; Financial cycle; Sales margin; ROE; Maturing. Those financial indicators were optimal and correlated and corresponded to each other. Besides indicators, the financial analysis made by the owner (manager), interpretation of information has great importance. That is why circumstances must be always taken into consideration while calculating financial indicators. Shliagа and Gal'tsev (2014) describe two approaches for evaluating the effectiveness of the company – monetary and resources. For monetary approach, results and costs are determined in revenues (inflow) and expenditures (outflow) of cash. For resource approach results characterized by the volume of made production and the costs – the amount of various types' resources spent. In modern conditions of development of Ukraine's businesses in Trokoz and Orlikovsky' (2014) opinion the most promising of latest management concepts for efficiency control is the concept of Business Performance Management (BPM) and Balanced Scorecard (BSC). BPM – a relatively new concept of governance denotes a holistic, process-oriented approach to management decisions aimed at improving the capacity of enterprises to assess their financial state and manage the performance of its activities at all levels by bringing together owners, managers, staff and external contractors within the overall integrated environment management. And the concept of BSC is a system of strategic management based on the measurement and evaluation of its effectiveness on a set of indicators, selected in such a way that consider all significant (in terms of strategy) aspects of its activities (Trokoz & Orlikovsky, 2014). Well-known statistical techniques, which can be used in describing the performance and recognizing the influence of which factors are bigger include: regression; descriptive statistics; correlation; analysis of variance; other multivariate methods; other (primarily nonparametric) (Capon et al., 1990). CEEOL copyright 2018 CEEOL copyright 2018 Britchenko, I., Monte, D. P., Kryvovyazyuk, I., Kryvoviaziuk, L. (2018). The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises. 92 Enterprise efficiency is complex characteristic, so in order to fully analyze the enterprise activity, make the right conclusions about its condition; the following indicators should be considered (Dudukalo, 2012): Profit margin; Total assets return; Fixed assets return; ROE; Return on investment; Residual Income. Financial ratios have played an important role in evaluating the enterprise's performance. Almost all existing methods include them. Financial ratios together with financial statements are instruments that help managers to monitor the company's performance and figure the best financial strategies out (Ching & Gerab, 2012). Although, nowadays the usage of nonfinancial indicators is frequently more promoted, financial indicators are able to evaluate the condition of an enterprise precisely based on its previous development (Kotane, 2015). Theoretically, financial ratios are divided into 5 groups (Robinson, Greuning, Henry & Broihahn, 2009): • Activity ratios indicate the efficiency of day-to-day tasks performed by company (for example, a collection of receivables and management of inventory); • Liquidity ratios show whether the company has the ability to meet its short-term obligations; • Solvency ratios show company's ability to meet long-term obligations; • Profitability ratios indicate the ability to generate profitable sales from its resources; • Valuation ratios measure earnings quantity connected to ownership of a specified claim. Existing approaches of efficiency estimation of management of enterprise's activity are not allowing consideration of efficiency in a comprehensive way (Dudukalo, 2012). This is due to the fact that each approach ignores the impact of factors of functional subsystems as a whole. In our opinion, only comprehensive assessment can provide the most useful information for the future decision-making process. For the evaluation of past periods and to develop appropriate strategies for the future, a comprehensive analysis should be carried out by the management of the company, it is so, because managers are better informed on the reasons of indicators' changes and what will be potential opportunities for their improvement. The comprehensive analysis was used in researches: Krivovyazyuk and Kryvoviaziuk' (2014) article contained comprehensive economic analysis as an instrument for improving efficiency of activity of engineering enterprises of Volyn region; in Kryvoviaziuk' (2014) article the comprehensive approach was used to diagnose innovative engineering companies; it was also used for strategy decision-making purposes for the enterprises after conducted diagnostics of the enterprises (Krivovyazyuk, Kryvoviaziuk & Strilchuk, 2013). CEEOL copyright 2018 CEEOL copyright 2018 – Economic Studies (Ikonomicheski Izsledvania), 27 (1), p. 87-108. 93 Research Methodology The researched sample consists of 15 enterprises: 7 enterprises from Portugal and 8 enterprises from Ukraine, which operate in the industrial sector of the economy (paper, pulp and energy; building materials; construction and real estate; steel; automotive industries). The choice was guided by subsequent requirements: companies should have been listed and had free access of data; they should relate to the industrial sector of the economy of both countries; they are characterized by a similar structure of capital and assets. Economic conditions of the economies of countries are similar from the standpoint of access to resources and methods of state regulation. It allows adequate comparing the efficiency and effectiveness of Ukrainian and Portuguese enterprises. The multiple linear regression model was used to study the relationship between a dependent variable and one or more independent variables. The model is able to identify the independent effects of a set of variables on the dependent variable (Greene, 2003). The general form of the linear regression model is given in equation 1: y = f (x1, x2, ..., xk) + ε [1], where y – the dependent variable; xk – the independent variable; ε – a random disturbance of stable relationship; n=1,2,...,k. The generalized model to be applied in this work is as follows (equation 2): ikikiiiii XXXY ,,,2,21,1,0 .... ⋅++⋅+⋅+= ββββ [2], where: Yi is the dependent variable for observation i (for comprehensive efficiency indicator the variable of ATR was used; for performance indicator the variable of ROA was used), with i = 1 to n; β0,i is the constant; β1,i to βk,i are the coefficients of independent variables X1,i to Xk,I for observation i X1,i to Xk,i, are the variables that may explain the efficiency or performance like calculated indicators given in Appendix I. Reliable regression analysis requires fulfilment of certain conditions "classical" assumptions (Greene, 2003): a) Collinearity; It means that two or more of the independent /explanatory/ variables in a regression have a linear relationship. This causes a problem in the interpretation of the regression results. If the variables have a close linear relationship, then the estimated regression coefficients and T-statistics may not be able to properly isolate the unique effect/role of each variable and the confidence with which we can presume these effects to be true (Gupta, 1999). CEEOL copyright 2018 CEEOL copyright 2018 Britchenko, I., Monte, D. P., Kryvovyazyuk, I., Kryvoviaziuk, L. (2018). The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises. 94 Durbin-Watson and collinearity statistics were used. Diagnostic approach to check for multicollinearity after performing regression analysis is to display the Variance Inflation Factor (VIF – a measure of how much the variance of an estimated regression coefficient increases if the explanatory variables are correlated) Higher the value of VIF, greater degree of collinearity. If VIF>10 there is strong evidence that collinearity is affecting the regression coefficients and consequently they are poorly estimated. Another check for collinearity is the Durbin-Watson statistic. Normally its value should lie between 0 and 4. A value close to 2 suggests no correlation; one close to 0 – negative correlation, and a value close to 4 – positive correlation ("Regression diagnostics", 2016, p. 47). b) Normality; Normal distribution can be checked using quantile-quantile (Q-Q) plots and the Kolmogorov-Smirnov Test (K-S Test). K-S Test is a nonparametric test of the equality of continuous, one-dimensional probability distributions that can be used to compare a sample with a reference probability distribution (one-sample K-S Test), or to compare two samples (two-sample K-S Test). If the p-value (given in results output as Sig.) is less than 0.05 then data cannot be considered as normally distributed. c) Homoscedasticity is an assumption that Standard Deviations (S.D.) of the error terms are constant and do not depend on the x-value. Consequently, each probability distribution for the dependent variable has the same S.D. regardless of the independent variable value. Breusch-Pagan and Koenker test is used to test for heteroskedasticity in a linear regression model. It tests whether the estimated variance of the residuals from a regression are dependent on the values of the independent variables. The test assumes that heteroskedasticity is not present. If the resulting p-value of Breusch-Pagan and Koenker is less than significance level of 5 %, the obtained differences in sample variances are occurred based on random sampling from a population with equal variances. Linear regression implements a statistical model that, when relationships between the independent variables and the dependent variable are almost linear, shows optimal results, but in other case the model is faulty. Another limitation of the linear regression modelling is the complete necessity of assumptions fulfilment in order for obtaining reliable results; it's limitation for predicting numeric output; possible inappropriate use for modelling nonlinear relationships; difficulty in explanation what the model actually shows and last but not least it's complexity and labour-intensity. Comparison of efficiency and performance of Portuguese and Ukrainian enterprises After taking into consideration of all researched articles, methods and approaches, firstly, conduction of comprehensive financial and economic analysis and determination of enterprise efficiency, using as proxy the Asset Turnover Ratio (ATR) and such indicators as: Quick ratio; Liquidity Ratio (LiqR); Cash ratio and debt ratio; Asset utilization or turnover ratios; Profitability ratios; Growth ratios; Asset structure and solvency ratios as the factors that may explain it was made. Secondly, in order to analyse profitability (company's performance) the ROA was used and among factors that explain it the EBITDA margin; Profit margin; NWC turnover ratio; Fixed asset to total assets; Current asset to total assets; CEEOL copyright 2018 CEEOL copyright 2018 – Economic Studies (Ikonomicheski Izsledvania), 27 (1), p. 87-108. 95 Net operation expenses to net sales ratio; Sales growth ratio; LR; Debt-to-Equity (as capital structure proxy); Interest coverage ratio. The descriptive statistics on indicators is exhibited іn Appendix IІ. According to the table, higher quick ratio' mean of Ukrainian enterprises shows that their ability to cover short-term obligations with liquid assets is slightly better. LiqR ratio is also slightly better in Ukrainian enterprises showing higher ability to pay off its short-term debts obligations with its current assets. In case of the cash ratio shows higher availability of cash and cash equivalents in Portuguese enterprises, also in both countries the level of liquidity in terms of cash is poor. The receivables turnover mean in both countries has high value, but it is slightly better in Ukrainian enterprises, where they are seemed to have an efficient collection of accounts receivable and companies have more customers that pay off their debts quickly. Inventory turnover ratio mean has a higher level in Ukrainian companies. Despite the fact that Ukrainian enterprises have almost all preconditions for good performance, subsequent indicator – NWC turnover ratio shows negative value, which means their use of working capital to generate sales, is not efficient. On the other hand, Portuguese companies in these terms are efficient. The ATR mean has similar low meaning implying not enough sum of revenue generated. Equity turnover ratio showing the more efficient use of equity to generate revenue in Portuguese enterprises, which mean is higher and equals to 3.645 (S.D. = 2.478). Ukrainian enterprises utilized investment in fixed assets to generate revenue more effectively (FATR mean is higher). Both Gross profit margin and Profit margin values in Portuguese companies are higher. EBITDA margin is slightly higher in Ukrainian enterprises and equal to 13.7 %. Both sides of enterprises have low ROA, Portuguese companies 0.1 % and Ukrainian – 0.7 %, which shows the effective but not efficient use of assets to generate earnings. The Operating expense to net sales ratio equals to 1.033 (S.D. = 0.38) in Ukrainian side of firms, which indicates high value of costs. In Portuguese enterprise its value is 0.945 (S.D. = 0.10) showing more positive proportion (sales higher than expenses). Mean growth rates for assets, net profit and sales better in Ukrainian enterprises indicating the clear trend of increase. Portuguese assets and net profit growth rates have negative meaning and indicate the declining trend. Researching structure of total assets: average of Current assets to total assets ratio in both sides are around 40%, but Ukrainian companies show more variability in its capital structure (Ukrainian S.D. = 20.4% against 14.5% for Portuguese companies). Long-term assets in average are 59 % of total assets (again, according to S.D., the ratio varies more among Ukrainian companies). Accordingly, average percentage of stocks in current assets is higher in Ukrainian enterprises 42.4 %; average percentage of Cash and cash equivalents is higher in Portuguese enterprises and is 21 %. In those cases better S.D. was presented by Portuguese side. CEEOL copyright 2018 CEEOL copyright 2018 Britchenko, I., Monte, D. P., Kryvovyazyuk, I., Kryvoviaziuk, L. (2018). The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises. 96 Analysing the structure of total debt, it may be concluded that in average the short-term financial debt higher in Portuguese enterprises (23 %; S.D. = 11%), but short term in general is higher in Ukrainian firms (61.2 %, S.D. = 29.6%). Total financial debt in total debt higher in Portuguese entities (60.3 %, S.D. = 14.9%). According to interest coverage ratio, Portuguese entities on the contrast to Ukrainian can pay interest on the outstanding debt (4.78 > -0.661). LR has slightly higher meaning in Portuguese side, where 77 % (S.D. = 15.4%) of capital comes in the form of debt (loans). During the research the model assumptions were checked; analysis was performed while estimating the model and determining factors of efficiency/performance and measuring the impact of each variable in average in the whole sample and also for each country; the analysis of efficiency/performance was made in the whole sample, for each country and industry. Results of checking of assumptions for efficiency given in Table 1. There is no clear collinearity, although while conducting a linear regression analysis, we checked closer collinearity statistic and there were several cases with VIF higher than 10 – which indicated the influence of collinearity on the regression coefficients and consequently they are poorly estimated. After eliminating outliers, the results indicate that there is no collinearity between variables. Table 1 Results of assumptions check for efficiency model Test Indicator Before crossing out of outliers After crossing out of outliers Regression analysis Adjusted R Square 0,975 0,989 Durbin-Watson 1,825 1,707 Number of possible models 12 6 Predictors (Constant) including FATR, CATR, LiqR, Quick ratio, Inventory to current assets ratio, Current assets to total assets ratio, ROA, EBITDA margin (Constant) including FATR, CATR, EBITDA margin, ROA, LiqR, LR Check of residuals KolmogorovSmirnov Test Sample size 90 65 Asymp. Sig. (2tailed) 0,058 0,082 Koenker test (Sig.) 0,018 0,629 Normality was visually checked using Q-Q plots, which showed the existence of outliers. K-S Test checked if residuals had a normal distribution and because the p-value was higher than 0.05, they have a normal distribution. Homoscedasticity check showed that indicator meanings have the same finite variance after eliminating outliers. CEEOL copyright 2018 CEEOL copyright 2018 – Economic Studies (Ikonomicheski Izsledvania), 27 (1), p. 87-108. 97 The optimal model was chosen due to the rule: "the higher adjusted R square is better", the one with Adjusted R square equal to 0.989. This chosen model is presented in equation 3. ATR = 0.701* FATR + 0.451* CATR – 0.136*EBITDA margin + 0.126* ROA – – 0.076* LiqR – 0.039*LR [3] The biggest positive influence has FATR (0.701) and CATR (0.451), smaller positive influence has ROA (0.126). Negatively influencing enterprise efficiency are EBITDA margin (-0.136), LiqR (-0.076) and LR (-0.039). The final models of efficiency by country are given in following equations and Table 2. Table 2 The model of efficiency for Portuguese and Ukrainian enterprises Variables Portugal Ukraine Standardized Coefficients T Sig. Standardized Coefficients T Sig. Beta Beta (Constant) 3,608 0,001 1,702 0,111 Fixed Asset Turnover Ratio (FATR) 0,727 38,433 <0,001 Current Asset Turnover Ratio (CATR) 0,464 32,208 <0,001 Short-term financial debt to total debt 0,037 2,551 0,017 Leverage Ratio (LR) -0,058 -3,479 0,002 Net profit growth ratio 0,045 2,962 0,006 EBITDA margin -0,040 -2,857 0,008 Short-term debt to total debt 0,825 8,571 <0,001 Return on Assets (ROA) 0,511 5,593 <0,001 Interest coverage ratio -0,351 -3,640 0,003 Adjusted R Square 0,994 0,859 Durbin-Watson 1,785 1,530 F-test 919,053 35,508 Sig. <0,001 <0,001 ATR (Port) = 0.727*FATR + 0.464*CATR + + 0,037*Short-term financial debt to total debt – 0.058*LR – 0.04*EBITDA margin [4] The biggest positive influence on ATR in Portugal has FATR (0.727) and CATR (0.464), smaller positive influence has Net profit growth ratio (0.045) and Short-term financial debt to total debt (0.037). Small negative impact made by LR (-0.058) and EBITDA margin (0.136). ATR (Ukr) = 0.825* Short-term debt to total debt + 0.511*ROA – – 0.351*Interest coverage ratio [5] The biggest positive influence at Ukrainian enterprises has a short-term debt to total debt (0.825), also ROA has a positive impact (0.511), the opposite correlation with ATR has CEEOL copyright 2018 CEEOL copyright 2018 Britchenko, I., Monte, D. P., Kryvovyazyuk, I., Kryvoviaziuk, L. (2018). The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises. 98 Interest coverage ratio (-0.351). Accordingly, the factors that explain efficiency among Portuguese enterprises are different from Ukrainian ones. Efficiency analysis. Our sample consists of 90 cases. Reviewing of normality showed the existence of several outliers. After correcting sample by the use of regression analysis, calculation of p-value and selecting reliable variables, 49 valid cases are left. In this part, the research hypothesis (RH1: Enterprise efficiency indicator (comprehensive indicator – ATR) equals to 1) was checked using the one sample t-test (Table ). The model results can be described as next: 0 – means inefficiency; 1 – efficiency. Table 3 Result of One-Sample T-test for Asset Turnover Ratio Descriptive statistics n Mean Std. Deviation Std. Error Mean 65 0,731204 0,3834501 0,0475611 One-Sample T-test for Asset Turnover Ratio (Test Value = 1) T Df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper -5,652 64 <0,001 -0,2687961 -0,363810 -0,173782 Given the results (Table ), ATR mean is 0.73 (S.D. = 0.38) which is statistically significantly different from the test value of 1. It has been concluded that enterprises are efficient. Nonparametric 2-independent samples t-test is used to compare the means of efficiency for two independent groups of Ukrainian and Portuguese enterprises (Table 4). Table 4 Result of Mann Witney after eliminating outliers Ranks Test Statistics for ATR Country n Mean Rank Sum of Ranks Mann-Whitney U 269,000 Asset turnover ratio Portuguese 42 27,90 1172,00 Wilcoxon W 1172,000 Ukrainian 23 42,30 973,00 Z -2,936 Total 65 Asymp. Sig. (2-tailed) 0,003 First of all the distribution should be checked. P-value is less than 0.05 which means that efficiency of Ukrainian and Portuguese enterprises have statistically significant different efficiency. In order to compare efficiency by country descriptive statistics are displayed in Table . Table 5 The level of efficiency results by country n Minimum Maximum Mean Std. Deviation Asset Turnover Ratio (Portugal) 34 0,2044 1,4639 0,6280 0,3064 Asset Turnover Ratio (Ukraine) 30 0,2356 1,6740 0,9197 0,4419 CEEOL copyright 2018 CEEOL copyright 2018 – Economic Studies (Ikonomicheski Izsledvania), 27 (1), p. 87-108. 99 Given the average of efficiency by country it seems that in average Ukrainian enterprises are more efficient. In order to find out if there is a difference in efficiency by sector in which enterprise is functioning, Shapiro-Wilk test was used (sample does not follow a normal distribution and n<30). Descriptive statistics and results of Kruskal Wallis test is shown in Table . Table 6 The level of efficiency results by industrial sector Asset Turnover Ratio n Minimum Maximum Mean Standard Deviation Shapiro-Wilk sig. Industry Paper 24 0 2,2304 0,7407 0,7370 0,002 Automotive 12 0 1,4639 0,6923 0,5640 0,067 Building materials 18 0 1,0403 0,5547 0,3562 0,040 Steel 18 0 1,5248 0,6351 0,5134 0,113 Building 18 0 0,6814 0,4965 0,1976 0,000 After checking significance p-value in Shapiro-Wilk test to standard α=0.05 – in this case α>0.05 in some industries. Thus, there is a difference in efficiency regarding the industry sector. As in descriptive statistics of Table is shown, the average efficiency is slightly higher in the paper industry and slightly lower in building enterprises. Results of checking of assumptions for performance given in Table 7. Table 7 Results of assumptions check for profitability model Test Indicator Before crossing out of outliers After crossing out of outliers Regression analysis Adjusted R Square 0,917 0,923 DurbinWatson 1,619 1,396 Number of possible models 8 5 Predictors (Constant) including Profit margin, EBITDA margin, log(TA), Debt to equity ratio, Number of employees, Operating expense to net sales ratio (Constant) including Profit margin, FATR, EBITDA Margin, Country, Debt to equity ratio Check of residuals KolmogorovSmirnov test Sample size 68 63 Asymp. Sig. (2-tailed) 0,840 0,986 Koenker test (Sig.) 0,748 0,095 A closer look at the variables highlighted few cases which prove the existence of collinearity, which was avoided by eliminating outliers. K-S Test for normality resulted in CEEOL copyright 2018 CEEOL copyright 2018 Britchenko, I., Monte, D. P., Kryvovyazyuk, I., Kryvoviaziuk, L. (2018). The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises. 100 improved significance after crossing outliers. Homoscedasticity check showed that heteroskedasticity is not present as an indicator. The optimal model for performance was chosen due to the same rule as efficiency and it is given in equation 6. ROA = 0.678*Profit margin + 0.236* FATR + 0.277*EBITDA margin + + 0.137*Country + 0.122* Debt to equity ratio [6] All of the variables have a positive influence, the biggest impact belongs to Profit margin (0.678). These factors explain 92.3% of performance's variance. The final model for Portuguese and Ukrainian enterprises is given in equations 7-8 and Table 8. ROA (Port) = 0.137*FATR – 0.221*CATR + 0,152* Debt to equity ratio + + 1.110* Profit margin – 0.102* Interest coverage ratio [7] In case of Portuguese enterprises, variables are statistically significant, and each factor influences dependable variable differently. The biggest positive influence on ROA has Profit margin (1.110), a bit smaller impact have Debt to equity ratio (0.152) and FATR (0.137). Small negative impact is made by CATR (-0.221) and Interest coverage ratio (0.102). Table 8 The model of profitability for Portuguese and Ukrainian enterprise Variables Portugal Ukraine Standardized Coefficients T Sig. Standardized Coefficients T Sig. Beta Beta (Constant) 0,548 0,588 1,314 0,206 CATR -0,221 -8,224 <0,001 FATR 0,137 4,572 <0,001 Debt to equity ratio 0,152 4,785 <0,001 Interest coverage ratio -0,102 -2,895 0,007 Profit margin 1,110 30,729 <0,001 0,668 7,375 <0,001 EBITDA margin 0,433 4,781 <0,001 Adjusted R Square 0,979 0,883 Durbin-Watson 1,830 0,546 F-test 311,594 68,991 Sig. <0,001 <0,001 ROA (Ukr) = 0.668* Profit margin + 0.433* EBITDA margin [8] CEEOL copyright 2018 CEEOL copyright 2018 – Economic Studies (Ikonomicheski Izsledvania), 27 (1), p. 87-108. 101 In the model for Ukrainian enterprises, two factors have a different level of influence on ROA. The biggest positive impact has Profit margin (0.668), EBITDA margin also has a positive impact (0.433). Profit margin influences both models of performance for Ukrainian and Portuguese enterprises, but there is a significant difference between those two models. Analysis of performance. Our sample consists of 90 cases. Analysis of normality showed the existence of several outliers. Correction of the sample was made using the regression analysis, calculation of p-value and selecting reliable variables. In the end, we get 63 valid cases. In this part, the research hypothesis (RH2: Enterprise performance indicator higher than 0) was checked using the one sample t-test (Table ). Table 9 Result of One-Sample T-test for Return on Assets Descriptive statistics n Mean Std. Deviation Std. Error Mean 63 0,018210 0,0622157 0,0078384 One-Sample T-test for Return on Assets (Test Value = 1) T Df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper -125,652 62 <0,001 -0,981789 -0,997459 -0,966121 Given the results (Table 11), ROA mean is 0.018 (S.D. = 0.062) which is statistically significantly different from the test value of 1. It has been concluded that enterprises are not as profitable and they could be characterized as enterprises with a low-performance level, which still shows that companies on average have a positive performance. Results of nonparametric 2-independent samples t-test have shown that meaning of p-value is smaller than 0.05 indicating the difference between countries performance models (Table 10). Table 10 Result of Mann Witney after eliminating outliers for ROA Ranks Test Statistics for ROA Country n Mean Rank Sum of Ranks Mann-Whitney U 233,000 Return on Assets Portuguese 41 26,68 1094,00 Wilcoxon W 1094,000 Ukrainian 22 41,91 922,00 Z -3,143 Total 63 Asymp. Sig. (2-tailed) 0,002 In order to compare profitability descriptive statistics by state are displayed in Table . Table 1 Profitability statistics by country n Minimum Maximum Mean Std. Deviation Return on Assets (Portugal) 41 -0,1131 0,0823 0,0002 0,0419 Return on Assets (Ukraine) 22 -0,1338 0,1947 0,0517 0,0793 CEEOL copyright 2018 CEEOL copyright 2018 Britchenko, I., Monte, D. P., Kryvovyazyuk, I., Kryvoviaziuk, L. (2018). The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises. 102 As can be observed from Table 11, Ukrainian enterprises have higher average ROA, meaning higher profitability than Portuguese enterprises. This indicates that there is a difference in performance level among Ukrainian and Portugal companies. In order to find out if there is a difference in profitability in enterprises by sector they are functioning in, reasoning by small samples of enterprises performance by industrial sectors, Shapiro-Wilk Test (Table 12) was used. Table 12 Profitability level by industrial sector Return on Assets n Minimum Maximum Mean Standard Deviation Shapiro-Wilk sig. Industry Paper 15 -0,0106 0,1947 0,0855 0,0609 0,500 Automotive 6 -0,0144 0,0410 0,0115 0,0230 0,478 Building materials 14 -0,1338 0,0499 0,0167 0,0477 0,212 Steel 12 -0,1131 0,0823 -0,0193 0,0621 0,630 Building 16 -0,0790 0,0416 0,0163 0,0271 0,000 After checking significance p-value in Shapiro-Wilk Test some industries do not follow a normal distribution and have less than 30 cases, which imply that level profitability by sectors has a significant difference. Thus, there is a slight difference in profitability between industrial sectors, for example, paper industry is the most profitable one among the studied sample. Automotive and building enterprises also give profit, and according to the results of descriptive analysis steel and building materials sectors of the economy in the sample have losses regarding the industry sector. Conclusions In order to conduct a comparison of Ukrainian and Portuguese enterprises, a descriptive and inferential analysis was performed as well as multivariate regressions (through OLS regressions) were applied to identify the factors that may explain the efficiency (measured by ATR) and performance as profitability (measured by ROA) based on collected data. The final conclusion can have next statements: 1. On average the companies in the sample are efficient. According to the results average efficiency (ATR) of all enterprises equal to 0.73 (S.D. = 0.38) which in the interval from 0 to 1 is significantly closer to the efficient level that is why enterprises are considered as efficient. While assessing efficiency by country better efficiency belonged to Ukrainian enterprises (mean = 0.92; S.D. = 0.44) compared to Portuguese (mean = 0.63; S.D. = 0.31). There was no significant difference revealed of CEEOL copyright 2018 CEEOL copyright 2018 – Economic Studies (Ikonomicheski Izsledvania), 27 (1), p. 87-108. 103 efficiency in industrial sectors, but average efficiency is slightly higher in paper industry and slightly lower in building enterprises. 2. Although the average of ROA enterprises (mean = 0.02; S.D. = 0.06) showed that enterprises have low-performance level it is still considered as a positive (between countries there is a slight difference in performance level among Ukrainian and Portugal companies in favour of Ukraine). 3. Companies efficiency is influenced by FATR, CATR, EBITDA margin, ROA, LiqR, LR. 4. Companies performance is influenced by EBITDA margin; Profit margin; NWC turnover ratio; FATR, CATR; Net operation expenses to net sales ratio; Sales growth ratio; LR; Debt-to-Equity; Interest coverage ratio. In order to improve performance and efficiency enterprises are suggested to pay more attention to the factors determined as a factors with high influence level. More detailed suggestions include next: • for Ukrainian enterprises – paying attention to the factors of short-term debt to total debt, ROA, Interest coverage ratio in order to be more efficient; Profit margin and EBITDA margin to make their performance better. • for Portuguese enterprises – in order to improve efficiency to observe and develop factors of fixed assets turnover ratio, current assets turnover ratio, Short-term financial debt to total debt, Leverage Ratio, EBITDA margin. As for profitability, fixed assets turnover ratio, current assets turnover ratio, Debt to equity ratio, Profit margin and Interest coverage ratio are suggested to be tracked. Optimization of efficiency and management of the analyzed enterprises can be found in the results of a comprehensive analysis of the factors of influence on the efficiency and effectiveness of their activities. The dynamism of those factors (It) serves as an information base for the development and adoption of tactical and strategic decisions, as well as for improving the management of the investigated corporations. This research indicated robust results with statistical significance, and thus the conclusions are relevant. Among limitations of the present work were set of requirements that companies should have been listed and had free access to data and function in the industrial sector. In the future, it is advised to consider expand the sample to other countries and include more enterprises, sub-sampling based on individual enterprises and non-researched sectors of the economy, also, testing the model on sub-periods. References Adzhavenko, М. N. (2014). Theoretical bases of defining the essence of the category "development effectiveness" of the enterprises [Аджавенко, М. Н. (2014). Теоретические Основы Определения Сущности Категории «Эффективность Развития» Предприятий. Бизнес Информ, 2, 264-270]. [On-line]. Available: http://cyberleninka.ru/article/n/teoreticheskieCEEOL copyright 2018 CEEOL copyright 2018 Britchenko, I., Monte, D. P., Kryvovyazyuk, I., Kryvoviaziuk, L. (2018). The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises. 104 osnovy-opredeleniya-suschnosti-kategorii -effektivnost-razvitiya-predpriyatiy, Access date: 29.03.2016. Aguilera-Caracuel, J., Guerrero-Villegas, J., Vidal-Salazar, M. D. & Delgado-Márquez, B. L. (2015). International Cultural Diversification and Corporate Social Performance in Multinational Enterprises: The Role of Slack Financial Resources. Management International Review, 55(3), 323-353. [On-line]. Available: http://doi.org/10.1007/s11575-014-0225-4, Access date: 29.03.2016. Banerjee, A. & De, A. (2014). Determinants of Corporate Financial Performance Relating to Capital Structure Decisions in Indian Iron and Steel Industry An Empirical Study. Paradigm, 18(1), 35-50. Capon, N., Farley, J. U., & Hoenig, S. (1990). Determinants of financial performance: a metaanalysis. Management Science, 36(10), 1143–1159. Ching, H. Y., Gerab, F. (2012). Determinants of financial performance in Brazilian companies: a multi-ratio model using multivariate statistical method. Journal of Global Business and Economics, 5(1), 79-99. Delen, D., Kuzey, C. & Uyar, A. (2013). Measuring firm performance using financial ratios: A decision tree approach. Expert Systems with Applications, 40(10), 3970-3983. [On-line]. Available: http://doi.org/10.1016/j.eswa.2013.01.012, Access date: 29.03.2016. Dudukalo, G. О. (2012). Analysis of the methods of assessment of enterprise management efficiency [Дудукало, Г. О. (2012). Аналіз методів оцінювання ефективності управління діяльністю підприємства. Електронне наукове фахове видання "Ефективна економіка", 3]. [On-line]. Available: http://www.economy.nayka.com.ua/?op=1&z=1031, Access date: 28.03.2016. Greene, W. H. (2003). Econometric analysis (5th ed). Upper Saddle River, N.J: Prentice Hall. Gupta, V. (1999). SPSS for Beginners. VJBooks Inc. Kijewska, A. (2016). Determinants of the return on equity ratio (ROE) on the example of companies from metallurgy and mining sector in Poland. Metalurgija, vol. 55(2), 285-288. Kotane, I. (2015). Evaluating the importance of financial and non-financial indicators for the evaluation of company's performance. Management Theory and Studies for Rural Business and Infrastructure Development, vol. 37(1), 80-94. [On-line]. Available: http://doi.org/10.15544/mts.2015.08, Access date: 29.03.2016. Kotane, I., & Kuzmina-Merlino, I. (2012). Assessment of financial indicators for evaluation of business performance. European Integration Studies, 6, 216-224. [On-line]. Available: http://doi.org/10.5755/j01.eis.0.6.1554, Access date: 29.03.2016. Krivovyazyuk, I.V. (2012). Functioning and development of the enterprises in condition of crisis: systematic-analytical approach: monograph. [Кривов'язюк, І.В. (2012). Функціонування та розвиток підприємств в умовах кризи : системно-аналітичний підхід : Монографія. Луцьк : ЛНТУ, 392]. Kryvovyazuk, I.V. & Kryvoviaziuk, L.V. (2014). The use of comprehensive economic analysis to improve the efficiency of activity of engineering enterprises of Volyn region [Кривов'язюк, І.В., Кривовязюк, Л.В. Використання комплексного економічного аналізу для підвищення ефективності господарювання машинобудівних підприємств Волинської області. Економічні науки. Серія "Регіональна економіка". Збірник наукових праць. ЛНТУ, 11 (43), 133-149]. Kryvovyazuk, I.V., Kryvoviaziuk, L.V., Strilchuk, R.M. (2013). Selection of the strategy of engineering enterprises development based on diagnostics results of their state [Кривов'язюк, І.В., Кривовязюк, Л.В. & Стрільчук, Р.М. (2013). Вибір стратегії розвитку підприємств машинобудування на базі результатів діагностування їх стану. Економічний форум, 2, 154-164]. CEEOL copyright 2018 CEEOL copyright 2018 – Economic Studies (Ikonomicheski Izsledvania), 27 (1), p. 87-108. 105 Kryvoviaziuk, L.V. (2014). An integrated approach to the diagnosis of innnovation-active engineering enterprises of Volyn [Кривовязюк Л.В. Комплексний підхід в діагностиці інноваційно активних машинобудівних підприємств Волині. Науковий вісник ЛНТУ. Серія «Економічні та гуманітарні науки». Науковий збірник, 14. Луцьк : РВВ ЛНТУ, 115-121]. Mĺkva, M. (2013). Importance of non-financial indicators for measuring enterprise performance. In: Eurobrand: 6th International Multidisciplinary Scientific Conference, Požarevac, 24-26(5), 27. Regression diagnostics (2016, April 10.). Oxford Journal. [On-line]. Available: http://www. oxfordjournals.org/our_ journals/tropej/online/ma_chap5.pdf. Robinson, T.R., Greuning, H.V., Henry, E. & Broihahn, M.A. (2009). International Financial Statement analysis. CFA Institute, John Wiley & Sons, Inc., Hoboken, New Jersey. Ross, S. A., Westerfield, R., & Jordan, B. D. (2008). Fundamentals of corporate finance. Tata McGraw-Hill Education. Shliagа, O. V. & Gal'tsev, M. V. (2014). The ways of the enterprise' efficiency increasing. [Шляга, О. В. & Гальцев, М. В. (2014). Шляхи підвищення ефективності роботи підприємства. Економічний Вісник Запорізької Державної Інженерної академії, 7, 66-75]. Trokoz, D. & Orlikovsky, M. (2014). The last concepts of performance management of modern enterprises. [Трокоз, Д. І. & Орликовський, М. О. (2014). Новітні концепції управління ефективністю діяльністю сучасних підприємств. Ефективна економіка, 5]. [On-line]. Available: http://www.economy.nayka.com.ua/?op=1&z=3034, Access date: 29.03.2016. Vătavu, S. (2014). The determinants of profitability in companies listed on the Bucharest stock exchange. Annals of the University of Petrosani, Economics, 14(1), 329-338. CEEOL copyright 2018 CEEOL copyright 2018 Britchenko, I., Monte, D. P., Kryvovyazyuk, I., Kryvoviaziuk, L. (2018). The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises. 106 Appendix I Table А1 Indicators used in the work and their formulas and meaning Group Indicator Meaning Formula Liquidity ratios Quick ratio Shows ability to meet its shortterm obligations with liquid assets (excluding inventories); Higher is better. Current ratio A measure of short-term liquidity; Higher – larger margin of safety. Current Assets/Current liabilities Cash ratio Shows ability to pay its short-term debts by cash Cash/Current liabilities Asset utilization or turnover ratios Receivable turnover ratio Indicates the efficiency with which a firm manages the credit it issues to customers and collects on that credit. Inventory turnover ratio Shows how many times a company's inventory is sold and replaced over a period. NWC turnover ratio Shows how effectively a company is using its working capital to generate sales; Higher is better. Asset turnover ratio (ATR) Shows ability to generate more revenue per euro of assets. Equity turnover ratio Determine the efficiency with which management is using equity to generate revenue. Fixed asset turnover ratio Measures operating performance Current asset turnover ratio Analyze the efficiency of usage of current assets. Profitability Ratios Gross profit margin Used to assess a firm's financial health. EBITDA margin A measurement of a company's operating profitability as a percentage of its total revenue. Return on equity (ROE) Measures a corporation's profitability. Return on assets (ROA) Shows how efficient management is at using its assets to generate earnings. Operating expense-toNet sales ratio The smaller ratio shows the greater the organization's ability to generate profit if revenues decrease. CEEOL copyright 2018 CEEOL copyright 2018 – Economic Studies (Ikonomicheski Izsledvania), 27 (1), p. 87-108. 107 Profit margin Shows how much out of every dollar of sales a company actually keeps in earnings. Growth Ratios Assets growth ratio Growth rates refer to the amount of increase that a specific variable has gained within a specific period and context. Net Profit growth ratio Sales growth ratio Asset Structure Ratios Current assets-toTotal assets ratio, Indicate the extent of total funds invested for the purpose of working capital Inventoryto-Current assets ratio Shows part of inventory in structure of current assets. Cash and cash equivalentsto-Current assets ratio Shows part of cash and cash equivalents in structure of current assets. Long-term assets-toTotal assets ratio Shows part of fixed assets in structure of total assets. Solvency Ratios Short-term financial debt-toTotal debt Shows part of short-term financial debt in structure of total debt. Short-term debt-toTotal debt Shows part of short-term debt in structure of total assets. Interest coverage ratio Determine how easily a company can pay interest on outstanding debt. Debt Ratio Leverage ratio (LR) Shows how much capital comes in the form of debt (loans), or assesses the ability of a company to meet financial obligations. Debt to Equity ratio Indicates how much debt a company is using to finance its assets relative to the amount of value represented in shareholders' equity Debt / Equity Total financial debt-toTotal debt Shows part of financial debt in structure of total debt. Source: based on Ross, Westerfield and Jordan (2008). CEEOL copyright 2018 CEEOL copyright 2018 Britchenko, I., Monte, D. P., Kryvovyazyuk, I., Kryvoviaziuk, L. (2018). The Comparison of Efficiency and Performance of Portuguese and Ukrainian Enterprises. 108 APPENDIX II Table А2 Descriptive staistics of economic and financial indicators by country sample (Portugal and Ukraine) Indicators by country Portuguese Ukrainian n Minimum Maximum Mean S.D. n Minimum Maximum Mean S.D. Quick ratio 42 0,258 1,479 0,802 0,240 48 0,107 7,519 1,356 1,314 Liquidity Ratio 42 0,434 2,128 1,078 0,357 48 0,465 12,084 2,672 2,759 Cash ratio 42 0,032 0,997 0,240 0,245 48 0,001 1,689 0,171 0,340 Receivable turnover ratio 42 1,526 11,959 5,134 2,773 48 0,000 28,170 7,400 8,283 Inventory turnover ratio 42 0,918 12,709 4,214 2,555 48 0,411 37,842 6,840 5,742 Net Working Capital turnover ratio 42 -574,690 513,507 0,237 121,537 48 -327,787 80,740 -5,238 51,290 Asset Turnover Ratio 42 0,204 1,464 0,628 0,306 48 0,000 2,230 0,626 0,656 Equity turnover ratio 42 0,747 12,308 3,645 2,478 48 0,000 13,772 1,516 2,246 Fixed Asset Turnover Ratio 42 0,259 3,487 1,166 0,765 48 0,000 7,231 1,319 1,794 Current Asset Turnover Ratio 42 0,525 4,153 1,721 0,958 48 0,000 7,875 1,772 1,914 Gross profit margin 42 0,126 0,809 0,547 0,159 48 -0,179 0,350 0,122 0,098 EBITDA margin 42 -0,173 0,484 0,137 0,119 29 -0,352 0,836 0,145 0,291 Profit margin 42 -0,320 0,190 0,010 0,090 48 -2,430 0,210 -0,060 0,380 Return on Equity 42 -4,661 0,533 -0,104 0,756 48 -0,574 2,179 0,039 0,393 Return on Assets 42 -0,113 0,082 0,001 0,042 48 -0,275 0,210 0,007 0,111 Operating expense to net sales ratio 42 0,782 1,280 0,945 0,104 45 0,517 3,354 1,033 0,376 Assets growth ratio 35 -0,230 0,410 -0,016 0,129 40 -0,475 1,531 0,077 0,305 Net profit growth ratio 35 -1,640,18 1,485 -47,45 277,143 40 -4,712 91,282 3,369 15,281 Sales Growth ratio 35 -0,434 0,773 0,016 0,237 40 -0,714 1,444 0,056 0,365 Current assets to total assets ratio 42 0,194 0,607 0,401 0,145 48 0,190 0,939 0,412 0,204 Inventory to current assets ratio 42 0,042 0,471 0,246 0,129 48 0,045 0,945 0,424 0,235 Cash and cash equivalents to current assets ratio 42 0,031 0,606 0,210 0,172 48 0,001 0,303 0,057 0,068 Long-term assets to total assets ratio 42 0,393 0,806 0,597 0,147 48 0,061 0,810 0,587 0,205 Short-term financial debt to total debt 42 0,019 0,503 0,230 0,110 48 0,000 0,811 0,191 0,238 Short-term debt to total debt 42 0,127 0,930 0,532 0,208 48 0,072 1,000 0,612 0,296 Interest coverage ratio 42 -9,308 70,569 4,784 13,985 48 -331,766 101,631 -0,661 53,261 Leverage Ratio 42 0,360 0,976 0,769 0,154 48 0,071 3,676 0,521 0,525 Total financial debt to total debt 42 0,355 0,862 0,603 0,149 48 0,000 0,952 0,473 0,292 Debt to equity ratio 42 0,560 40,23 5,760 6,370 48 -8,930 9,310 1,420 2,