EFFECT OF
SHARE OWNERSHIP CONCENTRATION, AUDIT COMMITTEE MEETING FREQUENCY, TYPE OF
EXTERNAL AUDITOR, AND RISK MONITORING COMMITTEE SIZE ON OPERATIONAL RISK
DISCLOSURE IN NON-BANK FINANCIAL SERVICES INSTITUTIONS (LJKNB) FOR THE 2019-2023
PERIOD
Jane Naomi1,
Lolita Akbar2, Ardila Galuh
Savitri3, Rachmi Syamsi4, Dewi
Hanggraeni5
Universitas Indonesia1,2,3,4,5,
Universitas Pertamina1,5
[email protected]1, [email protected]2, [email protected]3, [email protected]4, [email protected]5
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ARTICLE
INFO |
ABSTRACT |
|
Keywords: Corporate governance;
risk disclosure; operational risk |
In
an increasingly complex global business environment, effective corporate
governance is one of the main pillars to maintain economic stability and
encourage sustainable growth in the financial sector. This study aims to
analyze the Effect of Share Ownership Concentration, Audit Committee Meeting
Frequency, Type of External Auditor, and Risk Monitoring Committee Size on
Operational Risk Disclosure in Non-Bank Financial Services Institutions
(LJKNB) for the 2019�2023 Period. The content analysis method was used to
collect operational risk disclosure data from the annual reports of 42 LJKNB
listed on the IDX during the period 2019 to 2023. Using GLS regression
analysis, this study shows the influence of governance on the disclosure of
operational risks quantitatively and qualitatively. The results show that the
concentration of share ownership, the number of audit committee meetings, and
the external auditors of the Big 4 have a significant positive effect on the
disclosure of quantitative operational risks, while the number of risk
monitoring committees has a significant negative effect. The four governance
variables did not have a significant effect on the qualitative disclosure of
operational risks. |
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|
Introduction
In an
era of increasingly fierce global competition, corporate governance is a key
factor in maintaining the stability and sustainability of the financial sector (Ho, 2005). In Indonesia, Non-Bank Financial Services Institutions
(LJKNB) have a strategic role in supporting financial inclusion and risk
diversification through services such as insurance, multifinance, pension
funds, and other financial institutions supervised by the Financial Services
Authority (OJK). LJKNB contributes greatly to the provision of financial
services to underserved communities while strengthening domestic financial
stability (Tripalupi & Anggahegari, 2020). However, the high
operational risks inherent in LJKNB activities require the implementation of
strong governance to ensure efficiency, transparency, and effective risk
mitigation. In recent decades, attention to operational risk management in the
financial sector has increased in an effort to reduce potential losses. This is
due not only to certain regulatory considerations, but also to the occurrence
of huge operational losses in the financial sector (Neifar & Jarboui, 2018). Operational risks include
system failures, human error, and weaknesses in internal processes and
controls, which can have serious implications for a company's sustainability.
These risks can disrupt the company's daily operations and have an impact on business
sustainability. Therefore, effective operational risk management is very
crucial for LJKNB. One of the main indicators of good risk governance is the
disclosure of operational risks, which reflects the transparency of the company
in communicating the risks faced to stakeholders (Abraham & Cox, 2007).
Operational
Risk Management (ORM) and its disclosure practices in financial institutions
have recently attracted significant attention from academics, professionals,
and regulators (Helbok & Wagner, 2006). The Financial Services
Authority (OJK) has issued regulations such as OJK Regulation No.
44/POJK.05/2020 concerning the Implementation of Risk Management for Non-Bank
Financial Services Institutions, which requires LJKNB to implement risk
management effectively, covering strategic, operational, insurance, credit,
market, liquidity, legal, compliance, and reputation risks. This regulation
aims to encourage transparency, increase stakeholder trust, and reduce
information asymmetry between companies and related parties. Many LJKNBs only
provide risk reports that are formal in nature to meet regulatory obligations,
without providing information that is truly comprehensive and relevant to
stakeholders. This reduces the value of transparency, which should be the main
pillar of corporate governance. One example is the annual report which contains
the disclosure of operational risks. Some companies simply list common risks
without providing an in-depth analysis of their impact or mitigation measures
taken. For example, an insurance company might report information technology
risks in general, but not explain the specific impact of system failures on the
claims process or customer service. The obstacles that are often faced are the
lack of human resources with adequate risk management competencies, as well as
the absence of an internal mechanism to evaluate the quality of risk
disclosure. This shows that there is a gap between the expected regulations and
the implementation that occurs on the ground.
Agency
theory is the main conceptual foundation in this research. This theory
emphasizes the importance of a supervisory mechanism to reduce conflicts of
interest between the principal and the management (agent). In the context of
risk disclosure, this conflict can occur when management is reluctant to
disclose the actual operational risks in order to maintain the company's image.
Governance mechanisms, such as audit committees and risk monitoring committees,
are designed to mitigate these conflicts by ensuring risk disclosures are
transparent. In addition, stakeholder theory broadens the perspective by
emphasizing that operational risk disclosure is not only important for
shareholders but also for other stakeholders, including employees, customers,
regulators, and the wider community.
The
main problem behind this study is the low level of operational risk disclosure
in the Non-Bank Financial Services Institution (LJKNB) sector in Indonesia.
Although existing regulations have established comprehensive risk disclosure
standards, many LJKNB companies only meet the requirements in a minimalist
manner. Risk disclosures often do not provide substantial and in-depth
information to stakeholders, making it difficult for them to accurately
evaluate the company's operational risks. This indicates that there is a gap
between regulatory expectations and implementation on the ground, which can
weaken stakeholder confidence in corporate transparency.
In
addition, the lack of empirical research that specifically discusses the
disclosure of operational risks in the LJKNB sector is a challenge in itself.
Most previous studies focused more on the banking sector or public companies in
general, which have different governance and risk characteristics. The LJKNB
sector, which includes insurance, multifinance, and pension funds, has unique
operational risk dynamics, including reliance on information technology
systems, complex manual processes, and exposure to external risks. However,
research exploring the factors that affect operational risk disclosure in this
sector is still rare, so there is a gap in the relevant academic literature.
Previous
research has discussed the importance of risk disclosure in corporate
governance, but most studies have been conducted on the banking sector or
public companies in developed countries (Abraham & Cox, 2007). Research on operational
risk disclosure in the LJKNB sector in Indonesia is still limited, although
this sector has different risk and regulatory characteristics (Fitriana & Wardhani, 2020). This creates a gap in the
literature that needs to be filled with more in-depth empirical research. Using
panel data from 2019 to 2023, the study aims to identify the factors
influencing operational risk disclosure in LJKNB and provide evidence-based recommendations
for regulators and industry players.
In
addition, the international literature has extensively discussed the
relationship between corporate governance and risk transparency. Most of the
studies were conducted in developed countries, where market structures, regulations,
and corporate governance dynamics differ significantly from the Indonesian
context. The LJKNB sector in Indonesia has unique characteristics that are
influenced by local regulations, diverse levels of financial literacy, and
different market structures. This creates a gap in the existing literature and
highlights the need for more specific research in this sector. Thus, this study
aims to explore the dynamics of operational risk transparency in the LJKNB
sector by considering the unique regulatory and market context in Indonesia.
Furthermore,
the understanding of the factors that affect operational risk disclosure is
also still limited. Existing literature has highlighted the importance of
elements such as the concentration of shareholding, the frequency of audit
committee meetings, and the role of external auditors. However, research on how
these factors interact with each other in influencing operational risk
disclosure is still inadequate. This limitation indicates the need for more
comprehensive research to understand the dynamics of risk disclosure in the
LJKNB sector. Therefore, this study seeks to fill this gap by providing a more
in-depth and relevant empirical analysis for the LJKNB sector in Indonesia.
This
research relies on a strong theoretical framework to identify factors that
affect the disclosure of operational risks in Non-Bank Financial Services
Institutions (LJKNB). One of the main factors is the concentration of
shareholding, which refers to an ownership structure with the dominance of
majority shareholders. The literature shows that concentrated ownership can
provide greater control to majority owners, allowing them to influence
strategic policies, including risk management and transparency. Majority owners
usually have a great stake in ensuring effective risk management to protect
their investments, although in some cases this can reduce disclosure if they
have an incentive to hide certain risks. The frequency of audit committee
meetings is also an important element in risk governance. The frequency of
audit committee meetings allows its members to provide assessments related to
the selection of accounting principles, disclosures, and estimates used by the
Company (Greco, 2011). An active audit committee, through a high frequency of
meetings, is believed to be able to increase supervision of the risk management
process. This activity reflects the company's level of commitment to
transparency and accountability, as audit committee meetings are typically used
to evaluate risk disclosures as well as ensure regulatory compliance.
Furthermore,
the type of external auditor plays a significant role in strengthening the
credibility of the operational risk disclosure report. External auditors who
are independent and reputable in the market tend to be more trusted by
stakeholders in auditing company reports, resulting in reports that are more
transparent and free from bias. Finally, the size of the risk monitoring
committee is also an important determinant. Larger committees tend to have
members with diverse backgrounds and expertise, which can help identify and
manage operational risks more effectively. This diversity of expertise allows
companies to understand risks from multiple perspectives, improve the quality
of risk management, and encourage more comprehensive information disclosure.
This
research offers a significant contribution in understanding the disclosure of
operational risks in the LJKNB sector through several aspects of novelty.
First, this study integrates various variables that were previously often
studied separately. These variables include governance factors, such as the
frequency of audit committee meetings and the size of the risk monitoring
committee, ownership structure such as the concentration of share ownership,
and external factors such as the type of external auditor. The integration of
these variables in a single analytical framework provides a more holistic
picture of the factors that affect operational risk transparency.
Second,
this research specifically focuses on the LJKNB sector in Indonesia. Most
previous studies have highlighted the banking sector or public companies in
general, while the LJKNB sector has different risk and regulatory
characteristics. By focusing attention on LJKNB, this research contributes to
filling the literature gap related to sectors that receive less attention
despite having an important role in the Indonesian financial system.
Third,
this study uses a longitudinal approach with panel data from the period 2019 to
2023. The use of longitudinal data allows for the analysis of trends and
dynamics of operational risk disclosure and provides more comprehensive
insights (Xu et al., 2019). This approach allows for a more in-depth analysis
of trends and dynamics of operational risk disclosure over the past five years.
Using longitudinal data, the study was able to identify patterns of change as
well as factors that consistently affect operational risk transparency over
time, which are often overlooked in cross-sectional studies.
The
main objective of this study is to identify and analyze the influence of
several factors on the level of operational risk disclosure. First, this study
aims to evaluate the effect of stock ownership concentration on operational
risk disclosure. Concentrated ownership is often associated with a greater
degree of control by majority shareholders, which can influence a company's
strategic policies, including risk management transparency.
The
second objective is to explore the impact of the frequency of audit committee
meetings on operational risk disclosure. An active audit committee with a
regular meeting schedule reflects a higher level of oversight of the company's
risk management, so it is expected to encourage information transparency.
Third, this study examines the role of external auditors in influencing the
credibility and disclosure of operational risk reports. Independent and
reputable external auditors are considered to be able to increase stakeholder
confidence in the published risk reports.
Fourth,
this study assesses the contribution of the size of the risk monitoring
committee to the disclosure of operational risks. Larger committees typically
have members with diverse expertise that can strengthen a company's ability to
identify and manage operational risks effectively. Finally, this study aims to
provide empirical-based recommendations that can be used by regulators,
policymakers, and industry players to improve governance practices and risk
disclosure in LJKNB.
In
addition, this study uses an empirical approach based on panel data to provide
a more in-depth and robust analysis. The data panel was chosen because of its
ability to capture cross-sectional and time-series variations, resulting in
more comprehensive results than static data approaches. The variables used in
this study include dependent variables, namely the Qualitative and Quantitative
Operational Risk Disclosure Index, which are measured to evaluate the extent to
which operational risks are disclosed by the company.
As
independent variables, this study involves the concentration of stock
ownership, the number of audit committee meetings per year, the type of public
accounting firm (Big4 or non-Big4), and the size of the risk monitoring
committee. These variables were chosen because of their relevance in
influencing the level of operational risk disclosure as identified in previous
literature. In addition, the control variables used include the size of the
company (measured in billions of assets) and the risk measurement approach,
which reflects whether the company has a formal risk measurement mechanism.
To
analyze the relationship between variables, this study applies the Generalized
Least Squares (GLS) regression method to panel data. The GLS method was chosen
because it was able to overcome heteroscedasticity and autocorrelation that
often appeared in the panel data, so that the estimation results were more
efficient and reliable. With this analytical framework, the research is
expected to provide applicable recommendations for the industry as well as
in-depth insights to support evidence-based decision-making.
Research
Methods
Sample
and Data Selection
The final sample consisted
of 42 Non-Bank Financial Services Institution Companies listed on the Indonesia
Stock Exchange. The initial sample includes 57 companies. After going through
the selection process, the sample used was 42 companies. First, we need to
remove the company whose annual report is not available, then the company that
has some reports or data missing. Our final data is collected from annual
reports and/or reference documents available on the websites of the relevant
banks for the financial years 2019, 2020, 2021, 2022 and 2023. Details of the
sampling procedure are presented in Table 1.
Table 1 Sample Selection Stages
|
Sample Selection Stages |
|
|
Initial sample |
57 |
|
Exclusion of companies whose annual
report data is not available |
7 |
|
Exclusion of companies whose annual
report data is incomplete |
8 |
|
Final Sample |
42 |
|
Research period |
5 |
|
Total observations |
210 |
Regression
Model
To verify the research
hypothesis, we applied a statistical methodology by implementing two linear
panel regressions. We will test the effect of corporate governance mechanisms
on the quantity (first regression (1)) and quality (second regression (2)) of
OR disclosure for the entire sample. The model used according to the research
of (Neifar
& Jarboui, 2018) with modifications is as
follows:
Equation
1
QNOR=
α + β1CONC + β2ACOMF + β3BIG + β4RCSIZE + β5FSIZE
+ β6RMA + ε�
Equation
2
QLOR=
α + β1CONC + β2ACOMF + β3BIG + β4RCSIZE + β5FSIZE
+ β6RMA + ε�
Information:
QNOR
���������������������� = Operational Risk
Disclosure Quantity Index
QLOR
����������������������� = Operational Risk
Disclosure Quality Index
CONC
����������������������� = Percentage of largest
shareholders
ACOMF
������������������� = Number of audit
committee meetings in a year
BIG
���������������������������� = Presence of Big4
external auditors
RCSIZE
������������������� = Number of members of the
risk monitoring committee
FSIZE
����������������������� = Company size (total
assets)
RMA
�������������������������� = Risk measurement
approach
The
dependent variable in this study is the operational risk disclosure index. The
measurement of operational risk disclosure variables is adopted from the
content analysis method obtained from the analysis of the company's annual
report. Annual reports are used in this study because of their wide scope and
availability. (Krippendorff,
2018) describes content
analysis as "viewing data as a representation not of physical events but
of texts, images, and expressions that are made to be seen, read, interpreted,
and acted upon for their meaning, and therefore must be analyzed with such usage
in mind". In our research, the main objective is to attract qualitative,
quantitative, financial and non-financial data related to operational risks in
LKJNB companies. The data is collected manually and encoded according to the
coding instrument. The disclosure index is compiled based on the research of (Neifar
& Jarboui, 2018) with modifications.
The
quantitative and qualitative operational risk disclosure indices are calculated
using the formula:
QNOR=
QLOR=
nj=
Number of components expected to be disclosed by the company j.
Xi,j
= Number of components disclosed by company j.
i=
1 if there is a disclosure, 0 if there is no disclosure
Based
on the results of previous theoretical and empirical research, the specific
characteristics of LJKNB's corporate governance and the availability of data,
four corporate governance mechanisms were included as independent variables in
the research model, namely: (1) the composition of the largest shareholders,
(2) external auditors (3) the number of audit committee meetings and (4) the
number of members of the risk monitoring committee (Al‐Hadi
et al., 2016; Neifar & Jarboui, 2018). The study included two control variables, namely the size
of the company (total assets) and the company's risk measurement approach.
Results
and Discussion
Assumption Testing
This research
model focuses on panel data to exploit the temporal dimension (5 years) and the
individual dimension (42 companies of Non-Bank Financial Services
Institutions). Non-Bank Financial Services Institutions are institutions that
carry out activities in the insurance, pension fund, and financing institutions
sectors (OJK), 2014). Several tests need to be performed to qualify the panel
data, including multicollinearity testing and heteroscedasticity testing. The
selection of the model was carried out by the Chow test, the Haussmann test,
and the Lagrange multiplier test. The Chow test is used for the selection
between fixed effect and common effect models. The hausman test is used to
compare fixed effect or random effect models. Meanwhile, the Lagrange
Multiplier test is a test to determine the Common Effect or Random Effect model.
Based on the test results, it is known that the model with the variable QNOR
uses the fixed effect model (FEM), while the model with the QLOR variable uses
the random effect model (REM).
Multicollinearity
Before
conducting the regression analysis, multicollinearity was tested using the
Variance Inflation Factor (VIF) to detect the presence of noise in the model.
Table 1 shows that no independent variable has a tolerance value of less than
0.100 which means there is no correlation between independent variables. The
VIF value does not exceed 10 for all variables, so multicollinearity does not
occur. Thus, it can be concluded that this model does not have a correlation
between independent variables (non-multicollinearity) (Gujarati, 1995).
Table 2
Multicollinearity Test
|
Var. Independent |
VIF |
Tolerance |
|
Ownership
Concentration |
7.55 |
0.133 |
|
Audit Committee Meeting |
4.63 |
0.216 |
|
Auditor's
Type |
1.31 |
0.764 |
|
Risk
Committee Size |
3.02 |
0.331 |
|
Var. Control |
|
|
|
Firm
Size |
6.69 |
0.149 |
|
RMA |
1.76 |
0.569 |
Heteroscedasticity
Heteroscedasticity
testing is used to test whether the variance in the model is not fixed. It is
known that the model with the QLOR dependent variable uses the REM model
where� the Random Effect Model (REM)
model treats the interference variable or error as a random component and
accommodates the GLS method so that heteroscedasticity testing is not required.
In the
model with the QNOR dependent variable, it is known that the chi square test
statistic has a value of 0.00003 with a p-value of 0.000. The p-value < a
significant level, which means that heteroscedasticity or variance occurs in
the model is not fixed.
Table 3 Heteroskedastitias Test
|
Statistics |
QNOR |
|
𝜒2 |
0.00003 |
|
p-value |
0.000 |
Descriptive
Statistics
Table
4 shows the descriptive statistics for corporate governance variables used in
the analysis of the bank sample in this study. The table shows the minimum,
maximum, statistical average and standard deviation values.
Table 4
Descriptive Statistics
|
|
Mean |
Stdev |
Min |
Max |
|
|
Quantitative Operational risk disclosure |
0.147 |
0.052489 |
0 |
0.31 |
|
|
Qualitative Operational risk disclosure |
0.494381 |
0.178084 |
0.15 |
0.92 |
|
|
Ownership Concentration |
54.67907 |
20.51352 |
2.9 |
92.07 |
|
|
Audit Committee Meeting |
4.77619 |
2.590451 |
2 |
15 |
|
|
Auditor's Type |
0.190476 |
0.393615 |
0 |
1 |
|
|
Risk Committee Size |
1.942857 |
1.615289 |
0 |
9 |
|
|
Firm Size |
7.841715 |
1.895492 |
3.73767 |
12.37286 |
|
|
RMA |
0.438095 |
0.497339 |
0 |
1 |
|
The
average quantitative operational risk disclosure was 0.147 with a standard
deviation of 0.052, indicating a relatively low variation. The average
qualitative operational risk disclosure was higher, at 0.494, with a standard
deviation of 0.178, which indicates greater variation compared to quantitative
disclosure. The range is between 0.15 to 0.92, indicating that almost all
companies are qualitatively disclosing this risk. This indicates that many
companies are still reluctant or unable to disclose their operational risks in
the form of concrete figures. This could be due to the complexity of
quantitatively measuring operational risk or the lack of awareness of the
importance of quantitative disclosure.
The
concentration of holdings varies widely with an average of 54.67907 and a very
high standard deviation, 20.51352. The range from 2.9 to 92.07 shows
significant differences in the concentration of shareholding between companies,
with some companies being highly concentrated and others more dispersed. A high
concentration of ownership can have implications for potential conflicts of
interest and a decrease in the quality of supervision (Herlambang and Hapsari,
2023). The average number of audit committee meetings is about 4.78 per year
with a standard deviation of 2.59, indicating moderate variation. The average
frequency of audit committee meetings shows that most companies have carried
out their functions quite well. However, the range ranges from 2 to 15
meetings, which means some companies only meet the meeting minimum, while
others hold meetings very frequently.�
The average risk committee size is about 1.94 members with a standard
deviation of 1.62. The number of committee members varies from 0 to 9,
indicating that some companies do not have a risk committee, while others have
a relatively large committee size. In accordance with POJK No.73/POJK.05/2016
concerning Good Corporate Governance for Insurance Companies, POJK No.
29/POJK.05/2020 for Financing Companies, and POJK No. 27 of 2023 for Pension
Fund Business, companies are required to form a Risk Monitoring Committee
consisting of a minimum of 3 (three) people, of which 1 person acts as the
Chairman of the Committee and 2 people as Committee Members. This indicates
that there are companies that do not have a risk committee or have less than
the number of risk committee members required by the regulations, indicating
non-compliance with applicable regulations. This can have implications for the
quality of corporate governance and risk management implemented.
The
company size, measured in logs, has an average of 7.84 and a standard deviation
of 1.89, with a range from 3.74 to 12.37. This shows the research sample covers
different types of companies, ranging from small companies to large companies.
Multivariate
Regression Analysis
Table 5
Multivariate regression results
|
|
QNOR |
QLOR |
||||
|
|
Coef. |
z-stat |
P > |z| |
Coef. |
z-stat |
P > |z| |
|
CONC |
0.00147 |
2.26 |
0.024 |
0.00026 |
0.42 |
0.676 |
|
ACOMF |
0.0080 |
6.83 |
0.000 |
0.0052 |
1.55 |
0.121 |
|
BIG |
0.0145 |
2.77 |
0.006 |
0.0343 |
1.07 |
0.286 |
|
RCSIZE |
-0.0068 |
-5.96 |
0.000 |
0.0016 |
0.25 |
0.805 |
|
FSIZE |
-0.0009 |
-1.27 |
0.205 |
-0.0031 |
-1.18 |
0.237 |
|
RMA |
0.01031 |
3.39 |
0.001 |
0.1074 |
3.93 |
0.000 |
|
Constant. |
0.1129 |
12.98 |
0.000 |
0.4191 |
8.42 |
0.000 |
|
|
Rsquare= |
0.1427 |
chi = 4.54 |
R square = 0.1064 |
chi = 21.56 |
|
This study uses GLS regression analysis to examine the influence of corporate governance mechanisms on the disclosure of Operational Risk (OR) for quantitative (QNOR) and qualitative (QLOR) for all samples. Based on the regression results in table 5, the following results are obtained:
�
The effect of corporate governance
mechanisms on the disclosure of Operational Risk (OR) for quantitative (QNOR)
Based on the regression results in table 5,
the regression equation model for QNOR is obtained as follows:
QNOR=0.1129+0.00147x1+0.0080x2+0.0145x3−0.0068x4−0.0009x5+0.01031x6
The regression
coefficient for Ownership concentration is 0.00147. This coefficient has a
positive value, meaning that when the value of Ownership Concentration
increases, the Operational risk disclosure quantitative will increase. When� the value of Ownership concentration
decreases, the Operational risk disclosure quantitative will decrease. From the
results of the calculation, a p-value < significance level (α = 0.05)
was obtained, which means that ownership concentration had a significant
positive effect on operational risk disclosure quantitative. The results of
this study are in accordance with previous research which stated that the
concentration of stock ownership has a positive effect on the disclosure of
operational risks (Neifar & Jarboui, 2018).
The regression coefficient
for Audit committee meeting is 0.0080. This coefficient has a positive value,
meaning that when the number of audit committee meetings increases, the
Operational risk disclosure quantitative will increase. When the Audit committee
meeting decreased, the Operational risk disclosure quantitative also decreased.
From the calculation results, a p-value < significance level (α = 0.05)
was obtained, which means that the audit committee meeting had a significant
positive effect on the operational risk disclosure quantitative. This is in
line with previous research by (Greco, 2011), which showed that the frequency of audit committee
meetings increases their effectiveness in overseeing a company's financial
reporting practices. Audit committee meetings include evaluation of accounting
principles, information disclosure, and estimation. Regular meetings empower
committees to better fulfill their governance responsibilities and monitor the
company more effectively. In addition, a higher frequency of meetings can
prevent fraudulent activities (Cheng et al., 2006) and improve the quality of information disclosure (Allegrini & Greco, 2013).
The regression coefficient
for auditor type is 0.0145. This coefficient explains that when the variable
auditor type (1) is an external auditor from the Big Four, the value� of the operational risk disclosure quantitative
will be 0.0145 units greater than that of the auditor type (0), namely the
external auditor not from the Big Four. From the results of the calculation, a
p-value < significance level (α = 0.05) was obtained, which means that
auditor type (1) has a significant influence on operational risk disclosure
quantitative. This is in accordance with the research of Ruwita and Harto
(2014) and Wardhana and Cahyonowati (2013). Large public accounting firms such
as the Big Four often encourage clients to provide more transparent information
to maintain audit quality and public trust.
The regression coefficient
for Risk committee size is -0.0068. This coefficient has a negative value,
meaning that when the number of Risk committee sizes increases, the Operational
risk disclosure quantitative will decrease. When the Risk committee size
decreases, the Operational risk disclosure quantitative will increase. From the
results of the calculation, a p-value < significance level (α = 0.05)
was obtained, which means that the risk committee size has a significant
negative effect on Operational risk disclosure.
The regression coefficient
for firm size is -0.0009. This coefficient has a negative value, meaning that
when� the firm size increases, the
Operational risk disclosure quantitative will decrease. When� the firm size decreases, the Operational risk
disclosure quantitative will increase. From the results of the calculation, a
p-value > significance level (α = 0.05) was obtained, which means that
firm size has a negative and insignificant effect on the Operational risk
disclosure quantitative.
The regression coefficient
for the risk measurement approach is 0.01031. This coefficient explains that
when� the variable risk measurement
approach uses the standard approach (1), the value� of the Operational risk disclosure
quantitative will be 0.01031 units greater than�
the risk measurement approach using other approaches (0). From the
results of the calculation, a p-value < significance level (α = 0.05)
was obtained, which means that the risk measurement approach (1) has a
significant influence on the Operational risk disclosure quantitative.
From the calculation results, this model has an F value of 4.50 > F table so it can be concluded that this model is significant. With an R square value of 14.27%, it means that the variables Ownership Concentration, audit committee meeting, auditor type, risk committee size, firm size, and risk measurement approach contribute 14.27% to the Operational risk disclosure quantitative while the other 85.73% are explained by other variables.
�
The effect of corporate governance
mechanisms on qualitative Operational Risk (OR) disclosure (QLOR)
Based on the regression
results in table 5, the regression equation model for QLOR is obtained as
follows:
QLOR=0.4191+0.00026x1+0.0052x2+0.0343x3+0.0016x4−0.0031x5+0.1074x6
The regression coefficient for Ownership concentration is
0.00026. The regression coefficient has a positive value, meaning that when the
value of Ownership concentration increases, the Operational risk disclosure
qualitative will increase. When the value of Ownership concentration decreases,
the Operational risk disclosure qualitative will also decrease. From the
results of the calculation, a p-value > significance level (α = 0.05)
was obtained, meaning that Ownership concentration had a positive and insignificant
effect on Operational risk disclosure qualitative.
The regression coefficient for the Auditor committee meeting
is 0.0052. This coefficient has a positive value, meaning that when the number
of audit committee meetings increases, the Operational risk disclosure
qualitative will increase. When the number of audit committee meetings
decreases, the qualitative operational risk disclosure will also decrease. From
the calculation results, a p-value > significance level (α = 0.05) was
obtained, which means that the audit of the committee meeting had a positive and
insignificant effect on the Operational risk disclosure qualitative. The
results of this study are in line with previous research, where the number of
audit meetings each year produced insignificant results on the quality of
operational risk disclosure (Yoso, A., Christopher, D., Dessen, L. S. P., Putri, L. S., Anisah, L.,
& Hanggraeni, 2021).
The regression coefficient for auditor type is 0.0343. This
coefficient explains that when the auditor type variable (1) is an external
auditor from the Big Four, the Operational risk disclosure qualitative value
will be 0.0343 units greater than that of�
auditor type (0), namely external auditors not from the Big Four. From
the calculation results, a p-value > significance level (α = 0.05) was
obtained, which means that auditor type (1) has a positive and insignificant
effect on Operational risk disclosure qualitative.
The regression coefficient for Risk committee size is
0.0016. This coefficient has a positive value, meaning that when the number of
Risk committee sizes increases, the Operational risk disclosure qualitative
will increase. Similarly, when the number of Risk committee sizes decreases,
Operational risk disclosure qualitative will decrease. From the results of the
calculation, a p-value > significance level (α = 0.05) was obtained,
which means that the risk committee size had a positive and insignificant effect
on the operational risk disclosure qualitative.
The regression coefficient for firm size is -0.0031. This
coefficient has a negative value, meaning that when the firm size value
increases, the Operational risk disclosure qualitative will decrease. When the
Firm size value decreases, the Operational risk disclosure qualitative will
increase. From the results of the calculation, the p-value > significance
level (α = 0.05) was obtained, which means that firm size has a negative
and insignificant effect on operational risk disclosure qualitatively.
The regression coefficient for the risk measurement approach
is 0.1074. This coefficient explains that when the variable risk measurement
approach uses the standard approach (1), the value of the operational risk
disclosure qualitative will be 0.1074 units greater than that of the risk
measurement approach using other approaches (0). From the calculation results,
the p-value < the significance level (α = 0.05) was obtained, which
means that the risk measurement approach (1), namely the standard approach, has
a significant effect on the operational risk disclosure qualitative.
From the calculation results, this model has a chi value
< table chi, so it can be concluded that this model is significant. With an
R square value of 10.64%, it means that Ownership Concentration, audit
committee meeting, auditor type, risk committee size, firm size, and risk
measurement approach contribute 10.64% to Operational risk disclosure
qualitative while the other 89.36% is explained by other variables.
Conclusion
This research makes an important
contribution to understanding the influence of corporate governance on the
disclosure of operational risks in the Non-Bank Financial Services Institution
(LJKNB) sector in Indonesia. Based on the analysis of panel data for the
2019�2023 period, it was found that governance variables such as the
concentration of share ownership, the frequency of audit committee meetings,
and the existence of the Big 4 external auditors had a significant positive
influence on the quantitative disclosure of operational risks. These findings
confirm that the ownership structure and oversight mechanisms through the audit
committee play a strategic role in improving operational risk transparency.
However, the size of the risk
monitoring committee shows a significant negative influence on the quantitative
disclosure of operational risks, indicating that the oversized structure of the
risk monitoring committee may become less efficient in ensuring the
transparency of risk management. Meanwhile, no significant influence of all
governance variables was found on qualitative operational risk disclosure,
which shows that the quality of disclosure is more influenced by
non-quantitative factors such as organizational culture or management's
perception of the importance of risk disclosure.
Therefore, this study concludes that
the concentration of share ownership, the frequency of audit committee
meetings, and the type of external auditors of the Big 4 have a significant
positive influence on the quantitative disclosure of operational risks in
Non-Bank Financial Services Institutions (LJKNB) during the 2019�2023 period.
In contrast, the size of the risk monitoring committee showed a significant
negative influence. However, the four governance variables did not have a
significant effect on the qualitative disclosure of operational risks. These
findings emphasize the importance of strengthening corporate governance to
increase operational risk transparency in the LJKNB sector.
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