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This paper, which is derived from comprehensive research based on the microeconomic theory of investment and the theoretical approach to measuring the financial performance of firms, presents a conceptual model to define, assess, and measure the impact of corporate investment on business performance. In terms of investment, the focus falls only on tangible fixed assets, whereas business performance is defined solely as performance measured by the relevant financial indicators. Several research hypotheses are tested on an extensive sample of Slovenian firms. A statistically significant correlation between investment and financial performance indicators is found for the period 2000–2017. This correlation is particularly strong with net sales revenues, added value, and operating cash flow (EBITDA). Since the global financial crisis occurring at the break of the last decade is also included in the designated period, the creditless growth of investment together with the simultaneous deleveraging that took place after the financial crisis is explored and compared with the growth of selected financial performance indicators.
GEA College Faculty of Entrepreneurship, Ljubljana, Slovenia
Metka Tekavčič
School of Economics and Business, University of Ljubljana, Ljubljana, Slovenia
*Address all correspondence to: vladimir.bukvic.ce@gmail.com
1. Introduction
The purpose of this research is to determine how corporate investment influences the business performance of firms. This also can be considered as our research question. We explore this issue in the case of Slovenian firms, specifically, how successful they were with their investments in the period from 2010 to 2017. The reason why this topic has been chosen as a research subject lies in the fact that in spite of the relatively high capex being spent in firms in the last decade, quite a few of these firms have not performed well for many years. How many of them did not perform well is also the subject of our research, with investments achieving neither a satisfactory return on equity, ROE, nor the planned adequate cash flow.
We might venture to claim that the investment projects should have been justified by investment programs, or that a better business performance should have been foreseen, expressed either by higher net sales revenues, higher earnings before interests and taxes plus depreciation and amortization, EBITDA, a higher net profit, a higher ROE, a higher return on assets, ROA, or a higher positive cash flow, CF, etc. If we suppose that at the time when the investment decision was made, the investment projects as such had been assessed as profitable and economically justified, that is, economically sound, well set up, and promising for the investors, then the question might be raised, did these investment projects turn out to be as efficient as anticipated or as they should have been, or to put it another way, did these investments improve the business performance of the firms. Similarly, the investment project implementation with respect to what had been planned is also questioned, for example regarding the suitability of its technology and equipment, the planned investment budget, its sufficient and reasonable financial resources, reliable market projections, agile management, qualified labor force, etc. There is also doubt about achieving the required rate of return on investment projects and other relevant financial ratios by which business performance is measured and which are expected to be met by various stakeholders, mainly owners and creditors. Surely, not everything listed above is valid for all the firms. Among them, there are some who have improved their business performance due to their investments.
The research problem in our study can be addressed operationally in the following way. We base our research on resources as key drivers by which successful investment project implementation and the sustainable and profitable growth of a firm should be assured. As strong evidence for such a statement, we rely on the theoretical standpoints and comprehensions of various authors, and from the perspective of the operationality we set up a simple conceptual and measurement model, which links investments in tangible fixed assets and the business performance of the firms, expressed by a number of relevant financial indicators and ratios. From this model, a basic research thesis is erected: corporate investments in tangible fixed assets have a positive impact on business performance.
The existing research has mainly considered the effects of individual investments and their performance, and very rare the researchers have studied how investments do influence the business performance of the firms. Our research is grounded on a holistic view of the impact of investments on the business performance of firms, which can be accounted for as a novelty in this field. The potential contribution of our paper is to highlight the impact of Slovenian firms’ investments on their business performance in a rather long time span, including the big financial crisis as well, which can be also considered as a novelty in the area of corporate investment activity.
Based on the literature review, the authors develop a simple conceptual and measurement model to study the performance of the firms deriving from their investment. They establish a set of financial indicators and ratios, relating mainly to increase in sales, productivity, profitability, and cash flow, and find their correlation with the investment in tangible fixed assets. They try to find out if these correlations are statistically significant and how strong they are. Some of this research is quite similar to what some researchers have already done using the data for their national industries. Their scientific contribution in this field is an integral approach, a set of two groups of financial measures of the business performance and establishing or confirming their relevance to assess the effect of investments on business performance. Such a concept—corporate investments generally influence firms’ performance—has not been used before, and it is empirically tested on a rather big sample of Slovenian firms.
On top of that, the authors also study the behavior of the firms as investors, and they show how the firms as investors were able to exploit investment opportunities, what their prevailing motives to invest were, how often and when they invested (investment dynamics), what their investment growth in the longer study period was, what efficiency of their investment implementation was, and last but not least what economic effects they achieved by their investments. Such a complex and all-embracing analysis of the investment activity of the firms in the real economic sector in a longer period of time (after the last big financial crisis) at the national level has not been carried out recently either.
The relevance of this research can be pinpointed by the fact that investment activity is crucial for the firms’ sustainable growth and their long-lasting performance, and that the interest of the managers should be increased by the appropriate recovery of their consciousness and education in the sense that they consider all the resources that define and influence their investment ability differently than they do currently. Investment ability manifests in investment implementation and business performance. For this reason, it is very important that firms do not pay attention only to the pre-investment period when they make investment decisions, but also to the implementation of their investment and to the post-investment period, when they have to accompany and measure the financial results of their investments to find out how successful and efficient their investment was. It is especially relevant to know what financial indicators and ratios the investment influence. All this is the authors’ important contribution to the existing body of literature.
In the theoretical part of our research, the concepts and basic issues related to corporate strategic investments and their impact on business performance are presented. The scientific method of description and scientific methods of classification, comparison, analysis, and synthesis are used. A central issue in implementing this investigation is to find out whether there exists a correlation between investments in tangible fixed assets and the financial performance of firms. We do not deal with the total factor productivity (TFP). It is not an investigation to obtain any relevant measures of TFP.1
The empirical part of our research is based on the use of several research methods. As a basic method of our empirical research work, the statistical method of primary data analysis is used. Preliminary data were obtained by a questionnaire sent to Slovenian large and medium-sized enterprises (SMEs), classified from A to J according to the SKD 2008, V2 classification. Only the firms in the non-financial sector were observed. The financial data for the firms that responded to the questionnaire were collected from the GVIN (BISNODE—D&B) database. For testing the hypotheses, the chi-square test, t-statistics, and linear regression were used.
As already mentioned above, before testing our research hypotheses, the investment activities of the firms from our sample in the study period are presented from various aspects and illustrated graphically. Some results of this kind of research based on a sample of Slovenian firms are quite surprising.
At the end of this paper, we summarize the main findings of our research, in the first place the results of testing our research hypotheses. Limitations are also exposed, as are guidelines for further research work in the field of studying business performance due to investments.
2. Theoretical and conceptual background, and research hypotheses
In the theoretical framework of our research, first, a literature review is highlighted supporting the relevance of the research question. Thus, business performance related to corporate investments is presented and addresses our research hypothesis. Second, strategic corporate investments are defined with the emphasis on the dynamics of investment and its funding. Third, a conceptual and measurement model of investment impact on business performance is set up.
2.1 A literature review from the perspective of the relevance of the research question
The author of this paper tends to show in a systematic and critical manner how the existing literature deals with the relationship between investments and a firm’s performance, how it measures the effects of the investments on business performance, and to what conclusions the researchers have come so far studying this issue. Only on this basis one can better understand what the contribution of this paper stated above is like, how this paper is attached to the findings of the existing research work, what in essence this paper adds to the existing body of literature, and last but not least, why this research question is relevant.
For many years, a number of authors, such as Schultes [4], have studied the numerous factors that influence the performance of investments, and quite a few academicians and experts, for example, Grazzi et al. [5], have followed similar topics in the field of business, especially as they relate to investments in fixed assets (tangible and intangible), and studied the measurement of their efficiency from the point of view of business performance. A relatively strong interest in this field has emerged especially with regard to strategic investments and their role in strategic planning. They are considered as a key driver of a firm’s growth and progress [6].
Assessment of the impact of corporate investments was not a relevant research topic in the past, mainly due to a lack of data on investments. One of the first steps in this field was made by Doms and Dunne [7] who investigated corporate investments of American firms. The other researchers followed their case and they have found similar results: the years of investment inactivity or only of repairs and maintenance of tangible fixed assets followed the years of intensive investment activity in the firms and in the whole industry. Carlson and Laseen [8] showed that models of non-convex cost of adjustment offer a more suitable frame for better understanding of investment decisions and they reject those models that assume regular capital accumulation samples.
Although a lot of research has been done assessing the impact of various factors on investment project performance [9, 10], there are only a few empirical studies aimed at investigating the correlation between individual investment project performance and the firms’ financial performance. We should mention the research work done by Pollack and Adler [11]. They assert that there is a positive relationship between these two kinds of performance, which sounds logical and can be supported by project management theory. In some cases, the size of investment projects, innovation, and technological uncertainty, investment projects are not supposed to generate only profits, but they should also bring about strategic organizational benefits, such as product diversification to increase market share, the creation of new technical competences, the installation of new production lines, and the acquisition of new markets [12]. Ekrot et al. [13] advocate the thesis that a firm’s performance and efficiency, strongly based on project management organization, depend to a great extent on the performance of each individual project. Serrador and Turner [14] have found that the efficiency of investment projects measured by time, budget frame, and scope correlates significantly with a broader range of qualitative performance indicators, for instance, customer satisfaction, and general firm performance, the latter being expressed by financial ratios, which is also the subject of our research.
Some literature exists that studies the relationship between the increase of the firms’ wealth based on investment and their business performance as revealed by productivity and growth rate [15, 16, 17, 18, 19], by employment growth [20], by sales growth [21], or by other production factors [18, 22].
Models advocating the “learning by doing” principle argue that there is a certain time needed for workers to learn how to use new technology. For this reason, their productivity following the investment will very likely be U-shaped. This means that it decreases at the very beginning and then starts to increase, eventually reaching a higher level. The majority of empirical researchers [15, 17, 19, 22] provide evidence that the effect of investment on productivity growth is negative in the short run. Researchers who study long-term effects do not support a positive relationship between investment and productivity growth either. This causes quite an enigma from both the theoretical and the empirical aspects. Why invest in fixed assets if these investments do not generate benefits? The above-mentioned authors have studied this relationship in greater detail using a more sophisticated approach and providing evidence in the case of Italian and French firms that investments de facto improve the firms’ performance. Meanwhile, Power [15] has not found any evidence of a positive correlation between productivity and high recent investment spikes. Still, on the other hand, Huggett and Ospina [17] have found that productivity in fact decreased right after the implementation of a big investment. Bessen [16] has come to the conclusion that the productivity in newly built production plants increases over time, which he ascribes to the process of learning by doing. Power [15] has revealed a positive correlation between labor productivity and the age of production plants. Shima [19] has even observed a negative relationship between technical efficiency and the age of equipment. Kapelko et al. [23] have studied a sample of Spanish firms and they have come up with an interesting finding, namely that investment spikes cause productivity to decrease in the first year after an investment (cf. Ospina), that the relationship between technical changes and investment spikes is U-shaped, and that the effects of investment spikes on the dynamics of productivity changes differ depending on the size of the firm.
Based on a different econometric approach, Nilsen et al. [15] have found a positive and significant effect of investment implemented in the same year on labor productivity. It is interesting, though, that these effects disappeared throughout the following years. Their study also revealed that the group of firms with a bigger investment spike in at least 1 year of the sample period demonstrated a significantly higher productivity level than the group of firms with no bigger investments. Similarly, Grazzi et al. [5] have found a positive relationship between investment spikes and the firm’s sales growth. Having studied this particular relationship in the case of Italian and French firms, they realized that if the firms had at least one bigger investment in the study period, they increased their sales volume and profitability as well. The effect of the investment was strongest right after its implementation, in the period of the first year of its operation, afterwards, it decreased.
2.2 Corporate strategic investments, the dynamics of investment, and its funding
Investments are expenses designed for increasing or maintaining the stack of capital. We deal only with net investments designating a real capital increase. Meanwhile, following the statistical definition, an investment is everything that cannot be consumed, and following the general definition, an investment is every expense designed for increasing income in the future [24]. While investment expenses or capex can be aligned into several categories, the subject of this research is long-term corporate investments comprising corporate expenses for durable goods (equipment, premises).
Whenever we analyze corporate investments, the following questions are raised: How much capital do the firms want to use, at what given costs, and what return of capital and product level should be considered? What defines the desired stack of capital, that is, that stack of capital the firms want to possess in the long run? Clearly, firms cannot adjust their stack of capital to the level needed in their production right away. They need a certain period of time. We speak of an adjustment rate at which the firms adjust themselves from the existing stack of capital to its desired level. The adjustment rate defines the investment rate. Thus, investments express an adjustment rate of the economy to its desired level [24]. Technological modernization of production processes, such as robotization in firms, is an example of such an adjustment on the micro level. Today, we are witnessing the 4th industrial revolution, where cyber-physical systems, the internet of things, IoT, artificial intelligence, AI, and fast-growing production efficacy methods are broadly applied in the corporate industrial sector and elsewhere.
When Weissenrieder [6] asked himself what investments create value, he sorted investments into two groups, namely strategic and non-strategic investments. Strategic investments are those that pursue the goal “to create new value for the owners and to ensure the firm’s growth.” Non-strategic investments are those that maintain and save the value made by strategic investments. Strategic investments, such as investments in new product development or investment in acquiring new markets, are followed by more non-strategic investments. A strategic investment can be an investment in tangible fixed assets, which is the subject of our research, or in intangible fixed assets. It is irrelevant whether we talk about capex or not. Everything that counts as a cash expense in a firm is closely tied to new value creation and can be, according to Weissenrieder [6], defined as a strategic investment.
In relation to capital adjustment in firms, there are several studies [7, 25, 26, 27] that have found that firms adjust their production factors, such as capital, in a lumpy fashion.
A team of researchers [5] supports the thesis that decision-making dealing with rather big investment projects and their temporal dimension is linked with the managers’ expectations about future business opportunities, and with investment cycles. From the perspective of investment dynamics, Gourrio and Kashyap [28] provide evidence that the majority of the aggregate investment changes are explained by the changes in the number of firms being in the phase of comprehensive investments and having so-called investment spikes. Similarly, just as in macroeconomics, where we are interested in how to explain changes in aggregate investments and how these changes affect economic growth, we would also like to understand heterogeneous behavior at the micro level.
Sometimes firms renounce their investment, sometimes they are captured by a real wave of investment. Caballero [29] asserts that accounting for such lumpy investments is critical because it has an impact on the formation of the dynamic behavior of aggregate investments. Gourrio and Kashyap [28] have supported this thesis with their research of American and Chilean firms. They called the waves of investments investment spikes. The investment growth rates are mainly due to the firms’ investment spikes.
As the size of corporate investments depends on the available financial resources, besides own funds also borrowings, there arises the following issue: In the first decade of this century, the dynamic growth of corporate investments has been supported mainly by debt.
At the onset of the financial crisis, the delayed opening of the economy and the late arrival of international financial markets led to interaction among the financial accelerator channel, the liquidity channel, the banking credit extension channel, and the capital surge. A drastic reversal of foreign capital flows, triggered by banks from the most developed EU countries, caused a contagion of illiquidity, which drastically affected all the countries in the region. It led to bankruptcies and liquidations of firms [30].
After the great financial crisis at the break of the first decade, investment growth slowed down, firms were obliged primarily to deleverage, and at the very beginning, commercial banks stopped crediting the firms (credit crunch). Later after the financial crisis, the criteria and conditions for acquiring credits and loans became very strict. Thus, after the year 2010, we can observe an economic creditless recovery. This phenomenon is known as the Phoenix Miracle [31]. For this reason, we have established another hypothesis and tested it on the case of Slovenian firms, that is: The rating, defining, and monitoring of firms by commercial banks is closely tied to the firms’ indebtedness, which can point us to an important source of corporate investment funding, and which strongly influences corporate investment activities.
The rating of firms shows to a certain extent the credit capability of firms, but ultimately not whether they are able to exploit investment opportunities on the market and ensure themselves sustainable growth and development.
2.3 Conceptual and measurement model
Figure 1 presents the conceptual and measurement model relating to the hypothesis that investment in tangible fixed assets as an independent variable directly influences financial performance as a dependent variable, expressed by some most commonly used financial indicators and financial ratios.
Figure 1.
Conceptual and measurement model of investment impact on business performance. Source: Author.
The business performance of a firm, defined in our conceptual and measurement model as a dependent variable, can be measured and assessed by a wide range of financial indicators. In our research, the following financial indicators are used: Net sales revenues, Added value, EBITDA, and Net profit or net loss. Furthermore, the following financial ratios are used: Profit margin, ROA, EBITDA/Assets, ROE, Net profit or net loss per employee, Added value per employee, Sales revenues/Operating costs (thriftiness), and Net sale revenues per employee. Both groups, financial indicators and financial ratios, derive from the accounting databases of the firms in our sample.
The questionnaire was designed according to the relevant guidelines [32, 33]. Respondents chose among pre-defined possible answers. The closed questions design was preferred since it makes the alignment of answers easier and more reliable, hence facilitating statistical analysis.
The questionnaire consisted of two sections. The first section consisted of key questions inquiring about the opinions of respondents (mainly financial managers and CEOs), about the investment activity in their firms. The questions in this section were split into two subsections. The first one deals with the investment activity in their firms and is relevant for this paper. The second subsection deals with the investment ability. As this research is rather comprehensive and complex, the investment ability is a subject of another paper.2 Anyhow, the questionnaire as a whole is enclosed in this paper.
The second section of the questionnaire gathered general data on the respondents, such as their position in the firm and age, as well as general data on their firms, for example, the firm’s year of incorporation, size, average number of employees, and technical staff.
The first draft of the questionnaire was pilot tested on a convenience sample of 20 financial managers and CEOs. The final version was designed with minor amendments.
The questionnaire analysis relating to the investment activity of the firms in the study period is presented in Section 4.1 of this paper.
3.2 Data collection and sample
The primary data were collected in the period from January to April 2017 by means of the questionnaire being distributed to 1142 Slovenian large and medium-sized enterprises, sorted from A to J according to the Slovenian Standard Classification of Activities (SKD) 2008, V2. The segmentation into large and medium-sized firms was based on the Slovenian Companies Act (Paragraph 55, ZGD-1-NPB14). In total, 293 questionnaires were completed (of which 91.14% were useable). Thus, we have received 267 valid questionnaires (with a respondent rate of 23.40%). The sample consists of large firms (29.21%) and medium-sized firms (70.79%). Firms from all Slovenian statistical regions [12] were included in the sample. In terms of their legal and organizational status, the majority of the firms in the sample were limited liability companies (74.54%) and stock companies (21.35%). Almost 72% of the firms in the sample fall in the age span between 11 and 30 years, which means that the majority of the firms in our sample are mature from the perspective of their life cycle.
The financial data of the firms that sent back the questionnaires were acquired for the period 2010–2017 from the GVIN database, generated from the annual reports of the firms.
3.3 Data analysis
The causal links in our proposed conceptual model have been tested by bivariate analysis. This is a statistical method used to analyze the relationship between two variables. It enables us to draw conclusions from the sample and generalize them to the entire population. It means that we are able to infer the behavior of the population as a whole based on the results of the sample analysis. This has been carried out by setting up hypotheses, which can be either confirmed or rejected by statistical inference.
By means of the SPSS 25 software platform, we have calculated Pearson’s and Spearman’s correlation coefficients.
Contingent tables (Crosstabs) have also been used to study links between variables or constructs in our conceptual model and thereby test our research hypotheses. Additionally, we wanted to test the link between two nominal variables. Crosstabs are multidimensional frequency distributions, which generally enable one to infer about the link between two variables.
Values of dependent variables Y, which are in our case financial performance indicators, that is, Net sale revenues, Added value, EBITDA, and Net profit or Net loss, need to be expressed by the independent variable X, in our case by investments in tangible fixed assets, in the form of linear connection:
Y=a+βX+εE1
Our research sample can be written as:
y=â+β̂xE2
The regression line is a line with the equation y=a¯+β¯x, which best fits the data in the plane (x1, y1), (x2, y2), …, (xn, yn) (it is determined by the least-squares method) and serves as a mathematical model used to estimate the expected value of the variable Y by a given value of the variable X.
The validity of the linear model can be tested by a variance analysis based on size by the model explained variance for an alternative hypothesis:
H1:R2≠0linear model is appropriateE3
The reliability of the calculated parameters of the regression line can be tested by the t-test:
H1:β≠0a≠0.E4
Let us also state, that explanatory variables in the context of regression are sometimes referred to as endogenous. Thus, ordinary least squares (OLS) can produce biased and inconsistent estimates. In our statistical analysis, we have not included any instrumental variables to avoid biased estimates, which can be considered as one of the limitations of our research.
By testing the hypotheses, we have to arrange the time series of the chosen variables first, for we have conducted a time series analysis. The investment in tangible fixed assets was calculated as the difference between two sets of data for the consecutive years, that is, as a difference between two book values of the tangible fixed assets in year t + 1 and year t. If the book value of the tangible fixed assets in year t + 1 was higher than the book value of the tangible fixed assets in year t, the following conclusion can be made: a firm has increased the book value of its tangible fixed assets, a firm has invested. If the book value in year t + 1 was lower than that in previous year t, a firm has depreciated its tangible fixed assets more than it has invested.
An increase in the book value of fixed assets could be also influenced by a revaluation of the fixed assets. We have not accounted for this issue because the requisite data, that is, revaluation reserves data were not available. For this reason, our calculations might not be quite accurate, but there was no inflation worth mentioning; in fact, in the last years of our study period, there was even deflation. However, we can assume that the firms did not revaluate their tangible fixed assets, or if they did so (of course only a few of them), this might not have caused a serious problem, it can imply only a negligible error in our analysis. However, this issue can be considered as a certain limitation of our research.
Further, we have to calculate for each year of the study period relevant financial indicators or financial ratios for each firm in our sample. To get relevant indicators and ratios for the whole sample, we have scaled them with the net sales revenues of the firms. Similarly, we have done such a scaling with the investment in tangible fixed assets. To get the investment in tangible fixed assets for the whole sample, we have scaled them with the total assets of the firms. Thus, we have avoided possible heteroscedasticity problems in our regression analysis.
4.1 An outline of Slovenian firms’ investment activity in the period 2010–2017 viewed from various aspects
4.1.1 Exploitation of investment opportunities
Our research embraces in its 8-year period also the last 2 years of the great financial crisis and economic recession, that is, the years 2010 and 2011. Therefore, it is logical that almost 15% of the firms in our sample responded that they were primarily constrained to deleverage due to the credits and loans acquired in the past. This means that the firms have not or have only partially taken advantage of those business opportunities on the market that required some investments to be made.
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 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.
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).
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.
Movement
Number of firms
%
Increase of book value of tangible fixed assets
157
58.81
Decrease of book value of tangible fixed assets
107
40.07
Unchanged book value of tangible fixed assets
3
1.12
Total
267
100
Positive growth
150
56.18
Negative growth
105
39.33
Zero growth
12
4.49
Total
267
100
Average rate of increase of book value of tangible fixed assets
16%
Average rate of decrease of book value of tangible fixed assets
8%
Average growth rate of investment for all the sample firms
6%
Table 1.
Number and structure of the firms according to book value of their tangible fixed assets in the study period 2010–2017.
Source: AJPES database for the 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.
Figure 3.
Achievement of the economic effects of the investments implemented. Source: Investment ability of the companies, questionnaire 2018.
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 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.
Ratio
Rating
NFD/EBITDA
A
B
C
E
Unknown
Total
≤2
151
10
1
2
164
>2 in ≤5
47
13
2
2
1
65
>5
16
17
3
1
37
n.a.
1
1
Total
215
40
6
2
4
267
Table 2.
Number of firms in terms of rating and indebtedness measured by the NFD/EBITDA ratio.
Source: Questionnaire and AJPES database for 2017.
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 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.
Indebtedness
Inadequate
Adequate
Total
Rating
Good
Count
68a
149b
217
Expected count
87.3
129.7
217.0
% within rating
31.3%
68.7%
100.0%
% within indebtedness
64.8%
95.5%
83.1%
% of total
26.1%
57.1%
83.1%
Std. residual
−2.1
1.7
Bad
Count
37a
7b
44
Expected count
17.7
26.3
44.0
% within rating
84.1%
15.9%
100.0%
% within indebtedness
35.2%
4.5%
16.9%
% of total
14.2%
2.7%
16.9%
Std. residual
4.6
−3.8
Total
Count
105
156
261
Expected count
105.0
156.0
261.0
% within rating
40.2%
59.8%
100.0%
% within indebtedness
100.0%
100.0%
100.0%
% of total
40.2%
59.8%
100.0%
Table 3.
Relationship between the rating of the firms and their indebtedness measured by the NFD/EBITDA ratio. (rating*indebtedness crosstabulation).
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.
Chi-Square test for the rating of the firms and their indebtedness.
0 cells (0.0%) have an expected count of less than 5. The minimum expected count is 17.70.
Computed only for a 2 × 2 table.
Source: Questionnaire and AJPES database for 2017.
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 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.
Spearman’s correlation coefficient for two variables, the rating of the firm and the NFD/EBITDA ratio.
Correlation is significant at the 0.01 level (2-tailed).
Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples.
Source: Questionnaire and AJPES database for 2017.
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 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:
∏i=1nai1n=a1a2a3…annE5
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.
Model
R
R square
Adjusted R square
Std. error of the estimate
1
0.821a
0.673
0.652
4054964.139
Table 6.
Model summary.
a Predictors: (Constant), Tangible fixed assets.
Model
Sum of squares
df
Mean square
F
Sig.
1
Regression
5.085E+14
1
5.085E+14
30.926
0.000b
Residual
2.466E+14
15
1.644E+13
Total
7.552E+14
16
Table 7.
ANOVAa
a Dependent variable: net sales revenues.
b Predictors: (constant), tangible fixed assets.
Model
Unstandardized B
Coefficients std. error
Standardized coefficients beta
t
Sig.
1
(Constant)
−2365465.1
9981459.460
−2.370
0.032
Tangible fixed assets
1.505
0.271
0.821
5.561
0.000
Table 8.
Coefficientsa.
a Dependent variable: Net sales revenues.
Model
B
Bias
Std. error
Bootstrapa Sig. (2-tailed)
BCa 95% Confidence Interval Lower Upper
1
(Constant)
−23654658.1
−818984.712
11901364.94
.116
−47733387.8
−666241.520
Tangible fixed assets
1.505
0.027
0.338
0.012
0.894
2.193
Table 9.
Bootstrap for coefficients.
a Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples.
Source: Questionnaire and AJPES database for 2017.
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.
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. 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.
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.
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.
4.2.2.1 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 5.
Movement of added value for the 30 biggest sample firms in the period 2000–2017.
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.
4.2.2.2 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).
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.
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.
4.2.2.3 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 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.
Figure 8.
Impact of investment in tangible fixed assets on select financial ratios for the sample firms in the period 2000–2017.
4.2.2.4 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. These returns do not pertain to the firms or their owners, though, they are returns produced only by the investments.
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.
Figure 9.
Distribution of NFD/EBITDA ratios for the sample firms for the years 2007, 2010, and 2017.
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 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.
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.
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.
Figure 11.
Estimate of the conceptual model of the impact of investment in tangible fixed assets on business performance for Slovenian firms. Source: Author.
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 medium-sized 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 non-financial 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.
Identification number of the company /Matična št. je sicer Company Registration Number
2.2
Name of the company
2.3
The company is situated at/in …
2.4
Address of the company
2.5
Size of the company
2.6
The year of the company’s foundation
2.7
Legal and organizational status of the company
2.8
Core business/activity of the company according to SKD 2008
2.9
Predominant activity of the company according to SKD 2008
2.10
Average number of employees in 2017
2.11
Average number of employees in technical (investment) department in 2017
2.12
If your company is a stock company, is it listed on a stock exchange?
2.13
Current rating at the parent bank the company mainly works with
2.13.1
Class A: the companies for which the banks do not foresee any problems with settling their liabilities
2.13.2
Class B: the companies which for the time being have a weak financial strength, but it does not seem to be getting worse and they frequently settle their liabilities with delay
2.13.3
Class C: the companies which do not have sufficient long-term financial resources and the bank does not receive from them satisfactory current information or appropriate documentation regarding their debt
2.13.4
Class D: the illiquid and insolvent companies, whereat there is a high probability for not settling the liabilities
2.13.5
Class E: the companies which are supposed not to be able to settle their liabilities; thus they define their “expected” solvency and according to this estimation they run their proper credit policy
2.14
Management of the company
2.14.1
The company has one-member board (general manager/president of the management board)
2.14.2
The company has a managing board consisting of several members
2.15
Ownership structure of your company
2.15.1
One private shareholder/associate has at least 50% share in the company
2.15.2
Two biggest private shareholders/associates together have at least 50% share in the company
2.15.3
One shareholder/associate owned by the state has at least 50% share in the company
2.15.4
Two biggest shareholders/associates together have at least 50% share in the company, whereat one of them is state owned
2.15.5
One shareholder/associate is a financial holding and has more than 50% share in the company
2.15.6
Two biggest shareholders/associates together have at least 50% share in the company, whereat one of them is a financial holding
2.15.7
Neither one shareholder/associate by himself/herself nor the two biggest shareholders/associates together have at least 50% share in the company
3
Implementation of the investment opportunities in the last 8 years
3.1
Have you in the last 8 years (from 2010 to 2017) succeeded in taking advantage of those business opportunities in the market which required investments?
3.1.1
Entirely
3.1.2
Partly
3.1.3
No
3.2
We have not taken advantage of (all) the business opportunities being offered to our company in the market in the last 8 years, because:
3.2.1
our company has not had enough of its own financial resources for the necessary investments
3.2.2
our company has not succeeded in acquiring (borrowing) debt financial resources for the necessary investments
3.2.3
first our company had to free from debts (deleveraging) acquired in the past
3.2.4
the strategic guidelines (directives) for the necessary investments (the investments were not planned in our strategic business plan) have not been confirmed
3.2.5
the owners /through their supervisory board/ have not accepted/confirmed the business plans
3.2.6
we have not been ready for the implementation of the new investments /in the sense of getting ready with the project documentation and acquiring all the required licenses and permits
3.2.7
the investments have been too demanding with respect to the necessary funds
3.2.8
the investments have been too demanding with respect to technology
3.2.9
our company has not had sufficient human resources /lacked qualified physical labor force/
3.2.10
our company has not had sufficient human resources /lacked technical skill/
3.2.11
our company has been overtaken by competition
3.2.12
our company has not received new orders (in pipeline) from the existing clients
3.2.13
our company has not received new customers/clients for its products/services relating to the new planned investments
3.2.14
in the meantime, some organizational changes in our company occurred
3.2.15
other /please explain what …/
4
Investment activity of our company in the last 8 years (from 2010 to 2017)
4.1
What has encouraged your company to invest in the last 8 years?
4.1.1
Competition which invests
4.1.2
Increase in the customers’ orders
4.1.3
Increase in the sale in the previous year
4.1.4
Increase in export
4.1.5
Increase in productivity
4.1.6
New business opportunities in the market
4.1.7
Relatively high degree of the write-off of your equipment
4.1.8
Technological progress (a need for modernization)
4.1.9
Innovativeness and own R&D activities
4.1.10
Substitution of manual work with the process automation
4.1.11
Increase of profit
4.1.12
Offer of favorable financial resources
4.2
Has your company implemented bigger and financially demanding investments in the last 8 years:
4.2.1
equally, each year in approximately the same value
4.2.2
concentrated with an investment spike in one or two years at the beginning of the 8 year period
4.2.3
concentrated with an investment spike in one or two years at the end of the 8 year period
4.2.4
concentrated with an investment spike in one or two years in the middle of the 8 year period
4.3
How has your company implemented the biggest investments in fixed assets in Slovenia in the last 8 years? This question relates to one or more investments the joint value of which exceeded 100 thousand €:
4.3.1
The biggest investments were successfully finished before the deadline
4.3.2
The biggest investments were successfully finished on time
4.3.3
The biggest investments were successfully finished in the expected volume (size)
4.3.4
The biggest investments were successfully finished in the scheduled financial frame
4.3.5
The biggest investments were not realized according to the time schedule
4.3.6
The investment implementation was delayed due to acquiring the licenses and permits
4.3.7
During the investment implementation, some important technical changes occurred
4.3.8
The delay of the investment implementation was due to force majeure (weather, strikes, epidemic diseases, etc.)
4.3.9
During the investment implementation, the suppliers of the equipment were late
4.3.10
During the investment implementation, the constructors did not adhere to the time schedule
4.3.11
The investments were not realized in the expected volume (size)
4.3.12
The cost of the investment was exceeded due to the price increase
4.3.13
The cost of the investment was exceeded due to the excessive and additional unexpected works
4.3.14
The funds for the planned investments were not provided on time
4.3.15
The financial resources for the investments were different from those originally planned
4.3.16
The borrowed funds were bigger than the originally planned
4.3.17
In the end, the investments required additional employment, more workers than planned
4.3.18
Orders of the customers decreased either during the investment implementation or at the very end
4.4
Please estimate the economic effects of the investments (their performance) by choosing an appropriate answer below
4.4.1
Economic effects of the investments are bigger than the originally planned
4.4.2
Economic effects of the investments are achieved in the span from 91–100% of the originally planned
4.4.3
Economic effects of the investments are achieved in the span from 71–90% of the originally planned
4.4.4
Economic effects of the investments are achieved in the span from 51–70% of the originally planned
4.4.5
Economic effects of the investments are achieved in the span under 51% of the originally planned
5
Financial flexibility of the company
5.1
Capability to acquire financial resources
5.1.1
Capability to acquire financial resources (also in financial distress and during the financial crisis)
5.1.2
Capability to borrow in the long run when necessary
5.1.3
Cumulation of cash reserves to be able to borrow in the future
5.1.4
Capability to use financial leverage for new investments
5.2
Capability to manage the risk of being able to pay
5.2.1
Capability to manage the financial risk (exchange rate, interest rate, investment)
5.2.2
Capability to protect itself against a sudden drop of cash-flow (smaller vulnerability)
5.2.3
Disposal of cash reserves
5.2.4
Capability to maintain a good rating with banks (B and higher than B)
5.3
Capability to maintain capital adequacy in the long-run
5.3.1
Capability to maintain an appropriate capital structure (Debt to Equity Ratio) while respecting a balance golden rule
5.3.2
Capability to restructure short-term financial resources considering their time span and price
5.3.3
Disposal of cash reserves
6
Knowledge - competencies and dynamic capabilities
6.1
Technical competences
6.1.1
The employees have plenty of technical knowledge and skill
6.1.2
There are clearly defined needs for professional knowledge in our company
6.1.3
The employees learn fast and they are able to manage new technologies and implement them in the processes
6.1.4
Development of new products /services is supported by own knowledge in the company
6.1.5
The employees are able to receive and transmit good practices (technical solutions) from outside of the company and within it
6.1.6
The employees’ capability of innovation is comparable to the competition’s or is even bigger
6.1.7
Managers with technical competencies influence the innovativeness of the employees
6.1.8
Managers with technical competencies influence the permanent learning of the employees
6.2
Organizational and managerial competences
6.2.1
Middle management (leaders of sectors and services) is familiar with the strategy of the company
6.2.2
Middle management in our company takes part in investment planning
6.2.3
Business processes in our company are backed up with modern IT
6.2.4
From an organizational perspective, the management successfully delegates the tasks and empowers the employees
6.2.5
Management effectively supervises and controls the implementation of the tasks and projects and takes timely measures if deviations from goals and objectives occur
6.2.6
Implementation of the demanding tasks and projects is based on teamwork
6.2.7
There is a two way and effective communication among the employees in the company (each employee receives all the necessary information for the execution of his/her tasks)
6.2.8
For all the stakeholders involved in investment projects, there is an effective awarding system set up in our company
6.3
Project competences
6.3.1
Implementation of demanding projects is based on project management
6.3.2
For the majority of the suppliers for the investment implementation, the company acquires at least three bids
6.3.3
Strategic suppliers are involved in the design and development of the investment projects
6.3.4
As early as in the phase of the investment project design the crucial risk in the phase of implementation is assessed by the project managers who also prepare several scenarios
6.3.5
Project managers master all the phases of the investment projects
6.4
Dynamic capabilities
6.4.1
Investment project managers (within the company) are familiar with the development strategy of the company
6.4.2
Investment project managers (within the company) are involved in the design of the company’s development strategy
6.4.3
Top and middle management are involved in investment designing
6.4.4
Managers responsible for individual processes are able to perceive the strengths /weaknesses and opportunities/threats in the company and environment ahead of the competition
6.4.5
Managers responsible for individual processes continually observe and research the markets, technologies, and business environment
6.4.6
Top and middle management are able to identify business opportunities
6.4.7
Top and middle management are able to achieve the goals of the company
6.4.8
Top and middle management are able to decide, take decision, and then undertake the necessary measures to reach the goals
6.4.9
Top and middle management are able to combine and transform the resources
6.4.10
Top and middle management are able to transform the organizational structure in line with changes in the environment
7
Performance of the company in the last 8 years
7.1
Non-financial aspect
7.1.1
New products/services related to the new investments were marketed faster than those by the competition
7.1.2
The performance of the new products/services related to new investments was high
7.1.3
New investments increased the market share of our products/services
7.1.4
Turnover of workforce in the company is low
7.1.5
Turnover of technical staff in the company is low
7.1.6
Satisfaction of our customers increased after the investments were finished
7.1.7
Satisfaction of our employees increased after the investments were finished
8
Choose if you want to receive the feedback and results of this questionnaire and research
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Notes
There is a bulk of literature focusing on the estimation of productivity [1, 2, 3].
See a paper “Impact of companies’ investment ability on their performance” [34].
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.
Written By
Vladimir Bukvič and Metka Tekavčič
Submitted: 31 March 2022Reviewed: 19 April 2022Published: 21 July 2022
Open access peer-reviewed chapter
