Corresponding Author: Seno
Sasongko
Email: [email protected]
INTRODUCTION
Global economic conditions and especially in
Indonesia, are currently unstable due to the impact of the Covid-19 outbreak.
Covid-19 has actively spread to almost all countries worldwide and has harmed
the economy (Nasution et al., 2020). The reason is to restrict human mobility in social
interactions and economic activities to keep transmission under control (Hamdun et al., 2022). The widespread development of Covid-19 also impacts
the stability of the financial system and bank operations in a country, so
special attention and policies are needed to keep bank functions running with
the aim of the economy not declining significantly (Abubakar &
Handayani, 2021).
The problem that arose in the banking sector as a result
of the Covid-19 outbreak was decreasing on bank profitability, this condition
happened due to bank�s credit quality has worsened because the difficulty of
debtors in fulfilling their obligations to pay their credit, which had an
impact on increasing Bank Non-Performing Loans (NPL), which then continued to
decrease the loans disbursed, because the Bank tightened channelling credit, on
the other hand, the demand for credit from the public has also decreased due to
the impact of the pandemic (Utami & Yustiawan,
2021).
Declining financial performance can be observed from
the profits earned by banks during the pandemic. To estimate the level of
profit that will be obtained by a company, the comparison of profits or
profitability ratios can be used. Profit is a parameter to see whether a bank's
performance is good or not. Return on Assets (ROA), or the comparison of
profits to all assets, is one of several instruments that can be used to
calculate a bank's capability to maximize profit (Alim, 2014).
Return on Assets (ROA) is a comparison of returns on several
assets used by companies (Chandra et al., 2020). The increased ROA value shows that the Bank has improved
performance. The movement of Return on Assets (ROA) and Profits at Commercial
Banks from 2019 to 2021 can be seen in the following figure:
Figure 1. ROA and Profit Data for Conventional
Commercial Banks for 2019-2021
Source: OJK, 2019-2022 (data processed)
In 2020, ROA was recorded to have decreased from 2.47%
to 1.59% at the same time in the previous year. This condition was caused by
declining profits due to debtors' deteriorating credit quality conditions along
with the COVID-19 outbreak. Banks booked more enormous reserves in line with
debtor credit quality conditions, most of which experienced a deterioration.
In 2021, the profitability of Conventional Commercial
Banks was reported to have recovered with an increase in banking ROA from 1.59%
to 1.85% in the same period in the previous year. The ROA figure, which showed
an increase, was triggered by profit before tax which increased by 16.17% (yoy)
after the previous year, which decreased -by 24.28% (yoy). At the same time,
the average total assets remained constant.
Figure 2. Distribution of Credit and DPK (Third Party
Funds) for 2019-2021
Source: OJK, 2019-2022
In 2020, lending decreased slightly compared to 2019
due to the conditions of the Covid 19 pandemic, and in 2021 lending began to
increase.
Figure 3. Distribution of Credit
and DPK (Third Party Funds) for 2019-2021
Source: OJK, 2019-2022
Figure 4. LDR and BOPO Data
for Conventional Commercial Banks for 2019-2021
Source: Indonesian Banking Statistics 2019-2022 (data
processed)
Based on the data above, a pandemic has reduced
or weakened the Bank's mediation function. The number of NPLs has increased,
credit distribution has slowed, and banks have also faced rising BOPO fees.
Hence, the conditions for banks could be better than before the pandemic in
carrying out their intermediary function. The phenomenon of various
attenuations is a gap phenomenon that is of interest to the author for further
study.
As previously explained, bank strength plays a vital
role in economic stability and growth. Bank stability depends on profitability
and capital adequacy (Rustendi, 2019). Studies from previous research related to risk
management and profitability of commercial banks still show different results.
Furthermore, only a few researchers have placed the
affirmation of CEO tenure in the causal relationship between risk management
and the profitability of conventional commercial banks in Indonesia. Therefore,
this research is expected to provide different results regarding the gaps in
previous research (Pahlevi & Rahab,
2022).
The CEO's tenure influences the company's environment,
stakeholders, strategy, and activities, which directly or indirectly impacts
financial performance. Thus, studying the relationship between the CEO's tenure
and the company's financial performance is essential.
The addition of CEO Tenure in this study is based on
the premise that the longer the CEO has served in a company, the company's
resources will be managed and allocated more effectively to maximize the
profits to be generated. Experience, unique expertise, and more knowledge
possessed by a CEO will make a difference in managing a company.
This research is a development of research (Ejoh et al., 2014), (Alshatti, 2015), (Ndoka & Islami,
2016), (Katerina, 2018), (Kidane, 2020), (Sante et al., 2021), and (Mardiansyah &
Suryani, 2021). We are examining the effect of implementing risk
management on profitability in the Indonesian banking industry. Contrary to
previous studies or research, this research assesses the role of implementing
risk management by using Non-Performing Loan (NPL) indicators for credit risk,
Net Interest Margin (NIM) for market risk, Loan to Deposit Ratio (LDR) for
liquidity risk and BOPO for operational risk, to bank profitability proxied by
Return on Assets (ROA), as well as by adding CEO Tenure as measured by the
number of years in which the CEO has served in that position as a moderator variable.
In addition, this research was conducted in abnormal economic conditions,
namely when the economy was experiencing a decline due to the impact of the
Covid-19 pandemic. The Convid-19 pandemic has affected almost all business
sectors including the banking industry.
This study focuses on the role of risk management on
bank profitability because profitability proxied by ROA has a positive
relationship with financial performance and a negative relationship with
risk-taking. It can be stated that risk management indicators have relevance to
current and future profits. This research also aims to see and examine the
influence of CEO Tenure on the causal relationship between risk management and
bank profitability.
Based on this description, this research aims to
examine the Effect of Risk Management Implementation on the Profitability of
Conventional Commercial Banks with CEO Tenure as a Moderator Variable During
the Covid-19 Pandemic.
METHODS
Population and Research Sample
A population
is a group of elements with specific characteristics that can be used to make
conclusions. The population in this research are
commercial banks listed on the Indonesia Stock Exchange (IDX) during the
2020-2021 period. The sampling method was carried out by purposive sampling, aiming
to obtain a representative sample. This research uses secondary data collected
from annual reports of conventional general banking companies listed on the
Indonesia Stock Exchange (IDX) for 2020-2021. This data can also be found on
the Indonesian Capital Market Directory, IDX homepage www.idx.co.id, as well as
direct download on the company's website as a sample. Moderation
regression analysis is a data analysis technique that utilizes SPSS v26.
Several criteria were used to determine the number of samples in this research.
The
criteria that form the basis for determining the number of samples are shown in
table 1.
Table 1. Sampling Criteria
|
Criteria
|
Amount
|
|
Conventional commercial bank companies listed on
the IDX for the 2020-2021 period
|
43
|
|
Conventional commercial bank companies that do
not submit Financial Statements
|
(2)
|
|
The number of companies used as samples in the
study
|
41
|
|
The total sample used in the study (41 X 2 years)
|
82
|
Table 1 above states that the
number of samples obtained was 41 conventional banking companies from a
population of 43 conventional banking companies in Indonesia that were listed
on the IDX.
Research
Variables
Table
2. Summary of Research Variables
|
Variable
|
Operational definition
|
Measurement
|
Measurement Scale
|
|
Non-Performing
Loans (NPL)
|
Provision
of credit that is experiencing problems
|
Percentage of
non-performing loans to total loans granted.
|
Ratio
|
|
Net
Interest Margin (NIM)
|
The
ratio is used to measure the capability of bank management to use its
productive assets to obtain net interest income.
|
Percentage of net
interest income to average earning assets.
|
Ratio
|
|
Loan
to Deposit Ratio (LDR)
|
The
ratio of credit facilities to total deposits
|
Percentage of the
total loan amount to total deposit ratio
|
Ratio
|
|
BOPO
|
The
profitability ratio shows the proportion of total expenses to the Bank's
total operating income.
|
Percentage of total
operating costs Ratio to total operating income
|
Ratio
|
|
CEO
Tenure
|
The
annual number in which the CEO has held that position in a company
|
Number of years the
CEO has been appointed
�CEO Tenure =
|
Ratio
|
|
Return
on Assets (ROA)
|
The
ratio that shows the return (yield) of several assets used by the company
|
Profit before tax
to total assets
|
Ratio
|
Data Analysis Method
This research aims
to examine the effect of risk management on profitability and the effect of
risk management on profitability, with CEO tenure as a moderator
variable. The
data obtained in this research will be tested first to fulfil the basic
assumptions before testing the hypothesis. The formed regression model is
feasible to use if the classical assumption test is fulfilled. This research
uses classic assumption tests for normality, autocorrelation,
multicollinearity, and heteroscedasticity tests.
This research uses inferential statistical
analysis, namely Moderated Regression Analysis. The data is processed, and the
SPSS version 26 program is used for hypothesis testing. The moderator variable
can be determined by comparing the three regression equations formed. The
Moderated Regression Analysis model equation formed is:
Y
= α + β1X1 + β2X2 +
β3X3 + β4X4 +
ε ....................................................... (1)
Y
= α + β1X1 + β2X2 +
β3X3 + β4X4 +
β5Z + ε .............................................. (2)
Y
= α + β1X1 + β2X2 +
β3X3 + β4X4 +
β5Z + β6 X1*Z + β7X2*Z
+ β8X3*Z + β9X4*Z +
ε ....................................................................................................... (3)
Where:
Y�������� : ROA
X1������������ : NPLs
X2������������ : NIM
X3������������ : LDR
X4������������ : BOPO
Z��������� : CEO of Tenure
X1*Z��� : Interaction between NPL and CEO Tenure
X2*Z��� : Interaction between NIM and CEO Tenure
X3*Z��� : Interaction between LDR and CEO Tenure
X4*Z��� : Interaction between BOPO and CEO Tenure
The way to determine the type of moderator variable is
to check the beta value (b) or
the results of the regression coefficient test. The
classification of moderator variables can be seen with the following
information:
Table 3. Sampling Criteria
|
Test results
|
Moderation Type
|
|
β5 not
significant β6, β7, βs, β, significant
|
Pure
Moderator (pure moderation)
|
|
β5 significant β6, β7, β8, β, significant
|
Quasi
Moderator (pseudo moderation). The moderator variable can moderate the
relationship between the independent and dependent variables and, at the same
time, functions as an independent variable.
|
|
β5 significant β6,7,8,9
not significant
|
Predictor
Moderator Variable (predictor moderation). The moderator variable functions
as an independent variable in the established regression equation
|
|
β5 not
significant β6, β7, β8, β, Significant
|
Homologiser
Moderator (potential moderator. This variable has the potential to become a
moderator variable.
|
RESULTS AND DISCUSSION
Descriptive Statistics f
Table 4. Descriptive Statistical Analysis
|
|
N
|
Minimum
|
Maximum
|
Means
|
std. Deviation
|
|
ROA
|
76
|
-.1475
|
.0431
|
.003349
|
.0284594
|
|
NPLs
|
76
|
.0000
|
.2227
|
.038722
|
.0329310
|
|
NIM
|
76
|
-.0352
|
.1352
|
.040761
|
.0247331
|
|
LDR
|
76
|
0.1235
|
1.6229
|
.814153
|
.2869513
|
|
BOPO
|
76
|
.5170
|
2.8786
|
.978999
|
.3752032
|
|
CEOTENURE
|
76
|
1.0000
|
14,000
|
4.013158
|
3.5345662
|
|
Valid N (listwise)
|
76
|
|
|
|
|
The
variable Return on Assets (ROA) has the smallest value of -0.1475, namely Bank
Rakyat Indonesia Agroniaga and the largest is 0.0431, namely Bank Mestika
Dharma Tbk, the mean is 0.0033, and the standard deviation value is 0.028. This
mean figure of 0.0033 means that the average banking company in Indonesia has a
return on assets value of 0.33 per cent. This condition illustrates that
conventional commercial banks can still realize profits from assets managed in
the 2020-2021 period. The lowest ROA value was experienced by Bank Rakyat
Indonesia Agroniaga, where in December 2021 it recorded a net loss of Rp. 3.04
trillion compared to 2020, which included a net profit of Rp. 31.26 billion.
While the highest value was experienced by Bank Mestika Dharma Tbk where in
December 2021 it recorded a net profit of Rp. 519.58 million.
The
Non-Performing Loan (NPL) variable shows the smallest value of 0.00, namely
Bank Artos Indonesia Tbk and the highest is 0.22 or around 22%, namely the
Regional Development Banks of West Java and Banten Tbk; the mean is 0.0387. The
standard deviation value is 0. 0329. This 0.0387 mean figure illustrates that
the average conventional commercial Bank has a non-performing credit rate of
3.87 per cent. The average NPL value of conventional commercial banks is in good
condition, and non-performing loans in conventional banks can be handled
properly.
The
Net Interest Margin (NIM) variable shows the lowest value of -0.0352, namely
Bank Capital Indonesia, Tbk and the highest is 0.1352, namely Bank Amar
Indonesia, the mean is 0.0407 and the standard deviation value is 0.0247. This
mean value of 0.0407 illustrates that the average conventional commercial Bank
has a level of interest income on earning assets of 4.07 per cent and can be
said to be good because the NIM value is more than 2 per cent.
The
Loan to Deposit Ratio (LDR) variable shows the lowest value of 0.1235, Bank
Capital Indonesia, Tbk and the highest of 1.6229, Bank Woori Saudara Indonesia
1906, Tbk; the mean value is 0.814153 and the standard deviation value of is
0.287. This mean figure of 0.8141 or 81.4% illustrates that the average
conventional commercial Bank has a reasonably good level of credit composition
compared to the number of funds obtained. This average value shows that
conventional commercial banks still have a good level of liquidity. The LDR
ratio also provides an overview of banking channelling credit to the community,
compared to managing funds collected from the public. A high LDR illustrates
the implementation of the Bank's mediation function in lending. Banks must
continue to exercise strict control over loans given, apart from being able to
provide interest benefits, lending also carries the risk of contributing to
losses to the company, if the loans distributed do not run smoothly or become
problematic (Non-Performing Loans) because banks must reserve losses.
The
Variable Operating Costs to Operating Income (BOPO) shows the smallest number
of 0.5170, namely Bank Mestika Dharma Tbk and the highest is 2.878, namely Bank
Rakyat Indonesia Agroniaga Tbk, the mean is 0.979, and the standard deviation
is 0.375. This mean figure of 0.979 illustrates that the average conventional
commercial Bank has an average close to 100%, where the BOPO value of 97.9 per
cent must be a concern for banks in the future in order to reduce the BOPO ratio
below 90% following the direction of the Bank Indonesia (BI).
The
CEO Tenure variable that shows the lowest value is 1, there are 16 banks with
this value, and the highest is 14, namely Bank OCBC NISP Tbk and Bank Artha
Graha Internasional Tbk, the mean is 4.0131 and the standard deviation is
1.0233728. The mean value of 4.0131 illustrates that the average CEO has served
at a conventional commercial bank for about 4 years.
Classical
Assumption Test Results
Normality test
The
Normality Test aims to see whether the regression model formed and whether the
dependent variable (dependent) and the independent variable (independent) have
a normal distribution. The regression model
is good if it shows a normal or close-to-normal distribution. The study of the
research model was carried out in two parts because when the first part was
tested, it did not include fulfilling the normality assumption of the
residuals, where the results obtained in the One-Sample Kolmogorov Smirnov test
showed an asymp number. Sig (2-tailed) is less than 0.05, or the residual data
from the first part of the test are generally not distributed. Not normally
distributed data can be transformed to normalize data (Ghozali, 2018). The form of change made in this study is the SQRT (kx) transformation
form because the histogram graph formed is moderate negative skewness.
The second stage of testing was carried out by removing 6 data
classified as outlier data and then by transforming the SQRT (kx) data on the
dependent variable (ROA), independent variables (NPL, NIM, LDR, BOPO) and
moderator variables (CEO Tenure). The conclusion of the normality test after
carrying out the data transformation can be shown in table 5.
Table 5. Kolmogorov-Smirnov Test Results
|
|
|
Unstandardized Residuals
|
|
N
|
|
76
|
|
Normal
Parameters a, b
|
Means
|
.0000000
|
|
Std. Deviation
|
.12584370
|
|
Most
Extreme Differences
|
absolute
|
092
|
|
Positive
|
092
|
|
Negative
|
-.077
|
|
Test
Statistics
|
|
092
|
|
Symp.
Sig. (2-Tailed)
|
|
.176 c
|
Source:
SPSS 26 output, data processing
The
results of the One-Sample Kolmogorov-Smirnov test show the easy rate. Sig (2-tailed) is above 0.05 with a value of 0.176. Thus,
it can be concluded that the residual data is typically
distributed.
Autocorrelation Test
The
model is considered free from autocorrelation if the Durbin-Watson number has a
value of du < dw < 4-du. The results of the Durbin-Watson test can be
shown in table 6.
Table 6. Durbin Watson test results
|
DW
|
DU
|
4-DU
|
Information
|
|
1,947
|
1,867
|
2,134
|
There is no autocorrelation
|
Source: SPSS 26 output, data is processed
The results of the
autocorrelation test in table 6 above show that the Durbin-Watson value is
1.867. The DW value occupies a position between 2 and 4-du, so it can be stated
that autocorrelation does not occur, so the regression model formed is feasible
for further studies.
Multicollinearity
Test
Table
7. Multicollinearity Test Results
|
Variable
|
tolerance
|
VIF
|
Conclusion
|
|
NPL
|
.861
|
1.162
|
There is no multicollinearity
|
|
NIM
|
.911
|
1.098
|
There is no multicollinearity
|
|
LDR
|
.839
|
1.191
|
There is no multicollinearity
|
|
BOPO
|
.851
|
1.175
|
There is no multicollinearity
|
|
CEO
TENURE
|
853
|
1.172
|
There is no multicollinearity
|
Source: SPSS 26 output, data processing
Table 7. mentioned above, does not show symptoms of multicollinearity.
This can be shown from the VIF value <10 and the tolerance value > 0.1.
Heteroscedasticity
Test
Table
8. Heteroscedasticity Test Results (Park Test)
|
|
Unstandardized
Coefficients
|
Standardized Coefficients
Beta
|
t
|
Sig.
|
Collinearity Tolerance
|
VIF Statistics
|
|
Model
|
B
|
std. Error
|
|
1
|
(Constant)
|
-5,793
|
.285
|
|
-20.306
|
.000
|
|
|
|
NPLs
|
.089
|
.313
|
.036
|
.284
|
.777
|
.826
|
1,211
|
|
NIM
|
.295
|
.304
|
.119
|
.969
|
.336
|
.857
|
1.166
|
|
LDR
|
.300
|
.311
|
.122
|
.965
|
.338
|
.810
|
1.235
|
|
BOPO
|
.589
|
.364
|
.208
|
1.616
|
.111
|
.786
|
1.273
|
|
CEOTENURE
|
.603
|
.308
|
.246
|
1.956
|
.054
|
.822
|
1.216
|
Source: SPSS 26 output, data processing
Based on table 8 above, the
Park test results show that the independent variable's indicator coefficient is
not significant, and the value is more than 0.05. It can be concluded that the
regression model does not have heteroscedasticity.
Hypothesis
Test Results
Simultaneous
Significance Test Results (Ftest)
Table 9. F Test Results
|
Model
|
F
|
Sig
|
|
Regression
1
|
179.934
|
.000
|
|
Regression
2
|
150.108
|
.000
|
|
Regression
3
|
128.114
|
.000
|
Source: SPSS Output 2
Table 9 above
presents the results of statistical
calculations showing that the F-count value in model 1 is 179.934 with a probability of 0.000, in model 2 it is 150.108 with a probability of 0.000, and in model 3 it is 128.114 with a
probability of 0.000. The probability figures in models 1, 2, and 3 are much
smaller than 0.05, so it can be concluded that the fit model or regression
model can be used to predict profitability or in other words, it can be said
that:
1)
NPL, NIM, LDR, and BOPO simultaneously influence profitability (ROA).
2)
NPL, NIM, LDR, BOPO, and CEOTENURE simultaneously influence
profitability (ROA).
3)
NPL, NIM, LDR, BOPO, CEOTENURE*NPL, CEOTENURE*NIM, CEOTENURE*LDR, and
CEOTENURE*BOPO all have a simultaneous effect on profitability (ROA).
Coefficient of
Determination
Table 10
of the statistical processing below shows the adjusted R square as
follows:
Table
10. Coefficient of Determination
|
Model
|
Adjust R Square
|
|
Regression 1
|
0.905
|
|
Regression 2
|
0.909
|
|
Regression 3
|
0.931
|
Source: SPSS 26 output, data processing�������
Table 10 above shows the coefficient of determination of the three
equation models formed. In regression model 1, the Adjusted R Square number is 0.905. This means that 90.5% of the ROA variation can be explained by
variations of the four independent variables (NPL, NIM, LDR, BOPO), while other
causes explain the remaining 9.5% outside the model in this study. Adjusted R Square in the
regression model 2 obtained a value of 0.909, which means that 90.9% of the ROA variation can be explained by
variations of the five independent variables (NPL, NIM, LDR, BOPO, CEO Tenure).
In comparison, the remainder is other reasons outside the model in this study,
explaining 9.1 %. Regression model 3 shows the value of. Adjusted R Square of 0.931, where 93.1% of the variation in ROA can be
explained by variations of the nine independent variables (NPL, NIM, LDR, BOPO,
CEO Tenure, CEOTENURE*NPL, CEOTENURE*NIM, CEOTENURE*LDR, CEOTENURE*BOPO), while
other reasons outside the model in this study explain the remaining 6.9%.
The coefficient of determination from the regression model equation 1 to
regression model 3 shows a more excellent value, so it can be assumed that the
presence of a moderator variable (CEO Tenure) will strengthen the effect of the
NPL, NIM, LDR, and BOPO variables on financial performance (ROA).
Moderation
Regression Analysis (MRA) Test Results
The
results of the Moderated Regression Analysis can be seen in table 11.
Table 11. Moderation Regression
Analysis Test Results
|
Variable
|
|
t-Count
|
sig
|
|
Constant
|
9.220
|
3.964
|
.000
|
|
NPLs
|
.659
|
1.041
|
.302
|
|
NIM
|
1.406
|
6.564
|
.000
|
|
LDR
|
-.360
|
-.675
|
.502
|
|
BOPO
|
-3.984
|
-5.482
|
.000
|
|
CEOTENURE
|
1.261
|
.802
|
.425
|
|
CEOTENURE*NPL
|
-.333
|
-1.086
|
.282
|
|
CEOTENURE*NIM
|
-.087
|
-2.237
|
.028
|
|
CEOTENURE*LDR
|
.183
|
.723
|
.472
|
|
CEOTENURE*BOPO
|
1.407
|
4.190
|
.000
|
Source: SPSS 26 output, data processing
Table 11. above shows the equation of the
regression model formed, namely:
Y = 9.220 + 0.659 NPL + 1.406 NIM � 0.360 LDR � 3.984 BOPO + 1.261 CEOTENURE � 0.333 CEOTENURE*NPL � 0.087 CEOTENURE*NIM + 0.183 CEOTENURE*LDR
+ 1.407 CEOTENURE*BOPO
The
explanation of the regression model equation 3 above is as follows:
1.
A
constant of 9.220 means that if it is not influenced by the
eight independent variables where all the independent variables are 0, then the
ROA number will be the same, namely 9.220.
2.
The NPL
coefficient is 0.659 with a significance value of 0.302 and exceeds 0.05. This explains that there is no influence, and it is not
significant. The coefficient value of 0.659 indicates that if the value of the NPL variable is
increased by one unit, it will be able to increase ROA by 0.659 with other variables being constant.
3.
The NIM
coefficient is 1.406 with a significance value of 0.000 and less than 0.05. This explains that there is a positive influence, and
the value is significant. The coefficient value of 1.406 indicates that if the value of the NIM variable is increased by one
unit, it will be able to increase the ROA by 1.406 with other variables being constant.
4.
The LDR
coefficient shows the number - 0.360 with a
significance value of 0.502 and
exceeds 0.05. This explains that there is a negative influence but not significant.
The coefficient number is -0.360, indicating that if the LDR variable number is
increased by one unit, the ROA number will decrease by 0.360 with the other variable numbers unchanged.
5.
The BOPO
coefficient shows the number � 3.984 with a
significance value of 0.000 and less than 0.05. This explains that there is a
negative and significant influence. The coefficient value of -3.984 indicates that if the BOPO variable number is increased by one unit, the
ROA number will decrease by 3.984 with the
other variable numbers unchanged.
6.
The CEO
Tenure coefficient shows 1.261 with a significance value of 0.425 and exceeds 0.05. This explains that there is no influence, and it is not
significant. The coefficient value of 1.261 indicates that if the CEO tenure
variable value is increased by one unit, the ROA value will increase by 1.261
with the other variable numbers unchanged.
7.
The
coefficient of the moderator variable, the interaction between CEO Tenure and
NPL, shows the number - 0.333 with a
significance value of 0.282 and exceeds
the number 0.05. This explains that there is a negative influence but not
significant. The coefficient value is -0.333, indicating that if the value of
this variable is increased by one unit, the ROA number will decrease by 0.333 with the other variable numbers unchanged.
8.
The
coefficient of moderator variable, the interaction between CEO Tenure and NIM, shows
the number - 0.087 with a significance value of 0.028 and less than 0.05. This explains that there is a negative and significant
influence. The coefficient value is -0.087, indicating that if the number of this variable is
increased by one unit, the ROA number will decrease by 0.087 with the other variable numbers unchanged.
9.
The
coefficient of moderator variable, the interaction between CEO Tenure and LDR,
shows the number 0.183 with a significance value of 0.472 and exceeds the number 0.05. This explains that there is no influence and is
not significant. The coefficient value of 0.183 indicates that if the value of this variable is increased by one unit,
the ROA number will increase by 0.183 with the
other variable numbers unchanged.
10. The coefficient of moderator variable, the
interaction between CEO Tenure and BOPO, shows the number 1.407 with a significance value of 0.000 and less than 0.05. This explains that there is a positive and significant
influence. The coefficient value of 1.407 indicates
that if the number of this variable is increased by one unit, the ROA number
will increase by 1.407 with the other variable numbers unchanged.
Partial Test Results
(t-test)
The interpretation of the results of the
t-test in this research is as follows:
1.
Effect
of NPL on ROA
Non-Performing
Loans (NPL) on the t-test results do not significantly affect ROA. This is
shown from the t count of 0.871, where the t count is less than the t table of
1.996, and the probability obtained is 0.387 and exceeds 0.05. Thus hypothesis
1 is rejected.
2.
Effect
of NIM on ROA
The Net
Interest Margin (NIM) on the t-test results significantly influence ROA. This
is shown from the t count of 2.457, where the t count exceeds the t table of 1.996,
and the probability obtained is 0.016, which is less than 0.05. Thus hypothesis
2 is accepted.
3.
The
Effect of LDR on ROA
The Loan
to Deposit Ratio (LDR) on the t-test results has no significant effect on ROA.
This is shown from the t count of 0.388, where the t count is less than the t
table value of 1.996, and the probability obtained is 0.699 and exceeds 0.05.
Thus hypothesis 3 is rejected.
4.
Effect
of BOPO on ROA
Operating
Costs Operating Income (BOPO) on the results of the t-Test has a significant
effect on ROA. This is shown from the t count of 23.850, where the t count
exceeds the t table of 1.996, and the probability obtained is 0.000, which is
less than the number 0.05. Thus hypothesis 4 is accepted.
5.
Effect of CEO Tenure Moderation with NPL on ROA
CEO Tenure does not moderate NPL on ROA on the results
of the t-test. This is shown from the t count of 1.086, where the t count is
less than the t table value of 1.996 and the probability obtained is 0.282 and
exceeds 0.05, thus hypothesis 5 is rejected. Comparing
the three regression models formed in the Moderated Regression Analysis, it can
be concluded that because β5 is not significant and β6
is not significant, CEO Tenure to NPL to ROA is
a homologous moderator variable; that is, this variable has the potential to become a
moderator variable.
6.
Effect of CEO Tenure Moderation with NIM on ROA
CEO Tenure moderates and influences NIM on ROA on the
results of the t-test. This is shown from the t count of 2,237, where the t
count exceeds the t table of 1,996 and the probability obtained is 0.028, less
than 0.05, thus hypothesis 6 is accepted. Comparing the three regression models
formed in the Moderated Regression Analysis, it can be concluded that because β5 is insignificant. Β7 is significant, then CEO Tenure about NIM on ROA is a
pure moderator variable, namely pure moderation.
7.
The Moderation Effect of CEO Tenure with LDR on ROA
CEO Tenure did not moderate LDR on ROA on the results
of the t-test. This is shown from the t count of 0.723, where the t count is
less than the t table value of 1.996 and the probability obtained is 0.472 and
exceeds 0.05, thus hypothesis 7 is rejected. Comparing the three regression
models formed in the Moderated Regression Analysis, it can be concluded that
because β5
is not significant
and β8 is not significant,
CEO Tenure concerning LDR to ROA is homologous; that is, this variable
has the potential to become a moderator variable.
8.
Effect of CEO Tenure Moderation with BOPO on ROA
CEO Tenure moderates and influences BOPO on ROA on the
results of the t-test. This is shown from the t count of 4.190, where the t
count exceeds the t table of 1.996, and the probability obtained is 0.000, less
than 0.05. Thus hypothesis 8 is accepted. Comparing the three regression models
formed in the Moderated Regression Analysis, it can be concluded that
because β5 is insignificant. β9 is significant, then
CEO Tenure concerning BOPO to ROA is a pure moderator variable, namely
pure moderation.
Discussion
of Research Results
Effect
of NPL on ROA
The results of this research show
that ROA is not affected by Non-Performing Loans (NPL). This indicates that
bank profitability is not sufficiently affected by non-performing loans. This
result is in line with the results of previous research conducted by (Sunaryo
et al., 2021), (Kidane
2020),
(and Ristati et al., 2018); on the other hand, the results
of this research are not the same as the results of research (Mardiansyah &
Suryani, 2021), (Ansori &
Safira, 2018), (Dewi &
Srihandoko, 2018), (Anam,
2018) which states that there
is a significant effect of credit risk (NPL) on profitability (ROA). This shows that with the increase in non-performing loans
(NPL), the Bank's income and profits will decrease; thus, the ROA value will
also decrease.
The NPL variable in
this research does not affect ROA because NPL is not a determinant of
increasing or decreasing profits earned by a bank. This shows that the increase
in the NPL rate has yet to affect profits at Conventional Commercial Banks in
Indonesia during the Covid-19 pandemic in 2020 and 2021, or vice versa; the
decline in the NPL rate has also not affected the profit level. This can happen
because the comparison of credit risk (NPL) at Conventional Commercial Banks in
the research sample taken has a figure that is still maintained, namely below
5%, so it is possible that bank profits can still increase even though the NPL
value has increased. This situation can also be interpreted as even though the
NPL value increases, it is not necessarily capable of leaving harmful
consequences for the profits obtained by the Bank. This results from the number
of Allowance for Earning Assets (PPAP) that must be formed for credit that is
experiencing problems and is still at a reasonable level and can be absorbed by
the Bank's profits so that it does not have an impact on profitability.
Banking performance
will be maintained even though the NPL rate is relatively high, close to 5%,
because banks can still get a source of income from non-credit or commonly
called fee-based income (not from interest income), which includes securities,
funds placed with other banks, bank capital participation in other financial
institutions, shipping costs, collections, letters of credit, safe deposit
boxes, credit cards, payment points (payments for safekeeping accounts), bank
guarantees, foreign exchange trading, commercial paper and traveller's checks,
and others - other. A small NPL also does not affect profitability because
Conventional Commercial Banks have sufficient capital in accordance with OJK
regulations so that the Bank's capital can handle measured credit risk.
Effect of NIM
on ROA
Based on the results of this
study, the results show that Net Interest Margin (NIM) influences Return on
Assets (ROA). These results indicate that the difference between all interest
costs and all interest income increases, which causes an increase in profit
before tax, thereby increasing the value of Return on Assets (ROA). This
research results follow the results of previous research (Mardiansyah
& Suryani, 2021). However, they do not align with
research results (Ristati
et al., 2018), where Net Interest Margin (NIM)
does not affect ROA.
In the banking world, Net Interest Margin
(NIM) illustrates that market risks that arise due to movements in market
variables can impact the profits obtained by banks. NIM is a ratio that shows
the net interest income earned by a bank as a result of the Bank's ability to
manage its productive assets. Interest income minus interest costs will result
in net interest income. Assets said to be productive are assets that can
provide interest, such as securities instruments and loans extended to debtors
by banks. The ratio of NIM, which is getting bigger, will increase the interest
income from productive assets managed by the Bank. Thus, it may affect the
value of the Bank's ROA in a better direction.
The results of this study explain that market
risk, in this case, NIM, still affects bank profitability (ROA) even though
economic conditions are affected by the Covid-19 pandemic. Conventional
Commercial Banks must maintain the NIM level to maintain good financial
performance.
The effect of
LDR on ROA
The research results show that
the Loan Deposit Ratio (LDR) does not affect Return on Assets (ROA). This result contradicts the results of
research conducted by (Reviyana
& Wuryanti, 2016), (Ristati
et al., 2018) and (Prasetyo
& Darmayanti, 2015), which shows liquidity risk (LDR)
significantly affect bank profitability (ROA). Increasing the amount of credit
given will increase the income received by the Bank.
The results of this research are
the same as the results of research carried out previously by (Sunaryo
et al., 2021). LDR does not affect bank profits (ROA); this
shows that an increase or decrease in commercial bank liquidity does not affect
bank profits. This result may occur because the Bank wants to decide on
something other than a lower or higher LDR value and maintains it at a
measurable level.
Liquidity risk must be appropriately
controlled by banks. However, the effectiveness of banks in channelling credit
can be seen from the LDR value; each Bank internally has its policy considering
economic conditions, funds raised, etc. The risk of bank liquidity will
increase when the Bank's LDR value is above the standard, but on the other
hand, the Bank has potential benefits to be gained if the LDR value is
relatively high, namely increased profitability, if credit is given effectively
and there are no excessive bad loans. The quality of the loans disbursed is
good and carried out carefully. Considering that the average banking LDR value
is still within the normal range and the NPL ratio is still quite good, in this
condition, the Bank's profitability (ROA) is not affected by LDR.
Effect of
BOPO on ROA
The research results show that
Operational Income Operating Costs (BOPO) negatively and significantly affect
Return on Assets. The
results of this research are the same as the results of research conducted by (Sukma et al.,
2019), where operational risk (BOPO) has a
significant negative effect on profitability (ROA). BOPO has a negative effect
in that if BOPO increases, it indicates a decreased efficiency level, so
profitability (ROA) will also decrease. This is because the income earned by
the Bank is influenced by the amount of efficiency that the Bank makes in
carrying out its operations. The better the efficiency of a bank, the Bank's
performance will improve, and profits can be maintained within a reasonable
range.
Research results show results that are
different from those carried out by (Reviyana
& Wuryanti, 2016), (and Ristati
et al., 2018), where operational risk (BOPO) has a
significantly positive effect on bank profitability (ROA). This shows that the
increase in operational costs incurred by banks causes the Bank's ability to
obtain profits to increase; in other words, banks can manage costs at a high
level and are expected to generate greater profits.
ROA is negatively affected by BOPO,
indicating that the higher the BOPO value, that is, by incurring costs, the
Bank will reduce the profitability it will obtain. This is understandable
because high operational costs will burden profits in carrying out its
intermediary function. The Bank's operating income cannot cover high
operational costs. This can happen because the BOPO value, which has increased,
indicates increased operational costs, so a bank can be influential if it can
control these costs. Efficient operational costs will increase profit (profit).
Various innovations can be made to increase efficiency including making changes
with the help of information technology to be able to make various kinds of
savings, with the aim of employee and company performance remaining productive,
with controlled costs.
CEO
Tenure cannot Moderate NPL against ROA
The results of this study show
that CEO Tenure cannot moderate the effect of NPL on ROA. This is possible
because the external factors that affect NPL are more significant than the
CEO's power in the Bank's internal processes. The activity of extending credit
to the public is the duty of banking. Non-performing loans must always be
closely monitored and supervised in handling and solving them. Many factors
influence the occurrence of non-performing loans, including current economic
conditions (pandemic), which are not factors that bank, including management,
can control. In 2020 and 2021, the economy will be significantly affected by
the many restrictions on the movement of individuals and communities, so these
factors will very dominantly affect economic activities and impact the
company's ability to generate profits to pay the Bank's obligations. The
economy is slowing down and companies must prioritize internal costs, weakening
the ability to make payments to banks and increasing the percentage of non-performing
loans during a crisis.
CEO Tenure can Moderate NIM on ROA
The results of this research show
that CEO Tenure can moderate the effect of NIM on ROA. These results also show
that CEO Tenure can strengthen the link between NIM and bank profitability. The
CEO, as the captain who is responsible for the company's performance, will try
his best so that the company's financial performance runs optimally and what is
ensured that the Bank he leads must continue to generate profits by first
ensuring that interest income remains high-quality and market challenges and
economic fluctuations. High, but the CEO will ensure that market risk can be
handled properly so that the Bank's financial performance continues to be
handled correctly and through the Covid-19 pandemic. Tremendous pressure
occurred when the Covid 19 pandemic began; many sectors were affected, causing
banks to provide various kinds of relief for debtors, resulting in decreased
and depressed net interest income.
CEO
Tenure cannot Moderate LDR against LOA
This research shows that CEO
Tenure cannot moderate the effect of LDR on ROA. The Loan Deposit Ratio
compares the value of the loans disbursed by the Bank with the funds deposited
by third parties or the public collected by the Bank. The ideal conditions for
the LDR value for each Bank will vary. However, with good policies, lending to
the community should increase the company's performance in terms of
profitability, provided that the credit released to the public is manageable
and smooth in paying obligations to the Bank (principal and interest according
to the agreement).
LDR in this research does not
affect bank profit performance, so in testing the CEO Tenure variable as a
moderator factor, it also has the same result. Namely, it has no effect where
the CEO Tenure cannot moderate the LDR variable. Several previous studies had
effective results, and some had no effect, so this test still follows previous
studies.
CEO Tenure can Moderate BOPO against ROA
This research shows that CEO
Tenure can moderate the effect of BOPO on ROA. Tests with the results of a
significant negative influence in this study illustrate that if BOPO increases,
bank profitability (ROA) will decrease. However, conversely, if BOPO decreases,
ROA will increase. CEO tenure has proven to have a role in strengthening the
relationship.
The Covid 19 pandemic
must be addressed by management, especially the CEO, in the BOPO policy that
will be taken to get through the crisis. A good and efficient BOPO will affect
a bank's ability to generate profits. Relatively high operating expenses compared to banking
performance can reduce bank profits and ROA. Banks are required to make
improvements in cost efficiency in order to achieve sustainable profitability.
Conditions from 2020 to 2021, where the economy is still overshadowed by the
recovery process from the pandemic, have caused many businesses to fail and
close. Banks in this condition are also required to make improvements and work
processes that are more effective so that the pressure on interest income (NIM)
can be balanced with various savings in terms of costs so that banks can still
maintain profits.
CONCLUSION
The results of this research indicate several
things that can be concluded based on this research. NPL has a positive effect,
and its value is not significant to the Return on Assets of conventional
commercial banks. NIM has a positive influence and a significant value on the
ROA of conventional commercial banks. LDR has a negative effect, and its value
is not significant to the Return on Assets of conventional commercial banks.
BOPO has a negative and significant impact on conventional commercial bank ROA.
CEO Tenure is unable to moderate the NPL causality relationship to conventional
commercial bank ROA. CEO Tenure can
moderate the causality relationship between NIM and ROA for conventional
commercial banks. CEO Tenure could not moderate the causal relationship between
LDR and conventional commercial bank ROA. CEO Tenure can moderate the causality
relationship between BOPO and conventional commercial bank ROA.
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