The Role of Corporate Governance in Enterprise Risk Management (ERM) Disclosure

Khoerunnisa¹*, Imam Abu Hanifah², Wulan Retnowati³

Universitas Sultan Ageng Tirtayasa, Indonesia^1,2,3
Email: [email protected]

Abstract

The purpose of this study is to determine the effect of the existence of the risk management committee, the reputation of the auditor, and the size of the board of commissioners on the disclosure of enterprise risk management (ERM). The existence of the risk management committee is measured by a dummy variable proxy, Which is used to gauge auditor reputation, and the total number of commissioners on the board is used to gauge the board's size. The measurement of ERM disclosure as a dependent variable is measured by disclosure items based on the ERM Framework issued by COSO (2004). Using the purposive sample approach, this study examined a subset of businesses that were listed in the LQ45 Index from 2012 to 2014. The data used is obtained from the annual report listed on the Indonesia Stock Exchange. There were 15 companies during 2012-2014 that met the criteria. The analysis method used is multiple linear regression analysis. The results showed that the existence of the risk management committee and the reputation of the auditor had a significant effect on the disclosure of enterprise risk management, while the size of the board of commissioners did not have a significant effect on the disclosure of enterprise risk management.

Keywords: Enterprise Risk Management, Existence of Risk Management Committee, Auditor Reputation, Size of the Board of Commissioners.

INTRODUCTION

In the midst of an economic situation full of uncertainty in business competition and the complexity of business risks that must be faced by companies, the risk management system is one of the main tools to reduce and handle any risks that may arise (Beasley et al., 2008; Layyinatusy, 2013).

Risk is very important for the company. The treatment of risk has developed in accordance with the phenomena that occur in the organization or company. Initially, companies tend to try to control risk to provide assurance related to company goals. The risks associated with this uncertainty occur due to the lack or unavailability of sufficient information about what will happen (Layyinatusy, 2013; Sirait et al., 2022).

Uncertainty can have beneficial or detrimental consequences. Uncertainty that creates profitable possibilities is known as opportunity, while uncertainty that creates adverse effects is known as risk.

In general, risk can be interpreted as a situation in which an adverse possibility exists for a person or company (Aven, 2013). This led to the idea of implementing enterprise risk management.

Risk management disclosure is a form of corporate responsibility that controls management activities to minimize the occurrence of fraudulent practices in financial statements. One way that a firm shows that it is superior to others is by implementing and disclosing enterprise risk management (ERM), which is based on transparency (Hidayah et al., 2021).

Corporate risk management is a process influenced by the board of directors, management, and other personnel, applied in strategy setting and throughout the company. It is designed to identify potential events that may affect the entity and manage risk within risk appetite to provide reasonable assurance based on the achievement of corporate objectives (COSO Framework, 2004).

Enterprise risk management (ERM) programs have more benefits by providing more information about the company's risk profile. This is because outsiders are more likely to have difficulty assessing the financial strengths and risks of highly financial and complex companies. Companies can communicate the risk profile to external parties both financially and nonfinancially thanks to ERM, which also acts as a symbol of the organization's dedication to risk management (Handayani & Yanto, 2013; Hoyt & Liebenberg, 2011).

The growing complexity of business activities also triggers various business risks that will be faced by companies, even technological developments, globalization, and the development of business transactions such as hedging cause greater difficulties businesses have in controlling the risks they must take (Beasley et al., 2008). Therefore, to face all these challenges, the implementation of a formal and structured risk management system is a must for companies (Meizaroh and Lucyanda, 2011).

The cases of Enron and Worldcom and the global crisis that hit the world in 2008 caused much debate about the importance of good corporate governance (Abid & Ahmed, 2014). Failure to implement good corporate governance has been covered in the Sarbanes Oxley Act, which highlights the significance of risk management implementation in businesses to stop financial reporting fraud. The application of strong corporate governance, namely the transparency concept, which necessitates the use of enterprise-wide risk management, is directly linked to the execution of risk management (Hidayah et al., 2021; Saebah et al., 2023).

The existence of a risk management committee (RMC) influences ERM disclosure. Companies with RMCs can devote more time, energy, and ability to evaluating all internal controls and dealing with possible risks. The RMC can improve the quality of risk assessment and supervision and encourage companies to disclose the risks faced (Meizaroh and Lucyanda, 2011).

The Big Four can offer advice on sound corporate governance procedures and help internal auditors review and enhance risk management's efficacy to raise the standard of company risk assessment and oversight (Hidayah et al., 2021; Hoyt & Liebenberg, 2011).

The Board of Commissioners has a role to oversee the implementation of risk management and ensure that the company has an effective risk management program. Large board sizes can reduce the influence of managers so that boards can perform supervisory functions effectively ((Jankensgård, 2019) in (Agista & Mimba, 2017)). The large number of board members increases opportunities to exchange information and expertise so as to improve the quality of ERM (Desender, 2011) in (Jatiningrum, 2012).

Previous studies on enterprise risk management (ERM) disclosures have been conducted but have shown inconsistent results. In Indonesia, research on enterprise risk management (ERM) has not been widely conducted, even though the development of ERM has begun to increase. Therefore, research on ERM is very interesting to do considering that ERM is a new issue.

Meizaroh and Lucyanda (2017) conducted research on the influence of corporate governance and ownership concentration on enterprise risk management disclosure. The results showed that the existence of a risk management committee (RMC) and the reputation of the auditor had a significant influence on the disclosure, yet the commissioners' size showed insignificant results.

However, Jatiningrum and Fauzi's (2012) research on the influence of corporate governance and ownership concentration on enterprise risk management disclosure showed different results. In the study, the size of the board of commissioners had a significant influence. Meanwhile, the existence of a risk management committee (RMC) and the reputation of the auditor do not significantly affect the disclosure of enterprise risk management.

This study was conducted to determine the influence of variables on the existence of The size of the board of commissioners, the auditor's reputation, and the risk management committee (RMC) on enterprise risk management (ERM) disclosure in Indonesia. This study is anticipated to offer factual data on the implementation of corporate governance and the application of enterprise risk management in companies incorporated in the LQ45 Index for 2012 - 2014 listed on the Indonesia Stock Exchange (IDX).

Differences in the results of previous studies can be caused by differences in the basis of reference used, such as the year of study and different populations/samples. The existence of inconsistencies with previous research allows further research to be carried out. Therefore, this study seeks to examine corporate governance's influence more deeply on enterprise risk management (ERM) disclosure.

Based on the description above, researchers are interested in conducting research because their results differ from those of previous researchers. The author will then submit a thesis titled "The Role of Corporate Governance on Enterprise Risk Management Disclosure.”

METHOD

Research Approach

This study's research strategy is based on a quantitative methodology. The quantitative approach is a positivist-based research methodology that looks at specific populations or samples, collects data using research tools, and analyzes quantitative and statistical data to assess applied hypotheses (Sugiyono, 2010).

Sampling Method

A population is a broad category made up of persons or items that researchers have selected to be researched and from which conclusions will be made because they possess particular attributes (Sugiyono, 2010). Companies included in the LQ45 Index that are listed on the Indonesia Stock Exchange comprise the study's population (IDX) during 2012-2014 to better reflect current conditions.

Samples are part of the number and characteristics possessed by the population (Sugiyono, 2010). The sample in this study is companies listed on the IDX that are included in the LQ45 Index. LQ45 Index companies were chosen because they are companies with bluechip stock categories, namely companies that have stable income and not many liabilities. In addition, LQ45 Index Companies have great liquidity because the greater the risk, the greater the liquidity.

The sampling technique in this study is the purposive sampling method. According to Sugiyono (2010) stated that purposive sampling is a sampling technique with certain considerations and criteria in accordance with the research objectives. The reason why sample selection uses The reason for purposive sampling is that not every sample meets the author's defined criteria. Thus, the approach of purposive sampling is used as a technique to determine the sample criteria used in this study. The sample criteria in this study are as follows:

1. The sample is a company that was consecutively included in the LQ45 Index listed on the Indonesia Stock Exchange (IDX) from 2012 to 2014.

2. The Company published consecutive annual reports from 2012-2014.

3. The data from 2012-2014 is complete and can provide complete information in accordance with the variables contained in this study.

Data Collection Methods

The type of data used in this study is secondary data. According to Sugiyono (2010), secondary data is a source that does not directly provide data to data collectors, for example, through other people or documents. The secondary data is in the form of the company's annual report, LQ45 Index 2012-2014.

The data used in this study comes from the Indonesia Stock Exchange (IDX) 's official website, www.idx.co.id, the company's official website, literature in the library, and journals related to its research.

Data collection techniques in this study are documentation techniques, namely by collecting literature in the library, annual reports published by LQ45 Index companies listed on the Indonesia Stock Exchange (IDX), which are research samples through the official website, as well as archives that have to do with data related to variable calculations.

Data Analysis Methods

In this study, a series of tests were carried out to assess the suitability and reliability of the regression model used. This test aims to ensure that classical assumptions are met, including the absence of multicollinearity, autocorrelation, heteroscedasticity, and the distribution of data normality. Traditional assumption testing is a multi-step process. First, the Kolmogorov-Smirnov test is used to perform a normalcy test, which evaluates the residual distribution to ensure the presence of normality. In addition, normality is also evaluated through normal probability plots at regression outputs. The next step is the multicollinearity test, which is performed by examining the tolerance and variance inflation factor (VIF) values to assess the correlation between independent variables in the regression model. Next, an autocorrelation test is used to evaluate whether there is a correlation between the fault of the confounding in the previous period and the current period. This test is useful especially in time series data. Lastly, the heteroscedasticity test is run by looking for patterns in the scatterplot graph between the dependent variable's residual and expected values. Decision-making in classical assumption tests refers to certain criteria for each test.

In addition, in multiple linear regression model analysis, independent variables are incorporated into the regression equation to evaluate their effect on the dependent variable (Osborne & Waters, 2019). After that, a series of statistical tests were carried out to test the significance of the model. The R2 test is used to measure the model's ability to explain variations in the dependent variable. Moreover, a partial t-test is employed to examine the significance of the impact of individual independent variables on the dependent variable. Ultimately, the simultaneous F test is utilized to assess the collective effect of all independent variables on the dependent variable. Each test is carried out with a certain level of significance, and decision-making is carried out based on the resulting significance value. Thus, this entire testing process helps ensure the reliability and validity of the regression models used in this study.

RESULTS AND DISCUSSION

Existence of Risk Management Committee (FIRM_RMC)

The data is obtained from the annual reports of companies that are members of the LQ45 Index. After the recapitulation, the existence of the risk management committee was obtained as follows:

Table 1. Existence of RMC Companies LQ45 Index 2012-2014

No.	Kode	2012	2013	2014
1	AALI	0	0	0
2	ASII	1	1	1
3	BBCA	1	1	1
4	BBNI	1	1	1
5	BBRI	1	1	1
6	BDMN	1	1	1
7	BMRI	1	1	1
8	INDF	0	0	0
9	INTP	0	0	0
10	JSMR	No.	Code	2012
2013	2014	1	AALI	0
0	0	2	ASII	1
1	1	3	BBCA	1
1	1	4	BBNI	1
1	1	5	BBRI	1

Source: Data processed from the annual report

Auditor Reputation (AUD_REP)

The data is obtained from the annual reports of companies that are members of the LQ45 Index. After the recapitulation, the total reputation of the auditor is obtained as follows:

Table 2. Auditor Reputation of LQ45 Index Companies 2012-2014

1	1	6	BDMN	1
1	1	7	BMRI	1
1	1	8	INDF	0
0	0	9	INTP	0
0	0	10	JSMR	0
0	0	11	KLBF	0
0	0	12	LPKR	0
0	0	13	LSIP	0
0	0	14	SMGR	0
0	0	15	TLKM	0
0	0	0	0	0
11	No.	Code	2012	2013
2014	1	AALI	1	1
1	2	ASII	1	1
1	3	BBCA	1	1
1	4	BBNI	1	1

Source: Data processed from the annual report

Board of Commissioners Size (COM_SIZE)

The data is obtained from the annual reports of companies that are members of the LQ45 Index. After the recapitulation, the total board of commissioners is obtained as follows:

Table 3. Size of the Board of Commissioners of LQ45 Index Companies 2012-2014

No.	Kode	2012	2013	2014
1	AALI	7	7	6
2	ASII	11	10	11
3	BBCA	5	5	5
4	BBNI	7	7	8
5	BBRI	8	8	7
6	BDMN	8	8	6
7	BMRI	7	7	7
8	INDF	8	8	8
9	INTP	7	7	7
10	JSMR	6	6	6
11	KLBF	6	6	6
12	LPKR	7	8	9
13	LSIP	9	9	8
14	SMGR	6	6	7
15	TLKM	5	6	7

Source: Data processed from annual report

Enterprise Risk Management (ERM) disclosure

This variable uses a checklist index Utilizzando il quadro ERM pubblicato dal Comitato delle Organizzazioni Sponsor del Rapporto Treadway (Mohd-Sanusi et al., 2017). The number of items the company expects to disclose is 108 items.

Table 4. ERM Disclosure Items

No	Code	Enterprise Risk Management Dimension	Number of items
1	A	Internal Environment	13
2	B	Goal Setting	6
3	C	Incident Identification
		 Financial Risk	10
		 Compliance Risk	5
		 Technology Risks	4
		 Economy Risk	2
		 Reputation Risk	4
4	D	Risk Assessment	25
5	E	Risk Response	26
6	F	Activity Control	7
7	G	Information and Communication	3
8	H	Monitoring	3
		Total disclosure items	108

Source: COSO ERM Framework

Data Calculation

Existence of Risk Management Committee (FIRM_RMC)

This variable uses a dummy proxy variable, where a value of 1 is given for companies that have RMC and a value of 0 is given for companies that do not have RMC. Variable data on the existence of RMC on 15 companies sampled in this study were taken from the annual report. The results can be seen in the table below:

Tabel 5. Variables of the existence of RMC Companies LQ45 Index 2012-2014

No	Kode	2012	2013	2014
1	AALI	0	0	0
2	ASII	1	1	1
3	BBCA	1	1	1
4	BBNI	1	1	1
5	BBRI	1	1	1
6	BDMN	1	1	1
7	BMRI	1	1	1
8	INDF	0	0	0
9	INTP	0	0	0
10	JSMR	0	0	0
11	KLBF	0	0	0
12	LPKR	0	0	0
13	LSIP	0	0	0
14	SMGR	0	0	0
15	TLKM	0	0	0

Source: Processed data

Auditor Reputation

This variable uses a dummy proxy variable. If the company is audited by the Big Four Public Accountants, it is given a value of 1 and if it is not given a value of 0. Data on auditor reputation variables for 15 companies sampled in this study were taken from the annual report. The results can be seen in the table below:

Table 6. Variables of Auditor Reputation of Companies LQ45 Index 2012-2014

No	Kode	2012	2013	2014
1	AALI	1	1	1
2	ASII	1	1	1
3	BBCA	1	1	1
4	BBNI	1	1	1
5	BBRI	1	1	1
6	BDMN	1	1	1
7	BMRI	1	1	1
8	INDF	1	1	1
9	INTP	1	1	1
10	JSMR	0	0	0
11	KLBF	1	1	1
12	LPKR	0	0	0
13	LSIP	1	1	1
14	SMGR	1	1	1
15	TLKM	1	1	1

Source: Processed data

Size of the Board of Commissioners

The size of the board of commissioners is the number of all members of the board of commissioners in the company. Variable data on the size of the board of commissioners for the 15 companies sampled in this study were taken from the annual report. The results can be seen in the table below:

Table 7. Variable Size of the Board of Commissioners of LQ45 Index Companies 2012-2014

No	Kode	2012	2013	2014
1	AALI	7	7	6
2	ASII	11	10	11
3	BBCA	5	5	5
4	BBNI	7	7	8
5	BBRI	8	8	7
6	BDMN	8	8	6
7	BMRI	7	7	7
8	INDF	8	8	8
9	INTP	7	7	7
10	JSMR	6	6	6
11	KLBF	6	6	6
12	LPKR	7	8	9
13	LSIP	9	9	8
14	SMGR	6	6	7
15	TLKM	5	6	7

Source: Processed data

Enterprise Risk Management (ERM) disclosure

This variable data was obtained based on the ERM Framework issued by the Committee of Sponsoring Organizations of The Treadway Commission (Mohd-Sanusi et al., 2017). There are 108 ERM disclosure items covering eight dimensions. This variable is measured by the following formula:

IPERM = Total item yang diungkapkan

108

From the variable ERM disclosure of 15 companies sampled In this analysis derived from the annual report, the findings are presented in the subsequent table:

Table 8. Disclosure Results of Enterprise Risk Management (ERM) of LQ45 Index Companies 2012-2014

No	Kode	2012	2013	2014
1	AALI	0,944	0,944	0,944
2	ASII	0,907	0,907	0,907
3	BBCA	0,815	0,815	0,815
4	BBNI	0,954	0,954	0,954
5	BBRI	0,843	0,843	0,843
6	BDMN	0,824	0,824	0,824
7	BMRI	0,917	0,917	0,917
8	INDF	0,907	0,907	0,907
9	INTP	0,796	0,796	0,796
10	JSMR	0,880	0,880	0,880
11	KLBF	0,898	0,898	0,898
12	LPKR	0,852	0,852	0,852
13	LSIP	0,954	0,954	0,954
14	SMGR	0,972	0,972	0,972
15	TLKM	0,972	0,972	0,972

Source: Processed data

Data Analysis

Descriptive Statistical Analysis

Descriptive statistics provide an overview or description of data observed through metrics such as mean, standard deviation, maximum, and minimum values for each variable (Ghozali, 2006). The mean provides the average value of the data under consideration. Standard deviation indicates the extent to which the data deviates from this mean. The maximum value represents the highest recorded data point, while the minimum value signifies the lowest recorded data point relative to the average. The variables used include independent variables, namely the existence of a risk management committee (FIRM_RMC), auditor reputation (AUD_REP) and the size of the board of commissioners (COM_SIZE), and dependent variables, namely disclosure of enterprise risk management (ERM). Based on the results of data processing with the help of SPSS (Statistical Product and Service Solution) version 20.0, the calculation results are as follows:

Table 9. Descriptive Statistical Test Results

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
ERM	45	,796	,972	,89567	,057217
FIRM_RMC	45	0	1	,42	,499
AUD_REP	45	0	1	,87	,344
COM_SIZE	45	5	11	7,18	1,419
Valid N (listwise)	45

Source:Processed data

Innovation Indicators

Based on the descriptive statistical table above, it is known that the number of samples or N in this study was 45 samples. The sample came from 15 companies that are members of the LQ45 Index with an observation period of 3 years, namely from 2012-2014. The variable disclosure ERM (Y) has a minimum value of 0.796 obtained by PT Indocement Tunggal Prakasa Tbk and a maximum value of 0.972 obtained by PT Telekomunikasi Indonesia Tbk and PT United Tractors Tbk. The average is 0.89567 with a standard deviation of 0.057217, meaning that the standard deviation is lower than the average value. Based on this range, it indicates that the distribution of data for the ERM is good. This shows the high awareness of management to implement and disclose the company's risk management.

The variable of the existence of a risk management committee (FIRM_RMC) has a minimum value of 0 and a maximum value of 1. The average value of this variable is 0.42 with a standard deviation of 0.499. This indicates that the sample of companies in this study on average already has RMC. Companies that have RMC can devote more time, energy, and ability to evaluate internal control and resolve various risks that may be faced by the company ((Januarti, 2012) in (Agista & Mimba, 2017)).

The auditor reputation variable (AUD_REP) has a minimum value of 0 and a maximum value of 1. The average value of the auditor reputation variable was 0.87 with a standard deviation of 0.344. This illustrates that more than 50% of the samples in this study have been audited by the Big Four Public Accountants. Employing Big Four Public Accountants is viewed as possessing a strong reputation and expertise in recognizing potential company risks (Agista & Mimba, 2017).

The variable size of the board of commissioners (COM_SIZE) has a minimum value of 5 and a maximum value of 11. The average variable value of the board of commissioners size is 7.18 with a standard deviation of 1.419. This shows that the number of members of the board of commissioners in the company is sufficient, which is an average of seven people.

Classical Assumption Test

Classical assumption testing is conducted to assess and validate the suitability of the regression model employed in this study. It aims to confirm the absence of multicollinearity, autocorrelation, and heteroscedasticity within the regression model, as well as to verify the normal distribution of the resulting data (Layyinatusy, 2013). The classical assumption test comprises evaluations for normality, autocorrelation, heteroscedasticity, and multicollinearity (Ghozali, 2006). The classical assumption test can be described as follows:

a. Normality Test

Normality testing is performed with the Kolmogorov–Smirnov Test performed against regression model residual data. This test aims to test whether in regression models, disruptive or residual variables have a normal distribution (Ghozali, 2006). The total sample data testing is presented in the following table:

Table 10. Normality Test Results

One-Sample Kolmogorov-Smirnov Test
		Unstandardized Residual
N		45
Normal Parameters^a,b	Mean	0E-7
	Std. Deviation	,05158462
Most Extreme Differences	Absolute	,141
	Positive	,064
	Negative	-,141
Kolmogorov-Smirnov Z		,946
Asymp. Sig. (2-tailed)		,333
a. Test distribution is Normal.
b. Calculated from data.

Source: Processed data

From the table above shows the Asmp Sig value of 0.333 and the value of the independent variable that has a significance greater than the value of 0.05, the data used is normally distributed. The amount of data that produces normally distributed residual values is 45 samples.

The determination of a normally distributed variable or not can also be seen through a normal probability plot whose spread of variable points should be located not far around the Y = X line and the histogram that forms a normal curve. The plot graph of this study is seen in the figure below:

Figure 1. Plot Graph

From the figure above, it can be seen that the variable points are around the diagonal line and the spread follows the direction of the diagonal line, this shows that the data has been distributed normally.

b. Multicollinearity Test

This test was conducted to test whether the regression model found a correlation between independent variables. Multicollinearity can be seen from the tolerance value and VIF (variance inflation factor). If the tolerance value > 0.10 and VIF < 10 then it indicates the absence of multicollinearity. And vice versa if tolerance < 0.10 and VIF > 10 can be interpreted as multicollinearity (Ghozali, 2006). The results of this test can be seen in the following table:

Table 11. Multicollinearity Test Results

Model		Collinearity Statistics
Model		Tolerance	VIF
1	(Constant)
	FIRM_RMC	,870	1,149
	AUD_REP	,888	1,127
	COM_SIZE	,978	1,022

Source: Processed data

Based on the table above, the tolerance value > 0.10 and VIF < 10, so it can be concluded that there is no multicollinearity between independent variables in the regression model. This indicates that the independent variables in this study exhibit no correlation with one another, or that there is no interrelationship between the independent variables.

c. Autocorrelation Test

This test aims to test whether in a linear regression model there is a correlation between the fault of the confounding in period t with the error of the previous confounding t-1 peride (Ghozali, 2006). In this test used Durbin-Watson test (DW test). The test results can be seen in the following table:

Table 12. Autocorrelation Test Results

Model	Durbin-Watson
1	2,135

Source:Processed data

Based on the test results above, when compared with DWthe table can be described as follows:

Tabel 13. Perhitungan Durbin Watson

Information	DW value (d)	DWtable values		Analysis	Conclusion
Research Model	2,135	d_L	d_u	(d_u< d < 4-d_u)	No autocorrelation
		1,3832	1,6662

Sumber: Data yang diolah

Based on the results of autocorrelation testing in the table above, it is known that the DW value is calculated at 2.039 and compared to the DWtable. From the calculation results, it is known that n = 45, k = 3 with α = 0.05 obtained DW table values dL = 1.3832 and du = 1.6662. Based on the results of these calculations, it is known that the DWcalculate value (d) is between the value of the table du and the value of 4 - du (du < d < 4 - du), so it can be concluded that this research model has no symptoms of autocorrelation.

d. Heteroscedaticity Test

This test aims to test whether in the regression model there is an inequality of variance from the residual of one observation to another (Ghozali, 2006). This test is performed using a point graph (scatterplot). The results of the heterokedasticity test in this study are presented in the following figure:

Source: Processed data

Figure 2. Heteroschedaticity Test Results

By looking at the scatterplot graph, it can be seen that the points spread randomly, and scattered both above and below the number 0 on the Y axis.

Multiple Linear Regression Model

Testing the research hypothesis using multiple linear regression analysis. Multiple linear regression tests serve to explain the relationship between independent and bound variables that are interpreted through an equation that has been made. The results of testing with multiple linear regression can be seen below:

Tabel 14. Hasil Regresi Linier Berganda Coefficients

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
Model		B	Std. Error	Beta	t	Sig.
1	(Constant)	,831	,046		18,164	,000
	FIRM_RMC	-,046	,017	-,401	-2,654	,011
	AUD_REP	,056	,025	,334	2,233	,031
	COM_SIZE	,005	,006	,125	,880	,384

a. Dependent Variable: ERM

Source: Data Processing

From the table above can be written the linear regression equation as follows:

ERM = 0.831 – 0.046 FIRM_RMC + 0.056 AUD_REP + 0.005 COM_SIZE + ε

From the regression model above can be interpreted as follows:

1) A constant of 0.831. This number shows that the ERM disclosure variable will be 0.831 if each variable is FIRM_RMC, AUD_REP and COM_SIZE is zero.

2) The regression coefficient FIRM_RMC (X1) is -0.046. A negative coefficient value states that FIRM_RMC negatively affect the disclosure of ERM. This illustrates that for every 1% increase in FIRM_RMC%, the ERM disclosure will decrease by 0.046 assuming the other independent variables are considered constant.

3) The regression coefficient AUD_REP (X2) is 0.056. The positive coefficient value states that AUD_REP have a positive effect on the disclosure of ERM. This illustrates that every 1% increase in AUD_REP will increase ERM disclosure by 0.056 assuming the other independent variable is considered constant.

4) The regression coefficient COM_SIZE (X3) is 0.005. The positive coefficient value states that COM_SIZE have a positive effect on the disclosure of ERM. This illustrates that every 1% increase will increase ERM disclosure by 0.005 assuming the other independent variables are considered constant.

Tabel 15. Nilai Adjusted R²

Model Summary^b
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Durbin-Watson
1	,433^a	,187	,128	,053439	2,135
a. Predictors: (Constant), COM_SIZE, AUD_REP, FIRM_RMC
b. Dependent Variable: ERM

Source: Processed data

The Adjusted R2 value is 0.128. This indicates that the contribution of the percentage of influence of all independent variables is 12.8%, while the remaining 88.2% is determined by other variables that were not studied in this study. A low R2 score may indicate that the independent variable's capacity to explain the variance in the dependent variable is severely constrained.

b. Test t (Partial)

According to Ghozali (2006), a t test was conducted to determine the influence of each independent variable on the dependent variable. From the results of the t test, it can be seen that the independent variable will have a partial effect on the dependent variable. If the significant value (p-value) < 0.05. If t significance < p-value (0.05) then Ho is rejected and if t significance > p-value (0.05) then Ho is accepted. The t test can be seen from the table below:

Table 16. Test Results t

Model	Beta	t	P value	α	Hypothesis	Result
1 (Constant)	,831	18,164	,000	0,05
FIRM_RMC	-,046	-2,654	,011	0,05	Significant influence	Hypothesis accepted
AUD_REP	,056	2,233	,031	0,05	Significant influence	Hypothesis accepted
COM_SIZE	,005	,880	,384	0,05	Significant influence	Hypothesis rejected

Source: Processed data

From the t test above, the following conclusions can be drawn:

1) The effect of the existence of the Risk Management Committee (FIRM_RMC) on Enterprise Risk Management disclosure.

The first hypothesis proposed states that the existence of a risk management committee has a significant effect on enterprise risk management (ERM) disclosure. Based on table 20 shows that the FIRM_RMC variable has a T count of -2.654 while the T of the table is 2.01954 (T count < T table) and a significance value of 0.011 (p-value < 0.05). So it can be concluded that FIRM_RMC variable does not have a significant effect on ERM disclosure. This means that H1 is rejected or in other words the existence of a risk management committee has no significant effect on Enterprise Risk Management (ERM) disclosure.

The results of this study are not consistent with the results of research conducted by Meizaroh and Lucyanda (2017), Sari (2013) and (Handayani & Yanto, 2013)

2) Auditor's reputation for Enterprise Risk Management disclosure.

The second hypothesis proposed in this study is that the reputation of the auditor has a significant effect on the disclosure of Enterprise Risk Management (Pérez-Cornejo et al., 2019). Based on table 20 shows that the AUD_REP variable has a calculated T value of 2.233 while the table T value is 2.01954 (T count > T table) and a significance value of 0.031 (p-value < 0.05). Thus, it can be concluded that AUD_REP variable has a significant effect on ERM disclosure. This means that H2 is proven or in other words the auditor's reputation has a significant effect on the disclosure of Enterprise Risk Management.

The results of this study are in line with research conducted by Sari (2013), (Handayani & Yanto, 2013) and Meizaroh and Lucyanda (2017).

3) The Board of Commissioners' measure of Enterprise Risk Management disclosure.

The third hypothesis proposed in this study is that the size of the commissioners' board significantly affects the disclosure of Enterprise Risk Management (Abbas et al., 2021). From the results of this study obtained a calculated T value of 0.880 while the table T value of 2.01954 ( T count < T table) and significance value of 0.384 ( p-value < 0.05). So it can be concluded that H3 is not proven or rejected and in other words, the size of the board of commissioners has no effect on the disclosure of Enterprise Risk Management. This study's results align with the results of research conducted by Meizaroh and Lucyanda (2017).

c. F Test (Simultaneous)

The F (simultaneous) test determines whether the independent variables jointly or simultaneously affect the dependent variable (Ghozali, 2006). The significance level of simultaneous tests used in this study was α= 5% (0.05). The results of simultaneous tests in this study are shown in the table below:

Table 17. Hasil Uji F

ANOVA^a
Model		Sum of Squares		df		Mean Square		F	Sig.
1	Regression	,027	3		,009		3,147		,035^b
	Residual	,117	41		,003
	Total	,144	44
a. Dependent Variable: ERM
b. Predictors: (Constant), COM_SIZE, AUD_REP, FIRM_RMC

Source: Processed data

From table 20 above, the calculated f value is 3.147 while the table t is 2.83 (the calculated f value > t table) and the significance value is 0.035 (p-value < 0.05). So it can be concluded that the independent variables consisting of FIRM_RMC, AUD_REP, and COM_SIZE together significantly affect the dependent variable, namely the disclosure of ERM. This means that H4 is proven, or in other words, the variables of the existence of the risk management committee, the reputation of the auditor, and the size of the board of commissioners together affect the disclosure of Enterprise Risk Management.

Interpretation of Results

The Effect of the Existence of the Risk Management Committee on Enterprise Risk Management (ERM) Disclosure

Based on the results of hypothesis testing, it can be seen that the variable of the existence of the risk management committee does not have a significant effect on the disclosure of ERM. This can happen because it is associated with a policy or regulation from the Government of Indonesia that requires new FIRM_RMC by banking companies only. And companies that do not have FIRM_RMC, the results of their ERM disclosure have not been carried out optimally, most of the companies just disclose the risks set by the government. This means that the company's awareness of the importance of risk management is still low; they just follow the regulations. Naturally, until now the internal control function, especially for the banking sector, is still considered low. Thus it can be concluded that this study rejects the first hypothesis.

The Effect of Auditor Reputation on Enterprise Risk Management (ERM) disclosures

According to the findings from hypothesis testing, it can be inferred that the auditor's reputation variable significantly impacts ERM disclosure. This indicates that the Big Four are considered to have the expertise to identify risks so as to improve the quality of the company's risk assessment and supervision. In addition, there is greater pressure on Big Four audited companies to implement and disclose ERM (Agista & Mimba, 2017). Thus the study accepts the second hypothesis.

The Effect of the Size of the Board of Commissioners on Enterprise Risk Management (ERM) Disclosure

Based on the results of testing the hypothesis above, it can be concluded that the variable size of the board of commissioners has no effect on the disclosure of ERM. This indicates that the larger the size of the board, the greater the chance of internal conflict.

The large size of the board can also slow down the decision-making process because it has to unite various views and opinions of members ((Namoga, 2017) in (Agista & Mimba, 2017)). This results in ineffective board members in overseeing the implementation of ERM. It can be concluded that the results of this study reject the third hypothesis

The Effect of the Existence of the Risk Management Committee, the Reputation of the Auditor and the Size of the Board of Commissioners on the disclosure of Enterprise Risk Management (ERM)

Based on the results of the above hypothesis testing, it can be concluded that the independent variables, namely the existence of a risk management committee, the reputation of the auditor, and the size of the board of commissioners, together significantly affect the disclosure of ERM. We can conclude that the fourth hypothesis is supported by the study's findings.

CONCLUSION

Based on the results of the analysis and discussion that have been described, the conclusions of this study are as follows: The results of the t-test analysis showed that the variable of the existence of the risk management committee (X1) did not have a significant effect on the disclosure of enterprise risk management (Y). This is indicated by the calculated T value of -2.654 while the table T is 2.01954 (T count < T table) and the significance value is 0.011 (p-value < 0.05).

The results of the t-test analysis show that the auditor's reputation variable (X2) significantly affects enterprise risk management (Y) disclosure. This is indicated by the calculated T value of 2.233 while the table T value is 2.01954 (T count > T table) and the significance value is 0.031 (p-value < 0.05). The results of the t-test analysis show that the variable size of the board of commissioners (X3) has no effect on the disclosure of enterprise risk management (Y). This is indicated by the calculated T value of 0.880 while the table T value is 2.01954 (T count < T table) and the significance value is 0.384 (p-value < 0.05).

Together, the existence of the Risk Management Committee, the reputation of the auditor and the size of the board of commissioners have a significant effect on the disclosure of Enterprise Risk Management (ERM) because it has a calculated f value of 3.147 while the table t value is 2.83 (the value of f is calculated > t table) and the significance value is 0.035 (p-value < 0.05).

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Copyright holders:

Khoerunnisa, Imam Abu Hanifah, Wulan Retnowati (2024)

First publication right:

AJEMB – American Journal of Economic and Management Business