p–ISSN: 2723 - 6609 e-ISSN: 2745-5254
Vol. 5, No. 8 August 2024 http://jist.publikasiindonesia.id/
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3874
The Influence of Profitability, Leverage, Company Size,
Ownership Structure, and Board of Commissioners on Risk
Management Disclosure
Ari Istiqomah
1*
, Andry Priharta
2
, Riyanti
3
Universitas Muhammadiyah Jakarta, Indonesia
Email:
[email protected]
1*
, [email protected]
2
, [email protected]
3
*Correspondence
ABSTRACT
Keywords: risk
management disclosure;
profitability; leverage;
company size; ownership
structure.
The research aims to analyze and determine the influence of
profitability, leverage, company size, ownership structure,
and board of commissioners on risk management disclosure.
This research was conducted using an associative
quantitative research method with data analysis tools using
multiple linear regression using SPSS 20 software with a
population of 18 companies and a sample of 10 companies
engaged in the oil and gas energy sector listed on the
Indonesia Stock Exchange for the period 2018–2022. The
results of this study show that leverage and company size
partially have a significant effect on risk disclosure, while
profitability, ownership structure, and board of
commissioners partially have a non-significant effect on risk
management disclosure. The results of the smear test showed
that the results of profitability, leverage, company size,
ownership structure, and board of commissioners together
affected risk disclosure by 54%.
Introduction
Every organizational entity is always faced with uncertainty that can take the form
of opportunities and threats to achieve the set goals and objectives. The source of this
uncertainty can come from the internal or external environment of the organization
(Arifianto, 2019). Threats and opportunities that are manifestations of this form of
uncertainty can be called risks which, if not managed properly, can become a distraction
in efforts to achieve organizational goals and objectives (Ardimas & Wardoyo, 2014).
According to the official website of the Ministry of Energy and Mineral Resources
of the Republic of Indonesia, companies engaged in the oil and gas energy sector are
high-risk businesses. The risks of this business can be divided into operational risk,
market risk, and policy risk (Agustin, Anwar, & Bramana, 2023).
Specifically, according to the Centre of Risk Management and Sustainability
(CRMS), 5 biggest risks are often experienced by companies engaged in the oil and gas
energy industry, which are as follows:
Ari Istiqomah, Andry Priharta, Riyanti
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3875
Political Risk. The main thing that will affect oil and gas companies in the political
sphere is government regulations that regulate the movement of such companies
(Setiantp, 2016). Usually, there will be regulations or regulations that govern where,
when, and how oil and gas companies can move to extract oil and gas in a country
(Setiawan, Augustine, & Purwanti, 2021). That said, in general, this political risk will
increase more when the investor's company is a foreign company. Oil and gas companies
will tend to choose countries that have more stable political systems, easier investment
acquisition history, and long-term leases. However, some companies also choose
countries that are rich in oil and gas even if the country does not suit their preferences
(Maretha Sitinjak, Kristiana, Kurniasari, & Sasmito, 2018).
Geological Risk. The large number of oil and gas sources that are easy to obtain but
also tend to be easily intercepted makes the exploration of oil and gas sources move to
less friendly places. Examples include the construction of an oil refinery in the middle of
a rough ocean. However, many unconventional oil and gas extraction techniques can help
investors get results even though it may seem impossible at first. This geological risk
itself refers to the difficulty of extraction as well as the possibility of a smaller-than-
expected outcome (Rahmad, Raharjo, Widi Pramudiohadi, & Ediyanto, 2017). Oil and
gas geologists work hard to minimize the geological risks that occur by conducting
regular tests, so it is rare to find results that are far from previously estimated.
Price Risk. Oil and gas prices are the main factors to determine whether an oil and
gas investment is economically feasible or not. The geological barriers found to the ease
of extraction are the main factors in determining price risk. This is because to carry out
unconventional extraction, of course, more costs are also needed. A project will certainly
become unprofitable if there is a decline in oil and gas prices. This is the reason why a
company must conduct a forecasting system during the project period.
Financing Risk. Of all the risks mentioned above, this is the biggest risk and is
affected by the four risks above. The heavier the regulations and exploration costs, the
more expensive the operational costs that must be borne by the company. In addition, oil
and gas companies must also consider the salaries that must be paid to qualified workers.
This is why oil and gas companies need quite dense capital and the players are getting
less and less tips from time to time.
Because of their high investment needs, oil and gas entrepreneurs are usually
multinational entrepreneurs. In addition to trying at the multinational level, some
entrepreneurs are not only engaged in the oil and gas business but also do business in
other fields. The investment climate of a country is an important consideration in
determining the location of the oil and gas business. In addition to the investment climate,
the profits of the oil and gas business are considered by entrepreneurs. Therefore, every
entrepreneur must master the amount that affects the oil and gas business, including
reserves that produce production, costs, prices, and taxes. In the downstream sector,
investments that produce production will only be carried out if there is a profit, while
profits are a function of production, price, cost, and tax. In this case, costs are influenced
by technology and the environment, while production is a function of demand.
The Influence of Profitability, Leverage, Company Size, Ownership Structure, and Board of
Commissioners on Risk Management Disclosure
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3876
(Notonegoro, 2020), said that 2020 can be said to be one of the toughest years for
the oil and gas industry. All business segments of the oil and gas industry were hit. From
the upstream business segment, the oil and gas industry was hit by low oil prices. The oil
and gas business, both the upstream and downstream business segments, is a capital-
intensive business. In general, capital-intensive business activities have a fixed cost
structure that is relatively larger than the variable cost. Although production and sales
fall, oil and gas well maintenance costs, oil and gas refinery maintenance costs, oil and
gas transmission and distribution pipeline maintenance costs, and other oil and gas
infrastructure costs cannot be automatically reduced. This is considering that there are
certain conditions where a decrease in costs can have the consequence of a much greater
increase in costs in subsequent production activities and processes. One of the
consequences of low world economic growth, low consumption activities, and low
production activities is that energy demand as its carrying capacity will also be low.
According to (Ikitemur, Karabacak, & Igonor, 2020), risk management is a practice
of assessing, controlling, identifying, and mitigating risk. Risk management disclosure is
one of the most important elements of risk management. Risk disclosure is when the
reader of the annual report receives information about opportunities, dangers, losses,
threats, or risks affecting the company.
Companies and investors cannot avoid risks, but companies and investors can take
steps to anticipate the occurrence of risks.
According to (Pemayun & Budiasih, 2018), the disclosure of corporate risk
management needs to be carried out in a balanced manner, meaning that the information
submitted is not only positive but includes negative information, especially related to the
risk management aspect.
Based on the description of the background of the above problems, profitability,
leverage, company size, ownership structure and board of commissioners on risk
management disclosure obtained inconsistent results, so it is important to conduct
research and further evaluation, besides that research on risk management disclosure in
Indonesia is still relatively limited.
Thus, this study will discuss "The Influence of Profitability, Leverage, Company
Size, Ownership Structure and Board of Commissioners on Risk Management Disclosure
in Energy, Oil & Gas Companies Listed on the IDX for the 2018-2022 Period".
Ari Istiqomah, Andry Priharta, Riyanti
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3877
Table 1
Previous Research
This research aims to:
1. Knowing and analyzing whether profitability, leverage, company size, ownership
structure, and board of commissioners together have a significant effect on risk
management disclosures
2. Knowing and analyzing whether profitability has a significant effect on risk
management disclosure.
3. Determine and analyze whether leverage has a significant effect on risk management
disclosures.
4. Knowing and analyzing whether the size of the company has a significant effect on
risk management disclosure.
5. Determine and analyze whether the ownership structure has a significant effect on risk
management disclosure.
6. To know and analyze whether the board of commissioners has a significant effect on
risk management disclosure.
Research Methods
According to (Rosdianto, 2018), research methods are scientific ways to obtain data
with specific purposes and uses. Meanwhile, according to Sedarmayanti & Hidayat
(2013), the research method is a discussion of the theoretical concepts of various methods,
and their advantages and disadvantages, which in scientific papers are followed by the
selection of the methods used.
This study uses an associative quantitative research method, which is a research
method that uses numerical data or numbers to measure and analyze the phenomenon
being studied. This method involves collecting structured and measurable data, as well as
No Judul Hasil
Variabel (X) yang
diukur
Variabel (X) Pembeda
1
Sari dkk (2021). Pengaruh tingkat
leverage, profitabilitas dan ukuran
perusahaan public terhadap
pengungkapan risiko
Leverage & Profitabilitas tidak
terpengaruh secara signifikan , sedangkan
ukuran perusahaan berpengaruh signifikan
Leverage, Profitabilitas,
Ukuran Perusahaan
Menambahahan variabel struktur
kepemilikan dan dewan komisaris.
2
Tarantika & Solikhah (2019).
Pengaruh karakteristik dewan
komisaris dan reputasi auditor
terhadap pengungkapan manajemen
risiko
Ukuran persahaan & dewan komisaris
berpengaruh positif signifikan sedangkan
leverage & struktur kepemilikan tidak
berpengaruh positif
Ukuran Perusahaan,
Dewan Komisaris
Menambahahan variabel leverage,
profitabilitas, struktur kepemilikan
dan ukuran perusahaan
3
Saskara & Budiasih (2022).
Pengaruh leverage dan profitabilitas
pada pengungkapan manajemen
risiko
Leverage & Profitabilitas berpengaruh
positif pada pengungkapan manajemen
risiko
Leverage dan
Profitabilitas
Menambahahan variabel ukuran
perusahaan, struktur kepemilikan,
ukuran perusahaan dan dewan
komisaris
4
Fitriani & Setyawan (2022).
Determinan pengungkapan risiko,
Dewan komisaris berpengaruh positif dan
signifikan terhadap pengungkapan risiko
perusahaan
Dewan Komisaris
Menambah variabel penelitian lain
yaitu Leverage, ukuran
perusahaan, profitabilitas, struktur
kepemilikan, dan profitabilitas.
5
Kusumaningrum & Arifin (2022).
Determian pengungkapan
manajemen risiko dan pengaruhnya
terhadap return saham
Kepemilikan manager dan kepemilikan
institusi asing berpengaruh positif
sedangkan kepemilikan publik tidak
berpengaruh terhadap pengungkapan
pengungkapan manajemen risiko.
Struktur kepemilikan
Menambah variabel penelitian lain
yaitu Leverage, ukuran
perusahaan, profitabilitas, dan
profitabilitas.
The Influence of Profitability, Leverage, Company Size, Ownership Structure, and Board of
Commissioners on Risk Management Disclosure
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3878
using statistical techniques to analyze the data and generate generalizations that can be
applied to a wider population.
Data Source, Time, and Place of Research
The source of data for this research is from the financial statement data of
companies listed on the Indonesia Stock Exchange located on Jalan Jendral Sudirman
Kav. 52-53 Jakarta Indonesia, by downloading on the official website as follows
https://www.idx.co.id.
The time of this research was carried out from the end of 2023 to the beginning
of 2024 with the financial report data taken for the period 2018 to 2022 in oil and gas
energy companies located in Indonesia and listed on the Indonesia Stock Exchange.
Data Collection Methods
The data collection methods used in this study include:
1. Literature Studies
Literature study is a method in the form of written sources obtained from various
literature such as journals, books, theses, online media, print media, and other scientific
research related to the object of research.
2. Documentation
Documentation is a data collection method in which researchers only use existing
sources to support hypotheses and also to gain a deeper understanding of the research
subject without the need to collect data directly through observation or interview methods.
Population and Sample
The population in this study is all oil and gas energy companies listed on the
Indonesia Stock Exchange, namely 18 companies, namely PT Medco Energi
Internasional Tbk, PT Elnusa Tbk, PT Perusahaan Gas Negara Tbk, PT Energi Mega
Persada Tbk, PT Apexindo Pratama Duta Tbk, PT Rukun Raharja Tbk, PT AKR
Corporindo Tbk, PT Surya Esa Perkasa Tbk, PT Super Energy Tbk, PT Capitalinc
Investment Tbk, PT Mitra Investindo Tbk, PT Perdana Karya Perkasa Tbk, PT Radiant
Utama Interinsco Tbk, PT Sigma Energy Compressindo Tbk, PT Ratu Prabu Energi Tbk,
PT Ginting Jaya Energi Tbk, PT Sunindo Pratama Tbk and PT Astrindo Nusantara
Infrastruktur Tbk.
Data Analysis Methods
The data analysis method of this study uses the multiple linear regression method
with descriptive and verifiable analysis.
According to (Ghozali, 2016), multiple linear regression is a regression model that
involves more than one independent variable. Multiple linear regression analysis was
carried out to determine the direction and how much influence the independent variable
had on the dependent variable.
Descriptive analysis is a research method used to describe the data that has been
collected, while verifiable analysis is a research method used to test hypotheses using
numerical calculations or statistics (Sugiyono, 2017) .
The following are the presentations and results of the classic assumption test, namely:
1. Normality Test
Ari Istiqomah, Andry Priharta, Riyanti
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3879
According to (Ghozali, 2016), the normality test is carried out to test whether, in a
regression model, an independent variable (x) and a dependent variable (y) or both have
a normal or abnormal distribution. If a variable is not distributed normally, the results of
the statistical test will decrease. The data normality test can be done using the
Kolmogorov-Smirnov one-sample test, which provides that if the significance value is
above 5% or 0.05, the data has a normal distribution. Meanwhile, if the results of the one
sample Kolmogorov Smirnov test produce a significant value below 5% or 0.05, then the
data does not have a normal distribution.
2. The Car Wash
A regression model can be said to be good when it is free from autocorrelation.
According to (Ghozali, 2016) Autocorrelation tests can arise due to sequential
observations throughout time and are related to each other. The autocorrelation test aims
to test whether, in a linear regression model, there is a correlation between the
perturbrillator error in the t-period and the error in the t-1 period (previously). If there is
a correlation, then it is called an autocorrelation problem. This problem arises because
the residual (pervert error) is not free from one observation to another. If the observation
data is above 100 data, it is better to use the Lagrange Multiplier test.
3. Multicollinearity Test
According to (Ghozali, 2016), the multicollinearity test aims to find out whether
the regression model finds a correlation between independent variables or independent
variables. The effect of this multicollinearity is that it causes high variability in the
sample. This means that the standard error is large, so when the coefficient is tested, the
t-count will be a small value from the t-table. This shows that there is no linear
relationship between the independent variable that is affected and the dependent variable.
4. Heteroscedasticity Test
This test aims to test whether, in a regression model, there is a variant discomfort
from residual in one observation to another. If the variant is different, it is called
heteroscedasticity. One way to find out whether there is heteroscedasticity in a multiple
linear regression model is by looking at the scatterplot graph or from the predicted value
of the bound variable, namely SRESID with a residual error, namely ZPRED. If there is
no specific pattern and does not spread above or below the zero number on the y-axis,
then it can be concluded that there is no heterogeneity. According to (Ghozali, 2016), a
good research model does not have heteroscedasticity.
The formula of the multiple linear regression of this study is as follows:
Y = α + β1 X1 + β2 X2 + β3X3 + β4X4 + β5X5 + e
Information:
Y = Risk Management
a = Constanta
β = Koefisien estimate
X1= Profitability
The Influence of Profitability, Leverage, Company Size, Ownership Structure, and Board of
Commissioners on Risk Management Disclosure
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3880
X2 = Leverage
X3 = Company Size
X4 = Ownership Structure
X5 = Board of Commissioners
e = Error
Results and Discussion
Descriptive Statistical Analysis
Descriptive statistical data is a general description, regarding the research object
that is a research sample described by statistical data and is expected to provide an initial
overview of a problem being researched. The variables studied in this study include
several variables, namely profitability, leverage, company size, ownership structure,
board of commissioners, and risk management disclosure. The descriptive data can be
seen from the mean, maximum, minimum, and standard deviation values of each research
variable studied and can be seen in the following table:
Table 2
Deskriptive Statistics
From the data mentioned above, it can be interpreted as follows:
1. From the profitability data, it can be seen that the average is 0.0710 with a standard
deviation of 1.2714, the standard deviation value is greater than the average value,
indicating that the profitability of the companies sampled in this study varies greatly.
The average profitability value of 0.0710 shows that the companies studied can
generate an average profit after tax of Rp 0.07 from every Rp 1 of each revenue.
2. Profitability has a maximum value of 0.81, this shows that the company studied can
generate a maximum profit after tax of Rp. 0.81 from every Rp 1 of revenue. High
profitability is due to the company's ability to generate high revenue with low
expenses. The minimum value of 0.00 indicates that the company being studied cannot
generate profit from every Rp 1 of revenue received.
3. From the table data, it can be seen that the leverage variable has an average of 0.6062
with a standard deviation of 0.17750, the standard deviation value is lower than the
average value indicating that the leverage variable in this study does not vary. The
average leverage value is closer to the minimum, so the average leverage value in this
study is quite low, which means that the use of debt to finance assets in oil and gas
Ari Istiqomah, Andry Priharta, Riyanti
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3881
energy companies is quite low and indicates that the use of debt is more allocated to
finance operational activities.
4. From the company size data in the table, it can be seen that the average value is 29.8428
with a standard deviation of 1.54891. The standard deviation obtained is smaller than
the average indicating that the company size variable used in this study does not vary.
The company size has a maximum of 32.28, this indicates that the company studied
has an increase in assets of 32% from the previous year. Meanwhile, the minimum
value of 27.06 indicates that there is an asset management in the company studied by
27.06% from the previous year.
5. The distribution of data for the ownership of companies whose shares are owned by
institutions in the table shows the results of an average value of 0.5612 and a standard
deviation value of 0.19631, showing that the average share ownership of oil and gas
companies studied is 56% and the variables studied have a sample that does not vary
because the deviation value is smaller than the average value. The minimum value of
0.23, and the maximum value of 0.98 indicate that the institutional share ownership in
the research object is the lowest 23% and the highest 98% of the total share ownership.
6. The distribution of data for the board of commissioners in the table shows that the
average value of 0.3884 and the standard deviation of 0.11576 show that the average
composition of the independent board of commissioners owned by the oil and gas
energy companies studied is 38% and the variables studied have a sample that does
not vary because the deviation value is smaller than the average value.
A minimum value of 0.25 and a maximum of 0.75 indicates that the independent
board of commissioners owned by the company under study is a minimum of 25% and a
maximum of 75% of the total existing board of commissioners.
The risk disclosure in the table shows that the average value of 18.2200 and the
standard deviation of 3.68278 show that the average risk disclosure made in the oil and
gas energy companies studied is 18 items, and the variables studied have a sample that
does not vary because the deviation value is smaller than the average value. A minimum
value of 14.00 and a maximum of 29.00 indicates that the risk management disclosures
made by the companies studied are a minimum of 14 items and a maximum of 29 items
out of a maximum of 41 items disclosed.
Classical Assumption Test
According to (Ghozali, 2016), the classical assumption test is the initial stage used
before multiple linear regression analysis. This test is carried out to be able to provide
certainty so that the regression coefficient is unbiased consistent and accurate in
estimation. The tests carried out in this study are normality, multicollinearity,
autocorrelation, and heteroscedasticity tests. This classic assumption test uses the
Statistical Program Package for the Social Scientist 20 (SPSS 20).
1. Normality Test
According to (Ghozali, 2016), the normality test was carried out to test whether
independent and dependent variables have a normal distribution or not. There are two
ways to test the distribution of data, namely by graph analysis and statistical testing. The
The Influence of Profitability, Leverage, Company Size, Ownership Structure, and Board of
Commissioners on Risk Management Disclosure
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3882
test was carried out using the one-sample Kolmogorov-Smirnov (K-S) nonparametric
statistical test. The normality assumption is fulfilled when the plot output points follow
the plot diagonal line and the normality assumption yields an α > of 0.05. The data
distribution decision-making guidelines for the results of the normality test are as follows:
1. If the value of Asymp. Sig (2-tailed) < 0.05, it can be concluded that the distributed
data is abnormal.
2. If the value of Asymp. Sig (2-tailed) > 0.05, then it can be concluded that the data is
normally distributed
From the processing of SPSS data, the following results were obtained:
Table 3
Results of the One-Sample Kolmogrov-Smirnov Test Normality Test
Figure 1
Results of Residual Standard Regression P-Plot Normality Test
Unstandardiz
ed Residual
N
38
Normal Parameters
a,b
Mean
0E-7
Std.
Deviation
2.25729573
Most Extreme
Differences
Absolute
.195
Positive
.195
Negative
-.124
Kolmogorov-Smirnov Z
1.204
Asymp. Sig. (2-tailed)
.110
Ari Istiqomah, Andry Priharta, Riyanti
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3883
The analysis of the table and Figure 4.1 above shows that the value of Sig
Kolmogorov-Smirnov (K-S) > 0.05 and the output points of the plot follows the diagonal
line of the plot, so it is concluded that the data is normally distributed.
1. Uji Autokorelasi
The autocorrelation test aims to determine the pattern of influence of independent
variables in this study, so a multiple linear regression equation is prepared. Multiple
regression in this study was used to determine the influence of independent variables on
bound variables. The regression regression analysis produces a regression coefficient that
shows the direction of the causal relationship between the independent variable and the
bound variable. The results of the autocorrelation test of the Durbin-Watson method are
presented in the following table 4:
Table 4
Autocorrelation Test Results
From Table 4 above, the conclusion of the dw test for observation (n) is 75, the
independent variable (k) is 5 variables, and the Durbin Watson value with α = 5% is
obtained as 1.7698. Therefore, du < dw, 4-du (1.7698 < 1.794 < 4 – 1.7698), means that
it can be concluded that there are no autocorrelation symptoms in the data.
2. Multicollinearity Test.
The multicollinearity test was carried out to avoid bias in the research results.
There should be no multicollinearity between independent variables in a regression model
because it can affect the conclusions to be drawn. According to (Ghozali, 2016), the
multicollinearity test aims to find out whether the regression model finds a correlation
between independent variables or independent variables. The criteria to find out whether
or not there is multicollinearity in the regression model is to look at the tolerance and
variance inflation factor (VIF) values. The cutoff value that is commonly used to indicate
the presence of multicollinearity is a tolerance value ≤ 0.10 or equal to the VIF value ≥
10. The criteria for taking the use of tolerant values and VIF according to Ghozali (2016)
are as follows:
a. Tolerance Value ≤ 0.01 or VIF ≥ 10, then there is multicollinearity among the
independent variables.
b. Tolerance Value > 0.01 or VIF < 10, then there is no multicollinearity among
independent variables
3. Heteroscedasticity Test
According to (Ghozali, 2016), the heteroscedasticity test is used to test whether
there is a variant inequality in the model from the residual of one observation to another.
If there is no heteroscedasticity or what can be called homoscedasticity, then the data is
The Influence of Profitability, Leverage, Company Size, Ownership Structure, and Board of
Commissioners on Risk Management Disclosure
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3884
tested using a scatterplot. The basis for testing heteroscedasticity according to Ghozali
(2016) is as follows:
a. If there is a specific pattern, such as the dots that form a certain regular pattern (joining,
widening, then narrowing), then there is an indication that heteroscedasticity has
occurred.
b. If there is no clear pattern, as well as points spreading above and below the number 0
on the Y axis, then there is no heteroscedasticity.
Uji Hipotesis
a. Test F (Simultaneous Test)
According to (Sugiyono, 2017), the F test is used to show whether all independent
or independent variables included in the model have a joint influence on dependent or
bound variables. The hypothesis was tested by comparing the value of Fcal with Ftabel
with the degree of validity, namely df1 = k and df2 = n-k- 1, and the significance level
used was 5%. When the value of the F prob < a significance level of 5%, the conclusion
obtained from the results of data analysis using the F test is that the independent variables
together have a significant effect on the bound variables. The criteria for the results of F
calculation compared to F table with a significant level of 0.05 or α = 5% are as follows:
1. Fcal > the Ftable, the independent variable has a significant effect on the dependent
variable.
2. Fcal < Ftable, the independent variable does not have a significant effect on the
dependent variable.
From the results of data through SPS, the results of the F test are obtained as follows:
Table 5
R Test Results
Model Summary
R
R Square
Adjusted R Square
Std. Error of the
Estimate
1
.779a
.606
.545
2.42725
a. Predictors: (Constant), Commissioner, Profitability, Leverage, Size,
Ownership
Source: SPSS data processing results (2024)
Coefficient of Determination (R2) According to Sugiyono (2019), is the ability of
variable X (independent variable) to influence variable Y (dependent variable), the larger
the coefficient of determination indicates the better X's ability to explain Y. From the R
test table above, an adjusted R2 value of 0.545 is obtained. Thus, it can be concluded that
the contribution of variables – profitability, leverage, company size, ownership structure,
and board of commissioners together to the risk management disclosure variable is 54%,
while the remaining 46% is influenced by other variables that are not studied.
b. Test t (Partial test).
Hypothesis testing using the t-test aims to find out whether a hypothesis is accepted
or rejected. Data analysis using the t-test will show how far an independent variable
Ari Istiqomah, Andry Priharta, Riyanti
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3885
individually affects the bound variable. Hypothesis testing is carried out by comparing
the count with the table using the degree of freedom (dk) which is n – 2. When the prob
< a significance level of 5%, the conclusion obtained from the results of data analysis is
that the independent variable has a significant influence on the dependent variable. The
criteria for hypothesis testing using the t-test can be described as follows:
1. tcount ≥ ttable or Sig value < 0.05, then H0 is rejected and Ha is accepted. This can be
interpreted that there is a partially significant influence of independent variables on
dependent variables.
2. Calculate < ttable or Sig value > 0.05, then H0 is accepted and Ha is rejected. This can
be interpreted that there is no partially significant influence of independent variables
on dependent variables.
Table 6
T Test Results
From the table above, it is known as follows:
a) The Sig value of the profitability variable is 0.550 (> 0.005) and β1 = -1.825, so it is
concluded that the profitability variable has a negative and insignificant effect on the
disclosure of risk management.
b) The Sig value of the leverage variable is 0.002 (< 0.005) and β2 = 12.099, so it is
concluded that the leverage variable has a positive and significant effect on risk
management disclosure.
c) The Sig value of the company size variable is 0.002 (< 0.005) and β3 = 1.144, so it is
concluded that the company size variable has a positive and significant effect on risk
management disclosure.
d) The Sig value of the company's ownership variable is 0.518 (> 0.005) and β4 = -2.108,
so it is concluded that the company's ownership variable has an insignificant negative
effect on risk management disclosure.
e) The Sig value of the variable of the Board of Commissioners is 0.042 (> 0.005) and
B5 = 8.354, so it is concluded that the variable of the Board of Commissioners has a
positive and insignificant effect on the disclosure of risk management.
From the overall results of data processing on PSS, it can be included in the multiple
linear regression equation as follows:
Y = α + β1 X1 + β2 X2 + β3X3 + β4X4 + β5X5 + e
Y = -25.187 - 1.825X1 + 12.099X2 +1.144X3 – 2.108X4 + 8.354X5 + e
The Influence of Profitability, Leverage, Company Size, Ownership Structure, and Board of
Commissioners on Risk Management Disclosure
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3886
From the multiple linear regression equation above, it can be explained as follows:
1. The value of the constant (a) is negative, which is -25.187, meaning that if the
profitability, leverage, company size, company ownership, and commissioners are
equal to zero (0), then the risk management disclosure decreases.
2. The value of the profitability regression coefficient (X1) is -1,825 This value shows a
negative influence (opposite direction) between the profitability variable and the risk
management disclosure. This means that if the profitability variable increases by 1%,
then on the contrary, the risk management disclosure variable will decrease by 1,825.
Assuming that the other variables remain constant.
3. The regression coefficient value for the leverage variable (X2) has a positive value of
12,099. This shows that if leverage increases by 1%, then the risk management
disclosure will increase by 12,099 assuming other independent variables are
considered constant. A positive sign means that it shows a unidirectional influence
between independent variables and dependent variables.
4. The regression coefficient value for the company size variable (X3) has a positive
value of 1,144 This shows that if the size of the company increases by 1%, then the
risk management disclosure will increase by 1,144 assuming that other independent
variables are considered constant. A positive sign means that it shows a unidirectional
influence between independent variables and dependent variables.
5. The value of the regression coefficient of company ownership (X4) is -2,108 This
value shows a negative influence (opposite direction) between the variable of company
ownership and risk management disclosure. This means that if the profitability variable
increases by 1%, then on the contrary, the risk management disclosure variable will
decrease by 2,108. Assuming that the other variables remain constant.
6. The regression coefficient value for the board of commissioners (X5) has a positive
value of 8,354. This shows that if the board of commissioners experiences a 1%
increase, then the risk management disclosure will increase by 8,354 assuming other
independent variables are considered constant. A positive sign means that it shows a
unidirectional influence between independent variables and dependent variables.
7. The results of the study showed the values of the regression coefficient of profitability
(-1,825), leverage (12,099), company size (1,144), company ownership (-2,108), and
board of commissioners (8,354), because 12,099 > 8,354, 1,144, -1,825 and -2,108,
leverage is the dominant variable that affects risk management disclosure.
Based on the test with the SPSS program on the hypothesis that has been described
above, it can be interpreted as follows:
1. The influence of profitability, leverage, company size, company ownership structure,
and board of commissioners together affects risk management disclosure.
The hypothesis is tested by comparing the value of Fcal with Ftabel with the
degree of validity. F calculated from the results of SPSS processing obtained 9.853, while
df1 = 5 and df2 = 33, then for F table a value of 2.512 was obtained and the significance
Ari Istiqomah, Andry Priharta, Riyanti
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3887
level used was 5%. Fcal (9.853) > Ftable (2.512) and a sig value of 0.000 (< 0.05)
conclude that the independent variable has a simultaneous (together) effect on the
dependent variable.
Overall, a combination of these factors can affect a company's risk disclosure
policy. It's important to remember that good risk management disclosure helps build
stakeholder trust, increase transparency, and allow stakeholders to make more informed
decisions. This is in line with stakeholder theory.
Thus the first hypothesis (H1), profitability, leverage, company size, company
ownership structure, and board of commissioners together affect risk management
disclosure is acceptable.
2. Profitability affects risk management disclosure.
From the results of the T-test, the Sig value of the profitability variable was
obtained at 0.550 (> 0.005) and β –1.825, so it was concluded that the profitability
variable had a negative and insignificant effect on the disclosure of risk management.
This result is in line with previous research conducted by (Devi, Budiasih, &
Badera, 2017) which stated that the profitability variable did not have a significant effect
on risk management disclosure, but was different from the results of a study conducted
by (Asmoro, Setianingsih, & Putranti, 2023) which stated that profitability affected risk
management disclosure.
In stakeholder theory, it is emphasized that stakeholders have the right to obtain
information about various company activities that affect stakeholders. Profitability does
not have a significant effect on risk management disclosure is to be expected Although
profitability is an important factor in a company's financial health, its relationship with
risk management disclosure is not always direct. Many other factors can influence a
company's decision to disclose information about risks such as the strategic focus of the
company.
If profitability does not affect risk management disclosures, management must
ensure that they remain transparent in disclosing the risks the company faces to
stakeholders, regardless of the level of profitability. This helps build trust and maintain
good relationships with stakeholders. Meanwhile, investors must dig up comprehensive
information about the risks faced by the company, regardless of the current financial
performance.
Thus, the second hypothesis (H2), profitability affecting risk management
disclosure is rejected.
Leverage affects risk management disclosure.
From the results of the T-test, the Sig value of the leverage variable was obtained
at 0.002 (< 0.005) β 12.099, so it was concluded that the leverage variable had a
significant effect on risk management disclosure.
This result is in line with previous research conducted by (Octaviani & Sutriani,
2019) which stated that leverage has an effect on risk management disclosure, but is
different from the results of research conducted by (Kusumosari & Solikhah, 2021) which
stated that leverage does not affect risk management disclosure.
The Influence of Profitability, Leverage, Company Size, Ownership Structure, and Board of
Commissioners on Risk Management Disclosure
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3888
The level of leverage of a company can affect the perception of risk by
stakeholders. Companies with high levels of debt may be considered riskier, and therefore
tend to make more detailed disclosures about the risks they face, especially those related
to debt servicing and other financial risks. This is in line with stakeholder theory and
signaling theory where stakeholders have the right to get information about company
activities and get signals or pieces of information that are relevant to stakeholders.
To manage the risks associated with high levels of leverage, it is important for
management to adopt effective risk management practices and to communicate openly
with investors about the risk management strategies taken. On the other hand, investors
also need to pay attention to the leverage level of the company in conducting their
investment risk analysis and understand the implications associated with investing in
companies with a high level of leverage.
Thus the third hypothesis (H3) of leverage affecting risk management disclosure is
acceptable.
The size of the company affects the disclosure of risk management.
From the results of the T-test, the Sig value of the company size variable was
0.002 (< 0.005) and β 1.144, so it was concluded that the company size variable had a
significant effect on risk management disclosure.
These results are in line with previous research conducted by (Kusumosari &
Solikhah, 2021) where the results stated that company size affects risk management
disclosure.
The size of the company can affect the complexity of operations and the level of
risk faced. Larger companies may have more divisions, branches, and international
operations, all of which lead to a wide array of risks. Because of this, larger companies
tend to make more detailed disclosures about the risks faced to provide a better
understanding to stakeholders. This is in line with the stakeholder theory where
stakeholders have the right to get information related to company activities.
The size of the company can encourage better risk management disclosure
activities, while small companies may have less pressure to disclose their risks publicly
because they may not be directly monitored by regulatory agencies or the public like large
companies. However, this could be an opportunity for management to increase investor
openness and trust by adopting more proactive disclosure practices. On the other hand,
investors should conduct a careful risk analysis related to the size of the company they
are considering investing in.
Thus, the fourth hypothesis (H4) of the size of the company affecting the disclosure
of risk management is acceptable.
The ownership structure of the company affects the disclosure of risk management.
From the results of the T-test, the Sig value of the company ownership variable
was 0.518 (> 0.005) and β -2.108, so it was concluded that the company ownership
variable had a negative and insignificant effect on the disclosure of risk management.
This is in line with the results of research conducted by (Tarantika & Solikhah,
2019) which states that ownership structure does not affect risk management disclosure.
Ari Istiqomah, Andry Priharta, Riyanti
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3889
In line with stakeholder theory, both public and private companies must provide
sufficient information to their stakeholders to enable informed decision-making. If a
company's ownership structure does not affect risk management disclosure, it is possible
that the company's ownership structure is not the only factor affecting risk management
disclosure, but can have a significant impact on the company's risk disclosure policy.
In this case, management should remain focused on effective risk management
and transparent disclosure, while investors need to conduct additional analysis and rely
on alternative sources of information to understand the risks that the company may face.
Thus, the fifth hypothesis (H5) of the company's ownership structure affecting
risk management disclosure is rejected.
The Board of Commissioners affects the disclosure of risk management.
From the results of the T-test, the Sig value of the variable of the board of
commissioners was 0.042 (> 0.005), so it was concluded that the variable of the board of
commissioners had an insignificant effect on the disclosure of risk management.
This is in line with the results of research conducted by (Octaviani & Sutriani, 2019)
which states that the board of commissioners has no effect on risk management
disclosure, but is not in line with the results of (Kusumosari & Solikhah, 2021) research
which states that the board of commissioners affects risk management disclosure.
The existence of a strong and independent board of commissioners can encourage
companies to better disclose the risks they face, as they are responsible for overall risk
management and the interests of shareholders, this is in line with stakeholder theory and
signal theory. If the board of commissioners does not affect the disclosure of risk
management, the disclosure of risk management may be more influenced by factors such
as the size of the company, the risk management practices applied, and the company's
policies regarding information disclosure.
By realizing that the number of boards of commissioners does not necessarily
reflect the quality of risk management disclosures, both management and investors can
focus their attention on more important aspects, such as quality of information, effective
collaboration, and competent risk management. Thus, the sixth hypothesis (H6) of the
board of commissioners affecting risk management disclosure is rejected.
Conclusion
Based on hypothesis testing and research discussion taking into account the
limitations of the research, the following research conclusions can be stated:
1. Profitability, leverage, company size, company ownership structure, and board of
commissioners simultaneously have a significant effect on risk management
disclosure.
2. Profitability has a negative insignificant effect on risk management disclosure, which
is not in line with the hypothesis.
3. Leverage has a significant effect on risk management disclosure.
4. The size of the company has a significant effect on risk management disclosure.
The Influence of Profitability, Leverage, Company Size, Ownership Structure, and Board of
Commissioners on Risk Management Disclosure
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3890
5. Corporate ownership has a negative and insignificant effect on risk management
disclosure, which is not in line with the hypothesis.
6. The board of commissioners has an insignificant effect on risk management disclosure,
which is not in line with the hypothesis.
Ari Istiqomah, Andry Priharta, Riyanti
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3891
Bibliography
Agustin, Oppie, Anwar, Yuniarti, & Bramana, Sally Maria. (2023). Analisis Rasio
Profitabilitas Terhadap Optimalisasi Laba Pada PT Grand Titian Residence. Jurnal
Media Wahana Ekonomika, 20(1), 202–215.
Ardimas, Wahyu, & Wardoyo, Wardoyo. (2014). Pengaruh kinerja keuangan dan
corporate social responsibility terhadap nilai perusahaan pada bank go public
yang terdaftar di BEI.
Arifianto, Darmawan Agus. (2019). Analisis Manajemen Risiko Teknologi Informasi
Pada Aplikasi Monitoring Gangguan Nossa (New Operation Support System
Assurance) Pada Tioc (Telkom Integrated Operation Center) Unit Dso Surveillance
Menggunakan Framework Iso 31000. Program Sistem Informasi S1 Fakultas
Teknik Universitas Widyatama.
Asmoro, Wiwiek Kusumaning, Setianingsih, Novie Astuti, & Putranti, Eti. (2023).
Pengaruh Sumber Dana Eksternal dan Internal Perusahaan Terhadap Rentabilitas
Modal Sendiri. Owner: Riset Dan Jurnal Akuntansi, 7(1), 523–533.
Devi, Sunitha, Budiasih, I., & Badera, IDNB. (2017). Pengaruh pengungkapan enterprise
risk management dan pengungkapan intellectual capital terhadap nilai perusahaan.
Jurnal Akuntansi Dan Keuangan Indonesia, 14(1), 2.
Ghozali, Imam. (2016). Desain penelitian kuantitatif dan kualitatif: untuk akuntansi,
bisnis, dan ilmu sosial lainnya.
Ikitemur, Gokhan, Karabacak, Bilge, & Igonor, Andy. (2020). A mixed public-private
partnership approach for cyber resilience of space technologies. In Space
Infrastructures: From Risk to Resilience Governance (pp. 120–130). IOS Press.
Kusumosari, Larassanti, & Solikhah, Badingatus. (2021). Analisis kecurangan laporan
keuangan melalui fraud hexagon theory. Fair Value: Jurnal Ilmiah Akuntansi Dan
Keuangan, 4(3), 753–767.
Maretha Sitinjak, Lucky, Kristiana, Haryanti, Kurniasari, Widuri, & Sasmito, Wisnu
Djati. (2018). Manajemen Keuangan Terapan: Keputusan Investasi & Personaliti
Disc.
Notonegoro, Muhammad Ridho. (2020). Tahapan Komunikasi Partisipatif Dalam Proses
Pengelolaan Wisata Underwater Desa Bangsring (Kecamatan Wongsorejo,
Kabupaten Banyuwangi). Jurnal Sains Komunikasi Dan Pengembangan
Masyarakat [JSKPM], 4(6), 767–780.
Octaviani, Rika, & Sutriani, Elma. (2019). Analisis data dan pengecekan keabsahan data.
Pemayun, Ari Widhiasmana, & Budiasih, IGAN. (2018). Pengaruh religiusitas, status
sosial ekonomi dan love of money pada persepsi etis mahasiswa akuntansi. E-
The Influence of Profitability, Leverage, Company Size, Ownership Structure, and Board of
Commissioners on Risk Management Disclosure
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3892
Jurnal Akuntansi, 23(2), 1600–1628.
Rahmad, Basuki, Raharjo, Sugeng, Widi Pramudiohadi, Eko, & Ediyanto, Ediyanto.
(2017). Pengantar Eksplorasi Geologi Batubara dan Kualitas Batubara. Lembaga
Penelitian dan Pengapdian kedapa Masyarakat Univesitas Pembangunan ….
Rosdianto, Haris. (2018). Implementasi model pembelajaran POE (predict observe
explain) untuk meningkatkan pemahaman konsep siswa pada materi hukum
Newton.
Setiantp, Buddy. (2016). Mengungkap Strategi Investor Institusi Sebagai penggerak
utama kenaikan harga saham. BSK Capital.
Setiawan, Temy, Augustine, Yvonne, & Purwanti, Ari. (2021). Pengaruh etika terhadap
profitabilitas: mediasi image dan CSR: studi perusahaan tambang, minyak, dan
gas-Jejak Pustaka. Jejak Pustaka.
Sugiyono, F. X. (2017). Neraca Pembayaran: Konsep, Metodologi dan Penerapan (Vol.
4). Pusat Pendidikan Dan Studi Kebanksentralan (PPSK) Bank Indonesia.
Tarantika, Risna Ade, & Solikhah, Badingatus. (2019). Pengaruh Karakteristik
Perusahaan, Karakteristik Dewan Komisaris dan Reputasi Auditor Terhadap
Pengungkapan Manajemen Risiko. Journal of Economics, Management,
Accounting, and Technology, 2(2), 142–155.