Syntax Literate: Jurnal Ilmiah Indonesia
p–ISSN: 2541-0849 e-ISSN: 2548-1398
Vol.
9, No. 9, September 2024
THE INFLUENCE OF MANAGERIAL COMMUNICATION, WORKLOAD, AND COMPENSATION ON TURNOVER INTENTION WITH JOB SATISFACTION AS A MEDIATION VARIABLE ON XYZ INSURANCE EMPLOYEES IN TANGERANG
Nadia
Erlikasna Br Pandia1, Ardi2, Richard Andre Sunarjo3
Universitas Pelita Harapan, Banten, Indonesia1,2,3
Email: [email protected]1, [email protected]2
This study aims to
determine the influence of managerial communication, workload, and compensation
on turnover intention, with job satisfaction serving as a mediating variable. The
study was conducted at the XYZ Insurance office in Tangerang, involving 138
employees, with sample size determined using the Krejcie-Morgan formula and
purposive sampling method. Data was collected through questionnaires
distributed via Google Forms, containing 40 questions with 1-5 Likert scale.
The data was analyzed using the partial least squares structural equation
modeling (PLS-SEM) method. The results indicate that managerial communication,
workload, and compensation negatively affect turnover intention among XYZ
Insurance employees. Furthermore, the study demonstrates that job satisfaction
mediates the relationship between managerial communication, workload, and
compensation on turnover intention.
Key
Words: Managerial Communication, Workload,
Compensation, Job Satisfaction, Turnover Intention.
As a highly regulated industry, general insurance
companies require new employees to undergo extensive training and certification
to perform their jobs effectively. The costs associated with human resource
development in insurance are significant. Therefore, these companies face
substantial losses if trained employees decide to leave. Beyond the high costs
of training, employee departures can disrupt company operations due to the
limited availability of skilled workers in the insurance sector. Finding replacements
takes time, and once found, the expense of training new hires is substantial.
For instance, one crucial certification for employees in key insurance business
processes is the Insurance Risk Management Expert Certification (AMRP), which
costs IDR 17,500,000 per employee (Barafort, Mesquida, & Mas, 2018)
The
conditions described above prompt insurance companies to address employee
turnover more attentively. Turnover is a significant issue for XYZ Insurance,
evidenced by a rising turnover rate throughout 2023. This issue affects both
new employees and those with over five years of service. A key strategy to
mitigate employee turnover is understanding the factors influencing turnover
intention, which refers to an employee's desire to leave the company for other
opportunities (Jaharuddin & Zainol, 2019).
XYZ
Insurance has identified several factors contributing to turnover through exit
interviews conducted by the Talent Management Department with all employees who
resigned in 2023. The primary reasons for resignation include issues with
managerial communication, workload, and compensation.
Previous
research indicates that employee workload significantly impacts turnover
intention. Increased workload can elevate stress levels, reducing employee
commitment to their current employer. It also leads to hasty work attitudes,
increasing workplace pressure and turnover intention (Omar et al., 2020).
Compensation is another major factor in employee turnover at XYZ Insurance.
Research by Vizano et al. (2021) found that inadequate compensation negatively
affects job satisfaction and increases turnover intention.
Given the
turnover challenges faced by XYZ Insurance and insights from previous research
on turnover intention, a study is needed to investigate the impact of
managerial communication, workload, and compensation on turnover intention,
with job satisfaction as a mediating factor among XYZ Insurance employees in
Tangerang. This research will also delve into the role of job satisfaction as a
mediator influencing these variables and turnover intention.
Research conducted by De Leon (2021) on two newly merged banks in the Philippines indicates that managerial communication significantly influences turnover intention. This suggests that increased miscommunication within a company can lead to higher turnover intention. However, Hidayat and Tannady's (2023) research presents a slightly different perspective, showing that while work communication affects turnover intention, the relationship is not direct. In other words, improving managerial communication skills in a company can reduce turnover intention. Based on these previous studies, the proposed hypothesis is:H1: Managerial Communication has a negative influence on Turnover Intention
As discussed in the previous section, excessive workload is a key factor influencing an employee's decision to change jobs. Research conducted by Junaidi et al. (2020) shows that workload has a positive effect on turnover intention. This means that any increase in workload will automatically raise turnover intention. Additionally, Omar et al. (2020) found that an employee's desire to leave a company is significantly influenced by the existing workload at insurance companies in Malaysia, highlighting the need for management to address this issue. Based on these research findings, the following hypothesis is proposed:H2: Workload has a positive influence on Turnover Intention
According to Yeo et al. (2020), compensation has a positive effect on turnover intention. This means that increasing the compensation given to employees reduces their turnover intention. Similarly, research by Sofi’i et al. (2023) found that compensation can decrease employees' desire to leave the company. This indicates that higher compensation lowers turnover intention, showing that compensation negatively affects turnover intention (Vizano et al., 2021). Additionally, research by Chang et al. (2023) found that compensation is closely related to turnover intention in the internet industry. Based on these previous studies, the following hypothesis is proposed:H3: Compensation has a negative influence on Turnover Intention
The findings on
the impact of managerial communication on turnover intention, as well as its
influence on job satisfaction, form the basis for testing job satisfaction as a
mediating variable between managerial communication and turnover intention.
Duarte and Silva (2023) show that job satisfaction can mediate the relationship
between managerial communication and turnover intention. This means that
improved employee satisfaction with managerial communication within the company
leads to higher job satisfaction, which in turn reduces employees' desire to
leave the company. This research supports the following hypothesis:
H4: Job satisfaction can mediate the
influence of Managerial Communication on Turnover Intention
This
research also evaluates the role of job satisfaction as a mediator in the
relationship between workload (independent variable) and turnover intention
(dependent variable). Jayasri and Annisa (2023) concluded that job satisfaction
can mediate the effect of workload on turnover intention. However, Illahi et
al. (2022) found that job satisfaction does not mediate the relationship
between workload and turnover intention. In contrast, Dwinijanti et al. (2020)
demonstrated that job satisfaction can act as a mediator between workload and
turnover intention. Given the variations in previous research findings, the
author proposes the hypothesis based on the more dominant findings:
H5: Job
satisfaction can mediate the influence of Workload on Turnover Intention
Research
by Sofi’i et al. (2023) concluded that job satisfaction can negatively mediate
the relationship between compensation and turnover intention. This means that
better compensation management leads to higher employee job satisfaction at PT
Infomedia Nusantara, which in turn reduces the intention to change jobs. This
finding is supported by research from Illahi et al. (2022) and Aman-Ullah et
al. (2023), both of which also concluded that job satisfaction mediates the
relationship between compensation and turnover intention. Based on these
findings, the author proposes the following hypothesis:
H6: Job
satisfaction can mediate the influence of Compensation on Turnover Intention
The research used Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS version 4 software for data analysis. PLS-SEM facilitates analysis of intricate relationships between dependent and independent variables without requiring normally distributed data or large samples, employing bootstrapping methods. The data analysis process encompassed data organization, assessing variable quality (outer model), and evaluating relationships between variables (inner model). This approach enhances the ability to comprehensively interpret data outcomes and test hypotheses based on variable measurements in the study.
Results and Discussion
The following are the results of tests carried out in
inferential data analysis:
According to Ghozali and Latan (2015),
the aim of testing the outer model is to assess validity through convergent
validity and discriminant validity, as well as evaluating model reliability
through composite reliability and Cronbach's alpha. The following are the
results of the outer model test:
Figure
1. Outer Model Algorithm Results
Source: Data Processing Output
(2024)
Convergent
validity testing involves evaluating each indicator of the constructs.
According to Afthanorhan (2013), an indicator is deemed valid if it has a
loading factor exceeding 0.70, while a loading factor between 0.50 and 0.60 is
considered acceptable. Indicators with outer loadings below 0.50 are removed
from the model based on this criterion. Besides outer loadings, convergent
validity testing also includes measuring the average variance extracted (AVE).
AVE testing
assesses how much variation is captured by a construct from its indicators,
accounting for measurement error. AVE is considered more critical than
composite reliability, with a minimum recommended value of 0.50. Based on data
processing, all indicators show outer loading results above 0.70, except for
the WL 1 indicator which has an outer loading of 0.668, still considered valid
according to criteria. Additionally, based on AVE test results, all variables
demonstrate values above 0.5, indicating the validity of all indicators for
further testing purposes.
The
next step is to compare the correlation between variables with the root AVE
(√AVE). The measurement model has good discriminant validity if the √AVE of each
variable is higher than the correlation between variables. The results show
that the square root of the average variance extracted (AVE) for each construct
is greater than the correlation between one construct and other constructs in
the model. Based on this statement, the construct in the estimated model meets
the discriminant validity criteria. Then the results obtained were that the
relationship value of the variable with the indicator was higher than the
relationship value of the indicator with other variables. These results
indicate the conclusion that all indicators for each variable in this study are
able to measure the target variable.
To ensure there are no problems
related to measurement, the final step in evaluating the outer model is to test
the reliability of the model. Reliability testing was carried out using
composite reliability indicators and Cronbach's Alpha. If all latent variables
have Composite Reliability and Cronbach's Alpha values ≥ 0.70, then the
construct has good reliability, or in other words, the questionnaire used in
this research is consistent. The results obtained from the SmartPLS program for
testing composite reliability and Cronbach's Alpha show values that meet the
criteria, namely that all latent variables are reliable. This is because all
values for the latent variables have good composite reliability and
Cronbach's Alpha are greater than or equal to 0.70. These tests provide the
conclusion that the questionnaire used as a research tool is reliable or
consistent.
After the model has met the
criteria for the outer model, the structural model or what can also be called
the inner model is then tested. Inner model testing in order to analyze the
relationship between independent and dependent variables in this research
model. The testing stages of the structural model (inner model) are carried out
by carrying out various tests. The following are the test results for the inner
model:
The R-square test is a test that
aims to find out how much the independent variable contributes to the dependent
variable. The R-square test results used in this research are adjusted
R-square. This is because this research model has more than one independent
variable. If the R-square value is small, it means that the independent
variables in this research model cannot represent all the information to
predict the dependent variable or in other words the variation in the existing
independent variables is still very limited. However, if the R-square value of
the test results is close to one, it indicates that the independent variables
can represent all the information needed to predict the dependent variable (Sarstedt, Ringle, & Hair, 2021).
The
result is that the adjusted R-square value is 0.569. This means that the
dependent variable that can be explained by all the independent variables is
56.9% and the remainder is explained by other variables outside this research
model is 43.1%.
Table
2. Direct and Indirect Hypothesis Test Results
|
Hypothesis |
T Statistics |
P Value |
Standardized Path Coefficient |
Confidence Interval (CI) |
Decision |
|
|
5
% |
95% |
|||||
|
H1: Managerial Communication has a negative influence
on Turnover Intention |
4,718 |
0,000 |
-0.305 |
-0.406 |
-0.193 |
Supported |
|
H2: Workload has a positive influence on Turnover
Intention |
2,257 |
0.012 |
-0.165 |
-0.296 |
-0.057 |
Not supported |
|
H3: Compensation has a negative influence on
Turnover Intention |
2,550 |
0.005 |
-0.176 |
-0.292 |
-0.067 |
Supported |
|
H4: Job satisfaction can mediate the influence of
Managerial Communication on Turnover Intention |
3,869 |
0,000 |
-0,149 |
-0,211 |
-0,084 |
Supported |
|
H5: Job satisfaction can mediate the influence of
Workload on Turnover Intention |
2,873 |
0,002 |
-0,096 |
-0,156 |
-0,046 |
Supported |
|
H6: Job satisfaction can mediate the influence of
Compensation on Turnover Intention |
2,646 |
0,004 |
-0,099 |
-0,163 |
-0,042 |
Supported |
Source: Data Processing Output (2024)
The second hypothesis in this study
examines the purported positive influence of workload on turnover intention.
Based on the research outcomes presented in Table 2, this hypothesis is
rejected, indicating that workload does not positively affect turnover
intention. On the contrary, the findings demonstrate that workload actually has
a negative impact on turnover intention. These results diverge from previous
studies by Dwinijanti et al. (2020), Junaidi et al. (2020), Omar et al. (2020),
and Situmorang and Wardhani (2022), but are consistent with the findings of
Hidayat and Tannady (2023), which suggest that a heavier workload increases
employee engagement and commitment. This research, conducted among 145
Generation Z employees in Indonesia, indirectly suggests that generational
differences may influence how XYZ Insurance employees perceive their workload.
Essentially, employees at XYZ Insurance do not believe that a heavier workload
increases their desire to leave the company.
The third hypothesis posited by
researchers in this study investigates the influence of compensation on
turnover intention. According to the path coefficient analysis presented in
Table 2, compensation exhibits a significant negative influence on turnover
intention. This indicates that higher compensation levels perceived by XYZ
Insurance employees correlate with reduced intentions to leave the company.
These findings corroborate previous research by Aman-Ullah et al. (2023),
Asriani and Riyanto (2020), Chang et al. (2023), Vizano et al. (2021), and Yeo
et al. (2020), which consistently suggest that compensation negatively impacts
turnover intention.
Next, the fourth hypothesis examines
whether job satisfaction mediates the influence of managerial communication on
turnover intention. As indicated in Table 2, job satisfaction negatively
mediates the impact of managerial communication on turnover intention. This
implies that when employees experience good managerial communication, their job
satisfaction increases, subsequently reducing their desire to leave XYZ
Insurance. These findings align with research by Duarte and Silva (2023),
underscoring that internal communication significantly influences job
satisfaction, which in turn impacts employees' decisions to remain with or
leave the company.
The fifth hypothesis investigates
whether job satisfaction mediates the influence of workload on turnover
intention. Previous studies by Jayasri dan Annisa (2023) and Dwinijanti et al.
(2020) have demonstrated that job satisfaction can indeed mediate the effect of
workload on turnover intention.
The sixth hypothesis examines
whether job satisfaction mediates the influence of compensation on turnover
intention (Rubel & Kee, 2015).
Previous research by (Huang & Su, 2016)
supports the notion that job satisfaction can mediate the relationship between
compensation and turnover intention, indicating that higher satisfaction with
compensation reduces turnover intention.
This
research investigates the impact of managerial communication, workload, and
compensation on turnover intention among XYZ Insurance employees in Tangerang.
The study employs a research model encompassing ten hypotheses and utilizes
data analysis through SmartPLS 4.0. The findings indicate that managerial
communication negatively influences turnover intention, as does workload and
compensation. Additionally, job satisfaction serves as a mediator in the
relationships between managerial communication, workload, compensation, and
turnover intention.
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