FACTA UNIVERSITATIS
Series: Economics and Organization Vol. 18, No 4, Special Issue, 2021, pp. 383 - 396

https://doi.org/10.22190/FUEO210617027R

Original Scientific Paper

INTANGIBLE ASSETS IMPACT ON SUSTAINABLE GROWTH

RATE OF ENTERPRISES IN THE REPUBLIC OF SERBIA1

UDC 330:004.7(497.11)

Amer Rastić1*, Tatjana Stevanović2, Ljilja Antić2

1 Business College of Applied Studies” Prof. PhD Radomir Bojković”, Kruševac, Serbia
2 University of Niš, Faculty of Economics, Niš, Serbia

ORCID iD: Amer Rastić N/A
Tatjana Stevanović https://orcid.org/0000-0003-1270-7129

Ljilja Antić https://orcid.org/0000-0001-8796-4619

Abstract. The digital economy unites a dual typology of resources in enterprises, which

can be tangible and intangible. In the language of accounting, it is about tangible and

intangible assets. Due to the involvement of digital technologies in companies, intangible

assets or intellectual capital became prominent. The sustainable growth of companies in

Serbia has great importance for both management and external stakeholders. The

presented paper examines the impact of intangible assets, formatted with the VAIC model,

on the Sustainable growth rate (SGR) of companies in Serbia. The selected list of

companies refers to the most profitable industry sector of the Serbian economy, assessed

according to the Serbian Business Registers Agency for 2018. In order to confirm the

hypothesis, the synthesis method, the analysis method, and the correlation analysis

method were used. There was a significant positive impact of intangible assets on the

sustainable growth rate of enterprises and a negative impact of physical assets, which,

however, is not statistically significant. Since no research has been recorded in our

country that sheds light on the correspondence between intangible assets and SGR, the

study has a strong practical significance for this purpose. These results represent at the

same time a reference point for our economy and for future entrepreneurs on the way to

intensive involvement of intangible assets in companies.

Key words: intangible assets, digital economy, sustainable competitive advantage,

sustainable growth rate

JEL Classification: Q56

Received June 17, 2021 / Revised September 27 / Accepted November 21, 2021

Corresponding author: Amer Rastić

* PhD student at University of Niš, Faculty of Economics, Serbia
Business College of Applied Studies „Prof. PhD Radomir Bojković“, Topličina 12, 37000 Kruševac, Serbia

| E-mail: amerstudent@hotmail.com

https://orcid.org/0000-0003-1270-7129
https://orcid.org/0000-0001-8796-4619
mailto:amerstudent@hotmail.com

384 A. RASTIĆ, T. STEVANOVIĆ, LJ. ANTIĆ

1. INTRODUCTION

The role and importance of intangible assets strongly correspond to the emergence of

the digital economy or Industry 4.0. In other words, Industry 4.0 is determined by the

process of digitization and the connection of digital and physical objects (so-called cyber-

physical systems). It was first promoted by the German government in 2012 (germ.

Industrie 4.0) as a progressive step in the digitalization of German society (see more

Cagle et al., 2020, p. 106; Pozdnyakova et al., 2019, p. 12, 17; Sukhodolov, 2019, pp. 3-

10). Industry 4.0 is characterized by extensive automation, robotization in the production

and service sphere, increasing workforce efficiency and efficiency in companies in general,

reducing the anthropological impact on the environment through the use of economical

technology, and increasing demand for high intellectually capable workforce, especially in

information technology (Prokofyev et al., 2019, p. 95).

Current technology of the digital economy refers to Big Data, Blockchain, Cloud

technology, Internet of Things (IoT) or a network of various devices where data is an object of

exchange, and artificial intelligence (AI), which refers to computer systems capable of

performing tasks that require human intelligence. In addition, the digital economy involves

Virtual Reality or computer-simulated environment technology and Ubiquitous Computing

technology. Ubiquitous computing is defined as connected computer systems in the

environment in which we live and work, such as devices in smart houses (Popkova &

Haabazoka, 2019, p. 8). However, employees still represent the main carriers of economic

activities, whose knowledge converges towards digital knowledge (Popkova & Haabazoka,

2019, p. 7).

This makes it clear that knowledge and information represent the starting point for

creating the resources and values of today's economy. Accordingly, the assets of business

entities are increasingly knowledge-intensive (Ghosh & Mondal, 2009, p. 369). In other

words, in the digital knowledge economy, economic value is mainly derived from intangible

assets, to a much greater extent than from physical assets (Chu et al., 2011, p. 249). This is

particularly attributable to the European economic context (Sardo & Serrasqueiro, 2017, p.

771). It is, according to Stewart, “something that cannot be touched, yet slowly makes you

rich” or, according to Sullivan, “knowledge that can be converted into profit” (Ghosh &

Mondal, 2009, p. 370).

The competitiveness of the company is therefore established in the patterns of intangible

assets exploitation. Sustainable competitive advantage is largely determined by a company's

sustainable growth rate (SGR). SGR can be also associated with economic, environmental,

and social initiatives to secure the future (Xu et al., 2020, p. 2).

The work is organized as follows. After the introduction, a review of the literature was

presented, followed by a theoretical explanation of the relationship between intangible assets

and sustainable enterprise growth rates, and research hypotheses were proposed. The next part

is dedicated to the empirical analysis of the data, followed by a discussion of the obtained

results. The final part of the paper includes concluding remarks.

Intangible Assets Imapct on Sustainable Growth Rate of Enterprises in the Republic of Serbia 385

2. LITERATURE REVIEW

2.1. Intangible assets

Knowledge resources have rapidly become important in many disciplines such as

accounting, economics, and strategic management (Asiaei, Jusoh & Bontis, 2018, p. 294). The

literature noted relatively early texts on the importance of intangible assets. Swedish

economist Westerman (1768) points out that the Swedish transport industry at that time

lagged behind the main competitors due to lack of professional knowledge (Serenko & Bontis,

2013, p. 478).

Clear guidelines for the development of intangible assets were established by Penrose as

the founder of resource-based theory in 1959 (although the name resource-based theory is

mentioned in 1984 in Wernerfelt's work, "Resource Based View of the Firm") in her work

“The Growth of the Firm”. Instead of perceiving companies as administrative units, Penrose

described the company as a resource base made available to managers. Hence, it was

concluded that competitive advantage is provided by ownership of certain key resources that

are rare (Pike, Fernström & Roos, 2005, p. 490).

Increased corporate investments in intangible assets include, in addition to investments

in pure forms of intangible assets, the intangible enrichment of the value of manufactured

products and provided services (Mehta & Madhani, 2008, p. 11). According to the

methodology of resource-based theory, intangible assets are viewed as equal to physical and

financial assets (Gupta & Raman, 2020, p. 51). For creating value that is a consequence of

investments in intangible assets, and in order to achieve a competitive advantage of the

company, extraction of the given value is also required. By extraction, or extraction of the

value of intangible assets, is meant primarily its conversion into monetary value.

Thus, achieving a competitive advantage in the digital economy has been redefined by the

impact of digital technology and market globalization. In the new circumstances, there is a

vertical disintegration, accentuation of innovations, and intensive use of informatics

technologies. In other words, this process has produced the accumulation of intangible assets

reflected in innovations, employees, and organization (Ciprian et al., 2012, p. 683). Finally, it

is pointed out that intangible assets represent the most potent position of assets that affect

value creation (Đuričin & Janošević, 2009, p. 10).

In the context of creating and using knowledge, companies in order to achieve a

competitive advantage, focus on the following areas (adapted to Novićević, Antić &

Stevanović, 2006, p. 9):

i. Computer and communication technologies (AI, Big Data, IoT, Blockchain, Cloud

technology, virtual reality, versatile computing and 3D printing),

ii. Analytical methods (which involve intelligent analytical softwares).

Intangible assets, or in management terminology ”intellectual capital”, are in the literature,

albeit unofficially, divided into three parts: human capital, structural capital, and relational

capital (Cabrilo & Dahms, 2018, pp. 621–648; Wang et al., 2016 pp. 1861–1885). Gupta and

Raman (2020) emphasize that the term “intangible assets” is attributable to accounting, while

the term “intellectual capital” is present in the science of human resources management. The

term “knowledge resources” exists between economists in general (Gupta & Raman, 2020, p.

49). The separation of terms according to the field of study has been respected in academic

texts (Naidenova & Parshakov, 2013, p. 640).

According to Bontis et. al., human capital is manifested as an individual stock of

knowledge in an organization that results from employees (Bontis et al., 2000., p. 87). Also,

386 A. RASTIĆ, T. STEVANOVIĆ, LJ. ANTIĆ

Bontis et. al., indicate that human capital is the primary component of intangible assets as a

value creator. The management of this capital is attractive in the sense of its conversion into a

sustainable competitive advantage through increased business performance (Bontis & Fitz-

enz, 2002, pp. 225, 227). We also notice that these assets are profiled in economics textbooks

as “Key competencies” or as “Core competencies” as the main strategic determinant for

achieving a sustainable competitive advantage (Michalisin et al., 1997, p. 374; Novićević,

Antić & Sekulić, 2006, p. 41).

Capital that supports infrastructure for employees is interpreted as structural capital

(Chowdhury, Rana & Azim, 2019, p. 787). Structural capital refers to databases, software

platforms, algorithms, codes, organizational structure, documentation, and business

processes or "everything of knowledge that remains in the company, after the end of the

working day" (Bontis et al., 2000, p. 88). Relational capital refers to the company's relations

with consumers and suppliers, includes distribution channels, brand and everything that

creates and maintains the company's intangible assets by involving the company in

interaction with the external environment. It "includes knowledge materialized in all the

relationships that a company develops with suppliers, trade associations or the

government." (Bontis et al., 2000, pp. 88-89).

The question is, how to calculate intangible assets and how to quantify their impact?

Among many models that exist, the frequently cited model for calculating intangible assets

is the VAIC model (VAIC is an abbreviation for Value Added Intellectual Coefficient). The

model represents one of the most significant contributions in the valuation of intangible

assets (Gupta & Raman, 2020, p. 50).

The VAIC model is based on the fact that the exploitation of physical and intangible

assets creates added value (VA). VA implies the difference between output and input

values. The output value, represents the value of total income, and the input value includes

all costs except employee costs, which are treated as intangible assets of the enterprise

(Andriessen, 2004, p. 365).

Specifically, VA can be determined as the sum of operating profit, investments in

human resources, and depreciation costs (of fixed assets and intangible assets) (Dzenopoljac

et al., 2017, p. 888):

VA = Operating profit + Employee costs + Depreciation (1)

One of the weak points of the VAIC model is the condition that the company that is

involved in the calculation needs to have a positive profit. If there are losses, according to the

VAIC model it means that the company doesn’t create new added value (for more see Chu et

al., 2011, p. 252-253).

Capital employed (CE) refers to the value of net assets and includes physical and financial

capital, or in other words, tangible capital. CE is used to start and maintain a business.

Tangible assets in this sense play a fundamental role in determining the value of a company

(Dzenopoljac et al., 2017, p. 888). Capital Employed Efficiency (CEE) is calculated as the

ratio of balance sheet net assets and value-added VA (Dzenopoljac et al., 2017, p. 888):

CEE
VA

= (2)

HCE (Human Capital Efficiency) is calculated as ratio between VA and investments

in human resources (employee costs) (Dzenopoljac et al., 2017., p. 888):

Intangible Assets Imapct on Sustainable Growth Rate of Enterprises in the Republic of Serbia 387

HCE
HC

= (3)

Structural Capital Efficiency (SCE) is calculated as ratio between SC and VA. For the

calculation of SC, the value of HC is subtracted from VA (Dzenopoljac et al., 2017, p. 889):

SCE
VA

= (4)

SC = VA – HC (5)

Dzenopoljac et. al. (2017) state that it is not difficult to notice that the sum of HCE

and SCE represents the total efficiency of intangible assets (ICE, Intellectual Capital

Efficiency). The rationalization of the model is based on the assumption that companies

with a higher ICE ratio exploit intangible assets more efficiently and, consequently, have

a higher amount of intangible assets (Dzenopoljac et al., 2017., p. 889).

2.2. Sustainable growth rate of the company

The sustainable growth rate of the company is a very important business and financial

performance of the company, especially in situations of economic imbalance. Otherwise,

SGR refers to the maximum and consistent growth rate that a company can achieve without

mobilizing additional funds in the form of borrowing. Growth below a sustainable growth

rate can affect the loss of a company's competitive advantage due to reduced business

efficiency. Growth above a sustainable growth rate involves additional borrowing by the

company, which can worsen its financial health (Stanić, 2015, p. 118). In other words, this

represents a short-term expansion of sales growth because such a goal is ultimately

unsustainable. Accelerated growth overloads corporate resources and requires new

borrowing in order to prevent corporate insolvency (Xu et al., 2021).

An increase in debt while maintaining the same level of insolvency can only be

implemented if the increase in the percentage of debt in total sources is equivalent to the

increase in the percentage of capital. The growth rate is therefore a complex long-term

indicator that belongs to the business and financial performance of the company. Any

growth that deviates from a sustainable growth rate can be considered unsustainable growth

(Xu et al., 2021).

The expression of SGR is clarified through several modalities, among which the most

famous is the first, Higgins model of SGR. In a more concise edition, according to Higgins

(1977), SGR is expressed as (Arora, Kumar & Verma, 2018).

SGR = ROE (Return on Equity) x b (Retention Rate) (6)

ROE indicator is an indicator with a long tradition and is calculated as:

Shareholders' equity

Net profit
ROE = (7)

388 A. RASTIĆ, T. STEVANOVIĆ, LJ. ANTIĆ

On the other hand, as we know, retention rate b indicates the number of funds remaining

for the company to reinvest in business activities after the payment of dividends. It is

calculated as:

Net profit payed dividends
b

Net profit

−
= (8)

2.3. Intangible assets and SGR

Studies have concluded that intangible assets are a key element in achieving competitive

advantage (Sardo & Serrasqueiro, 2017; Mention & Bontis, 2013; Zéghal & Maaloul,

2010). In other words, intangible assets significantly correspond to the business and financial

performance of the company. These studies, which involve researches of relationship between

intangible assets and business-financial performance, shed light on the impact of intangible

assets on short-term indicators of performance in companies. SGR, however, is an accounting

measure that covers a longer period of time and business-financial expansion of the company.

Consequently, for the realization of SGR and, ultimately, sustainable competitive advantage, it

is necessary for companies to create value by exploiting intangible assets.

A study by Xu, et. al. (2020) examined the impact of intangible assets on the sustainable

growth rate of agricultural smart high-tech and non-high-tech enterprises in China. The results

obtained suggest that human capital reflects the main impact on the SGR. In another study

(Xu et al., 2021), a sample based on Chinese companies in the field of tourism, agriculture,

and renewable energy industry was selected. The study concludes that physical and intangible

assets reflect a positive impact on the sustainable growth of the company. In the context of

intangible assets, the intensity of the positive impact is distributed primarily on human capital,

then structural and to a lesser extent relational capital.

A study that covers India’s evidence investigated the impact of intangible assets in

India’s enterprises on their sustainable growth rates. The results of the study indicate a

positive significant impact of all variables of intangible assets on a sustainable growth

rate. Intangible assets in this study are represented in a slightly modified edition.

Intangible assets in this study are constituted from physical capital, human capital,

relational capital, innovation capital, and process capital (Mukherjee & Sen, 2019).

A study from 2008, conducted in China, proved the positive significant impact of

intangible assets (intellectual capital) on SGR, where, according to results of this study,

“human capital is the root of the momentum of enterprise growth” (Shui-ying & Ying-yu,

2008).

The study, which covers Korea's evidence, also demonstrated the positive impact of

intangible assets, more specifically human and relational capital, on the sustainable growth of

manufacturing companies. The positive impact of physical assets on the sustainable growth of

these companies has also been proven (Xu & Wang, 2018). Investments in physical assets are

inseparable from investments in intangible assets. In other words, it is necessary to involve

physical assets in this consideration when we are trying to measure the impact of intangible

assets on sustainable growth.

Although SGR has not been the subject of such studies, according to previous studies

related to other business and financial performance, companies in Serbia are still insufficiently

exploiting intangible assets, materialized in innovation, employees and organization of the

company, to achieve a sustainable competitive advantage (Dženopoljac et. al., 2016).

Intangible Assets Imapct on Sustainable Growth Rate of Enterprises in the Republic of Serbia 389

2.4. Proposed hypotheses

According to the previous text, using the mathematical formats listed above, the impact of

intangible assets on SGR can be explained through the main and auxiliary hypotheses:

Hypothesis H1. There is a positive impact of intangible assets on SGR;

H1a. Companies with a higher ICE ratio have a higher rate of

sustainable growth SGR;

H1b. Companies with a higher CEE ratio have a higher rate of

sustainable growth SGR.

Or if we draw an overview of these relations (Figure 1):

Fig. 1 Overview of Hypothesis H1
Source: Authors own drawings

Finally, this research aims to find a valid conclusion about Hypothesis 1. Precisely, the

aim is to verify how much the intangible assets of the company, formatted by the VAIC

model, are an influential predictor of the sustainable growth rate, formatted by Higgins

(1977), of the company.

3. METHODOLOGY

3.1. Data source

To check the validity of Hypothesis 1, it is necessary to select the data source and

select a suitable sample. In obtaining a suitable sample of companies to test Hypothesis 1,

we were guided by the following prerequisites:

▪ sample needs to contain companies that reported significant net profit result during the
observed period, that is, the leaders are in their branch;

▪ companies in the sample are knowledge-intensive with a relatively high share of
balance sheet reported intangible assets, such as labor costs, development investments,

and research and development costs;

▪ the financial statements of the selected companies in the sample were previously
audited.

Considering these preconditions, the database for sample selection, which is published by

the Serbian Business Registers Agency (SBRA) in its annual publication for 2018, is suitable.

These publications are reports of the “100 most companies” (Serb. „Izveštaj o sto naj

privrednih društava“) which are issued for each year. At the time of making this research,

we were not able to download the publication for 2019, and we used a list of companies

in this publication published for 2018 including their financial statements for 2019. There

is a database in the form of a list that involves companies that have achieved the highest

390 A. RASTIĆ, T. STEVANOVIĆ, LJ. ANTIĆ

annual net profit for the period. With the elimination of companies that do not have

complete financial statements for the period, a sample of 67 companies that achieved the

highest net profit in 2018 is selected (Table 1).

Table 1 Selected list of companies

1. Naftna Industrija Srbije, NIS 23. Farmina Pet Foods 45. Delta Agrar

2. Telekom Srbija 24. Imlek 46. Apatinska Pivara

3. Telenor, Beograd 25. Koteks Viscofan 47. Luxury Tannery

4. Javno preduzeće Srbijagas 26. Frikom 48. Fabrika Hartije

5. Tigar Tyres 27. JP Elektroprivreda Srbije 49. Impol Seval

6. JKP Beogradske elektrane 28. CRH Srbija 50. JP Srbijašume

7. Coca-Cola 29. Agromarket 51. Milan Blagojević-Namenska

8. Philip Morris 30. Heineken Srbija 52. Pharmaswiss

9. SBB 31. Titan Cementara 53. Phuket

10. Hemofarm 32. Karin Komerc 54. Marbo

11. Matijević 33. Sport Vision 55. OMV

12. Real Knitting 34. Contitech Fluid 56. Atlantic Grand

13. Delhaize 35. Ball 57. Naftagas

14. JP Jugoimport 36. Almex 58. HD-Win

15. Elektromreža Srbije 37. Pink 59. Zdravlje

16. Tetra Pak 38. Peštan 60. Elixir

17. JP Pošta Srbije 39. Yugoroskaz 61. Galenika

18. Henkel Srbija 40. RZD International 62. Phiacademy

19. Lafarge 41. Direct Media 63. Forma Ideale

20. Mozzart 42. Metalfer 64. Knjaz Miloš

21. Bambi 43. Soko Štark 65. Sport Time Balkans

22. Messer Tehnogas 44. Drenik 66. Mladost

67. Auto Čačak

Source: Authors made a suitable list of companies according to SBRA – The Serbian Business Registers

Agency. (2020). STO NAJ... privrednih društava u 2018. godini [The top hundred enterprises in 2018]. Retrieved

from https://www.apr.gov.rs/upload/Portals/0/GFI%202019/STO_NAJ/STO_NAJ_2018_16102019.pdf

The sample includes financial statements (balance sheets and income statements) of

these companies for the period 2015-2019. We collect relevant financial statements for

this period manually inputting identification numbers or names of companies in the

SBRA (2020) search engine. Companies that didn’t publish financial statements for the

given period or realized negative operating profit were eliminated from the study.

3.2. Construction of the regression model

To test Hypothesis H1, it is necessary to construct a regression model. Constructing a

regression model requires the involvement of a dependent variable and independent

variables in a regression equation with a specific constellation of relationships between

variables. The dependent variable refers to the SGR. The independent variables are

formatted with the VAIC model and refer to ICE and CEE. The construction of the

regression model according to the equation looks like:

SGRi, t = β0 + β1ICEi, t + β2CEE i, t + ε i, t (9)

https://www.apr.gov.rs/upload/Portals/0/GFI%202019/STO_NAJ/STO_NAJ_2018_16102019.pdf

Intangible Assets Imapct on Sustainable Growth Rate of Enterprises in the Republic of Serbia 391

More precisely, using the technique of multiple standard regression analysis based on

specified regression model, it is possible to determine (adapted according to Pallant,

2009, p. 147):

▪ how well the variables ICE and CEE can predict the outcome of the SGR in the
sample;

▪ which variable (ICE or CEE) best predicts the SGR in the sample;
▪ after eliminating the impact of other variables, how much particularly, the selected

intangible asset, can predict an outcome of the SGR enterprise.

3.3. Research results

3.3.1. Descriptive statistics and correlation analysis

According to Table 2, the average SGR value is 16.74. When we talk about intangible

assets efficiency coefficient (ICE), it is 4.24. However, this is higher than the efficiency

coefficient of physical assets (CEE), which is 0.84. This actually mildly shapes the initial

impression in our analysis that intangible assets have a stronger impact on VA creation.

Table 2 Descriptive Statistics

Mean Std. Deviation N

SGR 16.742801 62.6553640 335

ICE 4.2374 2.34498 335

CEE .844540 1.5264232 335

Source: Authors own calculations

As an integral element of the preliminary analysis, Table 3. checks the normality of

the distribution for the given variables. The values of the variables were found not to be

normally distributed. Further, this will shape our next analysis.

Table 3 Normality test results

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

SGR .307 335 .000 .344 335 .000

ICE .159 335 .000 .766 335 .000

CEE .305 335 .000 .384 335 .000

a. Lilliefors Significance Correction

Source: Authors own calculations

Correlation is a more suggestive technique, in that way, it doesn’t give definitive answers.

It suggests the existence of a possible relationship between variables (Barrow, M. 2009, p.

231). Since the values of the variables are not normally distributed, the correlation analysis

was performed based on the Spearman coefficient (rs).

Table 4 contains correlation findings between presented variables in the regression model

and reports the following:

392 A. RASTIĆ, T. STEVANOVIĆ, LJ. ANTIĆ

▪ A weak positive relationship between the ICE coefficient and the dependent
variable SGR was identified, where rs = 0.122 (p <0.05). In other words, this is

the first indication that a higher ICE coefficient also means a higher SGR;

▪ A medium-strong positive relationship between CEE and SGR was identified, rs =
0.432 (p <0.05). This also indicates that the higher CEE coefficient also means a

higher SGR.

Table 4 Normality test results

Correlations

SGR ICE CEE

Spearman's rho SGR Correlation Coefficient 1.000 .122* .432**

Sig. (2-tailed) . .025 .000

N 335 335 335

ICE Correlation Coefficient .122* 1.000 -.034

Sig. (2-tailed) .025 . .533

N 335 335 335

CEE Correlation Coefficient .432** -.034 1.000

Sig. (2-tailed) .000 .533 .

N 335 335 335
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).

Source: Authors own calculations

3.3.2. Analysis of the regression model

The results of the regression analysis according to Table 5. indicate that the model

significantly affects the variability of the variable SGR. According to the amount of the

adjusted coefficient of determination (Adjusted R2) is 2.9%, this model explains 2.9% of the

variability of SGR. In other words, regression model with the coefficients of intangible assets

efficiency (ICE) and invested capital efficiency (CEE) explains 2.9 % of changes in SGR.

Table 5 Explanatory power of the model

Model Summaryb

Model R R Square
Adjusted R

Square
Std. Error of
the Estimate

Change Statistics

R Square
Change F Change df1 df2

Sig. F
Change

1 .187a .035 .029 61.7301839 .035 6.043 2 332 .003

a. Predictors: (Constant), ICE, CEE

b. Dependent Variable: SGR
Source: Authors own calculations

The explanatory power (2.9%) is not high. However, the model is statistically significant

for p <0.05 (Table 6).

Intangible Assets Imapct on Sustainable Growth Rate of Enterprises in the Republic of Serbia 393

Table 6 Statistical significance of the model

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 46057.627 2 23028.814 6.043 .003b

Residual 1265124.382 332 3810.616

Total 1311182.010 334

a. Dependent Variable: SGR

b. Predictors: (Constant), ICE, CEE

Source: Authors own calculations

The next typical step in interpreting the results of regression analysis is to interpret

whether the independent variables (ICE, CEE) in the model make an individual and

isolated contribution to the change in SGR (Table 7).

Table 7 Individual contribution of independent variables (ICE, CEE) to SGR in the model
Coefficientsa

Model

Unstandardized

Coefficients

Standar
dized

Coeffic

ients

t Sig.

95.0%

Confidence

Interval for B Correlations

Collinearity

Statistics

Std.

Error Beta

Lower

Bound

Upper

Bound

Zero-

order Partial Part

Toleran

ce VIF

1 (Constant) .046 7.231 .006 .995 -14.177 14.270

ICE 4.563 1.440 .171 3.168 .002 1.729 7.396 .171 .171 .171 1.000 1.000

CEE -3.123 2.213 -.076 -1.411 .159 -7.476 1.230 -.077 -.077 -.076 1.000 1.000

a. Dependent Variable: SGR

Source: Authors own calculations

Standardized beta coefficients for independent variables indicate their individual and

isolated contribution to the dependent variable. In the given model, the standardized beta

coefficient for ICE is β1 = 0.171. In other words, intangible assets compressed in the ICE

reflect a significant positive impact on the SGR variable. The Hypothesis 1a is confirmed. In

other words, a higher ICE also means a higher SGR.

The impact of CEE on SGR is negative, but not statistically significant. Hypothesis 1b is

not confirmed. In other words, a higher CEE does not necessarily mean a higher SGR, but

lower SGR. Additionally, this relationship is negative, but this is not confirmed with statistical

significance.

Finally, we can state that Hypothesis H1 is partially confirmed, because ICE reflects the

positive impact on SGR and CEE does not reflect a positive statistically significant impact on

SGR.

3.3.3. Useful implications of research results

The results unequivocally indicate a statistically significant positive relationship between

the efficiency of intangible assets usage (ICE) and the sustainable growth rate SGR of

companies. These findings are consistent with research that Xu et al. (2021) conducted.

However, the impact of CEE on SGR is negative and not statistically significant, which is not

consistent with research that Xu et al. (2021) and Xu & Wang, 2018) conducted because they

394 A. RASTIĆ, T. STEVANOVIĆ, LJ. ANTIĆ

proved positive impact of CEE on SGR. The usefulness of these conclusions can be converted

into instructions for business entities.
Management structures in companies are advised to be more aware of intangible assets

and increase their investments, especially in human and structural capital (ICE). As Xu et. al.
(2021, p. 11) stated, the sustainable growth of modern enterprises should rely more on
intellectual capital than on capital employed. Specifically to each company, managers should
strengthen the logic of creating intangible assets like developing more employee supportive
corporate culture and promoting R&D activities to build innovations. Also, managers need to
incorporate information technology through different initiatives. On the other side, because
capital employed is synergistically connected with intellectual capital, managers should also
improve the efficiency of CEE in order to make an additional positive impact on SGR.
Additionally, managers should reduce the scale of liabilities in companies. To cover these
processes, developed management accounting infrastructure is necessary for the assessment of
the intangible assets exploitation efficiency. In this way, management is further referred for
corrective actions in order to optimize these processes related to intangible assets.

3.3.4. Limitations of the conducted research

Despite the best intention to proceed the research in the absence of certain limitations,
the obtained results are acceptable having in mind certain limitations. The limitations,
however, do not undermine the essentials to which the results of the analysis refer. The
first limitation relates to sample size. We believe that with a larger sample in the analysis,
results will more strongly emphasize found links between ICE, CEE, and SGR. Second, the
VAIC model has its limitations, which are also involved in the given research. VAIC model
doesn’t cover relational capital, also, the VAIC model doesn’t include R&D costs within
structural capital (for more see Chen, Cheng & Hwang, 2005, p. 162). Third, intangible assets
can be hardly represented by a simple sum of components due to their synergistic nature.

4. CONCLUSION

Intangible assets strongly correspond to the digital economy and Industry 4.0. and
with the development of AI, Big Data, Cloud computing, Virtual Reality, etc. Thus,
knowledge becomes the main source of value creation in companies. The sustainable
competitive advantage of an enterprise is established on the patterns of intangible assets
exploitation rather than on the exploitation of physical assets. Sustainable competitive
advantage is closely related to the sustainable growth rate of the company. The sustainable
growth rate of a company is also associated with economic, environmental, and social
initiatives in securing the future.

In this paper, research was conducted which sheds light on the impact of intangible
assets of 67 most profitable companies in Serbia on their sustainable growth rate. Results
involves significant positive impact of intangible assets on the sustainable growth rate of
the companies. The intangible assets impact is predominant in relation to the impact that
reflects physical assets on the sustainable growth rate of the observed companies. This
identification represents a contribution in relation to previous research conducted in
relation to intangible assets. Also, it represents an incentive for managers of companies in
Serbia to focus more intensively on intangible assets creation and exploitation. This is
especially due to the evidence in this research that intangible assets provide a better
sustainable growth rate for companies.

Intangible Assets Imapct on Sustainable Growth Rate of Enterprises in the Republic of Serbia 395

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UTICAJ NEMATERIJALNE AKTIVE NA ODRŽIVU STOPU

RASTA PREDUZEĆA U REPUBLICI SRBIJI

Digitalna ekonomija objedinjuje dvojaku tipologiju resursa u preduzećima, koji mogu biti

materijalni i nematerijalni. Jezikom računovodstva, reč je o materijalnoj i nematerijalnoj aktivi.

Usled involviranja digitalnih tehnologija u preduzećima do izražaja dolazi nematerijalna aktiva ili

intelektualni kapital. Održivi rast preduzeća u Srbiji je od izuzetnog značaja kako za menadžment,

tako i za eksterne interesente. U predstavljenom radu se ispituje uticaj nematerijalne aktive,

formatirane VAIC modelom, na održivu stopu rasta (eng. Sustainable growth rate, u daljem tekstu

SGR) preduzeća u Srbiji. Odabrana lista preduzeća odnosi se na najprofitabilniji sektor naše

privrede ocenjen prema Agenciji za privredne registre za 2018. godinu. U cilju potvrđivanja

hipoteza, sintetički metod, metod analize i metod korelacije je upotrebljen. Dokazan je značajan

pozitivan uticaj nematerijalne aktive na održivu stopu rasta preduzeća i negativan uticaj fizičke

aktive, koji međutim nije statistički značajan. Budući da u našoj zemlji nije zabeleženo istraživanje

koje rasvetljava korespondiranje nematerijalne aktive i SGR, studija u ovu svrhu ima snažan

praktični značaj. Navedeni rezultati predstavljaju ujedno i orijentacionu tačku našoj privredi i

budućim preduzetnicima na putu ka intenzivnom involviranju nematerijalne aktive u preduzećima.

Ključne reči: nematerijalna aktiva, digitalna ekonomija, održiva konkurentska prednost, održiva

stopa rasta

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