Syntax Literate:
Indonesian Scientific Journal p–ISSN: 2541-0849 e-ISSN: 2548-1398
Vol. 9, No. 9,
September 2024
THE
INFLUENCE OF COMPENSATION, PERCEIVED SUPERVISOR SUPPORT, AND CAREER DEVELOPMENT
ON INTENTION TO STAY WITH JOB SATISFACTION AS A MEDIATION VARIABLE IN XYZ
MANUFACTURING EMPLOYEES
Monika
Teresia1, Ardi2, Richard Andre Sunarjo3
Universitas Pelita Harapan, Tangerang,
Indonesia1,2
Universitas Raharja,
Tangerang, Indonesia3
Email: [email protected]1,
[email protected]2
Abstract
This research
aims to examine the company's efforts to retain competent employees in the
textile industry. The research background shows that the textile industry in
Indonesia has a significant contribution to the country's economy, but is faced
with challenges in maintaining a competent workforce. The research was
conducted at the company PT XYZ, one of the textile manufacturing companies in
Tangerang. The method used is quantitative with the SEM-PLS analysis technique.
Data was collected through questionnaires distributed to employees. This
research aims to examine the influence of compensation, perceived supervisor
support, and career development on intention to stay which is mediated by job
satisfaction. The research results show that the work environment, superior
behavior, career development and compensation are factors that influence
employees' desire to continue working at the company. This research emphasizes
the importance for organizations to create a conducive work environment, build
good relationships between employees and superiors, and match the workload with
the compensation received. These findings provide managerial implications for
textile companies in managing human resources to maintain business
sustainability and competitiveness.
Keywords: Intention to Stay, Compensation, Perceived
Supervisor Support, Career Development, Job Satisfaction, Manufacture
Introduction
The textile industry and textile products are
one of the sectors that contribute to Indonesia's economic growth (Ministry of
Industry of the Republic of Indonesia [Kemenperin],
2022). Textiel Inrichting
Bandoeng (TIB) in 1922 was the beginning of the establishment of modern
textiles in Indonesia. In 2022, Indonesia will commemorate 100 years of the
textile industry and it is hoped that it will continue to develop through
innovation and new processes in manufacturing. This industry has
competitiveness which is supported by an integrated system and competitive
demand.
The textile industry supports the country's
foreign exchange earnings of USD 13.02 billion. Indonesia's economic growth
received assistance from the textile sub-export of 5.67% in 2021 and 5.33%
during January – May 2022. Apart from that, as a social safety net, in 2021
this industrial sector can absorb 3.65% of the workforce which is equivalent to
18.79% of the total number of workers for the manufacturing industry.
Based on data from the Central Statistics
Agency (BPS, 2023), there are 32,193 types of manufacturing industry in
Indonesia. For the textile and apparel industries, the numbers are 131 and 141
units respectively. Meanwhile, the latest data for the number of textile and
apparel workers is 1,215,219 workers in 2020, consisting of 519,299 textile
workers and 695,920 apparel workers.
PT XYZ is a textile manufacturing industry in
Tangerang and has 429 employees. PT XYZ employees are divided into 2 large
groups, namely production and non-production units. Production unit employees
are workers who directly work in production processes and activities, including
operator level to factory manager. Meanwhile, non-production employees are
workers who are not directly involved in production, such as HRD, finance,
sales marketing and other supporting departments.
PT XYZ is supported by a competent workforce
in accordance with their fields to support its business development. This is
certainly something good for the company's performance because it is able to
retain its workforce. Therefore, companies must not be careless about the
workforce dynamics that may occur. Efforts to retain competent employees still
need to be made. One effort that can be made is to pay attention to the
workforce that still survives today because they are an important asset that
has a role in achieving organizational goals.(Popescu, 2019).
Human resources or employees contribute to
every aspect of the organization, whether positive or otherwise. It is
important for an organization to create a good working atmosphere to facilitate
employee needs. Organizational productivity cannot be separated from the role
of human resources within it. The work environment, behavior of superiors,
evaluation of employee work, and commitment to achieving organizational goals
are factors that make employees feel part of the organization.(Musawer, et al., 2021). Magner stated that employees will be
comfortable staying in an organization if they are involved in the
decision-making process(Ghosh et al.,
2013).
But on the other hand, employees also have the
desire to move from their current job and this can be a threat to the
organization. Retaining employees is important, especially for potential
talents because they have the characteristics that companies need in terms of
skills, knowledge, and even their ability to adapt to the organization.
Therefore, managing human resources is an important thing that needs to be done
in every organization to retain its employees.
Haque stated that retaining employees is
important considering the tight competition in the labor market, high
expenditure on recruitment and training activities(Malik &
Malik, 2023). On the other hand, employee dissatisfaction
with the company's efforts to provide and support human resource systems is one
of the reasons they leave their jobs. Several previous studies have revealed
factors that cause employees to not stay, namely company culture, relationships
with colleagues, lack of support, lack of opportunities to develop,
dissatisfaction with compensation, managerial role, work environment.(Ghosh et al.,
2013).Ghosh et al.
(2013)states that an organization needs to plan a
strategy to retain employees in order to maintain and achieve business
stability.
Sukri et al.
(2023)concluded that an individual's desire to stay
with a company is a dedication and willingness to continue working at their
current job. Aboobaker et al.(Knezović & Neimarlija, 2023)defines the desire to stay as an individual's
voluntary willingness to remain in an organization. Companies need to consider
providing a supportive work environment so that employees want to remain in the
organization or in their jobs.Fathima & Umarani (2023)states that an employee's desire to remain
employed at the current company for a long period of time is also known as
retention intent or intent to stay.Bandyopadhyay &
Srivastava (2023)revealed that job satisfaction, job mentoring,
intrinsic motivation, conflict management style, perceived organizational
support, perceived equity and supervisory leadership are factors that can
influence employees to stay with the organization. Therefore, employee
retention needs to be developed so that employees feel comfortable and want to
continue working there.
There are several things that can increase
employees' desire to stay.Juariyah et al. (2020)in his research, it was revealed that one of
the factors that makes employees stay in their jobs is compensation.
Compensation Compensation is an important factor in
an organization. Compensation is something obtained as a result of performance
or services provided(Aisyiah & Khoirunnisa, 2022). Apart from that, compensation is also
related to job satisfaction(Azzuhairi et al., 2022). With compensation, companies can retain and
employ the employees they want.
Another factor that influences a person's
desire to stay with a company is career development(Febriyanthy & Sary, 2024). Career development(Houssein et al., 2020)is a continuous choice made continuously by
individuals to choose their work. Career development is an individual's
subjective assessment of the career they are currently pursuing. In addition,
career development does not only include work but also includes the work
environment, interactions and changes that may occur in the organization. Apart
from that, in the research conductedRahayu et al. (2019)shows that career development is also related
to job satisfaction.
Lloyd stated that perceived supervisor support
is related to turnover intention(Afzal et al.,
2019). Social exchange theory states that employee
commitment to their superiors is a reciprocal relationship where employees
expect to receive rewards in return for their efforts and commitment to their
superiors. Perceived supervisor support is the supervisor's role in
understanding and appreciating the work of their employees, looking after their
welfare, and appreciating their efforts(Chami-Malaeb, 2022).Showing
personal concern, providing attention to subordinates, providing regular
assistance, and encouraging employees to make better decisions are some ways a
boss can support their employees. Apart from that, perceived supervisor support
is also related to job satisfaction(Winarto & Chalidyanto, 2020).
Based on the results of previous
research and several factors that influence employees' desire to stay, this
research aims to analyze the influence of compensation, perceived supervisor
support, and career development on the desire to stay with job satisfaction as
a mediating variable for employees of the XYZ manufacturing company in
Tangerang. Job satisfaction as a mediating variable will also be explored to
determine its influence on this variable. Based on the research background, this
research aims to:
1) To test and analyze whether compensation has
an effect on the desire to stay at XYZ manufacturing employees?
2) To test and analyze whether perceived supervisor
support influences the desire to stay at XYZ manufacturing employees?
3) To test and analyze whether career development
influences the desire to stay at XYZ manufacturing employees?
4) To test and analyze whether compensation has
an effect on job satisfaction of XYZ manufacturing employees?
5) To test and analyze whether perceived
supervisor support has an effect on job satisfaction of XYZ manufacturing
employees?
6) To test and analyze whether career development
has an effect on job satisfaction for XYZ manufacturing employees?
7) To test and analyze whether job satisfaction
can mediate compensation on employees' desire to stay in XYZ manufacturing
employees?
8) To test and analyze whether job satisfaction
can mediate perceived supervisor support on employees' desire to stay in XYZ
manufacturing employees?
9) To test and analyze whether job satisfaction
can mediate career development on the desire to stay in XYZ manufacturing
employees?
10) To test and analyze whether job satisfaction
influences the desire to stay with employees at XYZ manufacturing company?
This research uses a causal quantitative research method approach to see
the influence of independent variables on the dependent variable. In this
context, research analyzes the influence of intention to stay as an independent
variable on the dependent variable which includes compensation, perceived
supervisor support, and career development. The population in this study were
employees who worked at one of the manufacturing subsidiaries of PT XYZ in
Tangerang, totaling 87 employees. This research uses a saturated sample method,
where all members of the population are used as samples. The sampling technique
applied was non-probability sampling with purposive sampling, selecting active
employees with a minimum work period of one year and job levels from staff to
manager.
Data collection in this research was carried out using primary data
methods obtained directly from the first source through questionnaires.
Questionnaires were distributed online using Google Forms to collect the
necessary data from respondents. Data analysis was carried out using structural
equation modeling (SEM) techniques with a partial least squares (PLS-SEM)
approach to test the interdependence of variables in the research model. The
software used for data analysis was SmartPLS version
4, which allows identification and testing of complex relationships between
research variables.
Results and
Discussion
This
research examines the influence of compensation, perceived supervisor support,
career development and job satisfaction on the intention to stay of employees
who work at the XYZ manufacturing company. The analysis was carried out using
the Partial Least Square - Structural Equation Modeling (PLS-SEM) analysis
technique using SmartPLS 4 version 4.1.0.3 software.
The analysis process consists of two stages, namely outer model testing and
inner model testing. At the outer model testing stage, all indicators in the
model are tested for validity and reliability. Meanwhile, inner model testing
aims to test hypotheses based on the values between the independent variables
in the research model. The following are the results of the analysis carried
out in this research:
Outer
modelcarried
out to ensure the validity and reliability of the indicators used. This stage
is carried out before testing how the constructs or variables relate to each
other (Hair et al, 2022). In the
validity test, 2 measurements are carried out, namely convergent validity and
discriminant validity. Meanwhile, the reliability test was carried out by
measuring the Cronbach alpha value.
Figure 1. Outer Model Analysis Results
Convergent
Validity
Convergent
validity is carried out with the aim of measuring the magnitude of the
correlation between the construct and the latent variable. The convergent
validity test can be seen by the outer loading and average-variance extracted
(AVE) values. If the outer loading value is more than 0.70 and the AVE is more
than 0.50, then the indicator used has passed the validity test (Hair et al,
2022).
Table 1. Validity Test Results
|
Variable |
Indicator |
Outer Loading |
Information |
AVE |
Composite Reliability |
|
Compensation |
CS1 |
0.362 |
Invalid |
0.463 |
0.869 |
|
CS2 |
0.770 |
Valid |
|||
|
CS3 |
0.740 |
Valid |
|||
|
CS4 |
0.658 |
Invalid |
|||
|
CS5 |
0.791 |
Valid |
|||
|
CS6 |
0.596 |
Invalid |
|||
|
CS7 |
0.625 |
Invalid |
|||
|
CS8 |
0.794 |
Valid |
|||
|
Perceived
Supervisor Support |
PSS1 |
0.829 |
Valid |
0.736 |
0.951 |
|
PSS2 |
0.912 |
Valid |
|||
|
PSS3 |
0.887 |
Valid |
|||
|
PSS4 |
0.927 |
Valid |
|||
|
PSS5 |
0.845 |
Valid |
|||
|
PSS6 |
0.873 |
Valid |
|||
|
PSS7 |
0.712 |
Valid |
|||
|
Career
Development |
CD1 |
0.755 |
Valid |
0.574 |
0.904 |
|
CD2 |
0.787 |
Valid |
|||
|
CD3 |
0.722 |
Valid |
|||
|
CD4 |
0.838 |
Valid |
|||
|
CD5 |
0.725 |
Valid |
|||
|
CD6 |
0.666 |
Invalid |
|||
|
CD7 |
0.797 |
Valid |
|||
|
Job
Satisfaction |
JS1 |
0.808 |
Valid |
0.567 |
0.939 |
|
JS2 |
0.710 |
Valid |
|||
|
JS3 |
0.854 |
Valid |
|||
|
JS4 |
0.881 |
Valid |
|||
|
JS5 |
0.557 |
Invalid |
|||
|
JS6 |
0.571 |
Invalid |
|||
|
JS7 |
0.738 |
Valid |
|||
|
JS8 |
0.756 |
Valid |
|||
|
JS9 |
0.855 |
Valid |
|||
|
JS10 |
0.787 |
Valid |
|||
|
JS11 |
0.821 |
Valid |
|||
|
JS12 |
0.609 |
Invalid |
|||
|
Intention
to Stay |
ITS1 |
0.911 |
Valid |
0.763 |
0.957 |
|
ITS2 |
0.860 |
Valid |
|||
|
ITS3 |
0.866 |
Valid |
|||
|
ITS4 |
0.886 |
Valid |
|||
|
ITS5 |
0.896 |
Valid |
|||
|
ITS6 |
0.883 |
Valid |
|||
|
ITS7 |
0.809 |
Valid |
Table 1
shows that there are several indicators that have an outer loading value of
less than 0.70 so they are categorized as invalid. According to Hair et al
(2022), outer loading values in the range of 0.40 to 0.70 do not need to be
excluded if the AVE value is not below 0.50 and the composite reliability value
is below 0.60. After several indicators were removed, retesting was carried out
with the following results:
Table 2. Actual Validity Test Results
|
Variable |
Indicator |
Outer
Loading |
Information |
AVE |
Composite
Reliability |
|
Compensation |
CS1 |
Issued |
0.659 |
0.885 |
|
|
CS2 |
0.824 |
Valid |
|||
|
CS3 |
0.815 |
Valid |
|||
|
CS4 |
Issued |
||||
|
CS5 |
0.792 |
Valid |
|||
|
CS6 |
Issued |
||||
|
CS7 |
Issued |
||||
|
CS8 |
0.816 |
Valid |
|||
|
Perceived Supervisor Support |
PSS1 |
0.827 |
Valid |
0.736 |
0.951 |
|
PSS2 |
0.912 |
Valid |
|||
|
PSS3 |
0.888 |
Valid |
|||
|
PSS4 |
0.927 |
Valid |
|||
|
PSS5 |
0.847 |
Valid |
|||
|
PSS6 |
0.873 |
Valid |
|||
|
PSS7 |
0.712 |
Valid |
|||
|
Career Development |
CD1 |
0.835 |
Valid |
0.649 |
0.902 |
|
CD2 |
0.874 |
Valid |
|||
|
CD3 |
0.786 |
Valid |
|||
|
CD4 |
0.783 |
Valid |
|||
|
CD5 |
Issued |
||||
|
CD6 |
Issued |
||||
|
CD7 |
0.746 |
Valid |
|||
|
Job Satisfaction |
JS1 |
0.853 |
Valid |
0.674 |
0.935 |
|
JS2 |
0.759 |
Valid |
|||
|
JS3 |
0.850 |
Valid |
|||
|
JS4 |
0.904 |
Valid |
|||
|
JS5 |
Issued |
||||
|
JS6 |
Issued |
||||
|
JS7 |
0.763 |
Valid |
|||
|
JS8 |
0.791 |
Valid |
|||
|
JS9 |
Issued |
||||
|
JS10 |
Issued |
||||
|
JS11 |
0.818 |
Valid |
|||
|
JS12 |
Issued |
||||
|
Intention to Stay |
ITS1 |
0.911 |
Valid |
0.763 |
0.958 |
|
ITS2 |
0.860 |
Valid |
|||
|
ITS3 |
0.866 |
Valid |
|||
|
ITS4 |
0.888 |
Valid |
|||
|
ITS5 |
0.896 |
Valid |
|||
|
ITS6 |
0.883 |
Valid |
|||
|
ITS7 |
0.809 |
Valid |
|||
After
re-testing the validity, it was found that all outer loading values were
above 0.40 and the AVE value was above 0.50 so that the convergent validity
test was valid.
Discriminant
Validity
Discriminant
validityis
the extent to which a variable differs from other variables. A model is considered
to have good discriminant validity when the squared AVE value of each
independent variable exceeds the correlation relationship between that variable
and other variables. In this research, the method used is heterotrait-monotrait
ratio (HTMT) because it is considered more accurate than fornell-lacker
(Hair et al, 2022). Indicators within a construct are considered to have
discriminant validity if the HTMT between constructs or variables is <0.90
(Hair et al, 2022), in other words if the HTMT value is below 0.90 then it can
be said to be valid.
Table 3. HTMT Test Results
|
Variable |
Career Development |
Compensation |
Intention to Stay |
Job Satisfaction |
Perceived Supervisor Support |
|
Career Development |
|
|
|
|
|
|
Compensation |
0.656 |
|
|
|
|
|
Intention to Stay |
0.712 |
0.756 |
|
|
|
|
Job Satisfaction |
0.677 |
0.629 |
0.730 |
|
|
|
Perceived Supervisor Support |
0.638 |
0.609 |
0.653 |
0.883 |
|
In table 3,
it can be seen that all variables have values below 0.90 and have fulfilled
the discriminant validity aspect. It can be concluded that all indicators in
this research model have proven capable of measuring the targeted variables.
Reliability
Reliability
testing is used to see the level of consistency of a measuring instrument if
measurements are carried out twice or more on the same research. Accuracy and
consistency of answers to each variable is needed in measuring each variable so
that an instrument can be said to be reliable. The reliability test is carried
out by looking at the composite reliability (rho_c)
value which is expected to be more than 0.70 (Hair et al, 2022).
Table 4. Reliability Test Results
|
Variable |
Number of Items |
Cronbach's
Alpha |
Composite
Reliability |
|
Career Development |
4 |
0.864 |
0.902 |
|
Compensation |
7 |
0.830 |
0.885 |
|
Intention to Stay |
5 |
0.948 |
0.958 |
|
Job Satisfaction |
7 |
0.936 |
0.935 |
|
Perceived Supervisor Support |
7 |
0.939 |
0.951 |
In table 4
it can be seen that the Cronbach's alpha and composite reliability values for
all constructs have exceeded the value of 0.70. This shows that all constructs
have met reliability, so all variables in this research are reliable.
Inner
Model
The
structural model or inner model in this research defines the causal
relationship between latent variables. This test is carried out to predict the relationship
between latent variables by showing the direction of the relationship between
latent variables. Before testing the hypothesis, model quality parameters such
as variance inflation factor (VIF), R-square, f-square, Q-square, and Q-square
prediction are also considered.
Figure 2. Inner Model Analysis Results
Multicollinearity
The
multicollinearity test aims to test whether there is a strong correlation or
relationship between two or more independent variables in a model.
Multicollinearity is seen from the variance inflation factor (VIF), a VIF value
of less than 3 is considered ideal, while a value above 5 indicates the
possibility of multicollinearity problems in the research model. If the VIF
value is in the range of 3 to 5, this indicates that the multicollinearity test
provides an acceptable value or is still within acceptable limits.
Table 5 Multicollinearity Test Results
|
Variable |
Intention to Stay |
Job Satisfaction |
|
Career Development |
1,807 |
1,716 |
|
Compensation |
1,710 |
1,674 |
|
Intention to Stay |
|
|
|
Job Satisfaction |
3,527 |
|
|
Perceived Supervisor Support |
3,338 |
1,720 |
Based on
table 5, it can be seen that the Variance Inflation Factor (VIF) value for all
research variables does not exceed the value 5. This shows that there is no multicollinearity
problem in the research model. This research model is considered acceptable.
Determinant
Coefficient (R-Square)
The
determinant coefficient or R-square test is carried out to find out how much
the independent variable contributes to the dependent. If the R-square value is
close to 1, it indicates that the independent variable can provide all the
information needed to predict the dependent variable. The coefficient of
determination describes the explanatory power and predictive accuracy of the
research model. In the partial least square (PLS) method, the coefficient of
determination is measured by considering the adjusted R-square value, the
higher the value, the greater the influence of the independent variable on the
dependent.
Table 6. R-square test results
|
Variables |
R Square |
R Square
Adjusted |
|
Intention to Stay |
0.646 |
0.629 |
|
Job Satisfaction |
0.716 |
0.706 |
Based on
table 6, the R-square value for the intention to stay variable is 0.629. This
shows that around 62.9% of the variation in the intention to stay variable can
be explained by the independent variables in this research model, namely
compensation, perceived supervisor support, career development, and job
satisfaction. Meanwhile, the remaining 37.1% was influenced by other factors
outside the research model. The R-square value for the job satisfaction
variable is 0.706 or 70.6%. This shows that the job satisfaction variable in
this research model is able to be influenced by the variables
compensation, perceived supervisor support, career development and intention to
stay. Meanwhile, the remaining 19.4% was influenced by other variables outside
this research.
Effect size (F-Square)
The
f-square test is carried out to determine whether there is a change in the R2 value
when certain variables are removed from the model so that it can be seen
whether the omitted variables have an impact on the research model construct.
F-square analysis helps in evaluating the extent to which a particular
construct contributes to the variability of the variables targeted in the
research model. The f-square test provides data regarding the size of the
effect as an assessment to assess the substantial impact of predictor
variables. If the F-square value shows values of 0.02, 0.15, and 0.35,
respectively, then these values represent small, medium, and large effects.
Table 7. F-square test results
|
Track |
F Square |
Information |
|
CD → ITS |
0.090 |
Little effect |
|
CD → JS |
0.053 |
Little effect |
|
CS → ITS |
0.206 |
Medium effect |
|
CS → JS |
0.021 |
Little effect |
|
JS → ITS |
0.082 |
Little effect |
|
PSS → ITS |
0.001 |
Little effect |
|
PSS → JS |
0.941 |
Great effect |
Table 7
shows that the largest f-square value on the influence on intention to stay is
compensation with a value of 0.206. This indicates that the compensation
variable has sufficient influence in the research model. Meanwhile, perceived
supervisor support has a large effect size on job satisfaction with a value of
0.941.
Predictive
Relevance (Q2)
The
predictive relevance test is a test carried out to evaluate the level of
success in predicting the relevance of a variable. If the Q-square number is
below 0.25 then the predictive ability is low, while a Q-square value of more
than 0.50 indicates high predictive ability. The Q-square calculation process
is carried out using the blindfolding method or in SmartPLS
4 it is carried out using PLSPredict/CVPAT.
Table 8. Predictive Relevance Test Results
|
Variable |
Q2 (=1-SSE/SSO) |
|
Intention
to Stay |
0.569 |
|
Job
Satisfaction |
0.689 |
In table 8
it can be seen that the Q-square value is more than 0.50, so it is assumed that
the intention to stay and job satisfaction variables have strong predictive
ability. The Q-square value of 0.569 for intention to stay indicates that this
research model is able to explain around 56.9% of the variability in intention
to stay based on additional independent variables, and the remaining 43.1% may
be caused by other external factors. variables in this research model. The
Q-square value of 0.689 for the job satisfaction variable shows that this
research model has a strong level of ability in describing the relationship
between variables, especially job satisfaction as a mediating variable.
Hypothesis testing
Analysis
in hypothesis testing uses path coefficient assessment. The purpose of this
test is to measure how much influence the variables in the research model have
and the suitability of the direction of the calculation results to the
previously proposed hypothesis. To carry out this test, bootstrapping was
carried out using SmartPLS.
Table 9. Hypothesis Test Results
|
Hypothesis |
Path Coefficient |
Information |
|
H1: Compensation → Intention to stay |
0.353 |
Supported |
|
H2: Perceived Supervisor Support → Intention to Stay |
0.035 |
Supported |
|
H3: Career Development → Intention to Stay |
0.240 |
Supported |
|
H4: Compensation → Job Satisfaction |
0.101 |
Supported |
|
H5: Perceived Supervisor Support → Job Satisfaction |
0.677 |
Supported |
|
H6: Career Development → Job Satisfaction |
0.161 |
Supported |
|
H7: Compensation → Job Satisfaction → Intention to Stay |
0.032 |
Supported |
|
H8: Perceived Supervisor Support → Job satisfaction → Intention to
Stay |
0.216 |
Supported |
|
H9: Career Development → Job Satisfaction → Intention to Stay |
0.051 |
Supported |
|
H10: Job Satisfaction → Intention to Stay |
0.320 |
Supported |
Based on
table 9, it can be concluded that of the ten hypotheses proposed in the
research framework, all hypotheses show positive coefficient values in line
with the proposed hypothesis.
The Influence of Compensation on Intention to
Stay
In
statistical analysis, the null hypothesis (H0) and alternative hypothesis (H1)
will be tested based on the statistical results found. In this context, the
theoretical hypothesis used is that "compensation has a positive influence
on intention to stay". To test this hypothesis, a statistical hypothesis
can be formulated as follows:
a) H0
(Null Hypothesis): There is no positive influence between compensation on
intention to stay (ß=0).
b) H1
(Alternative hypothesis): There is a positive influence between compensation on
intention to stay (ß≠0).
The path
coefficient value of 0.353 indicates that there is a positive influence on
compensation and intention to stay so that H0 is rejected and H1 is accepted.
Referring to the path coefficient value, it is known that an increase in one
unit of compensation will increase the intention to stay by 0.353. These data
show that compensation has a positive relationship with intention to stay.
The influence of perceived supervisor support
on intention to stay
In
statistical analysis, the null hypothesis (H0) and alternative hypothesis (H1)
will be tested based on the statistical results found. In this context, the
theoretical hypothesis used is that "perceived supervisor support has a
positive influence on intention to stay". To test this hypothesis, a
statistical hypothesis can be formulated as follows:
a) H0
(Null Hypothesis): There is no positive influence between perceived supervisor
support on intention to stay (ß=0).
b) H1
(Alternative hypothesis): There is a positive influence between perceived
supervisor support on intention to stay (ß≠0).
The path
coefficient value of 0.035 indicates that there is a positive influence on
perceived supervisor support and intention to stay so that H0 is rejected and
H1 is accepted. Referring to the path coefficient value, it is known that an
increase in one unit of perceived supervisor support will increase the
intention to stay by 0.035. These data show that perceived supervisor support
has a positive relationship with intention to stay.
The Influence of Career Development on
Intention to Stay
In
statistical analysis, the null hypothesis (H0) and alternative hypothesis (H1)
will be tested based on the statistical results found. In this context, the
theoretical hypothesis used is that "career development has a positive
influence on intention to stay". To test this hypothesis, a statistical
hypothesis can be formulated as follows:
a) H0
(Null Hypothesis): There is no positive influence between career development on
intention to stay (ß=0).
b) H1
(Alternative hypothesis): There is a positive influence between career
development on intention to stay (ß≠0).
The path
coefficient value of 0.240 indicates that there is a positive influence on
career development and intention to stay so that H0 is rejected and H1 is
accepted. Referring to the path coefficient value, it is known that an increase
of one career development unit will increase the intention to stay by 0.240.
These data show that career development has a positive relationship with
intention to stay.
The Effect of Compensation on Job
Satisfaction
In
statistical analysis, the null hypothesis (H0) and alternative hypothesis (H1)
will be tested based on the statistical results found. In this context, the
theoretical hypothesis used is that "compensation has a positive influence
on job satisfaction". To test this hypothesis, a statistical hypothesis
can be formulated as follows:
a) H0
(Null Hypothesis): There is no positive influence between compensation on job
satisfaction (ß=0).
b) H1
(Alternative hypothesis): There is a positive influence between compensation on
job satisfaction (ß≠0).
The path
coefficient value of 0.101 indicates that there is a positive influence on
compensation and job satisfaction so that H0 is rejected and H1 is accepted.
Referring to the path coefficient value, it is known that an increase in one
unit of compensation will increase job satisfaction by 0.101. These data show
that compensation has a positive relationship with job satisfaction.
The Influence of Perceived Supervisor Support
on Job Satisfaction
In
statistical analysis, the null hypothesis (H0) and alternative hypothesis (H1)
will be tested based on the statistical results found. In this context, the
theoretical hypothesis used is that "perceived supervisor support has a
positive influence on job satisfaction". To test this hypothesis, a
statistical hypothesis can be formulated as follows:
a) H0
(Null Hypothesis): There is no positive influence between perceived supervisor
support on job satisfaction (ß=0).
b) H1
(Alternative hypothesis): There is a positive influence between perceived
supervisor support on job satisfaction (ß≠0).
The path
coefficient value of 0.677 indicates that there is a positive influence on
perceived supervisor support and job satisfaction so that H0 is rejected and H1
is accepted. Referring to the path coefficient value, it is known that an
increase in one unit of perceived supervisor support will increase job
satisfaction by 0.677. These data show that compensation has a positive
relationship with job satisfaction.
The Influence of Career Development on Job
Satisfaction
In
statistical analysis, the null hypothesis (H0) and alternative hypothesis (H1)
will be tested based on the statistical results found. In this context, the
theoretical hypothesis used is that "career development has a positive
influence on job satisfaction". To test this hypothesis, a statistical
hypothesis can be formulated as follows:
a) H0
(Null Hypothesis): There is no positive influence between career development
and job satisfaction (ß=0).
b) H1
(Alternative hypothesis): There is a positive influence between career
development and job satisfaction (ß≠0).
The path
coefficient value of 0.161 indicates that there is a positive influence on
career development and job satisfaction so that H0 is rejected and H1 is
accepted. Referring to the path coefficient value, it is known that an increase
of one career development unit will increase job satisfaction by 0.161. These
data show that compensation has a positive relationship with job satisfaction.
The influence of Compensation on Intention to
Stay is mediated by Job Satisfaction
In
statistical analysis, the null hypothesis (H0) and alternative hypothesis (H1)
will be tested based on the statistical results found. In this context, the
theoretical hypothesis used is that "compensation has a positive influence
on intention to stay which is mediated by job satisfaction". To test this
hypothesis, a statistical hypothesis can be formulated as follows:
a) H0
(Null Hypothesis): There is no positive influence between compensation on
intention to stay which is mediated by job satisfaction (ß=0).
b) H1
(Alternative hypothesis): There is a positive influence between compensation on
intention to stay which is mediated by job satisfaction (ß≠0).
The path
coefficient value of 0.032 indicates that there is a positive influence on
compensation and intention to stay which is mediated by job satisfaction so
that H0 is rejected and H1 is accepted. Referring to the path coefficient
value, it is known that an increase in one unit of compensation will increase
the intention to stay which is mediated by job satisfaction by 0.032. These
data show that compensation has a positive relationship with intention to stay
which is mediated by job satisfaction.
The influence of Perceived Supervisor Support
on Intention to Stay is mediated by Job Satisfaction
In
statistical analysis, the null hypothesis (H0) and alternative hypothesis (H1)
will be tested based on the statistical results found. In this context, the
theoretical hypothesis used is that "perceived supervisor support has a
positive influence on intention to stay which is mediated by job
satisfaction". To test this hypothesis, a statistical hypothesis can be
formulated as follows:
a) H0
(Null Hypothesis): There is no positive influence between perceived supervisor
support on intention to stay which is mediated by job satisfaction (ß=0).
b) H1
(Alternative hypothesis): There is a positive influence between perceived
supervisor support on intention to stay which is mediated by job satisfaction
(ß≠0).
The path
coefficient value of 0.216 indicates that there is a positive influence on
perceived supervisor support and intention to stay which is mediated by job
satisfaction so that H0 is rejected and H1 is accepted. Referring to the path
coefficient value, it is known that an increase in one unit of perceived
supervisor support will increase the intention to stay which is mediated by job
satisfaction by 0.216. These data show that perceived supervisor support has a
positive relationship with intention to stay which is mediated by job
satisfaction.
The influence of Career Development on
Intention to Stay mediated by Job Satisfaction
In
statistical analysis, the null hypothesis (H0) and alternative hypothesis (H1)
will be tested based on the statistical results found. In this context, the
theoretical hypothesis used is that "career development has a positive
influence on intention to stay which is mediated by job satisfaction". To
test this hypothesis, a statistical hypothesis can be formulated as follows:
a) H0
(Null Hypothesis): There is no positive influence between career development on
intention to stay which is mediated by job satisfaction (ß=0).
b) H1
(Alternative hypothesis): There is a positive influence between career
development on intention to stay which is mediated by job satisfaction (ß≠0).
The path
coefficient value of 0.051 indicates that there is a positive influence on
career development and intention to stay which is mediated by job satisfaction
so that H0 is rejected and H1 is accepted. Referring to the path coefficient
value, it is known that an increase of one career development unit will
increase the intention to stay which is mediated by job satisfaction by 0.051.
These data show that career development has a positive relationship with
intention to stay which is mediated by job satisfaction.
The Influence of Job Satisfaction on
Intention to Stay
In
statistical analysis, the null hypothesis (H0) and alternative hypothesis (H1)
will be tested based on the statistical results found. In this context, the
theoretical hypothesis used is that "job satisfaction has a positive
influence on intention to stay". To test this hypothesis, a statistical
hypothesis can be formulated as follows:
a) H0
(Null Hypothesis): There is no positive influence between job satisfaction and
intention to stay (ß=0).
b) H1
(Alternative hypothesis): There is a positive influence between job
satisfaction and intention to stay (ß≠0).
The path
coefficient value of 0.320 indicates that there is a positive influence on job
satisfaction and job satisfaction so that H0 is rejected and H1 is accepted.
Referring to the path coefficient value, it is known that a one unit increase
in job satisfaction will increase the intention to stay by 0.320. These data
show that job satisfaction has a positive relationship with intention to stay.
Importance
– Performance Analysis (IPMA)
Importance-performance
analysis (IPMA) is used to identify the most important variables and indicators
that have the best performance, and influence the dependent variable or target
construct in the research model. In IPMA analysis, two axes will be formed,
namely the x-axis and the y-axis. The x-axis contains the importance value
obtained from the total effect value and the y-axis contains the average value.
The value of the effect size is used as a reference for dividing the IPMA
analysis results into quadrants. The purpose of this analysis is to identify
factors that have shown poor performance and require improvement.
Figure 3. IPMA construct result
From
Figure 3, it can be found that the job satisfaction variable is at the top
right of the image, which indicates that this variable is very important and
shows high performance. Thus, the higher the employee's job satisfaction, the
higher the intention to stay they feel.
Figure
4. IPMA indicator results
Figure
5. IPMA indicator quadrant
Figure 5
shows the results of indicators that show the level of importance and
performance of various variables. These factors are distributed across
quadrants and indicate their level of importance and performance. In the
figure, it is found that the indicators in this research are divided into four
quadrants. The indicators in the top right group are considered very important
because they produce a large effect size and good performance, therefore they
are grouped in quadrant I. Variables in quadrant I are variables that need to
be maintained because they have good performance. Quadrant II is in the bottom
right group and is considered quite important but could become the next
priority if the indicators in quadrant I have been improved. The indicators in
the left group are indicators that are considered not very important because
they have a low effect size. In quadrant III, although it produces quite high
performance, the effect is not very significant. Quadrant IV has small effect
size and performance values.
Discussion
The
purpose of this research is to empirically test the research model in the
context of compensation, perceived supervisor support, career development, job
satisfaction and intention to stay for XYZ manufacturing employees. Based on
the research framework that has been prepared, there are ten hypotheses that
will be tested. The results of the hypothesis test showed a positive effect,
indicating that the ten hypotheses were supported in this research. Structural
analysis also shows that based on the r-square and q-square values, this
research model has a good level of predictive relevance.
The
research results show that the variable that has the most influence on
intention to stay is compensation of 0.353, followed by job satisfaction of
0.320. Meanwhile, regarding job satisfaction, the most influential variable is
perceived supervisor support at 0.677. This figure shows that the higher the
compensation felt by employees, the higher the intention to stay as well as the
perceived job satisfaction. The higher the job satisfaction, the higher the
employee's intention to stay. Apart from that, high perceived supervisor
support is also followed by high job satisfaction felt by employees. The
findings of this research show the importance of these variables in increasing
employees' intention to stay.
Hypothesis
1 in this study tests the effect of compensation on intention to stay. The
research results show that compensation has a positive influence on intention
to stay. This indicates that if the company wants to increase intention to
stay, employee compensation needs to be increased. The results of this research
are also in line with research conducted byAman-Ullah et al., (2023)where individuals who feel satisfied with the
compensation they receive will tend to choose to stay with the company where
they work. Juariyah et al (2020) stated the same
thing that compensation is one of the factors that influences someone to stay
with their organization.
Hypothesis 2 in this study tests the influence
of perceived supervisor support on intention to stay. The research results show
that perceived supervisor support has a positive influence on intention to
stay. An indication of this result is that if employees feel they have support
from their superiors, their tendency to stay with the company increases.
Research conducted by Chami-Malaeb (2022) on nurses regardingperceived
supervisor supportgave similar results in this study where
perceived superior support can help a person's tendency to move.
Hypothesis 3 in this study tests the
influence of career development on intention to stay. The research results show
that career development has a positive influence on intention to stay.With career development, organizations can develop
and plan career development so that employee turnover rates decrease. Research
conducted by Rahayu et al (2019) shows that employees
who feel that their talents are supported and appreciated by the company tend
not to look for other jobs.
Hypothesis 4 in this study tests the
effect of compensation on job satisfaction. The research results show that
compensation influences job satisfaction. Employees who feel that they receive
sufficient salary will feel satisfied with their work and this is in line with
research results. Sinuruya & Ekawati
(2023) found that compensation and job satisfaction have a positive
relationship, the higher the compensation, the level of satisfaction increases.
Aman-Ullah et al (2023) in their research stated that fair and adequate
compensation has a positive impact on job satisfaction. Employees who feel
appreciated will be more motivated.
Hypothesis 5 in this study tests the
influence of perceived supervisor support on job satisfaction. The results of
this research indicate that perceived supervisor support has a positive
influence on job satisfaction. The results of research conducted by Afzal et al
(2010) show that superior support helps employees develop a positive attitude
towards the company which ultimately also improves performance. Winarto and Chalidyanto (2020) in
their research stated that employees who feel satisfied are employees who
receive support from their superiors.
Hypothesis 6 in this study tests the
influence of career development on job satisfaction. The research results show
that career development has a positive influence on job satisfaction. Feriyadit et al (2024) found that career development has
the potential to increase a person's job satisfaction. Employees who are more
satisfied with their careers feel their goals are fulfilled. Achmad et al (2023) also found similar results that career
development influences job satisfaction. Job satisfaction is the result of a
person's career development(Ashraf,
2019).
Hypothesis 7 in this study tests the
effect of compensation on intention to stay which is mediated by job
satisfaction. The research results show that compensation mediated by job
satisfaction has a positive influence on intention to stay. This is in line
with research conducted by Fransinatra & Asyik (2023), Aman-Ullah et al (2023) that compensation has
a contribution to a person's intention to stay. Agustine
& Nawangsari (2020) conducted the same research
with job satisfaction as a mediating variable and found that compensation had a
negative effect on turnover. This is in line with research results that
employees who are satisfied with compensation will also feel satisfied with
their work and this will influence their intention to stay at the company.
Hypothesis 8 in this study tests the
influence of perceived supervisor support on intention to stay which is
mediated by job satisfaction. The research results show that perceived
supervisor support mediated by job satisfaction has a positive influence on
intention to stay. Perceived supervisor support influences a person's
commitment to their organization (Winarto & Chalidyanto, 2020). Alkhateri
& Nusari (2018) revealed that job satisfaction
can mediate the influence of superior support on nurses' turnover intention and
the same thing can be seen in the results of this study. Dhir et al (2020) also
found that employees who feel supported by their superiors tend to have
satisfaction at work and this influences loyalty to the organization.
Hypothesis 9 in this study tests the
influence of career development on intention to stay which is mediated by job
satisfaction. The research results show that career development mediated by job
satisfaction has a positive influence on intention to stay. Job satisfaction
reduces employees' tendency to move. Jena & Nayak (2023) found that career
development provided by the organization increased employee job satisfaction.
This encourages employees to stay with their current organization.
Hypothesis 10 in this study tests
the influence of job satisfaction on intention to stay. The research results
show that job satisfaction has a positive influence on intention to stay.
Employees who are satisfied with their jobs and other related aspects have a
lower tendency to move from their jobs (Azis et al,
2019) and this is in line with the results of this research. The results of
this research are also in line with those conducted by Thakur & Arora
(2022) that employees who feel satisfied with their work will tend to feel
comfortable and therefore choose to stay at their job.
Conclusion
This research illustrates that in the context
of the manufacturing industry, the challenge of retaining employees is
important. Based on the results of the research conducted, the following
conclusions were obtained: (1) Compensationhas a positive influence on intention to stay.
(2) Perceived supervisor supporthas a
positive influence on intention to stay. (3) Career developmenthas
a positive influence on intention to stay. (4) Compensationhas a positive
influence on job satisfaction. (5) Perceived
supervisor supporthas
a positive influence on intention to stay. (6) Career developmenthas
a positive influence on intention to stay. (7) Compensationwhich is mediated by
job satisfaction has a positive influence on intention to stay. (8) Perceived supervisor supportwhich is mediated by job
satisfaction has a positive influence on intention to stay. (9) Career developmentwhich is mediated by job
satisfaction has a positive influence on intention to stay. And (10) Job satisfactionhas a positive influence on
intention to stay.
Compensation that is adequate and appropriate
to employee contributions is an important factor in retaining employees.
Employees will feel appreciated and motivated to continue working at the
company if they receive compensation that is commensurate with their
performance and responsibilities. Apart from that, the opportunity to develop a
career is a very important factor for employees. Employees want opportunities
to grow and develop professionally. If the company provides a good career
development program, this will increase employee satisfaction and loyalty.
Supervisor support can also influence the level of employee satisfaction and
loyalty to the company. Leaders who are able to create a positive work
environment, provide support, and appreciate employee contributions will make
employees feel valued and committed to the company.
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Copyright holder: Monika Teresia, Ardi, Richard Andre Sunarjo (2024) |
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First publication rights: Syntax Literate: Jurnal
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