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1246
JOSS :
Journal of Social Science
BOARD CHARACTERISTICS AND FINANCIAL PERFORMANCE OF
COMPANIES FROM THE AGENCY THEORY PERSPECTIVE
Lukman
1
, Siti Ridloah
2
, Hanina Humaira
3
Universitas Negeri Semarang, Indonesia
Email : l[email protected]m, siti.ridloah@mail.unnes.ac.id,
haninahumaira@students.unnes.ac.id
*Correspondence: [email protected]d
KEYWORDS
Firm Financial
Performance;
Director Size;
Frequency of
Director Meeting;
Female Director;
Independent
Commissioner.
ABSTRACT
This study aims to examine the director size, frequency of director meeting,
female director, and independent commissioner on the firm financial
performance. The population in this study are companies listed in the LQ45
index for the 2017-2020 period. In this study, purposive sampling selected
27 companies as a sample, with the number of observations as many as 108
observations. This study uses an analytical method that analyzes regression
with a fixed-effect model approach and hypothesis testing. The results show
that director size positively and significantly affected the firm financial
performance. The female director negatively and significantly affected the
firm financial performance, the frequency of director meeting and
independent commissioner has no significant negative effect on the
company's financial performance, while leverage and growth as control
variables has no significant positive effect on the firm financial
performance. Investors are advised to pay more attention to factors such as
director size and female directors to be taken into consideration before
investing. This study suggests that future researchers can use other proxies
in measuring the firm financial performance, both from a financial
perspective such as ROE and a market value perspective such as Tobin's Q.
Further researchers are also advised to examine other factors related to the
board characteristics.
INTRODUCTION
The competition in the current era of Industry 4.0 is evolving rapidly, leading to the
emergence of intense industrial competition. This necessitates companies to continually
enhance their performance to achieve the corporate objective of maximizing profits for the
well-being of shareholders. The ability of a company to generate profits from its business
activities can be utilized as a benchmark for assessing the financial performance of the
company (Wardoyo & Veronica, 2013).
The financial performance of a company is a reflection of the level of achievement of
results obtained from the implementation of the company's activities during a specific period
(Priatna, 2016). The attainment of financial performance serves as a foundation for investors
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to assess and evaluate a company, along with its future prospects (Lestari & Putri, 2021). A
sound financial performance continually enhances the attractiveness for investors to invest
their capital (Kumalasari & Wijayanto, 2020). This fosters investor confidence that the better
a company's financial performance, the higher the return they will obtain by investing their
capital in that company. Setyawati & Amelia (2018) state that a benchmark for assessing a
company's financial performance can be found in the company's financial statements.
Financial statements provide information regarding the financial position, performance, and
changes in the financial position of the company during a specific period, serving as
management's accountability to those in need of the company's financial information
(Ratnaningsih & Alawiyah, 2018). This is exemplified by the profitability ratio of the
company, namely Return on Assets (ROA).
ROA is a ratio that represents the division of a company's net profit by its total assets
(Maftukhah, 2013). ROA is utilized to gauge the financial performance of a company in
generating profits by leveraging the assets owned by the company (Wijayanto, 2010). A higher
ROA value reflects a better financial performance of a company. his indicates that Return on
Assets serves as a foundational metric for evaluating company management, determining
whether the company's management can generate a favorable return on the total assets owned
by the company or not.
From the average financial report data, not all issuers exhibit satisfactory performance
each year. Many issuers demonstrate less favorable financial performance. The LQ45 Index
comprises 45 companies that have undergone a selection process based on high liquidity,
substantial market capitalization, and solid fundamental company performance as evidenced
by financial reports (Indonesia Stock Exchange, 2022). However, upon examining the average
return on assets values in the financial reports of each company, the financial performance of
companies listed in the LQ45 index has experienced a decline over several years, as illustrated
in Figure 1.1 below:
Fig. 1: The Average Return on Assets Graph of Companies Listed in the LQ45 Index for
the Period 2017-2020
Source: Indonesia Stock Exchange (data processed, 2022)
Based on the above data, it is evident that the average return on assets values for
companies listed in the LQ45 Index have shown a declining trend in financial performance
from 2017 to 2020. The return on assets values over the past four years were 8.07% in 2017,
7.40% in 2018, 6.94% in 2019, and 5.39% in 2020. Oktavianto (2014) asserts that one
mechanism to enhance a company's financial performance is through the improvement of
8.07
7.4
6.94
5.39
0
2
4
6
8
10
2017 2018 2019 2020
The Average Financial Performance of
Companies Listed in the LQ45 Index for the
Period 2017-2020
Board Characteristics and Financial Performance of
Companies from the Agency Theory Perspective
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corporate governance practices. According to Devilia & Prasetyo (2021), improved financial
performance is often perceived as the outcome of the implementation of effective corporate
governance mechanisms and structures within a company.
Effective corporate governance mechanisms provide assurance to investors for a fair,
timely, and efficient return on their investment, ensuring that management acts in accordance
with the company's objectives for the benefit of the company (Sukandar & Rahardja, 2014).
According to the agency theory developed by Jensen & Meckling (1976), corporate
governance serves as a solution to agency conflicts, which arise from the differing interests
between agents and principals. Agents, representingmanagement, may exhibit opportunistic
behavior, prioritizing personal interests over those of other parties (Yulianto, 2013). Al
Farooque et al. (2020) assert the necessity for companies to establish a well-functioning and
effective corporate governance system to reduce or even eliminate agency problems.
Good Corporate Governance (GCG) is a set of rules governing internal and external
relationships among shareholders, managers, governments, creditors, stakeholders, and
employees, each having rights and responsibilities to direct and control the company
(Wicaksana, 2010). The mechanisms of good corporate governance (GCG) are expected to
monitor company managers, enabling them to act more effectively in improving financial
performance and company value (Agustina & Ardiansari, 2018).
The board of commissioners and the board of directors, as the top management of the
company, play a crucial role in implementing corporate governance principles to enhance the
financial performance of the company. This research integrates the characteristics of the board
of directors and commissioners because both boards have their respective roles in managing
and ensuring that the company operates in line with its objectives. The variables of board
characteristics used include director size, frequency of director meetings, female director, and
independent commissioner.
The first factor influencing the financial performance of the company is director size.
The Board of Directors plays a vital role in the company (Yopie et al., 2018). The Board of
Directors is considered one of the mechanisms of corporate governance that can be utilized to
mitigate agency problems (Duppati et al., 2019). A larger board can provide a wide range of
information, ideas, knowledge, and perspectives in decision-making, ultimately benefiting
shareholders (Vu et al., 2018). An increasing size of the board of directors enhances
supervision over management and facilitates more effective decision-making (Palaniappan,
2017).
Research on the impact of director size on financial performance has been conducted.
Research by Gulzar et al. (2020), Shettima & Dzolkarnaini (2018), Queiri et al. (2021) state
that director size has a positive influence on the financial performance of companies.
However, in contrast, research by Palaniappan (2017), Kiptoo et al. (2021), Kao et al. (2018)
assert that director size has a negative impact on financial performance.
The second factor influencing the financial performance of a company is the Frequency
of Director Meetings. Board meetings serve as a crucial source of information used to enhance
the effectiveness of the board of directors in improving the financial performance of the
company. A higher frequency of director meetings is expected to aid in the coordination and
communication of information regarding the company's developments to the directors (Al-
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Amin & Rosyadi, 2018). Buchdadi et al. (2019) state that more frequent board meetings
enhance the ability to oversee and provide useful advice to the company, thereby improving
financial performance through enhanced team performance.
Several previous studies conducted by (2020), Al Farooque et al. (2020), Puni &
Anlesinya (2020) suggest that the frequency of director meetings has a positive impact on the
financial performance of companies, while Palaniappan (2017), Queiri et al. (2021); Mardiyati
(2016) state that the frequency of director meetings has a negative impact on financial
performance.
The third factor influencing the financial performance of a company is Female Directors.
According to Shettima & Dzolkarnaini (2018) the presence of women in the board strengthens
weak corporate governance. Female directors play a role as a better monitoring mechanism
because they possess high independence of thought (Adams & Ferreira, 2009). Papangkorn et
al. (2021) state that the presence of women on the board brings new ideas and different
perspectives to the decision-making process, leading to better decision-making.
Previous research on the impact of female directors on the financial performance of
companies has shown differing results. Studies by Ahmadi et al. (2018), Assenga et al. (2018),
Brahma et al. (2020) state that female directors have a positive influence on the financial
performance of companies. However, in contrast, research by Roudaki (2018); Iswadi (2016),
Ahmad et al. (2019) suggests that female directors have a negative impact on the financial
performance of companies.
The fourth factor influencing the financial performance of a company is Independent
Commissioners. Independent commissioners, as part of the good corporate governance
mechanism, are tasked with directly overseeing the actions of directors to prevent arbitrary
behavior (Maulana, 2020). Hadiprajitno & Krisnauli (2014) state that a larger size of
independent commissioners makes it easier to supervise management more effectively.
Consequently, management will maximize performance and improve asset turnover ratios,
minimizing agency costs incurred by the company.
Previous research on the independent commissioner variable's impact on the financial
performance of companies has also yielded inconsistent results. The findings of studies by
Ahmadi et al. (2018), Palaniappan (2017), Kiptoo et al. (2021), Kao et al. (2018) suggest that
independent commissioners have a positive impact on the financial performance of
companies. However, the results of studies by Queiri et al. (2021), Prasetio & Rinova (2021),
Mulyadi (2016) state that independent commissioners have a negative impact on the financial
performance of companies.
This research is conducted to address a research gap identified in previous studies.
Additionally, the study is motivated by the phenomenon of a gap between company financial
report data available in the LQ45 index during the years 2017-2020 and agency theory, as
indicated in the following table:
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Fig. 2: Average Variable Graph of Companies Listed in the LQ45 Index for the Period
2017-2020
Source: Indonesia Stock Exchange (data processed, 2022)
The above figure illustrates that director size in LQ45 index companies from 2017 to
2020 remained stable and consistent at an average of 8 individuals. This stability does not
align with the declining trend in ROA values from 2017 to 2020. This contradicts the agency
theory, which posits that companies with a larger board of directors tend to enhance financial
performance.
In the figure, the variable frequency of director meetings in LQ45 index companies from
2018 to 2019 remained stable at 37 times. This stable value does not correspond to the
declining ROA values in the same period. Additionally, in 2020, the frequency of director
meetings increased to 41 times, while in the same year, the ROA rate experienced a decrease
to 5.39%. This does not align with the agency theory, which suggests that companies with a
higher frequency of director meetings tend to have higher financial performance.
Based on the data presented in Figure 1.4 above, the proportion of female directors in
LQ45 index companies increased consecutively from 2017 to 2019 by 10.85%, 11.81%, and
13.07%, respectively. However, this increase in female directors was followed by a decline in
the financial performance of companies in 2017 to 2019 by 8.07%, 7.40%, and 6.94%.
This contradicts the agency theory, which states that companies with a high proportion of
female directors will improve financial performance.
In Figure 1.5 above, it is evident that the variable independent commissioners in LQ45
index companies experienced consecutive increases in the years 2018-2020 by 40.75%,
42.57%, and 42.82%, respectively. However, this rise in independent commissioners was
accompanied by a decline in the financial performance of companies in 2018-2020 by 7.40%,
6.94%, and 5.39%. This phenomenon contradicts the agency theory, which asserts that
companies with a higher number of independent commissioners will enhance financial
performance.
In addition to the board characteristics represented by director size, frequency of director
meetings, female directors, and independent commissioners, there are other factors that can
influence the financial performance of companies, such as leverage and growth. This study
employs leverage and growth as control variables.
According to Wicaksono & Ardiansari (2018) the board of directors is a key entity in a
company responsible for carrying out operational and managerial activities. Based on agency
8.07
7.40
6.94
5.39
8 8 8 8
39
37 37
41
10.85
11.81
13.07
12.79
41.99
40.75
42.57
42.82
0
10
20
30
40
50
2017 2018 2019 2020
The average values of the research variables for companies in the
LQ45 Index for the period 2017-2020
ROA DSIZE FDMEET FEMD INDCOM
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theory, it is explained that an increased number of board members brings benefits to the
company, such as better management control and monitoring, as well as more optimal
decision-making. This, in turn, impacts the improvement of the company's financial
performance (Azis & Hartono, 2017).
A larger board can also enhance financial performance by providing creative ideas,
knowledge, and more effective information (Gulzar et al., 2020). Therefore, this is consistent
with research conducted by Gulzar et al. (2020), Shettima & Dzolkarnaini (2018), Queiri et
al. (2021) indicating a positive relationship between director size and financial performance.
Ha1: Director size has a significant positive effect on financial performance.
In fulfilling their duties and functions, the board of directors needs to hold meetings,
which serve as a means of communication and coordination among board members in
determining the direction and goals of the company. Monitoring the frequency of board
meetings held in a year can be used to assess the effectiveness of the company's board of
directors.
According to agency theory, the frequency of board meetings can enhance financial
performance because an increased frequency encourages idea sharing, performance
disclosure, and discussions to solve agency problems (Yakob & Hasan, 2021). It can also
expedite the resolution of operational issues to enhance financial performance and maximize
shareholder wealth (Malik & Makhdoom, 2016). This aligns with research conducted by Al-
Matari (2020), Al Farooque et al. (2020), Puni & Anlesinya (2020), indicating a positive
relationship between the frequency of director meetings and financial performance.
Ha2: Frequency of director meeting has a significant positive effect on financial performance.
An important aspect related to the structure and function of the board of directors is the
diversity of its members. Gender diversity is part of the broader concept of board diversity
(Manmeet Kaur & Madhu Vij, 2017). According to agency theory, the presence of women
on the board strengthens weak corporate governance because women play a more efficient
monitoring role, resulting in fewer board attendance issues and better control over board
activities, thus minimizing agency problems in the company (Adams & Ferreira, 2009). Ting
et al. (2021) state that the presence of women on the board provides a broader perspective in
reducing risks, leading to more objective decision-making. This is in line with research
conducted by Ahmadi et al. (2018), Assenga et al. (2018), Brahma et al. (2020), indicating a
positive relationship between female directors and financial performance.
Ha3: Female director has a significant positive effect on financial performance
Independent commissioners are board members who are not affiliated. One of the main
functions of independent commissioners is to independently perform monitoring functions on
the company's management performance.
According to agency theory, solving agency problems through oversight is crucial, the
more supervisors there are, the lower the agency conflict, reducing agency costs (Jensen &
Meckling, 1976). As agency problems decrease, and agency costs are reduced, financial
performance can improve. This aligns with research conducted by Ahmadi et al. (2018),
Palaniappan (2017), Kiptoo et al. (2021), Kao et al. (2018) indicating a positive relationship
between the size of independent commissioners and financial performance.
Ha4: Independent commissioner has a significant positive effect on financial performance
Board Characteristics and Financial Performance of
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Fig. 3: Conceptual Framework
METHOD RESEARCH
This study falls under quantitative research. The data used were obtained from the
official website of the Indonesia Stock Exchange, www.idx.co.id, and the official websites of
each respective company. The population in this study consists of companies listed in the
LQ45 index for the period 2017-2020, totaling 66 companies.
The sampling technique in this study used purposive sampling method with the
following criteria: (a) Companies listed in the LQ45 index during the period 2017-2020; (b)
Companies consistently not included in the LQ45 index list for the period 2017-2020; (c)
Companies listed in the LQ45 index for the period 2017-2020 that did not provide data on the
frequency of board meetings in the annual report. Based on these criteria, a sample of 27
companies was obtained with a 4-year observation period, resulting in a total of 108
observations.
This research utilized Eviews 9 software for data processing. The data analysis methods
employed in this study include descriptive analysis, model estimation selection, classical
assumption tests, Goodness of Fit testing, regression analysis, and hypothesis testing. The
linear regression equation used in this study is as follows:
ROA = α+β1DSIZE+β2FDMEET+β3FEMD+β4INDCOM+LEV+GROWTH+e
Where:
α = Constant
β = Regression Coefficient
ROA = Return on Asset
DSIZE = Director Size
FDMEET = Frequency of Director Meeting
FEMD = Female Director
INDCOM = Independent Commissioner
LEV = Leverage
GROWTH = Growth
e = error
Company Financial Performance
The financial performance of a company is a depiction of the financial condition,
indicating the extent to which a company has performed well during a specific period
(Pahlawan et al., 2018). Financial performance can be measured using profitability ratios.
Profitability ratios illustrate the company's ability to generate acceptable levels of profit. In
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this study, the company's financial performance variable is proxied using Return on Assets
(ROA). This is because Return on Assets is a financial ratio related to profitability,
demonstrating how well a company can generate maximum profits with existing financing
activities (Hansen, 2017). Return on assets serves as a foundation for investors to assess the
company's management, determining whether the management can effectively generate
returns through the total assets owned by the company. The formula for Return on Assets
(ROA) is as follows:
ROA =
Net Income
Total Assets
Director Size
The board of directors is responsible for all forms of operational activities, management,
and various interests, both internally and externally, to achieve the company's goals
(Pahlawan et al., 2018). Director size in this study is measured using the following formula
(Ahmadi et al., 2018):
Director Size
= ∑ Company’s Board of Directors Members
Frequency of director meeting
Board of directors’ meetings involve coordination among board members in performing
their duties as company managers. Increasing the frequency of director meetings as an effort
to enhance monitoring will impact better financial performance (Rahadi & Octavera, 2020).
Frequency of director meeting in this study is measured using the following formula (Gulzar
et al., 2020):
Frequency of Director Meeting = ∑ Board of Directors' Meetings in 1 Year
Female director
Female directors are the number of women in a company occupying positions in the
board of directors. Diversity in the board of directors has a positive impact because greater
diversity within the board of directors will have a significant impact in minimizing the
likelihood of conflicts. Female director in this study is measured using the following formula
(Assenga et al., 2018):
Female Director =
∑ Female Board of Directors
∑ Board of Directors
Independent commissioner
An independent commissioner is a commissioner who does not come from an affiliated
party (Wulansari & Irwanto, 2018). The function of an independent commissioner is to
oversee management performance and provide a good and independent influence on decision-
making (Saputri & Khoiruddin, 2019). The increasing number of commissioners will enhance
their role and function in overseeing and controlling the actions of the board of directors. In
this study, the independent commissioner is measured using the following formula (Putri &
Muid, 2017):
Independent Commissioner =
∑ Female Board of Directors
∑ Board of Directors
Board Characteristics and Financial Performance of
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Leverage
Leverage, as a control variable, describes the extent to which a company is financed by
debt with the company's ability represented by its capital (Azis & Hartono, 2017). Leverage
in this study is measured using the following formula (Gulzar et al., 2020):
Leverage =
Total Debt
Total Assets
Growth
Growth as a control variable, describes a company's ability to maintain its position in
economic and industry developments within the economy in which the company operates
(Meidiawati & Mildawati, 2016). This research uses sales growth to measure the company's
growth with the formula as follows (Gulzar et al., 2020):
Growth =
Net sales t
Net sales (t-1)
RESULTS AND DISCUSSION
Descriptive Statistics
The results of the descriptive statistical test are as follows:
Table 1
Descriptive Statistical Test Results
ROA
DSIZE
FDMEET
FEMD
INDCOM
LEV
GROWTH
Mean
0.069488
7.805556
38.81481
0.121311
0.420312
0.525453
1.079557
Median
0.044064
7.000000
37.00000
0.105556
0.387500
0.488553
1.052756
Maximum
0.366601
12.00000
112.0000
0.500000
0.833333
0.964624
2.692553
Minimum
-0.057224
4.000000
10.00000
0.000000
0.200000
0.081420
0.520170
Std. Dev.
0.073974
2.128833
22.66874
0.131892
0.125149
0.225638
0.255852
Observations
108
108
108
108
108
108
108
Source: Eviews 9 Output (processed data, 2022)
The variable representing company financial performance (ROA) in this study has a
minimum value of -0.057224 or -5.7224% and a maximum value of 0.366601 or 36.6601%.
This indicates that the data distribution ranges from -0.057224 or -5.7224% to 0.366601 or
36.6601%. Overall, the company's financial performance variable (ROA) has an average
value of 0.069488. Based on this, it can be concluded that the average return on assets for
companies listed in the LQ45 index during the period 2017-2020 is 0.069488 or 6.9488%.
The variable director size (DSIZE) in this study has a minimum value of 4.000000 and
a maximum value of 12.00000. This indicates that the data distribution ranges from 4.000000
to 12.00000. Overall, the director size variable (DSIZE) has an average value of 7.805556.
Based on this, it can be concluded that the average number of directors in companies listed in
the LQ45 index during the period 2017-2020 is 8 directors.
The variable frequency of director meeting (FDMEET) in this study has a minimum
value of 10.00000 and a maximum value of 112.0000. This indicates that the data distribution
ranges from 10.00000 to 112.0000. Overall, the frequency of director meeting variable
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(FDMEET) has an average value of 38.81481. Based on this, it can be concluded that the
average number of director meetings in companies listed in the LQ45 index during the period
2017-2020 is 39 per year.
The variable female director (FEMD) in this study has a minimum value of 0.000000
and a maximum value of 0.500000 or 50.0000%. This also indicates that the data distribution
ranges from 0.000000 to 0.500000 or 50.0000%. Overall, the female director variable
(FEMD) has an average value of 0.121311. Based on this, it can be concluded that the average
percentage of female directors in companies listed in the LQ45 index during the period 2017-
2020 is 12% of the total board of directors.
The variable independent commissioner (INDCOM) in this study has a minimum value
of 0.200000 or 20.000% and a maximum value of 0.833333 or 83.3333%. This also indicates
that the data distribution ranges from 0.200000 or 20.0000% to 0.833333 or 83.3333%.
Overall, the independent commissioner variable has an average value of 0.420312. Based on
this, it can be concluded that the average percentage of independent commissioners in
companies listed in the LQ45 index during the period 2017-2020 is 42% of the total board of
commissioners.
The variable leverage (LEV) in this study has a minimum value of 0.081420 and a
maximum value of 0.964624. This explains that the data distribution ranges from 0.081420
to 0.964624. Overall, the leverage variable has an average value of 0.525453. Based on this,
it can be concluded that the average leverage of companies listed in the LQ45 index during
the period 2017-2020 is 0.525453.
The variable growth (GROWTH) in this study has a minimum value of 0.520170 and a
maximum value of 2.692553. This indicates that the data distribution ranges from 0.520170
to 2.692553. Overall, the growth variable has an average value of 1.079557. Based on this, it
can be concluded that the average growth of companies listed in the LQ45 index during the
period 2017-2020 is 1.079557.
Model Estimation Selection
Table 2
Chow Test Results
Effects Test
Statistic
d.f.
Prob.
Cross-section F
15.052170
(26,75)
0.0000
Cross-section Chi-square
197.365905
26
0.0000
Source: Eviews 9 Output (processed data, 2022)
Based on the table above, the Cross-section Chi-square value is 0.0000 < 0.05,
indicating that the selected model is the fixed effect model.
Hausman Test
The next step after conducting the Chow test is the Hausman test. The Hausman test is
carried out to compare between the fixed effect model and the random effect model in
determining the best model used in the panel regression model (Ghozali & Ratmono, 2013).
The results of the Hausman test are as follows:
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Table 3
Hausman Test Results
Test Summary
Chi-Sq. Statistic
Chi-Sq. d.f.
Prob.
Cross-section random
40.693286
6
0.0000
Source: Eviews 9 Output (processed data, 2022)
Based on the table above, it can be seen that the Cross-section random value < α, which
is 0.0000 < 0.05, indicating that the selected model is the fixed effect model. Based on the
model estimation selection conducted, it can be concluded that the best model used in this
study is the fixed effect model.
Classical Assumption Test
Normality Test
According to Ghozali & Ratmono (2017) the normality test aims to examine whether in
a regression model, the disturbance or residual variable has a normal distribution. If the
probability value of JB > 0.05, it means that the data is normally distributed. Conversely, if
the probability value of JB < 0.05, it means that the data is not normally distributed. Here are
the results of the normality test in this study:
Fig. 4: Normality Test Results
Source: Eviews 9 Output (prcessed data, 2022)
Figure 4 shows that the Jerque–Bera probability value is 0.000000 < 0.05, indicating
that the residuals are not normally distributed. The non-normality of the data can be addressed
by using data transformation. Based on the graph, it can be observed that the histogram falls
into the category of moderate negative skewness, suggesting that the most suitable
transformation is in the form of SQRT(k-x). Here are the results of the normality test on the
linear regression model equation after transformation.
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Fig 5: Normality Test after Transformation Results
Source: Eviews 9 Output (processed data, 2022)
Based on the figure above, it can be observed that the Jerque–Bera probability value is
0.240110 > 0.05 (5%), indicating that the data is normally distributed. Therefore, it can be
stated that the regression model is normally distributed.
Multicollinearity Test
Multicollinearity test, according to Ghozali & Ratmono (2017) is used to determine
whether there is high correlation among independent variables in a regression model. The
regression model is considered free from multicollinearity if the coefficient between
independent variables is < 0.90. Here are the results of the multicollinearity test in this study:
Table 4
Multicollinearity Test Results
DSIZE
FDMEET
FEMD
INDCOM
LEV
GROWTH
DSIZE
1.000000
0.429403
0.208901
0.593689
0.446859
-0.095838
FDMEET
0.429403
1.000000
0.460875
0.704653
0.380026
-0.038068
FEMD
0.208901
0.460875
1.000000
0.461261
0.193791
-0.041541
INDCOM
0.593689
0.704653
0.461261
1.000000
0.394553
-0.091336
LEV
0.446859
0.380026
0.193791
0.394553
1.000000
0.057432
GROWTH
-0.095838
-0.038068
-0.041541
-0.091336
0.057432
1.000000
Source: Eviews 9 Output (processed data, 2022)
Based on the test results shown in Table 4, it can be observed that the correlation
values among the variables director size (DSIZE), frequency of director meeting (FDMEET),
female director (FEMD), independent commissioner (INDCOM), leverage (LEV), and
growth (GROWTH) are still below the multicollinearity threshold of 0.90. Thus, it is
confirmed that there is no multicollinearity.
Heteroskedasticity Test
According to Ghozali & Ratmono (2017), the heteroskedasticity test is used to
determine whether there is a difference in variance between the residuals of one observation
and the residuals of another observation in a regression model. In this study, the
heteroskedasticity test employs the Breusch-Pagan-Godfrey test. Below are the results of the
heteroskedasticity test in this study:
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Table 5
Heteroskedasticity Test Results
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic
4.261249
Prob. F (6,101)
0.0007
Obs*R-squared
21.81673
Prob. Chi-Square (6)
0.0613
Source: Eviews 9 Output (processed data, 2022)
Based on the test results in Table 5, the Prob. Chi-Square Obs * R-squared has a
probability value of 0.0613 > 0.05. Therefore, it can be concluded that there is no
heteroskedasticity in the data above.
Autocorrelation Test
The autocorrelation test aims to examine whether there is a correlation between
disturbance errors (residual) at period t and errors at period t-1 (previous) in a regression
model. Autocorrelation can be detected or tested using the Durbin-Watson statistic. The
results of the autocorrelation test are presented in the following table:
Table 6
Autocorrelation Test Results
R-squared
0.906243
Mean dependent var
0.537764
Adjusted R-squared
0.866240
S.D. dependent var
0.089428
S.E. of regression
0.032707
Akaike info criterion
-3.755999
F-statistic
22.65431
Durbin-Watson stat
2.028762
Prob(F-statistic)
0.000000
Source: Eviews 9 Output (processed data, 2022)
Based on the autocorrelation test results above, the Durbin-Watson statistic is obtained
with a value of 2.028762. The decision-making basis for the presence of autocorrelation
involves checking the Durbin-Watson table, specifically whether dU < DW < 4-dU. In this
study, the Durbin-Watson table is consulted using a significance level of 0.05, considering
108 observations, 4 independent variables, and 2 control variables (k=6), resulting in a dU
value of 1,8049. The calculated Durbin-Watson statistic of 2.028762 is greater than the upper
limit (dU = 1.8049) and less than 4-dU (4-1.8049= 2,1951). Therefore, it can be concluded
that there is no autocorrelation.
Goodness of Fit Test
Coefficient of Determination (R2)
According to Ghozali & Ratmono (2013) this test of the coefficient of determination
aims to assess how well the model explains the variation in the dependent variable. The
model's ability to explain the dependent variable can be observed from the Adjusted R-
Squared values in the following table:
Table 7
Coefficient of Determination Test Results (R2)
R-squared
0.906243
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Adjusted R-squared
0.866240
Source: Eviews 9 Output (processed data, 2022)
In Table 6, the Adjusted R-Squared value is observed to be 0.866240 or 86.62%. This
can be interpreted as follows: the ability of the independent variables in this study, namely
director size, frequency of director meeting, female director, and independent commissioner,
as well as the control variables leverage (LEV) and growth (GROWTH), can explain 86.62%
of the variation in the company's financial performance, proxied by ROA. The remaining
percentage is accounted for by other variables not included in this research model.
Simultaneous Significance Test (F-Statistic Test)
According to Ghozali & Ratmono (2017), the F-statistic test aims to determine whether
all independent variables included in the model have a simultaneous effect on the dependent
variable. The results of the F-statistic test in the research model are presented in Table 8:
Table 8
F-Statistic Test Results
F-statistic
22.65431
Prob(F-statistic)
0.000000
Source: Eviews 9 Output (processed data, 2022)
Based on the results of the F-statistic above, it can be seen that the probability value of
the F-statistic in the model is 0.000000. Therefore, it can be explained that the probability
value of the F-statistic in this model is < the significance level α of 0.05. Thus, the null
hypothesis (H0) is rejected, and the alternative hypothesis (Ha) is accepted. This means that
the independent and control variables used in this study simultaneously influence the
dependent variable, namely, the financial performance of the company.
Hypothesis Testing
Multiple Linear Regression Analysis
Based on the selected estimation model, which is the fixed effect model, panel data
regression is conducted in this study using the fixed effect model. The results of the fixed
effect model regression in Eviews version 9 are presented in Table 9:
Table 9
Fixed Effect Model Regression Test Results
Variable
Coefficient
C
0.550116
DSIZE
0.029820
FDMEET
-0.012241
FEMD
-0.198154
INDCOM
-0.167118
LEV
0.019353
GROWTH
0.031741
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Source: Eviews 9 Output (processed data, 2022)
The multiple linear regression analysis using the fixed effect model produced the
following equation:
ROA = 0.550116 + 0.029820DSIZE - 0.012241FDMEET - 0.198154FEMD -
0.167118INDCOM + 0.019353 + 0.031741 + e
The constant is 0.550116, indicating that if the variables director size (DSIZE),
frequency of director meeting (FDMEET), female director (FEMD), independent
commissioner (INDCOM), leverage (LEV), and growth (GROWTH) are considered constant
or equal to 0, the average financial performance of the company is 0.550116.
The regression coefficient for director size (DSIZE) is 0.029820. This means that if
director size (DSIZE) increases by 1 unit, the financial performance of the company will
increase by 0.029820 units, assuming that the independent variables and other control
variables remain constant. The director size (DSIZE) variable has a positive effect on the
financial performance of the company.
The regression coefficient for frequency of director meeting (FDMEET) is -0.012241.
This implies that if the frequency of director meeting (FDMEET) increases by 1 unit, the
financial performance of the company will decrease by 0.012241 units, assuming that the
independent variables and other control variables remain constant. The frequency of director
meeting (FDMEET) variable has a negative effect on the financial performance of the
company.
The regression coefficient for female director (FEMD) is -0.198154. This means that if
female director (FEMD) increases by 1 unit, there will be a decrease in the financial
performance of the company by 0.198154 units, assuming that the independent variables and
other control variables remain constant. The female director (FEMD) variable has a negative
effect on the financial performance of the company.
The regression coefficient for independent commissioner (INDCOM) is -0.167118.
This implies that if independent commissioner (INDCOM) increases by 1 unit, there will be
a decrease in the financial performance of the company by 0.167118 units, assuming that the
independent variables and other control variables remain constant. The independent
commissioner (INDCOM) variable has a negative effect on the financial performance of the
company.
The regression coefficient for leverage (LEV) is 0.019353. This means that if leverage
(LEV) increases by 1 unit, there will be an increase in the financial performance of the
company by 0.019353 units, assuming that the independent variables and other control
variables remain constant. The leverage (LEV) variable has a positive effect on the financial
performance of the company.
The regression coefficient for growth (GROWTH) is 0.031741. This means that if
growth (GROWTH) increases by 1 unit, there will be an increase in the financial performance
of the company by 0.031741 units, assuming that the independent variables and other control
variables remain constant. The growth (GROWTH) variable has a positive effect on the
financial performance of the company.
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Hypothesis Testing (t-Statistic or Partial Test)
According to Ghozali & Ratmono (2017) the t-statistic or the test of individual
parameter significance fundamentally shows how much an independent variable influences
the dependent variable while assuming other independent variables are constant. The t-
statistic or partial test results can be seen in the following table:
Table 10
t-Statistic Test Results
Variable
Coefficient
t-Statistic
Prob.
C
0.550116
5.257318
0.0000
DSIZE
0.029820
2.282991
0.0253
FDMEET
-0.012241
-1.838049
0.0700
FEMD
-0.198154
-2.666902
0.0094
INDCOM
-0.167118
-1.820726
0.0726
LEV
0.019353
0.404409
0.6871
GROWTH
0.031741
1.355469
0.1793
Source: Eviews 9 Output (processed data, 2022)
Based on the table above, the results can be explained as follows:
1. The variable director size (DSIZE) has a coefficient value of 0.029820 with a probability
of 0.0253 < 0.05, indicating that director size has a positive and significant effect on the
financial performance of the company.
2. The variable frequency of director meeting (FDMEET) has a coefficient value of -
0.012241 with a probability of 0.0700 > 0.05, indicating that frequency of director
meeting has a negative and insignificant effect on the financial performance of the
company.
3. The variable female director (FEMD) has a coefficient value of -0.198154 with a
probability of 0.0094 < 0.05, indicating that female director has a negative and significant
effect on the financial performance of the company.
4. The variable independent commissioner (INDCOM) has a coefficient value of -0.167118
with a probability of 0.0726 > 0.05, indicating that independent commissioner has a
negative and insignificant effect on the financial performance of the company.
5. The variable leverage (LEV) has a coefficient value of 0.019353 with a probability of
0.6871 > 0.05, indicating that leverage has a positive and insignificant effect on the
financial performance of the company.
6. The variable growth (GROWTH) has a coefficient value of 0.031741 with a probability
of 0.1793 > 0.05, indicating that growth has a positive and insignificant effect on the
financial performance of the company.
The following is a summary of the research results in this study:
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No
Hypothesis
Result
1.
Ha1: Director size has a positive and significant effect on
the financial performance of the company.
Accepted
2.
Ha2: Frequency of director meeting has a positive and
significant effect on the financial performance of the
company.
Rejected
3.
Ha3: Female director has a positive and significant effect
on the financial performance of the company.
Rejected
4.
Ha4: Independent commissioner has a positive and
significant effect on the financial performance of the
company.
Rejected
Source: Eviews 9 Output (processed data, 2022)
Influence of Director Size on Company Financial Performance
In the t-statistics table, the variable director size has a coefficient of 0.029820 with a
significance probability of 0.0253 < 0.05. This result indicates that director size has a positive
and significant effect on the financial performance of the company, thus accepting alternative
hypothesis one (Ha1).
Director size in a company plays a role in determining both short-term and long-term
policies. The positive and significant impact of director size on the financial performance
aligns with agency theory. According to agency theory, a larger director size in a company
provides benefits such as better management control and monitoring, optimal decision-
making, reducing agency costs, and improving overall financial performance (Masitoh &
Hidayah, 2018). Despite the potential increase in expenses for director remuneration with a
larger director size, the benefits in terms of minimizing fraud and avoiding agency conflicts
far outweigh the costs, contributing to improved financial performance (Sitompul & Muslih,
2020).
A larger director size generates diverse skills and knowledge, which can be used
efficiently, reduces managerial dominance in decision-making, and strategic planning, thus
enhancing the financial performance of the company (Kakanda et al., 2016). This result is
consistent with studies by Gulzar et al. (2020); Shettima & Dzolkarnaini (2018); Queiri et al.
(2021) which found a positive and significant relationship between director size and financial
performance.
Influence of Frequency of Director Meeting on Company Financial Performance
In the t-statistics table, the variable frequency of director meeting has a coefficient of -
0.012241 with a significance probability of 0.0700 > 0.05. This result indicates that the
frequency of director meetings has a negative and insignificant effect on the financial
performance of the company, thus rejecting alternative hypothesis two (Ha2).
The negative and insignificant impact of the frequency of director meetings on financial
performance contradicts agency theory, which suggests that frequent director meetings can
aid in monitoring, performance disclosure, preventing information asymmetry, providing
useful advice on policies and strategic directions, and solving agency problems (Yakob &
Hasan, 2021).
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The finding of a negative and insignificant impact on financial performance does not
align with agency theory. This research discovered that the frequency of director meetings in
companies listed in the LQ45 index for the period 2017-2020 has a negative and insignificant
effect on financial performance. This result suggests that higher frequencies of director
meetings may decrease financial performance, and vice versa.
The non-significant result indicates that the frequency of director meetings may not
represent all companies listed in the LQ45 index from 2017 to 2020 but only affects the
research sample. This might be due to the frequency of director meetings not being optimally
and effectively conducted to prioritize the company's interests. Sandy et al (2020) suggested
that board meetings are often just a formality to fulfill obligations. Furthermore, the meetings
may not consistently address issues related to improving financial performance or important
company-related information but may also cover unrelated topics (Wijayanti & Mutmainah,
2012).
This result is supported by studies conducted by Gulzar et al. (2020); Sobhan (2021);
Yopie & Andriani (2021) indicating that the frequency of director meetings has a negative
and insignificant effect on the financial performance of the company.
Influence of Female Director on Company Financial Performance
In the t-statistics table, the variable female director has a coefficient of -0.198154 with
a significance probability of 0.0094 < 0.05. This result indicates that female directors have a
negative and significant effect on the financial performance of the company, rejecting
alternative hypothesis three (Ha3).
The negative impact implies that an increase in the number of female directors will
decrease the financial performance of the company and vice versa. The representation of
female directors negatively affects financial performance because women are not strongly
represented in corporate governance in Indonesia (Iswadi, 2016). From the descriptive
statistics table, the average value of the female director variable is 12.13%, indicating that the
board of directors is dominated by males at 87.87%, meaning the presence of female directors
is still a minority.
The presence of women is believed to decrease the financial performance of the
company due to the risk-averse nature of women compared to men (Manurung et al., 2020).
This difference in risk response may lead companies to miss more profitable investment
opportunities, resulting in lower income. The cautious nature of women also impacts decision-
making, leading to longer decision-making times. Based on this research, female directors
show a significant negative impact on the financial performance of the company. This result
is supported by studies by Roudaki (2018); Iswadi (2016); Ahmad et al. (2019), stating that
female directors have a significant negative effect on the financial performance of the
company.
Influence of Independent Commissioner on Company Financial Performance
In the t-statistics table, the variable independent commissioner has a coefficient of -
0.167118 with a significance probability of 0.0726 > 0.05. This result indicates that
independent commissioners have a negative and insignificant effect on the financial
performance of the company, thus rejecting alternative hypothesis four (Ha4).
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The negative and insignificant impact of an independent commissioner on the financial
performance of the company in this study contradicts agency theory. From the perspective of
agency theory, the presence of an independent commissioner can improve the quality of
supervision functions over financial performance and provide good and independent influence
on decision-making, thus minimizing agency problems (Jensen & Meckling 1976).
Contrary to agency theory, this study found that an independent commissioner in
companies listed in the LQ45 index for the period 2017-2020 has a negative and insignificant
effect on the financial performance of the company. The negative impact implies that a higher
proportion of independent commissioners in the company's board of commissioners will
reduce financial performance and vice versa.
The non-significant result indicates that an independent commissioner may not
represent all companies listed in the LQ45 index from 2017 to 2020 but only affects the
research sample. This suggests that the proportion of independent commissioners in a
company does not guarantee effective monitoring to minimize agency conflicts and the
possibility of managerial behavior prioritizing personal interests over shareholder interests
(Suaidah & Setyoningrum, 2021). Mahardika & Riyadi (2018) state that an independent
commissioner does not provide a significant contribution and impact on the financial
performance of the company because their presence is merely a formality to comply with
regulations created by the Financial Services Authority in Regulation of the Financial
Services Authority Number 33/POJK.04/2014, thus not enforcing good corporate governance
effectively.
Moreover, an independent commissioner may not function effectively due to strong
control and a dominant role held by majority shareholders, rendering the monitoring function
performed by the independent commissioner ineffective (Situmorang & Simanjuntak, 2019).
The presence of an independent commissioner tasked with overseeing management is not
always professional because of inadequate knowledge and information about the financial
performance of the company, making independent boards unable to review managerial actions
or uncover management errors (Apriliani & Dewayanto, 2018). This result is supported by
the studies of Gulzar et al. (2020), Sobhan (2021) and Wijayanti & Mutmainah (2012) which
state that an independent commissioner has a negative and insignificant effect on the financial
performance of the company.
CONCLUSION
This research aimed to examine the influence of director size, frequency of director
meetings, female director, and independent commissioner on the financial performance of
companies listed in the LQ45 index. The study also employed leverage and growth as control
variables. The findings revealed that director size has a significant positive impact on financial
performance, female director has a significant negative impact, while frequency of director
meetings and independent commissioner have a non-significant negative impact on financial
performance. The control variables, leverage, and growth, have a non-significant positive
impact on financial performance. One limitation of this study is that only director size
significantly influences financial performance. Therefore, it is recommended to explore other
factors related to board characteristics that may affect financial performance. Subsequent
research is also advised to use other proxies to measure company financial performance,
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considering both financial perspectives like ROE and market value perspectives like Tobin's
Q, to ensure result consistency when different proxies are used. Additionally, testing in other
sectors with a more extended research period can be conducted to strengthen the research
outcomes.
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Copyright holders:
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, Hanina Humaira3
(2023)
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