4.2. Descriptive statistics
The research was carried out on a group of 68 Polish SMEs. The research involved commercial units operating within the largest purchasing groups in Poland operating in the construction industry. After winsorizing the outlier data, our purpose is to provide a table of descriptive statistics showing a general picture of the state of distribution of observations of each variable. In other words, for a better understanding and accurate comparison between data values in the period before COVID-19 (BC) and during the COVID-19 pandemic (DC), comparative descriptive statistics are shown in
Table 1.
What stands out from the table one is that the average annual rate of return on assets (ROA), return on equity (ROE), and return on sales (ROS) have improved slightly during COVID-19 compared to before the pandemic. In addition, during COVID-19, the range of fluctuations related to the ratio of ROA is between 0.0109 and 0.2001, the ratio of ROE is between 0.0225 and 0.3989, and the ratio of ROS is between 0.010 and 0.1. However, the difference in volatility between the two variables of ROA and ROE was equal to (0.2075 − 0.0090 = 0.1985) 0.1985 and (0.4179 − 0.0179 = 0.4007) 0.4007, respectively, which was higher when compared to the Corona crisis. In general, the closeness of the average of the three financial performance measures during and before the COVID-19 crisis confirms the fact that Polish SMEs have tried to maintain their financial security in the uncertainty economic situation of the pandemic by adopting appropriate strategies so they can compete with others.
Regarding the liquidity ratios, the averages related to liquidity and quick ratios increased during COVID-19 compared to the previous period. This implies that the SMEs studied have adopted conservative strategies. They have resorted somewhat to more cash during the crisis to be more financially resilient in necessary situations. Moreover, let us look at the descriptive statistics of efficiency ratios such as Receivables turnover, Liabilities turnover, Inventory turnover, and operation cycle. The results show that companies have adopted more conservative policies during the crisis than in the past. For instance, since the average collection period of the company’s receivables is shorter than before, the firms are said to be less under financial pressure to secure their credit and increase working capital, thus increasing their revenue. Consistent with the conservatism strategy, we find that the average receivables turnover is shorter than liabilities turnover during the COVID-19 pandemic. This is beneficial for the company as evidenced by research (Habib). The company credits its activities with the cheapest source of financing, which is a trade credit. Furthermore, given that the average operation cycle has become shorter than before the crisis, it indicates that SMEs operating in the renewable energy industry need less working capital because their receivables collection periods are shorter. It will take less time for the produced goods to become cash. However, an increase in average inventory turnover compared to before the crisis could be due to the company’s decreased inventory investment. In addition, the operational cycle of converting materials and goods into cash may be short. Suppose the structure of current assets of Polish SMEs in the energy sector is analyzed. In that case, we see that the average share of inventory and short-term investments in current assets has increased during the crisis, while the amount of accounts receivable dropped. Finally, the last column of descriptive statistics focuses on evaluating the normality of the distribution of observations of variables. According to the results of the Jarque–Bera test, as the amount of probability of all variables in this study is greater than five percent, the normality assumption of the variables is supported.
4.3. Significance Testing
The main purpose of this study is to compare financial security strategies during the pandemic and to determine the types of approaches managers adopt to deal with the crisis. There is a comparison of financial security management strategies during and before COVID-19. These strategies are conservative and aggressive strategies for managing financial liquidity and net working capital. Among the types of paired tests, the Student
t-test can be employed to compare mean differences between data when the observations have been obtained in pairs. In this paper, to compare the mean value of the two sets of data during and before COVID-19 and to evaluate its significance, the statistics of the paired
t-test, Satterthwaite–Watch
t-test, and Welch F-test can be used. In general, the paired Student t provides a hypothesis test of the difference between population means for a pair of random samples whose differences are approximately normally distributed [
54,
60]. According to the research literature, we generally have three types of
t-tests called one sample
t-test, independent samples
t-test, and paired samples
t-test [
57,
65]. Our study includes the third type. The paired
t-test is employed to compare mean dissimilarities when the observations have been gained in pairs, and are thus dependent. In fact, the null hypothesis of the
t-test states that both means are statistically equal, while the alternative hypothesis refers to the opposite of this claim [
57,
65]. In this research, examining the differences between financial management policies in the era before and after the COVID-19 crisis can be a very good example of the application of the paired
t-test. In this study, we assume there are 68 pairs of observations and that each pair is independent of the other pairs. It is assumed that the paired observations as
and
for two periods during the COVID-19 pandemic (DC) and before the COVID-19 (BC) and their differences as
for
i = 1, 2, … n. As mentioned earlier, the evidence shows that our observations are normally distributed with means
and
. The random variable D =
X −
Y is then normally distributed with mean
and variance
. Accordingly, the null hypothesis is
:
[
53]. In short, the formula to calculate the Student
t-test is as follows:
Similar to the Student
t-test, the Welch
t-test assumes that the population of the two groups are normal. However, the Student
t-test presumes that the two populations have the same variance while the Welch
t-test does not make any assumption on the variances. According to Ahad and Yahaya [
65], the Welch
t-test is the parametric test for comparing means between two independent groups without assuming equal population variances. This statistic is even robust for testing the mean equality when the homogeneity hypothesis is not satisfied. Unlike the Student
t-test, the Welch
t-test does not pool across heterogeneous sources of variability where the denominator is not based on the pooled variance estimate. The Welch
t-test is defined by the following formula [
65];
In other words, under the null hypothesis, t is roughly distributed as the t-distribution with degrees of freedom. Finally, in statistics and uncertainty investigation, the Welch–Satterthwaite equation is used to calculate an approximation to the effective degrees of freedom of a linear combination of independent sample variances, also known as the pooled degrees of freedom [
66,
67], corresponding to the pooled variance. Various researchers have made many efforts to calculate a suitable degree of freedom for the Welch’s t statistics [
67,
68,
69,
70], but all of them are considered as approximate degree of freedom (ADF) [
65]. The degrees of freedom associated with this variance estimate is approximated from the sample data using the Welch–Satterthwaite equation, shown below:
The null hypothesis of equal population mean is rejected when
p-value ≤ α, where α is the significance value. The
p-value of one tail test is then defined as:
where
is the calculated
t value. The
p-value of two tailed test considered in this study is given by [
54]:
Based on the existing research literature, the ratios of the return on assets (ROA), return on equity (ROE), and return on sales (ROS) have been used in various types of research as a criterion for evaluating the financial performance of companies [
35,
36]. Before analyzing the various financial policies, we first want to show that the economic crisis caused by COVID-19 has worsened the financial performance of Polish SMEs within the energy sector. Hence, these results are presented by comparing the average of the above three ratios during COVID-19 (DC) and before COVID-19 (BC) in
Table 2,
Table 3 and
Table 4 as follows:
The results show that the average ROA and ROE during the COVID-19 crunch have improved compared to the past, while the trend of ROS ratio has moved in the opposite direction; however, none of these results are meaningful and reliable because the p-value of statistics of the student t-test, Satterthwaite–Welch t-test, ANOVA F-test, and Welch F-test are greater than five percent. It seems that Polish SMEs operating in the RE sector have been able to stabilize and maintain their previous economic position. In fact, they have neither progressed nor regressed significantly; they have only maintained their economic position in the market. As their financial position in the market stabilizes, they may be waiting to see what the future holds for the market.
In the next step of this research, we intend to examine two of the most important liquidity ratios: liquidity ratio and quick ratio. Here, we analyze if COVID-19 has changed financial liquidity management strategies such as liquidity and quick proportions among the SMEs.
Given that the probabilities of the different statistics in
Table 5 are more than five percent, the results insignificantly show that companies are inclined to have a larger share of their current assets in cash during the crisis. Therefore, they have a higher ability to pay off short-term debt and more financial flexibility than unexpected events and continue competing with others. As the amount of
p-value of all statistics is insignificant, the results of the quick ratio in
Table 6 also state that mining firms have adopted a conservatism approach in which the companies’ ability to meet their short-term obligations with their most liquid assets has increased unimportantly compared to the past. As the results in the previous step confirmed that most companies have sought to maintain their economic position in the market, the liquidity ratios now show that companies have tried to improve their liquidity situation slightly. This is because the profitability index should not be seriously affected, which is in line with the policy of balancing liquidity and profitability. Furthermore, as far as we know, short-term receivables turnover, short-term liabilities turnover, inventory turnover, cash conversion cycle (CCC), and operating cycle (OC) are defined as corporate efficiency ratios focusing directly on corporate liquidity management [
27]. Therefore, we want to know if the economic crisis has led to fundamental changes in the above working capital management policies. The results for each are presented in
Table 7,
Table 8,
Table 9,
Table 10 and
Table 11, respectively.
The COVID-19 pandemic has led companies to adopt a more conservative policy towards customers and suppliers for better liquidity security. The results of
Table 7 show that mining enterprises receive much faster receivables from their customers. In contrast, the outputs of
Table 8 confirm that the duration of their debts to suppliers and creditors has been almost the same as in the past. Given the insignificance of the above statistical results, it can be concluded that the risk of lack of liquidity could not pose a serious threat to SMEs operating in the energy sector that forces them to collect more receivables faster and pay their debts later. Before the occurrence of the COVID-19 economic crisis, managers had a conservative mindset and had enough cash reserves for their companies to be able to prepare themselves in advance to face difficult situations without surprised.
The important point is that inventories will increase financial liquidity ratios when security reserves are generated [
10,
23]. The findings of
Table 9 highlight the fact that inventory turnover in days during the COVID-19 period are longer than before, even though it is not statistically meaningful. The high inventory turnover during the crisis may be because SMEs’ investment in inventories has decreased or the operational cycle of converting materials and inventories into cash is short.
Regarding the ratio of cash conversion cycle (CCC), it can be stressed that CCC has been widely applied as a useful and comprehensive measure of working capital management because it measures the liquidity risk entailed by growth [
28,
29,
30,
31,
55,
56]. CCC evaluates how long a firm will be deprived of cash when it upturns its investment in inventory to develop customer sales. A short CCC means a quick collection of receivables and delays in payments to suppliers, which is connected with positive financial performance because it affects the effective use of working capital [
56]. As mentioned, mining companies seem to have had conservative policies before the crisis and saved enough cash. Hence, due to the lack of significant impact of the Corona crisis on the shortening of the cash conversion cycle, we can conclude that SMEs did not need working capital and high cash because they had already considered the necessary measures before the COVID-19 crisis.
Table 11 presents the results of enterprise management efficiency indicators (operating cycle).
Companies that have managed the crisis well before and need less cash are usually expected to have shorter operating cycles during the economic crisis. Since the amount of
p-value for relevant statistics is more than five percent, the outcomes are not statistically significant. However, we realized that the COVID-19 pandemic has significantly caused companies to reduce their operating cycle. A company with a shorter operating cycle needs less working capital. Because these companies have a lower receivables collection period, it will take less time for their goods to be converted into cash; therefore, they do not need to increase current assets to support their current debt. Another key question in this study is whether mining companies prefer to source most of their assets from debt during the economic crisis. To this end, analyzing the debt ratio results in
Table 12 can determine the answer to this question.
The statistical outputs from
Table 12 indicate that the average debt ratio during COVID-19 has declined insignificantly in comparison with before. In fact, since lower debt ratios indicate less business risk, Polish companies in uncertain economic conditions have sought to build safer and less risky business environments. However, this finding is not statistically meaningful.
Regarding the structure of current assets, it can be generally said that there are two strategies called aggressive and conservative. It is obvious is that if small- and medium-sized companies do not adopt a suitable working capital strategy, their financial security will be jeopardized [
60], and they may collapse during the Corona financial crisis. Since SMEs do not have huge capital and are largely dependent on bank loans and commercial credits compared to larger companies, they are more vulnerable to working capital fluctuations and are less inclined to take on high risks [
61]. In order to understand this concept more deeply, we attempted to analyze the components of current assets structure of Polish SMEs to determine which of the aggressive or conservative approaches were aligned during the COVID-19 crunch. Hence, the last fundamental question of this paper is whether small- and medium-sized enterprises in the energy sector have made fundamental changes to their current asset structure policies during the COVID-19 crisis. To find this answer, this study compares the structure of SMEs’ current assets before and during the crisis in
Table 13,
Table 14 and
Table 15.
If we look carefully at the results of
Table 13, it is clear that the share of inventories in the current assets portfolio increased insignificantly during the Coronavirus pandemic compared to before. According to the conservative working capital perspective, maintaining high levels of inventory in current assets cannot only reduce risk of liquidity associated with the opportunity cost of funds that may have been invested in long-standing assets, but it also declines the cost of interruptions in the production process, provides cost, and protects against price variation and loss of business owing to shortage of product [
67]. In general, from a statistical point of view, the non-significant coefficient ratio of inventories in CA shows that the companies have moved very slightly and unimportantly towards a conservative approach in relation to inventories. If we analyze the results in
Table 15 well, we can find such a similar scenario regarding short-term investments. Since maintaining financial security and having better liquidity conditions is more important to Polish SMEs, they have tried to have a non-significant increase in short-term investments so that they can quickly convert them into cash to have better flexibility during the COVID-19 pandemic. Finally, the results of
Table 14 witness a significant decrease in the share of receivables in current assets structure during the COVID-19 pandemic in comparison with before, supporting the conservative approach. In other words, during the COVID-19 crisis, which has reduced the income of many people, companies have preferred to set structure their current assets in such a way that they take less risk in obtaining the necessary liquidity and have fewer credit sales. In the conditions of the Coronavirus crisis, when the economic situation is uncertain and the general livelihood situation is not very favorable, it seems that cash sales and having less accounts receivable from customers can be a more suitable solution to maintain the financial safety of SMEs.