Corporate Governance Mechanisms and Their Impact on Company Performance

This study examines the impact of corporate governance mechanisms on company performance using Structural equation modelling and Ordinary Least Squares (OLS) Regression. The study seeks to determine the nature of corporate monitoring mechanisms including shareholders, external auditors, and executive board.

1. Introduction
1.1 Background to study
Corporate governance entails advocacy for law compliance as well as ethical conduct
demonstration (Datta, 2018), which, is in order to ensure that the company is well
aligned to both the society’s ethical concerns and existing regulations for both
profitability and sustainability prospects. Over the recent years, business performance
has been associated with a number of factors apart from the number of consumers for
their products. Such factors include corporate governance. Beth (2003) in her article
on corporate governance and firm performance notes that “the belief that governance
best practices lead to superior firm performance is widespread” a notion which Beth
refutes to not always be true. A number of studies to examine whether governance
best practices are reflected in the performance of the company have been conducted,
including Azim (2012) study on “Corporate governance mechanisms
and their impact on company performance”
1.2 Purpose of study
The purpose of our study is to replicate the studies data analysis and output done by
Mohammed Azim on “Corporate governance mechanisms and their impact on
company performance: A structural equation model analysis” Azim (2012) using
Structural equation modelling and Ordinary Least Squares (OLS) Regression. Our
focus will be to prove the theories presented by Azim (2012). Additionally, the study
seeks to determine the nature of corporate monitoring mechanisms also addressed as
structures in this study. The mechanisms include:
i. Shareholders
ii. External auditors
iii. Executive board

1.3 Research questions
The research questions for our study are as those used in the original study by Azim.
i. Is there a substitution effect in the corporate monitoring structures? (Azim, 2012)
ii. Is there a complementary effect among the featured monitoring mechanisms?
(Azim, 2012)
1.4 Keywords
Structural equation modelling, Ordinary Least Squares regression, Corporate
governance, Stakeholders

2. Methodology
2.1 Data
The sample data used for this study was obtained from SIRCA and Morning Star
DatAnalysis for 613 Australia’s big companies for the financial year of 2015. It
contains details of shareholders’ activities as well as different monitoring bodies such
as auditors. The data has 9 variables with various levels for the 613 companies, hence
the total number of variables is 22.
2.2 Assumptions
During our study we made the following assumptions:
i. The data for the study is normally distributed
ii. In order to ensure originality of data analysis we assumed that were no missing
values in the data-set and therefore did not explore for missing data, impute or
reconstruct
iii. We also assumed that the sample data was representative enough for estimation
of the population distribution as well as sufficient for testing of hypotheses
iv. The dependent and mediating variables for the SEM are continuous
v. The residuals from a SEM analysis are univariate normally distributed

3. Analysis Results
Descriptive Statistics
From table 1, the top 20 shareholders hold approximately 59.2286% of the shares
making them the majority shareholders while the top 1 have 22.81424 of the shares in
the data-set of all the companies in our study. The average board size for our study
was 10 where the minimum board size was 3 while the maximum board size was 33.
The average number of board meeting were 10, however some company’s did not
hold board meetings for the financial year 2015 as from the data provided,
nevertheless, the maxim number of board meetings were 47. Approximately
22.7689% of the board of directors were independent in the companies. 24.59% of the
board the board members in 2015 had some financial literacy. The average number of
audit meetings convened were between 0-35. Additionally, the
1- Descriptive statistics
Descriptive Statistics
N Minimum Maximum Mean
Std.
Deviation Variance Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Statistic Std. Error
BIG4 608 0 1 .55 .498 .248 -1.961 .198
PNAF 610 0 8 .40 .803 .645 43.732 .198
Top 20 613 .00 99.99 59.2286 22.81424 520.490 .154 .197
TOP1 546 .0367 .8848 .227689 .1664301 .028 2.149 .209
BSIZ 613 3 33 10.26 4.723 22.304 1.491 .197
PBFL 613 .0000000
00000
1.000000
000000
.2459075
5629970
.1779665722
43531
.032 1.738 .197
PBIN 613 .0000000
00000
.8000000
00000
.2647227
2508730
.1923043983
02462
.037 -.798 .197
BM 613 0 47 10.04 5.044 25.438 8.333 .197
CHCE 613 0 1 .89 .318 .101 3.928 .197
ACM 612 0 35 2.83 2.648 7.010 35.702 .197

PAFL 613 .0000000
00000
1.000000
000000
.2895090
4994951
.3728331576
71259
.139 -.628 .197
PAI 613 .0000000
00000
1.000000
000000
.4389808
1255341
.4408575119
21781
.194 -1.707 .197
NCM 613 0 24 1.18 1.962 3.851 30.218 .197
PNI 613 0 1 .27 .409 .167 -.814 .197
RCM 613 0 24 1.68 2.209 4.880 17.660 .197
PRI 613 0 1 .36 .439 .192 -1.519 .197
Log( TA) 606 4 12 7.92 1.129 1.274 .786 .198
Valid N
(listwise)
534
In testing for correlation between the various variables, using the Spearman
correlation matrix, it is noted that there were a number of variables indicating high
correlations. For instance from table 2 below, there is a high correlation between
number of remuneration meetings and the proportion of independent members
(0.708>0.6). Nevertheless, the use of Structural Equation Modelling will enable the
management of multicollinearity problem.

2-Spearman correlation test
Correlations
BIG4
PNAF
Top 20
TOP1
BSIZ
PBFL
PBIN
BM
CHCE
ACM
PAFL
PAI
NCM
PNI
RCM
PRI
Spear
man's
rho
BI
G4
Correlatio
n
Coefficie
nt
1.000
.190**
.115**
.047
.385**
-.072*
.186**
.157**
.064
.311**
.161**
.245**
.267**
.215**
.240**
.158**
Sig. (1-
tailed)
.
.000
.002
.139
.000
.038
.000
.000
.058
.000
.000
.000
.000
.000
.000
.000
N
608
605
608
542
608
608
608
608
608
607
608
608
608
608
608
608
PN
AF
Correlatio
n
Coefficie
nt
.190**
1.000
.036
-.038
.298**
-.036
.092*
.149**
-.016
.183**
.120**
.125**
.190**
.141**
.212**
.168**
Sig. (1-
tailed)
.000
.
.190
.188
.000
.187
.011
.000
.347
.000
.001
.001
.000
.000
.000
.000
N
605
610
610
544
610
610
610
610
610
609
610
610
610
610
610
610
To
p
20
Correlatio
n
Coefficie
nt
.115**
.036
1.000
.359**
.080*
-.022
-.077*
.023
-.049
.066
.038
.022
-.023
-.047
.053
.017
Sig. (1-
tailed)
.002
.190
.
.000
.023
.290
.029
.288
.113
.051
.171
.292
.281
.124
.093
.334
N
608
610
613
546
613
613
613
613
613
612
613
613
613
613
613
613

TO
P1
Correlatio
n
Coefficie
nt
.047
-.038
.359**
1.000
-.071*
.006
-.195**
-.064
-.138**
-.091*
.016
-.091*
-.092*
-.086*
-.108**
-.118**
Sig. (1-
tailed)
.139
.188
.000
.
.049
.441
.000
.067
.001
.016
.356
.017
.016
.022
.006
.003
N
542
544
546
546
546
546
546
546
546
545
546
546
546
546
546
546
BS
IZ
Correlatio
n
Coefficie
nt
.385**
.298**
.080*
-.071*
1.000
-.155**
.161**
.359**
.158**
.545**
.235**
.419**
.493**
.396**
.492**
.335**
Sig. (1-
tailed)
.000
.000
.023
.049
.
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
N
608
610
613
546
613
613
613
613
613
612
613
613
613
613
613
613
PB
FL
Correlatio
n
Coefficie
nt
-.072*
-.036
-.022
.006
-.155**
1.000
.075*
-.069*
.009
-.046
.399**
-.051
-.068*
-.081*
-.056
-.023
Sig. (1-
tailed)
.038
.187
.290
.441
.000
.
.033
.045
.412
.130
.000
.105
.045
.022
.085
.282
N
608
610
613
546
613
613
613
613
613
612
613
613
613
613
613
613
PB
IN
Correlatio
n
Coefficie
nt
.186**
.092*
-.077*
-.195**
.161**
.075*
1.000
.115**
.128**
.221**
.091*
.588**
.218**
.408**
.215**
.461**
Sig. (1-
tailed)
.000
.011
.029
.000
.000
.033
.
.002
.001
.000
.012
.000
.000
.000
.000
.000

N
608
610
613
546
613
613
613
613
613
612
613
613
613
613
613
613
BM
Correlatio
n
Coefficie
nt
.157**
.149**
.023
-.064
.359**
-.069*
.115**
1.000
.152**
.358**
.138**
.217**
.243**
.194**
.342**
.266**
Sig. (1-
tailed)
.000
.000
.288
.067
.000
.045
.002
.
.000
.000
.000
.000
.000
.000
.000
.000
N
608
610
613
546
613
613
613
613
613
612
613
613
613
613
613
613
CH
CE
Correlatio
n
Coefficie
nt
.064
-.016
-.049
-.138**
.158**
.009
.128**
.152**
1.000
.199**
.151**
.161**
.155**
.078*
.108**
.089*
Sig. (1-
tailed)
.058
.347
.113
.001
.000
.412
.001
.000
.
.000
.000
.000
.000
.027
.004
.014
N
608
610
613
546
613
613
613
613
613
612
613
613
613
613
613
613
AC
M
Correlatio
n
Coefficie
nt
.311**
.183**
.066
-.091*
.545**
-.046
.221**
.358**
.199**
1.000
.445**
.556**
.438**
.310**
.474**
.316**
Sig. (1-
tailed)
.000
.000
.051
.016
.000
.130
.000
.000
.000
.
.000
.000
.000
.000
.000
.000
N
607
609
612
545
612
612
612
612
612
612
612
612
612
612
612
612
PA
FL
Correlatio
n
Coefficie
nt
.16
1**
.12
0**
.03
8
.01
6
.23
5**
.39
9**
.09
1*
.13
8**
.15
1**
.44
5**
1.0
00
.32
4**
.12
3**
.08
2*
.19
0**
.12
8**

Sig. (1-
tailed)
.00
0
.00
1
.17
1
.35
6
.00
0
.00
0
.01
2
.00
0
.00
0
.00
0 .
.00
0
.00
1
.02
1
.00
0
.00
1
N 608 610 613 546 613 613 613 613 613 612 613 613 613 613 613 613
PA
I
Correlatio
n
Coefficie
nt
.24
5**
.12
5**
.02
2
-.09
1*
.41
9**
-.05
1
.58
8**
.21
7**
.16
1**
.55
6**
.32
4**
1.0
00
.28
1**
.36
4**
.30
7**
.43
4**
Sig. (1-
tailed)
.00
0
.00
1
.29
2
.01
7
.00
0
.10
5
.00
0
.00
0
.00
0
.00
0
.00
0 .
.00
0
.00
0
.00
0
.00
0
N 608 610 613 546 613 613 613 613 613 612 613 613 613 613 613 613
NC
M
Correlatio
n
Coefficie
nt
.26
7**
.19
0**
-.02
3
-.09
2*
.49
3**
-.06
8*
.21
8**
.24
3**
.15
5**
.43
8**
.12
3**
.28
1**
1.0
00
.70
8**
.57
1**
.37
4**
Sig. (1-
tailed)
.00
0
.00
0
.28
1
.01
6
.00
0
.04
5
.00
0
.00
0
.00
0
.00
0
.00
1
.00
0 .
.00
0
.00
0
.00
0
N 608 610 613 546 613 613 613 613 613 612 613 613 613 613 613 613
PN
I
Correlatio
n
Coefficie
nt
.21
5**
.14
1**
-.04
7
-.08
6*
.39
6**
-.08
1*
.40
8**
.19
4**
.07
8*
.31
0**
.08
2*
.36
4**
.70
8**
1.0
00
.38
9**
.53
6**
Sig. (1-
tailed)
.00
0
.00
0
.12
4
.02
2
.00
0
.02
2
.00
0
.00
0
.02
7
.00
0
.02
1
.00
0
.00
0 .
.00
0
.00
0
N 608 610 613 546 613 613 613 613 613 612 613 613 613 613 613 613
RC
M
Correlatio
n
Coefficie
nt
.24
0**
.21
2**
.05
3
-.10
8**
.49
2**
-.05
6
.21
5**
.342**
.10
8**
.47
4**
.19
0**
.30
7**
.57
1**
.38
9**
1.0
00
.66
4**
Sig. (1-
tailed)
.00
0
.00
0
.09
3
.00
6
.00
0
.08
5
.00
0
.00
0
.00
4
.00
0
.00
0
.00
0
.00
0
.00
0 .
.00
0
N 608 610 613 546 613 613 613 613 613 612 613 613 613 613 613 613
PR
I
Correlatio
n
Coefficie
nt
.15
8**
.16
8**
.01
7
-.11
8**
.33
5**
-.02
3
.46
1**
.26
6**
.08
9*
.31
6**
.12
8**
.43
4**
.37
4**
.53
6**
.66
4**
1.0
00
Sig. (1-
tailed)
.00
0
.00
0
.33
4
.00
3
.00
0
.28
2
.00
0
.00
0
.01
4
.00
0
.00
1
.00
0
.00
0
.00
0
.00
0 .
N 608 610 613 546 613 613 613 613 613 612 613 613 613 613 613 613

**. Correlation is significant at the 0.01 level (1-tailed).
*. Correlation is significant at the 0.05 level (1-tailed).

4. Analysis of the results
During analysis, data relating to 2015 financial year for 613 Australian companies
was used for both Structural equation modelling and Ordinary Least Squares
regression. The model was run to examine how monitoring variables affected the
performance of the company. The data-set was tested using least squares and after w
results compared against OLS.
5.1Fit of structural models
5.1.1 Absolute fit measures
Lewi (2017) argues that, “in order to find out whether and how the observed value of
a given phenomena is significantly different from the expected value…” the chi-
square ought to be used. The ch-square test involves comparison of how the computed
p-value is different from the significance level provided which helps predict how well
the distance between the fitted line and other data points is minimized. The model has
high chi-square values that range from 31.2-2886.02 indicating that there is a
relatively strong interrelationship between the monitoring variables. This is true given
that the scaled Pearson chi-square is at 536.000 while the Pearson chi-square is at
588.007. The RMSE range from approximately 0.0028 to 0.0627 which according to
Browne and Cudeck (1993) indicate a good fit since it is ≧0.08.
3- Root mean square error of approximation
Variable Obs RMSE F P
pbv 536 0.0028 .3714427 0.8290
roe 536 0.0083 1.116927 0.3476
roa 536 0.0168 2.269835 0.0606
dy 536 0.0627 8.886511 0.0000

Test Statistics
BIG
4
PNA
F
Top
20
TO
P1
BSI
Z
PBF
L PBIN BM
CH
CE ACM
PAF
L PAI NCM PNI RCM PRI
Log
( TA
)
Chi-
Squa
re
6.73
7a
2298
6.407
b
356.
507c
31.2
53d
526.
418e
1449
.121f
2886
.062g
668.
721h
364.
974i
1125
.987j
1740
.013k
1741
.212l
1932
.127k
2692
.688k
1402
.551m
1813
.835n
.000
o
df 1 428 571 514 26 79 74 30 1 15 10 11 10 10 12 9 605
Asym
p.
Sig.
.009 .000 1.00
0
1.00
0
.000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.00
0
Goodness of Fitb
Value df Value/df
Deviance 588.007 535 1.099
Scaled Deviance 536.000 535
Pearson Chi-Square 588.007 535 1.099
Scaled Pearson Chi-Square 536.000 535
Log Likelihooda -785.369
Akaike's Information Criterion
(AIC)
1574.738
Finite Sample Corrected AIC
(AICC)
1574.761
Bayesian Information
Criterion (BIC)
1583.306
Consistent AIC (CAIC) 1585.306
Dependent Variable: Log( TA)
Model: (Intercept)

a. The full log likelihood function is displayed and used in computing
information criteria.
b. Information criteria are in small-is-better form.
Substitution effect between shareholders and board monitoring
In the study model, there are three groups of monitoring variables which comprise of:
i. Shareholders
ii. External auditors
iii. Board of directors
The study question involves whether monitoring variables have a substitutional or
complimentary effect. Jaffar and Zaleha (2016) in their research on the role of
monitoring mechanisms on a firms performance argue that, generally, “The
monitoring role of corporate governance as measured by the composition of
independent board members have shown a positive significant effect on the
company’s performance.” Practically, various monitoring mechanisms work together
to streamline the common shareholder-executive interest clash. Ideally, the executive
are mandated to check on the audits done by external auditors hence exacting an
influence on the audit process. The process of auditing often results in reports which
eventually reach the shareholder who acts as a watchdog for the auditors (both
external and internal).

4- Relationship between Monitoring variables and Performance variables
Coefficientsa,b
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) -.610 .299 -2.041 .042
Top 20 .004 .004 .054 1.039 .299
TOP1 -.039 .486 -.004 -.080 .936
BSIZ .028 .016 .085 1.720 .086
a. Dependent Variable: ROE
b. Weighted Least Squares Regression - Weighted by ACM
The table indicates a correlation between the performance variables and the
monitoring variables. This is also true for the research done by Azim (2012) which
indicated existence of correlation between the shareholders, auditors and the
executive board as monitoring variables. The correlation coefficient between return

on equity and shareholders is approximately 0.072424.Indicating that shareholders do
affect the return on equity
Robustness tests
In exploration of the relationship between the board, shareholders and the auditors in
researching the existence of substitution and complementary relationship in the
corporate monitoring tools, the study used Ordinary least squares regression for the
performance variable Return on equity since it is the only performance variable that
had enough observations for OLS regression. R2 was 18.18% ; adjusted R2 = 15.65%
while ANOVA: F = 7.19 with a p-value of 0.000. However, the SEM results and
those of OLS regression are considerably different and hence inconsistent. Such may
be due to factors such as:
i. Error mi-specification in the model which may include omission of an important
explanatory variable or inclusion of a redundant exogenous variables
ii. Incorrect specification of the model
1-Ordinary least Squares regression

When testing for robustness in the model, the structural equation modelling results
show that there is a substitution and complementary relationship between the
monitoring variables (shareholders, board of directors and auditors). The results were
higher than those of Azim (2012).
Robust regression
5-Robust regression
Number of obs = 539
F( 9, 529) = 8.34
Prob > F = 0.0000
roe Coef. Std. Err. t P>t [95% Conf. Interval]
top20 .0015495 .0005109 3.03 0.003 .0005459 .0025532
top1 .1348009 .0662633 2.03 0.042 .0046293 .2649725
acm .0163219 .0060309 2.71 0.007 .0044744 .0281694

pafl -.0016783 .0298049 -0.06 0.955 -.0602288 .0568721
pai .0462693 .0290546 1.59 0.112 -.0108072 .1033458
ncm .0121012 .009268 1.31 0.192 -.0061054 .0303078
pni -.0333545 .0335505 -0.99 0.321 -.0992631 .032554
rcm .0128354 .0080525 1.59 0.112 -.0029833 .0286542
pri .0238044 .0314163 0.76 0.449 -.0379116 .0855204
_cons -.221560
5 .034423 -6.44 0.000 -.2891831 -.1539379
When the financial crisis of 2007-2008 came, it seems evident that the monitoring
structures of listed companies could not be able to prevent the decisions that were
apparently risky to make. Azim (2012) argues that this the scenario had a thwarting
effect on the investors’ trust on the executives ability to act as a monitoring variable.
From his research, the effects of the GFC lowered the coefficients of the board.
However the data from 2015 financial year for major listed companies in Australia
indicate that the coefficients of the board is way higher than when Azim conducted
the featured research at .0015495n which is higher than −0.073 (0.014) at a 0.05 level of
significance.

Conclusion
Despite the scarcity of utilization of SEM, Azim (2012) through use of Ordinary Least
Square regression and Structural Equation modelling manages to prove the existence
of a cause-effect relationship between the monitoring structures of companies which
include: shareholders, executive board, and the company auditors. As such our study
has proven that there actually is a relationship between the monitoring structures and
the performance of listed companies. However, the study shows that the monitoring
variables have substitutional effects as they are complimentary. Depending on the
context, this indicates that in presence of co-ordination between a company’s there is
a likelihood to promote the company performance in case of GFC. Nevertheless, this
study indicate a positive improvement between the trust of investors on the
monitoring tools set up by a company, more-so the board following an improvement
on the correlation coefficient between the monitoring structures (variables) of a
company and the company performance, in line with Azim’s 2012 research which
point out to “The practical reality of the diversity of the study...” (Azim, 2012).
Therefore the research succeeded in replicating the results of Azim (2012). However
there were a number of limitations for the study which included, the inability to
transform the data entirely, for a better projection of the effect of monitoring factors
on company performance, such limitations should be mitigated to enhance the whole
process.

Bibliography
Azim, M. (2012). Corporate governance mechanisms and their impact on company performance:
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Appendix
Variable Notation and level
Percentage of shares held by the top
shareholder
TOP 1
Percentage of shares held by the top 20
shareholders
TOP 20
Size of the board BSIZ
Number of Board Meetings BM
Proportion of independent directors on
the board
PBIN
Proportion of directors with financial
expertise on the board
PBFL
Roles of CEO and Chairman CHCE
 0= CEO and Chairman is
the same individual
 1= CEO and Chairman are
different individuals
Board Committees Audit Committee
 Number of Meetings
(ACM)

 Proportion of independent
members (PAI)
 Proportion of members
with financial literacy
(PAFL)
Remuneration Committee
 Number of meetings
(RCM)
 Proportion of independent
members (PRI)
Nomination Committee
 Number of meetings
(NCM)
 Proportion of independent
members (PNI)
External Auditors
i. Audit firm: Dummy variable
employed
BIG4
 0= Not Big Four
 1= Big Four Audit Firm

ii. Non- Audit Fees: Proportion of non-
audit fees per total audit fees
PNAF
Performance measures
Return on equity
Return on asset
Price–earnings ratio
Market-to-book value
Dividend yield
ROE
ROA
PER
MBV
DY