Econometric Analysis of the Impact of Expert Assessments on the Business Activity in the Context of Investment and Innovation Development Rostyslav Yurynets 1[0000-0003-3231-8059], Zoryna Yurynets 2[0000-0001-9027-2349], Kokhan Marianna 3[0000-0002-9358-2200] 1 Lviv Polytechnic National University, Lviv, Ukraine 2.3 Ivan Franko Lviv National University, Lviv, Ukraine rostyslav.v.yurynets@lpnu.ua1, zoryna_yur@ukr.net2., marianna.kokhan@gmail.com3 Abstract. The purpose of this paper is to build a logistic regression model where the outcome is the logit of probability of business success in quantitative and qualitative terms and predictor variables are identified factors that contrib- ute to business success. The logit model is based on a sample of of 40 success- ful and unsuccessful businesses in Ukraine. The applied methodology with ob- tained results can serve to identify factors that contribute to improving business success, as well as a base for future research on the impact of selected factors on business success with a bigger sample of participants, and to improve deci- sion making in conditions of uncertainty and instability of the external envi- ronment in developing countries. Keywords: logit model, enterprise activity estimation, quantitative and qualita- tive terms, decision making Introduction Uncertainty and instability of the business environment in Ukraine are increasing despite the investment and innovation development of the country and a supportive international environment. In a competitive environment, enterprises are constantly forced to search for optimal solutions in order to gain or maintain an advantage over their rivals. In principle, in this situation, evaluation of the success of the enterprise without application of mathematical approach in decision making processes is par- ticularly difficult. Business-investors are interested in measures of business performance as base for their investment-decisions in the firm. Business success is defined in different ways, very often, a combination of financial performance and organizational performance is used [3]. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). Ramadan, Ajami, Mohamed, Lazarova-Molnar [19] highlight the role of modeling and simulation in enhancing decision-making processes in enterprises. Decision- making in enterprises holds different possibilities for profits and risks. Due to the complexity of decision making processes, modeling and simulation tools are being used to facilitate them and minimize the risk of making wrong decisions in the vari- ous business process phases. Despite the increasing pace of different types of enterprise activity modelling, little is known about correct evaluation of the success of the enterprise. Understanding the features of evaluation of the success of the enterprise is important for several reasons. First, many management scholars have inferred that a relationship exists between the correct assessment of the success of the enterprise and its internal capabilities, the stochastic processes in the organization, an advanced management practices, the ex- ternal environment. In the absence of these basic elements, decision making practices will be more risky and costly. Second, managers of many enterprises are faced with making a strategic decision about whether or not to use the modellingFinally, the using of quantitative and qualitative data in modelling can help managers, who are trying to improve decision making in conditions of the investment and innovation development of the economy in developing countries. This needs putting in place a model for evaluation of the business success. The role of the logit regression model and its economic interpretation is to provide a synthetic presentation of the calculated data that will aid in the formulation of policy of the enterprise, integration into social and economic environment and the evaluation of the success or failure of economic activity. This paper analyzes the effects of individual factors on the business success. The empirical analysis was conducted over the sample of food industry enterprises in Ukraine. The goal of the research was to establish how the value of some variable changes in response to change the values of a defined list of factors. Since the analyzed varia- ble “Business success” is a binomial variable, the model of logistic regression is used. The McFadden maximum likelihood method (logarithmic likelihood function) are used to find logistic regression coefficients. Finding an estimate of an unknown pa- rameter is simplified by maximizing the natural logarithm. A binary independent variable in logistic regression is obtained. The Newton-Raphson method are utilized to maximize the function L. The initial values of a parameters (W) are defined as the vector of linear regression parameters. The conjugate gradient method for the calcula- tion of logistic regression coefficients are used. The obtained parameter estimates are interpreted. The rest of the study is structured as follows. Section 2 presents the litera- ture review, in section 3 we define the methodology, section 4 presents the main prac- tical results and finally section 5 concludes. Literature review Managers need business success measurement systems to improve and better or- chestrate capabilities and their impact on the business, which is also called business performance. A quite elaborate body of research has suggested that the application of business success measurement systems has a positive effect on the improvement of capabilities and consequentially the improvement of business performance [14]. There is no universally accepted definition of business success has been interpret- ed in many ways [6]. There are two important dimensions of business success: 1) financial vs. other success; and 2) short- vs. long-term success. Business success can have different forms In business studies, the concept of success is often used to refer to a firm’s financial performance (high productivity, efficiency and effectiveness, business per- formance). Based on uncertainty, it is not easy for companies to choose measures to assess the performance of their business [12]. Luta [16] considers that managing the assessed performance is a very important process in enterprise. This process starts after the performance evaluation of the per- sonnel in the enterprise and depends on the performance score previously assessed. Mitsel, Alimkhanova [18] view the means of evaluating the operating efficiency of enterprises based on the DEA (Data Envelopment Analysis) and financial indica- tors are taken as the input and output parameters. The profitability of capital invested in different business activities and the improvement of employee engagement are used for performance demonstrations [17]. Buttenberg [3] investigate the challenges in measuring business success in young firms and focuses on financial as well as product-market-performance indicators (sales growth, revenue growth rate of sales to current customers, Market share, market share growth) that are specific to start-up firms in order to support their strategic deci- sion-making. The decisions of founder(s) on the acquisition, development and shed- ding of resources and capabilities as well as the factors and indicators taken into con- sideration when measuring business performance are pathbreaking for the develop- ment of the firm. Venkatraman and Ramanujam [21, 803-804] include operational performance alongside financial performance (such as sales performance or market share) in the definition and thereby enlarge the previously dominant models of man- agement research. The results of the study [7] validate that the non-financial dimensions (namely, image and customer loyalty, and product service innovation) are not valid dimensions for measuring business success, while the other two dimensions (namely, business growth and profitability) show a high degree of correlation. This indicates that busi- ness growth is aligned with profitability, that growth for profitability is a major con- cern, and that profitability still remains the key measure of business success. To measuring business success with the use of financial indicators, Horváthová and Mokrišová [10] were focused on success measurement applying aset of non- financial indicators. • Return on investment (ROI), return on assets (ROA), profit and growth rates of financial indicators are widely common measures used and reliable indicators for business success and organizational effectiveness. • To evaluate business success, the following mathematical methods are used: a matrix model, a linear programming model for addressing the problems of input and output transformations [10] data envelopment analysis (DEA) non-parametric approachordinary least squares (OLS); stochastic frontier approach (SFA); maxi- mum likelihood estimation (MLE); corrected ordinary least squares (COLS); thick frontier approach (TFA); modified ordinary least squares (MOLS); distribution- free approach (DFA) with the application of characteristics such as the EVA indi- cator, MVA, INEVA, WACC RONA, or indicators based on CVA, FCF and others [1; 20]. • A modeling techniques with the aim of the measurement of the business success should be developed from a combining statistical data and expert judgment, includ- ing various types of factors. Methodology The relationship between the factors and rates of success or failure of organizations should be established to undertake assessment of the business success. The rates of success and failure of organizations can be took on exactly two values. A binary vari- able is a variable with only two values (0 and 1). So, we need to build a model for predicting a binary variable. Constructing a regular multiple regression will not produce the desired result. But the reason why is that the calculated values of the dependent variable may not belong to the interval [0, 1]. In this case, the task of constructing a regression dependence may not be as a prediction of the values of a binary variable, but as a simulation of some continuous variable that may yield values inside the [0,1] range. Such problems can be described by linear probability models or logit and probit models. The predict- ed values can not only correspond to the values 0 and 1, but can also be interpreted as the probability of success of the enterprises. The modeling of the assessments of the business success in order to predict its fu- ture state was considered. This means the evaluation of a qualitative variable (busi- ness success - 1 or 0) by several quantitative factors. Discriminant analysis tools can be used to select the most informative quantitative variables. Logit regression allows to determine the success group of an enterprise. And furthermore, logit regression provides an opportunity to consider the likelihood that an enterprise would be catego- rized as a particular success group. The logit model looks likes this: p(х) = P(Y = 1| X = х) = (1 + exp(хTw))-1, (1) where w are unknown parameters which will need to be assessed. The logistic curve (the solid line) shown in Fig. 1. Fig. 1. Logistic curve Positive points of logit analysis: logit analysis takes into account the model of nonlin- ear dependence, logit analysis has the ability to interpret the resulting success rate of the company. The resulting indicator determines the nominal value of the business success. The following Error! Reference source not found. were used to build a model for assessing the success rate for enterprises. The prepared data (Table 1) will be used to estimate the unknown parameters of the econometric model: 𝑃(𝑦𝑖 = 1|𝑥𝑖 ) = 𝐹(𝑤0 + 𝑤1 𝑥𝑖1 + 𝑤2 𝑥𝑖2 + ⋯ + 𝑤5 𝑥𝑖5 ) + 𝜀𝑖 , i = 1,2,…,n (2) where Р(уi = l | xi ) is s the probability that the i-th value of the binary variable is 1 with the хi condition; 1 𝐹(𝑧) = – logistics function; 1+𝑒 −𝑧 εі – random component; x1 – expert evaluation of the quality of management in enterprise; x2 – expert evaluation of the qualifications of the staff; x3 – expert evaluation of the quality of products; x4 – net revenue from product sales / cost of purchasing; x5 – net revenue from product sales / number of employees. There are several ways to find logistic regression coefficients. In the study, the maximum likelihood method was used. This method is used to get the parameter es- timates of the general population from the sample data. Table 1. Data table (factor matrix) for evaluating business success Business suc- tions of the staff management in Net revenue Cost of pur- Number of from product Quality of Quality of Qualifica- employees enterprise products chasing № sales cess 9 1 9 9 8 278 3392,8 160496 10 0 5 8 9 208 4500 9845 11 1 10 6 9 1694 7214369 3463868 12 0 7 6 7 304 6374 11362 13 1 10 7 8 2320 6244672 3317232 14 0 7 8 8 153 14637 12843 15 1 9 7 8 4044 145 2616316 16 0 5 7 9 189 11738 10012 17 0 7 7 8 158 7384 9838 18 1 10 9 7 513 45620 119851 19 0 8 6 8 211 13726 12472 20 1 10 7 8 3339 83550,7 629267 21 1 9 8 8 624 46554 829712 22 0 8 9 8 325 5475 10984 23 0 7 8 9 187 4736 328 The likelihood function is the basis of the method and expresses the probability density (probability) of the simultaneous appearance of the sample results Y1, Y2,…, Yn: L(Y1,Y2,…,Yk;Θ)=p(Y1;Θ)⋅…⋅p(Yn;Θ) (3) According to the maximum likelihood method, the value of Θ=Θ(Y1,…,Yn) that maximizes the function L is accepted to be in estimation of an unknown parameter. The calculation process is being simplified by maximizing not the function L, but the natural logarithm ln(L). It has to do with the fact that the maximum of both func- tions is achieved with identical values of Θ: L*(Y;Θ)=ln(L(Y;Θ))→max (4) We do have a binary independent variable through logistic regression. Therefore, we denote the probability of occurrence of 1 (Pi =Prob(Yi=1)) by Pi. This probability will depend on Xi,, where Xi, is the row of the regressors matrix, W is the vector of regression coefficients: 1 𝑃𝑖 = 𝐹(𝑋𝑖 ), 𝐹(𝑧) = (5) 1+𝑒 −𝑧 The log-likelihood function is: 𝐿(𝒀, 𝐖) = ∏𝑛𝑦𝑖 =1 𝐹(𝑿𝒊 𝑾)𝑌𝑖 [𝟏 − 𝐹(𝑿𝒊 𝑾)]1−𝑌𝑖 (6) We use lnL instead of function L. It does not change the essence of the task, but allows us to get rid of the multiplication: 𝐿∗ = ln𝐿 = ∑𝑛𝒊=𝟏 𝒀𝒊 ln𝐹(𝑿𝒊 𝑾) + (𝟏 − 𝒀𝒊 )ln(𝟏 − 𝐹(𝑿𝒊 𝑾)) (7) Here the following designations are introduced: W = (W0, W1,…,Wm)T, Xi = (1, Xi1,…,Xim), (8) (8) XiW = W0 + W1Xi1 + W2Xi1 +…+ WmXim The Newton-Raphson method was used to maximize the function L. A Newton- Raphson method is used to perform the minimization which typically requires several iterations: −1 𝜕 ln 𝐿(𝑾𝑡 ) ∂2 ln 𝐿(𝑾𝑡 ) 𝑾𝑡+1 = 𝑾𝑡 − [ ] (9) 𝜕𝑾 ∂𝑾 ∂𝐖′ where 𝜕 ln 𝐿(𝑾) = (𝑓0 (𝑾), 𝑓1 (𝑾), … , 𝑓𝑚 (𝑾)) 𝜕𝑾 𝑓0 (𝑾) = ∑𝑛𝑖=1 𝐹(𝑿𝒊 𝑾) − ∑𝑛{𝑖:𝑌𝑖 =1} 1 (10) 𝑛 𝑛 𝑓𝑗 (𝑾) = ∑ 𝐹(𝑿𝒊 𝑾)𝑋𝑖𝑗 − ∑ 𝑋𝑖𝑗 , 𝑗 = 1,2, … , 𝑚 𝒊=𝟏 {𝑖:𝑌𝑖 =1} ∂2 ln 𝐿(𝑾𝑡 ) = ∂𝑾 ∂𝐖′ ∑𝒏𝒊=𝟏 𝐹(𝑿𝒊 𝑾)(𝟏 − 𝐹(𝑿𝒊 𝑾)) , … ∑𝒏𝒊=𝟏 𝐹(𝑿𝒊 𝑾)(𝟏 − 𝐹(𝑿𝒊 𝑾))𝑋𝑖𝑚 , ∑𝒏𝒊=𝟏 𝐹(𝑿𝒊 𝑾)(𝟏 − 𝐹(𝑿𝒊 𝑾))𝑋𝑖1 , … ∑𝒏𝒊=𝟏 𝐹(𝑿𝒊 𝑾)(𝟏 − 𝐹(𝑿𝒊 𝑾))𝑋𝑖𝑚 𝑋𝑖1 , ……… … ∑𝒏𝒊=𝟏 𝐹(𝑿𝒊 𝑾)(𝟏 − 𝐹(𝑿𝒊 𝑾))𝑋𝑖𝑚 , ∑𝒏𝒊=𝟏 𝐹(𝑿𝒊 𝑾)(𝟏 − 𝐹(𝑿𝒊 𝑾))𝑋𝑖𝑚 𝑋𝑖𝑚 ( … ) This is usually the initial values which has been determined to be the the vector of linear regression parameters: 𝑾(поч) = (𝑿𝑇 𝑿)−1 𝑿𝑇 𝒀 (11) For our research we are going to use the conjugate gradient method. Empirical results The parameters of the resulting logistic regression model in analytics software package STATISTICA are as follows: Fig. 2. The parameters of the resulting logistic regression model in analytics software package STATISTICA It is to be noted (Fig. 2) that the five-factor logit model ensures a high degree of reliability. Its reliability was confirmed by 𝑥𝑖 2 = 55,99. And it may even be concluded that the null hypothesis can be not rejected with near zero probability. Based on the foregoing, the logistic model are obtained: 𝑃(𝑦𝑖 = 1|𝑥𝑖 ) = (1 + 𝑒 65,1−6,02𝑥1−0,54𝑥2+1,27𝑥3−0,02𝑥4−0,16𝑥5 )−1 The adequacy of the constructed logistic model can be calculated by likelihood ra- tio index (LRI, McFadden's-R2 statistic): ln𝐿(𝒘) 𝐿𝑅𝐼 = 1 − = 0,97 (12) ln𝐿(𝐰) where 𝐿(𝒚, 𝒘) = ∏𝑛𝑦𝑖=1 𝐹(𝒙𝒊 𝒘)𝑦𝑖 [𝟏 − 𝐹(𝒙𝒊 𝒘)]1−𝑦𝑖 (13) ln𝐿(𝒘) – is the maximum value of the log-likelihood function. This is reached at a point whose coordinates are equal the estimates of the model parameters, 𝑤 = (𝑤0 , 𝑤1 , 𝑤2 , … , 𝑤𝑚 ) ln𝐿(𝒘𝟎 ) – the value of the logarithmic likelihood function which is calculated on the basis of the assumption that wl = w2 = ... = wт = 0. The calculated value of likelihood ratio index points to the adequacy of the con- structed model. An assessment of the business success rate at different values of factors has been carried out. The Figure 3 show how business success would change when x1 factors (expert evaluation of the quality of management in enterprise) for certain values of factor x3 (expert evaluation of the quality of products) and fixed values of other factors: x2 = 7 (expert evaluation of the qualifications of the staff), x4 = 18 (net revenue from prod- uct sales / cost of purchasing), x5 = 163 (net revenue from product sales / number of employees) are changed. That is, increased the quality of the management (x1) for different values of product quality and fixed values of other indicators (x2, x4, x5) leads to better the business success indicator. 1 0,9 0,8 0,7 0,6 y (x3=7) 0,5 y (x3=8) 0,4 y (x3=9) 0,3 0,2 0,1 0 7 8 9 10 Fig. 3. Dependence of business success on the expert evaluation of the quality of management (for certain values of expert evaluation of the quality of products) The strategic concept of management on the quality of tangible and intangible el- ements of product is a good way of gaining competitive advantage. Considering mod- ern business activities of companies, there are numerous reasons for emphasizing the importance of quality management [2]. One of the most important elements that must be taken into consideration is the relation between the price and quality of products. The innovation is actually the key to improving the quality of products. Management of an enterprises must be able to recognize opportunities, i.e. the sources of innova- tion that such changes bring about, which will certainly improve the quality of prod- ucts [13]. The Figure 4 show how business success would change when x1 factors (expert evaluation of the quality of management in enterprise) for certain values of factor x2 (expert evaluation of the qualifications of the staff) and fixed values of other factors: x3 = 7 (expert evaluation of the quality of product), x4 = 17 (net revenue from prod- uct sales / cost of purchasing), x5 = 144 (net revenue from product sales / number of employees) are changed. That is, increased the quality of the management (x1) for different values of the qualifications of the staff and fixed values of other indicators (x3, x4, x5) leads to better the business success indicator. 1 0,9 0,8 0,7 0,6 y (x2=7) 0,5 y (x2=8) 0,4 y (x2=9) 0,3 0,2 0,1 0 7 8 9 10 Fig. 4. Dependence of business success on the expert evaluation of the quality of management (for certain values of expert evaluation of the qualifications of the staff) Hasebrook [9] was calculated how the quality of management can determine busi- ness (financial) success based on data about 1,900 banks and 2,700 respondents. It was thus found that there was a positive correlation between management quality and qualifications of the staff. The result shows a correlation between 35% (2009) and 95% (2013). To provide the success of the management manageres need the profes- sional qualifications approach to enhancing staff professional qualifications in the process of creation of competitive relations. The Figure 5 show how business success would change when x2 factors (expert evaluation of the qualifications of the staff) for certain values of factor x1 (expert evaluation of the quality of management) and fixed values of other factors: x3 = 8 (expert evaluation of the quality of product), x4 = 19 (net revenue from product sales / cost of purchasing), x5 = 122 (net revenue from product sales / number of employees) are changed. That is, increased the x2 factor for different values of the quality of management and fixed values of other indicators (x3, x4, x5) leads to better the busi- ness success indicator. 1 0,9 0,8 0,7 0,6 y (x1=8) 0,5 y (x1=9) 0,4 y (x1=10) 0,3 0,2 0,1 0 7 8 9 10 Fig. 5. Dependence of business success on the expert evaluation of the qualifications of the staff (for certain values of expert evaluation of the quality of management) The motivational models directed at increasing the loyalty of employees must be competitive, able to retain the staff, improve its professional qualifications and focus on creating profitability of oriented at innovation enterprise. That’s why the system of motivation should include not only financial incentive instruments (high wages, bo- nuses, bonuses and other forms of financial encouragement), but also the tools of further professional career growth, increase of loyalty and self assessment of special- ists [15]. 1 0,9 0,8 0,7 0,6 y (x1=8) 0,5 y (x1=9) 0,4 y (x1=10) 0,3 0,2 0,1 0 6 7 8 9 Fig. 6. Dependence of business success on the expert evaluation of the quality of products (for certain values of expert evaluation of the qualifications of the staff) The Figure 6 show how business success would change when x3 factors (expert evaluation of the quality of products) for certain values of factor x1 (expert evaluation of the quality of management) and fixed values of other factors: x2 = 9 (expert eval- uation of the qualifications of the staff), x4 = 5 (net revenue from product sales / cost of purchasing), x5 = 99 (net revenue from product sales / number of employees) are changed. That is, increased the x3 factor for different values of the qualifications of the staff and fixed values of other indicators (x2, x4, x5) leads to decrease the busi- ness success indicator. The decline can be attributed to the slowdown in employee motivation at the en- terprises, the decline in public investments in the food industry, and the rise in crisis in the country. Generally speaking, the quality of products and employee performance depends on a large number of factors, such as motivation, appraisals, job satisfaction, training and development [11]. For Ukrainian enterprises the wages that does not meet the labor efforts and quali- fications may demotivating employees and disincentive to work. This leads to еm- ployees of a company act according to the principles of irresponsibility, reduce productivity, breaking the labor legislation and operational discipline. 1 0,9 0,8 0,7 0,6 y (x2=8) 0,5 y (x2=9) 0,4 y (x2=10) 0,3 0,2 0,1 0 6 7 8 9 Fig. 7. Dependence of business success on the expert evaluation of the quality of products (for certain values of expert evaluation of the quality of management) The managers should be concerned by the low level of motivating, which does not allow workers and members of their families a decent standard of living. This risk could lead to the departure, of some of skilled personnel working for the organiza- tions. The Figure 7 show how business success would change when x3 factors (expert evaluation of the quality of products) for certain values of factor x2 (expert evaluation of the qualifications of the staff) and fixed values of other factors: x1 = 9 (expert evaluation of the quality of management), x4 = 5 (net revenue from product sales / cost of purchasing), x5 = 99 (net revenue from product sales / number of employees) are changed. This situation illustrates that in the food industry, the quality of products is gradu- ally decreasing. Businesses were plagued by low level of quality product as a result of low level of product standardization, quality management, quality control. The main factors that contributed to the decline of the product quality are: low quality of raw materials, low level of the organizations of labour and production pro- cesses, outflow of skilled workers, poor and irregular productivity in production. Improving product quality is one of the most important things for achieving long term sales growth and profitability. Businesses seeking to improve product quality need to embed quality practices in their routine processes. So rather than just an after- thought, quality has to be intrinsic to companies’ performance and daily operations management. And though increasing quality of products is not an easy task, it rewards businesses with increased revenue and reduced costs. Improving product quality based on these principles: build a solid product strategy, implement a quality man- agement system (QMS), make quality a part of company culture, perform product and market testing, always strive for quality [5]. Let us evaluate business success with a built model. Іnformation on activities of enterprises is presented in Table 2. Table 2. The value of indicators to measure а new businesses success Qualifications Business suc- management in Cost of pur- Number of Quality of Quality of of the staff employees enterprise products chasing № cess 1 8 8 8 150 15000 25000 2 6 7 8 100 5000 24000 3 10 9 8 400 10000 25000 𝑃(𝑦1 = 1|𝑥𝑖 ) = (1 + 𝑒 65,1−6,02∙8−0,54∙8+1,27∙8−0,02∙1,67−0,16∙166,67)−1 = 0,98 𝑃(𝑦2 = 1|𝑥𝑖 ) = (1 + 𝑒 65,1−6,02∙6−0,54∙7+1,27∙8−0,02∙4,8−0,16∙240 )−1 = 0,958 𝑃(𝑦3 = 1|𝑥𝑖 ) = (1 + 𝑒 65,1−6,02∙10−0,54∙9+1,27∙8−0,02∙2,5−0,16∙62,5 )−1 = 0,46 The calculations show the possible success of the first and second enterprises. Based on this results, it can be concluded that the logistic regression is adequate for the task at hand and is well suited to analyze the business success. Conclusions Any evaluation of the business success can be made by modelling based on quanti- tative and qualitative data and analysing the results. The data collected on an activity of Ukrainian enterprises of were related to the various financial variables (net revenue from product sales, cost of purchasing, number of employees) and expert evaluation. Using the binomial logistic model the logit of probability odds of business success in dependence of quantitative and qualitative factors that contribute to business success was analyzed. Performed logistic regression over the selected set of quantitative and qualitative variables showed that quality of the management, qualifications of the staff are most statistically significant predictors. All these factors affect the increase of probability of business success. In addition, declining quality product significantly decrease the probability odds of business success. And, obtained results illustrate that in the food industry, the qual- ity of products is gradually decreasing. Businesses were plagued by low level of qual- ity product as a result of various factors. Based on the obtained results, the management of an enterprises can focus on those areas, which are preconditioned for business success and efficiency improve- ment. The applied methodology with obtained results can serve as a base for the fu- ture research on the impact of quantitative and qualitative factors on success for the enterprises and organizations of different forms of ownership, working in different spheres. Referenses 1. Assaf, A. G., Deery, M., Jago, L.: Evaluating the performance and scale characteristics of the Australian restaurant industry. In: Journal of Hospitality & Tourism Research, 35(4), 419–436. (2011) 2. Bajic, M.: Total Quality Management in Hospitality. In: Hotel house – Hotelska kuca, Ugoprogres, Belgrade. (2002) 3. Buttenberg, K.: Measuring Business Performance in Young Firms, In: Journal of Business and Economics, 8(5), 446-454. (2017) 4. Chittithaworn, C., Aminul, Md., Keawchana, T., Yusuf, D. H. M.: Factors Affecting Busi- ness Success of Small & Medium Enterprises (SMEs) in Thailand, In: Asian Social Sci- ence, 7(5), 179-190. (2011) 5. Ciric, N.: 5 Ways to Increase Product Quality, available at https://www.smallbusinessrainmaker.com/small-business-marketing-blog/5-ways-to- increase-product-quality (2020) 6. Foley, P., Green H.: Small business success. London: Chapman. (1989) 7. Garg, A. K., Joubert, R. J. O., Pellissier R. Measuring business performance: A case study, In: Southern African Business Review, 8(1), 7-21. (2004) 8. Greene, W. H.: Ekonometric analysis [Econometric analysis]. Кyiv: Osnovy. (2005) 9. Hasebrook, J.P.: HR Management Quality as a Driver of Business Success, In: Transfer, 4, 34-35. (2015) 10. Horváthová, J., Mokrišová, M.: Innovative approaches and their application in measuring business performance. In: CBU international conference on innovations in science and ed- ucation, March 22-24, Prague, Czech Republic, available at: 10.12955/cbup.v5.921 (2017). 11. Kamalian, A. R., Yaghoubi, N. M., Moloudi, J.: Survey of Relationship between Organiza- tional Justice and Empowerment (A Case Study). In: European Journal of Economics, Fi- nance and Administrative Sciences, 24, 165-171. (2010) 12. Kappel, M.: 6 Ways To Measure Small Business Success, available at: https://www.forbes.com/sites/mikekappel/2017/03/08/6-ways-to-measure-small-business- success/#232bd2fa18f4 (2020) 13. Kosar, L., Raseta, S., Comic, D.: Hotel house from orientation toward service to orienta- tion toward the guest. Hotel house – Hotelska kuca, Hores, Belgrade. (2004) 14. Koufteros, X., Verghese, A., Lucianetti, L.: The effect of performance measurement sys- tems on firm performance: A cross-sectional and a longitudinal study. In: Journal of Oper- ations Management, 32(6), 313-336 (2014). 15. Lavrushin, O. I.: Ustoychivost bankovskoy sistemy i razvitiye bankovskoy politiki [The Stability of the Banking System and Development Banking Policy: monograph]. Moskva, KnoRus, (2015). 16. Luta, M.: Managing and rewarding the estimated performance. In: Human and social sci- ences at the common conference, 5(1), DOI: 10.18638/hassacc.2017.5.1 (2017). 17. Marcão, R.P., Sousa, M.J., Pestana G.: Performing Enterprise Architectures Through Gam- ified Business Models. In: Handbook of Research on Business Models in Modern Compet- itive Scenarios, Publisher: IGI Global, 232-247 (2018). 18. Mitsel, Artur A., Alimkhanova, Aliya N. DE Analysis of Enterprises Activity. In: Global Economics and Management: Transition to Economy 4.0, 25-36. (2019) 19. Ramadan, N., Ajami, R., Mohamed, N., Lazarova-Molnar, S. Modeling and Simulation for Enterprise Decision- Making: Successful Projects and Approaches, In: Conference: Fifth International Conference on Industrial Engineering and Operations Management. DOI:10.1109/IEOM.2015.7093873. (2015) 20. Stern, J. M. Economic Value Added, available at: https://www.eva.com (2015). 21. Venkatraman, N., Ramanujam, V. Measurement of business performance in strategy re- search: A comparison of approaches. In: Academy of Management Review, 11(4), 801- 814. (1986)