Risks for Companies during the COVID-19 Crisis: Dataset Modelling and Management through Digitalisation

: The goal is to create a systemic risk proﬁle of companies during the COVID-19 crisis, which reﬂects their cause-and-effect relationships and risk management. The research objects are the following types of risks for companies listed in “Global-500” (Fortune) and the top 55 most competitive digital economies of the world (IMD) in 2017–2022: (1) risk of reduction in competitiveness (rank), (2) risk of reduction in revenue, and (3) risk of reduction in proﬁt. The research methodology is based on the method of structural equation modelling (SEM), which allowed for exploring the cause-and-effect relationships between risk changes and digital risk management for companies during the COVID-19 crisis. As a result, based on the SEM model, it was proven that risks for companies during the COVID-19 crisis only slightly increased compared with that at the pre-crisis level. It was determined that companies faced large risks during the COVID-19 crisis in developed countries. It was discovered that, due to successful adaptation, risk management of companies assuaged the manifestations of the COVID-19 crisis in the economy. The key conclusion is that, under the conditions of a crisis of a non-economic nature (e.g., the COVID-19 crisis), companies independently and successfully manage their risks with the help of measures of digitalisation: corporate risk management with the limitation of state intervention is preferable. The contribution to the literature consists of the development of the concept of risks for companies by clarifying the speciﬁcs of risks and risk management of companies during the COVID-19 crisis. The theoretical signiﬁcance lies in the fact that the authors’ conclusions rethought the risks for companies under the conditions of a crisis given the special context of a crisis of a non-economic nature (via the example of the COVID-19 crisis). The practical signiﬁcance is that the developed novel approach to risk management of companies through digitalisation, which is based on the experience of the COVID-19 crisis, will be useful for risk management of companies under the conditions of future crises of non-economic nature caused by epidemics/pandemics and/or environmental disasters.


Introduction
The most important feature of the COVID-19 crisis (which was accompanied by the economic decline in 2020), which distinguishes it from many other similar crises, is its unpredictability. Due to the critically high level of uncertainty of the COVID-19 crisis, caused by its unique non-economic nature, it is expedient to study this crisis from the perspective of risk. Thus, the social risks of the COVID-19 crisis were connected with the in economic (digital) growth; more successful fights against tax evasion (overcoming the shadow economy); and an increase in revenues and, therefore, state budget surplus. This is the basis for proposing hypothesis H 3 : the use of digital measures of risk management of companies during the COVID-19 crisis allowed for mitigating the economic manifestations of the crisis, increasing GDP (supporting economic growth), reducing the shadow economy, and increasing state budget surplus.
RQ 4 : How (and with what measures) can we manage risks under the conditions of a crisis of a non-economic nature given the experience of the COVID-19 crisis: measures of state or corporate management? Based on the experience of crises of an economic nature, to reduce the risk burden on businesses during the COVID-19 crisis, the existing literature suggests implementing external (state) management with the help of standard measures of protectionism (Phang et al. 2023;Salami et al. 2022;Velayutham et al. 2021) and special (which became actual during the pandemic) measures of development of the healthcare infrastructure (Abdel Fattah et al. 2022).
In this case, the existing literature on the topic of the digital economy notes the advantages of digitalisation of businesses to raise the competitiveness, return, and profitability of companies. In particular, the following management measures are offered for this: • Development of digital/technological skills of employees to implement digital innovations in business (Türk 2022); • Raising the activity of the use of big data and analytics to create and develop smart productions with a high level of automatisation of all business operations (Cui et al. 2022); • Performing digital transformation in companies to raise their effectiveness and digital competitiveness (Busco et al. 2023); • Dissemination of mobile broadband subscribers (transition to 4G and 5G mobile Internet) for the development of e-commerce (Attaran 2023) This is the basis for proposing hypothesis H 4 : digital measures (increase in digital/technological skills, growth in the activity of the use of big data and analytics, acceleration of digital transformation in companies, and increase in the number of mobile broadband subscribers) allowed for reducing the risks for companies (improving business indicators of listed domestic companies) during the COVID-19 crisis.
To fill the discovered gap, to search for answers to the posed RQs, and to verify the proposed hypotheses, we performed dataset modelling of the international experience of change in the risks for companies during the COVID-19 crisis, as well as management of these risks with the help of digitalisation of businesses.

Methodology
The research objects in this paper are the following types of risks of listed companies: (1) risk of the reduction in competitiveness (rank, position in the ranking of companies), (2) risk of the reduction in revenue, and (3) risk of the reduction in profit. To compile the most precise risk profile of companies during the COVID-19 crisis, we conducted a quantitative study based on the methodology of econometrics. The considered statistics were unified in "massive time series"-datasets-with their further analysis. The authors' term "massive time series" was offered in this paper to define the notion of a "dataset" according to the categorical apparatus of mathematical sciences and econometrics.
In mathematics, a data array or data structure is treated as the structure of data that stores a set of values (elements of the array) of the indicators of a certain set continuous range. A classic example of a data array is a table-it is used in this paper (Garcia and Lumsdaine 2005). A specific feature of an array as a data structure (unlike, for example, a linked list) is the constant computational complexity of access to the element of the array via the index (McMillan 2014).
A time series is treated in mathematics as statistical material, collected during different time periods, on the values of any parameters of the studied process. In a time series, the time of measuring or order number of measuring is indicated for each calculation (e.g., Risks 2023, 11, 157 5 of 44 calendar year-like in this paper). Time series substantially differs from a simple data sample, for during the analysis, the interconnection of measurements with time, not only statistical diversity and statistical characteristics of the sample, is taken into account-this was an argument in favour of the provision of the statistical basis of this research in the form of a time series.
Time series consist of two elements: a period of time, for which or as of which numerical values are given; numerical values of a certain indicator, called levels of the series. A time series analysis is treated in mathematics as a totality of mathematical and statistical methods of analysis for determining the structure of a time series. These include, in particular, the methods of regression analysis, which are used in this paper (De Gooijer and Hyndman 2006).
The time series studied in this paper includes data before the pandemic (2017-2019) and during the COVID-19 pandemic and crisis (2020)(2021)(2022). These periods were calculated and determined according to the following logic. The pandemic was announced by the WHO on 11 March 2020. Due to this, the entire 2020 year was a pandemic and crisis year because, according to the World Bank (2023), the growth rate of the world GDP was negative (−3.1%), which is a sign of a deep recession of the world economic system.
The pre-pandemic period was marked by growth rates of the world economy of 3.4% in 2017, 3.3% in 2018, and 2.6% in 2019 (World Bank 2023). During the pandemic (the end of which was officially announced by the WHO on 5 May 2023, due to which the year 2023 is COVID-19-neutral), the world GDP reduced by 3.1% in 2020 and then grew by 6% in 2021 and by 3.1% in 2022 (World Bank 2023).
The logic of differentiation of these periods is that, in 2017-2019, the pandemic did not have any influence on the economy. That is why, an analysis of business in the pre-pandemic period allows for revealing its natural risks. In 2020-2022, the COVID-19 pandemic and crisis determined the pandemic context of the business environment and influenced business risks.
The advantage of a dataset analysis is that the statistics fully cover the studied economic processes, with the error of results being at the minimum. The essence of the method of dataset analysis was described in many studies (e.g., Yuan et al. 2023), and the specifics of using this method during the study of risks for companies were reflected in the work of Popkova and Sergi (2021) and Sozinova and Popkova (2023). The authors' datasets, formed based on the official international statistics of respectable sources-Fortune (2023) and IMD (2023)-can be found in a separate file, submitted with this paper. The experimental design of this research is shown in Table 1.
To search for answers to RQ 1 and RQ 2 , task 1 was set: to measure risks for companies during the COVID-19 crisis and to determine the features of risks in developed and developing countries. It is solved with the help of the method of horizontal analysis. Sample 1 contains the world's largest listed companies from the ranking "Global 500" (Fortune 2023) in 2019-2022, with division into developed and developing countries. The annual growth in the values of rank, revenue, and profits of these companies in 2019-2022 is assessed-separately for developed and developing countries.
Sample 2 contains the top 55 (without gaps in data) most competitive digital economies of the world (IMD 2023) in 2017-2022. The annual growth in the following values is assessed: (1) digital management measures of risk management, such as digital/technological skills (DGT 1 ), use of big data and analytics (DGT 2 ), digital transformation in companies (DGT 3 ), and mobile broadband subscribers (DGT 2 ); (2) alternative measures of state regulation, aimed at reducing the risk burden on businesses, such as protectionism (GOV 1 ) and health infrastructure (GOV 2 ); (3) risks that are measured with the help of the indicator "listed domestic companies" (RISK); and (4) potential economic implications of risks and risk management of companies, such as gross domestic product (GDP) (ECON 1 ), tax evasion (ECON 2 ), and government budget surplus/deficit (GBD).

Research Question (RQ) Research Task Research Method Sample
RQ 1 : What is the level of risks for companies during the COVID-19 crisis (in 2020): higher or lower than the pre-crisis level (2019)?
Task 1: to measure risks for companies during the  crisis and to determine the features of risks in developed and developing countries Method of horizontal analysis Sample 1: "Global 500" (Fortune 2023) in 2019-2022 with division into developed and developing countries (rank, revenue, and profits) Sample 2: The top 55 most competitive digital economies of the world (IMD 2023)  To assess reliability, an important aspect is a normal distribution of variables that are used in the regression function. The assumption of normality is very important in a regression analysis since it allows for using different statistical techniques. Deviation from normality may lead to distortion or influence the reliability of the results. The reliability and correctness of the calculation of risks in this paper are ensured and assessed according to the methodology proposed by Popkova and Sergi (2021) and the method proposed by Sozinova and Popkova (2023). Histograms of the normal distribution of data, which are used in the paper, are given in Figure 1.
The performed evaluation showed that the variables fall under a normal distributiontherefore, it is possible to interpret and confirm the regression model.
To search for answers to RQ 3 and RQ 4 , task 2 was set: to determine cause-and-effect relationships between the change and digital management of risks for companies during the COVID-19 crisis. It is solved in the strategy of assessment with the use of structural equation modelling with the help of the method of regression analysis. Based on the data from Sample 2-the top 55 most competitive digital economies of the world (IMD 2023) in 2017-2022-we performed econometric modelling of the connection between listed domestic companies and alternative measures of risk management and potential implications for the economy. Research model (1) systemically reflects the connections of the indicators from the sample and has the following mathematical expression: (3)GBD = a GBD + b GBD × ECON 2 ; (1) Research model (1) reflects four dependencies: (1) dependence of the risks for listed companies on the totality of digital management measures of risk management and alternative measures of state regulation, aimed at reducing the risk burden on businesses; (2) dependence of GDP and tax evasion (separately) on the risks for listed companies and on the totality of the digital management measures of risk management and alternative measures of state regulation, aimed at reducing the risk burden on businesses; (3) dependence of the government budget surplus/deficit on tax evasion; and (4) dependence of the health infrastructure on the totality of digital management measures of risk management. Research model (1) reflects four dependencies: (1) dependence of the risks for listed companies on the totality of digital management measures of risk management and alternative measures of state regulation, aimed at reducing the risk burden on businesses; (2) de-  The reliability of the results of the regression analysis is verified with the help of the F-test. It should be noted that, when performing a regression analysis with the variables of a time series, it is very important to follow certain important steps to ensure the reliability and substantiation of the results. These steps usually include tests for unit roots to assess stationarity and residual tests to evaluate the appropriateness of the model.
To follow these steps and to guarantee the reliability of the results of the regression analysis, we performed, first, stationarity tests, with the help of the Augmented Dickey-Fuller test (ADF), which is very important for determining whether the variables demonstrate unit roots or are stationary. Stationarity is a fundamental assumption in an analysis of a time series; violation of this assumption may lead to imprecise and unreliable results.
The augmented Dickey-Fuller test (ADF) checks the value of the regression coefficient (a) in the autoregression equation of the first order. If a ≥ 1, the process has a unit root-in this case, the row is not stationary, and it is an integrated time row of the first order. If a < 1, the row is stationary. The Dickey-Fuller test was performed in this paper with the help of MS Excel software for each studied time row separately.
In the Dickey-Fuller test, the null hypothesis assumes that the time series has a unit root (it is non-stationary). Within this test, to conclude that the series is stationary, it is necessary to check the given null hypothesis, given the significance of the regression coefficients.
Second, residual tests are very important for the evaluation of the criteria of agreement and determination of potential problems with the model. Residual tests in this paper include the research on residuals for autocorrelation with the use of techniques such as the Durbin-Watson test.
Third, the structural break test-to determine whether there are important changes or breaks in the data. Structural breaks can take place due to various factors, e.g., changes in policy, economic turmoil, or other external events. Ignoring the possibility of structural breaks may lead to incorrect results. In econometrics and statistics, a structural break is an unexpected change (in time) in the parameters of the regression models, which may lead to big errors in forecasting and to the unreliability of the model on the whole. The Chow test, offered by the econometrist Gregory Chow (1960), is a verification of whether true coefficients are equal in two linear regressions at different sets of data. In econometrics, it is most often used in the analysis of a time series to check the presence of a structural break in the period that can be considered known a priori (e.g., a large historical event, such as a war). During the evaluation of a programme, the Chow test is often used to determine whether independent variables have different effects on different sub-groups of the population.
For models with linear regression, the Chow test is used to check the unit break of the mean for a given time period. This test evaluates whether the coefficients in the regression model are equal for different time periods (Davidson and MacKinnon 1993).
The null hypothesis of the Chow test states that there is no significant difference between the coefficients of the two regression models. Interpreting the results of the Chow test involves analysing the calculated F-statistic and comparing it with the critical value at a chosen significance level (usually 5% or 1%). If the F-statistic is greater than the critical value, it suggests that there is a significant difference in coefficients between the two regression models or subgroups being compared. This indicates the presence of a structural break or a significant difference in the relationships between variables over time or across subgroups.
It should be noted that the period of the sample (2017-2022) is rather large, which ensures the accuracy of the evaluation. This allows for effective use of the Chow test with the existing data. We compared regression coefficients for 2019 (pre-pandemic) and 2020 (during the COVID-19 crisis). The reliability of estimations obtained within this timeframe is ensured by a rather large number of observations in each sub-period-55 observations (54 degrees of freedom).
It should be also noted that the connection between cause and effect is not necessarily unidirectional. While changes in the causal variable directly influence changes in the effect variable, the connections are more complex and may include bidirectional or reverse dynamics. Acknowledging the limitations of a unidirectional perspective, for a better understanding of the cause-and-effect connection in the context of this research, we have additionally performed a correlation analysis-we calculated the coefficients of cross correlation, which also allows for performing a multicollinearity test of variables (avoiding their duplication in the econometric model).
These steps help with the concern raised about the potential bidirectional or feedback dynamics in real-world relationships. The bidirectional nature of the cause-and-effect relationship was studied with the help of correlation coefficients, which demonstrate the character (inhibiting each other with the negative sign of the correlation coefficient and catalysing each other with the positive sign of the correlation coefficient) and tightness (the closer the value of the correlation coefficient to 1, the tighter the connection) of the connection between the indicators.
Task 3 was set: to determine the potential of digital management of risks for companies under the conditions of a crisis of a non-economic nature via the example of the COVID-19 pandemic. It is solved with the help of the foresight method; it is used to insert in model (1) the maximum values of the digital management measures of risk management. Also, the method of trend analysis is used to evaluate the growth in the indicators' values.
Reliability of the empirical data is ensured due to the following: (1) a large number of observations (the full sample includes 330 observations and 229 degrees of freedom); (2) a long research period, which covers the pre-pandemic (2017-2019) and pandemic (2020-2022) periods (we studied six periods-calendar years); and (3) the use of methodology that, on the one hand, is rather complex for obtaining precise and reliable results and, on the other hand, is widely accessible for rechecking data.

Risks for Companies during the COVID-19 Crisis: Specifics of Developed and Developing Countries
To solve the first task of this research and to measure the risks for companies during the COVID-19 crisis, as well as to identify the specifics of risks in developed and developing countries, we performed a horizontal analysis of the data in sample 1. The obtained results are presented in Figures 2 and 3.
As shown in Figure 2, the position (rank) of companies in developed countries listed in the Fortune (2023) ranking during the COVID-19 crisis (in 2020 compared with 2019) deteriorated (growth rate-0.99), while during the pre-crisis period, it was improving (growth rate in 2019 equalled 1.01). After the crisis, the growth in profits was restored: the growth rates in 2021 and 2022 equalled 1.02.
Similarly, the profits of these companies during the COVID-19 crisis (in 2020 compared with 2019) reduced (growth rate-0.96), while during the pre-crisis period, they were increasing (growth rate equalled 1.07 in 2019). The negative effect of the COVID-19 crisis was prolonged-the decline in companies' profits continued and even increased in 2021 (growth rate-0.79). However, in 2022, profits were restored (growth rate was 2.08). The obtained results demonstrate significant risks faced by companies in developed countries during the COVID-19 crisis. For comparison, let us consider the experience of developing countries ( Figure 2).
As shown in Figure 3, companies' profits during the COVID-19 crisis (in 2020 compared with 2019) in developing countries did not change, and revenues grew (growth rate was 1.01), though the growth rate of revenues reduced compared with that of the pre-crisis period (growth rate was 1.09 in 2019). The position (rank) of listed companies from developing countries in the Fortune (2023) ranking during the COVID-19 crisis (in 2020 compared with 2019) improved significantly: the growth rate was 1.03. It should be noted that before (0.99 in 2019) and after (0.95 in 2021 and 0.96 in 2022) the COVID-19 crisis, their rank was decreasing. This shows that companies in developing countries faced a much lower level of risk during the COVID-19 crisis than companies in developed countries. On the whole, the risk of companies from developing countries was minimal and even reduced during the COVID-19 crisis. To specify the reasons for this unique phenomenon, let us consider the results of the horizontal analysis of the data from sample 2 ( Table 2). The results presented in Table 2 show that the risks for companies, on the whole for the world in 2020, during the COVID-19 crisis remained unchanged at the 2019 level. Thus, the annual growth rate of listed domestic companies equalled 1.01 in 2019 and 2020, and it was even better compared with the pre-crisis period in 2018 (0.99). At that, the measures of protectionism remained at the pre-crisis level (growth rate in 2020: 1.00). The health infrastructure improved significantly during the COVID-19 crisis due to the ambitious measures of state regulation for the fight against the pandemic (growth rate in 2020 was 1.01), but this is not enough to reduce the risks for companies.
Against this background, we should note the active implementation of the digital measures of risk management of companies. Thus, digital/technological skills grew by 1.01 in 2020 (compared with 1.00 in 2019 and 0.99 in 2018). The use of big data and analytics continued to grow with the pre-crisis rate: in 2019 and 2020, the growth rate equalled 1.03. The digital transformation in companies accelerated: the growth rate was 1.02 in 2020 against 0.94 in 2019 and 0.99 in 2018. Mobile broadband subscribers demon-  This shows that companies in developing countries faced a much lower level of risk during the COVID-19 crisis than companies in developed countries. On the whole, the risk of companies from developing countries was minimal and even reduced during the COVID-19 crisis. To specify the reasons for this unique phenomenon, let us consider the results of the horizontal analysis of the data from sample 2 ( Table 2).
The results presented in Table 2 show that the risks for companies, on the whole for the world in 2020, during the COVID-19 crisis remained unchanged at the 2019 level. Thus, the annual growth rate of listed domestic companies equalled 1.01 in 2019 and 2020, and it was even better compared with the pre-crisis period in 2018 (0.99). At that, the measures of protectionism remained at the pre-crisis level (growth rate in 2020: 1.00). The health infrastructure improved significantly during the COVID-19 crisis due to the ambitious measures of state regulation for the fight against the pandemic (growth rate in 2020 was 1.01), but this is not enough to reduce the risks for companies. role in the successful adaptation of companies to the conditions of the COVID-19 crisis and allow for avoiding high risks for companies. Among the economic implications of the COVID-19 crisis, we should mention the reduction in GDP (0.97 in 2020 vs. 1.01 in 2019), the substantial deepening of the government budget deficit (growth rate was 5.88 in 2020 vs. 1.36 in 2019) and the continuation of the successful fight against the shadow economy (growth rate of success in 2020 remained at the 2019 level and was assessed at 1.03).   Against this background, we should note the active implementation of the digital measures of risk management of companies. Thus, digital/technological skills grew by 1.01 in 2020 (compared with 1.00 in 2019 and 0.99 in 2018). The use of big data and analytics continued to grow with the pre-crisis rate: in 2019 and 2020, the growth rate equalled 1.03. The digital transformation in companies accelerated: the growth rate was 1.02 in 2020 against 0.94 in 2019 and 0.99 in 2018. Mobile broadband subscribers demonstrated visible growth (1.10), though it was lower compared with those at the pre-crisis level (1.14 in 2019).
The mentioned digital measures, which were actively implemented, could play a key role in the successful adaptation of companies to the conditions of the COVID-19 crisis and allow for avoiding high risks for companies. Among the economic implications of the COVID-19 crisis, we should mention the reduction in GDP (0.97 in 2020 vs. 1.01 in 2019), the substantial deepening of the government budget deficit (growth rate was 5.88 in 2020 vs. 1.36 in 2019) and the continuation of the successful fight against the shadow economy (growth rate of success in 2020 remained at the 2019 level and was assessed at 1.03).
Thus, an analysis of the experience of the top 55 most competitive digital economies of the world (IMD 2023) in 2017-2022 showed that the digital measures of risk management of companies were actively implemented during the COVID-19 crisis and could potentially have an important role for mitigating the economic implications of the crisis. To specify this, it is necessary to model-in more detail-the cause-and-effect relationships between the change and digital management of risks for companies during the COVID-19 crisis.

Cause-and-Effect Relationships of the Change and Digital Management of Risks for Companies during the COVID-19 Crisis
To solve the second task and to identify the cause-and-effect relationships between the change and digital management of risks for companies during the COVID-19 crisis, we conducted a regression analysis of the data from sample 2. Based on the experience of the top 55 most competitive digital economies of the world (IMD 2023) in 2017-2022, we performed econometric modelling of the connection between listed domestic companies, and alternative measures of risk management and potential implications for the economy. This allowed for specifying research model (1) and receiving the following system of equations of linear regression: (2) To ensure the correctness of the conclusions for econometric model (2), we used important steps in statistical analysis-including the F-test, stationarity test, the Augmented Dickey-Fuller test (ADF), and residuals analysis (Durbin-Watson test), as well as the Chow test (the unit break of the mean for a given time period)-to confirm the regression model and to evaluate its appropriateness. This guarantees the reliability and precision of analysis, providing a strong foundation for high-precision conclusions from the data.
The Dickey-Fuller test was performed for all variables: the regression of their values in period t and of their values in period t-1 was found ( Figures A1-A10). The obtained results are reflected in the regression curves, which showed that the values of almost all regression coefficients are below 1, except for the following: Based on this, on the whole, the time rows of all studied variables can be characterised as stationary. This allows for disproving the null hypothesis of the Dickey-Fuller test (that the time series has a unit root, i.e., is non-stationary), based on the level of significance, and concluding that the series is stationary.
Econometric model (2) reflects four dependencies: first, the dependence of the risks for listed companies on the totality of digital management measures of risk management and alternative measures of state regulation, aimed at reducing the risk burden on business. Its characteristics are demonstrated in detail in Table 3. The results obtained in Table 3 show that the risks for listed companies are increased by 28.30%, determined via the implementation of the set of the considered management measures. The F-test was passed at the level of significance of 0.001. It should be noted that the protectionism measures do not reduce but only raise the risks for companies, which is confirmed via the negative value of the regression coefficient.
An increase in digital transformation in companies by one point leads to an improvement in the business indicators (e.g., reduction in risks) of the listed domestic companies by 287.6776. An increase in the number of mobile broadband subscribers by 1% leads to an improvement in the business indicators (e.g., reduction in risks) of the listed domestic companies by 7.6846. We should note the substantial effect of the development of healthcare infrastructure, for which a growth rate by one point leads to improvement in the business indicators (i.e., reduction in risks) of the listed domestic companies by 103.3040.
The residual test (Durbin-Watson test) was performed based on the data on residuals given in Table A1: d = 963863383.4683/461648735.7215 = 2.0879. Since the test statistics, 2.0879, did not exceed the critical value (at the level of significance of 0.01 at n = 330 and m(k) = 6), there is no correlation between the residuals, i.e., the residuals are independent. Therefore, the assumption was confirmed, and the Durbin-Watson test was successfully passed.
A structural break test (the Chow test) was performed in Table 4, with the values of the coefficients of the regression before the pandemic (in 2019) and under the conditions of the COVID-19 pandemic and crisis (in 2020). As shown in Table 4, coefficients in the considered regression model are approximately equal for different time periods-the period before the pandemic (in 2019) and that during the COVID-19 pandemic and crisis (in 2020). This shows the absence of structural breaks.
The performed analysis of F-statistic and its comparison with the critical value (Ftable = 3.2036) at the selected level of significance (α = 0.01, i.e., 1%) showed that the observed F-statistic is below the critical level (F-observed in 2019 was 0.8011, and that in 2020 was 0.7810). Therefore, there is no significant difference in the coefficients between two regression models or sub-groups that are compared. This is a sign of the absence of a structural break or insignificant difference in the ties between variables in time or sub-groups. Thus, we confirmed the null hypothesis of the Chow test on the absence of a significant difference between the coefficients of the two regression models.
Second is the dependence of GDP and tax evasion (in isolation) on the risks for listed companies and on the totality of the digital management measures of risk management and alternative measures of state regulation, aimed at reducing the risk burden on businesses. Its characteristics are demonstrated in detail in Tables 5-8. The results obtained in Table 4 show that GDP decreased by 69.10%, determined via the risks for listed companies and the implementation of the set of considered management measures. The F-test was passed at the level of significance of 0.001.
The residual test (Durbin-Watson test) was performed based on the data on residuals that are given in Table A1: 6746900467.5637/3681271967.3473 = 1.8328. Since the test statistics, 2.0879, did not exceed the critical value (at the level of significance of 0.01 at n = 330 and m(k) = 7), there is no correlation between the residuals, i.e., the residuals are independent. Therefore, the assumption is correct, and the Durbin-Watson test was successfully passed.
A structural break test (the Chow test) was performed in Table 6, where the values of the coefficients of regression before the pandemic (in 2019) and during the COVID-19 pandemic and crisis (in 2020) are shown.
As shown in Table 6, the coefficients in the considered regression model are approximately equal for different time periods-the period before the pandemic (in 2019) and under the conditions of the COVID-19 pandemic and crisis (in 2020). This is a sign of the absence of structural breaks. The performed analysis of F-statistic and its comparison with the critical value (F-table = 8.5622) at the selected level of significance (α = 0.000001, i.e., 0.0001%) showed that the observed F-statistic is below the critical level (F-observed in 2019 was 7.9598, and that in 2020 was 6.2082). Therefore, there is no significant difference in the coefficients between two regression models or sub-groups that are compared. This is a sign of the absence of a structural break or insignificant difference in the ties between variables in time or sub-groups. Thus, we confirmed the null hypothesis of the Chow test on the absence of a significant difference between the coefficients of the two regression models.
It is noteworthy that the development of healthcare infrastructure does not provide support for economic growth but, on the contrary, slows it down. An increase in digital/technological skills by one point leads to an increase in GDP of USD 386.1310 billion. An increase in the activity of the use of big data and analytics by one point leads to an increase in GDP by USD 993.0822 billion. An increase in the number of mobile broadband subscribers by 1% leads to an increase in GDP by USD 7.0497 billion. An improvement in business indicators (i.e., reduction in risks) of listed domestic companies by 1 leads to an increase in GDP by USD 1.8530 billion.
The results obtained in Table 7 show that the success of the fight against tax evasion (de-shadowing of the economy) improved by 78.78%, determined via the risks for listed companies and implementation of the set of considered management measures. The F-test was passed at the level of significance of 0.001. An increase in the activity of the use of big data and analytics by one point leads to an increase in the success of the fight against tax evasion (de-shadowing of the economy) by 0.2690 points.
The residual test (Durbin-Watson test) was performed based on the data on residuals that are given in Table A1: 735.8002/328.7042 = 2.2385. Since the test statistics 2.0879 did not exceed the critical value (at the level of significance of 0.01 at n = 330 and m(k) = 7), there is no correlation between residuals, i.e., the residuals are independent. Therefore, the assumption was confirmed, and the Durbin-Watson test was successfully passed.
A structural break test (the Chow test) was performed in Table 8, where the values of the coefficients of regression before the pandemic (in 2019) and during the COVID-19 pandemic and crisis (in 2020) are shown. According to Table 8, coefficients in the considered regression model are approximately equal for different time periods-the period before the pandemic (in 2019) and that during the COVID-19 pandemic and crisis (in 2020). This is a sign of the absence of structural breaks.
The performed analysis of F-statistic and its comparison with the critical value (F-table = 16.4298) at the selected level of significance (α = 0.0000000001, i.e., 0.00000001%) showed that the observed F-statistic is below the critical level (F-observed in 2019 was 14.2800, and that in 2020 was 14.3165). Therefore, there is no significant difference in the coefficients between the two regression models or sub-groups that are compared. This is a sign of the absence of a structural break or insignificant difference in the ties between variables in time or sub-groups. Thus, we confirmed the null hypothesis of the Chow test on the absence of a significant difference between the coefficients of the two regression models.
An increase in the level of digital transformation in companies by one point leads to an increase in the success of the fight against tax evasion (de-shadowing of the economy) by 0.0961 points. An increase in the number of mobile broadband subscribers by 1% leads to an increase in the success of the fight against tax evasion (de-shadowing of the economy) by 0.0134 points. An increase in the measures of protectionism by one point leads to an increase in the success of the fight against tax evasion (de-shadowing of the economy) by 0.661 points. An increase in the development of the healthcare infrastructure by one point leads to an increase in the success of the fight against tax evasion (de-shadowing of the economy) by 0.1229 points. Improvement in business indicators (i.e., reduction in risks) of listed domestic companies by one leads to an increase in the success of the fight against tax evasion (de-shadowing of the economy) by 0.0001 points. Third is the dependence of the government budget deficit on tax evasion. Its characteristics are demonstrated in detail in Table 9.
The results obtained in Table 9 show that the state budget surplus increased by 17.32%, determined via the success of the fight against tax evasion (de-shadowing of the economy). The F-test was passed at the level of significance of 0.005.
The residual test (the Durbin-Watson test) was performed based on the data on residuals that are given in Table A1: 5182.6456/4452.1147 = 1.1641. Since the test statistics, 2.0879, did not exceed the critical value (at the level of significance of 0.01 at n = 330 and m(k) = 1), there is no correlation between the residuals, i.e., the residuals are independent. Therefore, the assumption is confirmed, and the Durbin-Watson test was successfully passed.
A structural break test (the Chow test) was performed in Table 10, where the values of the regression coefficients are shown for before the pandemic (in 2019) and during the COVID-19 pandemic and crisis (in 2020).
As shown in Table 10, the coefficients in the considered regression model are approximately equal for different time periods-the period before the pandemic (in 2019) and that during the COVID-19 pandemic and crisis (in 2020). This is a sign of the absence of structural breaks.
The performed analysis of F-statistic and its comparison with the critical value (F-table = 7.1386) at the selected level of significance (α = 0.01, i.e., 1%) showed that the observed F-statistic is below the critical level (F-observed in 2019 was 4.7260, and that in 2020 was 0.8955). Therefore, there is no significant difference in the coefficients between the two regression models or sub-groups that are compared. This is a sign of the absence of a structural break or insignificant difference in the ties between variables in time or sub-groups. Thus, we confirmed the null hypothesis of the Chow test on the absence of a significant difference between the coefficients of two regression models.  An increase in the success of the fight against tax evasion (de-shadowing of the economy) by one point leads to an increase in the state budget surplus by 0.3987%-i.e., budget deficit decreases. Fourth is the dependence of the health infrastructure on the totality of the digital management measures of risk management. Its characteristics are demonstrated in detail in Table 11.
The results obtained in Table 11 show that the development of the health infrastructure increased by 56.09%, determined via the totality of the digital management measures of risk management. The F-test was passed at the level of significance of 0.001.
A structural break test (the Chow test) was performed in Table 12, where the values of the regression coefficients are shown before the pandemic (in 2019) and during the COVID-19 pandemic and crisis (in 2020).
As shown in Table 12, the coefficients in the considered regression model are approximately equal for different time periods-the period before the pandemic (in 2019) and that during the COVID-19 pandemic and crisis (in 2020). This is a sign of the absence of structural breaks.  The performed analysis of F-statistic and its comparison with the critical value (Ftable = 7.3301) at the selected level of significance (α = 0.0001, i.e., 0.01%) showed that the observed F-statistic is below the critical level (F-observed in 2019 was 5.7474, and that in 2020 was 5.2092). Therefore, there is no significant difference in the coefficients between the two regression models or sub-groups that are compared. This is a sign of the absence of a structural break or insignificant difference in the ties between variables in time or sub-groups. Thus, we confirmed the null hypothesis of the Chow test on the absence of a significant difference between the coefficients of the two regression models.
An increase in the development of digital/technological skills by one point leads to an increase in the level of the development of health infrastructure by 0.6197 points. Growth in the level of digital transformation in companies by one point leads to a growth in the level of the development of health infrastructure by 0.3723 points. Growth in the coverage of mobile broadband subscribers by 1% leads to an increase in the level of the development of health infrastructure by 0.0284 points.
The reliability of econometric model (2) is confirmed via the successfully passed F-test, the Augmented Dickey-Fuller test, the Durbin-Watson test, and the Chow test for all regression equations. The performed tests proved the regression model and confirmed its expedience, guaranteeing the reliability and precision of the analysis.
For a better understanding of the cause-and-effect relationships in the context of this research, we performed a correlation analysis, calculating the coefficients of crosscorrelation (Table 13). The correlation analysis (Table 13) allowed for performing a test on the multicollinearity of variables, which has been successfully passed-the duplicating variables are absent in econometric model (2). Based on the results from Table 1, the bidirectional nature of the cause-and-effect relationship of the studied indicators is determined.
Digital/technological skills (DGT 1 ) demonstrated the closest bidirectional connection and catalytic effect with digital transformation in companies (DGT 3 , correlation: 0.68) and with the use of big data and analytics (DGT 2 , correlation: 0.62). This could be explained via the growth in demand for digital personnel in the labour market in the course of the development of digital business and the corresponding expansion of opportunities for the digitalisation of business in the course of an increase in its provision with the necessary digital personnel.
Also, we revealed a rather close bidirectional relationship with protectionism (GOV 1 , correlation: 0.46): digital personnel support import substitution. Protectionism, in turn, raises the demand for digital personnel as a production factor of digital business. The revealed close bidirectional relationship with health infrastructure (GOV 2 , correlation: 0.46) shows that the development of digital healthcare based on the telecommunication infrastructure motivates employees and consumers to master digital skills, and better mastering of these skills, in its turn, expands on opportunities for the development of digital healthcare.
We also revealed a weak negative connection (inhibiting effect) with tax evasion (ECON 2 , correlation: −0.04). It could be explained by the fact that the shadow economy reduces the motivation of employees in mastering digital skills due to the impossibility to obtain returns on investments into training. At that, the development of digital skills increases the sellers' power in the labour market and allows employees to influence em-ployers, requiring social guarantees and fighting the shadow economy, i.e., overcoming tax evasion.
The use of big data and analytics (DGT 2 ) demonstrated the closest bidirectional connection and catalytic effect with digital transformation in companies (DGT 3 , correlation: 0.82). This means that the higher the level of digitalisation, the more actively big data and analytics are used, which causes further digital development, which takes place in a cyclical manner.
Digital transformation in companies (DGT 3 ) demonstrated the closest bidirectional connection and catalytic effect with protectionism (GOV 1 , correlation: 0.44) and health infrastructure (GOV 2 , correlation: 0.39), which is largely predetermined by the pandemic and crisis context. Thus, digitalisation supports import substitution due to the growth of digital competitiveness of domestic businesses and allows for developing digital healthcare. Accordingly, protectionism and digital healthcare (including the fight against COVID-19 during the pandemic) support the digitalisation of businesses.
Mobile broadband subscribers (DGT 4 ) demonstrated the closest bidirectional connection and catalytic effect with health infrastructure (GOV 2 , correlation: 0.41). Thus, mobile communication facilitates the development of telemedicine, while digital healthcare stimulates consumers to more actively use mobile communications for the use of telemedicine services.
At that, we revealed a weak negative connection (inhibiting effect) with government budget surplus/deficit (GBD, correlation: −0.12). It can be explained by the fact that a budget deficit hinders investments in the development of mobile communications and reduces its accessibility. In turn, the development of mobile communications and an increase in the activities of their use raise economic activity (support economic growth and increase the taxation base), facilitate the development of digital finance, and contribute to the fight against the shadow economy.
Protectionism (GOV 1 ) demonstrated the closest bidirectional connection and catalytic effect with health infrastructure (GOV 2 , 0.66). This means that protectionism was implemented and, at the same time, the healthcare infrastructure was developed, for the fight against the COVID-19 crisis. A developed healthcare infrastructure supports import substitution. Protectionism allows for starting import substitution in healthcare and developing its infrastructure. At that, there is a weak negative connection (inhibiting effect) with tax evasion (ECON 2 , correlation: −0.09). It can be explained by the fact that the shadow economy hinders import substitution, and import substitution facilitates the fight against the shadow economy.
Health infrastructure (GOV 2 ) demonstrated a weak negative connection (inhibiting effect) with tax evasion (ECON 2 , correlation: −0.03). It can be explained by the fact that the shadow economy causes a deficit in revenues of the state budget, reducing the effectiveness of institutes and thus hindering the financing of the development of healthcare infrastructure. Under the conditions of the COVID-19 pandemic and crisis, the development of healthcare infrastructure ensured the fight against the shadow economy.
The listed domestic companies (RISK) demonstrated a weak negative connection (inhibiting effect) with tax evasion (ECON 2 , correlation: −0.03) and government budget surplus/deficit (GBD, correlation: −0.31). It can be explained by the fact that overcoming the shadow economy reduces business risks. A reduction in business risks eliminates the necessity for the shadow economy and facilitates its overcoming.
Gross domestic product, GDP (ECON 1 ), demonstrated a weak negative connection (inhibiting effect) with tax evasion (ECON 2 , correlation: −0.02) and government budget surplus/deficit (GBD, correlation: −0.27). It can be explained by the fact that the shadow economy reduces the official GDP, decreases the tax base, and causes a budget deficit. Accordingly, overcoming the shadow economy causes a reverse process: an increase in the official GDP, growth in the tax base, and growth in the state budget revenues.
These steps address the concern raised about the potential bidirectional or feedback dynamics in real-world relationships. The bidirectional nature of the cause-and-effect relationship was studied with the help of correlation coefficients, which demonstrate the character (inhibiting each other with the negative sign of the correlation coefficient and catalysing each other with the positive sign of the correlation coefficient) and tightness (the closer the value of the correlation coefficient to 1, the tighter the connection) of the connection between the indicators.
The cause-and-effect relationships are systemically shown in the model of structural equations (SEM) in Figure 4.

Potential of Digital Management of Risks for Companies under the Conditions of a Crisis of a Non-Economic Nature via the Example of the COVID-19 Pandemic
To solve the third task and to determine the potential of digital management of risks for companies under the conditions of a crisis of a non-economic nature via the example of the COVID-19 pandemic, we used the method of foresight and inserted into econometric model (2) the maximal values of the digital management measures of risk management. With the help of the method of trend analysis, we assessed the growth rate of the indicators' values ( Figure 5). Thus, the obtained results showed that the totality of the digital management measures of risk management makes a significant contribution not only to the improvement in business indicators (reduction in risks) of listed companies but also to the development of healthcare infrastructure and the fight against tax evasion (de-shadowing of the economy).
Moreover, the digital measures of risk management play a much more important role in the reduction in the risks for companies than alternative measures of state regulation, aimed at reducing the risk burden on business (protectionism and the development of healthcare infrastructure). A reduction in risks for companies supports economic growth (ensuring an increase in GDP) and improves the results of the fight against tax evasion (de-shadowing of the economy), thus reducing the state budget deficit.

Potential of Digital Management of Risks for Companies under the Conditions of a Crisis of a Non-Economic Nature via the Example of the COVID-19 Pandemic
To solve the third task and to determine the potential of digital management of risks for companies under the conditions of a crisis of a non-economic nature via the example of the COVID-19 pandemic, we used the method of foresight and inserted into econometric model (2)  Following the authors' foresight ( Figure 5), to fully unlock the potential of digital management of risks for companies under the conditions of a crisis of non-economic nature via the example of the COVID-19 pandemic, it is recommended that a set of measures be implemented: increase digital/technological skills by 49.40%, increase the activity of the use of big data and analytics by 90.46%, raise the level of digital transformation in companies by 74.84%, and increase the coverage of mobile broadband subscribers by 31.48%.
Due to this, the business indicators of the listed domestic companies will grow, and their risks will reduce by 241.72% (their number grows from 0.73 thousand in 2022 to 2.48 thousand). Also, the development of health infrastructure by 70.01% (from 5.83 points in 2022 to 9.90 points) is achieved. Following the authors' foresight, the implementation of digital measures of risk management of companies ensures the following advantages for the economy: an increase in GDP by 33.22% (from USD 1.58 trillion in 2022 to USD 2.11 trillion), improvement in the results of the fight against tax evasion (de-shadowing of the  Following the authors' foresight ( Figure 5), to fully unlock the potential of digital management of risks for companies under the conditions of a crisis of non-economic nature via the example of the COVID-19 pandemic, it is recommended that a set of measures be implemented: increase digital/technological skills by 49.40%, increase the activity of the use of big data and analytics by 90.46%, raise the level of digital transformation in companies by 74.84%, and increase the coverage of mobile broadband subscribers by 31.48%.
Due to this, the business indicators of the listed domestic companies will grow, and their risks will reduce by 241.72% (their number grows from 0.73 thousand in 2022 to 2.48 thousand). Also, the development of health infrastructure by 70.01% (from 5.83 points in 2022 to 9.90 points) is achieved. Following the authors' foresight, the implementation of digital measures of risk management of companies ensures the following advantages for the economy: an increase in GDP by 33.22% (from USD 1.58 trillion in 2022 to USD 2.11 trillion), improvement in the results of the fight against tax evasion (de-shadowing of the economy) by 36.95% (from 4.86 points in 2022 to 6.66 points), and reduction in the budget deficit by 43.40% (from −4.13% of GDP in 2022 to −2.34% of GDP).

Discussion
This paper's contribution to the literature consists of the development of scientific provisions of the concept of risks for companies through a clarification of the specifics of risks and risk management of companies during the COVID-19 crisis. Due to this, this paper filled the literature gap and provided answers to all posed RQs, which are given-in comparison with the existing literature-in Table 14. Table 14. Obtained answers to the posed RQs compared with those in the existing literature.

Research Questions (RQs) Answers in the Existing Literature New Answers that Were Received in This Paper
RQ 1 : What is the level of risks for companies during the COVID-19 crisis (in 2020): higher or lower than the pre-crisis level (2019)?
Risks for companies were very high during the COVID-19 crisis (Moreno Ramírez et al. 2022;Tan et al. 2022;Zhou and Li 2022) Risks for companies during the COVID-19 crisis increased slightly compared with those at the pre-crisis level RQ 2 : Which countries experienced the highest risks for companies during the COVID-19 crisis: developed or developing nations?
Companies faced large risks during the COVID-19 crisis in developing countries Dohale et al. 2023) Companies faced large risks during the COVID-19 crisis in developed countries RQ 3 : What are the consequences of risks for companies during the COVID-19 crisis for the economy: increase or reduction in crisis phenomena in the economy?
Due to the unpreparedness of companies for the COVID-19 crisis, the risks for them increased the economic decline (Mezghani et al. 2021;Yamen 2021) Due to successful adaptation, the risk management of companies mitigated manifestations of the COVID-19 crisis in the economy As shown in Table 14, first, a new answer to RQ 1 was obtained. Unlike Moreno Moreno Ramírez et al. (2022), Tan et al. (2022), and Zhou and Li (2022), it was substantiated that the risks for companies during the COVID-19 crisis increased only slightly (not much) compared with those at the pre-crisis level. Hypothesis H 1 was proved, confirming the work of Hohenstein (2022) and Tingey-Holyoak and Pisaniello (2021).
Second, a new answer to RQ 2 was obtain. Unlike Abdullah et al. (2022) and Dohale et al. (2023), it was substantiated that companies faced large risks during the COVID-19 crisis, not in developing countries but in developed countries. Hypothesis H 2 was proved, confirming the work of Kukoyi et al. (2022) and Metwally and Diab (2023) Third, a new answer to RQ 3 was obtain. Unlike Mezghani et al. (2021) and Yamen (2021), it was substantiated that the risks for companies did not increase the economic decline, but on the contrary, due to successful adaptation, risk management of companies mitigated the manifestations of the COVID-19 crisis in the economy. Hypothesis H 3 was proved, confirming the work of Inshakova et al. (2021), Leung et al. (2023), and Ngo et al. (2023).
Fourth, a new answer to RQ 4 was received. Unlike Abdel Abdel Fattah et al. (2022), Phang et al. (2023), Salami et al. (2022), and Velayutham et al. (2021), it was substantiated that companies managed their risks independently and much more successfully during the COVID-19 crisis with the help of measures of the digitalisation of businesses. That is, internal corporate management is preferable to external (state) management with the help of standard measures of protectionism and special measures of the development of healthcare infrastructure to reduce the risk burden for businesses during the COVID-19 crisis. Hypothesis H 4 was proved, confirming the work of Attaran (2023), Busco et al. (2023), Cui et al. (2022), and Türk (2022).

Conclusions
Thus, as a result of the performed research, a systemic risk profile of companies during the COVID-19 crisis was formed; it reflects their cause-and-effect relationships and risk management. In particular, the following results were received. First, via the example of companies from "Global-500", it was discovered that companies in developing countries faced a much lower level of risk during the COVID-19 crisis than companies in developed countries.
Thus, we determined the significant risks faced by companies in developed countries during the COVID-19 crisis: the position (rank) of companies listed in the Fortune (2023) ranking during the COVID-19 crisis deteriorated (growth rate: 0.99), and the profits of these companies reduced (growth rate: 0.96). Unlike them, in developing countries, the profits of companies during the COVID-19 crisis (in 2020 compared with 2019) did not change, and their revenues grew (growth rate: 1.01), and the position (rank) of companies listed in the ranking of Fortune (2023) improved significantly (growth rate: 1.03).
Second, based on the experience of the top 55 most competitive digital economies of the world (IMD 2023) in 2017-2022, we compiled an econometric model, which disclosed the cause-and-effect relationships of the change in and digital management of risks for companies during the COVID-19 crisis. The model showed that, due to successful adaptation, risk management of companies mitigated the manifestations of the COVID-19 crisis in the economy.
Thus, digital measures of risk management improve the business indicators (reduce risks) for listed companies, facilitate the development of healthcare infrastructure and support the fight against tax evasion (de-shadowing of the economy). A reduction in the risks for companies supports economic growth (ensures an increase in GDP) and improves the results of the fight against tax evasion (de-shadowing of the economy), thus reducing the state budget deficit.
Third, it was proved that digital measures of risk management play a much more important role in the reduction in risks for companies than alternative measures of state regulation, aimed at reducing the risk burden for business (protectionism and the development of healthcare infrastructure). The key conclusion of this research is that, under the conditions of a crisis of a non-economic nature (e.g., the COVID-19 crisis), companies manage-independently and successfully-their risks with the help of measures of the digitalisation of businesses-i.e., internal corporate risk management with limited state intervention is preferable.
The theoretical significance lies in the fact that the authors' conclusions rethought the risks for companies under the conditions of a crisis given the special context of a crisis of non-economic nature (via the example of the COVID-19 crisis). Unlike a crisis of an economic nature (e.g., the 2008 global financial and economic crisis), crises of an economic nature (e.g., the COVID-19 crisis in 2020) (1) cause less vivid risks for companies; (2) cause larger risks for companies in developed countries, creating opportunities for improvement in the business indicators of companies in developing countries; (3) require the management of risks at the level of companies, not at the level of the state; and (4) cause a preference for digital risk management.
The practical significance of the authors' results is that the developed novel approach to risk management of companies during the COVID-19 crisis, which is based on digitalisation, allows, with high effectiveness, for managing the risks of companies during a crisis of a non-economic nature. For this, a set of digital management measures of risk management was proposed, which include an increase in digital/technological skills, an increase in the activity of the use of big data and analytics, growth in the level of digital transformation in companies, and an increase in the coverage of mobile broadband subscribers.
The managerial significance of the authors' recommendations is connected to the fact that, under the conditions of future crises of a non-economic nature (caused by reasons related to epidemics/pandemics and environmental disasters), they will allow for improving business indicators of listed domestic companies and reducing risks down to 241.72% (as the authors' foresight showed).
The proposed recommendations' economic policy implications are that their implementation by companies during future crises of a non-economic nature will ensure advantages for the economy in the form of the development of health infrastructure up to 70.01%, an increase in GDP up to 33.22%, improvement in the results of the fight against tax evasion (de-shadowing of the economy) up to 36.95%, and a reduction in budget deficit down to 43.40% (as the authors' foresight showed).
As for the limitations of the performed research, it should be noted that it is based on the unique experience of the COVID-19 crisis, unparalleled in the 21st century, which does not allow for generalisation of the results obtained and for their application to all crises of a non-economic nature. This is a limitation of the authors' conclusions; to overcome it, future studies should elaborate on the experience of future crises of a non-economic nature (caused by reasons connected to epidemics/pandemics and environmental disasters) and should identify common regularities of the risks for companies under the conditions of all crises of the non-economic nature.
Summing up, it is necessary to point to weaknesses of the research design and approach, which are connected with the predominant consideration of economic reasons and consequences of the risks of companies during the COVID-19 crisis with a focus on the digital economy. At that, non-economic aspects belong to an error of the compiled econometric model and remained beyond the limits of this research. Thus, prospects for future studies lie in the elaboration on these non-economic aspects.
In particular, attention should be paid to socio-psychological and cultural aspects, which are connected to social distancing and social isolation during the lockdown. They could have played an important role in the practical implementation of risks of companies during the COVID-19 crisis. It is recommended that they be studied in future work as a continuation of this paper.