Almost 23% of the firms in our sample responded that they did not have sufficient funds to invest, and more than 8% of the firms did not manage to acquire borrowing funds. This also implies that a certain number of firms did not borrow money for new investments in that period because they already had high financial leverage, that is, an inadequate capital structure, or simply that they could not get new credits and loans due to the credit crunch.

More than 45% of the firms in our sample responded that, in the 8-year period, they totally exploited those business opportunities in the market that required some investments. This means that these firms increased their business if we exclude those who only modernized their production process (automation and robotization). As already mentioned, the period after the financial crisis was characterized by a credit crunch. Therefore, we can identify creditless economic growth, which was typical for Slovenia in the period from 2013 up until the end of 2015 [35]. Creditless growth is a special (marginal) form of financial leverage decrease. We even witnessed this decrease later, after the recovery of the Slovenian banking sector, with episodes of the economy recovering without a simultaneous or precursive credit growth recovery. This phenomenon has been perceived in the case of exits from crises by Calvo et al. [31]. The genesis of these crises was closely tied to the unexpected blockade of capital inflow into developing countries. The same authors, as well as others [36], found similar patterns at the time of exits from crises with different geneses, including in developed countries. This phenomenon of creditless growth is called the Phoenix Miracle.

We have checked if such a recovery without credits also took place in the case of our sample firms. **Figure 2** shows that investments in tangible fixed assets increased in parallel with bank credits and loans from the beginning of the previous decade up to the great crisis in 2009. This implies that bank credits and loans were a generator and

#### **Figure 2.**

*Increase of tangible fixed assets, net sales revenues, and added value versus decrease of financial liabilities (credits and loans) after the last recession. Source: Author (AJPES database for the period 2002–2017).*

accelerator of investment growth.3 After the great financial crisis and the global recession, investments in the greater part of the firms from our sample stagnated (investments took place in the amount of depreciation, or better said, the firms implemented only replacement investments). Investments started to grow again after 2014, while the post-crisis bank credits and loans apparently decreased up to 2016. The economic recovery of the firms in our sample was accompanied by either a decrease or negative growth in bank credits and loans.

#### *4.1.2 Main themes to invest*

The firms in our sample were mostly motivated to invest by technological progress (a need to modernize their technological processes), new opportunities on the market, and an increase in their customers' demand. These three motives or main themes represent more than two-fifths of all the given incentives and impulses to increase investments in the past 8-year period.

#### *4.1.3 Dynamics of investment*

More than one-half of the firms in our sample invested in the past 8-year period evenly, that is, without bigger investment spikes. This finding relates to more or less big and medium-sized firms. However, approximately one-fourth of all the firms in our sample invested in a concentrated manner, with an investment spike in 1 or 2 years at the end of the 8-year period. Investment activity was a little bit more pronounced in medium-sized firms in our sample. This can be explained by the fact that those firms that incurred excessive debt after the last financial crisis directed their accumulation into deleveraging and less so into purchasing new fixed assets. We can refer to the financial accelerator and support the above-given statements with findings from the study conducted by Bole et al. [37]. They advocate the thesis that the financial accelerator changes not only in individual phases of the business cycle (boom, bust, recovery) but also with various kinds of investments, including investment in the real economic sector, furthermore in various industries and regions, and last but not least even with respect to the solvency of commodity producers.

Besides data acquired by means of the questionnaire, we also acquired financial data from the AJPES database. Among other things, we looked for the book value of fixed assets of the firms in our sample for each year in the study period 2010–2017. Thus, we found out whether, firstly, their book value increased or decreased in the last 8 years, secondly, what was their average rate of growth or drop, and thirdly, by what kind of dynamics their value changed, that is, evenly or in a concentrated manner at the beginning, end, or in the middle of the studied period.

**Table 1** shows the number and structure of the firms that increased or decreased the book value of their fixed assets (2017/2010). The average growth rates of their increase and decrease, respectively, are shown as well. The latter has been calculated as a geometric mean of chain indices through individual years for each firm in our sample, and for all of the firms together as well.

3 In the SPSS 25 software platform, we carried out a linear regression between investments as a dependent variable and bank credits and loans as an independent variable. The value of R2 is 0.842, which means that bank credits and loans can explain the 84.2% variation in investments. For these data, the F statistic is F = 74,49, which is statistically significant at p < 0.001 level.

*Impact of Corporate Investment on Business Performance: The Case of Slovenian Firms… DOI: http://dx.doi.org/10.5772/intechopen.104994*

#### **Table 1.**

*Number and structure of the firms according to book value of their tangible fixed assets in the study period 2010– 2017.*

It can also be seen that 150 firms (a little less than three-fifths of the total) in our research sample had a positive investment growth (16%) in the past 8-year period, furthermore that 105 firms (two fifths) evidenced a negative investment growth (8%) in the same period, and, last but not least, that approximately 5% of the firms in the sample had zero investment growth. In the period 2010–2017, the average investment growth for all the firms in the research sample was 6% per year. This means that almost three-fifths of the firms invested more in that period than they depreciated their tangible fixed assets.

#### *4.1.4 Efficiency of investment implementation*

Almost four-fifths of the firms responded that they realized their investments in tangible fixed assets successfully at the time (only investments bigger than EUR 100,000 were taken into account). A little bit less than one-half of the firms reported that they implemented their investment projects within the scheduled financial budget, and almost two-fifths of the firms asserted that they stuck with the physical scale of their investments.

Considering the first three answers, indicating that the firms finished their biggest investments in tangible fixed assets on schedule (or even sooner), in the planned physical volume, and within their financial budget, we get into the cross-section of a very small number of firms (less than 1% of all the firms in our sample). If we consider the combinations of only two kinds of answers, we get very low percentages as well (a maximum of 7%). This supports the thesis that quite a few of the firms did not implement their investment projects successfully, which can imply their insufficient investment ability.

Whether the size of a firm has any impact on investment project implementation has been tested by the chi-square test χ2 . The test has shown that there is no statistically significant correlation between these two variables (Pearson's chi-square = 0.686, p = 0.421). The size of the firms in our sample does not influence investment project implementation neither in terms of financial budget nor time schedule.

#### *4.1.5 Achievement of the planned economic effects of the realized investment projects*

**Figure 3** shows what real economic effects compared to goals the firms realized with their investments in the 8-year period.

It can also be seen that more than two-thirds of the firms in our sample responded that they realized the economic effects of their investments in the range of 91–100% in comparison to what they had planned (a little bit less than one-half of the firms in the sample), or even exceeded it (one-fifth of the firms in the sample). This means that the investments of big and medium-sized Slovenian firms in tangible fixed assets should contribute considerably to business performance improvement. One-fifth of the firms in the sample estimated the economic efficiency of the implemented investment projects in a range of 71 to 90% in comparison to what they had planned, and less than one-tenth of the firms in the sample are critical of the results achieved (in the range of up to 70%; 2% below 51%). From this review, the conclusion can be drawn that the output of investments in tangible fixed assets was at a level of a little over two-thirds (68.17%).

In this case, we have also carried out the chi-square test χ2 to test the hypothesis whether the size of firms influences the achievement of the economic effects of their realized investments. The test has revealed that there is no correlation between these two observed variables.

#### **4.2 Hypotheses testing**

#### *4.2.1 Testing of the hypothesis: the rating of firms influences their borrowing as a relevant factor of the firms' investment activity*

The investment activities of firms are mainly restrained by financial restrictions, which to some extent exclude the possibility to exploit opportunities for growth. According to Fazzari et al. [38], Kaplan and Zingales [39], Dasgupta et al. [40], Gatchev et al. [41], Ostergaard et al. [42] and Drobetz et al. [43], the restrictions

#### **Figure 3.**

*Achievement of the economic effects of the investments implemented. Source: Investment ability of the companies, questionnaire 2018.*

#### *Impact of Corporate Investment on Business Performance: The Case of Slovenian Firms… DOI: http://dx.doi.org/10.5772/intechopen.104994*

derive from market imperfections, especially from information asymmetry and wrong choice, all being dependent on the firms' ratings. The latter is crucial for acquiring borrowing funds, that is, bank credits and loans. Due to these restrictions, firms cannot access the borrowing funds for their investments as economically justified by positive net present value (one of the dynamic investment criteria). For this reason, their investments can be funded only by their own funds. Therefore, the volatility of proper funds can be demonstrated through the volatility of their investments, although the elasticity of investments increases relative to operating cash flow. On the other hand, well-performing firms are not financially restricted, their investments are independent of short-term oscillations in business performance, and elasticity is zero or very low [35]. Fazzari et al. [38] claim that when operating cash flow increases, the firms with restricted access to funds and with good investment opportunities use this cash flow to fund their investments.

**Table 2** shows the ratings of firms from our sample. Besides the qualitative data (the classification of firms into rating categories based on answers from the questionnaire), we also used the NFD/EBITDA ratio as an approximate estimate for the rating of the firms, calculated from the data acquired for each year from the AJPES database. The NFD/EBITDA ratio explains relatively well the current capability of a firm to generate cash flow for repaying its debt, which has been supported by other authors who used this ratio as well [35]. In the financial crisis, the firms decreased their debt due to their own motives and reasons. The consequences of the customers' and suppliers' push, which increased the insolvency of business partners, cannot be overlooked either. In such a situation, the greater part of cash flow is assigned to lowering indebtedness. Consequently, the sensitivity of investments to operating cash flow is lower than usual.

For the last year of the study period (2017), we tried to check whether there is any correlation between these two sets of data. To find out, in the case of our sample of big and medium-sized firms, how the NFD/EBITDA ratio reflects the capability of a firm to generate cash flow for debt repayment and thus also the investment ability of the firm [34], all the firms were sorted into three segments according to their indebtedness. In the first segment, there are firms with an NFD/EBITDA ratio less or equal (≤2). At the beginning of our study period (in 2010), there were 109 such firms (40.8%), and at the end of the study period (2017), there were 164 such firms (61.4%). These firms were able to repay their financial debt within the time span of 2 years, which means that the banks were ready to lend them new credits and loans. As a matter of fact, we put into the first segment all those firms that were net creditors

#### **Table 2.**

*Number of firms in terms of rating and indebtedness measured by the NFD/EBITDA ratio.*

with negative net debt. These were the firms whose cash balance exceeded financial liabilities. In 2010, there were 42 such firms (15.7%), and in 2017 there were 77 such firms (28.8%). In the second segment, we put firms that were more indebted, having higher financial leverage. Their indebtedness ranged from 2- to 5-times EBITDA. In 2010, there were 72 such firms (27%), and in 2017 there were 65 such firms (24.3%). In the third segment, we put firms with very high financial leverage, having a NFD/ EBITDA ratio higher than 5. In 2010, there were 71 such firms (26.6%), and in 2017 there were 37 such firms (13.9%). The firms with a negative EBIDTA, that is, a negative operating cash flow, were excluded from our analysis. There were only a few.

For the last year of the study period (2017), we carried out the chi-square test. For each variable we set two categories, "good rating" and "bad rating", and "appropriate" and "inappropriate" indebtedness. Pearson's chi-square test, χ2 , examines if there is any correlation between two nominal variables, in our case between the rating of the firms and their financial leverage (indebtedness). The Crosstabs procedure generates a contingent table, the results of the chi-square test, its characteristics, and the significance value. The results are presented in **Tables 3** and **4**.

Pearson's chi-square test examines if the two perceived variables are independent. If the significance value is small enough (Sig. < 0.05), then we reject the hypothesis

*Each subscript letter (a, b) denotes a subset of indebtedness categories whose column proportions do not differ significantly from each other at the 0.05 level.*

*Source: Questionnaire and AJPES database for 2017.*

#### **Table 3.**

*Relationship between the rating of the firms and their indebtedness measured by the NFD/EBITDA ratio. (rating\*indebtedness crosstabulation).*

*Impact of Corporate Investment on Business Performance: The Case of Slovenian Firms… DOI: http://dx.doi.org/10.5772/intechopen.104994*

*Source: Questionnaire and AJPES database for 2017.*

*a 0 cells (0.0%) have an expected count of less than 5. The minimum expected count is 17.70.b Computed only for a 2 2 table.*

#### **Table 4.**

*Chi-Square test for the rating of the firms and their indebtedness.*

that variables are independent, and we can trust that the variables are somehow correlated [44]. The value of chi-square statistics, shown together with the degrees of freedom and the significance value, is 42.341, which is within the round-off error. This value is strongly significant (p < 0.001), which shows that the rating of firms has a strong impact on whether the indebtedness of firms is appropriate or inappropriate, or the other way around, that the indebtedness of firms has a strong impact on whether the rating of the firms is good or bad.The very distinctive result shows that there is a correlation between rating and indebtedness irrespective of whether the latter is appropriate or inappropriate. In other words, in our sample of answers, there is a distinctive difference (i.e., between the portion of firms having a good rating and the portion of firms having a bad rating) in the case of two kinds of indebtedness. By means of the z-test, we have found that well-rated firms are significantly less indebted, and inversely, that poorly-rated firms are significantly more indebted and have higher financial leverage. This important finding can be considered from another perspective as well, that is, in percentage: more than 60% of the firms with a good rating (A and B) are appropriately indebted, and more than 85% of the firms with a bad rating (C, D, and E) are inappropriately indebted. The following conclusion can be drawn: the indebtedness of a firm significantly influences the rating, that is, the rating of a firm is good if a firm is appropriately (less) indebted and hence has low financial leverage.

Similarly, we have calculated the correlation between these two kinds of data, shown in **Table 2**. It can be seen that there were 151 firms in 2017 whose financial managers reported the rating A (at least their commercial banks rated them like this) according to the financial data from the AJPES database, and these firms had an NFD/ EBITDA ratio of less than or equal 2. Such a result is logical. It is also logical that a firm with the rating E was in the category with the highest NFD/EBITDA ratio, with high financial leverage. However, it is not logical that at the same time 16 firms were rated A while being very much indebted, or that a firm with the rating C is in the first category, with low financial leverage.

We have calculated Spearman's correlation coefficient, r. Both sets of data have got an appropriate rank, rating A being assigned the highest rank, that is, 5, and rating E the lowest rank, that is, 1. The least indebted firms, that is, the firms having an NFD/ EBITDA ratio of less than 2, are given rank 3, medium indebted firms rank 2, and the most indebted firms rank 1. The results are shown in **Table 5**.

*b Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples.*

Source: Questionnaire and AJPES database for 2017.

#### **Table 5.**

*Spearman's correlation coefficient for two variables, the rating of the firm and the NFD/EBITDA ratio.*

#### *4.2.2 Testing of the hypothesis: Increase of investment in tangible fixed assets influences some financial indicators and ratios*

Let us further test the hypothesis stating that an increase of investment in tangible fixed assets significantly influences some financial performance indicators, such as Added value per employee, Profit margin, ROE, ROA, Net sales revenues per employee, Net profit per employee, EBITDA to Assets, and Net sales revenues to Costs of goods sold. For this analysis, we used a longer time series of financial data for the firms in our sample, encompassing the 18-year period from 2000 to 2017.

As already mentioned, the analysis is based on financial data from the AJPES database, and its chart of accounts derived from the general ledger of firms. Account No. 0010102 presents the net book value of tangible fixed assets. This value constantly changes over time, within an individual year, and over the years. This value changes due to depreciation and the sale-off of assets (disinvestment). Both of these reduce this value on said account. On the other hand, this value also changes due to the purchase of new assets (including those acquired by financial lease). As already explained, for the purpose of our analysis, revaluation, which could have influenced the net book value of assets, was ignored. As inflation during our study period was low (in some years there was even deflation), we assumed that it had no important impact on the aforementioned value. If the difference between the purchasing value of new tangible fixed assets and the depreciated value of the existing fixed assets or the value reduced by disinvestments is positive, we get net investments in tangible fixed assets. If we consider these net book values of tangible fixed assets throughout a longer period of time, we can find out from the differences (or from calculated chain indexes) whether the firms in our sample invested or disinvested. The difference

*Impact of Corporate Investment on Business Performance: The Case of Slovenian Firms… DOI: http://dx.doi.org/10.5772/intechopen.104994*

between the two annual balances (at the end of each calendar year, as of December 31, Year X) of the net book values of tangible fixed assets TFAt – TFAt-1 (Account No. 0010102) represents the net investment in tangible fixed assets in year t.

As we quite considerably prolonged our study period, and to assure comparability of the data through time, all values were properly corrected by deflators or inflators of the individual year (SURS—recalculation of the financial data in time series due to inflation for the period 2000–2017).

We computed the average values for each of the above-presented variables for each individual year for the entire sample of 267 firms.

Chain indexes have been computed for each firm included in the sample and on their basis the average growth rate for each variable. The geometric mean has been computed as follows:

$$\left(\prod\_{i=1}^{n} a\_i\right)^{\frac{1}{n}} = \sqrt[n]{a\_1 a\_2 a\_3 \dots a\_n} \tag{5}$$

For carrying out linear regression, investment in tangible fixed assets was taken as an independent variable, while several financial indicators, such as Net sales revenues, Added value, EBITDA, Net profit, ROA, and others, were accounted for as dependent variables for each year of the computed average values.

Impact of the increase of investment in tangible fixed assets on Net sales revenues. Linear regression for the first pair of dependent variables, that is, for tangible fixed assets and Net sales revenues is calculated and presented in **Tables 6**–**9**.

R2 is 0.673, which means that investment in tangible fixed assets represents more than two-thirds of the variation in Net sales revenues. In other words, if we try to explain why firms increase the sale of their products/services and commodities/materials, we can look at the variation in Net sales revenues. There is a great number of factors that can explain this variation. In 67%, though, this variation can be explained by our model as comprising only investments in tangible fixed assets. Certainly, there are also other factors, other variables that influence the increase in sales.

*Model summary.*

*a Dependent variable: net sales revenues.*

*b Predictors: (constant), tangible fixed assets.*

1.505 0.027 0.338 0.012 0.894 2.193

*a Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples.* Source: *Questionnaire and AJPES database for 2017.*

#### **Table 9.**

*Bootstrap for coefficients.*

Tangible fixed assets

The ANOVA tells us whether the model, overall, results in a significantly good degree of prediction of the outcome variable. The sums of squares and the degrees of freedom are calculated. From these two values, the average sums of squares (the mean squares) can be calculated by dividing the sums of squares by the associated degrees of freedom. The most important part of this calculation is the F-ratio and the associated significance value of that F-ratio. For these data, F is 30.93, which is significant at p < 0.001 (because the value in the column labeled Sig. is less than 0.001). This result tells us that there is a less than 0.1% chance that an F-ratio this large would happen if the null hypothesis were true. Therefore, we can conclude that our regression model overall predicts Net sales revenues significantly well. Such a result is quite logical and expected.

Calculation of linear regression for the investments in tangible fixed assets and Net sales revenues.

Other factors that can influence the bigger volume of sales (although they are not considered in our research) can be the increase in sales prices, the increase in productivity, export incentives or customs relieves, business process rationalization and improvement, organizational changes, etc.

As previously mentioned, the ANOVA shows whether our model predicts the outcome variable well enough. However, it does not show the contributions of individual variables, except in our model where only one independent variable exists, and we can infer that this variable is a good predictor.

The regression calculation provides estimates of the model parameters (the beta values) and the significance of these values. From this calculation, we can conclude that b0 is EUR 23.6 million, which can be interpreted as follows: when no money is spent on investment in tangible fixed assets (when X = 0), the model predicts that all firms in our sample will decrease their Net sales revenues in the amount of EUR 23.6 million. We can also read off the value of b1 from the regression calculation. It is 1.505.

#### *Impact of Corporate Investment on Business Performance: The Case of Slovenian Firms… DOI: http://dx.doi.org/10.5772/intechopen.104994*

Although this value constitutes the slope of the regression line, it is more useful to think of it as representing the change in the outcome associated with a unit change in the predictor. Therefore, if our predictor variable is increased by one unit (if the investment in tangible fixed assets is increased by EUR 100), then our model predicts that EUR 150.5 of extra Net sales revenues will be generated, which can be considered a good result with respect to the fact that an increase in investment contributes more than two thirds to the increase in Net sales revenues.

Let us look in this calculation at the values for t. The t-test tells us whether the value of b is different from zero (0). The statistical tool SPSS 25 provides the exact probability of the perceived value of t occurring if the value of b in the population were zero. If this observed significance is less than 0.05, then the result reflects a genuine effect. In our case, this holds entirely. For one t value, the probability equals 0.032, for the other t value, the probability equals 0.000. Thus, we can claim that the probability of these t values occurring if the values of b in the population were zero is less than 0.001. Therefore, the values of b are significantly different from zero. In the case of the b for investment in tangible fixed assets, this result supports the thesis that investment in tangible fixed assets makes a significant contribution (p < 0.001) to predicting the increase in Net sales revenues.

In the same calculation, the bootstrap confidence interval suggests that the population of b values for tangible fixed assets is likely to fall between 0.894 and 2.193, and because this interval does not include zero (0), we would conclude that there is a genuine positive relationship between investment in tangible fixed assets and Net sales revenues. Also, the significance associated with this confidence interval is p = 0.012, which is significant. **Figure 4** shows the distribution of correlation coefficients between these two variables for all the firms in our sample.

Linear regression has also been calculated for the other pairs of dependent variables. We were always interested in the impact of the independent variable, that is, investment in tangible fixed assets on financial indicators. Regarding Added value, this impact is medium strong (R2 = 0.531; b1 = 0.461; Sig.: 0.001). In the case of b for investment in tangible fixed assets, this result means that investment in tangible fixed assets significantly contributes (p = 0.001) to predicting the increase of Added value. An increase of tangible fixed assets by EUR 1000 generates EUR 461 of Added value.

#### **Figure 4.**

*Distribution of correlation coefficients for two variables, Investment in tangible fixed assets and net sales revenues, for all the sample firms in the period 2000–2017.*

#### **Figure 5.**

*Movement of added value for the 30 biggest sample firms in the period 2000–2017.*

#### *<IP_ADDRESS> Impact of the increase of investment in tangible fixed assets on Added value*

As this financial performance indicator is very important for the firms' benchmarking, the dynamics of Added value for the firms in our sample in the period 2000–2017 are shown in **Figure 5**. From this chart, a change in the relationship between the Added value of the 30 biggest firms in the sample and the Added value of the remaining firms in the sample over the 18-year period can be seen as well. Each year, except in the year of the last biggest financial crisis and global economic recession (2009), the 30 biggest firms in the sample taken together generated more Added value than the rest of the firms in the sample. Afterward, the scissors started to open gradually.

**Figure 6** shows, for all the firms in our research sample, a course of two performance indicators, that is, productivity expressed by Net sales revenues per employee and Added value per employee. The data are presented for the period 2000–2017. It can be seen how the financial crisis and economic recession hurt our sample firms. After the crisis, Added value per employee increased faster than Net sales revenues per employee, although the firms did not yet reach their prior levels.

**Figure 6.** *Movement of productivity for the sample firms in the period 2000–2017.*

*Impact of Corporate Investment on Business Performance: The Case of Slovenian Firms… DOI: http://dx.doi.org/10.5772/intechopen.104994*

#### *<IP_ADDRESS> Impact of the increase of investment in tangible fixed assets on financial performance ratios*

Impact of the independent variable, that is, investment in tangible fixed assets, on the financial indicator EBITDA is weak (R2 = 0.305; b1 = 0.145; Sig.: 0.02). In the case of b for investment in tangible fixed assets, the result suggests that investment contributes significantly (p = 0.02) to the prediction of EBITDA increase. The increase of investment in fixed assets by EUR 1000 generates an operating cash flow in the amount of EUR 145. This relates to one year. However, the investment generates operating cash flow for its entire life span, which lasts several years, depending on the type of tangible fixed asset. By all means, this is not high profitability, though, if we compare it to the profitability of common riskier financial investments.

Let us consider the question of how investment in tangible fixed assets influences Net profit. In the case of this particular financial indicator, the predicting value of the regression coefficient b becomes totally vague (R2 = 0.025). As a matter of fact, in the period 2000–2017, there was no profitability of investments implemented in tangible fixed assets by the firms in our sample and measured by Net profit. Taking into account interests, we get an answer as to why Net profit is relatively weak or even negative (loss). As already explained, the firms substantially increased their indebtedness due to investments before the financial crisis in 2008. This implied high rates of interest paid to creditors, which lowered their Net profit a great deal.

As there is a strong positive correlation between investment in tangible fixed assets and Net sales revenues (their impact amounts to more than two thirds), we could draw the conclusion that even an increase of Net sales revenues due to investments positively influences select financial performance ratios, such as ROA, ROE, EBITDA/ Assets, and Sales revenues/Operating costs.

Consequently, we could expect an increase in ROA (return on assets). Good exploitation of the production assets should imply a higher ROA. The question can be raised whether the tangible fixed assets were well used (does the production run in fewer than three or four shifts?), and last but not least, whether the firms in our sample met all the customers' needs. Linear regression shows a statistically significant but relatively weak correlation between ROA and Net sales revenues (R2 = 0.236; Sig.: 0.048).

#### **Figure 7.**

*Movement of profitability ratios and sales revenues/operating costs ratio for the sample firms in the period 2000– 2017. Source: AJPES database of the sample firms for the period 2000–2017.*

Similar findings about a weak correlation between the compared variables have been revealed with other financial performance ratios, specifically ROE, EBITDA/ Assets, and Net sales revenues/Operating costs. For all of them, linear regression with Net sales revenues has been calculated. The course of the relevant ratios for the firms in our sample for the period 2000–2017 is presented in **Figure 7**. The blue curve presenting ROE is strongly accentuated. This ratio was high in 2007 (0.35), before the financial crisis, then it kept decreasing up to 2010. The owners' capital of the firms in our sample reached average annual profitability of 10% no sooner than in 2017.

#### *<IP_ADDRESS> Trend of the increase of investment in Slovenian firms compared to the course of select financial indicators in the period 2000–2017*

**Figure 8** presents the trend of nominal average values of some of the most relevant financial indicators, including financial costs (interests), for the population of our sample firms in the period 2000–2017. It is understood that this 18-year time span also includes a period denoted by a financial crisis and global economic recession, which endured from 2008 to 2012. The dynamic growth of financial indicators, for instance, Net sales revenues, Added value, and Net profit, stopped in 2009 (of Net profit already in 2008). The inertia of the growing trend of the increase of investments in tangible fixed assets, however, lasted up until the end of 2009 (finishing the implementation of investments made before the crisis). In 2010, there is a considerable decrease in the book value of tangible fixed assets (touching bottom) as the book value of these assets decreased by 6% and remained at this level until 2015. A year later, the average book value of such assets increased by 3%, although it did not yet reach its pre-crisis level. On the other hand, after a considerable drop in 2009, Net sales revenues started to increase slightly, even during the crisis. Similar findings have been revealed for Added value. The crisis had the biggest impact on Net profit, which started to decrease considerably in 2008. It grew a little bit in the next 2 years but

#### **Figure 8.**

*Impact of investment in tangible fixed assets on select financial ratios for the sample firms in the period 2000– 2017.*

*Impact of Corporate Investment on Business Performance: The Case of Slovenian Firms… DOI: http://dx.doi.org/10.5772/intechopen.104994*

remained at half its 2007 value until 2013. From 2008 to 2013, the total financial costs (interest) for our sample firms were in fact higher than their total Net profit.

#### *<IP_ADDRESS> The changing of financial costs (interest) due to the indebtedness of firms and its impact on profit margin*

While estimating investment profitability, all the stakeholders who provided funds for the investments must be taken into account. As this includes financial institutions, the interests on credits and loans constitute returns generated by investment projects.

For this reason, we are also interested in how Financial costs (interests) changed in the study period – the relevant data are available for the sample firms for the period 2005–2017 – due to the financial leverage, and what is the linear regression between the NFD/EBITDA ratio and Financial costs. **Figure 9** shows three curves of NFD/ EBITDA ratio distribution for three temporal cross-sections (cuts), that is, for 2007 (before the financial crisis), 2010 (during the financial crisis), and 2017 (after the financial crisis). It can be observed that the red curve representing a normal NFD/ EBITDA ratio distribution for the sample firms for 2010 is asymmetric to the right (the same goes for the other two curves), more flattened (the other two curves are more squeezed, with higher peaks), more elongated (stretched) to the right, and generally lies above the other two curves. This means that in the year of the last biggest financial crisis, absolutely more firms had a higher NFD/EBITDA ratio (more EBITDA was needed to cover net financial debt). On the curve, this is visible to the right from value 0. The left side of the curve from value 0, which lies underneath the other two curves, implies a similar conclusion. Those firms in our sample that were not indebted—meaning that their NFD/EBITDA ratio was negative—had more cash and cash equivalents or a lower EBITDA or both at the time of crisis. Thus, more firms had an NFD/EBITDA ratio equal to 2 in 2007 and 2017 than in the years of the crisis.

As Net profit is the main source for repaying debt, we were also interested in the relationship between the indebtedness of our sample firms and their profit margin throughout the study period. This is shown in **Figure 10**. The profit margin started to improve right after the financial crisis and economic recession, and it reached 5% in 2017. The firms with higher financial leverage generated financial resources for repaying their debt. This thesis can be supported by the finding that the firms in our sample started to decrease their indebtedness in the same period. The NFD/EBITDA

**Figure 9.**

*Distribution of NFD/EBITDA ratios for the sample firms for the years 2007, 2010, and 2017.*

#### **Figure 10.**

*Movement of indebtedness (NFD/EBITDA ratio) and profit margin of the sample firms in the period 2000–2017. Source: AJPES database of the sample firms for the years 2000, 2007, 2010 and 2017.*

**Figure 11.**

*Estimate of the conceptual model of the impact of investment in tangible fixed assets on business performance for Slovenian firms. Source: Author.*

ratio—which was almost 2 in the year 2010—reached 1.20 in 2017. As already mentioned, this is a weighted average ratio of all 267 firms, calculated by means of the weights of the Net sales revenues of each firm.

Following our findings and statistical analyses, our conceptual model can be adjusted so that only those financial indicators are included where there exists a statistically strong and medium-strong correlation with investment in tangible fixed assets. From **Figure 11**, looking at the correlation coefficients, it can be understood that the correlation is strong with Net sales revenues, Added value, and EBITDA, and less so with Net profit. However, only very weak correlations exist between investment in tangible fixed assets and financial performance ratios. Therefore, we skipped them in **Figure 11**.

#### **5. Conclusion, limitation, and future directions**

This study is based on the micro theory of investment and theoretical approaches to measuring firms' financial performance. It relies on a simple conceptual model consisting of only two constructs, investment in tangible fixed assets on one side and financial performance on the other. By means of this model, we try to find out and assess how much investment in tangible fixed assets improves the business performance of firms, expressed and measured by the relevant financial indicators and financial ratios.

Let us first summarize the general findings from the empirical part of the research, based on the answers of the financial managers responding to the questionnaire. A little bit less than half of the sample firms exploited the investment opportunities in the study period 2010–2017 in their entirety. The other firms exploited their investment opportunities partly, while some firms exploited none of them since they were primarily obliged to deleverage or did not have enough funds at their disposal, neither their own nor borrowed. They could not access borrowing funds due to either the credit crunch or their excessively high financial leverage.

The prevailing motives of the firms to invest were a need to modernize technology processes, to exploit new opportunities on the market, and to meet the growing demands of customers (new increasing orders).

In the investigated 8-year period, more than one-half of the firms under study invested evenly, without bigger investment spikes, whereas approximately one-fourth of the firms invested in a concentrated manner, with an investment spike in 1 or 2 years toward the end of this period.

A little bit less than three-fifths of the firms evidenced a positive investment growth (16%), while two-fifths of them reported a negative growth (8%). All the firms in our research sample evidenced an average annual investment growth rate in intangible fixed assets of 6%.

In terms of investment implementation efficiency, almost four-fifths of the firms realized their investment in tangible fixed assets successfully, meaning on time, with a little bit less than one-half of the firms performing their investment within the scheduled financial budget, and almost two-fifths in the planned physical scale, that is, without additional works and assets. If all the aspects of efficiency are taken into account simultaneously, quite a few of the studied firms were not efficient enough throughout the realization of their investment projects. In this, the size of the firm did not play a special role.

From the point of view of achieving economic effects, investments in tangible fixed assets are supposed to contribute a great deal to the firms' business performance improvement, which partly agrees with the findings of our conceptual model.

To verify our conceptual model and test our research hypotheses, we analyzed a temporal series of financial data extending back to the year 2000. In this way, we captured a period of intensive investment in the first decade of this century until the occurrence of the great financial crisis in 2008.

The results of our research carried out on big and medium-sized Slovenian firms for the period 2000–2017 partly support our hypotheses set up in the introduction. Investment in tangible fixed assets positively influences the financial performance of firms, as expressed by financial indicators and financial ratios. Statistically significant (Sig., p < 0.000), there exists a strong correlation between investment in tangible fixed assets and Net sales revenues (R2 = 0.673), which has already been confirmed by studies undertaken by Licandro et al. (2001) and Grazzi et al. [5]. However, there is also a quite strong statistically significant (Sig., p < 0.001) correlation between investment in tangible fixed assets and Added value (R2 = 0.531), which has not yet been substantiated in the literature. Statistically significant is also the correlation between investment in tangible fixed assets and the operating cash flow (EBITDA); it can be designated as a medium-strong correlation (R2 = 0.305). This particular

relationship has not been studied yet or is at least not observed in the literature. Last but not least, there is a statistically significant correlation between investment in tangible fixed assets and Net profit (Sig., p < 0.02), which has been previously supported by Grazzi et al. [5]. However, in our case, this correlation is negligible (R2 = 0.025).

Our research has not revealed any significant correlation between investment in tangible fixed assets and the selected financial ratios we originally included in our conceptual model. There is no correlation found between investment in tangible fixed assets and financial ratios, specifically Added value/Employee, Profit margin, ROE, ROA, Net sales revenues/Employee, Net income/Employee, EBITDA/Assets, and Business revenues/Operating costs.

We are aware of the limitations of the present study, in terms of the relatively small sample size and company size, and the endogeneity of the variables included in our linear model. Our sample includes a relatively high number of large and mediumsized firms. If the survey had been conducted internationally, it would have included a greater number of large firms, where the impact of strategic investments is more pronounced. Endogeneity refers to situations in which an explanatory variable is correlated with the error term. By using an instrumental variable in a linear model more consistent estimates may be obtained.

Another limitation of our research is a lack of data referring to the revaluation reserves in the balance sheet of the firms in our sample, which might be considered as a certain deficiency in the calculations of the financial ratios.

The third limitation refers to the methodological part. Instead of conducting time series analysis, we use geometrical means, which caused a certain reduction of the observations in our model. Consequently, the results could be more accurate.

In the future, we also plan to introduce certain methodological improvements in the questionnaire, which will include a number of other determinants from sources found in the field of investment activity, and performance indicators, including nonfinancial ones. The relevant literature furthermore led us to consider the directions of causality in the model. Since our research is based on a cross-sectional database, we cannot prove causation but can only confirm the assumed paths. The direction of causality could be determined only by a longitudinal study, which represents an important opportunity for further research.

#### **A. Questionnaire**

*Impact of Corporate Investment on Business Performance: The Case of Slovenian Firms… DOI: http://dx.doi.org/10.5772/intechopen.104994*

#### *Six Sigma and Quality Management*

*Impact of Corporate Investment on Business Performance: The Case of Slovenian Firms… DOI: http://dx.doi.org/10.5772/intechopen.104994*

#### *Six Sigma and Quality Management*

*Impact of Corporate Investment on Business Performance: The Case of Slovenian Firms… DOI: http://dx.doi.org/10.5772/intechopen.104994*

#### **JEL:**

C12; D25

*Six Sigma and Quality Management*

#### **Author details**

Vladimir Bukvič1 \* and Metka Tekavčič2

1 GEA College Faculty of Entrepreneurship, Ljubljana, Slovenia

2 School of Economics and Business, University of Ljubljana, Ljubljana, Slovenia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Impact of Corporate Investment on Business Performance: The Case of Slovenian Firms… DOI: http://dx.doi.org/10.5772/intechopen.104994*

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Section 4


**2.3 Impact of the gamma (P.1 and descendent lineages) on the severity and spread of the disease**

The first case of the gamma variant was detected in Tokyo, Japan in Jan-2021 and patients were relocated from the Brazilian Amazon state. The WHO has classified the variant as a gamma and assigned it to lineage P.1. Ten of the non-synonymous defining mutations in the S gene are present in the gamma lineage, which evolved following a period of rapid genetic diversification. Of these changes, three (N501Y, E484K, and K417T) increased favourable stable interaction with the human ACE-2 receptor. This mutation caused an outbreak in the Manaus region of Brazil in December 2020, with gamma variant cases accounting for 42% of all cases [22].

#### *2.3.1 Transmission and hospitalisation*

In a study done in Brazil in the year 2021 by Chen and Lu, it was demonstrated that patients with gamma variations have high viral loads that are 10 times higher than those of patients with other lineages. Transmissibility and infectivity were twice higher than the other circulating strains in all age groups. Studies showed that the gamma variant can overcome the immunity developed from earlier infections, increasing the risk of a breakthrough infection. Additionally, the death rate was 10–80% greater in the group infected with the gamma variant [22]. A study by Funk et al. showed that gamma variant patients had significantly higher adjusted odds ratios for hospitalisation (2.6, 95% CI, 1.4–4.8) and ICU admission (2.2, 95% CI, 1.8–2.9) [47].

#### *2.3.2 Vaccine response and breakthroughs infection*

Studies revealed that the Gamma variant also had a decrease in anti-RBD antibody neutralisation, and it has been connected to reinfections and breakthrough infections in those who have received vaccinations [48–51]. The Gamma variant was found to reduce the antibody neutralising activity of Pfizer or Moderna vaccines, as the report showed the prevalence of breakthrough infections in fully vaccinated recipients. In

*Perspective Chapter: Emerging SARS-CoV-2 Variants of Concern (VOCs) and Their Impact… DOI: http://dx.doi.org/10.5772/intechopen.107844*

a 2021 study on fully vaccinated Gold Miners recipients in French Guiana, Vignier et al. showed that 60% of BNT162b2 vaccine recipients experienced breakthrough infection [52]. Wang et al. also demonstrated that gamma variants had a neutralising effect on multiple monoclonal antibodies and were more resistant to neutralisation by convalescent plasma and vaccinee sera. The gamma variant was estimated to be 3.8–4.8 times more resistant to BNT162b2 or mRNA-1273 vaccine recipients [23, 53].

### **2.4 Impact of the delta and delta+ (B.1.617) variants on the severity and spread of the disease**

The first instance of delta variations was discovered in India, and it was one of the most common SARS-CoV-2 variants that affected the majority of the world. Lineage B.1.617 was assigned to these variations. The L452R, T478K, D614G, and P681R are four of the 17 variations in the Delta variation that are of particular concern, due to their involvement in infectivity and transmission. In comparison to alpha, the infection and transmissibility rates were extremely high. Despite immunisation, this variation resulted in a large number of human deaths. L452R and E484Q is not found in the B.1.617.2 lineage [54, 55]. The B.1.617.2 lineage was first associated with infection in India, and the dominant variant in infection in the UK in 2021. It was highly infectious and spread more rapidly than the original version of the virus. A study conducted by Deng et al. showed that the mutation L425R in the California population resulted in an increase in the binding affinity of the spike protein of SARS-CoV-2 with ACE-2 host receptors, which increased the viral load and 20% transmission rate [56]. Few studies have shown that L452 is not directly linked with the host ACE-2 receptor. This mutation located in the RBD's hydrophobic plaques generates structural alterations that promote binding of spike protein with the ACE-2 receptor [57, 58]. In addition to this, there was 3–6 folds reduction in neutralisation capacity of vaccine elicited sera in experiments with pseudotyped virus (PV) particles [58]. Few studies demonstrated that the D614G mutation in the spike protein promotes viral multiplication in the upper respiratory tract and enhanced transmission rate [59, 60]. The P681R, which is a substitution at position 681, may increase the variant's celllevel infectivity [61, 62]. During outbreak of delta variants worldwide, a new variant was emerged in United Kingdom that carries a novel point mutation' K417N' in delta variant and designated as a delta plus. Lineage AY.1 or B.1.617.2.1 was assigned to this variant. The three most prominent mutation K417N, V70F, and W258L in spike were exclusively present in the Delta Plus variant [63]. Besides this, few other delta variants emerged, Delta-AY.2 in the USA and Delta-∆144 in Vietnam [64].

#### *2.4.1 Transmission and hospitalisation*

During the second wave of the pandemic, COVID-19 patients required more ICU time, oxygen, and non-invasive and invasive ventilatory support. According to estimates, hospital mortality in the second wave was double that of the first [65]. Ong et al. calculated the risk linked with the delta variant by observing the need for oxygen, ICU admission time, and death and found that delta was associated with increased disease severity when compared to non-VOCs [66]. Several studies revealed that a large percentage of vaccine recipients reported breakthrough infection during the delta variant wave [67–71]. Initial studies demonstrated that the delta variant increased the hospitalisation risk in the population, despite the first dosage of vaccination [24, 72, 73]. Non-vaccinated people and those who contacted viruses

within 14 days of vaccination were more prone to hospitalisation [74]. A similar study conducted by Twohig et al. showed delta variant infections linked to an increased hospitalisation rate (hazard ratio 226 [95% CI 132–389]) in comparison to alpha variant infections. All age categories experienced an increase in mortality during the Delta wave, with patients under 45 experiencing the most increase in infection (10.5 percent vs. 7.2 percent, p 0.001), compared to pre-Delta wave [24].


**COMPUTED TOMOGRAPHY – CLINICAL APPLICATIONS**

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**6. Non‐IgG ITS**

#### **6.1. General background**

The IgG class accounts for the bulk of locally synthesized Igs in MS and most knowledge to date concerns this class. All Ig classes may give rise to ITS. IgG, IgM, and IgA are, respectively, present in 100, 20–36, and 6–9% of MS patients [69, 78]. The relative absence of IgE is commonly interpreted as a clue to type I immunity [147].

#### **6.2. IgM chains**

in a study including 1800 MS patients, the number of OCB was considered to correlate with the course of the disease [127]. The mean number of OCB in the group of benign patients was 2.9 ± 3.6 compared to 5.7 ± 4.9 in the severe group (*p* < 0.06) [127]. Therefore, the evidence points to a poorer clinical prognosis in MS patients in association with ITS. On the other hand, OCB negativity correlates with a better outcome in many studies [70, 127–131], even after demo‐

**5. Intrathecal Ig synthesis in animal models—technical limitations**

IgG synthesis level in CSF is highly correlated with CD19+CD138+ plasmablast levels [133], but although ITS is commonly thought to be associated with CSF floating cells, this minute cell number may account for less than 0.1% of the whole ITS, meaning that the bulk of IgG synthesis is provided by resident IgG‐secreting cells, residing either in the meninges or in the perivascular areas [134]. In experimental allergic encephalomyelitis (EAE) models, the parenchymal level of B‐cell infiltration increases from null to 134 B‐cell/cm2 [135] and the whole

In MS, B‐cells remain a minor proportion of the infiltrating lymphocytes (<5%), are virtually absent from the parenchyma, and are mostly observed around the small veins and meninges

IT Ig synthesis in animal models has received little attention since the very small volume of CSF does not facilitate sampling. Although older experiments based on low‐sensitivity techniques failed to demonstrate any ITS during EAE, optimal techniques (IEF followed by anti‐IgG staining) have subsequently confirmed the local synthesis of OCB and/or an elevated IgG index [139–142]. In viral models of demyelination (i.e., measles or JHM strain of corona‐ virus infection), a mirror pattern of OCB is common but local OCBs are always revealed by immunoblot against viral antigens [21, 143]. We are not aware of any EAE experiments aimed at demonstrating the nonspecific pattern of ITS observed in MS, especially the animal coun‐ terpart of the human MRZ pattern using common animal viruses or vaccines. From a theoret‐ ical point of view, animals receiving intrathecal vaccination with foreign antigens such as albumin are able to mount a strong intrathecal response [144–146]. In a unique case challenged intrathecally with ovalbumin (OVA), an increase in AI‐herpes was also obtained apart from the expected increase in AI‐ovalbumin, suggesting that animal models are promising for future

The IgG class accounts for the bulk of locally synthesized Igs in MS and most knowledge to date concerns this class. All Ig classes may give rise to ITS. IgG, IgM, and IgA are, respectively,

are available regarding a possible role of meningeal B‐cell aggregates in ITS.

)[1, 138]. Plasma cells are occasionally found in demyelinated areas. No data

–105 cells per mouse or rat brain [136, 137].

graphic adjustment [132].

62 Trending Topics in Multiple Sclerosis

(4.6–6 cells/mm2

studies of nonspecific ITS [145].

**6. Non‐IgG ITS**

**6.1. General background**

number of infiltrating B‐cells is in the range of 103

Intrathecal IgM synthesis is always higher in MS than in controls [110]. IgM OCB may occur in about 20–75% of MS patients [78, 113, 118] and in 74% of CIS [125]. CD19+CD5+ lymphocytes are the predominant population of B‐cells in CSF and are characterized by the T‐cell inde‐ pendent secretion of IgM natural antibodies directed against phylogenetically conserved structures that target non‐proteic epitopes. In a series of 53 CIS/relapsing‐remitting multiple sclerosis (RRMS), 46 of them had oligoclonal IgM‐recognizing lipid antigens, 30 targeted phosphatidylcholine (PC) alone, seven targeted PC and had additional reactivity against other myelin lipids (phosphatidylethanolamine, phosphatidylinositol, and sphingomyelin), and nine targeted myelin glycolipids, mostly sphingomyelin [113]. IgM OCB against lipids may be restricted to the RR and SP phases since they were not observed in PP patients [27]. Pentameric IgM strongly activates the complement cascade and is involved in the lesions of plaques (pattern II).

#### **6.3. IgA**

The IgA index is elevated in at least 9% of MS patients [54, 78, 148] and the mean intrathecal IgA index is modestly but significantly higher in patients than in controls [110]. Local synthesis of IgA may be the only clue to local Ig synthesis in some MS patients devoid of local synthesis of IgG [31]. About a quarter of Ig‐secreting cells are IgA in choroid plexuses [149]. IgA cells are also common in plaques (3–9%) and demonstrate a high clonality (up to 80%) [149]. IgAs are mainly mucosal secretory antibodies but could be a major component of CNS immune responses in certain viral infections [148, 150]. OCB IgAs were reported to have a low frequency (2/33) in the earlier CSF studies [71, 150]. In a post‐mortem analysis of plaques extracted from four MS brains, IgA+ plasma cells were recovered in 18/24 plaques and sometimes predomi‐ nated on Ig+ plasma cells [148]. Clonality assessment in the plaques of two patients demon‐ strated a low diversity of clones and a clonal restriction confined to plaques [148]. The somatic mutations were highly concentrated in the complementary determining region (CDR) and FR3, which are considered critical for antigenic specificities, suggesting an antigen‐driven matura‐ tion of affinity as observed for IgG. The locally synthesized IgA were demonstrated to target injured axons [148].

#### **6.4. Free light chains**

Light chains are normally synthesized more than heavy chains, with an FLCκ/*λ* ratio of 2:1 in normal blood. Serum circulating levels of FLC are about 1000‐fold lower than Ig levels owing to rapid renal clearance (serum half‐life of 2–6 h), whereas the rate of clearance from CSF is similar with Ig [151]. Therefore, the level of κFLC in MS is equal or higher in CSF than in serum. Although the absolute value of CSF FLC is variable among studies, the FLC level is always higher in MS than in controls, and the κFLC/protein ratio is even higher [152]. The κFLC index is more sensitive (Se 95%, Sp 91%) than the IgG index and OCB [153]. Since elevated CSF FLC is predictive of impairment [116], it has even been proposed as a therapeutic target [47].