Vol. 5, No. 10, October 2024
E-ISSN:2723 – 6692
P-ISSN:2723– 6595
http://jiss.publikasiindonesia.id/
Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2655
The Effect of Job Demands and Job Resources on Turnover
Intention Mediated by Work Engagement at PT Prinal
Michael Heryanto
,
Hendratmoko
Kwik Kian Gie School of Business and Information Technology, Indonesia
Email: michael.heryanto.mh@gmail.com, hendratmoko@kwikkiangie.ac.id
Correspondence: michael.heryanto.mh@gmail.com
*
KEYWORDS
ABSTRACT
Job Demands; Job Resources,
Work Engagement; Turnover
Intention
This research aims to identify the influence of job demands and job
resources on turnover intention, with work engagement as a
mediating variable at PT Prinal. The retail industry faces high
turnover rates, posing a significant challenge at PT Prinal despite the
industry's enormous potential. The study involved 250 employees
of PT Prinal, using a non-probability sampling technique. Analysis
was conducted with SmartPLS v4.1.0.5, utilizing Partial Least Square
(PLS) and Structural Equation Model (SEM) techniques to test the
hypotheses. The findings show that greater job expectations
dramatically reduce employee engagement and increase the
likelihood of leaving the position. The job demands at PT Prinal are
generally counterproductive, as demonstrated by how they diminish
employee engagement. On the other hand, more excellent job
resources considerably raise employee engagement at work and
lower the intention to leave. Since job demands and job resources
directly affect turnover intention, work engagement plays a role in
mediating the interaction between these three variables. These
results highlight how crucial it is to manage job expectations and
resources to boost work engagement and increase employee
retention.
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Introduction
The retail industry is one of the key sectors contributing significantly to Indonesia’s economic
growth, particularly within the trade sector, which accounts for up to 12.85% of the GDP. Retail is
vital to economic development, with businesses spread across Indonesia and a high labor absorption
rate. Given the news release (Kementrian Koordinator Bidang Perekonomian Republik Indonesia,
2024), Amid the uncertainty, Airlangga hopes that the retail sector can sustain the country's economy.
More than 270 million people living in Indonesia, increasing urbanization, internet penetration of up
to 70%, and the expansion of the middle class, which drives demand for the retail industry, are some
of the elements that contribute to the growth of the retail sector.
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Figure 1.
Install Indonesia's Gross Domestic Product (GDP) in 2022
Source: Central Bureau of Statistics - Statista 2023 (reprocessed)
The retail industry's success in Indonesia as a motor of economic growth faces various
challenges. According to data from (Fuller et al., 2022), the biggest challenge of the retail industry is
the higher turnover of frontline retail employees compared to other sectors. Frontline in the U.S.,
according to McKinsey, leaving their jobs have the following reasons: (1) workplace flexibility (34%),
(2) career development (32%), (3) health and well-being (29%), (4) compensation (29%), and (5)
meaningful work (27%). Another negative culture in the retail industry is thought to contribute to the
high turnover rate in the retail sector (Berisha & Lajçi, 2021)
Figure 2.
Turnover in Various Industries (2021-2022)
Source: (Fuller et al., 2022b)
18,34
12,85
12,40
12,22
9,77
5,02
4,15
4,13
3,09
2,89
10,76
0,00
2,00
4,00
6,00
8,00
10,00
12,00
14,00
16,00
18,00
20,00
Indonesia's share of gross domestic product (GDP) in 2022 (%)
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A certain amount of turnover is expected and necessary to meet the organization's dynamic
renewal needs. If turnover is high, it must be closely monitored. Since many people are dissatisfied
with their jobs, industry players must prevent high turnover. If job dissatisfaction is the cause of
employee departure, then the reason needs to be determined and addressed immediately. High
turnover will be inversely proportional to the health and stability of an industry in achieving every
organization's goal. Negative attributes trigger employees to experience stress and dissatisfaction,
which will encourage turnover intentions to occur. (Pandey et al., 2019)
A deliberate and purposeful decision to leave an organization is known as turnover intention.
In addition to other aspects, the key factors that significantly influence turnover intention are work-
life balance, job satisfaction, and work engagement. (Laksono & Wardoyo, 2019). High turnover is
undesirable and a significant problem for companies. This is because employees are a company
investment that significantly impacts the company's effectiveness and efficiency. Companies need to
carry out strategies for employee retention. Employee retention is an important matter that has a
long-term impact on the health and success of an organization. (Manjula, 2023).
Turnover incurs significant direct (recruitment, selection, and time) and indirect (product
quality, achievement of company goals, organizational stability, profitability, operational
inconsistency) costs that can lead to decreased employee motivation. (Ongory, 2007). Turnover
refers to the loss of some employees by a business, which necessitates recruiting new personnel.
Employers bear the costs associated with hiring and preparing new employees for duty. Employee
morale and motivation will suffer if left unchecked. Workers who have yet to try to get a new job in
the past will start looking for vacancies and eventually quit.
Challenges for the retail industry with significant economic growth potential, but endemic
problems must be resolved. The inherent and difficult-to-change culture of the retail sector does not
make it an obstacle to improving employee retention. The company in the research subject from PT
Prinal has a high turnover percentage compared to similar industries every month. Various reasons
cause turnover at PT Prinal, and 69% of the turnover is influenced by factors that can be controlled
internally by the PT Prinal team. Other factors that influence turnover intentions are (1) job
insecurity, (2) organizational commitment, (3) work stress and environment, and (4) realistic job
information. (Al-Suraihi et al., 2021). Increasing benefits through incentives is one of the strategies
for employee retention. This treatment is challenging in the retail industry because the margin of
sales revenue is in the low group compared to other sectors. This encourages the need for different
strategies that are more effective in reducing turnover.
Table 1.
List of Reasons for Employee Resignation in May 2023 - April 2024 at PT Prinal
No.
Reason for Resignation
Qty
%
Total
1
G1 - without explanation / absent from work for
five consecutive days / sudden resignation
734
12.08%
84.74%
2
H.D. - Dissatisfied with Job Type / Getting a New
Job
921
15.16%
3
G.I. - Violating Company Regulations / SO
Problems / Cannot Follow SOPs
3430
56.46%
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No.
Reason for Resignation
Qty
%
Total
4
H.I. - Away from Work
33
0.54%
5
G3 - Unwilling to be Mutated
30
0.49%
6
H4 - Family Problems / Returning Home /
Pregnant / Getting a New Job
353
5.81%
15.26%
7
H5 - Continuing Education
244
4.02%
8
H6 - Health Problem / Family Sickness
113
1.86%
9
H9 - Own Business / Self-Employment
78
1.28%
10
G.J. - Deceased
2
0.03%
11
G.B. - Expiry of Contract Agreement / Non-
renewal / Contract Exhaustion
112
1.84%
12
H.C. - Expiry of contract agreement with good
judgment/contract extension
25
0.41%
6075
100%
Source: Internal data of PT Prinal (reprocessed)
Figure 3.
Turnover Period May 23 - April 24 at PT Prinal
Source: Internal data of PT Prinal (reprocessed)
PT Prinal has been in retail for over ten years since 2011. A long enough time to learn how an
organization can stand and be strong in facing all situations and conditions. Turnover-related
problems have yet to be resolved and have become a significant problem until now. High job demands
at PT Prinal, indicated by long working hours, working on holidays, multitasking, and pressure to
achieve targets, are thought to contribute to creating a high % turnover. This is different from the
availability of adequate job resources, such as opportunities for career advancement and practical
support from superiors. Improper job demands and resource management will lead to high turnover,
9%
12%
9%
8% 8% 8%
9% 9%
6%
3%
8%
0%
2%
4%
6%
8%
10%
12%
14%
Mei 23 Jun 23 Jul 23 Ags 23 Sep 23 Okt 23 Des 23 Jan 24 Feb 24 Mar 24 Apr 24
% Number of Resign to Total Manpower (Turnover) at PT Prinal
Period May 23 - April 24
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causing vacant positions in each section. The company's significant direct and indirect expenditures
and instability make achieving organizational goals challenging.
Previous studies by Fuller et al. (2022) identified that turnover among frontline retail
employees, particularly in the United States, is driven by workplace flexibility, career development,
well-being, compensation, and perceived job meaning. Additionally, Berisha and Lajçi (2021) noted
that a negative workplace culture within the retail industry can contribute to high turnover rates.
Pandey et al. (2019) further explained that high job demands lead to stress and dissatisfaction, fueling
turnover intentions.
This study focuses on the influence of job demands and job resources on turnover intention,
with work engagement as a mediating variable, offering a distinct perspective from prior research.
Unlike previous studies that predominantly examined external factors such as flexibility and
compensation, this research explores the role of internal job demands and resources within PT Prinal
in reducing employee turnover intentions. Furthermore, it adopts a structural approach using PLS-
SEM to identify work engagement as a mediating factor in the relationship between job demands,
resources, and turnover intention. The findings of this study are expected to provide practical insights
into human resource management in the retail sector and contribute to the development of more
effective employee retention strategies.
The main objective of this study is to identify the importance of job demands and job resources
as factors that influence employees' intention to leave their jobs through work engagement. Suppose
job resources influence reducing turnover intention by increasing work engagement. In that case, PT
Prinal and other retail businesses must pay attention to adequate job resources so that their
employees can overcome their job demands. There is an anomaly where the work culture in PT Prinal
and similar retail industries has some similarities. Still, the organizational stability found in other
retail companies tends to be better. The lower and controllable turnover percentage evidences this.
This condition requires PT Prinal to explore the role of job resources that can control job
demands and turnover intention.
This research is expected to help broaden the understanding of human resource management
and highlight the role of work engagement in moderating the relationship between job demands,
resources, and turnover intentions. In addition, it can bring results that every industry can apply to
reduce the value of turnover intentions. Suppose the negative attributes in the retail sector are
difficult to remove. In that case, there is a step that the retail industry can take to maintain the
turnover value, one of which is through the provision of job resources. Suppose the research findings
show that job resources do not have a significant effect. In that case, further investigation is needed
to find more successful variables in reducing the value of turnover intentions.
Materials and Methods
This research uses a quantitative methodology to measure and statistically analyze the
relationship between variables (Sugiyono, 2020). This study aims to provide PT Prinal with an
understanding of the variables that can decrease the intention to quit and increase staff retention.
Some practical impacts are plans to create better human resource management techniques, improve
working conditions, and strengthen leadership support.
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1. Sampling Technique
According to Sugiyono (2020), the sample is part of the size and composition of the population.
The author uses the probability sampling method in the sample collection procedure. The basic
random sampling approach is the type of probability sampling method applied. Random sampling is
carried out without taking into account population stratification.
According to (Hair, Risher, Sarstedt, & Ringle, 2019), A more appropriate sample size is a ratio of 10 1,
and the ratio of observations to indicators of each variable is usually at least five times greater than
the number of variables to be evaluated. Because there are 22 question indicators in this study, a
sample size of 22 x 10 = 220 samples is required.
2. Data Collection Technique
This study collected primary data or data collected from the first source (research subjects).
The data collection method used was a survey conducted through Google Forms. The Google form
provided was a questionnaire with some closed questions. This type of data collection involves giving
respondents a list of written questions to answer (Sugiyono, 2020).
3. Data Analysis Technique
With the help of the SmartPLS 4.0 application, the data analysis technique used in this study to
ascertain the impact of job demands and job resources on turnover intention through job attachment
simplifies the entire data calculation and analysis procedure. The author of this study mainly collected
data for this study by using questionnaires, and the validity and reliability of the questionnaires are
two essential requirements.
Partial Least Square (PLS)
Partial Least Square (PLS) is an analytical technique sometimes called modeling" because it
removes the assumptions of OLS (Ordinary et al.) regression, such as the requirements that the data
be multivariate and regularly distributed and multicollinearity between exogenous variables is not a
problem. Weak hypotheses and data, such as limited samples and data normality issues, can be tested
using Partial Least Squares (PLS). In addition to verifying the theory, PLS can be used to explain
whether there is a relationship between latent variables. To avoid the problem of factor
indeterminacy and treat the latent variable estimation procedure as a linear combination of
indicators, PLS, as a prediction technique, assumes that all variance measures are valuable variances
to be explained. (Dr. Duryadi, 2021). The outer and inner models are the two main models used in PLS
analysis. SmartPLS 4.0 is the program used in this study.
The Partial Least Square (PLS) method was employed in this study using the SmartPLS 4.0
software. For several critical reasons, PLS was selected over other statistical techniques, such as
covariance-based Structural Equation Modeling (CB-SEM). Firstly, PLS is more flexible for data that
do not meet multivariate normality assumptions, which is often challenging in field data collection
within retail companies like PT Prinal. Secondly, PLS is appropriate for relatively small to medium
sample sizes, such as the 250 respondents in this study, allowing for significant results even with a
sample size smaller than that required for CB-SEM.
Additionally, PLS enables researchers to explore latent variables and the relationships between
variables more deeply through a predictive modeling approach. This approach aligns with the
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research goal of testing theoretical relationships and predicting factors influencing turnover
intentions in the retail sector. Therefore, PLS offers higher flexibility and more robust results than
other SEM methods in the context of this study.
Analysis of Research Hypothesis Testing
Structural Equation Modelling (SEM) analysis was used in this study to evaluate the research
hypotheses. Through the use of equations, SEM examines the structure of relationships. These
equations describe each relationship between constructs (dependent and independent variables) and
other variables used in the analysis (Hair et al., 2019). The process of finding out the relationship
between variables is called bootstrapping. Standard errors are used in bootstrapping to calculate p
values and t values (t statistics). (Hair et al., 2019). (Hair et al., 2019) stated that a p-value below 0.05
(significance level = 0.05) could be considered significant, but a (two-sided) t-value of 1.69 is used for
a considerable value.
Results and Discussions
This research uses the partial least squares method, a multivariate statistical methodology that
simultaneously analyses the influence between variables to anticipate, explore, or create a structural
model. According to (Hair et al., 2021), measurement model assessment, structural model evaluation,
and evaluation of model goodness and fit are all included in the PLS model evaluation.
1. Evaluation of the Measurement Model
Job demands, job resources, work engagement, and turnover intention are measured reflectively in
the measurement paradigm of this study, which uses a reflective approach. According to (Hair et al.,
2021), reflective measurement models are evaluated using discriminant validity tests (cross loading,
fornell lesser, and HTMT), reliability tests (composite reliability and Cronbach's alpha ≥ 0.70), and
validity tests (loading factor ≥ 0.70 and average variance extracted AVE ≥ 0.50).
Validity and Reliability Test
Table 2.
Validity and Reliability Test Results
Variables
Measurement
Item
Indicators
Outer
Loading
Cronbachs
Alpha
Composite
Reliability
AVE
Job Demands
JD1
Workload
0.750
0.872
0.903
0.610
JD3
Emotional demands
0.800
JD6
Work-life balance
0.850
JD7
Working conditions
0.773
JD8
Suitability of instructions
0.787
JD9
Jobdesk suitability
0.720
Job
Resources
JR3
Social support
0.806
0.761
0.862
0.677
JR4
Leader support
0.869
JR5
Development
0.791
Turnover
Intention
TI3
Subjective social status
0.794
0.913
0.936
0.745
TI4
Organisational culture
0.791
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Six valid measurement items were used to measure the job demands variable. Item outer
loadings ranged from 0.720 to 0.850, indicating that the six questions accurately measured job
demands. With a combined reliability of 0.903 and Cronbach's alpha of 0.872, the degree of
dependability of this variable is satisfactory, above the reliability criterion of 0.70. With an AVE score
of 0.610, which is higher than 0.50 and indicates that the variation of this measurement item covers
61% of the construct being assessed, the convergent validity of this variable also meets good
standards. With the highest outer loadings of 0.850 and 0.800, respectively, among the six
measurement items, the work-life balance (JR6) and emotional demands (JR3) items showed the most
vital link to the concept of job stress faced by PT Prinal personnel.
Three valid measurement items were used to measure the job resources variable; their outer
loadings ranged from 0.791 to 0.869, indicating that the items were valid to reflect the measurement
of job resources. With a combined reliability of 0.862 and Cronbach's alpha of 0.761, the degree of
reliability of this variable is also adequate. With an AVE value of 0.677 higher than 0.50 and indicating
that the variation of this measurement item covers 67.7% of the construct being assessed, the
convergent validity of this variable meets a good standard. Of the three items, the leader support
measurement item (JR4) has the highest outer loading of 0.869, indicating that the leader support
aspect at PT Prinal has been measured well. However, other factors, such as social support and
development opportunities, need to be considered for further improvement.
Five valid measurement items were used to assess the switching intention variable. The item
outer loadings ranged from 0.791 to 0.930, indicating that all five items accurately reflect the
measurement of switching intention. With a composite reliability of 0.936 and Cronbach's alpha of
0.913, this variable has a highly acceptable level of dependability. With an AVE score of 0.745, which
is higher than 0.50 and indicates that the variation of this measurement item covers 74.5% of the
construct being assessed, the convergent validity of this variable meets a good standard. Of the five
items, the measurement items regarding personal expectations (TI6) and career growth (TI7) have
the highest outer loading, at 0.930 and 0.899, respectively, indicating a strong correlation with the
construct of turnover intention experienced by PT Prinal employees.
Four valid measurement items were used to measure the work engagement variable. Item
outer loadings ranged from 0.719 to 0.893, indicating that all four questions accurately reflected the
measurement of work engagement. With a composite reliability of 0.904 and Cronbach's alpha of
0.858, this variable has an adequate level of dependability. With an AVE value of 0.704, which is higher
Variables
Measurement
Item
Indicators
Outer
Loading
Cronbachs
Alpha
Composite
Reliability
AVE
TI5
Personal orientation
0.893
TI6
Personal expectations
0.930
TI7
Career growth
0.899
Work
Engagement
WE2
Power
0.873
0.858
0.904
0.704
WE3
Helpful
0.860
WE4
Dedication
0.893
WE6
Engagement
0.719
Source: Primary data processed, 2024
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than 0.50 and indicates that the variation of this measurement item covers 70.4% of the construct
being assessed, the convergent validity of this variable also meets a good standard. The measurement
items regarding strength (WE2) and dedication (WE4) have the most significant external loadings of
the four, at 0.893 and 0.873, respectively. This indicates a strong relationship with the work
engagement construct experienced by PT Prinal employees. Benefits and engagement must also be
improved to support better work engagement.
2. Structural Model Evaluation
Testing research hypotheses regarding the relationship between variables is associated with
structural model evaluation. There are several phases involved in the structural model evaluation
process. To begin with, measure the inner VIF (Variance inflation factor) to ensure no
multicollinearity problem between variables. There is no multicollinearity between variables if the
Inner VIF value is less than 5 (Hair et al., 2021). The path coefficient and p-value are used to evaluate
hypotheses about the relationship between variables in the second stage. A path coefficient that is
less than zero indicates a negative relationship, and the opposite applies to the hypothesis being
investigated. If the P-value is less than 0.05, it means that the variables have a significant influence on
each other.
Measuring the f square, which shows the direct influence of factors at the structural level, is
part of the third stage. The effect can be classified as low (0.02), medium (0.15), or high (0.35) based
on the f square value. In addition, the upsilon v statistic obtained by squaring the mediation coefficient
is used to assess the mediation effect. The mediation effect can be interpreted as low (0.02), medium
(0.075), or large (0.175) based on the upsilon v value ((Sarstedt et al., 2021); (Lachowicz et al., 2018);
(Ogbeibu et al., 2021).
Table 3.
Multicollinearity Test Results
Based on the test results, multicollinearity between variables is relatively easy, or the amount
of multicollinearity is low because the inner VIF value is less than 5. This finding verifies the
robustness (i.e., absence of bias) of the parameter estimation results in PLS-SEM.
Table 4. Direct Relationship Testing
Hypothesis
Path
Coefficient
P-values
95% Confidence Interval
Path Coefficient
F-square
Lower
Limit
Upper Limit
H1. Job Demands → Turnover
Intention
0.250
0.000
0.147
0.349
0.139
VIF
Job Demands → Turnover Intention
1.309
Job Demands → Work Engagement
1.229
Job Resources → Turnover Intention
1.513
Job Resources → Work Engagement
1.229
Work engagement → Turnover Intention
1.494
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H2. Job Demands → Work
Engagement
-0.232
0.001
-0.363
-0.094
0.065
H3. Job Resources → Turnover
Intention
-0.361
0.000
-0.490
-0.242
0.249
H4. Job Resources → Work
Engagement
0.436
0.000
0.312
0.558
0.231
H5. Work engagement →
Turnover Intention
-0.390
0.000
-0.496
-0.272
0.295
Table 5.
Testing the Indirect Relationship (Mediation)
Hypothesis
Path
Coefficient
P-values
95% Confidence Interval
Path Coefficient
Upsilon V
Lower
Limit
Upper Limit
H6. Job Demands → Work
engagement → Turnover Intention
0.090
0.005
0.033
0.157
0.008
H7. Job Resources → Work
Engagement → Turnover
Intention
-0.170
0.000
-0.241
-0.106
0.029
Figure 4. Bootstrapping Test Results
Managerial Implication
1. Job Demands on Turnover Intention
The first hypothesis (H1) is accepted, indicating a positive and significant influence between job
demands and the increase in turnover intention with a path coefficient (0.250) and p-value (0.000
<0.05). Any increase in job demands will increase turnover intention. With a 95% confidence level,
the effect of job demands on turnover intention ranges from 0.147 to 0.349. However, the impact of
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job demands in increasing turnover intention is moderate at the structural level (F square = 0.139).
The job demands setting program is essential because turnover intention can decrease by 0.147
(lower limit) with a good setting.
The study's results align with the findings of (Hoare & Vandenberghe, 2024). This shows that
job demands have a positive and significant relationship with turnover intention, especially on the
work-life balance indicator. Similarly, (Gu et al., 2020) research explained that job demands positively
predict turnover intention. Job demands have a positive role in turnover intention. The higher the job
demands, the output will be directly proportional to the turnover intention that will arise. Based on
the findings of (Patel & Bartholomew, 2021), job demands will positively affect burnout conditions,
which is continued by the conclusions of Augustin et al. (2022). (Augustin, Zamralita, & Saraswati,
2022) explain that burnout significantly contributes to turnover intention. This can explain in detail
in specific conditions that burnout can bridge job demands and turnover intention, apart from the
work engagement variables studied.
2. Job Demands on Work Engagement
The second hypothesis (H2) is accepted, showing a negative and significant influence between job
demands and decreasing work engagement with a path coefficient (-0.232) and p-value (0.001 <0.05).
Any increase in job demands will decrease work engagement. With a 95% confidence level, the effect
of job demands on work engagement ranges from -0.363 to -0.094. However, the impact of job
demands on reducing work engagement is low at the structural level (F square = 0.065). The job
demands management program is very important because work engagement will only decrease by -
0.094 (upper limit) with good management.
The results of this study are the findings of (Ugwu & Onyishi, 2020), which showed that job
demands (workload and emotional demands) have a negative and significant relationship with work
engagement. Although research by (Schaufeli & Bakker 2004) did not find a significant relationship
between job demands and work engagement, research by Schaufeli & Bakker (2004) found a
significant relationship between job demands and work engagement. (Montgomery, Spânu, Bəban, &
Panagopoulou, 2015) support the idea that job demands (organizational and emotional demands) can
lead to emotional exhaustion and depersonalization and are negatively related to passion and
dedication to work (work engagement). (Bakker, 2011) also revealed that various job demands will
result in different levels of engagement.
3. Job Resources on Turnover Intention
The third hypothesis (H3) is accepted, showing a negative and significant influence between job
resources on reducing turnover intention with a path coefficient (-0.361) and p-value (0.000 <0.05).
Any increase in job resources will reduce turnover intention. With a 95% confidence level, the effect
of job resources on turnover intention ranges from -0.490 to -0.242. However, the impact of job
resources on reducing turnover intention is moderate at the structural level (F square = 0.249). A
program to improve job resources is essential because turnover intention can decrease to -0.490
(lower bound) with a good arrangement.
Research from (Hoare & Vandenberghe, 2024) showed that most predictors of job resources
have a negative relationship with turnover intention influenced by emotional exhaustion. This study
confirms the importance of job resources in reducing turnover intention. Individuals generally want
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social support, value congruence with the organization, appropriate rewards, and opportunities to
learn and develop in their work. (Albrecht et al., 2021). Job resources can serve as motivation when
employees face high job demands that can lead to burnout. Based on the JD-R model, when employees
face high emotional needs, social support from co-workers becomes more visible and significant.
(Schaufeli & Bakker, 2004).
4. Job Resources on Work Engagement
The fourth hypothesis (H4) is accepted, showing a positive and significant influence between job
resources on increasing work engagement with a path coefficient (0.436) and p-value (0.000 <0.05).
Any increase in job resources will increase work engagement. With a 95% confidence level, the effect
of job resources on work engagement ranges from 0.312 to 0.558. However, the impact of job
resources on increasing work engagement is moderate at the structural level (F square = 0.231). A
program to improve job resources is essential because work engagement can increase to 0.558 (upper
limit) with a good arrangement.
Research results from (Russell et al., 2020) show that job resources have a significant positive
relationship with work engagement (b = 0.101, p = 0.001). The research results (Van Heerden, Du
Plessis, & Becker, 2022) found that job resources have a positive and significant relationship with
work engagement. According to the theory of conservation of resources (COR) (Hobfoll, 1989), if the
organization does not provide job resources (such as development opportunities, role clarity, and
social support), employees tend to withdraw from work because their motivation and commitment
decrease. The availability of social support, support from leaders, and development opportunities are
essential to increase work engagement. Employees will show high dedication and enthusiasm if they
can access learning opportunities, job variety, social support, and supportive leaders.
5. Work Engagement on Turnover Intention
The fifth hypothesis (H5) is accepted, showing a negative and significant influence between work
engagement and reducing turnover intention with a path coefficient (-0.390) and p-value (0.000
<0.05). Any increase in work engagement will reduce turnover intention. With a 95% confidence
level, the effect of job demands on work engagement ranges from -0.496 to -0.272. However, the
impact of work engagement in reducing turnover intention is low at the structural level (F square =
0.295).
Various studies show that low levels of work engagement will stimulate a person to leave the
organization (turnover intention) (Russell et al., 2020). Work engagement will create a positive and
fulfilling experience, focusing on maintaining health and achieving success in one's career. (Schaufeli
& Bakker, 2004). Employees with high engagement will produce quality work results and do not
intend to leave. In addition, highly engaged workers are indirectly obligated to repay their
organization with long tenure and tend to stay with the organization (Saks, 2006).
6. Job Demands on Turnover Intention Through Work Engagement
The mediation test results show that the sixth hypothesis (H6) is accepted, where work engagement
significantly acts as a mediating variable, namely mediating the indirect effect of job demands on
turnover intention with a mediation path coefficient (0.090) and p-value (0.005 <0.05). However, at
the structural level, the mediating role of work engagement is still relatively low (upsilon v=0.008).
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Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2667
(Ogbeibu et al., 2021). With a 95% confidence level, increasing the role of work engagement can
increase mediation to 0.157 (upper limit). Work engagement relates to a person's attitude, behaviour,
and intention (Saks, 2006).
Employees with high work engagement are expected to be more physically and emotionally
involved with their organization and less likely to leave it (turnover intention). (Bakker et al., 2007).
Various findings and this study show that work engagement is negatively related to turnover
intention (-0.390; 0.000). The research is supported by the findings of (Wan et al., 2018), which show
that work engagement acts as a partial mediation between work environment (job demands and job
resources) and turnover intention. This is because job demands and job resources influence turnover
intention. Employees with low work engagement are likelier to have a greater purpose when leaving
the organization.
7. Job Resources on Turnover Intention through Work Engagement
The mediation test results show that the seventh hypothesis (H7) is accepted, where work
engagement significantly acts as a mediating variable, namely mediating the indirect effect of job
resources on turnover intention with a mediation path coefficient (-0.170) and p-value (0.000 <0.05).
However, at the structural level, the mediating role of work engagement is still relatively low (upsilon
v=0.028). (Ogbeibu et al., 2021). With a 95% confidence level, increasing the role of work engagement
can reduce mediation to -0.106 (upper bound).
The research aligns with (Li et al., 2022), which shows that work engagement will positively
mediate the relationship between job resources and turnover intention. Supported by research from
(Shaukat et al., 2020), there is a mediating role of work engagement between job resources on
turnover intention. (Kissi et al., 2023). Based on the findings of (Artiningsih et al., 2023), work
engagement mediates the relationship between job resources and turnover intention. The results of
this study confirm that when employees have access to adequate job resources, they tend to be more
engaged in their work, which, in turn, reduces the intention to leave the organization.
Conclusion
PT Prinal personnel's job requirements, job resources, work engagement, and intention to leave
the company are the subjects of investigation. Thus, work engagement is negatively affected by job
expectations. The work engagement of employees at PT Prinal decreases as work expectations
increase. The demands of the job are an obstacle. Workplace expectations positively impact the
tendency to leave—the tendency of P.T. principal employees to leave increases along with job
pressure. Demands from work are an obstacle. Job attachment is positively affected by job resources.
The more work resources management provides, the greater the engagement of PT Prinal employees
at work. In addition, work engagement is negatively affected by job resources. Employees at PT Prinal
are less likely to plan to leave their jobs with more job resources management provides. Turnover
intention is negatively affected by work engagement. Employees' Job involvement at PT Prinal
positively correlates with low turnover intention. Since job demands directly impact turnover
intention, job engagement is a partial negative mediator between the two variables. Meanwhile, since
job resources directly affect turnover intention, work engagement is a partial negative mediator
between the two.
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Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2668
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