The Impact of Intellectual Capital on Firms’ Performance: Evidence from Saudi Arabia

The purpose of this study is to empirically investigate the relationship between intellectual capital (IC) measured by the value-added intellectual coefficient (VAIC) and firms’ performance (FP) in the Saudi context. Data are drawn from a sample of 25 Saudi firms listed on the Saudi Stock Exchange (Tadawul) for the period 2015-2018. Using the VAIC model, the multiple linear regression models were constructed to examine the relationship between intellectual capital (IC) and firms’ performance (measured in terms of financial and market performance). The findings indicate that there is a positive association between overall intellectual capital efficiency as well as each of its three components (human capital efficiency, structural capital efficiency, capital employed efficiency) and the firms’ financial performance. Additionally, there is a positive association between human capital efficiency (HCE), structural capital efficiency (SCE), and the firms’ market performance. Overall, the findings suggest that human capital efficiency (HCE) has a significant and positive impact on firms’ financial and market performance in Saudi Arabia. The VAIC method may be a useful tool for managers and investors in their decision process. This is the first study about the impact of intellectual capital on firms’ performance in four industry groups in Saudi Arabia using the VAIC model.


Introduction
The world moved from the production era, in which the physical assets of organizations: lands, buildings, etc., were a sole source of value creation to the knowledge era based on the idea that knowledge, instead of a physical asset, is the main driver of economic growth (Seetharaman, Helmi Bin Zaini Sooria & Saravanan, 2002). The information revolution, the digital world, and knowledge in the current era have made intellectual capital an important source of wealth creation alongside physical assets. Intellectual capital (IC) is a resource that provides a competitive advantage (Bayraktaroglu, Calisir & Baskak, 2019;Chen, Cheng& Hwang, 2005), which means it also has a positive impact on firms' performance for that firms need methodologies to identify and measure IC (Bayraktaroglu et al., 2019).
Given the intangible nature of IC, there is a difficulty in measuring it (Chang & Hsieh, 2011). The traditional accounting model remains focused on physical and financial assets and ignores most IC assets. Additionally, the International Accounting Standards/International Financial Reporting Standards (IAS/IFRS) did not contribute to redefining many of the concepts, principles, and valuation methods of IC assets. The relative lack of IC accounting recognition and its increasing role in the value creation process means that the financial statements have lost some of their value to users (Zé ghal & Maaloul, 2010). Many researchers have made significant contributions to solving the problem of classification and measurement of IC (Dzenopoljac, Yaacoub, Elkanj & Bontis, 2017).
The Value-Added Intellectual Coefficient (VAIC) model, developed by Pulic (1998), is one of the most used methods of the efficiency measurement for three components: physical capital, human capital, and structural capital within a firm. Many studies used the VAIC model (Bayraktaroglu et al., 2019;Maditinos, Chatzoudes, Tsairidis, & Theriou, 2011;Nadeem, Gan& Nguyen, 2017) to measure intellectual capital (IC). Evidence from these studies indicates that These are intellectual assets that companies do not trade it, and the value of its elements cannot be inferred from normal market transactions, such as the value of traditional tangible assets (Bontis, 1998). In a knowledge-based economy, the companies increase shareholders' value to the maximum from intellectual resources more than physical or financial resources) Chang & Hsieh, 2011). There is a consensus that organizational capabilities are based on knowledge management because they are the source of organizational sustainability and competitive advantage ) Chang & Hsieh, 2011). The United Nations estimates that knowledge economies now account for 7% of global GDP and grow at 10% annually. It is worth noting that 50% of productivity growth in the European Union is a direct result of the use and production of information and communications technology (Dahman, 2017). In Saudi Arabia, The Council of Economic and Development Affairs has set 13 executive programs to achieve the 96 strategic goals of the Kingdom of Saudi Arabia's 2030 vision. One of these programs is The Human Capital Development program, which aims to improve the outputs of the education and training system in all its stages from early education to continuous education and training to reach global levels through education, qualification, and training programs that keep pace with the developments of the times and its requirements ("Human Capital Development Program Saudi Vision 2030," 2016).

Definitions of IC
According to The International Accounting Standards Board (IASB;1998) is defined an intangible asset as: "an identifiable non-monetary asset without physical substance". Such an asset is identifiable when it is separable, or when it arises from contractual or other legal rights. Examples of intangible assets include computer software, licenses, trademarks, and patents. Under Standard 1 issued by the IASB (1997), intangible assets are separately recognized on the balance sheet. To manage and measure a specific phenomenon, it must first be clearly defined. Additionally, IC is a conceptually clear research topic but represents significant diversity when it comes to developing an accurate definition (Dzenopoljac et al., 2017).
Given the intangible nature of knowledge, various concepts have been proposed in academia, and each attempt to captures a specific phenomenon. As a result, IC is an ideological process are not just an intangible asset. It is a type of movement from owning the knowledge and having the skills to using it (Chang & Hsieh, 2011). Bontis (1998) defined IC as "the pursuit of effective use of knowledge as opposed to information" (p.67). According to (Maditinos et al., 2011) IC can be defined as the gap observed between the market value and the book value. Additionally, Seetharaman et al. (2002) argued that IC is the difference between the firm's market value and the cost of replacing its assets.
Based on the definitions presented, we can define IC as the knowledge that is used to create value for the organization to obtain a competitive advantage.
Additionally, SC "represents all non-human stocks of codified knowledge in an organization" (Ferreira & Martinez,2011, p.252). It includes operations, systems, structures, brands, intellectual properties, and other intangible properties owned by the company that are not visible on its balance sheet (Roos et al., 2001). Finally, CC is "considered a market-based asset that is obtained through an affiliation with a brand" (Ferreira & Martinez,2011, p.252). It represents all valuable relationships with customers, suppliers, and other relevant stakeholders (Roos et al., 2001).

Some Methods and Models for the Measurement of IC
Academia has drawn attention to the role of knowledge in business development ) Chang & Hsieh, 2011), and there is a large number of publications on this topic as well as a wide variety of methods (Osinski, Selig, Matos, & Roman, 2017). To effectively and efficiently manage IC, it is necessary to properly identify, understand, and measure its components, which poses a great challenge for academics and practitioners (Dzenopoljac et al., 2017). Many tools have been proposed in studies, but the reliability of the tool is still largely dependent on industry characteristics and the objectivity of the information (Chang & Hsieh, 2011 ( . These tools can be separated into two groups: The first group, determines each component of IC in monetary terms, such as the calculated intangible value (CIV), Tobin's Q, and VAIC, developed by Pulic (1998).
They determine the economic value of a firm's intangible assets and compares a firm's performance with its competitors) Bayraktaroglu et al., 2019).
The second group determines the IC without assigning a monetary value to it, such as Balanced Scorecard and Skandia Navigator (Bayraktaroglu et al., 2019;Dzenopoljac et al., 2017). They monitor what kind of IC elements a firm has, and what type of effects it has ) Bayraktaroglu et al., 2019). The (VAIC) model, developed by Pulic (1998), is one of the most widely used monetary measures of IC efficiency because this model measures the value added (VA) by any business along with the individual contributions of each asset class of human capital (HC), structural capital (SC), and capital employed (CE) in value creation-unlike other valuation-based measurements that cannot measure the value of a company's IC assets (Bayraktaroglu et al., 2019;Maditinos et al., 2011;Nadeem et al., 2017).

The Relationship Between IC and FP: A Literature Review
When examining the main elements of the IC and their impact on FP, many studies indicate a strong and positive relationship, based on how each IC component is measured (Dzenopoljac et al., 2017). In the literature, there are various studies, which have been conducted in diverse industries (banking/finance, IT, high technology, service, manufacturing, etc.) and countries (India, Australia, Greece, Taiwan, etc.) to examine the relationship between IC and FP by using the VAIC model.
There are many indicators often used in the VAIC model to measure FP, including three financial performance indicators: return on assets (ROA), return on equity (ROE) for profitability and assets turnover (ATO) for productivity, and one market performance indicator-market to book ratio (MB) (Bayraktaroglu et al., 2019). -Belkaoui (2003) investigated the relationship between IC and FP using 81 United States (US) multinational firms. The results revealed that intangible assets in general and IC in particular are a sustainable source for creating superior wealth. Chen et al. (2005) examined the relationship between IC and market value and the financial performance of Taiwanese firms listed on the Taiwan Stock Exchange (TSE) for the period from 1992 -2002. The results indicate a positive relationship between IC and firms' market value and financial performance. Also, investors place a high value on firms with better IC efficiency. (Zéghal & Maaloul, 2010) tested the impact of IC on the firm's economic, financial, and stock market performance of 300 United Kingdom (UK) companies for the year 2005. The findings show that the IC of companies has a positive impact on economic and financial performance and also indicates that capital employed was a major determinant of financial and stock market performance. (Chu, Chan, & Wu, 2011)  Al-Musali and Ismail (2014) conducted a study on all of the commercial banks listed on the Saudi Stock Exchange (Tadawul) -11 commercial banksfor 2008 -2010. They argued that the main resources of banks are intellectual and intangible and play the most important role in the value creation process, so it is important to explore the relationship between IC and bank performance. The results revealed that the IC performance of Saudi banks is low and HCE is positively associated with bank financial performance indicators (measured by ROA and ROE) and CEE is positively associated with bank financial performance indicators (ROE) while SCE has a nonsignificant association with financial performance indicators.

Riahi
In his review of empirical IC research, Inkinen (2015) focused on analyzing 54 experimental research papers to determine whether IC systematically affects FP. First, the main results show that IC mainly impacts FP through combinations and interactions between the IC dimensions. Second, IC influences FP through mediation (a variable that explains the relationship between the independent variable and the dependent variable, which is FP). Third, there is evidence of a strong relationship between the IC dimensions and the firm's innovation performance. Dzenopoljac et al. (2017) used a sample of 100 publicly traded Arab companies to assess the impact of IC components on the company's earnings, profitability, efficiency, and market performance for the period between 2011-2015. The results showed that earnings and profitability were significantly affected by structural and physical capital. Efficiency was determined primarily by physical capital. The performance of the market has been mainly affected by HC. Nadeem et al. (2017) used a sample of all publicly listed firms on the Australian stock exchange for a period from 2005 -2014. This study showed that IC efficiency is positively significant with FP, as measured by ROA and ROE. Nawaz and Haniffa (2017) examined the effect of IC on the FP of 64 Islamic financial institutions in 18 different geographical regions and categorized the data into three main groups, namely, Asia, Europe, and the Middle-East for the period from (2007 to 2011). The results showed a significant positive relationship between VAIC and FP (measured by ROA) and a significant positive relationship between FP and CEE and HCE; however, there was no significant relationship with SCE. Smriti and Das (2018) examined the relationship between IC and FP for a sample of 2,500 Indian companies in 15 industries for the period from (2001 to 2016). Their findings revealed that HC has a major impact on firm productivity (measured by ATO). Additionally, SCE and CEE have an important role in the firm's sales growth (SG) and market value (measured by Tobin's Q).
However, some studies added other elements to IC to expand and modify the VAIC methodology. Bayraktaroglu et al. (2019) examined the relationship between IC and FP for a sample of 400 manufacturing firms listed on the Istanbul Stock Exchange for the period from (2003 to 2013). They added customer capital (CC) efficiency and innovation capital to the VAIC model. The results reveal that innovation capital has a moderating role in SC's impact on firm profitability and that IC components have a moderating role on the relationship between CEE and profitability.

The Cons of a Positive Relationship between IC and FP
Chang and Hsieh (2011) added innovation capital to the VAIC model to investigate the efficiency of IC and its performance on all the publicly listed semiconductor companies on the Taiwan Stock Exchange-based on data from 367 companies for the period from (2000 to 2008). They concluded that a company's IC has a negative impact on its financial and market performance, but the association between innovation capital and companies' operating, financial, and market performance is significant. Firer and Williams (2003) tested the relationship between IC and FP for a sample of 75 publicly traded firms from South African and belonging to four sectors. The empirical results failed to find any relationship between the IC and the dependent variables (profitability, productivity, and market value). This means that South African firms mostly depend on their tangible resources despite efforts to increase the nation's IC base. Maditinos et al. (2011) examined the impact of IC on firms' market value and financial performance for a sample that consisted of 96 Greek companies belonging to four economic sectors for a period from 2006 to 2008. The empirical results failed to support any relationship between the value-added efficiency components and the dependent variables (profitability, growth, and market value) except for having a significant relationship between HCE and FP (measured by ROE).

The Neutral Effect of IC on FP
According to the aforementioned literature, which was conducted to examine the relationship between IC and FP, there is some evidence of a relationship between IC components and FP but there are differences regarding the nature of this relationship (Bayraktaroglu et al., 2019). This difference may be for several reasons: 1) variation in economic and political conditions between countries; 2) some studies have been conducted during the years of the global financial crisis; and 3) the use of multiple indicators to measure FP.

Conceptual Model
The conceptual model developed for this study is based on previews methodologies (Maditinos et al., 2011;Nawaz & Haniffa, 2017;Pulic,1998Pulic, ,2004 and investigates the relationship between IC and FP. The resource-based view of the strategy claims that performance differences arise because successful companies have valuable resources that others do not (Roos et al., 2001). The VAIC methodology defines IC as the total of human capital and structural capital. It includes three components; human capital efficiency (HCE), structural capital efficiency (SCE), and capital employed efficiency (CEE). In the previous section, we discussed a set of empirical research using the VAIC methodology and the results were mixed.
The design of the conceptual model of the relationship between IC and FP may be developed as follows:

Hypotheses Development
The hypotheses developed in this study arise from the literature review related to our subject. The main hypothesis assumes that there is a positive relationship between overall intellectual capital efficiency and firm performance.
From this main hypothesis, three sub-hypotheses related to the impact of each component of Intellectual Capital are derived and would be tested.
H1a: There is a positive relationship between human capital efficiency and firm performance.
H1b: There is a positive relationship between structural capital efficiency and firm performance.
H1c: There is a positive relationship between capital employed efficiency and firm performance.

Definition and Measurement of Variables
The variables used in our conceptual model can be classified into three categories: independent, dependent, and control variables.

Independent Variables
VAIC is the measurement of the independent variables in this study using the definition of Pulic (1998). It measures the value added (VA) by any business along with the individual contributions of each asset class of human capital (HC), structural capital (SC), and capital employed (CE) in value creation to assist management and other interested parties in effectively monitoring and evaluating the efficiency of VA. The present study includes four independent variables: HCE (human capital efficiency), SCE (structural capital efficiency), CEE (capital employed efficiency), and VAIC (the composite sum of the three separate indicators).
In the first phase, to compute efficiency levels, the value created by the company (VA) must be calculated. The VA is the difference between the output and input (Pulic,2004, p.64). Additionally, OUT is "total sales," and IN is the "cost of bought-in materials, components, and services" except the labor expenses are treated as a resource because of the important role of employees in the VA (Pulic,2004).

VA= OUT -IN
Value added can be calculated from the company accounts as follows: VA = OP + HC + D + A where: OP = operating; HC = employee costs; D = depreciation; and A = amortization (Pulic,2004 In the second phase: we calculate (CE, HC, and SC) as they were defined by Pulic (2004).
CE is a book value of the net assets of the company = total assets _ intangible assets HC includes the total salaries and wages for the company = total investment of employees (salary, wages, etc.) SC is the difference between VA and human capital =VA-HC This means there is an inverse relationship between human capital and structural capital in creating value for the company. (Pulic,2004).
Finally, we compute VAIC and its three components (CEE, HCE, and SCE).
HCE is the ratio of VA to human capital (HC) =VA/HC SCE is the ratio of Structural Capital (SC) to VA =SC/VA CEE is the ratio of VA to Capital Employed (CE) =VA/CE VAIC=CEE+ HCE +SCE There are several main reasons for the use of the VAIC model in this study:1) simplicity and availability of data; 2) objectivity, and reliability because the data are usually audited by a professional accountants ; and 3) comparability-in addition to its use by interested parties to effectively monitor and gain insight into the intangible assets of the company (Al-Qulaiti,2013;Bayraktaroglu et al., 2019;Chang & Hsieh, 2011;Firer & Williams, 2003;Maditinos et al., 2011;Pulic 1998Pulic , 2004Smriti & Das, 2018).

Dependent Variables
The present study includes two financial performance indicators and one market performance indicator as to the dependent variables.
The financial performance is measured using two indicators: Return on Assets (ROA) and Return on Equity (ROE). Return on assets is an indicator that measures the company's profitability relative to its total assets. Return on equity is an indicator that measures the company's efficiency in generating profits from each unit of shareholders' equity (Chen et al.,2005;Firer & Williams, 2003;Maditinos et al., 2011;Nawaz &Haniffa, 2017).
The market performance is measured using Tobin's Q which is the ratio of the market value of a company's assets to the replacement value of those assets . The market value of assets can be estimated as the sum of the market value of the company's equity and book values of its debt and the replacement value can be considered as equal to the book value of the total assets. It measures the wealth generated by a company for its shareholders ) Bayraktaroglu et al., 2019;Smriti & Das, 2018).

Control Variables
This study includes two control variables (the firm's size and the firm's activity) because the sample consists of firms from different industry groups.
-The firm's size is measured by the natural log of the total assets (Al-Qulaiti, 2013;Goebel, 2015) -The firm's activity consists of the nominal variable representing four industry groups within the service sector (Firer & Williams, 2003;Goebel,2015).

Econometric Models
Six multiple regressions would be used to test the developed hypotheses of the study.

Models Testing the Relationship Between the Overall Intellectual Capital Efficiency (VAIC) and FP.
Models,1,2 and 3 were attempts to examine the relationship between (VAIC) and FP measured by two financial performance indicators (ROA, ROE) and the market performance measured by Tobin's Q, respectively. They serve to test the main hypothesis which states that: "There is a positive relationship between overall intellectual capital efficiency and firm performance." These models are stated as follows: The second set of models (4, 5, and 6) serve to test the three sub-hypotheses (H1a, H1b, and H1c). They serve to examine the relationship between the components of the VAIC (CEE, HCE, and SCE) and FP measured by both financial performance indicators (ROA and ROE), and the market performance indicator (Tobin's Q), respectively.

Data Collection
To collect the data for testing the hypotheses, we used the convenience sampling method. The firms required for this study were selected from the Saudi Stock Exchange website (Tadawul), as this study was conducted for the Saudi context, and all of the relevant data and variables were available on this website for the period (2015-2018). The sample from the present study includes 25 Saudi firms listed on the Saudi Stock Market (Tadawul) and are from four industry groups: telecommunication services (four companies), diversified financials (four companies), banks (11 banks), health-care equipment and Svc (six companies). The selected data covered a period of four years, 2015 -2018. The panel data was adopted in this study because we needed observations of multiple phenomena obtained over multiple periods for the same firms. After the exclusion of one observation with a negative VA score, 99 observations belonging to 25 firms operating in four industry groups had been used in the analysis. An observation with a negative VA has been excluded because of the inability of the VAIC model to deal with the negative VA values, because "This would then mean that the company is expending more input resources than its output. The negative sign is carried through in all subsequent indexes, which does not generate meaningful analysis." (Chu et al., 2011, p.252) . All four industry groups are knowledge-based and important to the Saudi economy. (Bontis,1998) stated that intellectual assets are important in the professional services industry and the many new and knowledge-based companies.

Empirical Evidence of the Impact of IC on FP in the Saudi Context
This section presents the descriptive analysis and the correlation and multiple regression models to test the research hypotheses.

Descriptive Statistics
The descriptive statistics, namely, the frequency, mean, standard deviation, median, minimum, and maximum values of all variables are respectively shown in Table (1) and Table (2) (see Appendix A). This suggests that HCE remained the main value driver for firms during the study period, indicating the effective utilization of HC. Also, the sum of the mean values of HCE and SCE is 4.480 754 9, which is more than the mean value of CEE (0.107 305 9), indicating that the firms created more value from the intangible components of VAIC than from the physical and financial components.
It can be seen that the mean of 4.588 061 for VAIC, which indicates that the firm produced an average value of First, a one-way analysis of variance (ANOVA) was conducted to check if there are significant differences between the means of VAIC in the four industry groups.  Table (3) illustrates that there are significant differences between the means of VAIC in the four industry groups at a significant level of 5% (p= 0.000 9). This indicates that the ability of each industrial group to compete depends, to a large extent, on the creation of value through investment in IC.
Second, the one-way ANOVA was conducted on the four industry groups to check if there are significant differences between the means of each component of VAIC (HCE, SCE, and CEE). The results from this test are shown in Tables 4, 5, and 6. These results in Tables 4 and 5 suggest that there are significant differences between the means of HCE and CEE in the four industry groups at a significant level of (5%) (p= 0.000 1, p=0.000 0), respectively. This means that HC and CE are to play an important role in creating value in the four industry groups. The results in Table 6 suggest that no significant differences exist between the means of SCE in the four industry groups (see Appendix B).

Hypotheses Testing: Correlation and Regression Analysis
This research is based on the panel data, which contains observations of multiple phenomena obtained over multiple periods (2015 to 2018) for the same firms (25 companies from four industry groups) to empirically test the relation between IC and FP. There are some mandatory conditions to use multiple regressions based on the panel data.

Hausman Specification Test for a Fixed or Random Effect
Hausman specification tests are conducted for the panel data to check whether models that would be used to test the developed hypotheses were fixed-or random-effect models. When the probability of the test is less than 10%, then the fixed-effect model is most appropriate for the study. The unbiased estimator of this type of model is the "within" estimator. The results of the Hausman specification tests for the six models (in Table 7) show that the fixed-effect model is most appropriate for the study because the probability of the test is less than 10% for all of the models (see Appendix C).

The Impact of Overall Intellectual Capital Efficiency (VAIC) on FP .
Tables 8, 9, and 10 show the results of the multiple linear regressions for models 1, 2, and 3 respectively, to examine the relationship between VAIC and FP as measured by ROA, ROE and Tobin's Q at the sample level. As seen in Tables 8, 9, and 10 the R 2 is 0.306 1, 0.123 5, and 0.342 9 for the whole sample, indicating that the models 1, 2, and 3 can explain about 31%,12%, and 34% respectively, of the changes that occur in FP. Also, Tables 8 and 9 indicate that there is a positive and significant relationship between VAIC and the firm's financial performance as measured by ROA and ROE at a significant level (5%). This means that any increase in the value of VAIC leads to the improvement of the financial performance of the firms. Table 10 indicates that there is no relationship between VAIC and the firms' market performance as measured by Tobins'Q.
In regard to the control variables, Tables 8 and 9 show that there is a positive relationship between the firm's activity and the firms' financial performance as measured by ROA and ROE at a significant level (5%).Tables 9 and 10 indicate that there is a positive relationship between the firm's size and the FP measured by ROE and Tobins'Q at a significant level, 10% and 5% respectively (see Appendices D, E, and F).
There is a multicollinearity problem concerning the control variable (activity), thus it is automatically omitted from model 3.

The Impact of the Components of the VAIC (HCE, SCE, and CEE) on FP.
Tables 11, 12, and 13 show the results of the multiple linear regressions for models 4, 5, and 6 when examining the relationships between the components of the VAIC, namely HCE, SCE, and CEE, and the FP measured by ROA, ROE and Tobin's Q respectively. The results indicate that the R 2 in a model 4, 5, and 6 is 0.400 5, 0.279 7, and 0.342 8 for the whole sample. This means that the models 4, 5, and 6 can explain about 40%, 28%, and 34% of the changes that occur in FP. The result in Table 11 demonstrates that there is a positive and significant relationship between HCE, SCE, and CEE and the financial performance of firms (ROA).
Moreover, the results depicted in Table 12 indicate there is a positive association between (HCE) only and the firms' performance measured by ROE; however, there is no relationship between SCE, CEE and ROE. According to the results in Table 13, there is a positive and significant relationship between (HCE, SCE) and the market performance of firms measured by (Tobin's Q). In regard to the control variables, Tables11, 12, and 13 show that there is a positive relationship between the firms' size and the firms' performance as measured by ROA, ROE, and Tobin's Q at a significant level (5%) (see Appendices G, H, and I).
There is a multicollinearity problem concerning the control variable (activity), and it is automatically omitted from model 6.

Discussion
The results of the models 1 and 2 indicate that VAIC is positively related to firms' financial performance as measured by ROA and ROE, which means that IC is important for the firms' competitive advantages and its significant role in value creation.
These findings are consistent with previous studies (Al-Qulaiti, 2013;Chen et al., 2005;Chu et al., 2011;Nadeem et al., 2017;Nawaz & Haniffa, 2017;Zé ghal & Maaloul, 2010;). These studies confirmed that the efficiency and ability of intellectual resources to create added value positively affect the financial performance of firms. The results of model 3 failed to find an association between VAIC and firms' market performance measured by (Tobin's Q). This finding agrees with the previous studies (Chu et al., 2011;Maditinos et al., 2011). As a result, the main hypothesis H1 is partially confirmed.
Concerning the impact of each component of VAIC (HCE, SCE, and CEE) on a firm's performance, the results of the model 4 show that there is a positive association when the firm's performance is measured by ROA, which implies that IC and physical capital have a major impact in creating value for stockholders and other stakeholders. This result corroborates the previous studies conducted by Al-Qulaiti (2013) and Chu et al. (2011).
The findings of model 5 show that there is a positive association between HCE (only) and FP as measured by ROE. This is in line with the results of (Maditinos et al., 2011) who only found a statistically significant relationship is the one between HCE and (ROE). The explanatory power of models 4 and 5 was higher than that in models 1 and 2. This first implies that, the VAIC components may be a better predictor than the aggregate VAIC in predicting the firm's financial performance (Chu et al. 2011) and, second, the stakeholders may have emphasized various components of IC differently (Firer & Williams, 2003;Chen et al.,2005;Chu et al., 2011). Additionally, the explanatory power of the ROA models was higher than the ROE models using VAIC and the three components as independent variables because ROA reflects the effectiveness of a firm in utilizing all sources of funding (both debts and shareholders' equity) while ROE only includes shareholders' equity (Chu et al., 2011).
The findings of the model 6 show that there is a positive relationship between HCE and SCE and the market performance of firms measured by (Tobin's Q). This denotes that investors prefer firms that generate a higher return from intellectual resources. It seems that Saudi Arabian investors realize the importance of IC and focus on the human and structural capital of a firm when they estimate its real value. This result is in line with the study of (Bayraktaroglu et al., 2019) who observed a significant relationship between HCE and the market performance of firms. Smriti and Das (2018) also found that SCE had a positive impact on the market performance of firms.
From the aforementioned analysis, H1a is fully supported by the empirical data, while H1b and H1c are partially supported.
The results related to the control variables (in Tables 8 , 9, and10) show that there is a positive relationship between the firm's activity and the firms' financial performance(ROA, ROE), in addition to there is a positive relationship between the firm's size and the firms' financial and market performance (ROE, Tobin'sQ).This indicates that the optimum utilization of available assets leads to lower costs and contributes to improving the firms' financial and market performance (Al-Qulaiti, 2013).
Overall, it can be observed that HCE, among the components of VAIC, has a great impact on FP. This revealed that the managers of the selected firms in our study focus their attention on IC, especially HC in value generation, more than physical capital.
Our empirical findings contradict the results of Dzenopoljac et al. (2017) who found that profitability was greatly affected by structural and physical capital only, and market performance was negatively affected by HC. This difference may be attributed to several reasons: 1) the different countries and sectors that were included in the sample, which included manufacturing and service sectors; 2) the difference in the period because it covered the years that followed the global financial crisis; and 3) the difference in indicators used to measure FP.

Conclusion: Findings, Limitations, and Implications
This research aimed to investigate the association between the IC (on overall), each of its three components HCE, SCE, and CEE and FP (measured in terms of financial and market performance) for 25 Saudi listed firms in four industry groups for the period from 2015 to 2018. The study adopted the VAIC model as a proxy for IC and its components, which were used in previous studies (Bayraktaroglu et al., 2019;Chen et al.,2005;Chu et al., 2011;Dzenopoljac et al., 2017;Firer & Williams, 2003;Maditinos et al.,2011). The empirical findings denote that there is a positive association between VAIC and the firms' financial performance. This suggests the importance of IC in creating value for stockholders and other stakeholders. Also, there is a significantly positive association between HCE, SCE, and CEE and the firms' financial performance. It is observed that IC components have a predicting power that, for the firms' financial performance, is higher than VAIC overall.
However, findings show that no association exists between VAIC and the firms' market performance while a positive association exists between HCE and SCE and the firms' market performance. This reflects the positive reaction of investors towards firms with a higher efficiency for using intangible resources.
Finally, our results show that HCE has a significantly positive association with a firm's financial and market performance, and it seems to be the most important contributors to economic success in Saudi Arabia. . xtreg roa hce sce cee size activity, fe