The Impact of Top-Tier Management Diversity on Banks' Bottom Line
Employing Operational Performance
ZAENAL ABIDIN1,*, R. MAHELAN PRABANTARIKSO2 , MUHAMMAD AKBAR1, AMABEL
NABILA3
1Perbanas Institute,
Jakarta, 12940,
INDONESIA
2Sekolah Tinggi Ilmu Ekonomi Indonesia Banking School,
Jakarta, 12730,
INDONESIA
3University of Warsaw,
Warsaw, 00-927,
POLAND
*Correspondent Author
Abstract: - The purpose of this study is to examine the impact of diversity (age, gender, experience, and education)
on the bottom line of banks through operational performance in commercial banks in Indonesia. Profitability, as
measured by Return on Assets (ROA), is used to approximate the bank's bottom line. Meanwhile, the bank's
operational performance is measured by the Operational Efficiency Ratio (OER), Net Interest Margin (NIM), and
Non-Performing Loan (NPL). This study employs a purposive selection technique with an observation period of 53
banks' annual banking reports from 2021-2022 Commercial Banks. The data analysis methods used are descriptive
statistical analysis, moderation regression analysis, and hypothesis testing. The outcome of the bottom line is
significantly impacted by Top-tier Management or TTM (age), which has a considerable impact on operational
performance. The bottom line is also significantly impacted by TTM diversity, which is mediated by operational
performance. The bottom line is significantly impacted by operational performance as well. Gender, experience,
and educational diversity in TTM are not significant.
Key-Words: - Diversity, Top-tier Management, Bottom line bank, Operational performance, Strategic
Management, Indonesia.
Received: April 8, 2023. Revised: February 5, 2024. Accepted: February 25, 2024. Published: March 29, 2024.
1 Introduction
Understanding the elements that drive corporate
success has long been a cornerstone of academic and
practical research in strategic management. This field
of study is critical because it influences the strategies
and decisions that determine the performance of
businesses across a variety of sectors. Recent
advances in this discipline have highlighted the
importance of internal resources and talents, notably
the strategic role of a company's top-tier management
(TTM) in determining organizational outcomes. The
notion of TTM, which includes the Boards of
Commissioners and Directors, has emerged as a
significant component in this debate. According to
studies, the composition and diversity of TTM—in
terms of demographics, educational background,
experience, and other characteristics—have a
significant impact on a firm's strategic direction and
operational success. This research is especially
significant in industries such as banking, where the
complexity and opacity of processes highlight the
need for competent top-tier management. This study
aims to provide novel insights into the optimal
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composition of TTM and its impact on firm
performance by investigating the influence of TTM
characteristics such as age, education, experience,
and gender diversity on key performance indicators
such as non-performing loan (NPL), operational
efficiency ratio (OER), net interest margin (NIM),
and overall profitability.
Many academics, particularly those in the field of
strategic management, have discovered
characteristics that can influence firm performance.
Some analysts feel that external circumstances play a
significant part in determining a company's success.
Other studies, such as those based on resource-based
perspectives, are more focused on internal issues
influencing firm performance. According to the
resources-based theory, company resources are
heterogeneous, not homogeneous, and the productive
services available come from company resources that
give a unique character to each company, so it is a
source of good performance for the company, [1].
In this study, the Board of Commissioners and
Board of Directors are members of a company's Top-
tier Management (TTM) and are key resources in
building performance, [2], [3]. TTM is said to be
reflective of organizational performance even in
upper-echelon theories, [2]. TTM plays a critical
function in the corporation in formulating corporate
strategies that affect company performance, [4]. As a
result, the composition of TTM will have an impact
on firm performance.
TTM diversity describes variations in the
composition of Commissioners and Directors based
on traits. This diversity may depend on demographics
like nationality, gender, age, education, race, and
background or work history, [5], [6].
The greatest team members make up the best
TTM composition, which generates added value from
a combination of diverse individuals. Of course, a
TTM composition made up of a group of people with
comparable backgrounds and abilities will be
profitable. However, there will be more opportunities
if information is gathered in a diversified way since it
has a better possibility of having a good effect that is
far more than the sum of its parts. Several studies
have demonstrated that TTM diversity influences its
members' cognition, behavior, and decision-making,
which in turn influences firm outcomes such as
company success, [7].
Existing research indicates that the impact of
TTM composition on performance provides variable
results. Some studies discovered a positive
interaction, others found a negative relationship, and
still others found no relationship. Based on the level
of efficiency of female leaders in firms and how it
affects profitability, revealed that greater profitability
in family businesses will have a detrimental influence
on non-family businesses, [8].
The influence of diversity on business
accomplishment is more important for operational
effectiveness than financial performance, which is
the bottom line of business achievement), [4]. There
are numerous operational performances in the
banking business, such as Non-Performing Loan
(NPL), Operational Efficiency Ratio (OER), and Net
Interest Margin (NIM). Whereas the bottom line or
profit measure is usually Return on Assets (ROA)
and Return on Equity (ROE) as the final
performance, including banking.
One of the most crucial aspects of TTM is its
makeup. Indeed, some academics investigate the
best properties that a TTM must have. For instance,
whether TTM should be internal or external, or
whether schooling should be at least an
Undergraduate Degree or Graduate Degree. On the
other hand, other academics are more focused on
researching the makeup of the board of directors or
the best combination of directors. The composition
has been the subject of additional research, [4]. The
board of directors’ makeup rather than one of its
members' competencies is, therefore, the subject of
this investigation.
While examining the composition of TTM, one
of the intriguing aspects to investigate is the diversity
of TTM. TTM diversity has been widely explored
and provided varied and confusing results therefore it
is still extremely fascinating to be researched further,
[9].
For a variety of reasons, we have chosen to focus
on the banking business in our study. The opacity and
complexity of bank operations make monitoring bank
activities difficult for external stakeholders. As a
result, bank TTM plays an even more important
monitoring role than the non-financial sector.
Moreover, this study utilizes an innovative
approach, using current data from 2021 to 2022, to
evaluate the influence of TTM diversity on
Indonesian commercial banks' operational and
financial performance, with a focus on the
moderating function of operational measures such as
NPL, OER, NIM, and LDR. It stands out for its
complete analytical model, which takes non-banking
experience into account as part of TTM diversity and
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recognizes the multifaceted implications of diversity,
including possible dangers. This study's novel
viewpoint on the delicate interplay between TTM
diversity and bank performance, particularly during a
moment of global upheaval, is an important
contribution to the area of strategic management.
This study aims to investigate how age,
education level, experience, and gender diversity
affect TTM's operational success in terms of profit
generation. Experience in non-banking enterprises is
a part of the diversity of experience. Meanwhile,
NPL, OER, NIM, and bottom line (ROA) represent
operational performance.
2 Literature Review
2.1 Diversity of Top-tier Management (TTM)
TTM diversity denotes differences in the composition
of Commissioners and Directors based on
characteristics. Several research has used that
property to characterize TTM diversity. TTM
diversity utilizes observable variables such as
country, age, and gender, as well as invisible criteria
such as education and occupational history, [5].
Socioeconomic origin, citizenship, age, sex,
academic achievement, and employment all play a
role in variability, [6].
TTM variety will be a source of creativity and
varied viewpoints, [4]. Diverse TTM members will
have different understandings and points of view
while assessing a problem. Diversity can assist
companies in identifying and capitalizing on
opportunities to improve production, provide
services, attract, retain, motivate, and effectively use
human resources, improve decision-making
processes at all organizational levels, and a variety of
other benefits obtained as organizations with social
and modern awareness, [10].
2.2 Diversity of Gender
Many studies have been undertaken to investigate the
impact of gender diversity on businesses. According
to theoretical study, the upper echelon theory benefits
from gender diversity, [11]. When using theoretical
perspectives on social identity, researchers have
found negative effects of gender diversity, [7]. Due to
these theories, gender diversity may result in
decreased stock values, [12].
2.3 Diversity of Experience
Beyond gender diversity, TTMs' knowledge and
previous experience will have an impact on the firm.
TTM variety will be a source of creativity and varied
viewpoints, [4]. TTMs with a wide range of
experience might bring their previous experience to
their new companies.
TTMs with a wide range of experience might
bring their previous experience to their new
companies. TTM at a Banking company, for
example, who has expertise as a TTM in the
manufacturing industry, will be able to apply the
success of operational management in manufacturing
to banking.
On the other hand, diversity of experience
produces effects that are as diverse as gender
diversity. Diversity carries dangers that might affect
businesses, [4]. If the amount of TTM variety is
high, the possibility for conflict and inadequate
communication quality owing to differences in
viewpoints and opinions stemming from the diversity
of skill and experience possessed may induce
unwillingness to cooperate.
2.4 Diversity of Age
Businesses also gain from it in terms of creativity and
innovation, which improves the organization's
success. Age diversity contributed to the
experimental firms' low performance using data from
128 German enterprises, [13]. The bank's risk-taking
decreased with the size of the age gap between the
chairman and the CEO, [14]. Their findings are
attributed to the cognitive conflict between the CEO
and chairman, which makes the chairman more
autonomous.
2.5 Diversity of Education
Diverse knowledge and abilities on a board will
result in higher-quality decisions than decisions made
by people with identical histories, [15]. As indicated
by earlier studies, highly educated people from all
different types of educational backgrounds had an
increased possibility of innovating, [16]. A board of
directors (BOD) members are expected to contribute
their varied experience during board discussions and
this information can be translated into enhanced
goods, procedures, and offerings that will boost the
business's financial performance, [15]. No
statistically significant link between educational
diversity and a company's financial performance was
found, [17].
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The impact of TTM gender diversity on
operational performance is more appropriate than
directly affecting banks' bottom line or profit level,
[4]. As a result, the research intended to determine
the impact of TTM diversity on bank operational
performance. The operational performance measures
used are NPL, OER, and NIM.
3 Research Method
Fig. 1: Conceptual Model of Research
Source: Adapted from [5] and [6]
This research is inextricably linked to previous
empirical studies, but this analysis uses the most
recent data from 2021 and 2022, along with a more
thorough model. This study examines the influence
of diversity on the bottom line, ROA, and regulating
operational performance such as NPL with
operational performance such as OER, NPL, NIM,
and LDR. The purpose of this study was to
determine the impact of gender diversity and TTM
experience on the bank's bottom line through
operational performance. The effect of diversity on
the bottom line will be moderated by each
operational performance. Figure 1 summarizes the
operationality of the research variables.
3.1 Operational Research Variables
In this study, associative research will be used. The
attempts to determine the influence or relationship
between two or more components, [18]. This
research will be able to generate hypotheses that will
aid in the explanation, prediction, and management
of symptoms. Associative research distinguishes
three types of relationships: symmetric, causal, and
interactive/reciprocal. NPL, OER, and NIM are used
as factors mediating the link between the two to
examine the influence of gender diversity on bank
performance in this study. Table 1 summarizes the
operationality of the research variables.
TTM Diversity
a. Age
b. Level of Education
c. Gender
d. Experience
Bottom Line
Operational
Performance
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Table 1. Research Variables
Construct Indicator Operational
TTM
Diversity
Age
Gender
Education
Experience
banking experiences
Operational
Performance
OER
NIM
NPL
Bottom line
ROA
Source: Adapted from [22] and [ 23]
3.2 Population and Research Sample
The study population consists of 106 commercial
banks in Indonesia, 92 of which are conventional
banks and 14 of which are Islamic banks. There are 8
foreign bank branch offices within a conventional
bank. During 2021 through 2022 the research sample
available data is consistently 53 conventional
commercial banks, omitting international bank
branches. Table 2 (Appendix) contains a list of banks
included in this study.
3.3 Research Data Collection Techniques
This study focuses on commercial banks. The
analysis unit must have published an annual report
for the 2021 to 2022 review period, as well as
provided information on the personal information of
directors and commissioners (TTM) for the 2021-
2022 review period. Plenty of instances of bank data
can be regarded as secondary data because they were
obtained from regulatory websites and bank
documents in the form of Bank Annual Reports. This
information is regarded as reliable and accurate. The
two-year time range is from 2021 to 2022.
3.4 Research Data Processing Methods
The analytical method employs SEM-PLS with the
assistance of the SmartPLS 3.0 software. There are
two analyses in SEM-PLS: the inner model and the
outer model. The outer model establishes the
relationship between the latent variables and the
observed indicators (validity and reliability), whereas
the inner model establishes the relationship between
the independent and dependent latent variables.
The following is utilized in the PLS-SEM outer
model to infer correlations between variables and
indicators in studies. Two tests, namely a validity
test, and a reliability test, are run on this outer model.
To guarantee that the data obtained is legitimate,
exact, and correct, the determining signs associated
with the study's variables will first be tested. The
validity and reliability of the indicators utilized in
this study will then be tested based on the research
that has been done, [19].
The result will be utilized to establish the
correlation between the independent, dependent, and
moderating variables in the inner PLS-SEM model.
By employing bootstrapping, each variable's impact
on its value may be examined. By substituting the
initial sample, the sample size can be raised to 5000
samples, giving the bootstrap more room for error or
standard deviation. This affects how the structural
path test's predicted t-value is calculated. The t-test is
then used to assess how each variable differs from
the others in terms of its relationship. To make the
generated data more stable and simpler to analyze in
the future, these results will be close to the normal
value, [20].
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4 Results and Discussion
4.1 Descriptive Statistics
Table 2. Results of Descriptive Statistics
Minimum
Maximum
Mean
Std. Deviation
Age
.20
1.00
.9414
.12494
Gender
.40
1.00
.8394
.14704
Education
.00
1.00
.5962
.23911
Experience
.25
1.00
.7790
.19222
NPL
.01
14.09
2.6400
1.91785
NIM
-2.33
10.42
5.3484
1.94023
OER
46.50
237.74
84.8960
27.50232
ROA
-10.36
4.74
1.3765
2.15115
Source: Processing results with Smart PLS
The descriptive data for research indicators are
shown in Table 3. The proportion of TTM over 45
years old ranges from 20% to 100% for each bank,
with an average of 94.14% of TTM over 45 years
old. This predominance implies a preference for
experienced banking leaders. In terms of gender
makeup, the numbers show a majority of male
members in TTM, indicating possible gender
imbalances at the leadership level. Educationally, a
significant proportion of TTM members have at least
a master’s degree, demonstrating the banking sector's
emphasis on higher education. Each bank's male
TTM proportion spans from 40% to 100%, with an
average of 83.94%. TTM with a minimum master's
degree ranges from 0% to 100%, with an average of
59.6%; the remainder are TTM with bachelor's
degrees. The proportion of TTM having banking
experience ranges from 25% to 100%, with an
average of 77.9%, while the remainder has never
worked in banking, with the majority working in
regional development banks and state-owned firms.
TTM members have extensive banking expertise,
demonstrating the industry's preference for seasoned
experts who understand the complexity of the
financial market. It is worth noting that, while many
TTM members have direct banking expertise, there
are also representatives from regional development
banks and state-owned corporations. This diversity of
experience may contribute to a larger range of
viewpoints and knowledge among the bank's
executives.
The sample banks' NPL value varies between
0.01% and 14.09%, with a mean of 2.64%. NIM
values range from -2.33 to 10.42, with a mean of
5.35%. OER results range from 46.5 to 237.74, with
a mean of 84.90%. ROA values range from -10.36%
to 4.74%, with a mean of 1.376%. Financial
performance in 2022 has been improved than in 2021
since the COVID-19 pandemic remains to have an
impact in 2021.
Operational performance measures such as NPL
percentages and NIM exhibit a range indicating
variability in bank asset quality and interest revenue
management. The OER values vary greatly among
the sample banks, possibly reflecting variances in
cost management practices. The ROA, a key
profitability statistic, demonstrates that banks have
transitioned from negative to positive territory,
perhaps signaling recovery and expansion in the post-
pandemic period. The increase in financial
performance in 2022 over the previous year suggests
resilience and adaptability in the face of persistent
pandemic problems.
Overall, the data paints a picture of Indonesian
commercial banks that favor leadership experience
and educational attainment in their TTM
composition, despite indicators of operational
recovery and profitability improvement in an
uncertain economic situation.
4.2 Outer Model Analysis
4.2.1 Validity Test Results
Each indicator is valid for measuring its construct if
the standardized factor loading (SLF) value is more
than 0.5 and the p-value of the t-test is less than 0.05.
Table 4 shows the results of the indicators' validity
tests.
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Table 3. Phase I Validity Test Results
Indicator
SLF
Standard
Deviation
T Statistics
P-Values
Gender <- Diversity.TTM
-0.337
0.262
1.289
0.198
Education <- Diversity.TTM
-0.173
0.272
0.636
0.525
Experience <- Diversity.TTM
0.217
0.324
0.671
0.503
Age <- Diversity.TTM
0.952
0.294
3.235
0.001
OER <- Performance.Operational
0.945
0.024
39.364
0
NIM <- Performance.Operational
-0.459
0.203
2.261
0.024
NPL <- Operational Performance
0.718
0.133
5.401
0.000
ROA <- Bottom.Line
1
0
-
0.000
Source: Processing results with Smart PLS
TTM diversity is quantified using four indicators:
gender, education, experience, and age. According to
the table above, gender, education, and experience
have an SLF of <0.5, whereas age has an SLF of
>0.5. As a result, only valid age measures TTM
diversity, whereas the other three factors, namely
gender, education, and invalid experience, measure
TTM diversity.
Three metrics are used to assess operational
performance: OER, NIM, and NPL. According to the
table above, NIM has an SLF of < 0.5, whereas OER
and NPL have SLFs greater than > 0.5. As a result, it
is possible to conclude that OER and NPL are
legitimate for measuring operational performance,
however, NIM is not.
With an SLF of -0.337 and a p-value of 0.198,
the gender component of TTM diversity does not
have a statistically significant association since the p-
value is greater than the standard threshold of 0.05.
The SLF for education is -0.173 with a p-value of
0.525, implying that educational diversity is not a
significant predictor of TTM diversity in this model.
Experience has an SLF of 0.217 and a p-value of
0.503, indicating that it is not a statistically
significant indicator of TTM variety. The age
indicator has an SLF of 0.952, which is both high and
statistically significant (p-value = 0.001). This
suggests that age is a reliable and substantial
indicator of TTM variability.
Age appears to be the only parameter provided
that makes a substantial contribution to measuring
TTM variety. This research found no significant
association between gender, education, or experience
and TTM diversity. This shows that age diversity in
TTM is a more important consideration in this setting
than gender, education, or experience variety.
With an SLF of 0.945 and a p-value of 0.000,
OER is a very significant and powerful predictor of
operational performance. NIM has an SLF of -0.459,
which is below the 0.5 criterion, but it is still
statistically significant (p-value = 0.024). This
negative result may indicate an unfavorable
association with operational performance, but its
statistical significance implies it should not be
discounted without more research. The NPL indicator
has an SLF of 0.718 and a p-value of 0.000, making
it a reliable indicator of operational success. While
NIM does not satisfy the normal SLF criteria, its
statistical significance shows that it might still be a
useful indication of operational effectiveness, albeit
with a different connection than what is often
assumed.
The bottom-line indicator, ROA, has a default
SLF of 1, indicating that it is a direct assessment of
the banks' bottom-line performance.
Since the bottom line is only measured by one
indicator, ROA, the SLF is worth one. Following
that, invalid indications are eliminated from the
model, and validity tests are run again. The validity
test results after issuing invalid indicators are shown
in Table 5.
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Table 4. Phase II Validity Test Results
Indicator
SLF
Standard Deviation
T Statistics
P-Values
OER <- Performance.Operational
0.963
0.013
72.493
0.000
NPL <- Operational Performance
0.706
0.152
4.661
0.000
ROA <- Bottom.Line
1
0
-
0.000
Age <- Diversity.TTM
1
0
-
0.000
Source: Processing results with Smart PLS
Table 5. Reliability Test Results
Construct
Cronbach's Alpha
Composite Reliability
Average Variance Extracted (AVE)
Bottom.Line
1
1
1
Diversity.TTM
1
1
1
Performance.Operational
0.656
0.829
0.713
Source: Processing results with Smart PLS
Table 6. Inner Model t-Test Results
Coefficient
Standard Deviation
T Statistics
P-Values
Diversity.TTM -> Bottom.Line
0.26
0.122
2.133
0.033
Diversity.TTM -> Performance.Operational
-0.394
0.106
3.729
0.000
Performance.Operational -> Bottom.Line
-0.76
0.092
8.259
0.000
Source: Processing results with Smart PLS
According to Table 5, all indicators are
appropriate for measuring the construct. The
reliability test results are shown in the next section.
The Standardized Loading Factor (SLF) for OER
is 0.963, demonstrating a strong and positive
correlation with the Operational Performance
construct. This significant loading shows that OER is
an effective metric of bank operational success. The
T-statistic of 72.493, together with a p-value of
0.000, gives strong statistical proof that the link is
meaningful and not a random chance. The SLF for
NPL is 0.706, which is greater than the threshold of
0.5 and indicates a significant positive link with the
operational performance construct but is as strong as
OER. The T-statistic is 4.661, and the p-value is
0.000, demonstrating that NPL is a statistically
significant predictor of operational performance. The
SLF for ROA is one, indicating a perfect association
with bottom-line performance as a construct. This
may reflect a definitional link in which ROA is seen
as a direct measure of the bottom line in the context
of this study. Since the standard deviation and T-
statistics are not supplied, the p-value of 0.000
demonstrates that ROA is a reliable indicator of
bottom-line success. Similarly, the SLF for a measure
of TTM diversity is 1, showing a perfect connection.
This might imply that age diversity is the primary or
single component of TTM diversity in this study. The
absence of standard deviation and T-statistics, along
with a p-value of 0.000, indicate that the association
between age and TTM variety is decisive and
statistically significant.
4.2.2 Reliability Test Results
All indicators are reliable for measuring their
construct because the AVE value is more than 0.5
and the values for the composite reliability (CR) and
Cronbach alpha are both higher than 0.7. Table 6
displays the reliability test results.
According to Table 6, the bottom line and variety
of TTM are only measured by 1 indicator, resulting
in Cronbach alpha, CR, and AVE values of 1. While
the Cronbach alpha for operational performance is
less than 0.7, the CR and AVE values are high,
implying that the indicators that assess operational
performance are reliable.
TTM structures have Cronbach's alpha and
composite reliability (CR) values of 1. This suggests
complete internal consistency, which is quite
remarkable and may imply that each construct is
measured using a single indication. While this may
be appropriate in some cases (for example, when a
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construct is immediately observable and does not
require several items to capture its variation), it
seldom provides a thorough assessment of
dependability. The average variance extracted (AVE)
for these constructs is also one, indicating that a
single indicator captures all the variance for each
construct. This perfect AVE indicates no
measurement error, which is uncommon in practice
and may require additional validation or research to
verify that the structures are properly represented.
The Cronbach's alpha for operational
performance is 0.656, which is somewhat below the
usually recognized criterion of 0.7 for determining if
a group of indicators has adequate internal
consistency. However, this may not always imply a
problem because Cronbach's alpha is sensitive to the
number of items in a scale, and a slightly lower value
may be acceptable in scales with fewer items.
The CR for operational performance is 0.829,
which is higher than the permissible level of 0.7. This
shows that the composite indicators used to assess
operational performance are dependable, and the
construct has a high level of internal consistency.
The AVE for operational performance is 0.713,
which is above the minimal requirement of 0.5,
showing that the concept itself, rather than error,
accounts for most of the variation covered by the
indicators, demonstrating convergent validity.
4.3 Inner Model Analysis
The inner model is used to visualize the relationship
between research hypotheses. The results of the t-test
on the inner model are shown in Table 7.
According to Table 7, TTM diversity (age) has a
substantial impact on the bank's operational
performance, with a regression coefficient of -0.394
indicating that the bigger the TTM diversity value,
the lower the operational performance. TTM
diversity is determined by age indicators, therefore
the higher the share of top management aged >=45,
OER and NPL measurements of the bank's
operational performance indicate a decline.
TTM diversity also has a substantial impact on
the bottom line, with a positive regression coefficient
(0.26) indicating that the greater the value of TTM
diversity, the higher the bottom line. In other words,
the higher the share of top management aged >=45
years old, the higher the bottom line as assessed by
ROA.
The bottom line is significantly impacted by
operational performance, as indicated by the
regression coefficient of -0.760, which suggests that
the greater the value of operational excellence, the
lower the bottom line. To put it another way, the
higher the OER and NPL values, the lower the ROA
value, and vice versa, the lower the OER and NPL
values, the higher the ROA value. The path diagram
below depicts the entire relationship between
constructs and measurement indicators.
Figure 2 shows the size of the coefficient of
determination for each endogenous variable.
Fig. 2: Path Diagram
Source: Processing results with Smart PLS
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DOI: 10.37394/23207.2024.21.71
Zaenal Abidin, R. Mahelan Prabantarikso,
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Table 7. Coefficient of Determination
R
Square
R Square
Adjusted
Bottom.Line
0.8
0.796
Performance.Operatio
nal
0.156
0.147
Source: Processing results with Smart PLS
The model demonstrates that age diversity within
TTM has a direct, negative connection with
operational performance (path coefficient = -0.394).
This suggests that increasing age diversity among
TTM members is related to lower operational
effectiveness. However, the model also demonstrates
a positive direct association between age diversity
and the bottom line (ROA), with a path value of
0.260.
Operational performance is highly impacted by
NPL, with path coefficients of 0.963 and 0.706,
respectively, indicating strong construct linkages.
According to Table 8, changes in TTM (age)
diversity and operational performance explain 79.6%
of the bottom line, while the rest is explained by
other variables not included in the model. While
TTM diversity could only match 14.7 percent of
operational success, numerous other characteristics
could explain operational performance but are not
included in the model.
The bottom line's R-squared value is 0.8,
indicating that the model's variables (TTM age
diversity and operational performance) account for
80% of its variability.
The adjusted R-squared score of 0.796 shows a
very high degree of explanation after controlling for
the number of factors in the model. The R-squared
value for operational performance is 0.156, with an
adjusted R-squared value of 0.147, indicating that the
model accounts for 15.6% of the variance. This
implies that additional factors not included in the
model might account for the remaining 84.4% of the
variation in operational performance.
4.4 Analysis of Research Results
TTM (age) diversity has a significant impact on
operational performance and on the bottom line,
according to Table 8, while operational performance
mediated TTM diversity has a significant impact.
In other words, financial performance will
increase as the value of OER and NPL declines and
the bottom line, as defined by ROA, grows. The
higher the share of top management over the age of
45, the lower the bank's operational performance as
assessed by OER and NPL. The results of this study
are in line with those who found that the bank's risk-
taking decreased with the age difference between the
chairman and chief executive officer (CEO),
resulting in a lower risk of non-performing loans
(NPL) and lower operating expenses, [14].
Furthermore, a variety of ages was cited as a
contributing factor to the unsuccessful performance
of the experimental enterprises, [13].
Age diversity can have both beneficial and bad
consequences on the finance business. When making
decisions, age diversity brings together individuals
with diverse viewpoints, experiences, and problem-
solving approaches. However, age variety can often
cause communication issues and generation divides,
especially when it comes to technology uptake.
TTM with a diverse gender, education, and
experience, on the other hand, has no impact on
operational performance or the bottom line. The
findings of this study contradict prior research that
demonstrated the positive impacts of gender
diversity, [11]. Other researchers have discovered
that gender diversity has a negative impact, [7], [12].
As a result, in addition to the insignificance of gender
diversity, TTMs' diversity of skill and previous
experience does not affect the organization. This
contrasts prior research findings that suggested that
diversity of experience can bring old expertise to be
implemented in his new organization, [4].
Furthermore, the findings of this study differ from
previous studies, [15], [16], [21]. These studies
research findings found significance but are
consistent with one of the previous researchers, who
concluded that there was no connection between
educational diversity and financial performance of an
enterprise, [17].
This is possible since not all types of variety are
guaranteed to have a good influence. TTM has a
diverse gender, education, and experience, but its
good impact is dependent on how well members of
this management can interact and coordinate their
work. The impact of diversity on operational
performance and results may take some time to
manifest, particularly in the short term.
Additionally, the findings of the t-test in Table 8
show that operational performance has a significant
effect on the bottom line. Conversely, a lower ROA
value corresponds to greater OER and NPL values,
and a higher ROA value to a lower OER and NPL
value. This study's findings are congruent with
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Zaenal Abidin, R. Mahelan Prabantarikso,
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previous studies that discovered that gender diversity
has a negatively significant effect on NPL and OER.
Those two financial ratio variables had a significant
negative influence on ROA. Furthermore, it stated
that neither NPL nor NIM had a substantial effect on
ROA. Nonetheless, the OER ratio has a considerable
impact on ROA, [22], [23].
4.5 Direct Effect, Indirect Effect and Total
Effect
The following, Table 9, shows the extent of the direct
and indirect impact of TTM diversification and
operational success on the bottom line.
Based on Table 9, TTM diversity has a total
impact on the bottom line of 0.559, whereas
operational performance has a total impact of -0.76.
Thus, operational performance is the most important
factor influencing the bottom line. This is reasonable
given that past study findings show a wide range of
opinions on the impact of TTM on ROA (bottom
line) financial performance. As early as operational
performance, such as NPL, OER is indeed very
closely tied to Profit (ROA), since as the value of
NPL and OER falls, so does the ROA. TTM diversity
has a direct beneficial impact on the bottom line, with
a value of 0.260. This indicates that all things being
equal, increased TTM variety directly adds to a better
bottom line.
Furthermore, the TTM variety has an indirect
impact of 0.300 on the bottom line. This indirect
impact may be mediated by additional factors not
explicitly listed in the table but included in the
model.
The overall effect of TTM variety on the bottom
line is 0.559, which includes both direct and indirect
effects. This substantial overall effect implies that
TTM variety is an important element impacting the
bottom line, not just directly but also indirectly.
Operational performance hurts the bottom line,
with a value of -0.394. This shows that reductions in
operational performance measurements, such as an
increase in OER or NPL, are directly linked to a
lower bottom line.
There is no indirect effect stated for operational
performance, suggesting that its impact on the bottom
line is direct and not caused by other factors in the
model.
The overall effect of operational performance on the
bottom line is -0.760, which is both significant and
negative. This suggests that operational success has a
greater impact on the bottom line than TTM
diversity.
The negative coefficient for operational
performance indicates a possible inverse link with the
bottom line, implying that when operational
performance indicators deteriorate for example.
increased NPL and OER, the bottom line, as
measured by ROA, declines. This is compatible with
financial theory, since greater NPL ratios indicate
more non-earning assets, while higher OERs indicate
less efficient operations, both of which can reduce
profitability.
The findings emphasize the need for successfully
managing operational performance to achieve better
financial results. While TTM diversity benefits the
bottom line, the operational characteristics examined
have a greater and worse overall impact on
profitability. The report concludes that, while TTM
variety benefits the bottom line, banks must
emphasize operational savings to maintain financial
success. These findings are consistent with previous
research, which has found conflicting views on the
influence of TTM on financial success but a
consensus on the relevance of operational
performance measurements.
Table 8. Direct Effect, Indirect Effect and Total
Effect
Direct
Effect
Indirect
Effect
Total
Effect
Diversity.TTM ->
Bottom.Line
0.260
0.300
0.559
Diversity.TTM ->
Performance.Operational
-0.394
0.000
-0.394
Performance.Operational -
> Bottom.Line
-0.760
0.000
-0.760
Source: Processing results with Smart PLS
5 Conclusion
This study sheds light on how top-tier management
(TTM) diversity affects the operational performance
and profitability of Indonesian commercial banks. A
review of data from 53 banks between 2021 and 2022
revealed that age diversity within TTM had a
substantial impact on both operational performance
and the bank's bottom line. Notably, whereas TTM
diversity mediated by operational performance had a
significant effect, gender, experience, and education
differences had no discernible influence. The study
found that operational performance, as measured by
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Zaenal Abidin, R. Mahelan Prabantarikso,
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metrics such as operating expenses, operating
income, net interest margin (NIM), and non-
performing loans (NPL), had a greater impact on
profitability than TTM diversity, with a stronger
negative correlation to the bottom line. The findings
indicate that, while operational performance is
critical in determining a bank's financial success, the
impact of TTM diversity on financial outcomes is
complicated and varies with individual variables such
as age. However, the study suggests that the impacts
of diversity may not be immediately apparent in
short-term operational performance and outcomes.
Future studies should investigate longer periods to
properly understand the long-term effects of TTM
diversification on a bank's profitability. Overall, this
study emphasizes the subtle impact of TTM variety
in influencing Indonesian commercial banks'
financial outcomes as well as the critical necessity of
operational success in generating profitability.
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APPENDIX
Table 9. Bank Sample
No
Bank
1
BRI
2
MANDIRI
3
BNI
4
BTN
5
BCA
6
PANIN
7
ARTHA GRAHA
8
CAPITAL INDONESIA
9
BUMI ARTA
10
MAYAPADA
11
BJB
12
DKI
13
BPD DIY
14
BANK JATENG
15
BANK JATIM
16
BANK JAMBI
17
BANK SUMUT
18
BANK NAGARI
19
BANK SUMSEL BABEL
20
BANK LAMPUNG
21
BANK KALSEL
22
BANK KALBAR
23
BANK KALTIMTARA
24
BANK KALTENG
25
BANK SULSELBAR
26
BSG
27
BPD BALI
28
BANK NTT
29
BANK MALUKU MALUT
30
BANK PAPUA
31
BANK BENGKULU
32
BANK SULTENG
33
BANK SULTRA
34
BANK BANTEN
35
MESTIKA DHARMA
36
SINARMAS
37
GANESHA
38
BANK MEGA
39
BJJ
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No
Bank
40
MNC
41
BANK NEO
42
BANK RAYA INDONESIA
43
DIGITAL BCA
44
BANK NATIONALNOBU
45
BANK INA PERDANA
46
BSS
47
BANK JAGO
48
BMS
49
MAYORA
50
INDEX SELINDO
51
MANDIRI TESPEN
52
BANK VICTORIA
53
BANK ALLO
Source: Purposive Sampling Results Sample (processed)
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en_
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