Next Article in Journal
Modeling the Urban Freight-Transportation System Using the System Dynamics Approach
Previous Article in Journal
Risk Simulation of Urban Rainstorm Flood Disasters Considering Crowd Activities
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Systems
Volume 11
Issue 8
10.3390/systems11080408
systems-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Management of Operational Risk in the Context of Financial Performance of SMEs

by
Mária Hudáková
1,*,
Peter Kardoš
1,
Ján Dvorský
2,
Charles Randy Afful
3 and
Jitka Kloudova
4
1
Department of Crisis Management, Faculty of Security Engineering, University of Zilina, 010 26 Zilina, Slovakia
2
Department of Economics, Faculty of Operational and Economics of Transport and Communications, University of Zilina, 010 26 Zilina, Slovakia
3
Department of Business Administration, Faculty of Management and Economics, Tomas Bata University in Zlin, 760 01 Zlin, Czech Republic
4
Faculty of Business, Pan-European University, 110 00 Prague, Czech Republic
*
Author to whom correspondence should be addressed.
Systems 2023, 11(8), 408; https://doi.org/10.3390/systems11080408
Submission received: 13 June 2023 / Revised: 19 July 2023 / Accepted: 6 August 2023 / Published: 8 August 2023
(This article belongs to the Section Complex Systems and Cybernetics)
Versions Notes

Abstract

:
Owners and managers of small and medium-sized enterprises (SMEs) have changed their perspective on risk management due to the current global negative threats in the business environment. If they want to be successful and ensure the financial performance of their business, they must adopt a proactive approach to reducing strategic risks in connection with operational risks. The aim of the article is to identify and quantify the differences in operational risk management and its effect on the financial management of SMEs between four Central European countries. The statistical sample of SMEs consisted of 1090 owners and top managers. Data collection was carried out through a questionnaire in the period December 2022–January 2023. Statistical hypotheses were evaluated with the application of correlation analysis and linear regression modelling. Empirical findings have confirmed that operational risk management has a significant positive impact on improving the profitability of the enterprise and on reducing the enterprise’s indebtedness, as well as in the context of reducing the enterprise’s inability to pay its obligations. However, there are disparities in the perception of this influence between owners/managers with regard to the country in which they carry out their business activities. The findings are important for both national and multinational organizations and entities dealing with risk management in the business environment of small and medium-sized enterprises.

1. Introduction

In the conditions of the Visegrad Group (V4) countries, i.e., in Slovakia, the Czech Republic, Poland, and Hungary, small and medium-sized enterprises (SMEs) are a necessary and logical part of the economic organism of each country. SMEs have an irreplaceable role, especially in the area of job creation and regional development [1,2]. According to Khan et al. [3], a quality business environment, which creates conditions for achieving long-term sustainable economic growth, is a basic prerequisite for business development and increasing competitiveness in the V4 countries on an international scale. Business in the V4 countries has represented a period full of changes and uncertainty since 2020 [4,5,6]. Waiho et al. [7] state that the pandemic of the COVID-19 virus brought with it a number of negative impacts that affected not only the business environment but also the entire society. SMEs had to deal with the implementation of a number of anti-pandemic measures that limited business activity to varying degrees [8]. Subsequently, the war in Ukraine continues to exacerbate ongoing adverse effects and slow growth. In both the world economy and the EU economy, the prices of energy commodities are the most affected [9]. Inflation reaches record-high values as a result of energy prices. According to an EU study [10], even though the whole world is facing high inflation at record levels for the last several decades, the Eurozone is objectively the worst in international comparison. The consequences of the pandemic and Russian aggression in Ukraine have combined on the European continent. The combination of these two factors, when the post-pandemic recovery brought rising prices and was followed by a sharp spike in energy prices, caused major problems for Europe. According to Dvorský et al. [11], unpredictability and uncertainty in the business environment are considered the most fundamental problems also by investors, which decreases the competitiveness of countries in the long-term perspective.
The size of individual negative impacts on SMEs depends on the socioeconomic conditions of individual countries, but also on the ability and readiness of the enterprise to face these negative events [12]. The authors of the study draw attention to the vulnerability of European countries to the effects of negative threats and recommend applying innovative strategies to reduce their impacts [13]. Currently, industries have started to implement the concept of risk management more in their management strategy and adapt it to the newly created conditions [14]. According to Ciocoiu et al. [15], it is important to assess the extent to which individual elements of risk resistance are created and implemented. It is also necessary to clearly establish the responsibility of owners and managers of SMEs for the implementation of the risk management system, which is one of the key aspects of resilience, sustainability, and financial performance of enterprises [16].
An innovative view of risk management, influenced by the current negative threats from the business environment in the V4 countries, influences the formation of new attitudes of owners and managers of SMEs, placing more emphasis on the prevention of business crises [17]. Ferreira de Araújo Lima et. al. [18] state that it is very important and necessary to set up a risk management strategy that would reduce strategic risks influenced by external threats. Strategic risks have a strong influence on operational risks. New and proactive management of SMEs with a focus on risk management contributes to greater sustainability and increases the financial performance of SMEs [19]. These facts motivated the team of authors to conduct this study, the aim of which is to examine changes (differences) in operational risk management and its impact on the financial management of SMEs between four Central European countries.
In the V4 countries, business environment operational risks have been considered the top business risks of SMEs in the long term. This has been testified by the studies carried out by the authors of the article from 2018 to the present [5,11,20,21]. Currently, negative threats in the business environment give owners and managers of SMEs a new attitude toward risks. The originality of this article is the processed study on the current attitude of owners and managers of SMEs to operational risk management in connection with financial performance. Other authors have not dealt with this topic to such an extent and scope so far. Conducting an in-depth statistical analysis focused on the evaluation of the impact of operational risks on the enterprise financial management in the SMEs segment among the countries of the Visegrad group is a unique and original way to demonstrate causal links in the context of business sustainability with respect to the country of business.
The subject of the examination is the search for an answer to the following question:
RQ: Is the country of SME business a significant factor in the perception of operational risk in the context of financial management of the enterprise among owners/top managers in Central European countries?
The article is conceived as follows. The critical literature research in the field of operational risk and financial management of enterprise performance forms the basis for conducting quantitative research in the business environment of Central European countries. Another part of the article consists of the methodology of collecting empirical data; questionnaire structure and defining variables; formulation of statistical methods and tools for their verification; and the structure of the sample of respondents. Subsequently, the empirical results are presented in tables and regression equations. The discussion includes a comparison and dissemination of empirical findings from the business environment in the SME segment with other studies and results. In conclusion, the authors indicate the further direction of the scientific research focus and their activities. In addition, in the conclusion, they formulate the main findings, the limits of empirical research, and define the user of the achieved findings.

2. Theoretical Background

The ability to maintain the financial performance of the enterprise and the ability to effectively manage business risks are closely related [22]. Enterprise risk management is an important element of effective strategic management, which through its activities tries to reduce the negative impact of various types of risks—strategic, financial, operational, personnel, etc., on planned objectives [23]. The financial performance of SMEs is sensitive to changes in the business environment. The presence of threats in business (e.g., the COVID-19 pandemic; the Russia–Ukraine conflict) manifests itself in negative contexts in connection with operational risk management. The growth of SME costs in countries close to military conflict (countries of the Visegrad Group) is affected to the greatest extent. Many research studies examine the importance and benefit of risk management in enterprises and currently also assess various negative influences and impacts on the financial performance of enterprises [4,24,25,26]. The mentioned research studies and empirical research declare the growing interest of research teams in examining the issue of risk management in the enterprise, which is also testified by the following processed analysis.

2.1. Operational Risk

Operational risk exists in every organization regardless of its size. Lopez [27] defines operational risk as “the risk of loss arising from inadequate or failed internal processes, people, and systems or from external events”. This definition includes human error, fraud, and malice, information systems failures, personnel management problems, commercial disputes, accidents, fires, and floods. From a narrower perspective, the risk is limited exclusively to events occurring within the organization or, more specifically, to those caused solely by human error [23].
According to Agarwal and Ansell [28], operational risks include process or system failures, legality and compliance, illegal or criminal activity, and human factors. Bai et al. [29] define operational risk as the risk resulting from the execution of the enterprise’s business activities. The risk is that the enterprise’s internal procedures, policies, and systems are not adequate to prevent a loss, whether due to market conditions or operational difficulties [3]. Such deficiencies may arise from improper measurement or reporting of risk or from a lack of control over key managers [30].
Dumitrescu and Deselnicu [31] and Holla et al. [32] define operational risks mainly with the failure of production processes, systems, and services, i.e., these are risks resulting from business interruption. This mainly concerns limitations of the production process, technical breakdowns, accidents, failure to master the technological process, insufficient utilization of the production capacities, low rate of innovations, obsolete production equipment, non-compliance with the quality of the production process, and product failure, as well as external sources of risk, e.g., loss of suppliers, lack of resources and raw materials, etc.
Several authors (e.g., [28,33]) state that among the most serious sources of operational risks that significantly affect the development of SME business are outdated production equipment, missing technologies, and the inability to innovate. Obsolete production equipment or missing new technologies greatly affect the sustainability of SMEs. Tucek and Hrbáčková [34] state that new trends and technological developments but also the interconnectedness of economies and markets force enterprises to constantly deal with innovating production equipment, technologies, and procedures. Currently, terms such as intelligent enterprise, intelligent logistics, and transport systems, as well as the very concept of Industry 4.0, are increasingly coming to the fore.
According to Avilov et al. [35], it is necessary for SMEs to adapt to current trends in the field of production technologies, as well as the introduction of intelligent and self-learning systems into the production process. Enterprises often lack technology, which they then have to rent, which causes additional production costs [33]. Due to the influence of personnel, as well as financial and economic risks, enterprises have significant problems in the field of innovation in the current environment, and SMEs lose interest in innovation. They perceive the costs spent on research and development of new products as unnecessary costs; similarly, due to the significant conservative approach of the owners, there is no willingness to provide new services, and thus they lag behind the competition [36]. This is subsequently reflected in the financial stability and competitiveness of the enterprise.
According to Allahar [37], SMEs are afraid to invest, because the costs will be reflected in a higher price of products or services, as a result of which enterprises significantly lose their competitive advantage. Innovation has a profound and significant impact on an enterprise’s competitiveness through increasing productivity. For enterprises, it is necessary that the product always complies with the standards, which determine its quality. Product quality affects the prosperity, efficiency, and competitiveness of SMEs as well [38]. The manufacturer obtains higher sales for quality products even with an unlimited amount of sales, as a result of a higher price than the competition can achieve [39]. With quality, the enterprise can not only establish itself in existing markets but can also create new markets with its original products.
According to Gok et al. [40], quality makes it possible to increase exports compared to the competition, thereby obtaining foreign exchange, which enables the purchase of modern equipment and technologies abroad, and consequently the enterprise creates a lead over the existing competition.

2.2. Financial Performance of SMEs

Financial management is a key part of every SME, as the main goal of business is to increase the market value of the enterprise and maximize profit [41]. An enterprise must make a profit in order to provide a return to all investors and to grow and invest. According to Chang and Wu [42], financial management represents the main function of management and affects all parts of the enterprise. When choosing an appropriate financial policy, it is very important to monitor the enterprise’s performance with regard to predefined goals that are aligned with the interests of all entities. Ślusarczyk and Grondys [19] state that the measurement and evaluation of performance become not only an important process in assessing the success of the enterprise but also a process that, based on the analysis of the effectiveness of the enterprise’s financial operations, supports the development and increase in the long-term competitiveness of the enterprise.
According to Udoh [43], the basic tool for evaluating financial performance is the application of financial analysis. The financial analysis of SMEs enables a comprehensive assessment of the quality of business activities and the economic level of business entities. Zhao and Zeng [44] define financial analysis as an analytical activity that interprets financial information in assessing an enterprise’s performance and prospects, as well as in comparison with other enterprises and industry results. The financial analysis acts primarily as feedback on what the enterprise has achieved and what assumptions it has managed to fulfil, and therefore it is obvious that it forms an integral part of the financial management of the entire enterprise [45]. The main purpose of financial analysis is to provide a methodical apparatus that, through available resources, enables a comprehensive evaluation of the financial situation and assessment of the financial health of the enterprise [46]. Kotaskova et al. [47] define the financial situation as financial performance, which is measured mainly by profitability, and the financial position of the enterprise, which reflects the risks associated with the way the enterprise is financed. The concept of financial health expresses a satisfactory financial situation in which the enterprise is able to consistently achieve the level of appreciation of invested capital, which is required by investors in view of the risk of the given type of business. Olah et al. [48] perceive financial analysis as a tool for measuring performance from a time point of view, on two levels. The first of them is the fact that financial analysis provides us with a certain insight into the past, thanks to which it offers the possibility to evaluate the development of the enterprise based on a whole range of criteria. The second level is represented by the fact that financial analysis serves as a basis for financial planning in all-time planes. In addition to evaluating the current financial situation, it also enables short-term planning associated with the normal operation of the enterprise, as well as strategic planning associated with the long-term development of the enterprise.
As part of financial management, SMEs should, among other things, monitor and evaluate internal and external factors that influence financial analysis and thus affect the performance of the enterprise [49]. The enterprise cannot be analyzed as an isolated entity because its success is influenced not only by the influences of the external environment but also by its connection to the activities of the internal environment [17]. According to Christensen et al. [50], by analyzing the financial situation of the enterprise, it is possible to reveal the strong and weak points of the enterprise’s activities and, based on the prediction of development, to reveal potential risks that could cause or will cause financial difficulties in the future or a financial crisis of the enterprise. In general, the main task of financial analysis is considered to be the identification of the causes that condition the financial situation of the enterprise and actively contribute to its improvement and stabilization [51].
Financial analysis indicators are used to monitor revenues and costs and overall financial management in enterprises [52]. They focus on the information available in the profit and loss statement and balance sheet of the enterprise. In every enterprise, it is also important to solve the procedure for optimally solving the relationship between fixed and variable costs. If a large part of the operating costs are fixed, then the operating profit will decrease significantly with a decrease in sales. According to Bogodistov and Wohlgemuth, [53], this phenomenon refers to operating leverage (OL—Operating leverage), which expresses the sensitivity of the reaction of operating profit (EBIT—profit excluding tax and interest) to a change in the enterprise’s total revenues (TR—Total revenues). Knowing this relationship is an important prerequisite for ensuring the enterprise’s liquidity and long-term profitability, as well as managing production (operational) risk. According to Zhao and Zeng [44], operational risk is primarily influenced by the share of fixed costs in total costs. The rate, or the degree of operational risk, is determined by the degree of operational leverage [54]. Operational risk is mainly related to the reduction in demand for products, the quality of management, the intensity of competition in the relevant market, the degree of diversification of the enterprise’s product portfolio, the quality of products, the use of technology, the qualifications of employees, etc.

3. Research Methodology

The aim of the article is to identify and quantify the differences in operational risk management and its effect on the financial management of small and medium-sized enterprises (SMEs) between four Central European countries.

3.1. Data Collection

Data collection was carried out in the following steps. In the first step, research workplaces in V4 countries were approached in order to analyze the factors determining the sustainability and financial performance of SMEs in the business environment of Central Europe. The research team consists of the following research departments: i. Department of Economics, University of Zilina, Slovakia; ii. Department of Crisis Management, University of Zilina, Slovakia; iii. John von Neumann University, Hungary; iv. Department of International Business, University of Gdańsk, Poland; v. Department of Microeconomics, University of Gdańsk, Poland; and vi. Department of Business Administration, Tomas Bata University in Zlín, Czech Republic. In the second step, the final version of the questionnaire (English version) was created, named „Effect of selected factors on the sustainability in the segment of small and medium-sized enterprises“. The questionnaire was subsequently translated into the national language of the partner countries. The respondent was defined as the owner or top owner of a small and medium-sized enterprise (SME) that operates in the business environment in the selected country of the Visegrad Group (V4; CR—Czech Republic, SR—Slovakia; PL—Poland; HU—Hungary). In the third step, the renowned external agency MNFORCE was contacted in order to create a sample of respondents using Computer-Assisted Web Interviewing (CAWI Research Method). The minimum number of respondents in the selected research countries was the result of the sample size analysis. The main criterion for data collection was the proportional percentage expression of MPS according to demographic characteristics, which can be found from public sources at the national level (e.g., Statistical Office of the Slovak Republic, etc.). The data collection itself was carried out between December 2022 and January 2023.

3.2. Questionnaire and Variables

The questionnaire was created in electronic form. The questionnaire included a control question to verify the consistency of the respondent’s answers. The respondent was excluded from the sample set in case of different attitudes of the respondent to the control question. The online questionnaire was secured against automatic computer filling. At the beginning of the questionnaire, the respondents had to answer the question of whether they consent to the publication of their positions on the statements in the questionnaire for scientific purposes. A total of 26 respondents did not give their consent. Their attitudes were immediately excluded from the statistical survey. The respondents did not publish any sensitive information about themselves; there was complete anonymity of the respondents.
The questionnaire was constructed from several separate parts: demographic characteristics of the SME (e.g., size of the enterprise; the legal form of the enterprise; industry of enterprise; period of operation of the enterprise in the business environment; location of operation of the enterprise; country of the enterprise); demographic characteristics of the respondent (e.g., gender; age; the highest level of education; correlation of the level of education with the business sector; the position of the respondent in an SME); and formulated statements on selected sources of business risks (personnel risk, financial risk, market risk, enterprise reputation, legal risk, crisis phenomena in business, and operational risk). To evaluate the scientific questions of the article, the following variables were analyzed:
  • Independent variables (operational risk statements; ORS; e.g., Virglerova et al. [5]; Hudakova and Dvorsky [55]: Our enterprise has a sufficient utilization of the production capacities (ORS1). The enterprise suppliers’ prices for products and services are adequate (ORS2). Our enterprise has no problem with the distribution of our products/services (ORS3). Our enterprise has no problem with the suppliers (e.g., cooperation, numbers of suppliers, relationships; ORS4).
  • Dependent variables (financial performance of SMEs; FPS; e.g., Kotaskova et al. [47]; Olah et al. [48]: Our enterprise has sufficient profit (FPS1). The indebtedness of the enterprise is adequate (not a high share of debt; FPS2). Our enterprise has no problem with an ability to pay obligations (insolvency; FPS3).
The respondent had to answer the selected statements with one of the following types of answers (processed according to a 5-point Likert scale): I completely agree with the statement (1); …; I completely disagree with the statement (5). The questionnaire was designed so that positive responses to the independent variables linearly converged to positive responses to the dependent variables.

3.3. Statistical Hypothesis and Statistical Methods

To evaluate the main goal of the article, the following statistical hypotheses (SH) were formulated:
SH: There is a statistically significant influence of selected operational risk indicators (SH1: ORS1; …; SH4: ORS4) on financial management (SH1_1: FPS1; …; SH1_3: FPS3) among owners/top managers of SMEs with regard to the country in which do business (CR, PL, HU, SR, V4).
Descriptive statistics and their characteristics (mean; standard deviation; skewness; and kurtosis) were used for the initial analysis of variables with the partial aim of verifying the multiple normal distribution of the formulated variables. The subject of further statistical investigation was the application of correlation analysis. The aim of the correlation analysis was the calculation and verification of the statistical significance of the pairwise coefficients of dependence between independent and dependent variables. Verification of statistical significance was carried out with the application of t-stat. Due to the nature of the variables, linear regression modelling (LRM) can be considered a suitable mathematical–statistical method. In this context, it is important to emphasize that the goal of applying LRM to data evaluation is not to predict the dependent variable in the future, but only to verify the statistical significance of the selected independent variables (or their influence) on the selected independent variable. This statistical approach of impact evaluation was applied in several empirical studies, e.g., Belas et al. [56]; Dvorsky et al. [57]; Virglerova et al. [5]; and others. LRMs and their statistical significance were verified using the F-test. Regression coefficients were calculated by the method of least squares and their statistical significance was verified by t-Stat. Due to the fact that the data are not time series data, autocorrelation is not the subject of the analysis. Verification of the negative phenomenon of multicollinearity in LRM was verified using the Variance Inflation Factor (VIF).

3.4. Structure of Respondents in V4 Countries

Out of the total number of 1129 questionnaires, 2.3% (n = 26) of respondents did not consent to the publication of their positions for scientific purposes. The structure of the sample set of respondents (n = 1090) according to the country of operation of the SME was as follows: 301 (27.6%)—PL; 362 (33.2%)—CR; 162 (14.9%)—SR; 265 (24.3%) HU. A total of 64.6% of micro-enterprises filled out the questionnaire. The largest representation of enterprises according to the legal form of business are sole traders (54%) and limited liability companies (37.2%). Up to 33.5% of enterprises that have been operating in the business environment for more than 10 years participated in the research. A total of 39% of enterprises have their headquarters in the capital of the country. In total, 44.9% of women completed the questionnaire; 41.2% of respondents with a bachelor’s degree as their highest education; and 78.8% of SME owners. More than 35% of the respondents said that some activities in the enterprises they perform are correlated with their highest level of education. Most respondents indicated that their motivation to start a business was mainly money (64.6%) and mission (25.5%). The sample of respondents considers the most important characteristic of an entrepreneur to be flexibility (29.5%) and creativity (28.7%) in business.

4. Empirical Results

Descriptive characteristics of selected variables (dependent; independent) are the subject of Table 1.
The highest rate of agreement in the evaluation of dependent variables (FPS) is among Czech respondents for the statement FPS3 (CR: M = 1.677; see Table 1). On the other hand, the lowest rate of agreement in the evaluation of dependent variables (FPS) is among Slovak respondents for the statement FPS1 (SR: M = 2.720). The highest rate of agreement in the evaluation of independent variables (ORS) is among Polish respondents for the statement ORS1 (PL: M = 2.026). On the other hand, the lowest rate of agreement in the evaluation of dependent variables (FPS) is among Czech respondents for the statement FPS1 (CR: M = 2.859). Skewness and Kurtosis for each variable are in the interval of values from −2 to 2. The assumption of multiple normal distributions of the defined variables is accepted due to the stated values.
The results of the correlation analysis showed moderate to strong positive dependencies between selected FPS and ORS indicators in SR, PL, HU (pairwise correlation coefficients range from 0.4 to 0.63; see Table 2). According to the Czech respondents, the dependencies between FPS and ORS indicators show very weak to weak dependencies.
ORS the highest rate of agreement in the evaluation of dependent variables (FPS) is among Czech respondents for the statement FPS3 (CR: M = 1.677). On the other hand, the lowest rate of agreement in the evaluation of dependent variables (FPS) is among Slovak respondents for the statement FPS1 (SR: M = 2.720).
The highest rate of agreement in the evaluation of independent variables (ORS) is among Polish respondents for the statement ORS1 (PL: M = 2.026). On the other hand, the lowest rate of agreement in the evaluation of dependent variables (FPS) is among Czech respondents for the statement FPS1 (CR: M = 2.859). Skewness and Kurtosis for each variable are in the range of values from −2 to 2. The assumption of a multiple normal distribution of the defined variables is accepted due to the stated values.

4.1. Effect of Operational Risk Indicators on FPS1

By applying linear regression modelling, the statistical significance of LRM1 for V4 countries was verified in the joint model and separately for each country (see Table 3).
Table 3 shows that LRM1 separately in selected research countries that investigated the causal relationship between FPS1 and ORS indicators is statistically significant at the 0.001% significance level. Linear regression models explain from 18.5% (CR) to 46.0% (PL) of the variability of the dependent variable FPS1. Linear regression functions with regression coefficients take the following forms:
  • CR: LRM1: FPS1 = 0.775 + 0.217 × ORS1 + 0.130 × ORS2 + 0.177 × ORS3 + 0.133 × ORS4
  • SR: LRM1: FPS1 = 0.568 + 0.297 × ORS1 + 0.251 × ORS2 + 0.153 × ORS3 + 0.141 × ORS4
  • PL: LRM1: FPS1 = 0.310 + 0.351 × ORS1 + 0.146 × ORS2 + 0.288 × ORS3 + 0.198 × ORS4
  • HU: LRM1: FPS1 = 0.661 + 0.401 × ORS1 + 0.187 × ORS2 + 0.186 × ORS3 − 0.039 × ORS4
  • V4: LRM1: FPS1 = 0.540 + 0.318 × ORS1 + 0.150 × ORS2 + 0.228 × ORS3 + 0.116 × ORS4
The results of the verification of a negative phenomenon such as the presence of multicollinearity in regression models (LRM1; …; LRM3) are as follows:
  • CR: ORS1: VIF = 1.085; ORS2: VIF = 1.157; ORS3: VIF = 1.216; ORS4: VIF = 1.258
  • SR: ORS1: VIF = 1.315; ORS2: VIF = 1.673; ORS3: VIF = 2.342; ORS4: VIF = 2.271
  • PL: ORS1: VIF = 1.950; ORS2: VIF = 2.405; ORS3: VIF = 3.054; ORS4: VIF = 2.219
  • HU: ORS1: VIF = 1.691; ORS2: VIF = 2.156; ORS3: VIF = 2.245; ORS4: VIF = 1.921
  • V4: ORS1: VIF = 1.421; ORS2: VIF = 1.573; ORS3: VIF = 1.838; ORS4: VIF = 1.709
The presence of multicollinearity was not confirmed in any LRM. Dependencies between independent variables in LRMs are not statistically significant.

4.2. Effect of Operational Risk Indicators on FPS2

The results of the verification of the statistical significance of the independent variables (ORS1, …, ORS4) on the dependent variable FPS2 are the subject of Table 4. In order to achieve the highest possible consistency of the models, stepwise linear regression analysis was used.
Table 4 shows that LRM2 separately in selected research countries that investigated the causal relationship between FPS2 and ORS indicators is statistically significant at the significance level of 0.1% except for LRM2 in the Czech Republic (p-value of F-test = 0.086). In the Czech Republic, the causal relationship of ORS indicators on FRS2 has not been proven. Other linear regression models explain from 18.1% (HU) to 40.4% (PL) of the variability of the dependent variable FPS2. Linear regression functions with regression coefficients take the following forms:
  • SR: LRM2: FPS2 = 0.502 + 0.295 × ORS1 + 0.186 × ORS2 + 0.053 × ORS3 + 0.247 × ORS4
  • PL: LRM2: FPS2 = 0.573 + 0.095 × ORS1 + 0.237 × ORS2 + 0.094 × ORS3 + 0.356 × ORS4
  • HU: LRM2: FPS2 = 1.048 + 0.145 × ORS1 + 0.043 × ORS2 + 0.175 × ORS3 + 0.104 × ORS4
  • V4: LRM2: FPS2 = 0.976 + 0.166 × ORS1 + 0.099 × ORS2 + 0.109 × ORS3 + 0.181 × ORS4

4.3. Effect of Operational Risk Indicators on FPS3

The results of the verification of the statistical significance of the independent variables (ORS1, …, ORS4) on the dependent variable FPS3 are the subject of Table 5. In order to achieve the highest possible consistency of the models, stepwise linear regression analysis was used.
Table 5 shows that LRM3 separately in selected research countries that investigated the causal relationship between FPS3 and ORS indicators is statistically significant at the significance level of 0.1%. Linear regression models explain from 7.1% (CR) to 34.2% (PL) of the variability of the dependent variable FPS3. Linear regression functions with regression coefficients take the following forms:
  • CR: LRM3: FPS3 = 1.031 + 0.136 × ORS1 + 0.155 × ORS4
  • SR: LRM3: FPS3 = 0.517 + 0.249 × ORS1 + 0.060 × ORS2 − 0.034 × ORS3 + 0.376 × ORS4
  • PL: LRM3: FPS3 = 0.618 + 0.117 × ORS1 + 0.158 × ORS2 + 0.177 × ORS3 + 0.268 × ORS4
  • HU: LRM3: FPS3 = 1.078 + 0.115 × ORS1 + 0.128 × ORS3 + 0.201 × ORS4
  • V4: LRM3: FPS1 = 0.829 + 0.153 × ORS1 − 0.035 × ORS2 + 0.166 × ORS3 + 0.240 × ORS4
The interpretation of statistical hypotheses with regard to the achieved results (see Table 3, Table 4 and Table 5) is the subject of the following Table 6.

5. Discussion

The main findings of the quantitative research conducted in the V4 countries are as follows:
Owners/top managers think that the ability to use production capacity has the most significant impact on achieving sufficient profit regardless of the country in which the SME operates. Czech and Hungarian owners/top managers also consider other sources of operational risk to be significant (although to a lesser extent) in contrast to Slovak and Polish owners/top managers.
Czech SME owners/top managers believe that operational risk management does not affect the adequacy of the enterprise’s indebtedness. On the contrary, Hungarian, Slovak, and Polish owners/top managers consider operational risk management to be significant and have a positive impact on the enterprise’s indebtedness. In this context, the Hungarian respondents perceive that the distribution of products/services is a key aspect of the share of the enterprise’s debt. Slovak owners/top managers consider the most important factor to be the enterprise’s ability to use production capacities. Polish respondents perceive relationships with suppliers as key in connection with reducing the enterprise’s indebtedness.
Owners/top managers in Central European countries (CR, PL, SP, HU) believe that their relationships with suppliers are crucial in relation to the inability to pay the obligations (insolvency) of the enterprise. Hungarian respondents consider this source of operational risk to be the only significant factor in connection with the inability to repay the obligations (insolvency) of the enterprise. Slovak and Czech respondents, in addition to the mentioned source, perceive it as necessary to monitor the utilization of production capacities in the enterprise. Polish owners/top managers believe that the reasonableness of the enterprise’s suppliers’ prices for products and services is also important.
Based on the processed results, it is possible to declare that operational risk management is linked to financial performance, i.e., has a significantly positive effect on improving the profitability of the enterprise and reducing the indebtedness of the enterprise, as well as reducing the inability to repay the enterprise’s obligations.
The presented processed results are a link to the conclusions of the authors [21,55], who assessed the perceived sources of operational risks by the owners/top managers of enterprises in the V4 countries in 2017–2018 and 2019–2020. Among the main sources of operational risks in the countries, V4 in 2017–2018 were obsolete production facilities, low rate of innovations, and insufficient product quality [55]. In the years 2019–2020, insufficient product quality, low rate of innovations, obsolete production facilities, and insufficient utilization of the production capacities prevailed [5,21].
These results are also confirmed by many other authors [4,6,27,29,48], who conducted similar research on the issue of operational and financial risks of SMEs, the sources of which are of a different nature. Lopez [27] and Popp et al. [58] point to incorrectly defined procedures and process management as the most serious causes of operational risks. This is manifested in an insufficient definition of responsibilities and accountabilities. According to Kozubíková et al. [59], it can be about deficiencies in the planning process and deficiencies in the control process. It is important to pay attention to the prevention of operational risks [4]. Olah et al. [48] emphasize the permanent control of the production process. As another preventive measure, it is advisable to carry out random quality control of products and supervise the supply of necessary raw materials and create good relations with suppliers [58].
Operational risks must be seen as an important factor affecting the financial performance of most SMEs in the V4 countries [1,15]. According to Kotaskova et al. [47], financial risks are significant risks in any enterprise, whether it is a small or medium-sized enterprise. The current issue of financial risk management reaches the same level as SMEs. This is also evidenced by the further processed results of the survey by the authors Olah et al. [48], which point to the greatest intensity of SME financial risk sources: inadequate profit from business, unpaid receivables, inability to pay obligations (insolvency), and corporate indebtedness. This opinion is also confirmed by other authors who deal with this issue [59]. According to several authors [4,5], there is essentially a relationship between direct business operations and the amount of debt the enterprise owes, which directly affects the level of financial risk. The more debt a business has, the more likely it will be to default on financial obligations, given that financial risk indicates the possibility that the entity’s cash flow is not adequate to pay creditors and meet other financial liabilities. Apart from the importance of financial risk, it can lead to financial mismanagement in losses, indebtedness, and liquidity problems. Wolmarania and Meintjes [60] concluded that setting the right systems of internal financial management policies eliminates most of the failures of SMEs and prevents financial crises.
The results of processed worldwide studies show that operational risks are just as important as financial risks. According to the processed Allianz risk barometer study, business interruption is among the top serious business risks in the long term [61]. In addition, according to worldwide studies, e.g., The Global Risks Report [62], Dun & Bradstreet’s World Economic Forum 2022, and Key Global Risks for Businesses, operational risks are still among the top 10 most severe risks. The results of the processed study by KPMG [63] also define the following as top enterprise risks: cyber security, ESG/responsible risks, market risks, operational and strategic risks, people risks, etc.

6. Conclusions

The aim of the article is to identify and quantify the differences in operational risk management and its effect on the financial management of small and medium-sized enterprises (SMEs) between four Central European countries.
Empirical research indicates that the management of operational risk positively affects the financial management of the enterprise in the business environment of SMEs in the Central European area. The findings point to the fact that there are differences in the perception of the impact of operational risk on the enterprise’s financial management between the countries of the Visegrad Group. According to SMEs operating in the business environment of the Czech Republic, there is no significant influence of operational risk management on the adequacy of the enterprise’s indebtedness. On the contrary, in the other V4 countries, their positive impact on reducing the enterprise’s indebtedness has been demonstrated. The exact answer to the formulated scientific hypothesis (RQ) is yes. The country of SME is an important factor that determines the perception of operational risk in the context of SME financial management.
The findings are important for owners and top managers operating in the SME segment, not only in Central Europe, to realize the importance of operational aspects of business and possible sources of risks arising from them. The findings can serve as a basis for the creators of national and transnational policies dealing with the quality of the business environment. The findings can also be disseminated in the creation of supporting materials (e.g., preparation of workshops and educational courses for enterprises) dealing with the issue of risk management in the SMEs segment. The obtained results represent valuable information for SMEs not only in V4 countries but also in other countries of Central and Eastern Europe, which have similar business environments, such as Slovenia, Croatia, Romania, Lithuania, etc.
The findings obtained from quantitative research have certain specificities. The research was carried out in the segment of small and medium enterprises. Data collection took place in four Central European countries neighboring Ukraine. Owners and managers of SMEs subjectively evaluated financial management and operational management in the enterprise. The research was carried out during a turbulent period—during the ongoing Russian invasion of Ukraine. Applying linear regression modelling is important in finding causal relationships between variables in only one direction.
Considering the significant findings of the impact of operational management on the financial performance of enterprises in the SMEs segment, the subject of further research activities will be the demonstration of the impact of operational risk management on business sustainability, on the future of business, and in the context of the perception of the business entity regression. An analysis of the effect of operational risk on the financial performance of the enterprise with regard to the size of the SME could also bring interesting results. Further directions of the solved issue can bring interesting knowledge not only for owners and managers of SMEs, whose business is expanded within Europe (V4 countries), but also for other institutions supporting the development of the business environment in Europe.

Author Contributions

Conceptualization, M.H.; methodology, J.D.; validation, J.D.; formal analysis, P.K. and C.R.A.; investigation, M.H. and J.D.; resources, P.K., C.R.A. and J.K.; data curation, J.D.; writing—original draft preparation, M.H., J.D. and J.K.; writing—review and editing, M.H. and P.K.; visualization, J.D. and J.K.; supervision, M.H. and J.D. All authors have read and agreed to the published version of the manuscript.

Funding

This paper is output by the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic—VEGA No. 1/0243/20 Integrated risk management system in the conditions of contemporary changes in enterprise environment in Slovakia.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ključnikov, A.; Civelek, M.; Klimeš, C.; Farana, R. Export risk perceptions of SMEs in selected Visegrad countries. Equilibrium. Q. J. Econ. Econ. Policy 2022, 17, 173–190. [Google Scholar] [CrossRef]
  2. Yang, J.S. The governance environment and innovative SMEs. Small Bus. Econ. 2017, 48, 525–541. [Google Scholar] [CrossRef]
  3. Khan, K.A.; Çera, G.; Nétek, V. Perception of the Selected Business Environment Aspects by Service Firms. J. Tour. Serv. 2019, 10, 111–127. [Google Scholar]
  4. Belas, J.; Gavurova, B.; Dvorsky, J.; Cepel, M.; Durana, P. The impact of the COVID-19 pandemic on selected areas of a management system in SMEs. Econ. Res.-Ekon. Istraživanja 2021, 35, 3754–3777. [Google Scholar] [CrossRef]
  5. Virglerova, Z.; Dvorsky, J.; Kozubikova, L.; Cepel, M. Perception of non-financial risk determinants in SMEs in Visegrad countries. Oeconomia Copernic. 2020, 11, 509–529. [Google Scholar] [CrossRef]
  6. Virglerova, Z. Differencesin the Concept of Risk Management in V4 Countries. Int. J. Entrep. Knowl. 2018, 6, 100–109. [Google Scholar]
  7. Waiho, K.; Fazhan, H.; Dahlianis Ishak, S.; Azman Kasan, N.; Jung Liew, J.; Norainy, H.; Ikhwanuddin, M. Potential impacts of COVID-19 on the aquaculture sector of Malaysia and its coping strategies. Aquac. Rep. 2020, 18, 100450. [Google Scholar]
  8. Bartik, A.W.; Bertrand, M.; Cullen, Z.; Glaeser, E.L.; Luca, M.; Stanton, C. The impact of COVID-19 on small business outcomes and expectations. Proc. Natl. Acad. Sci. USA 2020, 117, 17656–17666. [Google Scholar] [CrossRef]
  9. Carvalho, C.; Eusepi, S.; Moench, E.; Preston, B. Anchored Inflation Expectationst. Am. Econ. J. Macroecon. 2023, 15, 1–47. [Google Scholar]
  10. Coleman, W.; Nautz, D. Inflation target credibility in times of high inflation. Econ. Lett. 2023, 222, 110930. [Google Scholar] [CrossRef]
  11. Dvorský, J.; Čepel, M.; Kotásková, A.; Bugánová, K. Differences in business risk effects on the future of smes due to the COVID-19 pandemic. Int. J. Entrep. Knowl. 2021, 9, 14–31. [Google Scholar]
  12. Mazzanti, M.; Mazzarano, M.; Pronti, A.; Quatrosi, M. Fiscal policies, public investments and wellbeing: Mapping the evolution of the EU. Insights Into Reg. Dev. 2020, 2, 725–749. [Google Scholar] [CrossRef] [PubMed]
  13. Gavril, S.; Coca, O.; Creanga, D.E.; Mursa, G.C.; Mihai, C. The Impact of the Crisis Generated by COVID-19 on the Population Concerns. A comparative Study at the Level of the European Union. Transform. Bus. Econ. 2020, 19, 703–719. [Google Scholar]
  14. Tullo, L. COVID-19 triggers great nonfinancial risk crisis: Nonfinancial risk management best practices in Canada. J. Risk Manag. Financ. Inst. 2020, 14, 40–58. [Google Scholar]
  15. Ciocoiu, C.N.; Prioteasa, A.L.; Colesca, S.E. Risk Management Implementation for Sustainable Development of Romanian SMEs: A Fuzzy Approach. Amfiteatru Econ. 2020, 22, 726–741. [Google Scholar] [CrossRef]
  16. Estiri, M.; Dahooie, J.H.; Skare, M. COVID-19 crisis and resilience of tourism SME’s: A focus on policy responses. Econ. Res.-Ekon. Istraž. 2022, 35, 556–5580. [Google Scholar]
  17. Dobrowolski, Z. After COVID-19: Reorientation of crisis management in crisis. Entrep. Sustain. Issues 2020, 8, 799–810. [Google Scholar]
  18. Ferreira de Araújo Lima, P.; Crema, M.; Verbano, C. Risk management in SMEs: A systematic literature review and future directions. Eur. Manag. J. 2020, 38, 78–94. [Google Scholar] [CrossRef]
  19. Ślusarczyk, B.; Grondys, K. Parametric Conditions of High Financial Risk in the SME Sector. Risks 2019, 7, 84. [Google Scholar] [CrossRef] [Green Version]
  20. Hudáková, M.; Masár, M. Assessment of the Key Business Risks of the SMEs in Slovakia and Their Comparison with other EU Countries. Entrep. Bus. Econ. Rev. 2018, 6, 145–160. [Google Scholar] [CrossRef]
  21. Hudakova, M.; Gabrysova, M.; Petrakova, Z.; Buganova, K.; Krajcik, V. The Perception of Market and Economic Risks by Owners and Managers of Enterprises in the V4 Countries. J. Compet. 2021, 13, 60–77. [Google Scholar] [CrossRef]
  22. Pisar, P.; Bilkova, D. Controlling as a tool for SME management with an emphasison innovations in the context of Industry 4.0. Equilibrium. Q. J. Econ. Econ. Policy 2019, 14, 763–785. [Google Scholar]
  23. Gates, S.; Nicolas, J.L.; Walker, P.L. Enterprise Risk Management: A Process for Enhanced Management and Improved Performance. Manag. Account. Q. 2012, 13, 28–38. [Google Scholar]
  24. Lv, S.; Xu, Z.; Fan, X.; Qin, Y.; Skare, M. The mean reversion/persistence of financial cycles: Empirical evidence for 24 countries worldwide. Equilibrium. Q. J. Econ. Econ. Policy 2023, 18, 11–47. [Google Scholar] [CrossRef]
  25. Dvorsky, J.; Kozubikova, L.; Kljucnikov, A.; Ivanova, E. Owners vs. Managers. Disparities of Attitudes on the Business Risk in SME Segment. Amfiteatru Econ. 2022, 24, 174–193. [Google Scholar] [CrossRef]
  26. Åslund, A. Responses to the COVID-19 crisis in Russia, Ukraine, and Belarus. Eurasian Geogr. Econ. 2020, 61, 532–545. [Google Scholar] [CrossRef]
  27. Lopez, J.R.C. How Can Enterprise Risk Management Help in Evaluating the Operational Risks for a Telecommunications Company? J. Risk Financ. Manag. 2021, 14, 139. [Google Scholar] [CrossRef]
  28. Agarwal, R.; Ansell, J. Strategic Change in Enterprise Risk Management. Strateg. Chang. 2016, 25, 427–439. [Google Scholar] [CrossRef] [Green Version]
  29. Bai, C.U.; Gao, W.L.; Sarkis, J. Operational Risks and Firm Market Performance: Evidence from China. Decis. Sci. 2021, 52, 920–951. [Google Scholar] [CrossRef]
  30. Alem, D.; Oliveira, F.; Peinado, M.C.R. A practical assessment of risk-averse approaches in production lot-sizing problems. Int. J. Prod. Res. 2020, 58, 2581–2603. [Google Scholar] [CrossRef]
  31. Dumitrescu, A.; Deselnicu, D.C. Risk assessment in manufacturing SMEs’ labor system. Procedia Manuf. 2018, 22, 912–915. [Google Scholar] [CrossRef]
  32. Holla, K.; Mitasova, V.; Pavlenko, T. Risk assessment model verification in hazardous industrial processes. Procedia Eng. 2017, 192, 324–329. [Google Scholar] [CrossRef]
  33. Goettlich, S.; Knapp, S. Uncertainty quantification with risk measures in production planning. J. Math. Ind. 2020, 10, 5. [Google Scholar] [CrossRef] [Green Version]
  34. Tucek, D.; Hrbáčková, L. Trends in Risk-Based Thinking and Risk Management Methods in Czech Plastic Cluster Production Companies. Int. Adv. Econ. Res. 2019, 25, 245–246. [Google Scholar] [CrossRef]
  35. Avilova, T.V.; Voytolovskiy, N.V.; Dikareva, V.A.; Chernysheva, A.M. Efficiency of applying risks management systems at industrial enterprises under market conditions. J. Appl. Econ. Sci. 2017, 12, 766–771. [Google Scholar]
  36. Shirai, K.; Amano, Y.; Uda, T. Cost reduction function considering stochastic risks in production process. Int. J. Innov. Comput. Inf. Control 2020, 16, 1257–1278. [Google Scholar]
  37. Allahar, H. Innovation management and value chain design: Case of a small professional services firm. Int. J. Innov. 2019, 7, 192–209. [Google Scholar] [CrossRef] [Green Version]
  38. Sethi, R. New product quality and product development teams. J. Mark. 2000, 64, 1–14. [Google Scholar] [CrossRef]
  39. Teplicka, K.; Hurna, S. New approach of costs of quality according their trend of during long period in industrial enterprises in smes. Manag. Syst. Prod. Eng. 2021, 29, 20–26. [Google Scholar] [CrossRef]
  40. Gok, O.; Ersoy, P.; Boruhan, G. The effect of user manual quality on customer satisfaction: The mediating effect of perceived product quality. J. Prod. Brand Manag. 2019, 28, 475–488. [Google Scholar] [CrossRef]
  41. Crovini, C.; Ossola, G.; Britzelmaier, B. How to reconsider risk management in smes? an advanced, reasoned and organised literature review. Eur. Manag. J. 2021, 39, 118–134. [Google Scholar] [CrossRef]
  42. Chang, B.G.; Wu, K.S. The nonlinear relationship between financial flexibility and enterprise risk-taking during the COVID-19 pandemic in Taiwan’s semiconductor industry. Oeconomia Copernic. 2021, 12, 307–333. [Google Scholar] [CrossRef]
  43. Udoh, S. Review of financial management in private firms: Unlocking the cash management model. Int. J. Entrep. Knowl. 2022, 10, 95–106. [Google Scholar] [CrossRef]
  44. Zhao, S.; Zeng, M. Theory of SMEs financial risk prevention and control. In Proceedings of the 2014 International Conference on Education, Management and Computing Technology, Tianjin, China, 14–15 June 2014; pp. 514–517. [Google Scholar]
  45. Ismanto, H.; Widiastuti, A.; Muharam, H.; Pangestuti, I.R. The Impact of Risk and Financial Knowledge, Business Culture and Financial Practice on SME Performance. Qual.-Access. Success 2020, 21, 3–9. [Google Scholar]
  46. Kaczmarek, J.; Alonso, S.L.N.; Sokołowski, A.; Fijorek, K.; Denkowska, S. Financial threat profiles of industrial enterprises in Poland. Oeconomia Copernic. 2021, 12, 463–498. [Google Scholar] [CrossRef]
  47. Kotaskova, A.; Lazanyi, K.; Amoah, J.; Belás, J. Financial risk management in the V4 Countries’ SMEs segment. Investment Manag. Financ. Innov. 2020, 17, 228–240. [Google Scholar] [CrossRef]
  48. Olah, J.; Kovacs, S.; Virglerova, Z.; Lakner, Z.; Kovacova, M.; Popp, J. Analysis and comparison of economic and financial risk sources in SMEs of the visegrad group and Serbia. Sustainability 2019, 11, 1853. [Google Scholar] [CrossRef] [Green Version]
  49. Tamulevičienė, D.; Androniceanu, A. Selection of the indicators to measure an enterprise’s value and its changes in the controlling system for medium-sized enterprises. Entrep. Sustain. Issues 2020, 7, 1440–1458. [Google Scholar] [CrossRef] [Green Version]
  50. Christensen, B.J.; Nielsen, M.Ø.; Zhu, J. The impact of financial crisis on the risk-return. Trade off and the leverage effect. Econ. Model. 2015, 49, 407–418. [Google Scholar] [CrossRef] [Green Version]
  51. Kulathunga, K.M.M.C.B.; Ye, J.; Sharma, S.; Weerathunga, P.R. How does technological and financial literacy influence SME performance: Mediating role of ERM practices. Information 2020, 11, 297. [Google Scholar] [CrossRef]
  52. Dodd, O.; Kalimipalli, M.; Chan, W. Evaluating corporate credit risks in emerging markets. Int. Rev. Financ. Anal. 2021, 73, 101610. [Google Scholar] [CrossRef]
  53. Bogodistov, Y.; Wohlgemuth, V. Enterprise risk management: A capability-based perspective. J. Risk Financ. 2017, 18, 234–251. [Google Scholar] [CrossRef]
  54. Leibowitz, M.L.; Kogelman, S. Operating Leverage and Inflation. J. Portf. Manag. 2023, 49, 159–168. [Google Scholar] [CrossRef]
  55. Hudáková, M.; Dvorský, J. Assessing the risks and their sources in dependence on the rate of implementing the risk management process in the SMEs. Equilibrium. Q. J. Econ. Econ. Policy 2018, 13, 543–567. [Google Scholar]
  56. Belas, J.; Škare, M.; Gavurova, B.; Dvorsky, J.; Kotaskova, A. The impact of ethical and CSR factors on engineers’ attitudes towards SMEs sustainability. J. Bus. Res. 2022, 149, 589–598. [Google Scholar] [CrossRef]
  57. Dvorský, J.; Čepel, M.; Simionescu, M.; Ďurana, P. The Influence of Competitiveness on Start-up in SMEs Segment. E&M Econ. Manag. 2021, 24, 102–117. [Google Scholar]
  58. Popp, J.; Oláh, J.; Machova, V.; Jachowicz, A. Private equity market of the Visegrad group. Ekon.-Manazerske Spektrum 2018, 12, 1–15. [Google Scholar] [CrossRef]
  59. Kozubíková, L.; Dvorský, J.; Cepel, M.; Balcerzak, A.P. Important characteristics of an entrepreneur in relation to risk taking: Czech republic case study. J. Int. Stud. 2017, 10, 220–233. [Google Scholar] [CrossRef]
  60. Wolmarans, H.; Meintjes, Q. Financial management practices in successful small and medium enterprises (SMEs). South. Afr. J. Entrep. Small Bus. Manag. 2015, 7, 88–116. [Google Scholar] [CrossRef] [Green Version]
  61. Deloitte Insight. Deloitte Uk Global Digital Risk Survey. 2021. Available online: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/risk/deloitte-uk-globaldigital-risk-survey-2021.pdf (accessed on 1 February 2021).
  62. The Global Risks Report 2023, 18th Edition, Insight Report. Available online: https://www3.weforum.org/docs/WEF_Global_Risks_Report_2023.pdf (accessed on 1 May 2023).
  63. KPMG. Global Pension and Sovereign Funds Risk Management Survey. Current and Emerging Risk Management Trends. 2023. Available online: https://assets.kpmg.com/content/dam/kpmg/xx/pdf/2022/03/global-pension-and-sovereign-funds-risk-management-survey.pdf (accessed on 1 April 2023).
Table 1. Descriptive characteristics of defined variables.
Table 1. Descriptive characteristics of defined variables.
V4FPS1FPS2FPS3ORS1ORS2ORS3ORS4
M2.3902.2441.9812.1722.5992.2122.272
SD1.0921.0290.9440.9341.1410.9801.042
V1.1931.0590.8910.8721.3020.9611.085
S−0.3940.2101.0630.029−0.7550.2670.014
K0.6050.7501.0420.6140.3530.7440.719
SRFPS1FPS2FPS3ORS1ORS2ORS3ORS4
M2.7202.4762.1102.4452.8542.4392.390
SD1.1801.0880.9400.9551.2591.0461.006
V1.3931.1830.8840.9111.5861.0941.012
S−1.031−0.2581.119−0.114−1.139−0.0020.108
K0.2220.4970.9450.3300.1310.7010.726
CRFPS1FPS2FPS3ORS1ORS2ORS3ORS4
M2.2852.2021.6772.2152.8592.0472.218
SD0.9080.9990.7430.8271.0910.8490.990
V0.8250.9980.5520.6851.1910.7210.980
S0.9730.5032.2190.082−1.0341.152−0.025
K1.0810.8271.2120.5530.1540.9480.757
PLFPS1FPS2FPS3ORS1ORS2ORS3ORS4
M2.4422.3272.2182.0262.2712.2312.281
SD1.1941.0651.0420.9381.0130.9631.041
V1.4261.1351.0850.8801.0260.9271.083
S−0.818−0.0060.1390.130−0.0610.148−0.142
K0.4080.6750.7570.7710.5730.6220.588
HUFPS1FPS2FPS3ORS1ORS2ORS3ORS4
M2.3052.0972.0682.1112.4622.2872.258
SD1.1110.9600.9781.0031.1591.0811.131
V1.2340.9220.9561.0061.3431.1691.278
S−0.4400.4410.7830.159−0.562−0.3440.022
K0.5480.8110.9560.7450.4690.5440.787
Note: M—Mean; SD—Standard Deviation; V—Variance; S—Skewness; K—Kurtosis. Source: own statistical processing.
Table 2. Correlation matrices according to the country of operation of SMEs in the business environment.
Table 2. Correlation matrices according to the country of operation of SMEs in the business environment.
V4FPS1FPS2FPS3ORS1ORS2ORS3ORS4SRFPS1FPS2FPS3ORS1ORS2ORS3ORS4
FPS11 FPS11
FPS20.471 FPS20.521
FPS30.380.491 FPS30.400.551
ORS10.480.320.311 ORS10.460.460.421
ORS20.440.320.240.471 ORS20.520.490.390.451
ORS30.480.340.380.470.501 ORS30.470.440.400.400.561
ORS40.410.350.400.370.490.611ORS40.450.480.510.370.540.731
CRFPS1FPS2FPS3ORS1ORS2ORS3ORS4PLFPS1FPS2FPS3ORS1ORS2ORS3ORS4
FPS11 FPS11
FPS20.211 FPS20.631
FPS30.240.291 FPS30.450.571
ORS10.290.090.191 ORS10.580.440.431
ORS20.280.070.080.181 ORS20.570.550.500.651
ORS30.300.030.160.240.231 ORS30.620.550.530.630.701
ORS40.290.090.230.170.330.371ORS40.540.590.530.450.610.721
HUFPS1FPS2FPS3ORS1ORS2ORS3ORS4
FPS11
FPS20.511
FPS30.400.601
ORS10.560.350.311
ORS20.510.340.280.601
ORS30.480.400.360.560.651
ORS40.370.360.380.480.600.651
Note: All pairwise dependence coefficients are statistically significant at the 0.05 significance level. Source: own statistical processing.
Table 3. Verification of the influence of ORS indicators on FPS1.
Table 3. Verification of the influence of ORS indicators on FPS1.
RALRM1
CRSRPLHUV4
R0.4400.6060.6830.6120.586
R20.1930.3670.4670.3740.343
Adj. R20.1840.3510.4600.3650.341
SE0.8200.9510.8790.8850.887
N3621623012651090
ANOVA
F- test21.37423.02664.99540.980141.801
Sig. 0.000 ***0.000 ***0.000 ***0.000 ***0.000 ***
Verification of the statistical significance of RC with the application of t-Stat.
Constant4.550 ***2.361 *2.180 *4.600 ***6.542 ***
ORS13.992 ***3.314 ***4.647 ***5.830 ***9.281 ***
ORS23.051 **3.276 ***1.8822.775 **5.093 ***
ORS33.161 **1.4073.135 **2.529 *6.134 ***
ORS42.722 **1.2652.726 **−0.6033.425 **
Note: RA—Regression Analysis; RC—Regression Coefficient; SE—Standard error; R2—Coefficient of determination; Adj. R2—Adjusted Coefficient of determination; N—number of respondents; LRM—Linear regression model; * α = 0.05; ** α = 0.01; *** α = 0.001. Source: own statistical processing.
Table 4. Verification of the influence of ORS indicators on FPS2.
Table 4. Verification of the influence of ORS indicators on FPS2.
RALRM2
CRSRPLHUV4
R0.1170.5970.6420.4390.428
R20.0140.3570.4120.1930.183
Adj. R20.0080.3400.4040.1810.180
SE08200.8840.8230.8690.932
N3621623012651090
ANOVA
F-test2.47022.02352.00716.36060.726
Sig. 0.0860.000 ***0.000 ***0.000 ***0.000 ***
Verification of the statistical significance of RC with the application of t-Stat.
Constant10.256 ***2.248 *4.299 ***7.427 ***11.274 ***
ORS11.4153.554 ***1.3462.149 *4.598 ***
ORS2-2.620 **3.269 **0.6493.191 **
ORS3-0.5261.0922.428 *2.761 **
ORS41.4562.379 *5.245 ***1.6345.116 ***
Note: RA—Regression Analysis; RC—Regression Coefficient; SE—Standard error; R2 —Coefficient of determination; Adj. R2—Adjusted Coefficient of determination; N—number of respondents; LRM—Linear regression model; * α = 0.05; ** α = 0.01; *** α = 0.001. Source: own statistical processing.
Table 5. Verification of the influence of ORS indicators on FPS3.
Table 5. Verification of the influence of ORS indicators on FPS3.
RALRM3
CRSRPLHUV4
R0.2760.5740.5920.4190.457
R20.0760.3290.3500.1760.208
Adj. R20.0710.3120.3420.16670.206
SE0.7160.7800.8460.8920.841
N3621623012651090
ANOVA
F- test14.77519.50440.04619.56371.430
Sig. 0.000 ***0.000 ***0.000 ***0.000 ***0.000 ***
Verification of the statistical significance of RC with the application of t-Stat.
Constant8.076 ***2.621 *4.510 ***7.555 ***10.572 ***
ORS12.945 **3.391 **1.6121.7614.698 ***
ORS2-0.9552.120 *-−1.242
ORS3-−0.3851.996 *1.8284.692 ***
ORS44.018 ***4.108 ***3.840 ***3.169 ***7.487 ***
Note: RA—Regression Analysis; RC—Regression Coefficient; SE—Standard error; R2 —Coefficient of determination; Adj. R2—Adjusted Coefficient of determination; N—number of respondents; LRM—Linear regression model; * α = 0.05; ** α = 0.01; *** α = 0.001. Source: own statistical processing.
Table 6. Evaluation of formulated hypotheses.
Table 6. Evaluation of formulated hypotheses.
LRM1LRM2LRM3
EPS1CRSRPLHUV4EPS2CRSRPLHUV4EPS3CRSRPLHUV4
ORS1SSSSSORS1RSRSSORS1SSRRS
ORS2SSRSSORS2RSSRSORS2RRSRR
ORS3SRSSSORS3RRRSSORS3RRSRS
ORS4SRSRSORS4RSSRSORS4SSSSS
Note: S—Hypothesis was supported; R—Hypothesis was rejected. Source: own statistical processing.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Hudáková, M.; Kardoš, P.; Dvorský, J.; Afful, C.R.; Kloudova, J. Management of Operational Risk in the Context of Financial Performance of SMEs. Systems 2023, 11, 408. https://doi.org/10.3390/systems11080408

AMA Style

Hudáková M, Kardoš P, Dvorský J, Afful CR, Kloudova J. Management of Operational Risk in the Context of Financial Performance of SMEs. Systems. 2023; 11(8):408. https://doi.org/10.3390/systems11080408

Chicago/Turabian Style

Hudáková, Mária, Peter Kardoš, Ján Dvorský, Charles Randy Afful, and Jitka Kloudova. 2023. "Management of Operational Risk in the Context of Financial Performance of SMEs" Systems 11, no. 8: 408. https://doi.org/10.3390/systems11080408

APA Style

Hudáková, M., Kardoš, P., Dvorský, J., Afful, C. R., & Kloudova, J. (2023). Management of Operational Risk in the Context of Financial Performance of SMEs. Systems, 11(8), 408. https://doi.org/10.3390/systems11080408

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop