Intellectual capital and profitability: a firm value approach in the European companies.

Martins, Maria Manuela ; Lopes, Ilidio Tomas

Introduction

To Simai (2003), the assumption that information and knowledge are key drivers, both in the process of production or as an essential part or the final commodities, is unquestionable and always has had impact on value creation. Knowledge emerges to make a spear, as well as a microchip: what have effectively changed was the quantity, the quality, the density of knowledge and information, the speed in which they are disseminated and changed, and the proportion of them which is embodied in the final products and services. In companies, knowledge is embodied in intangible assets. An intangible asset is a differentiating factor of business (Stewart 1997) and can become a competitive advantage, allowing companies to continue their activities. Lev (2001) argues that the increase in competition and the emergence of information and communication technologies has definitely changed the process of business value creation. Thus, intangible assets play an increasingly important role in the scope of developed economies. Bontis et al. (1999) also argue that the most successful companies are those that use their intangible assets better and faster than competitors. To Ichijo (2002), only a company that generates knowledge is able to be successful in the market, and only wins if it innovatively driven. A significant part of the market value of a company is not embodied in the intangible assets recognized in the balance sheet. The difference between the market value and the book value of a company represents the invisible value, embodied in non-capitalized intangibles.

Over the last two decades, new categories of intellectual capital and intangible resources have emerged in the economic literature, in particular in the new age business models structures (Edvinsson, Malone 1997; Schiuma et al. 2008; Survilaite et al. 2015). Traditionally, intellectual capital can be split into four categories: human capital, structural capital, organizational capital, and relational capital (Edvinsson, Malone 1997). However, Schiuma et al. (2008) and also refer to social and stakeholder capital, as subsets of organizational and structural capital, respectively. Thus, it is expected that the accounting treatment (recognition and disclosure) of intangible affect the firms' future returns, in particular their performance indicators, in particular their profitability (Zeghal, Maaloul 2010; Macerinskiene, Survilaite 2011; Kianto et al. 2013; Tudor et al. 2014; Salchi et al. 2014; Survilaite et al. 2015). As evidenced by Tudor et al. (2014), the level of intangibles has a direct relationship with profitability, by comparing the intangibles-to-total-assets ratio and other measures of profitability such as Return of Assets (ROA), Return of Capital Employed (ROCE), and Gross Margin (GM). Based on several models and approaches (Edvinsson, Malone 1997; Survilaite et al. 2015; Macerinskiene, Aleknaviciute 2015), intellectual capital and intangibles have been identified and managed as key drivers of performance and profitability. These immaterial resources are included on the firms' financial statements or disclosed in complimentary reports (Lopes 2010) towards the increase of value relevance of firms. Hence, the management of intellectual capital impacts on performance in terms of competitiveness, as well as financial revenues. According to Kianto et al. (2013: 119) "the management of intangibles is a key managerial mechanism for firms in the knowledge economy".

This paper aims to investigate the association between the degree of intangibility of European companies and their profitability level, and the association between the degree of intangibility and firms' value. It is structured as follows: the next section addresses the prior literature insights and research hypotheses. Methodology describes the research approach and methods (Sekaran, Bougie 2013; Lopes 2015), the data, the variables, and general descriptive measures. The next section analyses the empirical results and discussion, complemented by final remarks and expected future outcomes.

1. Prior research and hypotheses

Some authors (Brooking 1996; Edvinsson, Malone 1997; Lev, Zarowin 1999; Stewart 1997; Sveiby 1997; Zeghal, Maaloul 2010; Kianto et al. 2013; Tudor et al. 2014; Salchi et al. 2014; Survilaite et al. 2015) argue that intellectual capital explains the difference between the market value and the book value. Broadly, it can be defined as the wealth of knowledge-based companies. It has attracted over the last decades, a significant practical interest and impact (Petty, Guthrie 2000). Stewart (1997) argues that the intellectual resources such as knowledge, information and experience, are the tools for creating wealth and defines intellectual capital as the new wealth of organizations. Sullivan (2000) defines as intellectual capital the knowledge that can be converted into profits. Primarily, due to intellectual capital measurement issues and difficulties, companies are facing problems with their management (Andrikopoulos 2005). For Kok (2007), a method for determining the intellectual capital, or the intangible side of a company, is comparing the market value with its book value. These arguments are based on the intellectual capital assumptions. Intellectual assets of a company are intangible in nature and therefore do not have a way or a suitable financial value. They are characterized as hidden assets, since it is difficult to identify their unique contribution to a company value creation (Fincham, Roslender 2003). Intellectual capital is not reported in traditional financial statements since some of its elements do not meet the definition or recognition criteria (Lopes 2010). According to the International Accounting Standard (IAS) 38 (IFRF 2004), the definition of an intangible asset is an identifiable non-monetary asset without physical substance. An asset is a resource that is controlled by the entity as a result of past events, for example, purchase or self-creation and from which future economic benefits (inflows of cash or other assets) are expected. Therefore the three critical attributes of an intangible asset are: identifiability; control or power to obtain benefits from the assets; and future economic benefits, such as revenues or reduced future costs. The list of items that should not be included in the balance sheet includes the brands, mastheads, publishing titles, customer lists and items similar in substance internally generated (IAS 38). If an item does not meet the definition of intangible assets and the criteria for recognition as an intangible asset, the expenditure on this item should be expensed when it is incurred.

The research conducted by Riahi-Belkaoui (2003), focused on the relationship between intellectual capital and the performance of selected multinational companies of the USA, suggests that intellectual capital is positively associated with financial performance. In the same trend, the research of Alshubiri (2015) aims to demonstrate the impact of the intellectual capital from market capitalization on profitability in the financial sector, listed in Muscat Security Market of Oman. This research used the market capitalization methods (MCM) to measure intellectual capital as independent variables on profitability. The results indicated a statistically significant impact of Tobin's Q, on market to book value, and on profitability, based on ROE and EPS. Complimentarily, the research conducted by Chen et al. (2005) was applied to firms listed on the Taiwan stock exchange (TSE), and has investigated the relationship between intellectual capital and a firm's market value and financial performance. The results support a significantly positive relationship among intellectual capital, market value and financial performance.

Several other researches have been concluded over the last decade, supporting the assertions between intangibles and firms' performance and profitability. Thus, Salojarvi (2004) found that companies that implement active practices to manage their intangibles obtain better results in innovation and in the development of new products processes. To Liang, Yao (2005), net income is the most significant explanatory capability in market value of Taiwan information electronic company when examined on intangible assets, balanced scorecard and intellectual capital, respectively. Tan et al. (2007) evidence that intellectual capital and company's performance is positively related. Intellectual capital is correlated to future company performance, and the rate of growth of a company's IC is positively associated to the company's performance. Furthermore, the contribution of intellectual capital to company performance differs by industry. The researches of Oliveira et al. (2010), focused on the companies listed in BM&FBovespa, conclude that companies with higher degree of intangibility perform better. However, the results from Mosavi et al. (2012) were emerged from Iranian companies and revealed no conclusive evidence to support a definitive association between intellectual capitals, measured by VAIC. Furthermore, there is just a statistically significant relationship between human capital efficiency and financial performance and the degree of intangibility.

Nascimento et al. (2012) has analysed only companies in the Technology, Information and Telecommunications sector, listed in BM&FBovespa. That research investigates the correlation among the degree of intangibility and the performance indicators. These results show that no differences exist among the analysed segments. Vasconcelos et al. (2013) observed the behaviour of the degree of intangibility of the largest banks listed on the BM&FBovespa for the period 2007-2010 and found that (i) the explanatory notes were the accounting document most commonly used for the presentation or decomposition of intangibles, (ii) the most representative types of intangible assets were "expenditure on acquisition and software development", "software and systems" and "acquisition of payrolls" with regard to frequency, and "goodwill" and "acquisition of payrolls" with regard to average volume of investment; (iii) the predominant classification of intangible assets was "infrastructure assets", (iv) the degree of intangibility decreased over the study period, and (v) no symmetry was observed between variations in the index of investments in intangible assets and market value.

The diversity on intellectual capital models approaches over the last decade (Survilaite et al. 2015) has conducted researches to an increase usage of value added performance indicators. These indicators have the ability to capture the value creation over a certain period of time and can act as significant predictors of expected returns. In th is scope, Zeghal, Maaloul (2010), using data from UL listed companies, also concluded that there is a positive association between value added intellectual capital coefficient and economic performance, financial performance and stock market performance. The same association signal was obtained in relation to the association between value added capital employed coefficient and economic performance, financial performance and stock market performance. Thus, these evidences support the significant role of intellectual capital in creating value for stockholders as well as for other stakeholders. Salchi et al. (2014) have examined the relationship between six variables (e.g. structural capital efficiency, human capital efficiency, economic value added) and firms financial performance of the chemical and pharmaceutical firms listed in Tehran Stock Exchange. Their results suggest that all the relationships are significant except for the relationship between structural capital efficiency, economic value added, and financial performance.

Broadly, intangibles and intellectual capital are linked with firms' competitiveness (Kianto et al. 2013) and can act as predictors of future performance. Although measured through multiple and diversified approaches and indicators, those resources can be viewed "as strategic assets since their inclusion in the structure of the total assets allows companies to extract a competitiveness rent and, thus, to enhance the outcomes of their activity" (Tudor et al. 2014: 292).

Based on prior researches and outcomes, we formulate our hypotheses as follows:

[H.sub.1]: The European companies with major degree of intangibility are more profitability;

[H.sub.2]: The European companies with major degree of intangibility are more valuable.

2. Methodology and methods

2.1. Approach and data source

This paper follows a positivist or mainstream approach (Sekaran, Bougie 2013; Lopes 2015), based on the possibility to predict the firms' performance based on its knowledge intensity and intangibility level. Thus, we assume that our research can be replicable, based on its findings generalization. Thus, through a deductive reasoning, cause and effect relations are tested within structured and multilateral frameworks.

This research is based on 486 European companies. In the first step our sample was selected by considering all firms included in the Financial Times 2014 classification of the 500 largest European companies, with reference to 2013 market value. Fourteen companies were not included in the sample due to the information unavailability. Largest companies were selected towards the analysis of a set of companies that are economically important and that operate in multiple environments such as legal, institutional and economic conditions. The information about companies was extracted from Datastream database over the current year.

2.2. Variables

The degree of intangibility is calculated by dividing the Market Value by Book Value, following the same approach as other researches such as Riahi-Belkaoui (2003), Tudor et al. (2014), and Alshubiri (2015). This ratio represents how many times the market value is above, or below, the book value, assuming that higher the intangibility degree more relevant will be the intangible assets in the company. Based on the degree of intangibility of each company, the median was calculated allowing the categorization of companies into two different groups; 1. the intangible-intensive companies with a degree of intangibility equal or higher than the median composed by 244 companies and 2. the tangible-intensive companies with a degree of intangibility below the median composed by 242 companies. Thus, the Knowledge Intensity was classified by splitting companies in two groups, based on the median descriptive measure (Group 1--Knowledge Intensive Companies: with a Degree of Intangibility equal or above its median measure; Group 2--Non-Intensive Knowledge Companies: Degree of Intangibility below its median measure). Profitability was measured by the Return on Assets (ROA), Return on Equity (ROE), Return on Capital Employed (ROCE) and Return on Sales (ROS). These indicators are often used in financial and accounting literature in evaluating the performance of companies. ROA is calculated by dividing a company's annual earnings by its total assets providing insights as to how efficient management is in using its assets to generate earnings. ROE is calculated by dividing a company's annual earnings by its Shareholder's Equity and evidences how well a company uses investments to generate earnings growth. ROCE is calculated by dividing the Earnings Before Interest and Tax (EBIT) by the Capital Employed. This indicator is the difference between Total Assets and Current Liabilities. ROCE measures a company's profitability and the efficiency with which its capital is employed. ROS is calculated by dividing the Earnings Before Interest and Tax (EBIT) by the Sales and is used to evaluate a company's operational efficiency. All of these variables are supported by prior researches such as Lev, Zarowin (1999), Zeghal, Maaloul (2010), Kianto et al. (2013), Tudor et al. (2014), Salchi et al. (2014), and Survilaite et al. (2015).

The firm value is measured by Tobin's Q, defined as the sum of the market value of shares of the company and liabilities divided by the book value and liabilities. Thus, Tobin's Q is often used in financial and accounting literature in evaluating the companies. Table 1 resumes the variables description.

3. Empirical results and discussion

3.1. Descriptive analysis

The 486 companies were integrated into ten activity sectors and the number of companies from each sector is shown in Table 2. The main representative (24.9%) is the sector "Financials" (which includes financial services, nonlife insurance, life insurance, banks, real estate investment and services and real estate investment trusts). The second most representative sector (17.9%) is the "Industrials" (which includes industrial transportation, industrial engineering, construction and materials, support services, aerospace and defence, electronic and electrical equipment and general industrials), followed by the sector "Consumer goods" (which includes personal goods, beverages, food producers, household goods and home construction, automobiles and parts and tobacco), representing 12.6% of total. Table 3 evidences that the most represented country, in number of firms, is United Kingdom (22.2%), France (15%), and Germany (11.3%). Countries like Luxembourg and Romania evidence a very residual influence in this sample.

Based on the classification according to its activity (Eurostat 2014), the 486 companies included in the sample were split into KNOWLEDGE INTENSIVE OR NON-KNOWLEDGE INTENSIVE, as mentioned above. The first group is composed by 282 companies and the second group integrates 204 companies (Table 4).

Table 5 illustrates the main descriptive statistics measures, considering the sample and the classification according company's knowledge intensity. Table 6 evidences the main descriptive statistics measures, not considering the extremes values from the intangibility degree.

3.2. Hypothesis tests

We used the t-Student test to verify that the null hypothesis (H0) would, or not, be rejected. The null hypothesis is rejected in case of ROA, ROE ROCE and Tobin's Q, evidence that there is a difference between those indicators, observed for Knowledge Intensive (intangible-intensive) companies and for Non-Knowledge Intensive (tangible-intensive) companies. In case of ROS, the null hypothesis is not rejected, which supports the evidence that there are no statistically differences between the mean of ROS obtained for intangible-intensive companies and ROS observed in tangible-intensive companies. The same test was run to the sample with no extreme values of the degree of intangibility and the results obtained corroborate the previous results.

3.2.1. Degree of intangibility and the profitability Degree of intangibility and ROA

Table 7 evidences the descriptive measures of the degree of intangibility and ROA and the tests of the null hypothesis ([H.sub.0]). This hypothesis states that the mean of ROA of intangible intensive European companies is equal to the mean of ROA of intensive tangible European companies. Empirical evidence supports that the largest mean is observed in the group of intangible intensive companies. Furthermore, the results from t-Student test also supports the rejection of the null hypothesis, evidencing that there is a difference between the indicator ROA obtained in intangible-intensive companies and the same indicator observed in tangible-intensive companies.

Degree of intangibility and ROE

Table 8 includes the descriptive measures of the degree of intangibility and ROE, including the tests of the null hypothesis (H0), which states that the mean of ROE of intangible intensive European companies is equal to the mean of ROE of intensive tangible European companies. The empirical evidence indicates that the largest mean is observed in the group of intangible intensive companies. Complimentarily, the statistical results from t-Student test indicate the rejection of the null hypothesis. Thus, there is a difference between the ROE obtained by intangible-intensive companies and the ROE obtained for tangible-intensive companies.

Degree of intangibility and ROCE

Table 9 relates to the descriptive measures of the degree of intangibility and ROCE. In this scope, the null hypothesis is described as follows: the mean of ROCE of intangible intensive European companies is equal to the mean of ROCE of intensive tangible European companies. This supports the evidence that the largest mean is observed in the group 1 (intangible intensive companies). Furthermore, the results derived from t-Student test indicate the rejection of the null hypothesis which means that there is a statistically significant difference between the ROCE obtained by intangible-intensive companies and the ROCE obtained for tangible-intensive companies.

Degree of intangibility and ROS

In the next table (Table 10), we evidence the descriptive measures of the degree of intangibility and the indicator ROS, including the test related to the mean's differences. The null hypothesis states that the mean of ROS of intangible intensive European companies is equal to the mean of ROS of intensive tangible European companies. From the empirical evidence, we can conclude that the largest mean is observed in the group of tangible intensive companies. Thus, null hypothesis cannot be rejected, confirming that there is no difference between the ROS obtained by intangible-intensive companies and ROS observed in tangible-intensive companies.

These results are consistent with the results reported in several previous researches (Riahi-Belkaoui 2003; Chen et al. 2005; Tan et al. 2007; Oliveira et al. 2010; Zeghal, Maaloul 2010; Kianto et al. 2013; Tudor et al. 2014; Salchi et al. 2014). Thus, intellectual capital is positively associated with financial performance, acting as a key driver on the companies' value creation processes.

3.2.2. Degree of intangibility and firm value Descriptive measures of the degree of intangibility and ROE, including the tests of the H0, is evidenced in Table 11. The null hypothesis illustrates that the mean of Tobin's Q of intangible intensive European companies is equal to the mean of Tobin's Q of intensive tangible European companies. However, the results evidences that the largest mean is observed in the group of intangible intensive companies. The results obtained from t-Student test indicate the rejection of the null hypothesis. Thus, there is a difference between the Tobin's Q obtained by intangible-intensive companies and Tobin's Q obtained for tangible-intensive companies.

These results evidences that the relationship between the degree of intangibility and firm value is consistent with the results presented in the research conducted by Chen et al. (2005) and Zeghal, Malool (2010), which supports a significantly positive relationship between intellectual capital, market value performance, and companies' financial performance.

3.2.3. Knowledge intensity

Based on the classification of companies above (according the company's knowledge intensity), a similar statistical analysis was carried out separately for both groups (Knowledge Intensive companies and Non-Knowledge Intensive companies, respectively). The results are summarized in the next table (Table 12). Thus, we have a reasonable basis to conclude that for both groups the null hypothesis was rejected, except in the case of ROS. Thus, the mean of ROA, ROE, ROCE, and Tobin's Q, are statistically different, evidencing higher values in the first group (Knowledge Intensive Companies).

In the particular case of ROS, and as mentioned above, the null hypothesis cannot be rejected, concluding that there is no significant differences between knowledge intensity and the operational key performance indicator ROS. This evidence can suggest that knowledge intensity will significantly impact on external measures (e.g. ROE, and value added measures), accurately perceived and incorporated by actual and potential investors. In fact, ROS is a current efficiency measure, operational and internally focused, and driven to short run actions and strategies. Relating the sectors of activity under analysis, the null hypothesis is not rejected (p > 0.05) for ROE which evidences that there is no differences between means across sectors. However, some differences were found for ROA across the following activity sectors: "Financials" and "Consumer Goods" (p = 0.002); "Financials" and "Consumer Services" (p = 0.000); "Financials" and "Health Care" (p = 0.000); "Health Care" and "Utilities" (p = 0.017). In the case of ROCE, the statistical differences, at a significance level of 5%, were observed between "Health care" and "Utilities" (p = 0.006) and between "Health Care" and "Industrials" (p = 0.028). Finally, the most significant differences were observed when using the indicator Tobin's Q. Based on a significance level of 1%, we underline the most relevant differences across activity sectors: "Consumer Services" and "Financials" (p = 0.000); "Consumer Services" and "Oil and Gas" (p = 0.006); "Financials" and "Health Care" (p = 0.003); and "Utilities" and "Consumer Services" (p = 0.010). Other significant differences can be observed if we increase the analysis significance level.

As a concluding remark in the scope of our outcomes, we didn't find in previous researches, a direct association between the degree of intangibility and profitability of firms, based on its market value and knowledge intensity. Hence, these new insights constitute a new and important outcome towards the consolidation assertion that intangible resources and intellectual capital drive companies towards value creation and sustainability. Traditional and new measures can be incorporated in new intellectual capital models (Macerinskiene, Aleknaviciute 2015) towards the increase firms' value relevance and it dynamic perception by markets and other stakeholders.

Conclusions

This paper was focused on the association between the degree of intangibility of European companies (according Financial Times classification), its profitability level, and the firms' value. Measuring the profitability through the key performance indicator ROA, ROE, and ROCE, the most relevant findings of the empirical research evidence that there is a difference between the profitability and the firm value observed in intangible-intensive companies and tangible-intensive companies. This supports the accounting and economic traditional assertions that intangibles can act as significant predictors of performance and profitably. Furthermore, it also possible to support the assertion that financial markets can accurately perceived the importance of intangibles embodied in external key performance indicators such as ROE, ROA, ROCE, or Tobin's Q. However, if profitability is measured using the indicator ROS, we conclude that there is no difference between this indicator distribution and the degree of intangibility across the groups under analysis. This evidence can be supported by the indicator nature, strongly focused to operational and internal efficiency. Across the sectors under analysis, we also found some important differences. This confirms the different levels of knowledge intensity across sectors, and its subsequent impact on performance indicators disclosed to stakeholders. Broadly, our findings corroborate the principles stated on intellectual capital and intangibles literature, and related accounting standards, providing additional empirical evidence towards a positive contribution to the intellectual capital literature and its impact on the performance obtained over the years to come. As research limitations, we can underline the use of a limited set of performance and profitability indicators, the need to perform a similar analysis for a wide range of time, and the simplistic method used in the classification of companies in knowledge intensive and non-knowledge intensive companies. Our research directions are focused on the effort to surpass the mentioned limitations, by using complimentary and new research approaches and methods.

doi: 10.3846/btp.2016.673

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Disclosure statement

The authors report no financial interests or potential conflicts of interest.

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Maria Manuela MARTINS [1], Ilidio Tomas LOPES [2]

Accounting Department, Instituto Universitario de Lisboa (ISCTE-IUL), Business Research Unit (BRU-IUL), Av. Forgas Armadas, 1649-026 Lisboa, Portugal

E-mails: [1] manuela.martins@iscte.pt (corresponding author); [2] ilidio.tomas.lopes@iscte.pt

Received 23 July 2015; accepted 23 December 2015

Maria Manuela MARTINS. Professor and researcher at Instituto Universitario de Lisboa (ISCTE-IUL) and research at Business Research Unit (BRU-IUL). Graduate in Management, she obtained a Master Degree in Business Administration specialization in Information Systems Management and a PhD in Management, specialization in Accounting (Instituto Universitario de Lisboa (ISCTE-IUL)). Research interest: Knowledge Management, Management and Financial Accounting.

Ilidio Tomas LOPES. Professor and researcher at Instituto Universitario de Lisboa (ISCTE-IUL) and research at Business Research Unit (BRU-IUL). Graduate in Business Administration, he obtained a Master Degree in Statistics and Information Management and a PhD in Management, Specialization in Accounting (University of Coimbra, Portugal). Research interest: Knowledge Management, Management and Financial Accounting, Management Control Systems, and Research Methodologies. Table 1. Variables description Variable Description Intangibility degree Market Value/Book Value ROA Return on Assets: Net Income/Assets ROE Return on Equity: Net Income/Equity ROCE Return on Capital Employed: EBIT/ Capital employed ROS Return on Sales; EBIT/Sales Tobin's Q (Market Value + Liabilities) / (Book Value + Liabilities) Table 2. Activity sectors Activity sector N % Basic materials 44 9.1 Consumer goods 61 12.6 Consumer services 56 11.5 Financials 121 24.9 Health care 23 4.7 Industrials 87 17.9 Oil & gas 33 6.8 Technology 14 2.9 Telecommunications 21 4.3 Utilities 26 5.3 Total 486 100.0 Table 3. Countries Country N % Austria 7 1.4 Belgium 10 2.1 Czech Republic 2 0.4 Denmark 13 2.7 Finland 10 2.1 France 73 15.0 Germany 55 11.3 Greece 5 1.0 Hungary 2 0.4 Ireland 4 0.8 Italy 28 5.8 Luxembourg 1 0.2 Norway 10 2.1 Poland 10 2.1 Portugal 5 1.0 Romania 1 0.2 Russia 21 4.3 Spain 23 4.7 Sweden 24 4.9 Switzerland 38 7.8 The Netherlands 22 4.5 Turkey 14 2.9 UK 108 22.2 Total 486 100.0 Table 4. Knowledge intensity Company classification N % Knowledge Intensive 282 58.0 Non-Knowledge Intensive 204 42.0 Total 486 100 Table 5. Descriptive measure Variable N Mean Median Standard deviation Intangibility degree 486 3.0643 2.0700 3.4935 Knowledge intensive 282 2.8743 1.8900 3.4202 Non-Knowledge intensive 204 3.3269 2.3750 3.5843 ROA 486 0.0527 0.4087 0.0061 Knowledge intensive 282 0.0490 0.0343 0.0665 Non-Knowledge intensive 204 0.0576 .04702 0.0544 ROE 486 0.1403 0.1203 0.0182 Knowledge intensive 282 0.1332 0.1163 0.1600 Non-Knowledge intensive 204 0.1501 0.1262 0.2086 ROCE 374 0.0746 0.0644 0.0541 Knowledge intensive 183 0.0745 0.0673 0.0575 Non-Knowledge intensive 191 0.0748 0.0634 0.0508 ROS 486 0.1807 0.1218 0.3161 Knowledge intensive 282 0.1876 0.1293 0.3731 Non-Knowledge intensive 204 0.1712 0.1129 0.2143 Tobin's Q 486 1.7738 1.3270 1.5072 Knowledge intensive 282 1.6686 1.1843 1.4418 Non-Knowledge intensive 204 1.9193 1.4645 1.5851 Table 6. Descriptive measure not considering the extremes Variable N Mean Median Standard deviation Intangibility degree 438 2.5293 2.0700 1.6508 ROA 438 0.0496 0.0411 0.0539 ROE 438 0.1290 0.1213 0.1333 ROCE 344 0.0693 0.0633 0.0444 ROS 438 0.1838 0.1214 0.3280 Tobin's Q 438 1.5992 1.3292 0.8793 Table 7. The degree of intangibility and the ROA Degree of intangibility N Mean Median Standard deviation Intangible intensive 244 0.0750 0.0607 0.0680 Tangible intensive 242 0.0303 0.0173 0.0450 Degree of intangibility Max Min Intangible intensive 0.4519 -0.0959 Tangible intensive 0.3932 -0.0584 t Test for equality of means: [t.sub.(484)] = 8.505; p = 0.00. Table 8. The degree of intangibility and the ROE Degree of intangibility N Mean Median Standard deviation Intangible intensive 244 0.1935 0.1600 0.2075 Tangible intensive 242 0.0865 0.0847 0.1321 Degree of intangibility Max Min Intangible intensive 1.7805 -0.6227 Tangible intensive 1.4123 0.3366 t Test for equality of means: [t.sub.(484)]; p= 0.00. Table 9. The degree of intangibility and the ROCE Degree of N Mean Median Standard deviation intangibility Intangible intensive 229 0.8806 0.0757 0.0598 Tangible intensive 145 0.5345 0.0847 0.0347 Degree of Max Min. intangibility Intangible intensive 0.3608 -0.0338 Tangible intensive 0.1687 0.0342 t Test for equality of means: [t.sub.(372)] = 6.334; p = 0.00. Table 10. The degree of intangibility and the ROS Degree of N Mean Median Standard deviation intangibility Intangible intensive 244 0.1601 0.1392 0.1295 Tangible intensive 242 0.2009 0.0995 0.4282 Degree of Max Min. intangibility Intangible intensive 0.7415 -0.0684 Tangible intensive 5.5302 -0.2449 t Test for equality of means: [t.sub.(484)] = -1.401; p = 0.16. Table 11. The degree of intangibility and the Tobin's Q Degree of N Mean Median Standard deviation intangibility Intangible intensive 244 2.4652 1.8823 1.8840 Tangible intensive 242 1.0859 1.0239 0.2145 Degree of Max Min. intangibility Intangible intensive 17.3419 0.2315 Tangible intensive 1.9099 0.3963 t Test for equality of means: [t.sub.(484)] = 11.2 42; p = 0.00. Table 12. t Test for equality of means Variable Statistics Knowledge Intensive Companies ROA [t.sub.(280)] = 5.9 1 6; p = 0.00 ROE [t.sub.(280)] = 6.0 62; p = 0.00 ROCE [t.sub.(181)] = 3.232; p = 0.00 ROS [t.sub.(280)] = -0.6 05; p = 0.51 Tobin's Q [t.sub.(280)] = 8.775; p = 0.00 Non-Knowledge Intensive Companies ROA [t.sub.(202)] = 6.173; p = 0.00 ROE [t.sub.(202)] = 3.571; p = 0.00 ROCE [t.sub.(189)] = 5.967; p = 0.00 ROS [t.sub.(202)] = -1.82 6; p = 0.07 Tobin's Q [t.sub.(202)] = 6.824; p = 0.00