Burnout and Quality of Work Life on Job Performance: Mediating
Role of Job Satisfaction Among Financial Services Employees
MARIO CHRISSENDY DIAN SAPUTRA, ARYANA SATRYA
Department of Human Resource Management
Universitas Indonesia
Jalan Lingkar, Kota Depok, Jawa Barat, 16424
INDONESIA
Abstract: The ability of company management to sustain employee job performance in the pursuit of
organizational objectives is critical due to the intense competition among financial service companies. This study
investigates the relationship between burnout, work life quality, job satisfaction, and job performance, examining
how job satisfaction mediates the relationship between these factors. The study was conducted to 200 employees
of financial services companies in Indonesia. Data was obtained by distributing questionnaires. The method
employed is quantitative analysis utilizing SEM PLS analysis. The research findings indicated that: (1) Burnout
has a negative and significant impact on employees job performance; (2) Quality of Work Life has a positive and
significant impact on employees job performance; (3) Job satisfaction has a positive and significant impact on
employees job performance; (4) There is a significant indirect impact of Burnout on Job Performance with Job
Satisfaction; (5) There is a significant indirect impact of Quality of Work Life on Job Performance with Job
Satisfaction.
Keywords: Burnout, Quality of Work Life, Job Satisfaction, Job Performance, Financial Services Company
Received: April 2, 2024. Revised: August 24, 2024. Accepted: September 26, 2024. Published: October 18, 2024.
1. Introduction
Global business practices have been transformed
by the industrial 4.0 era in areas dominated by
technological advancements. The swift advancement
of technology has resulted in major changes in many
aspects of human existence, including the economic
area, which has led to the emergence of the digital
economy [1]. Financial institutions must rapidly
adapt in order to ensure their continued operation,
and one effective approach is to use financial services
as its operational base [2]. financial services
represent the successful integration of technology
based on financial services that enable transactions
without limitations of place or time [3].
Maximizing job performance and employee job
satisfaction requires offering good working
conditions, or Quality of Work Life (QWL) [4].
Quality of Work Life (QWL) refers to the condition
of a favorable work environment. These factors are
evident in the salary, welfare programs, flexible work
schedules, positive relationships, and opportunities
for individual growth [5].
However, the growing business competition
causes pressure not only on companies but also on
employees. This pressure can lead to increased stress
and decreased job satisfaction among employees,
ultimately impacting their performance and overall
well-being [6]. Therefore, it is crucial for
organizations to continuously assess and improve the
Quality of Work Life (QWL) initiatives to ensure a
positive work environment that supports employee
motivation and productivity [7].
The increased stress might cause employee
burnout [8]. Burnout is a work-related condition that
can lead to employees experiencing exhaustion [9].
Frequently, the failure to meet job demands can lead
to a decrease in work quality [10]. Long handling of
targets, pressure, and deadlines can lead to
employees experiencing stress, tiredness, and
emotional exhaustion, ultimately having a negative
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impact on the company [11].
The fast-paced nature of the financial services,
combined with high expectations for innovation and
growth, can contribute to increased stress levels
among employees. This trend is evident in the rate of
turnover among newly hired personnel at the
company, specifically around 33% within the first 6
months of employment. According to research
conducted by Fadilasari & Selviana (2023) at a
financial services, the work environment had a 16.5%
influence on burnout, while workload was
responsible for 58.2% of burnout. These findings
highlight the importance of addressing workload
management and creating a supportive work
environment to prevent burnout among employees.
Implementing strategies such as workload
distribution, stress management programs, and
regular check-ins can help mitigate the negative
effects of workplace stress on employee well-being
and company performance [13].
The in-depth discussion on the impact of burnout
and quality of work life on employee performance
has been extensively studied in research. Burnout has
reduced employees' performance in Tehran, Iran
[14]; Burnout has an impact on employee
performance at transportation service companies in
Bandung [15]; Quality of Work Life has a positive
influence on job performance for health service
employees in Iran [16]; Burnout has negatively
contributed to job satisfaction for IT employees [17].
Currently, there is limited discussion on the
research regarding the impact of Burnout and Quality
of Work Life on Job Performance, specifically in the
context of financial services employees in Indonesia.
Additionally, the role of Job Satisfaction as a
moderator in this relationship has not been well
explored. This article will provide a comprehensive
analysis and examination of the subject.
2. Literature Review
2.1 Burnout
Burnout is a state characterized by a simultaneous
feeling of physical and mental exhaustion resulting
from persistent feelings of frustration or stress.
Moreover, burnout is a psychological condition
characterized by three dimensions: emotional
exhaustion, depersonalization, and low personal
achievement and self-esteem while performing daily
tasks (Maslach & Leiter, 2017). Burnout syndrome is
a continuous and progressive human reaction to
excessive stress at work that leads to negative
impacts on the individual's wellness (Montero-
Marín, 2016). Burnout syndrome, from a
psychological perspective, leads to cognitive,
emotional, and behavioral problems, which in turn
show as negative behaviors towards work,
colleagues, and professional roles (Maslach & Leiter,
2016).
2.2 Quality of Work Life
Quality of life refers to the overall satisfaction and
happiness of an individual, considering several
aspects such as the physical health, mental well-
being, social relationships, and economic situation
(Naje & Jameel, 2024). Quality of Work Life is
associated with positive workplace settings, a
positive work environment, and sufficient work
engagement, hence promoting a sense of belonging
among employees in a company (Kalhor et al., 2018).
Moreover, Mawu et al. (2018) propose that the
measurement of Quality of Work Life can be
accomplished with the following indicators: (a)
appropriate and fair compensation, (b) safe and
healthy work environment, (c) opportunities to use
and develop workers' abilities, (d) social interaction
at work, (e) employee rights in the office.
2.3 Job Satisfaction
Job satisfaction refers to the emotional and
psychological condition that employees experience
as a direct outcome of their work, characterized by an
authentic feeling of satisfaction and fulfillment in
performing their job responsibilities (Dhamija et al.,
2019). It can be defined as a positive relationship
between employees and the organization (Bakotić,
2016). Meanwhile, Yang & Hwang (2014) classify
job satisfaction indicators into two categories: 1)
Intrinsic Factors, which pertain to the tasks and work
itself, and include how individuals perceive their
work in terms of challenges, employee capability,
and potential benefits. 2) Extrinsic factors pertain to
work elements that are irrelevant or have minimal
connection to the execution of job duties, a promising
professional progress, company benefits, and a
continuously developing work environment.
2.4 Job Performance
Job performance is an overall anticipated value
for an organization resulting from various behavioral
occurrences performed by each individual during a
specific period of time (Motowidlo & Kell, 2012) .
Job performance refers to the overall effectiveness of
an individual in utilizing and managing various
organizational resources, such as human, financial,
and physical resources, in order to accomplish the
aims and objectives of the company (Akhavan et al.,
2013). These findings align with earlier research
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indicating that job performance encompasses
employee behavior and represents the desired
outcomes of performance as determined by the
business (Bieńkowska & Tworek, 2020; Sonnentag,
2003).
3. Methods
3.1 Research Design
The research design was constructed using a
quantitative research methodology. This study
involves two independent variables, Burnout and
Quality of Work Life, one meditating variable, Job
Satisfaction, and one dependent variable, Job
Performance. The research design model is
illustrated in Figure 1.
Fig 1. Research Design Framework
3.2 Data Analysis
This research included 200 employees of financial
services companies in Indonesia. The data was
obtained using a questionnaire distributed from
January to March 2024. The study used the Partial
Least Square-Structural Equation Modeling (PLS-
SEM) method for data analysis, combining factor
analysis and regression to test relationships between
variables [18], [19]. PLS also measures errors
intrinsic to abstract evaluation concepts, providing a
basis for future research and development [20].
4. Results and Discussion
4.1 Validity and Reliability Test
Table 1. The Results of the Validity and Reliability
Test for Burnout Variables
Di
mens
ions
I
ndic
ator
Cod
e
Validity
Reliabilit
y
K
M
O
C
ompo
nent
Matri
x
D
escri
ption
C
ronb
ach’s
Alph
a
D
escri
ption
P
hysic
al
exhau
stion
B
O1
0
.72
2
0.
884
V
alid
0.
835
R
eliabl
e
B
O2
0.
875
V
alid
B
O3
0.
850
V
alid
E
motio
nal
exhau
stion
B
O4
0
.71
3
0.
865
V
alid
0.
808
R
eliabl
e
B
O5
0.
841
V
alid
B
O6
0.
846
V
alid
M
ental
exhau
stion
B
O7
0
.70
9
0.
831
V
alid
0.
849
R
eliabl
e
B
O8
0.
901
V
alid
B
O9
0.
896
V
alid
L
ow of
perso
nal
acco
mplis
hmen
t
B
O10
0
.73
4
0.
889
V
alid
0.
866
R
eliabl
e
B
O11
0.
874
V
alid
B
O12
0.
903
V
alid
(Source: Data Analysis, 2024)
Table 1 shows the validity results for the Quality
of Work Life variable for all dimensions show a
KMO value of more than 0.5 and a factor loading
value of more than 0.5. Therefore, all items can be
indicated as valid. The reliability test outcomes
indicate that the dimension representing the quality
of work life possesses a Cronbach's alpha value
exceeding 0.6, indicating its reliability. The findings
are presented in the following Table 2.
Table 2. The Results of the Validity and Reliability
Test for Quality of Work Life Variables
Di
mensi
ons
I
ndic
ator
Cod
e
Validity
Reliabilit
y
K
M
O
C
ompo
nent
Matri
x
D
escri
ption
C
ronb
ach’s
Alph
a
D
escri
ption
Re
asona
ble
Q
WL
1
0
.73
4
0.
906
V
alid
0.
870
R
eliabl
e
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Volume 2, 2024
and
fair
comp
ensati
on
Q
WL
2
0.
909
V
alid
Q
WL
3
0.
872
V
alid
A
health
y and
secure
workp
lace
Q
WL
4
0
.74
7
0.
899
V
alid
0.
897
R
eliabl
e
Q
WL
5
0.
911
V
alid
Q
WL
6
0.
925
V
alid
O
pport
unitie
s to
apply
and
devel
op
skills
for
emplo
yees
Q
WL
7
0
.73
6
0.
897
V
alid
0.
864
R
eliabl
e
Q
WL
8
0.
875
V
alid
Q
WL
9
0.
889
V
alid
So
cial
intera
ction
in the
workp
lace
Q
WL
10
0
.72
5
0.
922
V
alid
0.
884
R
eliabl
e
Q
WL
11
0.
916
V
alid
Q
WL
12
0.
865
V
alid
E
mploy
ee
rights
in the
office
Q
WL
13
0
.70
2
0.
925
V
alid
0.
842
R
eliabl
e
Q
WL
14
0.
915
V
alid
Q
WL
15
0.
836
V
alid
(Source: Data Analysis, 2024)
The validity results of the job satisfaction variable
for all dimensions reveal a KMO value of more than
0.5 and a factor loading value of more than 0.5.
Therefore, all items can be considered valid.
According to the reliability test results, the
Cronbach's alpha value of the dimensions of the job
satisfaction variable is reliable, with a value greater
than 0.6. The findings are shown in Table 3.
Table 3. The Results of the Validity and Reliability
Test for Job Satisfaction Variables
Di
mens
ions
I
ndic
ator
Cod
e
Validity
Reliability
K
M
O
C
ompo
nent
Matri
x
D
escri
ption
C
ronb
ach’s
Alph
a
D
escri
ption
Pa
yroll
K
K1
0
.66
6
0.
789
V
alid
0.
852
R
eliabl
e
K
K2
0.
923
V
alid
K
K3
0.
929
V
alid
W
orklo
ad
K
K4
0
.75
5
0.
924
V
alid
0.
904
R
eliabl
e
K
K5
0.
916
V
alid
K
K6
0.
913
V
alid
W
ork
envir
onme
nt
K
K7
0
.71
6
0.
942
V
alid
0.
914
R
eliabl
e
K
K8
0.
953
V
alid
K
K9
0.
880
V
alid
B
enefit
s
K
K10
0
.69
9
0.
842
V
alid
0.
837
R
eliabl
e
K
K11
0.
908
V
alid
K
K12
0.
860
V
alid
S
uperv
ision
K
K13
0
.76
2
0.
959
V
alid
0.
945
R
eliabl
e
K
K14
0.
954
V
alid
K
K15
0.
935
V
alid
(Source: Data Analysis, 2024)
The validity results of the job performance
variable for all dimensions reveal a KMO value of
more than 0.5 and a factor loading value of more than
0.5. Therefore, all items can be considered valid.
According to the reliability test results, the
Cronbach's alpha value of the dimensions of the job
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Volume 2, 2024
performance variable is reliable, with a value greater
than 0.6. The findings are shown in the following
Table 4.
Table 4. The Results of the Validity and Reliability
Test for Job Performance Variables
Di
mens
ions
I
ndic
ator
Cod
e
Validity
Reliability
K
M
O
C
ompo
nent
Matri
x
D
escri
ption
C
ronb
ach’s
Alph
a
D
escri
ption
T
he
objec
tives
have
been
achie
ved
J
P1
0
.73
6
0.
890
V
alid
0.
880
R
eliabl
e
J
P2
0.
926
V
alid
J
P3
0.
899
V
alid
A
bility,
com
mitm
ent,
motiv
ation
are
achie
vable
J
P4
0
.70
6
0.
865
V
alid
0.
909
R
eliabl
e
J
P5
0.
947
V
alid
J
P6
0.
949
V
alid
Di
rectio
n,
dedic
ation,
resili
ence,
and
strate
gy
have
been
imple
ment
ed
J
P7
0
.74
5
0.
891
V
alid
0.
880
R
eliabl
e
J
P8
0.
909
V
alid
J
P9
0.
912
V
alid
(Source: Data Analysis, 2024)
4.2 Outer Structural Model
The tests conducted to assess the Outer Model
using reflective indicators are Convergent Validity,
Discriminant Validity, Composite Reliability,
Average Variance Extracted (AVE), and Cronbach's
Alpha.
4.2.1 Convergent Validity
Indicator items and variable dimensions are
considered valid if the outer loading scores exceed
0.700. The results of the analysis indicate that the
reflective indicators show strong validity and
reliability. These findings support the validity of the
SEM PLS for the Outer Structural Model in this
study.
Figure 2. The analysis results for the outer model of
variable X1
Figure 2 shows that the most dominant aspect of
variable X1 (Burnout) is X1.4 dimension, which is
related to low of personal accomplishment, with the
highest outer loading score of 0.960. The BO2
variable has the highest score of 0.882 in the X1.1
dimension, indicating physical exhaustion. The
variable BO4 has the highest loading of 0.862 in the
X1.2 dimension, which indicates a significant level
of emotional exhaustion. The BO8 variable has a
maximum score of 0.902 in the X1.3 dimension,
indicating a significant level of mental exhaustion.
The lowest score seen in dimension X1.4 is
associated with low of personal accomplishment,
specifically BO12, which has an outer loading of
0.903.
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Figure 3. The analysis results for the outer model of
variable X2
Figure 3 shows that the most dominant dimension
in variable X2 (Quality of work life) is dimension
X2.2, which is related to a safe and healthy work
environment, with the highest outer loading score of
0.949. The QWL2 indicator in the X2.1 dimension
has a maximum score of 0.905, representing
appropriate and fair compensation. The QWL6
dimension has the highest indicator score of 0.928 in
the X2.2 dimension, which represents a safe and
healthy work environment. The highest score in
dimension X2.3 is the opportunity to use and develop
employees' skills, specifically referred to as QWL7,
with the outer loading score of 0.905. The QWL10
dimension has the highest indicator of social
interaction in the workplace, with an outer loading
score of 0.921 in the X2.4 dimension. In the X2.5
dimension related to employee rights in the office,
QWL13 emerges as the highest indicator, with an
outer loading of 0.924.
Figure 4. The analysis results for the outer model of
variable Y1
Figure 4 shows that the most dominant aspect of
the variable Y1 (job satisfaction) is the Y1.2
dimension, which is related to the type of job and has
the highest outer loading score of 0.951. The highest
indicator in the Y1.1 dimension of payroll is KK3
with an outer loading of 0.924. The highest indicator
in the Y1.2 dimension of work type is KK4 with an
outer loading score of 0.923. The highest indicator in
the Y1.3 dimension of the work environment is KK8
with an outer loading score of 0.955. The highest
indicator in the Y1.4 dimension of the award is KK11
with an outer loading score of 0.906. Furthermore,
the highest indicator in the Y1.5 supervision
dimension is KK13 with an outer loading of 0.959.
Figure 5. The analysis results for the outer model of
variable Y2
Figure 5 shows that the most dominant dimension
in the variable Y2 (Job Performance) is Y2.1, which
is the desired target that has been achieved with the
highest outer loading score of 0.922. The highest
indicator in the Y2.1 dimension, the targeted
objectives have been achieved, namely JP2 with an
outer loading of 0.926. The highest indicator in the
Y2.2 dimension of ability, commitment, and
motivation is available, namely JP6, with an outer
loading of 0.937. And the highest indicator in the
Y2.3 dimension of directions, determination,
persistence, and strategy has been implemented,
namely JP9 with an outer loading of 0.914.
4.2.2 Discriminant validity, Cronbach alpha,
composite reliability, and AVE
According to the discriminant validity (cross
loading) table, all items show cross loading figures
that are higher for their respective variables
compared to the loading figures for the other
variables, indicating that they contribute to the
formation of the construct. The factor loading values
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for each variable's items are still higher than those for
the related variables or indicators. The test results are
presented in Table 5.
Table 5. The test results from the outer model for
reflective indicators
Variable /
Dimension
Cron
bach's
Alpha
Com
posite
Reliabili
ty
Ave
rage
Varian
ce
Extrac
ted
(AVE)
X1 (Burnout)
0.956
0.961
0.67
5
X1.1 Physical
exhaustion
0.839
0.903
0.75
6
X1.2
Emotional
exhaustion
0.809
0.887
0.72
4
X1.3 Mental
exhaustion
0.849
0.909
0.76
9
X1.4 Low of
personal
accomplishment
0.867
0.918
0.79
0
X2 (Quality
Work of Life)
0.966
0.969
0.67
8
X2.1
Reasonable and
fair compensation
0.877
0.924
0.80
3
X2.2 A healthy
and secure
workplace
0.899
0.937
0.83
2
X2.3
Opportunities to
apply and develop
skills for
employees
0.864
0.917
0.78
6
X2.4 Social
interaction in the
workplace
0.884
0.928
0.81
2
X2.5 Employee
rights in the office
0.872
0.922
0.79
7
Y1 (Job
Satisfaction)
0.968
0.971
0.69
2
Y1.1 Payroll
0.855
0.913
0.77
9
Y1.2 Workload
0.906
0.941
0.84
2
Y1.3 Work
environment
0.916
0.947
0.85
6
Y1.4 Benefits
0.840
0.903
0.75
7
Y1.5
Supervision
0.945
0.965
0.90
1
Y2 (Job
Performance)
0.939
0.949
0.67
5
Y2.1 The
objectives have
been achieved
0.890
0.931
0.81
9
Y2.2 Ability,
commitment,
motivation are
achievable
0.910
0.943
0.84
7
Y2.3 Direction,
dedication,
resilience, and
strategy have been
implemented
0.888
0.931
0.81
7
The Cronbach's Alpha value for each
variable/indicator exceeds 0.7, indicating that the
variables X1 (Burnout), X2 (Quality of Work Life),
Y1 (Job satisfaction), and Y2 (Job performance) are
considered reliable. Variables with composite
reliability figures that are higher than 0.7 are
classified as having high reliability. The discriminant
validity, as indicated by the Average Variance
Extracted (AVE) measure, demonstrates that each
variable possesses an AVE value over 0.5.
4.3 Inner Structural Model
The PLS structural model was conducted using
the SmartPLS program in this study. The resulting
structural diagram is shown in the following Figure
6.
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Volume 2, 2024
Figure 6. Structural model diagram
According to figure 6, the equation for the
structural model is calculated as follows:
1. Y
1 = –0.485 X1 + 0.342 X2 + ei1; R2 = 0.400;
2. Y
2 = –0.203 X1 + 0.274 X2 + 0.347 Y1 + ei1;
R2 = 0.407.
Note:
X1: Burnout
X2: Quality Work of Life
Y1: Job Satisfaction
Y2: Job performance
ei: residual
The tests conducted to assess the inner model
include the use of the coefficient of determination
measured by R square, the predictive relevance
measured by Q square, and the Goodness of Fit Index
(GoF).
4.3.1 Determination Coefficient (R2)
Table 6. The Results of Determination Coefficient
Impact
R Square
X1, X2 Y1
0.400
X1, X2, Y1 Y2
0.407
The coefficient of determination (R-square)
obtained from model 1 is the impact of variables X1
(Burnout) and X2 (Quality of Work Life) on variable
Y1 (Job satisfaction) of 0.400 or 40.0%. In model 2,
the impact of variables X1 (Burnout), X2 (Quality of
Work Life), and Y1 (Job satisfaction) on variable Y2
(Job performance) is 0.407 or 40.7%.
4.3.2 Effect size (F²)
Table 7. The results of effect size
Exogenous
Model 1 (Y1)
Model 2
(Y2)
F
square
Eff
ect
F
square
E
ffect
X1
(Burnout)
0.3
83
Hig
h
0.0
49
L
ow
X2 (Quality
Work of Life)
0.1
91
Mo
derate
0.1
04
L
ow
Y1 (Job
Satisfaction)
0.1
22
L
ow
The F square value indicates the effect size or the
diversity in exogenous and endogenous variables.
The F square coefficient category is the low category
for F square between 0.02 to 0.15, the moderate
category for F square between 0.15 to 0.35, and the
high category for F square more than 0.35.
4.3.3 Predictive Relevance (Q²)
The total diversity of data that can be explained
by the model is measured by a formula:
The results of the Q square calculation indicate
that the diversity of data that can be explained by the
model is 0.644, or 64.4% of the information
contained in the data can be explained by the model.
This model is included in the good category (> 0).
4.3.4 Goodness of Fit Index (GoF)
Goodness of Fit testing of the model is carried out
to see the overall accuracy of the model by
multiplying the average coefficient of determination
value by the average communality (AVE) value.
The GoF calculation result is 0.525, so it can be
concluded that the accuracy of the model is in the
high category (>0.36).
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4.4 Hypothesis testing
The testing was conducted to provide an
assessment of the coefficients or parameters that
indicate the effect of one latent variable on other
latent variables. An impact is considered significant
if the p-value is less than 0.05, and it is considered
not significant if the p-value is more than 0.05. The
calculation results are presented in the following
Table 8.
4.4.1 Direct Effect Hypothesis
Table 8. The results of direct effect using T-statistics
Ef
fect
Path
coefficient
T
statistics
p-
value
s
Descr
iption
X
1
Y1
-0.485
7.302
0.
000
Signif
icant
X
2
Y1
0.342
3.734
0.
000
Signif
icant
X
1
Y2
-0.203
2.226
0.
026
Signif
icant
X
2
Y2
0.274
3.225
0.
001
Signif
icant
Y
1
Y2
0.347
3.380
0.
001
Signif
icant
Variable X1 (Burnout) has a negative and
significant effect on variable Y1 (job satisfaction),
with T-statistics values higher than the critical value
(7.302 > 1.96) and p-values smaller than α (0,000 <
0.050). A negative coefficient indicates that
increased burnout can significantly lower the Y1
variable. A study conducted by [21] has provided
evidence that there is a significant negative
relationship between burnout and job performance in
many professions and industries. This suggests that
organizations should give priority to addressing
burnout in order to enhance job satisfaction and job
performance. It is crucial for companies to implement
strategies to avoid or control burnout among
employees to maintain a positive work environment
[22].
The variable X2 (Quality of Work Life) has a
positive and significant effect on the variable Y1,
with T-statistics values greater than the critical value
(3.734 > 1.96) and p-values smaller than α (0,000 <
0.050). A positive coefficient suggests that improved
quality of work life can significantly improve job
satisfaction. It is supported by research conducted by
Perangin-Angin et al., (2020), which stated that there
is a significant correlation between quality of work
life, job performance, and job satisfaction among
factory employees in Medan, Indonesia. The study
concluded that investing in initiatives to enhance the
quality of work life can lead to higher levels of job
satisfaction among employees. Organizations that
place a high priority on enhancing the quality of work
life may experience positive impacts on employee
engagement, loyalty, and overall performance [24].
Variable X1 (Burnout) has a negative and
significant effect on variable Y2 (Job performance),
with T-statistics values greater than the critical value
(2.226 > 1.96) and p-values smaller than α (0.026 <
0.050). A negative coefficient indicates that
increased Burnout can significantly lower job
performance. This study indicated that job burnout
has a significant effect on reducing employees'
performance. It is crucial for organizations to address
burnout in order to maintain high levels of job
performance among employees [25]. Implementing
strategies to prevent and manage burnout can lead to
improved overall productivity and employee
satisfaction within the company [26]. Addressing
burnout not only improves job performance but also
enhances employee satisfaction, ultimately leading to
increased productivity within the organization [27].
By recognizing and mitigating the negative effects of
burnout, companies can create a more positive and
productive work environment for their employees
[27].
The variable X2 (Quality of Work Life) has a
positive and significant effect on the variable Y2 (job
performance), with T-statistics values greater than
the critical value (3.225 > 1.96) and p-values smaller
than α (0.001 < 0.050). A positive coefficient
suggests that improved quality of work life can
significantly improve job performance. The result is
supported by research conducted by Sari et al.,
(2019), which implies that Quality of Work Life has
a positive and significant influence on employee
performance in the tourism industry. The quality of
work life encompasses factors such as job
satisfaction, motivation, productivity, health, job
security, safety, and welfare at work [29]. Prior study
has discovered that the quality of work life has a
positive impact on productivity, and enhancing
productivity would also improve the quality of work
life [7]. Moreover, organizations should prioritize
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enhancing the quality of work life for their employees
in order to boost job performance [30].
Variable Y1 has a positive and significant effect
on variable Y2 (Job performance), with T-statistics
values greater than the critical value (3.380 > 1.96)
and p-values smaller than α (0.001 < 0.050). A
positive coefficient indicates that increased job
satisfaction can significantly improve job
performance. The work environment places
significant emphasis on the importance of employee
job satisfaction and performance [31]. Job
satisfaction is a key indicator of the general success
of an organization and has a significant influence on
various aspects of the company [32]. Studies have
demonstrated that employees who experience job
satisfaction are more likely to be actively involved
and driven, resulting in increased levels of
productivity and performance [33]. Therefore,
organizations should consider implementing
strategies to enhance job satisfaction in order to
ultimately improve overall job performance.
4.4.2 Indirect Effect Hypothesis
Table 9: The results of indirect effect
Effe
ct
Path
coefficient
T
statistics
p-
valu
es
Descripti
on
X1
Y1
Y2
-0.168
2.855
0.00
4
Significa
nt
X2
Y1
Y2
0.119
2.680
0.00
8
Significa
nt
The indirect effect of the X1 variable (Burnout)
on the Y2 (Job performance) variable through the Y1
(work fulfillment) is significant, with T-statistics
values higher than the critical value (2.855 > 1.96)
and p-values smaller than α (0,000 < 0.050). The
variable of job satisfaction mediates the impact of
Burnout on Job performance. Job satisfaction is
considered a partial mediation because the direct
influence of X1 on Y2 is significant. It is crucial for
companies to identify factors that contribute to a high
level of job satisfaction [30]. When employees have
a high level of job satisfaction, their work
performance will also be enhanced. Employees who
experience job satisfaction and a positive workplace
are more likely to exert more effort in their work,
leading to increased productivity and improved work
outcomes [34]. Moreover, it creates opportunities for
the company to achieve success.
The indirect effect of the X2 variable (Quality of
Work Life) on the Y2 (Job performance) variable
through the Y1 variable is significant, with T-
statistics values higher than the critical value (2.680
> 1.96) and p-values smaller than α (0.008 < 0.050).
The job satisfaction variable mediates the impact of
Quality of Work Life on Job performance. Job
satisfaction is considered a partial mediation because
the direct influence of X2 on Y2 is significant.
Improving the quality of work life can lead to higher
job performance through increased job satisfaction
[7]. By focusing on improving these aspects,
companies can ultimately achieve greater success in
terms of productivity and work outcomes [32].
5. Conclusion and Implication
Based on the prior overview of the data and
discussion, the following conclusions can be drawn:
(1) Burnout has a negative and significant impact on
the variable of job performance. A negative
coefficient signifies that an increase in Burnout might
have a significant negative impact on Job
performance. (2) The Quality of Work Life has a
positive and significant impact on job performance.
A positive coefficient suggests that enhancing the
Quality of Work Life might have a significant
positive impact on Job performance. (3) Job
satisfaction has a positive and significant impact on
job performance. A positive coefficient signifies that
enhancing job satisfaction can have a significant
impact on job performance. (4) The impact of
burnout on job performance through job satisfaction
is significant. The variable of job satisfaction serves
as a mediator for the impact of burnout on job
performance, demonstrating partial mediation due to
the direct effect on job performance.
The implications of this research can serve as a
reference for companies aiming to manage employee
satisfaction and enhance organizational performance.
Initially, it is important for companies to be attentive
and identify indications of burnout among their
employees. Then, companies should implement
strategies to mitigate or reduce the effects, such as
stress management programs or psychological
assistance. Furthermore, allocating resources
towards enhancing the quality of work life, such as
offering flexible hours or sufficient resources, can
provide advantages in increasing employee
engagement and performance. Moreover,
implementing strategies to enhance job satisfaction,
such as acknowledging accomplishments, fostering
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professional growth, and enhancing interactions
between supervisors and staff, can effectively
reinforce the correlation between job satisfaction and
job performance. Therefore, comprehending the
correlation among burnout, quality of work life, job
satisfaction, and work performance can assist
companies in formulating more efficient strategies to
accomplish the objectives.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Mario Chrissendy Dian Saputra carried out
conceptualization, writing-original draft, and
investigation.
Aryana Satrya was responsible for the supervision
and wrting-review & editing.
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
that are relevant to the content of this article.
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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