Syntax Literate:
Jurnal Ilmiah Indonesia p–ISSN: 2541-0849 e-ISSN: 2548-1398
Vol. 7, No. 11, November
2022
Analysis of Job
Satisfaction Antecedents and its Impact on Hospital Performance from The
Perspective of Healthcare Professionals at XYZ Hospital in East Java
Salsabilla Maula Zalfa El
Hamzah, Yohana Cahya F. Palupi
Meilani
Study Program of Hospital Administration
Master Program, Faculty of Economics and Business, Universitas Pelita Harapan
Jakarta, Indonesia.
Email: [email protected], [email protected]
Abstract
Company performance is an important component
in a wide range of empirical research, particularly business policy research.
The performance of the company is essentially a complex phenomenon with
multiple dimensions and known to be correlated with job satisfaction among
healthcare professionals. Therefore, understanding the factors associated with
job satisfaction is important. The purpose of this study was to analyze the
antecedents of job satisfaction and analyze their impact on hospital
performance from the perspective of healthcare professionals. This research was
conducted at XYZ Hospital. This research method is a quantitative study with a
total sample of 160 healthcare professionals
respondents who meet the research criteria. The research sample was taken using
a purposive sampling method and data collection was carried out by distributing
questionnaires online. The data obtained were analyzed using SmartPLS. The results of this study indicate that there are
five independent variables as antecedents of job satisfaction. These variables
are social support, work operation requirements, healthcare
professional-patient relationship, work-family conflict, and working
conditions. In social support variables, work operation requirements,
healthcare professional-patient relationship, and working conditions have a
positive effect on job satisfaction and have a significant effect while the
work-family conflict variable also has a significant impact but has a negative
effect on job satisfaction (T-statistic>1.645 and P-value <0.05). In
addition, job satisfaction also significantly-positively affects hospital
performance (T-statistic>1.645 and P-value <0.05). Furthermore, job
satisfaction is significant (T-statistic>1.645 and P-value <0.05)
mediates social support, healthcare professional-patient relationship,
work-family conflict, and working conditions on hospital performance.
Keywords: Antecedents; Job Satisfaction; Hospital
Performance.
Introduction
According to the World
Health Organization (WHO), hospitals are fundamental to the formation of a
healthcare system. In many regions of the world, external pressures,
health-care system defects, and hospital-sector deficits are currently driving
a new vision for hospitals. In this view, they play a crucial role in assisting
other healthcare providers, as well as in community outreach and home-based
services, and are essential to a well-functioning referral network. In addition
to reflecting the needs and values of the communities they serve,
hospitals must be robust and capable of maintaining and expanding services
during emergency situations. Effective hospitals are created with certain
groups in mind, such as children and the elderly.
The administration of a
hospital aspires for excellence in patient care by delivering high-quality
services given by highly motivated personnel. Frequently, hospitals struggle to
retain highly trained personnel. In such situations, hospital administrators
generally view healthcare workers as essential capital assets and adopt a range
of techniques to recruit and retain them. Empirical research reveals that a
higher level of job satisfaction is associated with a decrease in employee
turnover, which is correlated with improved patient care quality and hospital
performance (WHO, 2017).
The inner thoughts and
attitudes of health workers toward their job and other work-related factors,
such as the work environment, which represent their subjective sense of job
satisfaction, constitute their job satisfaction. A cross-sectional survey of primary
care critical public health practitioners revealed only modest work
satisfaction among health professionals (Chen, Liu, Liu, Ruan,
Yuan & Xiong, 2020). Patients are more likely to
receive high-quality care from health professionals who are fulfilled in their
employment. A low degree of job satisfaction, on the other hand, may be
indicative of decision-making and hospital management concerns, negatively
influencing the quality and efficiency of health services, damaging
doctor-patient relationships, and decreasing patient satisfaction (Zhang et
al., 2021). Investigating the elements that influence the job satisfaction of
health professionals can therefore contribute to the development of sound
healthcare policy and provide valuable insights for enhancing their job
satisfaction.
The "two-factor
theory" of Herzberg is one of the most well-known theories to study and
comprehend job happiness. This theory is also known as the motivation-hygiene
theory or dual-factor theory since it presents a collection of motivation and hygiene
components that effect job satisfaction and discontent. According to this idea,
job happiness is determined by a set of "motivational variables"
inherent to the position, such as the possibility for personal improvement,
acknowledgment for one's achievements, and career advancement. In contrast,
"hygiene variables" are external to the job and include
organizational policies, interactions with others, personal life, salary, and
job security. Despite the fact that the two-factor theory was first established
in 1966, many scholars believe it is still relevant and have utilized it to
better comprehend and analyze the job satisfaction of health care professionals
(Chen et.al., 2020).
XYZ Hospital health workers
chose job dissatisfaction as the main factor causing the decline in hospital
performance. The second cause is heavy workload and the third cause is
interprofessional conflict. Fifteen respondents who experienced job dissatisfaction
were then conducted a follow-up survey. Then, more in-depth questions were
asked to the respondents to be able to explain the causes of their job
dissatisfaction. The following is an explanation from XYZ Hospital health
workers regarding the job dissatisfaction experienced.
This study proposes a new
research model based on the modification of several previous research models
concerning the antecedents of job satisfaction, namely social support, work
operation requirements, healthcare professional-patient relationship, work-family
conflict, and working conditions, and their influence on hospital performance
as perceived by healthcare professionals at XYZ Hospital. This research is
anticipated to yield knowledge and progress toward enhancing hospital
performance, with the ultimate aim of yielding managerial implications that are
advantageous to all parties.
The aim of this study, based
on the research questions in the previous sub-chapter, is to analyze various
factors that contribute to job satisfaction. These factors include the positive
impact of social support, work operation requirements, healthcare professional-patient
relationships, and working conditions on job satisfaction. Additionally, the
study aims to examine the negative impact of work-family conflict on job
satisfaction. Furthermore, the study seeks to explore the positive impact of
job satisfaction on hospital performance. Through comprehensive analysis and
evaluation, this research aims to provide insights into the relationships
between these factors and job satisfaction, ultimately contributing to a better
understanding of the factors that influence employee satisfaction in a hospital
setting.
According to the World
Health Organization (WHO), hospitals are fundamental to the formation of a
healthcare system. In many regions of the world, external pressures,
health-care system defects, and hospital-sector deficits are currently driving
a new vision for hospitals. In this view, they play a crucial role in assisting
other healthcare providers, as well as in community outreach and home-based
services, and are essential to a well-functioning referral network. In addition
to reflecting the needs and values of the communities they serve,
hospitals must be robust and capable of maintaining and expanding services
during emergency situations. Effective hospitals are created with certain
groups in mind, such as children and the elderly.
The administration of a
hospital aspires for excellence in patient care by delivering high-quality
services given by highly motivated personnel. Frequently, hospitals struggle to
retain highly trained personnel. In such situations, hospital administrators
generally view healthcare workers as essential capital assets and adopt a range
of techniques to recruit and retain them. Empirical research reveals that a
higher level of job satisfaction is associated with a decrease in employee
turnover, which is correlated with improved patient care quality and hospital
performance (WHO, 2017).
The inner thoughts and
attitudes of health workers toward their job and other work-related factors,
such as the work environment, which represent their subjective sense of job
satisfaction, constitute their job satisfaction. A cross-sectional survey of primary
care critical public health practitioners revealed only modest work
satisfaction among health professionals (Chen, Liu, Liu, Ruan,
Yuan & Xiong, 2020). Patients are more likely to
receive high-quality care from health professionals who are fulfilled in their
employment. A low degree of job satisfaction, on the other hand, may be
indicative of decision-making and hospital management concerns, negatively
influencing the quality and efficiency of health services, damaging
doctor-patient relationships, and decreasing patient satisfaction (Zhang et
al., 2021). Investigating the elements that influence the job satisfaction of
health professionals can therefore contribute to the development of sound
healthcare policy and provide valuable insights for enhancing their job
satisfaction.
The "two-factor
theory" of Herzberg is one of the most well-known theories to study and
comprehend job happiness. This theory is also known as the motivation-hygiene
theory or dual-factor theory since it presents a collection of motivation and hygiene
components that effect job satisfaction and discontent. According to this idea,
job happiness is determined by a set of "motivational variables"
inherent to the position, such as the possibility for personal improvement,
acknowledgment for one's achievements, and career advancement. In contrast,
"hygiene variables" are external to the job and include
organizational policies, interactions with others, personal life, salary, and
job security. Despite the fact that the two-factor theory was first established
in 1966, many scholars believe it is still relevant and have utilized it to
better comprehend and analyze the job satisfaction of health care professionals
(Chen et.al., 2020).
XYZ Hospital health workers
chose job dissatisfaction as the main factor causing the decline in hospital
performance. The second cause is heavy workload and the third cause is
interprofessional conflict. Fifteen respondents who experienced job dissatisfaction
were then conducted a follow-up survey. Then, more in-depth questions were
asked to the respondents to be able to explain the causes of their job
dissatisfaction. The following is an explanation from XYZ Hospital health
workers regarding the job dissatisfaction experienced.
This study proposes a new
research model based on the modification of several previous research models
concerning the antecedents of job satisfaction, namely social support, work
operation requirements, healthcare professional-patient relationship, work-family
conflict, and working conditions, and their influence on hospital performance
as perceived by healthcare professionals at XYZ Hospital. This research is
anticipated to yield knowledge and progress toward enhancing hospital
performance, with the ultimate aim of yielding managerial implications that are
advantageous to all parties.
The aim of this study, based
on the research questions in the previous sub-chapter, is to analyze various
factors that contribute to job satisfaction. These factors include the positive
impact of social support, work operation requirements, healthcare professional-patient
relationships, and working conditions on job satisfaction. Additionally, the
study aims to examine the negative impact of work-family conflict on job
satisfaction. Furthermore, the study seeks to explore the positive impact of
job satisfaction on hospital performance. Through comprehensive analysis and
evaluation, this research aims to provide insights into the relationships
between these factors and job satisfaction, ultimately contributing to a better
understanding of the factors that influence employee satisfaction in a hospital
setting.
Research Methods
The research methodology used in this study follows a quantitative
approach with a survey-based design. The study does not involve any
intervention on the research subjects, making it a non-interventional study.
Data collection was conducted using a questionnaire instrument, and the study
utilized a cross-sectional design, collecting data from August to September
2022. The validity of the measurement was assessed through content validity,
construct validity (convergent and discriminant validity), and criterion
validity. The reliability of the instruments was evaluated using the Cronbach
Alpha test. The analysis of the data involved inferential statistical methods,
including hypothesis testing and path analysis using bootstrapping. The inner
model was evaluated using R-Square and significance values, while the quality
of the model was assessed using the Q2 value. The hypothesis testing involved
comparing the T-statistic values with the T-table values and examining the
direction of the coefficients. Additionally, importance-performance analysis
and a pilot test were conducted to ensure questionnaire validity and
reliability. The research employed SmartPLS software
for data processing and analysis.
The outer model analysis in this study utilizes SmartPLS
to assess the validity and reliability of the measurement instrument. The
analysis consists of two parts: the validity test and the reliability test. The
validity test involves evaluating indicator reliability (outer loading),
construct reliability (Cronbach's alpha and composite reliability), construct
validity (Average Variance Extracted), and discriminant validity (heterotrait-monotrait ratio). The pretest results using SmartPLS software indicate that out of the 40 indicators
proposed in the study, 39 indicators are reliable and valid for measuring the
constructs. One indicator, WO 5, falls below the required threshold and is
removed. The construct reliability, assessed through Cronbach's alpha and
composite reliability, is within the acceptable range of 0.7-0.95, indicating
non-redundancy. The construct validity is confirmed through the Average
Variance Extracted (AVE) values, which exceed 0.5 for all variables,
demonstrating convergent validity. Discriminant validity is established by
analyzing the heterotrait-monotrait ratio (HTMT),
with all values below 0.9, indicating that the constructs are empirically
distinct from each other. Overall, the findings support the reliability,
validity, and discriminant validity of the measurement instrument in this study.
In this study, 160
respondents responded and were used for actual tests conducted by the author.
The data in this study were obtained using an online questionnaire. The
respondents in this study were healthcare professionals at XYZ Hospital in East
Java.
In the analysis with
PLS-SEM, the first step is to evaluate the outer model by assessing the
relationship between the indicators and their latency (Hair et al., 2019).
Analysis on the outer model consists of two types of data testing, namely
reliability testing and validity testing. In this study, the variables used are
reflective variables so that reflective measurement model assessments are used.
In the reliability test two values were carried out, namely, indicator
reliability by looking at the outer loading and construct reliability by
looking at the composite realibility and Cronbach's
alpha values.
Figure 1. Outer Model Result
Source: Research Data Analysis Using Smart-PLS (2023)
The outer model results of
this study indicate the use of 39 reliable indicators for measuring the
construct. The indicators demonstrate outer loading values above 0.4,
indicating their reliability. Construct reliability is assessed through
composite reliability and Cronbach's alpha values, with all variables
exhibiting values ranging from 0.7 to 0.95, indicating internal consistency and
reliability without redundancies. The construct validity is assessed through
average variance extracted (AVE), which confirms convergent validity with all
AVE values exceeding 0.5. Lastly, discriminant validity is evaluated using the heterotrait-monotrait ratio of correlations (HTMT), where a
ratio below 0.9 is considered valid. These findings demonstrate the reliability
and validity of the indicators and constructs in this study.
Table 1.Discriminant Validity
Result
|
|
Healthcare Professional-Patient Relationship |
Hospital Performance |
Job Satisfaction |
Social Support |
Work Operation
Requirement |
Work-Family Conflict |
Working Condition |
|
Healthcare Professional-Patient Relationship |
|
|
|
|
|
|
|
|
Hospital Performance |
0.607 |
|
|
|
|
|
|
|
Job Satisfaction |
0.66 |
0.725 |
|
|
|
|
|
|
Social Support |
0.588 |
0.583 |
0.707 |
|
|
|
|
|
Work Operation
Requirement |
0.578 |
0.449 |
0.34 |
0.254 |
|
|
|
|
Work-Family Conflict |
0.223 |
0.101 |
0.203 |
0.119 |
0.419 |
|
|
|
Working Condition |
0.598 |
0.745 |
0.878 |
0.704 |
0.269 |
0.162 |
|
Table 1 displays the HTMT
values in this study, and it is evident that none of the HTMT values exceed
0.9. This indicates that the constructs in the study are valid and empirically
distinct from other constructs in the structural model, and the indicators are
capable of specifically measuring their respective constructs. With all 39
indicators deemed reliable and valid for measuring each construct, the analysis
can proceed to test the inner model. The inner model analysis involves
examining potential collinearity issues, evaluating the significance and
relevance of structural model relationships, hypothesis testing, path analysis,
and conducting an Importance Performance Mapping Analysis (IPMA) to aid
decision-making. Collinearity is assessed through the Variance Inflation Factor
(VIF) values, with values below 3 considered ideal.
Table 2. Multicolinearity
Result
|
Variabel |
Job
Satisfaction |
Hospital
Performance |
|
Healthcare Professional-Patient Relationship |
1.966 |
|
|
Job Satisfaction |
|
1.000 |
|
Social Support |
1.700 |
|
|
Work Operation Requirement |
1.434 |
|
|
Work-Family Conflict |
1.150 |
|
Table 2 above shows the VIF
value in this study, the VIF value of all variables has a value of less than 3
so that it can be concluded that there was no collinearity problem between the
research variables in this study.
Determinant
Coefficient (R-Square)
The next step in the inner
model analysis involves examining the coefficient of determination (R-Square),
which measures the variance explained in each endogenous construct and the
model's explanatory power or predictive accuracy. R-Square ranges from 0 to 1,
with higher values indicating stronger explanatory power. An R-Square value of
0.10 is considered satisfactory, while values between 0.25-0.5 are categorized
as weak, 0.5-0.75 as moderate, and above 0.75 as substantial or strong.
However, an R-Square value above 0.9 may indicate overfitting. The
interpretation of R-Square should be relative to the research context and
compared with values from related studies and similar models. A high R-Square
can indicate model overfitting, where the model becomes too complex and fails
to generalize to other samples from the same population. Careful consideration
is needed to avoid such issues (Hair et al., 2019; Sarstedt
et al., 2021).
Table 3. Determinant Coefficient Result
|
Variable |
R2 |
Interpretation |
|
Job
Satisfaction |
0.674 |
Moderate
explanatory power |
|
Hospital
Performance |
0.426 |
Weak
explanatory power |
Based on table 3 above, it
can be seen that the R2 value for the job satisfaction variable is 0.647, this
value indicates that the magnitude of job satisfaction can be explained by the
variables social support, work operation requirements, healthcare professional-patient
relationship, work-family conflict, and working conditions of 64.7% and for
35.3% the difference can be explained by other variables that are not present
in this study. For the hospital performance variable, it has an R2 value of
0.426 which indicates that hospital performance can be explained by the job
satisfaction variable of 42.6%.
In the analysis of the
structural model or inner model, the next step is to assess the predictive
ability of the proposed model using the F-Square (F2) value obtained from
bootstrapping data processing. The F-Square test examines the effect size,
specifically the impact of removing a predictor construct on the R-Square value
of the target construct. The F-Square value is similar to the size of the path
coefficient and indicates the rank relevance of predictor constructs in
explaining the dependent constructs. A small effect is indicated by an F-Square
value above 0.02, a moderate effect by a value above 0.15, and a large effect
by a value above 0.35. A value below 0.02 suggests no significant effect.
Table 4. F-Square Value
|
Variable |
F2 |
|
Social
Support
Job Satisfaction |
0.073 |
|
Work
Opera |
0.020 |
|
Healthcare
Professional-Patient Relationship
Job Satisfaction |
0.065 |
|
Work-Family
Conflict
Job Satisfaction |
0.082 |
|
Working
Condition
Job Satisfaction |
0.383 |
|
Job
Satisfaction
Hospital Performance |
0.742 |
Source: Research Data Analysis Using Smart-PLS (2023)
Based on the results of the
effect size data analysis in Table 4 above, it can be said that the variables
social support, work operation requirements, healthcare professional-patient
relationship, and work-family conflict have a small effect on job satisfaction
(0.073, 0,020, 0,065, and 0,082 respectively), working condition variables have
a large effect on job satisfaction (0.383, and job satisfaction variables have
a large effect on hospital performance (0.742), and there are no variables that
have an F2 value <0.02.
Predictive
Relevance: The Stone-Geisser’s Value (Q2)
In the structural model
analysis, the next step is the Q-Squared test (Q2), which assesses the
predictive relevance of latent variables in the research model. The Q2 value,
obtained through the blindfolding procedure, measures the model's ability to
predict the initial observed values. A Q2 value of 0, 0.25, and 0.5 indicates
small, medium, and large predictive relevance of exogenous constructs for
specific endogenous constructs, respectively. If the Q2 value is less than 0,
it signifies a lack of predictive relevance. The Q2 test employs the
out-of-sample method, simulating changes in data compared to the original
dataset to evaluate the model's quality when tested with different data in the
future. The blindfolding procedure with an omission distance of five to ten and
a cross-validated redundancy approach is used to obtain the Q2 test results in
the SmartPLS application, incorporating essential
elements of the path model and structure (Hair et al., 2019).
Table 5. Q-Square Value
|
Variabel |
Q2 |
|
Job
Satisfaction |
0.403 |
|
Hospital
Performance |
0.234 |
Source: Research Data Analysis Using Smart-PLS (2023)
From the table 5 above, it
can be concluded that the Q-Square value for the job satisfaction variable is
0.403, meaning that it has a medium predictive relevance value, while the
hospital performance variable has a small predictive relevance value because
the Q-Square value is 0.234.
Research
Hypothesis Test Result
In hypothesis testing, the
first step is to assess the relevance of the path coefficient, which ranges
from -1 to +1. Values closer to -1 indicate a strong negative relationship,
while values closer to +1 indicate a strong positive relationship. Path coefficients
exceeding +/-1 are considered unacceptable due to potential collinearity
issues. The aim is to evaluate the significance and strength of the
relationship and test the hypothesis. The next step involves using
bootstrapping, a resampling technique performed through the SmartPLS
application. Bootstrapping helps test the significance of coefficients. For
this study, a one-tailed statistical test was conducted as the direction of
influence was predetermined. The significance of the relationship is determined
by comparing T-statistics generated through bootstrapping with T-table values.
A T-statistic equal to or greater than 1.65 and a p-value less than 0.05
indicate a significant influence between the independent and dependent
variables. The inner model resulting from the bootstrapping process in SmartPLS is presented in the provided image (Sarstedt et al., 2021).
Table 6. Research Hypothesis Test Result
|
Hypothesis |
Path
Coefficient |
T-Statistic |
P-Value |
Interpretation |
|
H1:
Social Support
Job Satisfaction |
0.201 |
2.811 |
0.003 |
Hypothesis
Supported |
|
H2:
Work Operation Requirement
Job Satisfaction |
0.096 |
1.724 |
0.043 |
Hypothesis
Supported |
|
H3:
Healthcare Professional-Patient
Relationship
Job Satisfaction |
0.205 |
3.283 |
0.001 |
Hypothesis
Supported |
|
H4:
Work-Family Conflict
Job Satisfaction |
-0.175 |
3.454 |
0.000 |
Hypothesis
Supported |
|
H5:
Working Condition
Job Satisfaction |
0.482 |
8.045 |
0.000 |
Hypothesis
Supported |
|
H6:
Job Satisfaction
Hospital Performance |
0.653 |
11.862 |
0.000 |
Hypothesis
Supported |
Source: Research Data Analysis Using Smart-PLS (2023)
Table 6 shows the results of
hypothesis testing in this study, from this table it can be concluded that 6
hypotheses (H1, H2, H3, H4, H5, H6) proposed in this study are supported. The
conclusion is seen from the significant influence and the path coefficient
value which is in accordance with the direction of the hypothesis that has been
proposed in this research. Furthermore, the results of the significance test
for each hypothesis will be explained.
The Effect
of Social Support to Job Satisfaction
Based on the results of
hypothesis testing in this study, several conclusions can be made. Firstly,
social support has a significant positive impact on job satisfaction among
healthcare professionals at XYZ Hospital (H1). The healthcare professional-patient
relationship also significantly influences job satisfaction (H3). However, work
operation requirements (H2) and work-family conflict (H4) do not have
significant effects on job satisfaction. Working conditions (H5) positively
influence job satisfaction, and job satisfaction (H6) has a significant
positive impact on hospital performance. These conclusions are supported by the
T-Statistic values, p-values, and Standardized Coefficients obtained from the
analysis.
Mediating
Variable Analysis
After hypothesis testing,
the subsequent step involves analyzing the paths within the research model.
Mediating variable analysis focuses on the specific indirect effects, which
examine the influence of a variable on other variables, including both exogenous
and endogenous variables. Mediating variables play a role in mediating the
relationship between independent and dependent variables. Path analysis
utilizes the T-statistics and path coefficients to assess the specific indirect
effects. The main emphasis is on the coefficient value of the path that
connects the independent variable, mediating variable, and dependent variable.
The specific indirect effects are obtained through the bootstrapping process,
particularly using bias-corrected bootstrapping, which is effective in
detecting mediation. A statistically significant indirect effect is indicated
by a T-statistics value > 1.65 (two-tailed) and a P-value < 0.05,
providing evidence of mediation.
Table 7. Mediating Variable Analysis Result
|
Path |
Path
Coefficient |
T-Statistic |
P-Value |
|
Social
Support
Job Satisfaction
Hospital Performance |
0.131 |
2.684 |
0.004 |
|
Work
Operation Requirement
Job Satisfaction
Hospital Performance |
0.062 |
1.597 |
0.055 |
|
Healthcare
Professional-Patient Relationship
Job Satisfaction
Hospital Performance |
0.134 |
3.066 |
0.001 |
|
Work-Family
Conflict
Job Satisfaction
Hospital Performance |
-0.114 |
3.232 |
0.001 |
|
Working
Condition
Job Satisfaction
Hospital Performance |
0.315 |
6.818 |
0.000 |
Source: Research Data Analysis Using Smart-PLS (2023)
Table 7 presents the results
of the mediation test, specifically the indirect effects on hospital
performance through the mediating variable of job satisfaction. The social
support variable shows a positive and significant indirect effect on hospital
performance, with a path coefficient of 0.131, a T-statistic value of 2.684,
and a P-value of 0.004. On the other hand, the work operation requirement
variable does not have a significant indirect effect on hospital performance,
with a path coefficient of 0.062, a T-statistic value of 1.597, and a P-value
of 0.055. The healthcare professional-patient relationship variable has a
positive and significant indirect effect on hospital performance, with a path
coefficient of 0.134, a T-statistic value of 3.066, and a P-value of 0.002. The
work-family conflict variable exhibits a negative and significant indirect
effect on hospital performance, with a path coefficient of -0.114, a
T-statistic value of 3.232, and a P-value of 0.001. Lastly, the working
condition variable shows a positive and significant indirect effect on hospital
performance, with a path coefficient of 0.315, a T-statistic value of 6.818,
and a P-value of 0.000.
Importance-Performance
Map Analysis
PLS-SEM analysis of the
Important Performance Map Analysis (IPMA) provides valuable insights into the
role of construction antecedents and their implications for managerial action.
It is particularly useful for comparing PLS-SEM results in multigroup analyses.
The analysis involves five steps. Firstly, the analysis checks whether the
necessary requirements have been met. Then, performance values and importance
values are calculated for latent variables. These results are used to build an
importance-performance map for the target constructs. Finally, the IPMA can be
expanded to the indicator level to gain more specific information on effective
managerial actions. The IPMA analysis helps identify the variables that require
prioritization and attention from management based on their impact. By focusing
on data-driven insights rather than assumptions, management can address
important aspects highlighted by respondents. The important performance map can
be further enhanced by adding additional lines representing average importance
and performance values, which divide the map into four areas. This provides
guidance for prioritizing managerial activities that are crucial for the
selected target but require performance improvement.
Table 8. Construct Importance and Performance Analysis
Result
|
|
Construct
Importance for Turnover Intention |
Construct
Performances for Turnover Intention |
|
Social Support |
0.201 |
72.747 |
|
Work-Operation Requirement |
0.096 |
78.438 |
|
Healthcare Professional-Patient Relationship |
0.205 |
74.088 |
|
Work-Family Conflict |
-0.175 |
44.474 |
|
Working Condition |
0.482 |
66.208 |
|
Mean |
0.162 |
67.191 |
Table 8 shows the average
importance and performance values for the job satisfaction construct. Based on
the Important Performance Map Analysis (IPMA), the graph reveals that social
support and healthcare professional-patient relationship variables are considered
important and have good performance, positioned in the upper right quadrant.
These variables should be maintained and prioritized by the human resources
team. The working condition variable, located in the lower right quadrant, is
also important but requires improved performance. On the other hand, the
work-family conflict variable, in the lower left quadrant, is considered less
important and has poor performance. The human resources team should address and
reduce problems related to work-family conflict. Further analysis will be
conducted at the IPMA level, providing additional indicators in the subsequent
table and graph.
Table 9. Indicator Importance and Performance Analysis
Result
|
Variable |
Indicator |
Construct
Importance for Turnover Intention |
Construct
Performances for Turnover Intention |
|
Social Support |
SS 1 |
0.033 |
80.156 |
|
SS 2 |
0.037 |
76.406 |
|
|
SS 3 |
0.056 |
71.875 |
|
|
SS 4 |
0.048 |
77.031 |
|
|
SS 5 |
0.053 |
61.562 |
|
|
SS 6 |
0.038 |
77.500 |
|
|
Work-Operation Requirement |
WO 1 |
0.040 |
81.042 |
|
WO 2 |
0.028 |
81.875 |
|
|
WO 3 |
0.006 |
56.719 |
|
|
WO 4 |
0.038 |
75.833 |
|
|
Healthcare Professional-Patient Relationship |
HR 1 |
0.055 |
75.000 |
|
HR 2 |
0.033 |
70.417 |
|
|
HR 3 |
0.048 |
85.208 |
|
|
HR 4 |
0.062 |
70.312 |
|
|
HR 5 |
0.060 |
70.000 |
|
|
Work-Family Conflict |
WF 1 |
-0.052 |
53.125 |
|
WF 2 |
-0.020 |
43.594 |
|
|
WF 3 |
-0.030 |
44.062 |
|
|
WF 4 |
-0.053 |
37.031 |
|
|
WF 5 |
-0.049 |
43.906 |
|
|
Working Condition |
WC 1 |
0.107 |
66.250 |
|
WC 2 |
0.115 |
56.875 |
|
|
WC 3 |
0.139 |
62.917 |
|
|
WC 4 |
0.139 |
68.958 |
|
|
WC 5 |
0.113 |
73.750 |
|
|
Mean |
0.042 |
66.456 |
|
Table 9 above shows the
average IPMA indicator value, for the importance, it has an average of 0.042
and for the performance, it has an average of 66.456. Indicator IPMA Result
shows that HR3 (My job is a respectable job), SS4 (The leaders support my good
relationship with coworkers), HR1 (Patients respect me as a healthcare
professional), SS3 (The leaders care about my mental health), HR5 (I have a
good relationship with patients), HR4 (Patients believe in me), WC5 (The
hospital where I work can deal with errors that occur), WC4 (The department
where I work is comfortable), are in the upper right quadrant which indicates
that these things need to be maintained by XYZ hospital management. Meanwhile,
the indicators SS5 (I got help from coworkers), WC1 (The department I work for
has tools that meet patient needs), WC2 (The department I work for has enough
staff), WC3 (The department where I work has a good cultural atmosphere) are in
the lower right quadrant. This indicates that these indicators are important but
still require improvement, so XYZ Hospital management must prioritize these
matters for improvement.
The study findings indicate
that social support, healthcare professional-patient relationships, work-family
balance, and working conditions significantly influence job satisfaction among
healthcare professionals at XYZ Hospital. Healthcare professionals who receive
more social support, establish positive patient relationships, experience lower
work-family conflict, and have better working conditions tend to have higher
levels of job satisfaction. Creating a supportive work environment, fostering
collaborative relationships, implementing work-life balance practices, and
improving working conditions are crucial for enhancing job satisfaction.
Moreover, job satisfaction has a positive impact on hospital performance, as it
enhances employee engagement, morale, communication, and teamwork, leading to
improved patient care and outcomes. These findings highlight the importance of
addressing these factors to promote job satisfaction and ultimately enhance
hospital performance and patient care.
Conclusion
In conclusion, this study examined the research model focusing on
healthcare professionals at XYZ Hospital. The aim was to analyze the
antecedents of job satisfaction and their impact on hospital performance. The
results showed that social support, work operation requirements, healthcare
professional-patient relationship, working condition, and job satisfaction
significantly affect job satisfaction. Additionally, job satisfaction was found
to have a significant positive impact on hospital performance. The empirical
model demonstrated moderate predictive accuracy and relevance for job
satisfaction in predicting hospital performance. This study contributes by
proposing a modified model that can adequately predict job satisfaction
variables and suggests further replication and testing in other populations and
hospitals for future research.
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