The Influence of Profitability, Leverage, Company Size, Ownership Structure, and Board of Commissioners on Risk Management Disclosure

ABSTRACT


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: 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-thanexpected 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.(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 capitalintensive 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".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 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: 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.

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.

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 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.

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.

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.

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:

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: 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 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).

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 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: 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.

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: 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 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: 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 variablesprofitability, 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 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 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 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 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 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.
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.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.

Table 3 Results of the One-Sample Kolmogrov-Smirnov Test Normality Test Figure 1 Results of Residual Standard Regression P-Plot Normality Test One-Sample Kolmogorov-Smirnov Test
a. Test distribution is Normal.b.Calculated from data.