This paper investigates the impact of intangible assets on the financial performance of Slovak ICT companies in the context of the knowledge economy. The paper uses a panel data regression analysis to test the hypothesis that intangible assets have a significant positive efect on four indicators of financial performance: Return on Assets, Net Profit Margin, Assets Turnover, and Return on Equity. The paper analyzes a sample of 180 Slovak ICT companies for the period 2015-2019, using eight independent variables: Research and Development Intensity, Research and Development Intensity Squared, Software, Intellectual Property Rights, Acquired Intangible Assets, Leverage, Size, and Dummy variable for ICT sub-sectors. The paper applies various tests to select the appropriate estimation method and to check the adequacy of each model. The results partially confirm the hypothesis, as only Research and Development Intensity, Research and Development Intensity Squared, and Acquired Intangible Assets have a significant positive impact on some indicators of financial performance. The paper also finds that the influence of intangible assets varies depending on the type and measure of financial performance. The paper contributes to the literature on intangible assets and financial performance by providing empirical evidence from Slovak ICT companies. The paper also provides some implications for managers and policymakers to improve their intangible investment policy.
Over the past few decades, extensive discourse among researchers has centered on the evolving
role of various types of capital in shaping the economic value and sustainability of enterprises,
paving the way for their enduring success. Particularly noteworthy is the recognition of
intellectual capital’s pivotal role in this metamorphosis, influenced by shifts in production focus
and management strategies. This shift is marked by a transition from the conventional emphasis
on physical capital and labor to a spotlight on intellectual capital and the exchange of ideas,
buoyed by intellectual property rights, particularly patents for their integration with technology
[
This new paradigm necessitates a growing reliance on national intellectual capital to fortify the economies of developed nations. Moreover, for numerous enterprises, financial performance hinges on the judicious crafting of policies that foster both the creation of novel intangible assets and the eficient utilization of existing ones. This involves their seamless integration into enterprise operations, fostering efective partnerships, governance, and control. In this context, enterprises frequently encounter network efects and an elevated exposure to market and technological risks. Consequently, a recalibration of business strategies becomes imperative, encompassing initiatives that leverage intangible assets to their strategic advantage. This is especially pertinent for high-tech entities, characterized by their substantial reliance on intangible assets to develop innovative technological products and services.
In light of these economic transformations, the quest for fresh theories and strategies is
imperative to underpin informed decisions and management conduct in enterprises rich in
intangible assets. A pertinent avenue of investigation is the impact of intangible assets on
companies’ financial performance, specifically focusing on the context of Slovak enterprises
operating in the information and communications technology (ICT) sector. These companies,
encompassing those engaged in information processing, storage, transfer, production of
computing and telecommunication devices, and related services, epitomize high-tech enterprises.
Their value creation processes are intrinsically linked to the eficient utilization of intangible
assets. Investments in high-tech intangible assets within the ICT sector hold the promise of
enhancing their financial metrics. However, as Huňady et al. [
Over the last decade, the Slovak Republic has witnessed vigorous development in its ICT
sector. The number of individuals employed in the information and communication technology
services sector surged from 28,905 in 2009 to 53,676 in 2019 [
Given the pivotal role of intangible assets in shaping the eficiency of high-tech enterprises, a research hypothesis has been formulated. This study hypothesizes a significant positive correlation between intangible assets and the financial performance of ICT companies. Recognizing that the strength of this influence may also vary based on company size, levels of borrowed capital, and sub-sector categorization within the ICT industry, the analysis of the impact of intangible assets on financial performance takes into account these factors. The resulting insights are expected to inform recommendations for Slovak ICT companies’ intangible asset investment decisions.
Problems of influence of intangible assets in their broad (economic) understanding on financial
performance of high-tech companies are paid considerable attention of academicians. First
of all, this is conditioned by the decisive role of intellectual capital for such enterprises in the
context of the development of knowledge economy, which is based on ideas, R&D, innovations
and technological progress. Scientists analyze of the impact of diferent intangible values on
ifnancial performance: intangible assets (the concept of IAS 38 [
Table 1 lists the number of articles and their quotations, which reveal the relationship between “Intangible assets” / “Intellectual capital” and “Financial performance” in science-based databases of Scopus, Web of Science and Google Scholar.
The results of analysis of scientific databases are obtained (table 1) testify to the existence of a considerable number of publications in this direction of researches, as well as their influence on scientific works of other authors, which is confirmed by a considerable number of references to data of other authors and their constant growth from year to year. The cluster analysis of the key words of the articles from the databases of the Scopus and Web of Science on the basis of the use of VOSviewer allowed to confirm this conclusion. There was also a large number of publications that examined the impact of structural elements of intangible assets or intellectual capital (research and development, intangible resources, customer capital, structural capital, human capital, social capital, relational capital) on financial performance (figure 1). In addition, publications have been identified that investigate the impact of intangible assets or intellectual capital on other types of indicators that characterize the performance of the enterprise – firm performance, business performance, corporate performance, firm value, efectiveness, eficiency, profitability, ROA, competitive advantage etc. (figure 2).
Little attention is paid directly to the issue of impact of intangible values on financial
performance of ICT companies, although the presence of significant positive relationships between
with two variables is confirmed in the vast majority of results. Gan and Saleh [
Li and Wang [
Dženopoljac et al. [
Zhang [
Huňady et al. [
Qureshi and Siddiqui [
Lopes and Ferreira [
Radonić et al. [
The analysis of studies on the issues of the research made it possible to establish the existence of mutually contradictory evidence regarding the impact of intangible assets on the financial performance. In general, this does not allow the management of enterprises to efectively control intangible values aimed at creating internal value, and for investors – to receive clear signals for making efective investments. Considering the above, the following objectives were formulated: to measure the relationship between intangible assets and the financial performance of Slovak ICT companies; to investigate which components of intangible assets have the most significant or insignificant impact on the financial performance of Slovak ICT companies; to form recommendations for improving the investment policy of ICT companies, based on the level of significance of the elements of intangible assets from the point of view of increasing ifnancial results.
To determine whether intangible assets stimulate financial performance, was analyzed sample of 180 Slovak ICT companies for the period 2015–2019. In particular, the panel data information from financial statements of such enterprises, available in the open access, as well as the information from database “FinStat” was used to form panel data. Only those companies, for which the necessary information for the 5-year period was available, were included in the sample. The selected 180 companies provide a valid and complete set of data in order to carry out relevant statistical analysis.
Investigated enterprises proceeding from EU Economic Activity Classification and from the SK NACE 2 classification belongs to group 26 “Manufacture of computer, electronic and optical products”, includes direct production of computers, computer peripheral equipment (input device, output device, input/output device), communication equipment (public switching equipment, transmission equipment, customer premises equipment), measuring, medical, navigation, radio, optical and other electronic equipment, as well as production of various types of accessories for such products (electrical boards, magnetic and optical media, etc.). In order to take into account the influence sub-sectors afiliation on financial performance of ICT companies two groups were allocated in their composition. The first group included enterprises dealing with the production of diferent types of electronics and components, and the second group involved enterprises producing communication equipment and components.
Based on the form of ownership, most of the companies investigated – 160, companies with limited liability, 16 – is a joint-stock company, 2 – production cooperative, 1 – limited partnership, 1 – general partnership. By type of ownership, the companies investigated are divided as follows: private domestic – 64%; foreign – 21%; international with a predominant private sector – 13%; cooperative – 1%; state – 1%.
In the research for characteristics of financial performance of ICT companies were used four
dependent variables – Return on Assets, Net Profit Margin, Return on Equity, Assets Turnover,
and used in their work by researchers for simiral empirical analysis of the relationship between
intangibles values and company financial performance [
Types, calculation procedures, and abbreviations used in the Variables study are shown in table 2.
The dynamics of four indicators, that characterize financial performance of Slovak ICT companies (ROA, NPM, ROE, ATO) for the period 2015–2019 showed in figure 3.
Figure 3 displays the change in time of financial performance indicators for the 2015–2019 period. It allows to identify a number of common trends: Simultaneous growth in all indicators for 2017–2018 years; decrease in ATO, ROA and NPM indicators for 2015–2016 years, their growth in 2016–2018 years, as well as their simultaneous decrease in 2018–2019; during 2018–2019 years only growth of ROE indicator occurs. In general, common behavior was found for ATO, ROA and NPM, as well as almost completely diferent behavior of ROE compared to these indicators.
To understand the relationship between intangible assets and financial performance indicators, this study examined four following models:
Model 1: ROA = + 1 ⋅ RDI + 2 ⋅ RDI2 + 3 ⋅ SOFT + 4 ⋅ IPR + 5 ⋅ AIA + 6 ⋅ LEV + 7 ⋅ l_SIZE + 8 ⋅ DVICTSS +
Calculation (Source)
Dependent Variables Return on Assets Net turnover / Total Assets Net Profit Margin Net profit / Total Sales Assets Turnover Total Sales / Total Assets Return on Equity Net profit / Total Equity
Independent Variables
Intangible Assets Variables
Research and Development Intensity Capitalized R&D Costs / Total Sales Research and Development Intensity Squared Squared function of RDI
Software Software (Intangible Asset) Intellectual Property Rights Valuable Intellectual Property Rights Acquired Intangible Assets Acquired long-term intangible assets
are charged until the time of their use
Control Variables Leverage Total liabilities / Total Assets
Size Logarithm of Total Assets Dummy variable for ICT sub-sectors 1 for electronic producers, 0 for communication producers
Abbreviation
ROA NPM ATO ROE RDI RDI2 SOFT IPR AIA
LEV l_SIZE DVICTSS 7 ⋅ l_SIZE + 8 ⋅ DVICTSS
+ Model 2: NPM = + 1 ⋅ RDI + 2 ⋅ RDI2 + 3 ⋅ SOFT + 4 ⋅ IPR + 5 ⋅ AIA + 6 ⋅ LEV + Model 3: ATO = +
1 ⋅ RDI + 2 ⋅ RDI2 + 3 ⋅ SOFT + 4 ⋅ IPR + 5 ⋅ AIA + 6 ⋅ LEV + Model 4: ROE = + 1 ⋅ RDI + 2 ⋅ RDI2 + 3 ⋅ SOFT + 4 ⋅ IPR + 5 ⋅ AIA + 6 ⋅ LEV + where: ROA, NPM, ATO, ROE – dependent variables, where is entity and is time; – Identifier; – Variance introduced by the unit-specific efect for unit ; – Regression coeficient;
RDI, RDI2, SOFT, IPR, AIA – independent intangible variables, LEV, l_SIZE, DVICTSS – independent control variables; – error term.
The descriptive statistics (observation, mean, median, standard deviation, minimum, maximum) of a full sample are presented in table 3.
From table 3 it can be observed that the full sample is measured with 180 units. The largest deviations in variables are related to SOFT (5,95⋅104), IPR (2,89⋅104), AIA (1,61⋅105) and ROE (4,30). Large diferences between the minimum and the maximum values of ROA, ATO, and ROE show that the financial performance levels of ICT companies are quite distinct. For some variables (ATO, LEV, IPR, AIA, l_SIZE) the mean value is greater than the standard deviation value, as a result, the data in these variables have a small distribution. ROA, NPM, and ROE have a higher standard deviation than their mean. This indicates a relatively large set of ratios that will characterize the normal distribution curve and will not be outliers. The closeness of the mean (13,5) and median (13,3) values for l_SIZE indicates a high level of symmetry in the distribution of range values, that is, the size of the studied enterprises. The mean value of the LEV ratio is 0,438, and this means that approximately 44% of the total assets of ICT companies are financed through borrowed resources.
In general, correlation matrix of variables used in Models 1-4 (figure 5), testifies to absence
multicollinearity problem, since in most cases, the correlation coeficient is less than 0,5 (–0,5).
The only exception is the high correlation coeficient between variables RDI and RDI2 (0,9), which
is understandable given that RDI2 is a squared function of RDI. However, as Özkan [
The choice of estimate panel data parameter for each of the selected models plays an important role in the regression analysis of panel data. This parameter should be adequately correlated with the data used in the corresponding model. Proceeding from F-statistics test for Model 1 F(179; 712) = 1,17767 with p-value 0,0766456, which is more than 0,05 and confirms null hypothesis in relation to pooled OLS model. The need for such a choice estimate parameter for Model 1 also confirmed the application Breusch-Pagan test, according to which chi-square (1) > 2,04561 p-value = 0,152645, which is larger than 0.05 and confirms zero hypotheses. The use of F-statistics test and Breusch-Pagan test also confirmed the need for use pooled OLS model as a quality estimate parameter for Model 2. For Model 3 after application F-statistics test it was received F(179; 712) = 1,23387 with p-value 0,0331413, that is less than 0,05 and testifies to the adequacy of application Fixed efects method (FEM). However, this conclusion is refuted as a result Breusch-Pagan test, according to chi-square (1) > 3,58479 p-value = 0,0583107, which is larger than 0,05 and confirms zero hypothesis of adequacy pooled OLS model. Considering the results Hausman test (p-value = prob(chi-square (8) > 4,34179) = 0,825045), according to which more appropriate is the application of Random efects method (REM) than FEM, for Model 3 more appropriate also consider the application of pooled OLS model. For Model 4 after application of F-statistics test F(179; 712) = 1,32394 of p-value 0,00693691, which is less than 0,05 and shows the adequacy of application of FEM. This is the test followed by the p-value = P(chi-square (1) > 6,04321) = 0,0139599.
To verify the adequacy of the Panel data for Models 1-4 that is collected about ICT companies, it should be diagnosed using Normality test, Autocorrelation test and Heteroscedasticity test. Normality test for all Models 1-4 allowed to detect abnormal distribution of the error. For example, for Model 1 for chi-square (2) = 4119,75 p-value = 0, which is less than 0,05, and does not confirm zero hypotheses about the normal distribution of balances. Review null hypothesis about no first-order autocorrelation based on usage Wooldridge test for autocorrelation allowed to confirm it for all four models. In particular, for all Models 1-4 p-value it is more than 0,05 (0,73367; 0,923389; 0,193049; 0,227822), confirming null hypothesis. White test was used to check the heteroscedasticity of a models 1–3. Since the obtained p-value for each of the three models (0,284134; 0,999935; 0,421088) is more than the critical value, the zero hypothesis about the absence of heteroscedasticity is forgiven. For Model 4 with estimate parameter FEM was applied non-parametric Walk test, which also was established the presence of heteroscedasticity. In particular, chi-square(180) = 78593,1 p-value = 0 was received. Since p-value is less than 0,05, there is an inhomogeneous observation and a diferent variance of a Model 4 random error, which confirms the existence of heteroscodesticity.
To solve the problem of inadequacy of all Models 1–4 used by this data due to the problem
of improper distribution of the error and heteroscedasticity, the use of robust estimators is
proposed. They help minimize or eliminate impact of outliers in a Models 1-4, improving the
results of panel data regression analysis. Practice of use robust standard errors in regression
analysis was also used in research of scientists who study the impact of intangible assets and
their components on the performance of enterprises [
Model 1 (ROA). Tables 4–5 show the results of regression analysis performed using pooled OLS model. They show how the independent variable will afect the dependent variable, which of the regressions have significant influence, force and direction of such influence.
Coeficient 1,83324 −1,16410 0,110937 1,65440⋅10−6 1,34184⋅10−6 −4,98766⋅10−7 −0,137738 −0,0379800 0,168307
Standard error 0,632512 0,157720 0,0175566 5,38521⋅10−7 9,24827⋅10−7 1,38889⋅10−6 0,214780 0,0454674 0,105500
z 2,898 −7,381 6,319 3,072 1,451 −3,591 −0,6413 −0,8353 1,595
P-value 0,0038 <0,0001 <0,0001 0,0021 0,1468 0,0003 0,5213 0,4035 0,1106
Significance by t-statistics *** *** *** *** *** Note: *** Significant at the 1% level.
Based on the results of the regression analysis, const, RDI, RDI2, SOFT and AIA are statistically
significant (there are stars in the last column of table 4), having the highest level of significance
at the 1% level. Accordingly, these indicators have the highest impact on ROA. In addition
to RDI and AIA, other significant indicators have a direct impact on ROA and RDI and AIA
are rotating. The presence of a diferent direction of influence in RDI and RDI2 indicates the
presence of U-inverted relationship between R&D and ROA [
The overall content of the regression coeficient of Model 1 is that with an increase of 1 directly influencing the ROA, the last increase in the ratio will be increased. For example, if SOFT is increased by 1, the ROA will increase by 1,65440 ⋅1006. And for indicators that have a positive impact on ROA, their increase by 1 for ICT enterprises will result in corresponding decrease of ROA (depending on the coeficient of regression).
Table 5 indicates that the coeficient of determination (R-squared) of Model 1 is 0,047173. This means only that 4,7% of the variation of ROA can be explained by the variation of the independent variables (const, RDI, RDI2, SOFT, IPR, AIA, LEV, l_SIZE, DVICTSS).
Model 2 (NPM). Model 2 can be interpreted through the following equation: =̂ −0, 274718 + 0, 0626252 1 − 0, 00669466 2 − 5, 13111 ⋅ 10−8 3 + 2, 00654 ⋅ 10−7 4 − 5, 88982 ⋅ 10−8 5 − 0, 0630010 6 + 0, 0269143 7 − 0, 0517929 8 where: ̂ – NPM; 1 − 8 – the same as in Model 1.
Based on table 6, the most significant efect on NPM is changed to l_SIZE. Accordingly, with the growth of the enterprise volume by 1 increases the value of the NPM indicator by 0,0269143. Significant at the 5% level in NPM explanation have regressors const, RDI, RDI2 and AIA. Also significant at the 10% level is the DVICTSS regression, which has an indirect efect. Indirect efects on NPM are also afected by the RDI2 and AIA indicators. This means that, as investments in such types of intangible assets increase, the corresponding (depending on the regression coeficient) reduction of the dependent variable will occur. By comparing the coeficient of Model 2 with RDI and RDI2, it is possible to note the existence of the upper limit of investments in R&D of Slovak ICT companies, after which their negative impact on NPM will already be observed.
Table 7 indicates that the R-squared of Model 2 is 0,01, a very low value and does not allow to speak about the significant role of intangible assets in NPM provision. This means that 1,33% of the variation of the NPM can be explained by the variation of regressors.
Model 3 (ATO). Model 3 can be interpreted through the following equation: =̂ 2, 80330−1, 42622 1 −0, 134772 2 +2, 76920⋅10−6 3 +4, 61116⋅10−6 4 −4, 38715⋅10−7 5 − 0, 139781 6 − 0, 0951285 7 + 0, 150857 8 where: ̂ – ATO; 1 − 8 – the same as in Model 1.
For dependent variable ATO except for LEV and DVICTSS, all other regressions are significant. In particular, l_SIZE significant at the 5% level, and all other regressions (const, RDI, RDI2, SOFT, IPR and AIA) significant at the 1% level. Direct efects on ATO from the regression data are RDI2, SOFT and IPR, while others are afected. In particular, as in Model 1 for ROA, making a small amount of investments in R&D of Slovak ICT companies has a negative impact on ATO. Only their implementation from a certain volume, in particular, in the volume of RDI2, ensures Note: * Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level. Note: ** Significant at the 5% level; *** Significant at the 1% level. the growth of ATO. Based on an equal to 1,3 RDI2 growth by 1 increases the NPM value by 0,0269143. Table 9 indicates that the R-squared of Model 3 is 0,056. This means that 5,61% of the variation of the ATO can be explained by the variation of regressors.
Model 4 (ROE). Model 4 can be interpreted through the following equation: ̂= −1, 06067 − 2, 79903 1 + 0, 272431 2 + 5, 89712 ⋅ 10−6 3 + 8, 97938 ⋅ 10−7 4 − 1, 27997 ⋅ 10−6 5 + 8, 94081 6 + 0, 0265371 7 + 0, 392670 8 where: ̂ – ROE; 1 − 8 – the same as in Model 1.
Model 4 has five statistically significant regressors – RDI, RDI2, SOFT, AIA and LEV (table 10). All of them have the highest level of significance – 1%, therefore they have the greatest influence on the dependent variable (ROE). The equation of Model 4 shows that most of the independent variables (RDI2, SOFT, IPR, LEV, l_SIZE and DVICTSS) have a direct influence, and only two variables (const, RDI and AIA) have a rotational influence on the ROE. As in Models 1 and 3, Model 4 has a U-inverted relationship between R&D and ROA, characterized by the need to increase investment in R&D of Slovakia ICT companies to ensure their positive impact on ROE.
Table 11 indicates that the LSDV R-squared of Model 4 is 0,51. This is quite a high value compared to the 1–3 models, but not enough to speak about the significant role of intangible assets in providing of financial performance of ICT companies. This means that 51,61% of the variation of the ROE can be explained by the variation of the regressors.
The results obtained in the article partially confirm the conclusions of the analyzed works on the
role of intangible assets in the promotion of financial performance of high-tech companies. As
for some regressions, they are in conflict with such conclusions. The existence of a positive and
significant relationship between intangible assets and some financial performance measures was
confirmed, which is also set in the works of Li and Wang [
The results of the survey refutes the conclusions of Gan and Saleh [
The study has some limitations, which should be taken into account by other scientists when evaluating the results of a study. Firstly, given the suficient breadth of the term “financial performance”, a list of dependent variables used in the study can be specified. Second, the list of independent variables used in a study can be expanded by uncapitalized intangible assets that also afect the financial performance of Slovakia ICT companies. However, it is necessary to separate from the composition of diferent types of expenses of ICT companies those expenses connected with creation of intangible assets (client, ecological, social, etc.), as such data are not in financial statements of companies. Third, to determine the role of intangible assets in improvement of financial performance, research can be carried out not only on the examples of companies of ICT industry, but also on the example of other branches of economy. This will allow to carry out an interindustry comparison and establish in which areas of management of enterprises should pay the most attention to development of an intangible investment policy.
This research was undertaken with the objective of comprehending the ramifications of intangible assets on the financial performance of high-tech enterprises. The focus of this study was the analysis of 180 Slovak ICT companies over the period spanning 2015 to 2019. This inquiry gains particular pertinence against the backdrop of the pivotal role that the ICT sector plays in propelling the development of the Slovak economy. The Slovak Government has proactively established conducive institutional conditions to facilitate the growth of ICT companies and has initiated specialized programs to incentivize investments in this sector.
Panel data regression analysis served as the foundational methodology for this investigation. The financial performance was characterized through four dependent variables: Return on Assets, Net Profit Margin, Assets Turnover, and Return on Equity. For each of these indicators, a distinct model was constructed, incorporating eight independent variables. The intangible asset variables encompassed Research and Development Intensity, Research and Development Intensity Squared, Software, Intellectual Property Rights, and Acquired Intangible Assets. Additionally, three control variables were integrated: Leverage, Size, and a Dummy variable denoting ICT sub-sectors, within the temporal scope of 2015 to 2019. The selection of the optimal panel data parameter for each model was grounded in statistical tests, such as the F-statistics test, Breusch-Pagan test, and Hausman test (Models 1 to 3 – pooled OLS model, Model 4 – Fixed Efects Method). To assess the models’ compatibility with the generated data, the Normality test, Autocorrelation test (Wooldridge test for autocorrelation), and Heteroscedasticity test (White test, Walk test) were employed, substantiating the application of robust standard errors due to partial model adequacy.
The study’s hypothesis was afirmed to a certain extent through the outcomes of the panel regression analysis. The results unveiled that not all categories of intangible assets wield a significant positive influence on the financial performance of Slovak ICT companies. Specifically, Research and Development Intensity (RDI), Research and Development Intensity Squared (RDI2), and Acquired Intangible Assets (AIA) exhibited substantial impact across various degrees on the four distinct financial performance indicators. This emphasizes the rationale for management to channel investments into these specific categories of intangible assets for Slovak ICT companies. Furthermore, the contrasting directions of influence of RDI and RDI2 on financial performance indicators underscore the existence of a U-inverted relationship between R&D investment and the financial performance metrics. Depending on the model, RDI either operates beyond the threshold of returns on R&D investments or within it, while RDI2 exhibits the inverse relationship. These findings ofer managerial insights into optimizing R&D investments based on the desired financial performance outcomes. Remarkably, across all models, Acquired Intangible Assets (AIA) demonstrated substantial significance but negatively afected the financial performance of Slovak ICT enterprises. This indicates the need for more expeditious integration of these long-term intangible assets into the operational fabric of the companies.
Furthermore, the research underscores the need for a comprehensive system to plan the assimilation of intangible assets, tailoring them to the company’s exigencies as a core component of its intangible investment strategy. The analysis of control variables, including Leverage, Size, and Dummy variable for ICT sub-sectors, demonstrated selective impact on financial performance indicators. Notably, only the variable l_SIZE exhibited a significant influence on Net Profit Margin (NPM) and Assets Turnover (ATO), with implications for managerial considerations in optimizing these metrics. The variable DVICTSS exhibited limited significance on NPM, while LEV afected Return on Equity (ROE). These findings underscore the nuanced and selective nature of control variables’ influence on various financial performance indicators, with no discernible efect on Return on Assets (ROA).
In the broader context, this study serves to illuminate the intricate relationships between intangible assets and financial performance in the ICT sector. The insights garnered herein have the potential to inform prudent decision-making within the domain of intangible asset investments and their subsequent impact on financial performance for Slovak ICT companies. Furthermore, this research ofers valuable insights for policy and strategic adjustments aimed at fostering the incorporation of long-term intangible assets into business operations, thus enhancing financial performance. As the ICT sector continues to evolve and shape the Slovak economy, these findings can serve as a compass guiding efective strategies for leveraging intangible assets in this dynamic landscape.
As the global business environment continually evolves, future research endeavors could delve deeper into the interplay between various intangible asset categories and financial performance across diferent industries and geographical contexts. Such investigations would not only enrich the existing body of knowledge but also provide actionable insights for enterprises seeking to optimize their financial performance through targeted intangible asset investments.
This article is an output of the project of the Scientific Grant Agency of the Ministry of Culture of the Slovak Republic and Slovak Academy of Sciences (VEGA) no. 1/0517/20 (2020–2022) “Virtual Cryptochains as a Relevant Tool to Eliminate Economic Crime”