Improving Profitability Measurement: Impact of Intellectual Capital Efficiency on Return on Total Employed Resources in Smart and Knowledge-Intensive Companies

Abstract

This paper aims to emphasize the quantification of intellectual capital, not disclosed on the assets side of the balance sheet in the smart and knowledge-based enterprise, because it is very important for the more precise quantification of the profitability ratio, such as the return on assets (ROA). For this purpose, the paper suggests the EIC (efficiency of intellectual capital) methodology. It points out the necessity for the new profitability formula, gives methodological solutions for it, and investigates the impacts of intellectual capital (IC) efficiency indicators on traditional and new formulas of profitability in the case of knowledge-intensive and smart companies. The research confirms the importance of improving the profitability measurement in the knowledge economy era, where exists the dominance of intangible assets. It emphasizes the need for the correction of the denominator of the traditional ROA indicator. The comprehensive measurement of the total intellectual capital, especially its non-disclosed component in the balance sheet, provides information for more precise and accurate profitability measurements. The paper points out the issue of improving the traditional financial ratio, such as the ROA. This can be achieved by incorporating the value of intellectual resources, which are undisclosed in the balance sheet, in its denominator. This solution results in creating a new profitability indicator—return on total employed resources (EOR). This EOR indicator is more successful in capturing the enterprise’s intellectual performance compared with traditional profitability ROA indicators. This fact leads to the conclusion that EOR is better profitability indicator especially for smart and knowledge-intensive companies.

1. Introduction

In the modern economy, firms and national economies are focused on achieving sustainable growth and development, through improved relationships among key stakeholders, increased returns on employed assets, and higher value for shareholders [1]. In this environment, where the only certain thing is uncertainty, a key source of a firm’s sustainable competitive advantage is intellectual capital [1].

In order to achieve competitiveness, it is important for the company to focus on intellectual resources. These resources have been addressed in the literature through the resource-based, dynamic capabilities and knowledge-based theory at the end of the 20th century. These theories potentiate the importance of efficient usage of intellectual resources for improving business performance [2]. This means that the company is focused on the development of employee competencies, databases, innovation-supporting culture, and valuable relationships with stakeholders. These resources are difficult to copy, which allows the company to maintain an advantage in the market. In this way, barriers to competition are created. Intellectual capital is becoming a key value creator in the company and a source of competitiveness of the greatest importance in the new era—the era of knowledge and a smart economy.

Intellectual capital (IC) is a multidimensional category that represents all the intangible resources, both disclosed and undisclosed in the balance sheet. However, these undisclosed resources also create economic value and significantly affect the profitability and competitiveness level of smart enterprises. These hidden resources are not visible on the assets side of the balance sheet (unlike fixed assets, current assets, intangible assets, and goodwill).

IC can be generated internally but also bought at the market. If the company buys intangible assets, it is shown as an expense in the annual reports and not only as the cost of acquisition of those assets but also as the cost of preparation for the use of those properties. IC that is being developed within the smart organization requires investment in the human resources of an organization and other intellectual property [3] (p. 5).

Understanding the progression of intangible assets, their investments, and the advantages they yield can be challenging to grasp. However, when it comes to selecting valuation methods for intangible assets associated with a company’s expertise, it is thought that this financial insight could lead to gains for the organization while also streamlining its management. Therefore, traditional accounting-based valuation methods are an important starting point for overcoming the problem of disclosing all elements of IC in the assets side of the balance sheet [3].

According to the IAS 38 Intangible Assets [4], all elements of IC are not recognized and disclosed in the balance sheet. This emphasizes the necessity of improving the existing IC measurement methodologies and approaches. IC measurement controls the economic rationale for using intellectual resources, especially in smart and knowledge enterprises, abundant with a high portion of IC in their book or market value.

Effective IC management requires the permanent control of the efficient use of various types of intellectual resources. This is relevant for disclosed and undisclosed intellectual items in the balance sheet due to the limitations of financial reporting [5]. Numerous scholars have focused their attention on the issue of IC measurement in the past. Their efforts resulted in many suggested methodologies for evaluating the overall IC but also for value assessment and the measurement of IC segments.

The EIC conceptual framework [6,7] of IC efficiency points out the relevance of measuring the value of one part of the IC that is not visible on the balance sheet. This invisible and undisclosed component of the overall IC should be included in calculating the total efficiency of both visible and invisible resources. The EIC methodology uses publicly available information, which is presented in the financial accounting statements and supplemented with information from the stock exchange. The advantages of EIC methodology reflect in both the logical consistency and the simple quantification of different efficiency indicators. The EIC framework connects financial accounting evaluation and market evaluation in smart, digitalized enterprises.

2. Theoretical Aspects

2.1. Definition of IC

IC represents “the difference between firm’s market value and the cost of replacing its assets” [8]. Hall [9] treats IC as a portfolio of intangible elements or items that are the drivers of economic value in smart enterprises in modern business conditions. The knowledge management and IC literature point to a certain difference in the structure of IC components. Agostini and Nosella [10] differentiate in their model three segments of IC—human, structural, and relational capital.

Human capital (Hc) in an enterprise consists of the human potential of its managers and other employees. Han, Lin, and Chen [11] define human capital as the core resource that incorporates not only competence, knowledge, and skills but also experience, attitude, employee commitment, and individual attributes, which create profit and productivity for the company. Human resources are the basis for the development and growth of structural capital as a part of the IC. Sundara Pandian [12] indicates that structural capital (Sc) consists of processes, organizational culture, information systems, management philosophy, and networking systems. Relational capital (Rc) is capital contained in relationships with key external stakeholders, such as customers, suppliers, and the community. Also, it includes brands, corporate images or reputations, and exclusive contracts (licensing, franchising, etc.).

2.2. IC Valuation of Companies—EIC Methodology Approach

Total intellectual capital (IC), comprising undisclosed IC (ΔIC) and disclosed IC (Iag), is the sum of [6,7]

IC = Iag + ΔIC

(1)

and

IC = Iag + Hc + SRc

(2)

where Iag is intangible assets and goodwill, Hc is human capital, and SRc is structural and relational capital.

IC is disclosed in the balance sheet under the non-current assets as the sum of intangible assets and goodwill (Iag). Visible IC in the assets side of the balance sheet is presented according to the UK and Ireland GAAP [13] or IFRS [14] in

Table 1. Alternative usage of IFRS or the UK and Ireland GAAP for evaluating IC that is disclosed in the balance sheet of entities.

Entities	Regulations for the Evaluation of Visible IC
Micro-entities	FRS 105 (a) Section 13 Intangible Assets Other than Goodwill (b) Section 14 Business Combinations and Goodwill (c) Section 22 Impairment of Assets
Small entities	IFRS for SMEs	Section 1A Small Entities of FRS 102
Publicly listed companies and companies at the national level	IAS 38 Intangible Assets IFRS 3 Business Combination IAS 36 Impairment of Assets	FRS 102 The Financial Reporting Standard applicable in the UK and the Republic of Ireland Section 18 Intangible Assets other than Goodwill Section 19 Business Combinations and Goodwill Section 27 Impairment of Assets Reduced disclosures for subsidiaries and ultimate parents (Paragraphs 1.8 to 1.13 of FRS 102)

Source: authors’ presentation based on [13,14,15,16,17,18,19,20].

.

Since 2005, the UK’s publicly listed companies have been obliged to create consolidated financial statements in line with IFRS [14], an obligation that remains to this day. Almost all other companies had the choice of following IFRS [14] or the UK and Ireland GAAP [13]. If they are small entities, as defined by the Companies Act 2006 [21], they have the additional option of following IFRS for SMEs. However, since 2015, the UK and Ireland GAAP has been replaced by a new framework comprising several standards [13]: FRS 100 [22], FRS 101 [23], FRS 102 [18], and FRS 105 [19]. FRS 101 [23] and FRS 102 [18] are based on IFRS but with some changes made possible by the Companies Act 2006 [21] and several other amendments.

There are several differences in the recognition of intangible assets between IFRS [14] and FRS 102 [18]:

• If R&D costs meet the recognition criteria under IFRS [14], they will be shown on the balance sheet, while according to FRS 102 [18], if the recognition criteria are met, there is a choice to recognize these costs in the income statement or in the balance sheet.
• According to IFRS [14], intangible assets can have an indefinite useful life, while according to FRS 102, the expected life of intangible assets should not exceed 10 years.
• Under the IFRS option, goodwill may or may not be amortized but is subject to an annual impairment review, while under FRS 102, it is amortized over the expected useful life [14,18].

IAS 38 Intangible Assets [4] defines the conditions when an intangible asset will be recognized in the balance sheet. On the balance sheet of an enterprise can be displayed only the goodwill recognized in a business combination. “Goodwill arising on acquisition is calculated as the difference between the fair value of the price paid for the subsidiary and the fair value of the net assets acquired” [24]. According to IFRS 3 [17], goodwill is calculated in the following way: consideration transferred + non-controlling interest + fair value of previous equity interests = net assets recognized.

The IAS 36 Impairment of Assets [20] recognizes the impairment cases of intangible assets and goodwill acquired in business combinations [7].

The difference in terminology between the UK GAAP and IAS/IFRS is given in

Table 2. The difference in terminology between the UK GAAP and IAS/IFRS.

FRS 102 Terminology	Company Law Terminology
Individual (financial statements)	Entity (financial statements)
Reporting date	Financial year and date
Consolidated (financial statements)	Group (accounts/financial statements)
Parent	Holding undertaking
EU-adopted IFRS	IAS/IFRS
Finance costs	Interest payable and similar charges expenses

Source: authors’ presentation based on [13,14,18].

.

Non-disclosed IC is calculated as a summation of the human (Hc) and structural and relational capital (SRc).

In the IC literature, the value of one part of the IC, non-disclosed in the balance sheet (marked as ΔIC), is calculated by ([7], pp. 725–726)

ΔIC = Mc − E

(3)

and

E = As − L − Nci

(4)

where Mc is the market capitalization, E is the net assets or equity attributable to the parent enterprise’s shareholders, As is the book value of the disclosed assets, L is the total current and non-current liabilities, and Nci is the non-controlling (minority) interests or outside stockholders’ interests in subsidiaries ([7], pp. 725–726).

Non-disclosed IC (marked as ΔIC) consists of human (Hc), structural (Sc), and relational capital (Rc) ([7], p. 726):

ΔIC = Hc + Sc + Rc

(5)

or

ΔIC = Hc + SRc

(6)

All kinds of intellectual resources, disclosed and undisclosed on the balance sheet, have to be continually improved, developed, and increased because a competitive advantage depends on their characteristics and performance [25]. Krstić and Rađenović [25] point out the key advantages of the EIC methodological framework over other methodologies for IC efficiency measurement, especially regarding combining information about IC from both market and financial reporting systems.

2.3. EIC Methodology for Intellectual Capital Efficiency Measurement

Nowadays, the market value of many smart, knowledge companies exceeds their actual book value. Hence, the market valuation is becoming more and more IC-based, increasing the cap between the market and book values of firms. This leads to the increased interest in finding solutions for the more efficient economic usage of IC. Sardo and Serrasqueiro [26] indicate that efficient use of IC improves the creativity, technological capacity, innovativeness, and growth possibilities of the smart company. A prerequisite for adequate management of IC is its accurate measurement.

Namely, the EIC methodology ([7], pp. 733–735) overcomes numerous limitations of the widely applied VAIC model [27,28,29,30]. It suggests the calculation of efficiency in the use of total intellectual capital (Eic).

In the aim of calculation of Eic indicator, EIC methodology ([7], pp. 733–735) suggests the following steps (

Table 3. EIC methodology—four key steps.

EIC Methodology Steps	Element/Indicator	Formulas
Step 1	ICVA	ICVA = EBIT + Dfa + Amia + Iml + Pe, or ICVA = EBITDA + Pe
Step 2	Iag Hc SRc	Iag is visible in a balance sheet Hc = Pe + Ib SRc = IC − (Iag + Hc)
Step 3	Eiag Ehc Esrc	Eiag = ICVA : Iag Ehc = ICVA : Hc Esrc = ICVA : SRc
Step 4	Eic	Eic = ICVA : IC

Note: Iag—intangible assets and goodwill; Hc—human capital; SRc—structural and relational capital; EBIT—earnings before interest and tax; D_fa—depreciation of fixed or non-current assets; Am_ia—amortization of intangible assets with identifiable useful life (copyrights and patents); Iml—impairment loss of intangible assets with indefinite useful lives (goodwill); Pe—personal expenses and other investments in human resources; Ib—incentives and bonuses for managers and other employees; EBITDA—earnings before interest and tax, depreciation, and amortization; ICVA—intellectual capital value added; Eiag—efficiency in the use of intangible assets and goodwill; Ehc—efficiency in the use of human capital; Esrc—efficiency in the use of structural and relational capital; Eic—efficiency in the use of total intellectual capital. Source: authors’ presentation based on [7] (pp. 733–735).

):

• Step 1: calculation of the intellectual capital value added (ICVA) as a special value of economic result which is created by IC ([7], p. 733);
• Step 2: calculation of the elements of total IC (Iag, Hc, SRc) and total IC ([6], p. 729).

SRc is a part of IC, which is not incorporated within the disclosed value of Iag because of the rigorous criteria in the FRS 102 (Section 18) [18] and IAS 38 [4].

Structural capital is quite difficult to quantify, measure, and valorize in many enterprises, bearing in mind all the elements of this category, such as organizational structure and culture, top management philosophy, information systems, etc. ([7], p. 735). As well as for the other kinds of capital, it is also important for structural capital to provide its efficient usage for creating ICVA.

Relational capital is very specific in terms of precisely measuring its financial value, especially due to its content as it encompasses the shareholders’ relations, corporate image, and reputation ([7], p. 735). Relational capital can also contribute to ICVA creation.

If we emphasize the problem of valuing both structural and relational capital ([7], p. 735), we can calculate their values by the formula (SRc) given in Table 3.

• Step 3: calculation of the partial efficiency indicators ([7], p. 734): Eiag, Ehc, and Esrc;
• Step 4: calculation of total intellectual capital efficiency (Eic) ([7], p. 735).

ROA indicators are widely used in practice despite certain limitations. By their essence, they are based on the accounting concept of result—accounting profit. First of all, it is necessary to point out certain advantages that are of crucial importance for their application in practice. ROA indicators are treated as a summary and surrogate criteria of business success for several reasons. The accounting profit (earning) as an element of the ROA measure can be calculated periodically, that is, in very short periods of time (monthly, quarterly, yearly). Bearing this in mind, their short-term character and great importance in controlling the ongoing business activities of managers are highlighted. This makes it possible to prevent irresponsible and irrational attitudes of managers of smart companies.

However, certain limitations can be attributed to ROA indicators, which are reflected in the managerial decisions that are made based on them. Those limitations are in relation to their elements—profits (earnings) and total assets. In addition, although there is a positive correlation between the profit and the price of shares on the stock exchange, it must be emphasized that it is still weakly expressed. That is why it is stated that annual accounting profit is an imperfect surrogate (substitute) for economic profit or economic value added (EVA) [31]. The economic profit or EVA, as well as the value of the company, changes, but it is reflected through the accounting profit later. The explanation of the reasons why the accounting profit (earning) does not adequately reflect the economic profit (EVA) is that the factors that influence the accounting profit are not simultaneously the factors that reflect the economic profit and vice versa. Also, it is negative that the accounting profit only takes into account the costs of using borrowed capital and ignores the costs of equity. Omitting the valuation of the costs of equity limits the comparison of the results of different companies that have different shares of debt and own capital in the structure of liabilities. In addition, the accounting profit and the profitability indicator (ROA) recalculated based on it are largely dependent on the accounting methods.

The EIC methodology for IC measurement is aimed to provide a more accurate efficiency measurement of the total assets recorded in the balance sheet. That is the well-known, traditional indicator (

Table 4. Traditional formulas for the calculation of ROA indicators.

ROA Indicators	Formulas
ROA1	ROA1 = EBIT: As
ROA2	ROA2 = EBITDA: As
ROA3	ROA3 = net income: As
ROA4	ROA4 = net income attributable to shareholders of parent enterprise: As

Source: authors’ presentation.

) called return on assets (ROA) ([32], pp. 284–287), ([23], p. 98), ([24], p. 47).

However, the value of the denominator of this financial ratio (As) does not incorporate the total value of IC. This is because of the problems of recognizing and reporting the values of all intangible assets in accordance with very rigorous criteria defined in the FRS 102 (Section 18) [18] and IAS 38 [4], which restrict the cases in which the costs of internally developed intangible assets can be capitalized on the enterprise’s balance sheet.

The importance and necessity for a change of the traditional measurement of ROA [33,34] in the 21st century are pointed out by the data on the changed relation between tangible and intangible assets in the balance sheet. “Considering that various intangible items are difficult to verify and that the management structure of a company could use them to manage or manipulate reported earnings” [35] confirms the limitation that comes from the EBIT as the numerator of the ROA ratio. It is a reason for incorporating the net income and the net income attributable to shareholders in ROA calculations.

In particular, considering many additional limitations of ROA and other so-called accounting profit performance indicators [31], we point out the need to improve the calculation of ROA. The new profitability ratio has an additional element—ΔIC (

Table 5. The additional element of new profitability ratio formulas.

Visible assets	I Current assets - Cash and cash equivalents - Accounts receivable - Inventories - Other current assets II Non-current assets - Long-term investments - Plants, buildings, equipment, and other - Intangible assets and goodwill (Iag) - Other non-current assets	As	⇾ It is used for traditional profitability formulasROA
Invisible intellectual assets	I Human assets II Relational assets III Structural assets	ΔIC	⇾ In addition to As, it is used for new profitability formulasEOR

Source: authors’ presentation.

).

The problem of accurate measurement of the ROA also arises due to the accounting (balance sheet) treatment of certain elements of the company’s intellectual (intangible) assets according to the IAS 38—intangible assets [4], which are not recorded in the balance sheet. That is why the importance of the ROA as a financial indicator decreases, especially for companies with a significant share of intellectual resources (intangible assets) in the assets of the balance sheet, so-called smart companies.

In order to adequately calculate the profitability, the value of assets invested in intellectual resources should be added to the value of total assets in the denominator of the profitability rate, i.e., intellectual property that is not shown in the assets of the balance sheet. In this way, a profitability indicator would be obtained that better measures the efficiency of the use of all “visible” (disclosed) and “invisible” (non-disclosed) assets ([6], p. 83).

The traditional ROA indicator can be replaced with another indicator that measures the efficiency of the use of all employed resources (EOR) (material and immaterial, visible and invisible on the assets side of the balance sheet). This new profitability indicator can be calculated according to the formulas presented in

Table 6. Efficiency in the use of all employed resources (EOR) indicators.

EOR Indicators	Formulas
EOR1	EOR1 = EBIT: (As + ΔIC)
EOR2	EOR2 = EBITDA: (As + ΔIC)
EOR3	EOR3 = net income: (As + ΔIC)
EOR4	EOR4 = Net income attributable to shareholders of parent enterprise: (As + ΔIC)

Source: authors’ presentation.

([32], p. 292):

3. Materials and Methods

The key aim of the empirical research in this study was to explore if the constructed new EOR profitability indicators were more successful in capturing the enterprise’s intellectual performance compared with traditional profitability ROA indicators. The specific aim of the paper was to investigate the impact of partial efficiency measures of intellectual capital components and an aggregate indicator of IC efficiency (according to EIC methodology) on the smart firm profitability ratios. As partial measures of intellectual capital efficiency, the following indicators were employed: Eiag, Ehc, and Esrc. For measuring the profitability performance, the return on assets (ROA) and a new indicator, the efficiency in the use of all employed resources (EOR), were employed. Figure 1 depicts the conceptual framework.

In order to fulfill this specific objective, the following hypothesis was investigated:

H1. 

The greater partial IC efficiency indicators (Eiag, Ehc, Esrc) increase ROA.

H1a. 

The greater partial IC efficiency indicators (Eiag, Ehc, Esrc) increase ROA1.

H1b. 

The greater partial IC efficiency indicators (Eiag, Ehc, Esrc) increase ROA2.

H1c. 

The greater partial IC efficiency indicators (Eiag, Ehc, Esrc) increase ROA3.

H1d. 

The greater partial IC efficiency indicators (Eiag, Ehc, Esrc) increase ROA4.

H2. 

The greater partial IC efficiency indicators (Eiag, Ehc, Esrc) increase EOR.

H2a. 

The greater partial IC efficiency indicators (Eiag, Ehc, Esrc) increase EOR1.

H2b. 

The greater partial IC efficiency indicators (Eiag, Ehc, Esrc) increase EOR 2.

H2c. 

The greater partial IC efficiency indicators (Eiag, Ehc, Esrc) increase EOR 3.

H2d. 

The greater partial IC efficiency indicators (Eiag, Ehc, Esrc) increase EOR 4.

The partial IC performance measures were based on the EIC model developed by Krstić [6] and first implemented by Krstić and Bonić [7] for measuring the IC efficiency of the Software AG company. This model, apart from the efficiency in the use of total intellectual capital, also enables the calculations of partial performance indicators, such as Eiag, Ehc, and Esrc.

Although it represents a model for measuring IC, its simple usage and transparency in determining specific components of intellectual capital, as well as its consistency in calculating the partial performance indicators of IC efficiency, give it an advantage over Pulić’s VAIC model [36], which has been implemented in many studies [37,38,39] but also criticized because of its premises [28].

The empirical research used secondary data to test the defined hypotheses. The secondary data were collected by accessing relevant data, documents, and reports (data from the financial statements of the analyzed companies) on the official websites of the Interbrand top 50 most valuable brands for the period 2012–2019. Data after 2019 were not included in the analysis due to the effect of the COVID-19 pandemic and the deterioration of the company’s performances (market capitalization, Iag, Hc, SRc, total intellectual capital, current assets, non-current assets, EBIT, EBITDA, net income, ROA, and EOR) on that basis, which could distort the picture of real business results under normal business conditions. Study [40] points out the effects of some organizational (business) performances of knowledge-intensive enterprises in the COVID-19 crisis.

Smart, knowledge-intensive, or IC-intensive companies adopt emerging new technologies in a way that delivers their business strategy and operational excellence. They use a combination of technology, data, and analytics to create a personalized experience for customers [41]. The sample consisted of the smart companies listed on the top 50 Interbrand list. One of the basic criteria for choosing a company was the ranking list for the entire analyzed period from 2012 to 2019. Applying this criterion, the sample was reduced to 36 smart companies (36 brands). Those smart and knowledge-intensive companies had a significant difference between their market and book values, which testified that they possessed a significant stock of invisible and intangible intellectual (human, structural, and relational) resources.

Namely, the sample included companies whose brands were on the list of the 50 most valuable brands according to the Interbrand methodology, where only companies whose corporate brands were on this list in all years of the analyzed eight-year period were taken into account. In this way, the initial sample of 50 companies was reduced to a total of 36 companies (36 brands that were ranked in the top 50 brands in the eight-year period). The value of the brand as a criterion for the selection of the company was chosen considering that the brand is a dominant part of the intangible assets of modern companies, which indicated that these were smart companies—companies that were rich in intellectual assets. All the necessary data for the calculation of different efficiency indicators according to EIC methodology were taken from financial statements and annual reports of companies in the sample. The sample was reduced because of the lack of some financial information in the annual reports of certain companies from the initially chosen sample of 50 companies.

For the validation of the proposed hypotheses, correlation and regression analyses were employed. Due to the panel data characteristics, the panel data models were analyzed. The methodology underpinning the research is depicted in Figure 2.

First, the descriptive statistics of data were presented to observe the average, minimum, and maximum values of the analyzed indicators.

Second, the data were tested for normality by employing the Shapiro–Wilk and Shapiro–Francia normality tests, where p values less than 0.05 indicated the non-normal data distribution.

Third, based on the results from these tests, the Pearson or Spearman correlation coefficients were calculated to determine the relationship among the variables. The Pearson correlation was used if data were normally distributed, while the Spearman was used in the case of non-normal data distribution. These coefficients took values from −1 to +1, and values close to ±1 indicated a strong relationship among variables. According to Gupta [42], a weak correlation existed if the correlation coefficient was less than 0.2, moderate if it was in the range of 0.2–0.5, and strong if it was above 0.5.

Fourth, in order to choose the appropriate regression model F-test, the Breusch–Pagan LM test and the Hausman test were performed. The acceptance of the null hypothesis in the F-test pointed to the appropriateness of the pooled regression model (Pooled), while the rejection of the null hypothesis indicated the appropriateness of the fixed effects model (FEM). The acceptance of the null hypothesis in the Breusch–Pagan LM test pointed to the appropriateness of the Pooled model, while the rejection of the null hypothesis indicated the appropriateness of the random effects model (REM). Finally, the acceptance of the null hypothesis in the Hausman test revealed the appropriateness of the REM [43].

Fifth, the regression analysis was performed to investigate the impact of the partial IC efficiency indicators on the profitability indicators.

The data analysis was performed using the STATA software package (v.18).

4. Research Results

The research results were systematized and presented following the statistical analysis methodology presented in Figure 2. In

Table 7. Descriptive statistics.

Variable	Mean (Average Value)	Standard Deviation	Minimum	Maximum
Eiag	4.135268	5.36816	0.3646165	24.55744
Ehc	2.915885	2.026027	0.87	11.53
Esrc	0.8337643	2.003524	0.0618215	17.11948
Eic	0.8793207	2.911814	0.0552611	27.42476
ROA1	0.1066667	0.0744497	0	0.31
ROA2	0.140758	0.085496	0	0.3323678
ROA3	0.0810695	0.0584378	0	0.3
ROA4	0.0779167	0.0584605	0	0.3
EOR1	0.0448691	0.025851	0	0.14
EOR2	0.0616179	0.0352808	0.0028677	0.2027486
EOR3	0.0346524	0.0228427	0	0.16
EOR4	0.0342246	0.0228556	0	0.16

Source: authors’ calculation.

the descriptive statistics are presented. The average value of Eiag in the analyzed companies was 4.13, and the minimum value of 0.36 was recorded by IBM, while the maximum value of 24.56 was recorded by Nike. The average value of Ehc was 2.92, and the minimum value of 0.87 was recorded by General Electric, while the maximum value of 11.53 was recorded by Apple. The mean value of Esrc was 0.83, and the minimum value of 0.0618 was recorded by Coca-Cola, while the maximum value of 17.12 was recorded by Toyota. The minimum value of Eic was 0.055 and was recorded by Coca-Cola, while the maximum level was 27.42 and was recorded by Toyota. The average value of Eic in the analyzed companies was 0.88.

The average value of ROA1 was 0.11, and the maximum value of 0.31 was recorded by Apple. The average value of ROA2 was 0.14, and the maximum value of 0.33 was recorded by Apple. The average value of ROA3 and ROA4 was 0.08, and the maximum value of 0.3 was recorded by eBay. The average value of EOR1 was 0.04, and the maximum value of 0.14 was recorded by Samsung. The average value of EOR2 was 0.06, the minimum value was 0.0028, and the maximum value of 0.20 was recorded by Samsung. The average value of EOR3 and EOR4 was 0.03, and the maximum value of 0.16 was recorded by eBay.

The results of the Shapiro–Wilk and Shapiro–Francia tests for normality revealed that all variables were non-normally distributed, and hence, the non-parametric Spearman correlation test was performed (

Table 8. Correlation analysis.

	ROA1	ROA2	ROA3	ROA4	EOR1	EOR2	EOR3	EOR4
Eiag	0.4392 (0.0000)	0.5595 (0.0000)	0.3941 (0.0000)	0.4028 (0.0000)	0.1796 (0.0289)	0.1583 (0.0546)	0.1138 (0.1744)	0.1177 (0.1600)
Ehc	0.3991 (0.0000)	0.3979 (0.0000)	0.4068 (0.0000)	0.4382 (0.0000)	0.5910 (0.0000)	0.5127 (0.0000)	0.5625 (0.0000)	0.5834 (0.0000)
Esrc	−0.3502 (0.0000)	−0.2543 (0.0018)	−0.3086 (0.0002)	−0.3267 (0.0001)	0.0524 (0.5267)	0.1887 (0.0216)	0.0789 (0.3472)	0.0597 (0.4770)
Eic	−0.2062 (0.0119)	−0.0810 (0.3277)	−0.1729 (0.0382)	−0.1868 (0.0230)	0.1662 (0.0435)	0.2892 (0.0004)	0.1619 (0.0526)	0.1464 (0.0800)

Note: p values in ( ). Source: authors’ calculation.

).

The correlation between Eiag and all indicators of efficiency in the use of all employed resources (EOR1–EOR4) was low and positive. It was statistically significant for EOR1 and EOR2, while it was insignificant for EOR3 and EOR4. As regards the correlation between Eiag and ROA indicators, a moderate, positive, and statistically significant correlation existed with ROA1, ROA3, and ROA4, while with ROA2 the correlation was strong and positive. A moderate, positive, and statistically significant correlation existed between Ehc and all ROA indicators (ROA1–ROA4), while the correlation with all EOR indicators (EOR1–EOR4) was strong, positive, and statistically significant. The correlation between Esrc and all ROA indicators (ROA1–ROA4) was negative, moderate, and statistically significant, while between Esrc and EOR2, it was low, positive, and statistically significant. The correlation between efficiency in the use of total intellectual capital (Eic) and ROA1 was moderate, negative, and statistically significant, while between Eic and ROA3 and ROA4 it was low, negative, and statistically significant. The correlation between EIC and all EOR indicators (EOR1–EOR4) was positive and statistically significant, with EOR2 being moderate, whereas with EOR1, EOR3, and EOR4 it was low. The correlation between other variables was mainly low and statistically insignificant.

Model adequacy testing was performed, and the results are presented in

Table 9. Model adequacy test results.

Dependent Variable	F-Test H0: Pooled, H1: FEM	Breusch–Pagan LM H0: Pooled, H1: REM	Hausman Test H0: REM, H1: FEM
ln ROA1	33.27 (0.0000)	174.12 (0.0000)	38.65 (0.0000)
ln ROA2	66.18 (0.0000)	155.88 (0.0000)	16.55 (0.0009)
ln ROA3	16.58 (0.0000)	96.60 (0.0000)	16.94 (0.0007)
ln ROA4	16.38 (0.0000)	95.63 (0.0000)	16.50 (0.0009)
ln EOR1	23.45 (0.0000)	111.58 (0.0000)	15.34 (0.0015)
ln EOR2	60.34( 0.000)	142.82 (0.0000)	165.27 (0.0000)
ln EOR3	11.87 (0.000)	51.06 (0.0000)	12.40 (0.0061)
ln EOR4	11.35 (0.000)	45.10 (0.0000)	13.65 (0.0034)

Note: p values in ( ). Source: authors’ calculation.

. The fixed effect model (FEM) was adequate to assess the impact of partial IC efficiency indicators on all ROA and EOR indicators.

The regression analysis results are presented in

Table 10. Regression results for ROA indicators.

Independent Variable	Dependent Variable
	ln ROA1	ln ROA2	ln ROA3	ln ROA4
Constant	−3.848405 [−18.54] (0.000)	−3.021402 [−21.41] (0.000)	−4.297922 [−13.96] (0.000)	−4.2829 [−14.00] (0.000)
ln Eiag	0.2854089 [3.32] (0.001)	0.2812256 [4.78] (0.000)	0.210579 [1.69] (0.093)	0.1906696 [1.54] (0.126)
ln Ehc	1.511795 [6.17] (0.000)	0.9773559 [5.84] (0.000)	1.819542 [5.16] (0.000)	1.806142 [5.15] (0.000)
ln Esrc	−0.0312022 [−0.54] (0.587)	0.0068146 [0.17] (0.861)	0.0190412 [0.21] (0.838)	0.0162234 [0.18] (0.860)
${\mathrm{R}}^2$	0.9070	0.9489	0.8194	0.8203
$\overline{{\mathrm{R}}^2}$	0.8905	0.9399	0.7849	0.7859
F statistics	20.18 (0.0000)	21.91 (0.0000)	11.30 (0.0000)	11.12 (0.0000)

Note: t statistics are given in [ ]; p values are given in ( ). Source: authors’ calculation.

and

Table 11. Regression results for EOR indicators.

Independent Variable	Dependent Variable
	ln EOR1	ln EOR2	ln EOR3	ln EOR4
Constant	−4.096927 [−19.72] (0.000)	−3.289875 [−23.82] (0.000)	−4.113758 [−15.05] (0.000)	−4.176465 [−15.26] (0.000)
ln Eiag	0.2661066 [3.12] (0.002)	0.1965754 [3.42] (0.001)	0.2065751 [1.87] (0.064)	0.2377906 [2.15] (0.034)
ln Ehc	1.110574 [4.57] (0.000)	0.64954 [3.96] (0.000)	1.015174 [3.26] (0.001)	1.047368 [3.36] (0.001)
ln Esrc	0.1815787 [3.20] (0.002)	0.2434336 [6.38] (0.000)	0.2671788 [3.29] (0.001)	0.2665765 [3.28] (0.001)
${\mathrm{R}}^2$	0.8464	0.9228	0.7451	0.7427
$\overline{{\mathrm{R}}^2}$	0.8186	0.9092	0.6946	0.6917
F statistics	13.04 (0.0000)	21.91 (0.0000)	7.37 (0.0002)	8.03 (0.0001)

Note: t statistics are given in [ ]; p values are given in ( ). Source: authors’ calculation.

. Eight regression models were tested in total, investigating the impact of partial IC efficiency indicators on all ROA indicators (four models) and all EOR indicators (four models). The regression results for models investigating the impacts of partial IC efficiency indicators on ROA indicators are presented in Table 10. All four regression models were statistically significant as confirmed by the F statistics (p < 0.0000). The estimated models explained a 90.70% change in ROA1, a 94.89% change in ROA2, an 81.94% change in ROA3, and an 82.03% change in ROA4. The Ehc had the highest impact in all four models. Also, the ROA indicators were determined by the Eiag in the first three models, while the impact of Esrc was not statistically significant in all models.

The regression analysis results are presented in Table 10 and Table 11. Eight regression models were tested in total, investigating the impact of partial IC efficiency indicators on all ROA indicators (four models) and all EOR indicators (four models). The regression results for the models investigating the impact of partial IC efficiency indicators on ROA indicators are presented in Table 10. All four regression models were statistically significant as confirmed by the F statistics (p < 0.0000). The estimated models explained a 90.70% change in ROA1, a 94.89% change in ROA2, an 81.94% change in ROA3, and an 82.03% change in ROA4. The Ehc had the highest impact in all four models. Also, the ROA indicators were determined by the Eiag in the first three models, while the impact of Esrc was not statistically significant in all models.

The regression results for the models investigating the impact of partial IC efficiency indicators on EOR indicators are presented in Table 11.

All four regression models were statistically significant as confirmed by the F statistics at a 1% significance level. The estimated models explained an 84.64% change in EOR1, a 92.28% change in EOR2, a 74.51% change in EOR3, and a 74.27% change in EOR4. The Ehc had the highest impact in all four models. Also, the EOR indicators were determined by the Eiag, as well as by the Esrc in all models.

In the first model, if Ehc increased by 1%, the EOR1 would increase by 1.11%, ceteris paribus; if Eiag increased by 1%, the EOR1 would increase by 0.27%, ceteris paribus; and if Esrc increased by 1%, the EOR1 would increase by 0.18% at a 1% significance level. In the second model, if Ehc increased by 1% the EOR2, would increase by 0.65%, ceteris paribus; if Eiag increased by 1%, the EOR2 would increase by 0.20%, ceteris paribus; and if Esrc increased by 1%, the EOR2 would increase by 0.24% at a 1% significance level.

In the third model, if Ehc increased by 1%, the EOR3 would increase by 1.02%, ceteris paribus; if Eiag increased by 1%, the EOR3 would increase by 0.21%, ceteris paribus; and if Esrc increased by 1%, the EOR3 would increase by 0.27% at a 1% significance level. In the fourth model, if Ehc increased by 1%, the EOR4 would increase by 1.05%, ceteris paribus; if Eiag increased by 1%, the EOR4 would increase by 0.24%, ceteris paribus; and if Esrc increased by 1%, the EOR4 would increase by 0.27% at a 1% significance level.

Based on the above-presented regression results, Figure 3 depicts the verified hypotheses.

5. Discussion

The results obtained in this study cannot be compared with the results of other studies, as there are no previous papers that have applied EIC methodology and intellectual capital efficiency indicators (Eiag, Ehc, Esrc, and EIC), which are calculated based on EIC methodology. The results of other studies mainly use the VAIC methodological framework, which is a highly criticized measurement methodology [27,28,29,30,44].

The results of our study (based on the EIC model) are not comparable with other studies that are based on the VAIC/AVAIC/MVAIC model, because there are differences in the formulas for calculating the partial indicators of efficiency, for example, for the efficiency of structural capital as a segment of total IC. The VAIC/AVAIC/MVAIC model calculates the efficiency of structural capital as a ratio of structural capital to the value added. On the contrary, the EIC model calculates indicators according to the principle for partial efficiency indicators as ratios of value created (output) to employed capital (input). In the VAIC/AVAIC/MVAIC model, inconsistency exists in calculations of various partial efficiency indicators of IC elements: human capital efficiency is a ratio of the value added to human capital, and human capital efficiency is a ratio of the structural capital to the value added.

Nonetheless, some general discussion can be offered here. Namely, Campos et al. [45] highlighted that IC impacts business performance efficiency indirectly through “dynamic capabilities, network competence, technological capabilities, absorptive capabilities and innovation performance”.

Kweh et al. [46] revealed that human, structural, and relational capital are the key contributors to the high efficiency of the companies (using an example of 1395 firm-year observations for the period from 2010 to 2018), whereas only human and relational capital contribute to the company profitability.

Pigola et al. [47], using 81 studies from 2006 to 2020 based on a bivariate analysis, came to similar conclusions. Nkambule et al. [48] suggested that invisible IC (innovation, process, and customer capital) has a positive and significant impact on the performance of US smart firms and that human capital has a direct impact on firm efficiency.

Tiwari [49], based on a sample of 84 companies for 2009–2019, concluded according to the regression analysis that IC is positively related to profitability.

Study [50], based on the VAIC model, the adjusted VAIC (AVAIC), and the modified VAIC (MVAIC), found a positive and significant relationship between partial indicators of IC efficiency and financial performance (ROA and return on equity—ROE) for 35 Chinese agricultural listed companies for 2013 to 2019.

Study [51], based on the modified value-added intellectual coefficient (MVAIC) model, explores Chinese agricultural listed companies for 2014–2020 and the impact of partial IC efficiency indicators (human and structural) on financial leverage.

A study [52] based on VAIC explores the relationship between the partial indicators of intellectual capital efficiency from financing Islamic banks for 2009–2019 and finds that the efficiency of human capital has a positive relationship with ROA.

A recent study [53] supports the criticism of the VAIC model and explores the impact of IC on profitability using two methodological solutions, VAIC and a newly designed approach to IC valuation of authors of the study [53], and states that their approach has advantages compared with the VAIC because it measures efficiency ratios and IC stocks.

Xu and Li [54] proved that invisible IC (human, structural, and relational) enhances firm performance (profitability and productivity) in China’s manufacturing sector. Tiwari [49] and Xu and Li [54] based their studies on VAIC or modified VAIC, which is a highly criticized measurement methodology [27,28,29,30,44]. Many critics of VAIC/MVAIC are the key reasons for using the EIC methodology in this study.

6. Conclusions

The research confirms the importance of improving the profitability measurement in the knowledge economy era, where exists the dominance of intangible assets. It emphasizes the need for a correction in the denominator of the traditional return on assets (ROA) indicator. Better measurement of total intellectual capital, especially the component that is not visible on the assets side of the balance sheet, provides the information for better measurement. The paper points out the issue of improving the traditional financial ratio, such as ROA. This can be achieved by incorporating the value of intellectual resources, which are undisclosed in the balance sheet, in its denominator. This solution results in creating a new profitability indicator—efficiency in the use of all employed resources (EOR). This new profitability indicator in the denominator includes invisible intellectual resources. It is its advantage in comparison with traditional profitability indicator—return on assets (ROA). This missing element in better formulas for profitability measurement is intellectual capital, which is not recorded in the balance sheet (marked as ΔIC), which consists of human (Hc), structural (Sc), and relational capital (Rc). EIC methodology provides the solution to calculate this missing element in the profitability formulas that are especially relevant for knowledge-intensive, high-technology, smart companies.

The presented study has several implications, spanning from methodological, conceptual, and managerial. The study offers the efficiency of intellectual capital (EIC) methodological framework as a sound intellectual capital measurement solution, as opposed to the overwhelmingly criticized VAIC/MVAIC model [27,28,29,44]. The advantages of the EIC methodology for the listed joint stock companies in different industries are the following:

• The use of information from the annual financial accounting report and information related to the market valuation to overcome barriers in the valuation of the total intellectual capital by information of the system of financial accounting;
• The use of the new economic category—intellectual capital value added (ICVA), which is a reliable measure of intellectual potential in intellectual resource-intensive companies, and ICVA is a numerator in all intellectual capital efficiency indicators (efficiency in the use of intangible assets and goodwill, Eiag; efficiency in the use of human capital, Ehc; efficiency in the use of structural and relational capital, Esrc; efficiency in the use of total intellectual capital, visible and invisible, EIC).
• Calculate the intellectual capital efficiency indicators (Eiag, Ehc, Esrc, EIC) as output/input ratios, in other words, as the ratios of the value of output and the value of input;
• Useful for comprehensive intellectual capital performance measurement and operative control, intellectual capital performance benchmarking, intellectual capital performance management, and intellectual capital strategy control.

Further, the presented conceptual framework focuses on the efficiency of intellectual capital components, both visible and invisible, thus offering new insight toward the importance of adequate measuring and managing IC in knowledge enterprises. Traditional ROA profitability indicators have limitations in the precise measurement of the efficiency in the use of total disclosed assets in accordance with the relevant accounting standards. Thanks to the EIC methodological solution, the problem of quantifying the intellectual capital, which is not recorded in the balance sheet (ΔIC), was overcome. The intellectual capital that is not recorded in the balance sheet (ΔIC) is a vital missing component of the traditional formulas of return on assets (ROA) indicator calculation. In this way, our conceptual framework suggests an analytically better profitability measurement—the efficiency in the use of all employed resources (EOR) indicator, which is confirmed in the empirical segment of this study.

Based on all the above-obtained results, the defined hypothesis H1 and its sub-hypotheses (H1a, H1b, H1c, and H1d) could be only partially confirmed, suggesting that greater intellectual capital efficiency increases the profitability performance of the smart firm, but the statistical significance of the presented models depends on the chosen partial performance indicators. Concretely in this empirical study, only the Ehc has been proven to lead to greater profitability in all four models, while the results for Eiag are statistically significant in three models, whereas the Esrc is not statistically significant, and in the first model, the impact of the profitability is negative.

Regarding hypothesis H2 and its sub-hypotheses (H2a, H2b, H2c, and H2d), it has been accepted, thus confirming that the greater partial intellectual capital efficiency indicators (Eiag, Ehc, and Esrc) increase the efficiency in the use of all employed resources (EOR) indicators. These results point to the fact that constructed new EOR profitability indicators are more successful in capturing enterprise intellectual performance compared with traditional, profitability ROA indicators.

Finally, the managerial implications are also important, highlighting the fact that IC in smart firms must be carefully measured and managed as it represents a vital source of value creation and competitiveness. The highest impact of human capital on firms’ profitability has to be adequately addressed by managers and policymakers, and employees must be valued accordingly. Particularly, continuous training and development programs are essential for building employees’ competencies and skills, leading to improved human capital efficiency and ultimately smart firms’ profitability.

Nonetheless, with all the advantages of the study, the main limitation is the sample used in the study, more precisely, the number of smart companies included in the analysis. Therefore, the future research direction could be toward a more inclusive sample covering a wide range of firms from different industries to additionally validate the proposed methodological and conceptual framework.