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The Relationship between Corporate Governance and Intellectual Capital: The

Moderating Role of Firm Size

Article in International Journal of Law and Management · February 2019

DOI: 10.1108/IJLMA-02-2018-0033

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 International Journal of Law and Management
The relationship between corporate governance and intellectual capital: The
moderating role of firm size
Amina Buallay, Allam Hamdan,
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intellectual capital: The moderating role of firm size", International Journal of Law and Management,
Vol. 61 Issue: 2, pp.384-401, https://doi.org/10.1108/IJLMA-02-2018-0033
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IJLMA
61,2 The relationship between
corporate governance and
intellectual capital
384 The moderating role of firm size
Received 13 February 2018 Amina Buallay
Revised 13 February 2018
Accepted 15 November 2018
Brunel University, London, UK, and
Allam Hamdan
Department of Accounting and Economics, College of Business and Finance,
Ahlia University, Manama, Bahrain
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Abstract
Purpose – The purpose of this study is to examine the moderating role of firm size on the relationship
between corporate governance (CG) and intellectual capital (IC) efficiency.
Design/methodology/approach – The methodology was a pooled data for three years (2012-2014) for
171 listed firms, resulting in 489 observations.
Findings – The findings revealed that the inclusion of firm size as a moderating variable has influenced
positively only the relationship between CG principles and capital employed efficiency (CEE). Further, the finding
showed that the two IC components namely, human capital efficiency and structural capital efficiency, tend to be
higher with firms that high level of CG adoption. However, CEE tends to be higher with firms that have lower
level of CG adoption. Other finding shows that CG index was significant with the three IC components.
Originality/value – Such information will help the stakeholders, investors, decision-makers, regulators,
policymakers and scholars to improve their knowledge about IC. Furthermore, it will be useful for firms to place
their priorities regarding the internal system and financial plans for effective and efficient use of CG and IC.
Keywords Saudi Arabia, Corporate governance, Intellectual capital, Agency theory,
Resources-based theory
Paper type Research paper

1. Introduction
In a competitive world, Knowledge is power, aphorism has increased by the beginning
of twenty-first century (Rechberg and Syed, 2013). Nowadays, researchers focus
attention on intellectual capital (IC), which consists of knowledge and experience of an
employee, database and information systems, business relationship, goodwill and
alliance (Saunders and Brynjolfsson, 2016). Van der Meer-Kooistra and Zijlstra (2001)
claim that IC adds value to the firms by improving the exchange of knowledge and the
creation of new knowledge. Guthrie and Petty (2000) note that the IC has the potential to
improve the efficiency of both capital and labor markets. Researchers also find that IC
positively influences the performance and wealth of the firms (Celenza and Rossi, 2014;
International Journal of Law and
Management
Singh et al., 2016; Inkinen, 2015; Zerenler and Gozlu, 2008; Phusavat et al., 2011). The
Vol. 61 No. 2, 2019
pp. 384-401
importance of knowledge and IC is essential for the shareholders and investors to
© Emerald Publishing Limited ensure that managerial decisions are made to enhance shareholders’ wealth through the
1754-243X
DOI 10.1108/IJLMA-02-2018-0033 efficient use of IC (Appuhami and Bhuyan, 2015).
 Despite the fact that IC is a competitive strategic resource and it increases performance, The
there are problems with managing and controlling IC in organizations. Van der Meer- moderating role
Kooistra and Zijlstra (2001) argue that if IC is not properly managed, it will be suboptimal,
its value-added capacity will not be fully used. Managing the IC remains one of the vital
of firm size
challenges for the accounting profession, due to its complexity and diversity (Dzinkowski,
2000). Several researchers argue in support of the need to understand the role of corporate
governance (CG) in effectively protecting and managing IC in the firms (La Rocca et al., 2008;
Safieddine et al., 2009). CG ensures that decisions made by managers are made to enhance 385
shareholders’ interest through the efficient use of IC. However, few research studies show
how CG influences the IC in the firms. In particular, there is no enough understanding about
the linkage between CG and IC. Although, theories pretend to be inefficient so far in
determining how CG inside firms influences the IC.
There are few attempts to measure the relationships between CG and IC, particularly in
Saudi (Al-Musalli and Ismail, 2012). Considering the fact that firms’ main resources are
knowledge and IC and it functions the most significant role in firms’ value-creating process,
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it is necessary to have sufficient information about the CG efficiency in Saudi firms and
analyze, how well CG principles are adopted to use the IC.
Because of the importance of CG and IC to stakeholders; factors affecting the relationship
between CG and IC need to be identified. This study contributes to the literature in several
ways. First, it sheds the light on the few prior CG–IC research by considering all sectors in
the Saudi economy. Previous studies have examined CG and IC separately. Second, it
provides empirical evidence on the relationship between CG principles and IC by using data
from listed firms of the biggest country in the Gulf Council Countries (GCC). Thus, the
results are expected to broaden the understanding of CG and its impact on IC, which
eventually affect firms’ performance in GCC economies. Third, this study contributes to the
third stage of IC research by investigating the effect of CG principles, which influence the
board of directors (BOD) and managers’ behavior with regard to IC efficiency. Fourth, this
study uses a third variable by using the moderated model to enhance and strength the
relationship between CG and IC. Fifth, this study uses the VAIC model by using three
coefficients; namely, capital, structural capital and capital employed. Finally, such
information will help the stakeholders, investors, decision maker, regulators, policymakers
and scholars to improve their knowledge about IC. Furthermore, it will be useful for firms to
place their priorities regarding the internal system and financial plans for effective and
efficient use of CG and IC.
This study followed the model developed by Pulic (1998), which is called value added
intellectual (VAIC) to measure the IC efficiency. This study attempt to investigate whether
CG principles are affected by human capital, structural capital and capital employed in
Saudi listed firms.
Section 1 being introduction, further part of this study is divided into five sections, as
follows: Section 2 discusses literature review and developing hypotheses. Section 3 presents
the design and research methodology. Section 4 shows the descriptive statistics. Section 5
presents regression analysis results. Section 6 presents the study’s conclusion,
recommendations and the scope for further research.

2. Literature review and hypotheses

CG is a combination of policies, laws and instructions influencing the way a firm is managed
and controlled, it consists of a framework of rules to grant transparency and fairness in the
relationship between the firms and its shareholders, the framework of CG consists of both
external and internal contracts between employees and the shareholders, it includes
 IJLMA distribution of rewards and responsibilities and conditions to avoid conflicting interests
61,2 (Klapper and Love, 2004). Over two decades both CG and IC literatures have exposed the
significance to stakeholders and management and the prospects of developing the topic of
CG are considered along with areas for future research (Turnbull, 1997). CG and IC are
considered as the main value driver of the firm’s performance, it has become a significant
factors for those firms in enhancing their competitive advantage and reducing agency
386 problem. IC is not clear as CG and like many other intangible assets; it might be difficult to
identify and report in financial statements. This leads to a gap between firms’ value as
reported in the financial statement and actual market value (Rahman, 2012). The need for IC
valuation has increased; different methods to calculate the value of IC and its efficiency have
been developed, such as Skandia IC Report Method (Edvinsson and Malone, 1997),
Intangible Asset Monitor Approach (Sveiby, 1997) and value added intellectual coefficient
(VAIC) (Pulic, 1998). Among IC valuation methods, VAIC is widely used in calculating the
IC. Few studies have discussed the linkage between CG and IC. However, most of these few
studies have been carried out in developed countries. Al-Musalli and Ismail (2012) examine
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the level of IC of listed banks in GCC countries using VAIC and investigate the possible
impact of several CG variables, bank-specific characteristics and banking industry
characteristics on IC. They found that board size, board independence, family ownership
and institutional ownership have a significant relationship with IC. Moving from GCC
countries to Middle East countries, Alizadeh et al. (2014) examine the association between
CG and IC in the pharmaceutical companies in Tehran Stock Exchange for five years period
from 2004 to 2009, using a regression model. The independent variable was CG (i.e. board
size, duality of CEO and BOD and auditing committee). The results found that board size
has a negative impact on firms’ IC, while dualities of CEO and BOD and auditing committee
have no effects on IC. Another study was adopted by Altuner et al. (2015) in Istanbul to
examine the linkages among IC, CG and corporate social responsibility, the study was
conducted on manufacturing firms listed in Istanbul Stock Exchange for five years from
2007 to 2011. The results support a positive relationship among these important constructs.
Widening the literature to Asian countries, Ahmed Haji and MohdGhazali, (2013) examine
the relationship between IC disclosure and CG in Malaysian listed companies for the period
2008-2010. The study concludes that all CG attributes, namely, board size, independent
directors, board effectiveness and position of the chairman were significant while director
ownership was found to be consistent in negatively with IC. Broadening the literatures to
the European countries, Cerbioni and Parbonetti (2007) examine the relationship between
CG and IC in a sample of European biotechnology firms. The variables were independent
directors, board dimension, CEO and BOD duality. The findings suggest that BOD
independency is positively-related IC while CEO duality is negatively linked to IC. Another
study adopted by Li et al. (2008), adopting a study in UK for a sample of 100 UK listed firms
to investigate the relationship between IC disclosure and CG variables. The independent
variables are as follows: board composition, ownership structure, audit committee size and
frequency of audit committee meetings and CEO role duality. Findings show that IC
components have a significant association with all the CG except for role duality. Appuhami
and Bhuyan (2015) examine the influence of CG on IC in top service firms in Australia. The
findings show that CEO duality, board composition and remuneration committee
composition are significantly associated with IC, whereas, board size and audit committee
composition are insignificantly associated with IC. Another study conducted in different
stock exchanges by Saeed et al. (2015) to explore the role of IC as a mediator between CG and
firms performance relationship. This study use five CG measures, which can contribute to
the IC and then IC leads to corporate performance. The results show that CG measures and
 IC of firms yield higher corporate performance. Overall, literatures show that the direct The
relationship between CG principles and IC components is ambiguity. To fill this gap in the moderating role
literature, we use a moderated model to enhance the relationship. We adopt this study, as it
depends on both Agency theory developed by Jensen and Meckling (1976), who addresses
of firm size
problems that arise due to the conflict between management and shareholders interest and
the resources-based theory developed by Grant (1991), who considers that IC is the main
strategic asset in creating and maintaining firms’ competitive advantage. Therefore, it is
interesting to further explore the effect of firm size on the relationship between CG and IC of
387
listed firms in Saudi Arabia.
We construct Firm size to be positively enhancing the linkage between CG and IC.
Therefore, the main hypothesis can be divided into three sub-hypotheses:

H1. Firm size positively moderates the relationship between CG and human capital
efficiency (HCE) of Saudi listed firms.
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H1a. Firm size positively moderates the relationship between ownership of largest
shareholder and HCE of Saudi listed firms.
H1b. Firm size positively moderates the relationship between ownership of largest three
shareholders and HCE of Saudi listed firms.
H1c. Firm size positively moderates the relationship between board size and HCE of
Saudi listed firms.
H1d. Firm size positively moderates the relationship between independency of BOD and
HCE of Saudi listed firms.
H1e. Firm size positively moderates the relationship between duality of chairman and
CEO and HCE of Saudi listed firms.
H2. Firm size positively moderates the relationship between CG and structural capital
efficiency (SCE) of Saudi listed firms.
H2a. Firm size positively moderates the relationship between ownership of largest
shareholder and SCE of Saudi listed firms.
H2b. Firm size positively moderates the relationship between ownership of largest three
shareholders and SCE of Saudi listed firms.
H2c. Firm size positively moderates the relationship between board size and SCE of
Saudi listed firms.
H2d. Firm size positively moderates the relationship between independency of the BOD
and SCE of Saudi listed firms.
H2e. Firm size positively moderates the relationship between duality of chairman and
CEO and SCE of Saudi listed firms.
H3. Firm size positively moderates the relationship between CG and capital employed
efficiency (CEE) of Saudi listed firms.
H3a. Firm size positively moderates the relationship between ownership of largest
shareholder and CEE of Saudi listed firms.
H3b. Firm size positively moderates the relationship between ownership of the largest
three shareholders and CEE of Saudi listed firms.
 IJLMA
61,2 H3c. Firm size positively moderates the relationship between board size and CEE of
Saudi listed firms.
H3d. Firm size positively moderates the relationship between independency of the BOD
and CEE of Saudi listed firms.
388 H3e. Firm size positively moderates the relationship between duality of chairman and
CEO and CEE of Saudi listed firms.

3. Research design and methodology

3.1 The study sample and data source
The study is based on the selected sample of 171 listed firms in Saudi stock exchange for
three years from 2012 to 2014 with 498 observations. The Data used in this study were
collected from the Saudi stock exchange database (TADAWUL). Firms used in the sample
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were selected based on data availability. Firms have not been closed down or merged with
other firms during the research period. Data were obtained from the Saudi stock exchange
database; we used in our sample, the pooled data, which combine both time series data and
cross-sectional data in our sample.

3.2 The study methodology

The study aimed to investigate whether CG practices influence the efficiency of the IC of
Saudi listed firms. Thus, the study used the VAIC Model developed by Pulic (1998) to
measure the relationship between CG and IC efficiency. More interestingly, the study used
firm size as a moderating variable in addition to the other two control variables to examine
their influences on the relationship between CG and IC Efficiency.

3.3 Variables
3.3.1 Dependent variables. The dependent variable (IC) is measured using HCE, SCE and
CEE. To measure the value of IC, the efficiency of IC can be measured using VAIC,
following previous studies (Celenza and Rossi, 2014; Singh et al., 2016; Inkinen, 2015 and
Nimtrakoon, 2015, Sarea and Alansari, 2016). Table II shows the steps followed in the
study to reach the VAIC.
3.3.2 Independent variables. The independent variables (CG) is measured using the
Ownership of the largest shareholder, ownership of the three largest shareholders, size of the
BOD, independency of BOD and duality of chairman and CEO (Khamis et al., 2015; Hamdan
and Al-Sartawi, 2013; Bouaziz, 2014).
3.3.3 Moderator variable. Firm size was included in the model to analyze whether adding
a third variable (firm size) will influence the relationships between CG and IC components
(Chen and Chen, 2011)
3.3.4 Control variables. Two control variables will be discussed for all estimated models
of our study. They are: firm age (Fan et al., 2011) and the sectors (Firer and Mitchell
Williams, 2003).

3.4 Study model

To measure the relationship between CG and IC; the study estimates the moderator model as
follows.
 Listed Total Excluded Study
The
Sector companies observations observations sample moderating role
of firm size
Agriculture and Food Industries 16 48 3 45
Banks and Financial Services 12 36 0 36
Building and Construction 17 51 1 50
Cement 14 42 5 37
Energy and Utilities 2 6 0 6 389
Hotel and Tourism 4 12 0 12
Industrial Investment 15 45 4 41
Insurance 35 105 8 97
Media and Publishing 3 9 3 6
Multi-investment 7 21 0 21
Petrochemical Industries 14 42 0 42
Real Estate Development 8 24 0 24
Retail 15 45 0 45 Table I.
Telecommunication and Information Technology 4 12 0 12 TADAWUL
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Transport 5 15 0 15 database sample

Total 171 513 24 489 selection

Variable Formula

Value added (VA) Operating profit þ employee cost þ depreciation

þ amortization
Capital employed (CE) Equity þ long term liabilities
Human capital (HC) Total costs invested on employees
Structural capital (SC) Value added (VA) – human capital (HC)
Human capital efficiency (HCE) VA/HC Table II.
Structural capital efficiency (SCE) SC/VA Value added
Capital employed efficiency (CEE) VA/CE intellectual
Value added intellectual coefficient (VAIC) HCE þ SCE þ CEE coefficient

Corporate
Path A
Governance (X)

Firm Size (MO) Intellectual Capital

(Y)

Corporate
Governance * Firm Path B Figure 1.
Size (X.MO)
The study model
 IJLMA The moderator variable – firm size – in fact, acts like the second independent variable. When
61,2 the moderator variable is launched, the firm size has to maintain a causal relationship with
IC and plays the same function as CG (Namazi and Namazi, 2016).
To measure the moderating role of firm size on the relationship between CG and IC, the
study created an interaction variable (CGIndex*firm size), which included as an independent
variable. Hence, the study regression model is presented as the following:
390 ICit 5 b 0 þ b 1 CG1it þ b 2 CG2it þ b 3 CG3it þ b 4 CG4it þ b 5 CG5it
þ b 6 ðCGIndex  FsizeÞit þ b 7 Ageit þ b 8 Sector þ « it

where: ICit: is a continuous variable; the dependent variable is the IC components measured
by three models (e.g. HCE model, SCE model and CEE model). HCEit: the ratio of value
added, divided by human capital, of the company (i), in the period (t). SCEit: the ratio of
structural capital divided by value added, for the company (i), in the period (t). CEEit: the
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ratio of value added divided by capital employed, for the company (i), in the period (t). b 0: is
the constant. b 1–8: is the slope of the controls and independent variables. CG1it: is a dummy
variable, 0 if a shareholder has shares more than 20 per cent and 1 otherwise, for the
company (i), in the period (t). CG2it: is a dummy variable, 0 if the shareholders have shares
more than 50 per cent and 1 otherwise, for the company (i), in the period (t). CG3it: is a
dummy variable, 0 if the board members are not between 7 and 13 member and 1 otherwise,
for the company (i), in the period (t). CG4it: is a dummy variable, 0 if the boards of director
members are not controlled by greater than 50 per cent independent outside directors and 1
otherwise, for the company (i), in the period (t). CG5it: is a dummy variable, 0 if the chairman
is the same of CEO and 1 otherwise, for the company (i), in the period (t). Ageit: the number of
years, since the company was established, for the company (i), in the period (t). Sectorit: is a
dummy variable, the area of the economy in which companies work in the same field or have
related product or service, for the company (i), in the period (t). « it: is the random error.

4. Testing data validity

As presented in Table III, to secure approximation of data to a normal distribution, Jarque–
Bera parametric test was used. The decision basis of this test is to accept the null hypothesis
(H0) that the data are normally distributed (Gujarati, 2003). As shown in Table III, we
noticed that the (J-B) value for all variables was less than 0.05. This ascertains that the study
data are not normally distributed. To overcome this problem, the natural logarithm of these
variables was considered. Additionally, the size of the sample was big and not distributing
the data normally may not influence the credibility of the study. However, empirical
research that uses time series, like the case of this study, presupposes stability of these
series. Autocorrelation might occur in the model because the time series on which this study
is based is non-stationary (Gujarati, 2003). As for the strength of the general linear model
(GLM) basically depends on the hypothesis that every variable from the independent ones is
by itself independent. If this condition is not realized, the GLM will then be inapplicable. It
can never be considered good for the parameters’ evaluation. To actualize this, collinearity
diagnostics standard used incessant tolerance quotient for every variable of the independent
ones.

5. Descriptive results
As shown in Table III, for ownership of the largest shareholder, the mean percentage is
around 44.1 per cent, shows that the largest shareholder in Saudi companies own more
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Statistics
Jarque–Bera
Variables Label Measurement Mean SD Maximum Minimum (p-value)

Dependent Variable: IC
Human capital efficiency HCE Is the ratio of value added divided by human 5.934 10.357 127.766 0.000 0.000
capital
Structural capital efficiency SCE Is the ratio of Structural capital divided by value 0.826 1.398 24.826 0.000 0.000
added
Capital employed efficiency CEE Is the ratio of value added divided by capital 0.161 0.357 3.219 0.000 0.000
employed
Independent variables: CG
Ownership of largest CG1 Ownership of largest shareholder 0 If a 0.441 0.497 1.000 0.000
shareholder shareholder has shares more than 20% and 1
otherwise
Ownership of largest three CG2 Ownership of largest three shareholders 0 if the 0.612 0.488 1.000 0.000
shareholders shareholders have shares more than 50% and 1
otherwise
Size of BOD CG3 Size of BOD 0 if the board members are not 0.724 0.447 1.000 0.000
between seven and thirteen member and 1
otherwise
Independency of BOD CG4 Independency of BOD 0 if the boards of director 0.429 0.496 1.000 0.000
members are not controlled by greater than 50%
independent outside directors and 1 otherwise
Posts of chairman and CEO CG5 Duality of chairman and CEO 0 if the chairman 1.000 0.000 1.000 0.000
is the same of CEO and 1 otherwise
Moderator variable:
Firm size FSize The total assets of the company 22,795,164 60,986,570 435,000,000 143,895 0.000
Control variable:
Firm age FAge The number of years since the company was 21.263 14.901 60.000 0.000 0.000
established
Industrial dummy Sctr Dummy variable that equals one for industrial
companies

variables, descriptive
Table III.
Measuring of
of firm size
moderating role
The

and validity
391
 IJLMA than 20 per cent of a firm’s outstanding stocks, this means that the majority of firms in
61,2 the Saudi market are family-owned business, they have the voting power in the firm,
which significantly influences the strategic direction and the business operations of the
firm. This indicates that Saudi companies are controlled by a few individuals. The
mean percentage ownership of the three largest shareholders is 61.2 per cent, shows
that the ownership of the largest three shareholders is less than half of the shares in
392 Saudi listed firms. This may indicate that the firms focus on multiple shareholders
control. Added to that, the high percentage shows a strong monitoring by other
shareholders in the firms. One of the important CG practices is having the BOD between
7 and 13 members. The mean percentage for the board size is around 72.4 per cent,
board size is considered to be aligned with governance practices to take a strategic
decision that leads to efficient use of company resources. A BOD between 7 and 13
members can be reasonable, as more the numbers involved, the harder it becomes to
make decisions. For the independency of the BOD, the mean percentage is 42.9 per cent
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of board independency, which is nearly to half of the sample may adversely affect
disclosure and transparency and could be a possible reason for the conflict of interest.
The mean percentage for duality of CEO and the chairman, the duality takes place
when the chairman of the board and CEO roles are 100 per cent compliance in all listed
Saudi firms, which can lead to an effective board (Bouaziz, 2014; Awwad and Alkababji,
2014).

6. Preliminary analysis: corporate governance level and intellectual capital

The CG level was divided into two categories; firms with high CG and firms with low CG as
shown in Table IV. The path analysis based on the value of the calculated median of CG
index (60 per cent). To identify the significance in the variance between the means of the two
samples t-statistic test and z-statistic tests were used. The two IC components namely, HCE
and SCE tend to be higher with firms that have high CG level. However, the CG level found
to be higher with firms that have low CEE.
By using the t-statistic, the path analysis of CG found to be significance in the variance
between the means HCE and SCE while it was insignificant in CEE; similar results were
found by using the z-statistic. The results indicate that the strict adoption of CG leads to
inefficient use of capital employed whereas it has significantly affected the efficiency of
human capital and structural capital as well.

CG level Difference tests

High CG Low CG
Mean SD Mean SD
Intellectual capital of IC of IC of IC of IC t-statistic z-statistic

Human capital efficiency HCE 0.816 1.479 0.628 0.302 2.315** (0.021) 1.910** (0.037)
Structural capital efficiency SCE 0.158 0.374 0.131 0.135 2.203** (0.023) 2.482** (0.014)
Capital employed efficiency CEE 4.902 7.936 6.145 12.580 1.342 (0.180) 1.114 (0.265)

Notes: The t-statistic is based on parametric test Two-Independent Sample t-test and z-statistic is based on
Table IV. non-parametric test Mann–Whitney z-test. The upper value is for t-statistic test or z-statistic and the lower
Path analysis value in brackets (p-value) is the probability value for this test. The difference Significance at: *10; **5; ***1
between CG and IC per cent levels
 7. Granger causality test The
Causality test aims to find the direction of the relationship between CG and IC through moderating role
answering the question as to whether the CG index can cause or encourage IC. This is what
we are trying to answer in this part of the study. This step is to determine the causal
of firm size
direction of “Granger”. When there is one integrative vector a systematic error correction for
Engle and Granger (1987) is used. By applying this test to two slow terms, results emerged
as shown in Table V. Three proposed models of relationships have been developed. The
results show that there is no causal relationship toward the impact of CG on CEE. However, 393
there is a causal relationship toward the impact of CG adoption on HCE and SCE.

8. Empirical study
The study used GLM to test the moderating effect of firm size on the relationship between
CG and IC. We, therefore, run several tests to check whether data of this study could meet
the conditions of the GLM. For the strength of the GLM basically depends on the hypothesis
that every variable from the independent ones is by itself independent. If this condition is
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not realized, the GLM will then be inapplicable. It can never be considered good for
parameters’ evaluation. To actualize this, Collinearity Diagnostics Standard used incessant
Tolerance quotient for every variable of the independent ones. Variance inflation factor
(VIF) has to be found afterward. This test is the standard that measures the effect of
independent variables. Gujarati, (2003) stated that getting a VIF higher than 10 indicates
that there is a multicollinearity problem for the independent variable of concern. As
presented in Table VI, it can be noticed that the VIF values for all independent variables is
less than 10, which means that we do not have any collinearity problems in the study
models.
To test the autocorrelation problem in the study models, we used the Durbin–Watson
(D-W) test. Table VI shows that the D-W values of the HCE, SCE and CEE models are
beyond the (1.5-2.5) range. This indicates the presence of a positive autocorrelation in this
model. To overcome this problem (Lag 1) has to be considered when testing these models.
Empirical studies in finance face many measurement problems including relationship
study between CG and IC, many internal variables are related to a random error of
regression models. As this study is a longitudinal corporate data (panel data) during a
period of time, heterogeneity, simultaneity and reverse causality problems might exist
between these units (Adams et al., 2010; Wintoki et al., 2012). The problem of unobserved
heterogeneity appears when there is a set of latent variables that drive the relationship
between CG and IC. To reach accurate results and to avoid different measurement problems
on the relationship between CG and IC, we use the firm fixed-effect (FE) approach. Table VI
shows the empirical results.

Intellectual capital CG index

HCE 3.110*** (0.009)

SCE 3.304*** (0.000)
CEE 1.694* (0.067)

Notes: The null hypothesis states that there is no causal relationship between the slow factor (independent
variables in the horizontal side: CG index) and (dependent variable in the vertical side: IC variables) of the
table. The upper value is for “Fisher” F-Statistic test and the lower value in brackets (p-value) is the Table V.
probability value for this test. Symbols mean: that there is a causal effect for independent variable to Granger causality
dependent variable at *** 1, ** 5, * 10 per cent, respectively test
 IJLMA HCE model SCE model CEE model
61,2 Variables VIF b t-statistic b t-statistic b t-statistic

Panel A: CG variables:
Ownership of largest shareholder CG1 1.569 1.402 0.61 1.882 3.461*** 0.48 6.604***
0.542 0.001 0.000
Ownership of largest three CG2 1.641 2.09 0.86 3.491 2.507** 0.401 5.143***
shareholders
394 0.3`9 0.012 0.000
Size of BOD CG3 1.076 0.271 0.117 1.673 2.596** 0.491 6.673***
0.907 0.031 0.000
Independency of BOD CG4 1.105 1.949 0.854 3.351 3.351*** 0.487 6.71***
0.393 0.001 0.000
CG index CGIndex 4.55 0.48 3.537*** 7.008 5.61*** 1.801 6.017***
0.000 0.000 0.000
Panel B: Moderator variable:
Firm Size*Governance index SizeGov 3.678 0.42 0.566 0.42 0.566 0.145 6.127***
0.572 0.572 0.000
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Panel C: Control variables:

Firm age Age 1.057 0.023 1.108 0.023 1.108 0.005 7.274***
0.268 0.268 0.000
Sector Sctr 3.578 0.238 3.533*** 0.257 3.55*** 0.009 3.751***
0.000 0.000 0.000
2
R 0.021 0.016 0.121
Adj. R2 0.015 0.014 0.116
F-Statistic 3.55*** 10.897*** 23.821***
p-value 0.001 0.000 0.000
Hausman test ( x 2) 5.557*** 8.983*** 10.997***
p-value 0.000 0.000 0.000
Durbin–Watson stat 2.087 1.589 2.472

Notes: This table reports the regression results using the ordinary-least-squares with firm and year fixed-
effects (FE). All regressions are estimated with robust standard errors clustered at the firm level. t-Critical:
at df 489 and a confidence level of 99 per cent is 2.326 and level of 95 per cent is 1.645 and level of 90 per
cent is 1.282. F-critical (df for denominator n- b -1 = 489-8-1 = 480) and (df for numerator = b = 8 and
confidence level of 99 per cent is 2.79 and confidence level of 95 per cent is 2.09 and confidence level of 10
Table VI. per cent is 1.77. The upper value is for t-statistic test and the lower value in brackets (p-value) is the
FE Results probability value for this test. Symbols mean Significance at: *10; **5; ***1 per cent levels

When time-series and cross-sectional data are merged, we get longitudinal data that
gives more data information with more disparity, less internal correlation between
variables, more degrees of freedom and more efficiency (Gujarat, 2015). Longitudinal
regression models are divided into FE and random-effect (RE) approaches. The trade-off
between the two approaches depends on the assumptions set on possible correlation
between cross-sectional units (firms), the amount of « i error (other factors affecting firms’
IC) and regressed variables X’s (CG). If assumed that « i and X’s are not correlated, a RE
approach is best, otherwise FE approach is best. Our study can only assume a correlation
between error and independent variables of the study sample. This was confirmed by
“Hausman Test” where a H0 assumes that capabilities of FE and FE approaches are
same, but if a H0 is rejected then this indicates that random-effect approach is
inappropriate, and it is, therefore, preferable to use FE approach. “Houseman chi-
squared” model shown in Tables VI statistically significant, which mean that capabilities
of the FE model (FE) is best representing the relationship, confirming our assumption
that « i and X’s are correlated.
 8.1 Human capital efficiency model results The
As shown in Table VI, the slope coefficient of interaction term 0.420 indicates that moderating role
the moderating impact of firm size is insignificant on HCE as evident from the coefficient
and p-value (0.572).
of firm size
The results specify that the inclusion of firm size as a moderating variable has not
influenced the relationship between CG practices and HCE, which is not significant at 5 per
cent. Therefore, H1a-H1c and dare rejected. However, the results specify that the inclusion
of firm size as a moderating variable has influenced the relationship between HCE and CG
395
index, which is significant at 5 per cent (0.000). This indicates that the BOD and managers of
Saudi firms are not able to realize the full potential of the governance adoption to maximize
their HCE. To justify the results of CG effect on the HCE; Saudi Arabia has a newly
established CG culture implemented since 2010. The insignificant results in Saudi Arabia
might be caused by the fact that Saudi’s listed firms have recently adopted the CG
regulations and the effect of those practices has still not appeared; also, the practices have
not yet affected the HCE. Otherwise, we can say that the adoption of CG regulations by
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Saudi’s listed firms could not be in a proper or actual way to affect the human capital. As
well as, there are labor laws in Saudi, which protect the employees and it could be used
instead of CG regulations.
Other factors might be a cause of those results, first of all, Saudi firms greatly depend on
foreign labor; most Saudis refuse to take unskilled or menial jobs, as these are often
considered socially unsuitable. The policy of “Saudisation” aims to raise the share of skilled
and educated Saudi nationals employed in the domestic economy, which in return will have
a great impact on human capital utilization. Second, job creation for the young and a rapidly
growing population constitute the most serious stress points in the labor market. The issue
of labor market rigidity also needs to be addressed to reach to the efficiency of human
capital. Third, Saudi labor regulations restrict the hiring of women. Fourth, lengthy
dismissal procedures and high mandatory severance pay in the public and private sectors.
Fifth, Saudi market consist of large merchant families with strong connections to the family
dominate the private sector, which has benefited extensively from the business
environment. That said, some within the private sector, mostly the young and Western-
educated, acknowledge the need for reform and change (Country insight report: Saudi
Arabia, 2017). Finally, the results also can be explained by the unrest of the Arab Spring
that was between 2012 and 2014, Saudi Arabia (including in neighboring Bahrain and
Yemen) facing growing dissatisfaction in the country over unemployment, firms bankrupt
and corruption, which makes the foreign and Saudi human such as employee, managers and
BOD avoid working in Saudi firms, which affect the contribution toward HCE.
Based on these results, managers should pay huge attention to their human resources
and adopting CG to invest in employee knowledge, skills and capabilities or by attracting
highly knowledgeable and skilled employees. Also, firms should motivate the BOD to
strictly adopt the code of governance for better employee performance because as we noted
earlier, firms with more CG tend to have a better HCE. Besides that, BOD and managers of
Saudi firms should consider the governance practices to structure relevant strategies and
policies on how to obtain; best utilize, develop and retain their employees for better IC
efficiency.

8.2 Structural capital efficiency model results

As shown in Table VI, the slope coefficient of interaction term 0.42 indicates that the
moderating impact of firm size is on SCE as evident from the coefficient and p-value (0.572).
 IJLMA However, the inclusion of firm size as a moderating variable has influenced the
61,2 interaction between ownership of the largest shareholder and SCE, which is negatively
significant at 1 per cent (0.001). Therefore, H2a is accepted. To explain this result, traditional
agency theory claims that more concentrated ownership would enhance the ability of
shareholders to monitor the management of the company, preventing it from taking self-
serving decisions affecting the performance of the company negatively. Concentrated
396 ownership creates majority shareholders and minority shareholders with diverging
interests and objectives. In a market environment where laws protecting minority
shareholders are absent or weak, a situation of majority shareholders controlling the
company will be created and the performance of the company would be affected negatively.
Theoretically, it may be said that an increase in ownership concentration should lead to a
reduction in the costs of separation of ownership and control benefiting company
performance eventually. However, the larger shareholders may benefit from that
improvement privately at the expense of smaller shareholders.
As for ownership of the largest three shareholders, the inclusion of firm size as a
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moderating variable has influenced positively the interaction between ownership of the
largest three shareholders and SCE, which is significant at 5 per cent (0.012). Therefore, H2b
is rejected. The three largest shareholders in the organization hold shares with a total sum
exceeding 50 per cent, this means that those three are monopolizing and controlling the
organization, thus creates a group of controlling shareholders that would protect their
interests rather than the interests of the company itself or minority shareholders affecting
negatively on the efficiency of structural capital in the firms. Moreover, the inclusion of firm
size as a moderating variable has influenced the interaction between board size and SCE,
which is positively significant at 5 per cent (0.031). Therefore, H2c is rejected. To explain
this result, it can be concluded that the size of the BODs’ principal being between 7 and 13
members has a positive relationship with firm performance. It is believed that a smaller
board is able to direct and make better decisions and that a larger board size may lead to less
firm performance. Finally, the inclusion of firm size as a moderating variable has influenced
the interaction board independency and SCE, which is positively significant at 1 per cent
(0.001). Therefore, H2e is accepted. Several prior studies document the favorable impact of
outside directors on firm decisions aimed at enhancing shareholder wealth (Alves, 2014). In
Saudi firms, we found that SCE is significantly affected by the independency of BOD.
Overall, the results suggest that in Saudi scenario, the CG is under-developed and to have
such results, it means that the BOD and managers perceive the efficiency of the firms in
terms of tangible assets equally to in terms of intangible assets. Thus, the BOD and
managers in Saudi firms are considered the structural capital such as patents, trademarks
and databases as a source that contributes toward governance efficiency. This is a good
indicator that Saudi firms are aware on the importance of CG as an indicator in measuring
the SCE.

8.3 Capital employed efficiency model results

The results of the CEE Model presents the best adjusted R2 (0.121), thus, as shown in the
Table VI, the IC models are significant at 0.000 and 0.01. This means that the inclusion of
firm size as a moderating variable has significantly influenced the interaction between CG
principles and CEE, which is significant at 1 per cent (0.000).
Table VI shows that the CG practices have a positive impact with CEE at 1 per cent
significant level (0.000), therefore, H3a-H3c and H3e are accepted. These results indicate
that the adoption of CG practices is significantly contributed to a physical asset’s market
value and its replacement value. Based on these results, firms in Saudi increase their CEE by
 concentrating more on corporate decisions that are related to tangible and financial assets The
and invest in their capital, not the employees or the systems. To explain that, Saudi Arabia moderating role
possesses the second largest reserves of oil in the world (after Venezuela) and it ranked the
second largest producer of oil after the USA. While this has brought wealth to some sectors
of firm size
of society and lead the firms to concentrate significantly on further investment in their
physical and financial assets (D&B Country insight report: Saudi Arabia, 2017). Considering
this result, we believe that CG has a significance and usefulness in the Saudi market, as the
nexus between financial markets and markets for goods and services. Thus, better CG may
397
translate to better CEE because of the association with superior profitability and market
position. Overall, results of the regression analysis indicate that CG can explain the physical
assets efficiency of listed firms in Saudi Arabia demonstrating that an increase in the level
of CG affects firm`s performance in the country.
To conclude, in Saudi the CEE is a driving factor behind investment decisions and stock
valuation. Firms should motivate the BOD to strictly adopt the code of governance for better
CEE efficiency. This can explain the firms that adapting CG tend to have a better CEE.
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Thus, Saudi is moving on the right track as the firms have a highly experienced and
educated BOD about the importance of CEE, which is expected to lead to a bright economy
in the near future, and therefore, experience higher growth and a deep and valued IC culture.

8.4 Control variables

As shown in Table VI, firm age found to be significant with the CEE models. As the number
of years since the firms were established increased, it is positively affecting the CEE in the
firms. The results are in line with the results founded by Buallay et al. (2017), who found that
firm age as a control variable is significant for the efficiency of assets in relation to
governance practices.
For the sector, it positively affects the CEE models at 1 per cent level. However, the HCE
and SCE models are negatively significant at 1 per cent level. It means that the type of sector
is negatively contributed to HCE and SCE models.

9. Conclusion and recommendations

This study is to measure the relationship between CG and IC of listed firms in the Saudi
stock exchange. This paper is to contribute to IC literature in terms of examining the
relationship between the CG and IC, the IC, which was measured by using HCE, SCE and
CEE and the CG has been measured by using the ownership of the largest shareholder,
ownership of the three largest shareholders, size of the BOD, independency of BOD and
duality of chairman and CEO. In addition to that, the study also used two control variables
such as firm age and the sectors, as discussed in the literature review. Therefore,
theoretically, this study is to fill the gap in the literature review, the researchers used the
moderator role of firm size model to enhance the relationship between CG and IC and this
was depending on the agency theory, which addresses the problems that arise due to the
conflict between management and shareholders interest and the resources-based theory,
which considers that IC is the main strategic asset in creating and maintaining firms’
competitive advantage. However, the study answered the research question, whether the CG
index can cause or encourage IC? This step is to determine the causal direction of “Granger”.
When there is one integrative vector a systematic error correction for Engle and Granger
(1987) is used. However, the results confirm a moderating effect of Firm size on the
relationship between CG and CEE. These results showed that a combination of the CG index
with the firm size will enhance a firm’s CEE, although, neither the HCE nor the SCE should
 IJLMA be considered. Thus, the results of this study can be a starting point for similar research to
61,2 the relationship between CG and IC and other factors related to this matter in GCC.
The descriptive analysis results, on one hand, showed that the HCE and SCE tend to be
higher with firms that have high CG level. However, the CG level found to be higher with
firms that have low CEE.
The regression models results showed that the firm size is significant and has a positive
398 impact on the relationship between CG and CEE. This evidenced that firm size has a
moderating role on the relationship between CG and IC. Based on this, Saudi firms should
motivate the BOD to strictly adopt the code of governance for better CEE efficiency. In
addition, the results showed that CG has no significant impact on HCE. The analysis
evidenced that the CEE model is the best model in explaining the moderating role of firm
size on the relationship between CG and IC. Also, the CEE model was found to be highly
significant in the three components of the IC.
Value of IC would be realized through the interaction of CG index and firm size as
these two components are highly correlated. In spite of the strong existing theoretical
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argument for the contribution of CG to firm’s success, the current IC do not seem to
provide a good proxy that would assist users in predicting the link between IC and
firm’s success. Future researchers need to find better proxies of IC than what is
discussed in previous researche studies.
We suggest that capital market authority in Saudi to focus more on IAS 38 adoption to
assure that all listed companies in stock exchange are controlling and reporting the IC; also,
it should conduct a workshop about the importance of IC.
In Saudi, the laws associated with protecting IC are weak, therefore, we recommend
the capital market authority to pay more attention to IC to avoid the gap between firms’
value as reported in financial statement and actual market value. Moreover, the capital
market authority should have a clear and mandatory law associated with IC. Added to
that, the stakeholders such as investors, shareholders, creditors and debtors are
recommended to increase their knowledge about the term of IC and its importance in the
business to make better investment choices.
Generally, we suggest that organizers such as capital market authority, the ministry of
finance, external auditors and stock exchange organizer to take the IC into consideration to
assure more reliable financial information to all business parties.
To conclude, Saudi is moving on the right track as the firms have a highly
experienced and educated BOD about the importance of IC, which is expected to lead to
a bright economy in the near future, and therefore, experience higher growth and a deep
and valued IC culture.

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academic discipline”, Journal of Intellectual Capital, Vol. 14 No. 4, pp. 476-500.

Corresponding author
Amina Buallay can be contacted at: ameena.buallay.87@gmail.com

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