INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue V, May 2024
www.ijltemas.in Page 56
The Moderating Role of Supervisor Support in the Mediating
Effect of Career Satisfaction on High Performance Work Systems:
A Path to Enhanced Nurse Service Quality
Chusni Mubarakh
1
, Fitri Kartika
2
, Yani Restiani Widjaja
3
, Wisnu Prajogo
4
1
Department of Management, ARS University, Bandung, STIE YKPN, Sleman, Yogyakarta, Indonesia
2
Department of Management, ARS University, Bandung, Indonesia
3
Department of Management, ARS University, Bandung, Indonesia
4
Department of Management, STIE YKPN, Sleman, Yogyakarta, Indonesia
DOI : https://doi.org/10.51583/IJLTEMAS.2024.130508
Received: 30 April 2024; Accepted: 09 May 2024; Published: 08 June 2024
Abstract: This study examines the moderating role of supervisory support in the relationship between high-performance work
systems (HPWS) and nurse service quality, mediated by career satisfaction. We used Partial Least Squares Structural Equation
Modeling (PLS-SEM) to look at the data from Likert-scale questionnaires filled out by 187 nurses from Tjitrowardojo General
Hospital in Purworejo, Indonesia. Our findings reveal that HPWS significantly enhances nurse career satisfaction, which in turn
positively affects service quality. Moreover, supervisory support not only directly contributes to improved service quality but also
strengthens the impact of HPWS on career satisfaction and, indirectly, on service quality. The implications of these results
suggest that hospitals can enhance service quality by fostering supportive supervisory relationships and implementing robust
performance systems. These findings contribute to the existing literature by highlighting the crucial role of supervisory support in
enhancing the effectiveness of performance systems within healthcare settings.
Keywords: Career satisfaction; High performance work systems; Nurse service quality; Structural equation modeling; Supervisor
support; Work environment
I. Introduction
As a critical element of patient care and customer satisfaction, enhancing the quality of nursing services is one of the healthcare
industry's primary priorities. Many hospitals and health facilities in Indonesia, like Tjitrowardojo Purworejo General Hospital,
have begun using high-performance working systems (also known as high-performance work systems, or HPWS), in an attempt
to raise the calibre of these services. The goal of the human resource management strategy known as HPWS is to boost employee
happiness and engagement in order to enhance organisational performance. The goal of implementing HPWS is to foster a
supportive work environment where nursing staff members experience gratitude and support from management and oversight.
It has been discovered that job satisfaction and intentions to leave the nursing profession are significantly predicted by the quality
of the nurse-supervisor relationship[1]. Furthermore, research has demonstrated that flexible shift arrangements, incentive-based
pay plans, and a happy work atmosphere can all have a favourable impact on job satisfaction and, in turn, the quality of nursing
services.[1][2]. For nurses and other healthcare workers, supervisory support and high-performance work systems (HPWS) are
critical elements that determine career satisfaction and, in turn, the quality of care they offer. According to research by Tahiry &
[3] career adaptability has a mediating role in the relationship between career satisfaction and supervisor support[4].
This means that having a supportive supervisor can boost career satisfaction, which may improve the quality of services provided.
Additionally, research like that conducted by Kim and Seo[5] highlights the importance of elements like person-centred nursing
and work engagement in determining the calibre of nursing services. The association between work engagement and nursing
service quality was found to be mediated and moderated by person-centred nursing, indicating that approaches that prioritise
individualised care can improve the quality of nursing care. Furthermore, the Novita & Prasetyo study[6] emphasises how crucial
nurse performance and competency are in influencing patient satisfaction and service quality. This suggests that spending money
to develop nurses' competencies can result in better patient satisfaction and higher-quality services. Furthermore, the study by
Astuti et al. [7] emphasises how important it is for nurses to give care in a consistent, professional manner while paying close
attention to the patient's needs. This emphasises how crucial standardised care procedures are to guaranteeing superior nursing
services. In many professions, particularly healthcare, the relationship between job satisfaction and high-performance work
systems (HPWS) is critical. Studies show that HPWS has a favourable correlation with both overall employee satisfaction [8],
and work satisfaction among medical staff [5]. Further research has revealed that HPWS influences the perception of job success
through employability orientation and HPWS attribution [9].
HPWS significantly contributes to the development of innovative skills, improving organisational performance and
competitiveness[3]. Moreover, research indicates that supervisory assistance has a positive impact on workers' well-being, job
happiness, and work engagement, all of which have an impact on productivity inside the company as well as the personal and
professional lives of its employees[10]. Thus, important variables that can influence job satisfaction and the calibre of nursing
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue V, May 2024
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services are HPWS and supervisory support. Supervisory support and high-performance work systems (HPWS) are important
mediators of career satisfaction and, by extension, the quality of nursing services[11].
II. Literature Review
Theoretical Framework and Hypotheses Development
Figure 1. Theoritical Framework[12]
Classical Test Theory
Classical Test Theory (CTT) is a psychometric theory used to forecast test results[13]. First proposed by Charles Spearman in
1904[13], the concept continues to be utilised because of its efficient and uncomplicated method of measurement. According to
the CTT theory[13], the observed test scores (X) consist of two components: the true scores (T), which indicate the average score
one would achieve if they took the test multiple times independently, and the random measurement errors [13]. The equation X
= T + E gives this relationship. The main principles of classical test theory (CTT) encompass the absence of a relationship
between genuine scores and errors, as well as the anticipation that measurement errors will balance out to an average of zero
throughout the entire population.
This theory emphasises the overall test score and highlights the importance of reliability and validity as crucial elements in test
assessment. The fields of education and psychology commonly use CTT in the development and evaluation of tests with a fixed
length.
Despite its widespread use, CTT is not impervious to criticism. The measurement methods limitations include dependence on
raw scores, which may not precisely represent the actual test outcomes, and a deficiency in providing clear numeric significance.
It does not necessarily imply an individuals competence in carrying out particular activities.
The CTT framework is highly regarded for its straightforward approach to analysing and explaining reliability and validity
difficulties. It does not require a deep understanding of statistical distribution functions and mathematical models. Alternative
methods, such as Item Response Theory (IRT)[14], evaluate responses by considering the probability associated with individual
items rather than overall scores in the case of more intricate assessments[13].
High Performance Work System (HPWS)
The term HPWS refers to a collection of organisational procedures that emphasise job enrichment, skill development, and
employee engagement. These procedures can increase workers commitment to their work and levels of job satisfaction. These
procedures consist of reworking jobs, providing performance reviews, investing in training and development, and including staff
members in decision-making[2]. In contrast, one of the most important factors in determining employee satisfaction is
supervisory support. An engaged workplace can boost employee trust and job happiness. Supervisors that exhibit open
communication, respect, and concern for their staff members can foster this atmosphere[2]. In addition, studies have
demonstrated that positive management practices like vocal encouragement, constructive criticism, and problem-solving
assistance greatly increase worker job satisfaction[15]. The goal of high performance work systems, also known as high-
commitment workplaces, high-involvement work systems, high performance practices, and high performance workplaces, is to
improve organisational performance by developing employees skills and commitment [16]. A high-performance work
environment, on the other hand, increases the value, uniqueness, and unmistakability of employees knowledge and abilities,
resulting in improved performance and a competitive edge [17]. According to research, HPWSwhich entails actions including
in-depth training, selective hiring, involvement in decision-making, and performance managementinfluences employee
attitudes and behaviours, which in turn affect how well employees and organisations operate [18]. The context in which health
care organisations operate is dynamic, unclear, and ever-changing. Owing to these dynamics, organisations now face new
challenges and must adopt a new focus to continuously improve their performance in order to maintain their competitive
excellence. Improving organisational performance through the use of High Performance Work Systems (HPWS) and removing
barriers to organisational effectiveness is one of the most crucial factors in maintaining and attracting new customers [19].
High-Performance Work Systems (HPWS) are designed to maximize employee potential and are associated with improved job
satisfaction and performance. In the context of healthcare[19], HPWS can provide nurses with the resources, autonomy, and
support necessary to excel in their roles, thereby enhancing service quality. However, the mediating role of career satisfaction
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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between HPWS and service quality suggests a complex interplay where enhanced job satisfaction through systemic and
supervisory support can potentially lead to either an improvement or a plateau in service quality due to various underlying
factors[16]. High-Performance Work Systems (HPWS) are posited to positively affect nurses career satisfaction by providing
comprehensive support, including training and development, which align with nurses intrinsic and extrinsic motivations[20][18].
Supervisor Support
The role of supervisor support in this dynamic cannot be overstated. Supervisors who provide clear communication, fair
treatment, and support for professional development contribute to the job satisfaction of their nurses[21]. Moreover, this support
is not merely about improving morale but also about helping nurses navigate the complexities and stresses of their work, which in
turn can lead to improved service quality[22][23]. The current crisis has made clear how important supervisory support (SS) is to
the career happiness and adaptation of healthcare workers [24]. The impact of supervisory support on presenteeism was
substantial [20], because supervisory support are seen as a form of compensation in an organisation[25]. The impact of
supervisory support is crucial in fostering job satisfaction and lowering employees intentions to leave their jobs[26].
In advanced research, supervisory support can serve as both a moderation variable and an independent variable. Here's the
explanation and reason for the route. Because support from a superior can stand alone in affecting employee career satisfaction,
we can regard supervisory support as an independent variable. In this context, supervisors' support can directly impact employees'
work satisfaction without relying on other variables. Supervisory support can also act as a moderation variable if research wants
to explore how supervisory support affects the strength or direction of the relationship between HPWS and career satisfaction or
between career satisfaction and turnover intentions. For example, the effect of HPWS on career satisfaction may be stronger when
supervisor support is high, suggesting that supervisor support can reinforce the positive impact of HPMS on career satisfaction.
Social exchange theory. This theory suggests that the relationship between employees and superiors is a mutual relationship in
which support from superiors can improve the quality of such exchanges, allowing the superiors to be catalysts that strengthen the
link between the working environment and employees' reactions.
Organizational Support Theory. According to this theory, employees' perceptions of how much an organization values their
contributions and cares about their well-being can affect a variety of work outcomes, including career satisfaction and the
intention to move jobs. Support from supervisors, as representatives of organizations, can play an important role in shaping such
perceptions.
Career Satisfaction
Edwin Lockes definition, which describes job satisfaction as a pleasant or good emotional state arising from an appraisal of
ones job or employment experience[4], is the one that is most frequently used. Nevertheless, there are broader definitions of job
satisfaction than just the pleasant emotions that are typically thought to be the primary factors influencing how people see and
interact with their professions [27]. Studies have repeatedly shown that nurses, in particular, who work in circumstances that
present a variety of problems, frequently need to successfully manage their emotions. Because of this expectation of emotional
labour and the inherent pressures in the healthcare sector, nurses are under a lot of strain, which can negatively affect their job
satisfaction and, in turn, the standard of care that they offer [15].
Career satisfaction among nurses is an influential factor in healthcare delivery and is impacted by a myriad of elements ranging
from personal
58
ulfilment to professional growth opportunities[28]. Its well-established that career satisfaction can lead to
enhanced job performance[29], which is crucial in nursing due to its direct correlation with patient care and outcomes. Career
satisfaction among nurses is an essential factor influencing nurse service quality. It is suggested that when nurses perceive their
careers as fulfilling and aligning with their professional goals, they are more likely to deliver higher quality care to patients[26].
This notion is supported by the idea that a satisfied workforce is generally more productive and engaged, contributing positively
to overall organizational performance[30].
Nurse Service Quality
Nurse service quality is a critical outcome for healthcare institutions, often determining patient satisfaction and overall healthcare
effectiveness[31][32]. The quality of service provided by nurses is not solely dependent on their competencies but also on the
satisfaction they derive from their careers, which influences their motivation and attention to care[20][33].
Based on nursing expertise and advice directed at people, families, groups, or communities, both well and ill, nursing services are
a type of professional service that is an essential component of health services. Nurses must be highly motivated when delivering
nursing care services [34]. The ability to execute the Nursing Practice Act with accuracy, speed, ease, and expertise determines
the quality of nursing services provided by the institution. The effectiveness and efficiency of the current hospital systems
structural components determine what nursing services mean. Quality nursing care is one of everyones fundamental needs.
Professionals in the fields of health and nursing are currently working to improve the following areas: nursing care quality,
nursing devices, nursing professionalism, and nursing self-worth [35].
In this chapter, the researcher looks at the existing research and finds a gap: earlier studies haven't gone far enough in developing
theories or providing empirical evidence about how career satisfaction affects the quality of nurse service in high-performance
work systems. They also haven't looked into the role of supervisory support in this setting. To bridge this gap, the researcher
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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formulates eight hypotheses titled The Moderating Role of Supervisor Support in the Mediating Effect of Career Satisfaction on
High Performance Work Systems: A Path to Enhanced Nurse Service Quality based on the provided conceptual framework.
Theoretical foundations support these hypotheses and provide detailed explanations.
H1(+): In nursing staff, high-performance work systems (HPWS) are positively associated with career satisfaction (CS).
According to HRM theory, HPWS, which includes comprehensive training, employee empowerment, and performance
incentives, should enhance job satisfaction by providing employees with the skills, autonomy, and motivation they need to excel
in their roles. The Resource-Based View (RBV) suggests that organizational systems that provide resources, such as HPWS,
contribute to employee satisfaction by enabling better performance and career growth opportunities.
H2(+): HPWS have a direct positive impact on nurse service quality (NSQ).
The systems approach to quality management posits that well-designed work systems lead to better process outcomes, which in
healthcare translates to improved service quality by nurses. The theory of performance improvement posits that structured and
efficient work systems directly contribute to the quality of outputs, in this case the service provided by nurses.
H3(+): HPWS indirectly influence NSQ through career satisfaction (CS).
The job demands-resources model suggests that resources provided by HPWS can reduce job demands and increase job
resources, leading to higher job satisfaction, which in turn may lead to improved job performance and service quality. According
to mediation theory, HPWS positively impacts service outcomes by enhancing employee satisfaction, as engaged employees are
more likely to provide high-quality services.
H1H3: These hypotheses emphasize the pivotal role of HPWS in enhancing career satisfaction and, subsequently, the quality of
nursing services. They reflect the direct and indirect pathways through which structured, resource-rich work environments can
lead to better healthcare outcomes.
H4(+): Career satisfaction (CS) and supervisory support (SS) have a positive correlation.
According to Relational Exchange Theory, supportive relationships in the workplace lead to higher levels of employee
satisfaction because they fulfill psychological contract obligations. Social exchange theory implies that when nurses perceive
support from their supervisors, they are likely to experience higher levels of job satisfaction due to a sense of obligation and
reciprocity.
H5 (+): Supervisory support (SS) directly enhances nurse service quality (NSQ).
The impact theory argues that direct support from leadership has a tangible effect on employee performance and, subsequently,
on the quality of service they provide. The supportive leadership theory suggests that support from supervisors can improve
employees' performance by providing them with the necessary guidance, feedback, and resources.
H6 (+): Career satisfaction (CS) mediates the indirect positive effect of supervisory support (SS) on nurse service quality (NSQ).
The conservation of resources theory indicates that supervisory support can be a critical resource for nurses, leading to greater
career satisfaction, which in turn can conserve the psychological resources needed for providing high-quality service.
H4–H6: These posit the crucial role of supervisors in not only directly influencing nurse service quality but also in enhancing it
indirectly through boosting career satisfaction. It acknowledges the dual pathway through which SS affects NSQ.
H7 (+): The relationship between high-performance work systems (HPWS) and nurse service quality (NSQ) is strengthened when
supervisor support (SS) is high.
According to the Moderating Role of Leadership, supportive leadership amplifies the effectiveness of organizational systems and
suggests that SS strengthens the beneficial effects of HPWS on NSQ.
H8 (+): The level of Supervisor Support (SS) moderates the positive effect of Career Satisfaction (CS) on Nurse Service Quality
(NSQ), with the effect being stronger when SS is higher.
According to Social Support Theory, the presence of supportive relationships, such as those with supervisors, magnifies positive
work outcomes, specifically the influence of CS on NSQ.
H7–H8: These introduce the idea of moderation, suggesting that the positive pathways identified in H1H6 are contingent upon
the level of SS. They emphasize the nuanced role that supervisory support plays in either facilitating or constraining the real
benefits of HPWS and CS on NSQ.
III. Research Methodology
This research delves into the experiences of nurses working at Tjitrowardojo General Hospital, Purworejo. We surveyed a total of
187 nurses for this study[4]. The data collection involved distributing questionnaires on March 30, 2024, which utilized a Likert
scale ranging from 1 (strongly disagree) to 5 (strongly agree), allowing respondents to express the extent of their agreement with
various statements regarding their work environment and support systems.
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We disseminated the questionnaires to a randomly selected group of willing nurses, ensuring a degree of randomness in the
selection process. This approach helps reduce selection bias, contributing to the reliability of the gathered data.
The study uses partial least squares structural equation modeling (PLS-SEM), a multivariate statistical analysis method that is
becoming more popular in many research areas because it can handle complex models, such as latent constructs, with ease. PLS-
SEM is particularly adept at prediction and theory building in exploratory research, making it well-suited for this study's
objectives[36][37][38].
The PLS analysis process unfolds in several distinct stages.
This stage entails evaluating the measurement model, also known as the outer model. This stage assesses the reliability and
validity of the constructs used in the research. It ensures that the items used to measure the variables are consistent and accurately
reflect the constructs they intend to measure.
Testing of the model fit. This phase assesses the extent to which the collected data aligns with the proposed model, thereby
confirming the empirical evidence's support for the theoretical framework.
The hypothesis was tested. Hypothesis testing in PLS-SEM involves checking the path coefficients to determine the strength and
significance of the relationships between constructs.
Mediation and Moderating Analysis[39][40][12].
This advanced stage of analysis assesses the indirect effects between variables (mediation) and how certain variables might alter
the strength or direction of the relationship between other variables (moderation).
This study measures the high-performance work system using an established instrument from previous literature [17]. We gauged
supervisory support using measures [41], assessed career satisfaction using measurements [26], and evaluated nurse service
quality using metrics [33]. These measures, sourced from validated and peer-reviewed academic literature, bolster the
methodological rigor of the study and provide a solid foundation for the analysis and interpretation of the research findings.
Table 1. Demographic Information
Variable
Categories
Frequency
%
Gender
Male
47
25.7
Female
139
74.3
Total
187
100.0
Age Group
20-25
7
3.7
26-30
40
21.4
>30
140
74.9
Total
187
100.0
Length of Work
<1 Years
6
3.2
1-5 Years
38
20.3
6-10
Years
46
24.6
>10 Years
97
51.9
Total
187
100.0
These demographics serve as a backdrop for the analysis of how high-performance work systems and supervisory support can
mediate career satisfaction and improve nurse service quality. The data implies that the findings will reflect insights from a
predominantly seasoned workforce, which may possess well-formed attitudes towards the interplay of organizational support,
personal career fulfillment, and service delivery outcomes.
The demographic profile of the study participants is predominantly female, representing 74.3% (139 out of 187) of the sample,
while males account for 25.7% (47 out of 187). This gender distribution is reflective of the broader trends in the nursing
profession, where female representation is traditionally higher.
The sample's age distribution shows a concentration of more experienced individuals, with a significant majority of 74.9% (140
out of 187) being over 30 years old. The 2630 age group comprises 21.4% (40 out of 187), while the 2025 age group makes up
the smallest segment at 3.7% (7 out of 187). The preponderance of the older age group suggests that the respondents bring a
wealth of experience to the study, which may influence their perceptions of career satisfaction and the effectiveness of high-
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performance work systems.
When examining the length of service, it is evident that long-term employment is common among the participants. Those with
over ten years of experience constitute the largest category at 51.9% (97 out of 187), followed by the 610 year category at 24.6%
(46 out of 187). Respondents with 15 years of service represent 20.3% (38 out of 187), and those with less than one year account
for the smallest group at 3.2% (6 out of 187). The longevity of service is indicative of a stable workforce and suggests potential
deep-rooted perceptions of work systems and career development within the sample.
The sample's experience-rich profile provides fertile ground for exploring the intricate dynamics of how established work systems
and supervisory relationships impact career satisfaction. Given that the majority of respondents have significant work tenure, they
can offer nuanced feedback on the long-term effects of HPWS and the quality of supervisory support on their career satisfaction
and consequent service quality. The insights derived from such a demographic can guide healthcare organizations in tailoring
their high-performance work systems and supervisory support mechanisms to enhance the quality of care, considering the
profound influence of career satisfaction as a mediator in this relationship.
IV. Result And Discussion
According to table 2, Ten Global Model Fit
We used Partially Least Squares Structural Equation Modeling (PLS-SEM) to explore the connections between High Performance
Work Systems (HPWS), Supervisor Support (SS), Career Satisfaction (CS), and Nurse Service Quality (NSQ). The data
underpinning this research, collected from 187 nursing staff at Tjitrowardojo General Hospital, Purworejo, forms the bedrock of
the nuanced investigation presented herein.
The strong pathway presence in the model, as shown by the average path coefficient (APC) of 0.281 and a p-value below 0.001,
suggests that the implemented HPWS has a strong relationship with nurses' job satisfaction and service quality. The average R-
squared (ARS) and average adjusted R-squared (AARS) values are both above 0.48. This means that the model explains a
moderate to substantial amount of the variance in the dependent variables. This supports the strong relationship between the
variables that were studied.
The Average Variance Inflation Factor (AVIF) and Average Full Variance Inflation Factor (AFVIF) stood at 1.703 and 2.646,
respectively. These figures, well below the cautionary threshold of 5, affirm that multicollinearity is not of concern, thus
validating the independence and reliability of the predictors.
The model's goodness of fit (GoF), quantified at 0.618, surpasses the acceptable threshold of 0.25 and edges towards the ideal
marker of 0.36. The strong fit demonstrates a close match between the theoretical framework and the observed data.
The Simpson's paradox ratio (SPR) at 0.833 and the R-squared contribution ratio (RSCR) at 0.994 both surpass the required
benchmarks, indicating that the relationships within the model are consistent and directionally accurate. The Statistical
Suppression Ratio (SSR), perfect at 1.000, implies there are no suppressor variables confounding the model. Additionally, the
nonlinear bivariate causality direction ratio (NLBCDR) of 0.750 exceeds the threshold of 0.7, underscoring the validity of the
model's causality assumptions.
Table 2. Ten Global Model Fit [37][12]
Measure
Value
P
Acc
if
Ideally
APC
0.281
<0.001
-
-
ARS
0.489
<0.001
-
-
AARS
0.481
<0.001
-
-
AVIF
1.703
-
<= 5
<= 3.3
AFVIF
2.646
-
<= 5
<= 3.3
GoF
0.618
-
>= 0.25
>= 0.36
SPR
0.833
-
>= 0.7
= 1
RSCR
0.994
-
>= 0.9
= 1
SSR
1.000
-
>= 0.7
-
NLBCDR
0.750
-
>= 0.7
-
Ten global model fit [13][43][44]
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APC (Average Path Coefficient), at 0.281 with a significance level of p<0.001, the average strength of the relationships in the
model is good, and the paths in the model are generally significant.
ARS (Average R-squared), the ARS value of 0.489 indicates that, on average, the model explains a moderate to good amount of
variance in the dependent variables.
AARS (Average Adjusted R-squared), at 0.481, this suggests that the explanatory power of the model is consistent even after
adjusting for the number of predictors.
AVIF (Average Variance Inflation Factor), with a value of 1.703, it is below the threshold of 5, indicating no concerning
multicollinearity among predictors. It is also close to the ideal threshold of 3.3.
AFVIF, similarly, at 2.646, this value suggests a satisfactory level of multicollinearity.
GoF (Goodness of Fit) the value of 0.618 is well above the minimum acceptable level of 0.1, indicating that the model has a
large goodness of fit.
SPR (Simpson's Paradox Ratio), the value is 0.833, which is above the acceptable threshold, meaning that the model is
consistent in the direction of its relationships.
RSCR (R-squared Contribution Ratio), the very high value of 0.994 indicates that the model's R-squared values are reliable
and not influenced by any suppressor variables.
SSR (Statistical Suppression Ratio), a perfect score of 1.000 suggests that there is no suppression effect, where the inclusion of
a variable would increase the predictive validity of another.
NLBCDR (Nonlinear Bivariate Causality Direction Ratio), at 0.750, this indicates a good level of causality direction in the
model, suggesting that the specified direction of relationships is generally correct
Table 3. Combined loadings and cross-loadings [45]
HPWS
SS
CS
NSQ
Type (a
SE
P
value
HPWS
0.690
0.523
-0.261
-0.335
Reflect
0.073
<0.001
Ind2
0.659
-0.050
-0.466
0.090
Reflect
0.073
<0.001
_2
0.766
-0.074
-0.264
0.014
Reflect
0.072
<0.001
_3
0.755
-0.004
-0.173
0.144
Reflect
0.072
<0.001
_4
0.797
0.050
0.212
-0.004
Reflect
0.071
<0.001
_5
0.759
0.089
0.202
-0.139
Reflect
0.072
<0.001
_6
0.801
-0.055
0.059
0.075
Reflect
0.071
<0.001
_7
0.790
-0.001
0.353
-0.050
Reflect
0.071
<0.001
_8
0.768
0.057
0.091
-0.120
Reflect
0.071
<0.001
_9
0.753
-0.098
0.086
-0.054
Reflect
0.072
<0.001
_10
0.771
0.174
0.128
-0.202
Reflect
0.071
<0.001
_11
0.675
-0.248
-0.217
0.382
Reflect
0.073
<0.001
_12
0.738
-0.187
-0.136
0.234
Reflect
0.072
<0.001
_13
0.746
-0.175
0.249
-0.002
Reflect
0.072
<0.001
SUPERVI
-0.174
0.824
0.226
-0.096
Reflect
0.071
<0.001
_14
-0.317
0.837
0.270
-0.097
Reflect
0.070
<0.001
_15
0.168
0.849
-0.176
0.065
Reflect
0.070
<0.001
_16
-0.097
0.830
0.045
0.192
Reflect
0.070
<0.001
_17
-0.042
0.820
0.059
-0.170
Reflect
0.071
<0.001
_18
0.075
0.688
-0.382
0.302
Reflect
0.073
<0.001
_19
0.293
0.818
-0.220
0.180
Reflect
0.071
<0.001
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_20
-0.056
0.845
0.129
-0.110
Reflect
0.070
<0.001
_21
0.160
0.858
-0.016
-0.207
Reflect
0.070
<0.001
CAREER_
-0.106
-0.011
0.889
-0.063
Reflect
0.069
<0.001
_22
-0.032
0.080
0.880
-0.162
Reflect
0.070
<0.001
_23
0.205
-0.124
0.834
0.217
Reflect
0.070
<0.001
_24
-0.072
0.006
0.918
-0.004
Reflect
0.069
<0.001
_25
0.019
0.040
0.909
0.023
Reflect
0.069
<0.001
NURSE_S
0.316
-0.387
-0.000
0.801
Reflect
0.071
<0.001
_26
0.025
-0.136
-0.036
0.831
Reflect
0.070
<0.001
_27
0.197
-0.156
-0.193
0.877
Reflect
0.070
<0.001
_28
-0.099
-0.064
-0.058
0.842
Reflect
0.070
<0.001
_29
0.060
0.080
-0.141
0.903
Reflect
0.069
<0.001
_30
-0.231
0.341
0.236
0.797
Reflect
0.071
<0.001
_31
-0.153
0.121
0.227
0.865
Reflect
0.070
<0.001
_32
-0.104
0.183
-0.094
0.898
Reflect
0.069
<0.001
_33
-0.009
0.001
0.089
0.844
Reflect
0.070
<0.001
Notes: Loadings are unrotated and cross-loadings are oblique-rotated. SEs and P values are for loadings. P values < 0.05 are
desirable for reflective indicators.
According to table 3. Combined loadings and cross-loadings
The loadings The loadings signify the relationship strength between indicator variables and their respective latent constructs
(HPWS, SS, CS, NSQ). A loading of 0.7 or higher typically indicates a strong and acceptable relationship, suggesting that the
indicator reflects the construct well. The negative loadings for certain indicators on HPWS and NSQ hint at a possible inverse
relationship, which could be of substantive interest.
To assess discriminant validity, we use cross-loadings, which compare an indicator's loading on its own construct with its loading
on other constructs. An indicator should exhibit the highest loading on its intended construct, relative to its loading on other
constructs. The oblique-rotated cross-loadings should ideally reflect this pattern. The P values, all of which are less than 0.001,
indicate highly significant relationships between the indicators and their respective constructs.
The loadings for the HPWS items show strong correlations with their construct, with values such as 0.690 for HPWS itself,
indicating that these items are good indicators of the HPWS construct in this setting. Reflective-type indicators, all with p-values
less than 0.001, confirm the items' statistical significance. The loadings for SS items also show strong correlations, especially
with the construct of supervisory support (a loading of 0.824 for SS shows this), which means that these items accurately reflect
how nurses see supervisory support.
For the Career Satisfaction construct, the items show very high loadings, with CS at 0.889, indicating an excellent representation
of the career satisfaction of the nursing staff. Similarly, the Nurse Service Quality construct's items exhibit high loadings, with
values like 0.801 for NURSE_S, suggesting a strong relationship between the items and the overall service quality delivered by
nurses.
The analysis of these loadings and cross-loadings, particularly with their significant p-values, underlines the measurement model's
reliability and validity. The results indicate that the constructs used in the model accurately reflect the perceptions and
experiences of the nurses regarding HPWS, SS, CS, and NSQ.
Table 4. Correlations among l.vs. with sq. rts. Of AVEs (Fornell-larcker discriminatory validity)
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
HPWS
0.746
0.764
0.751
0.499
0.154
0.047
SS
0.764
0.802
0.676
0.541
0.105
0.038
CS
0.751
0.676
0.879
0.351
0.046
-0.130
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NSQ
0.499
0.541
0.351
0.849
0.168
0.208
SS*HPWS
0.154
0.105
0.046
0.168
1.000
0.773
SS*CS
0.047
0.038
-0.130
0.208
0.773
1.000
Note: Square roots of average variances extracted (AVEs) shown on diagonal.
P values for correlations
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
HPWS
1.000
<0.001
<0.001
<0.001
0.029
0.505
SS
<0.001
1.000
<0.001
<0.001
0.139
0.596
CS
<0.001
<0.001
1.000
<0.001
0.516
0.067
NSQ
<0.001
<0.001
<0.001
1.000
0.018
0.003
SS*HPWS
0.029
0.139
0.516
0.018
1.000
<0.001
SS*CS
0.505
0.596
0.067
0.003
<0.001
1.000
According to table 4, Cross-Loading & Fornell-larcker discriminatory validity [48]
High Performance Work Systems (HPWS). HPWS shows strong correlations with all other constructs, particularly Supervisor
Support (SS) with a correlation coefficient of 0.764, indicating a significant overlap in the concepts measured by HPWS and SS.
The square root of AVE for HPWS is 0.746, which exceeds the correlation with other constructs, demonstrating good
discriminant validity.
Supervisor Support (SS). SS correlates highly with both HPWS and Career Satisfaction (CS), with coefficients of 0.764 and
0.676 respectively. This implies that SS not only contributes significantly to the implementation of HPWS but also positively
impacts nurse career satisfaction. The square root of AVE for SS is 0.802, affirming strong construct reliability and appropriate
discriminant validity as it is higher than its correlation with other constructs.
Career Satisfaction (CS). CS shows a substantial correlation with HPWS (0.751) and a moderate correlation with Nurse Service
Quality (NSQ) (0.351), highlighting its central role as a mediator in the model. The correlation between CS and NSQ, although
lower than with HPWS and SS, is statistically significant, suggesting that higher career satisfaction can lead to better service
quality. The AVE for CS is 0.879, indicating excellent reliability and discriminant validity.
Nurse Service Quality (NSQ). NSQ is notably correlated with SS (0.541) and moderately with HPWS (0.499), indicating that
both the systemic and support dimensions significantly impact service quality. The square root of AVE for NSQ is 0.849, which
is higher than its correlations with other constructs, ensuring good discriminant validity.
Interaction Terms (SS-HPWS and SS-CS). The interaction term SSHPWS shows a correlation coefficient of 0.154 with HPWS
and 0.105 with SS, suggesting a moderate moderating effect of SS on the relationship between HPWS and other outcomes. SSCS
displays even lower correlation values with CS (0.046) and SS (0.038), but a significant correlation with NSQ (0.208). This
indicates that the combined effect of SS and CS significantly influences NSQ. The correlation of 0.773 between the two
interaction terms highlights their interconnectedness in affecting outcomes.
P-values for Correlations. The P-values associated with these correlations are predominantly below 0.05, indicating statistically
significant relationships among the constructs. Notably, the correlations between HPWS and SS, CS, and NSQ are all highly
significant (P < 0.001). The interaction terms show varying levels of significance, with SS-HPWS having a more significant
influence on NSQ than on other constructs.
According to table 5, Latent Variables Coefficient (Average Variance Extracted) [47].
R-squared values. The R-squared values for HPWS and SS are 0.587 and 0.394, respectively, indicating moderate to strong
explanatory power regarding the variance in these constructs. This suggests that the model explains a significant portion of the
variance in HPWS and SS.
Composite reliability. The high composite reliability scores for HPWS (0.946), SS (0.944), and CS (0.959) show that the
constructs are internally consistent, with scores above the 0.7 threshold that means the scales are reliable.
Cronbach's alpha. The Cronbach's alpha for all constructs is above 0.9, indicating excellent internal consistency and reliability
of the items within each construct.
Average variance extracted (AVE). AVE scores for all constructs are above 0.5, which is the commonly accepted threshold,
indicating satisfactory convergent validity.
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The full collinearity VIF (variance inflation factor). values below the threshold of 5 suggest that multicollinearity is not a
concern for this model, indicating that the predictors are relatively independent.
Q-squared (Stone-Geisser's Q2). Q-squared values, particularly for HPWS (0.602), indicate the model has predictive relevance
for the constructs, with values above 0 suggesting the model's predictive accuracy.
Skewness and kurtosis. The skewness and kurtosis values suggest that the data distribution does not deviate excessively from
normality, implying that the data are reasonably symmetric and do not have problematic tails.
Unimodality. The unimodal assessments (RS and KMV) indicate that the data do not show a significant departure from
unimodality, which is desirable for SEM analysis.
Normality tests (Jarque-Bera and Robust Jarque-Bera). Normal-JB suggests that some constructs do not follow a normal
distribution, whereas Normal-RJB indicates that, with robust assessment, some constructs exhibit normality. This highlights the
importance of employing robust statistical methods capable of handling non-normality in SEM.
Overall, the data in the table supports the validity and reliability of the SEM model that this study used. The model shows good
internal consistency, convergent validity, and predictive relevance. We can also use PLS-SEM techniques to examine the
relationships between HPWS, SS, CS, and NSQ, as well as their interaction terms, as they do not exhibit multicollinearity and
generally align with unimodality and normality assumptions. The results provide empirical support for the theoretical framework
suggesting that supervisory support moderates the relationship between HPWS and career satisfaction, which in turn impacts
nurse service quality. These insights are instrumental for healthcare organizations aiming to optimize their work systems and
supervisory support structures to enhance overall service quality.
According to table 6 Path Coefficient (Structural Model/Inner Model) [49]
CS works in conjunction with HPWS and SS.
The path coefficient from HPWS to CS is 0.561, and from SS to CS is 0.247, both of which are statistically significant with p-
values less than 0.001. This implies a strong positive influence of HPWS and SS on CS, suggesting that effective work systems
and supportive supervisory behaviors substantially increase career satisfaction among nurses. The interaction term SS*HPWS has
a path coefficient of -0.032 when predicting CS, but this is not statistically significant (p-value 0.324). This suggests that the
interaction between SS and HPWS does not have a discernible effect on career satisfaction.
The NSQ operates as a function of SS and CS. SS and CS positively influence NSQ, with path coefficients of 0.467 and
0.156, respectively.
The relationship between SS and NSQ is highly significant (p < 0.001), indicating that supervisory support directly contributes to
the quality of nursing services. CS's influence on NSQ, while statistically significant (p = 0.012), has a smaller path coefficient.
This demonstrates a positive but more modest impact of career satisfaction on service quality. A significant path coefficient of
0.226 and a p-value of less than 0.001 indicate that combining supervisory support and career satisfaction significantly enhances
nurse service quality.
The model's findings highlight the central role that HPWS and SS play in fostering an environment that promotes CS, which is
crucial in improving the quality of nursing services. The interaction effects indicate that while SS is effective on its own in
enhancing NSQ, its impact is even more pronounced when nurses also experience high levels of career satisfaction.
According to table 7, Linear and nonlinear (warped) relationships among latent variables [50]
Career satisfaction (CS). The 'Warped' indicator indicates the relationships between High-Performance Work Systems (HPWS)
and CS, Supervisor Support (SS) and CS, and the interaction term SS-CS. This suggests that the effects of HPWS and SS on CS
are not strictly linear but may follow a more complex, possibly curvilinear, relationship. The warped nature of these relationships
implies that increases in HPWS and SS may lead to greater increases in CS after certain thresholds are met, or there could be
diminishing returns at higher levels of HPWS and SS.
Nurse Service Quality (NSQ). The table demonstrates the 'warped' relationships between SS and NSQ, CS and NSQ, as well as
the interaction term SS-CS with NSQ. This indicates that as supervisory support and career satisfaction increase, their impact on
nursing service quality may intensify in a non-proportional manner. For instance, a threshold may exist where minor
enhancements in CS result in significant improvements in NSQ, or vice versa.
HPWS and SS. There is no indication that HPWS and SS have warped relationships directly with other constructs. This suggests
that the study did not find evidence of nonlinearity in these relationships, or that these specific relationships were not the focus of
the nonlinear analysis.
The presence of warped relationships in the model adds a layer of complexity to the interpretation of the data. It implies that
simply examining the direction and strength of the relationships (as done in linear models) may not fully capture the dynamics at
play. Instead, the model acknowledges the nuanced nature and potential influence of factors that cause the relationship between
work systems, supervisory support, career satisfaction, and service quality to deviate from a straight line.
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According to table 8, Indirect and total effects[51][49][46][52][51]
Indirect Effects Analysis
The table assesses the indirect effects that occur when a path between two variables is mediated by a third variable. In this case, it
shows the indirect effects on Nurse Service Quality (NSQ) from High-Performance Work Systems (HPWS) and Supervisor
Support (SS), mediated by Career Satisfaction (CS).
HPWS to NSQ through CS: The indirect effect size is 0.087, which is statistically significant with a P value of 0.039. This
indicates that HPWS positively affects NSQ through its impact on CS. The effect size of 0.044 suggests that this indirect pathway
has a small but significant influence on NSQ.
SS to NSQ through CS: The indirect effect size is 0.038, which is not statistically significant, as indicated by a P value of 0.220.
This implies that the path from SS through CS to NSQ is not strong enough to be considered significant in this model.
SS-HPWS to NSQ through CS: An indirect effect size of -0.005 with a P value of 0.460 suggests no significant indirect effect of
the interaction term SS-HPWS on NSQ through CS.
Total Effects Analysis
Total effects encompass both direct and indirect effects of one variable on another within a path model.
Career Satisfaction (CS). The total effect of HPWS on CS is 0.561, and from SS on CS is 0.247. Both effects are significant
with P values of less than 0.001. This suggests a strong and significant total effect of both HPWS and SS on career satisfaction of
nurses.
Nurse Service Quality (NSQ). The total effect of SS on NSQ is quite substantial, with a value of 0.506, and it is statistically
significant (P < 0.001). The effect of CS on NSQ is also significant with a total effect size of 0.156 and a P value of 0.012. The
interaction term SS-CS shows a significant total effect size of 0.226 on NSQ, indicating a meaningful impact on NSQ when both
supervisor support and career satisfaction are considered.
Standard Errors and Effect Sizes
Standard errors are associated with all indirect and total effects, providing an estimate of the margin of error in the effects. The
standard errors for the indirect effects are all around 0.050, and similarly, for total effects, they range from 0.049 to 0.069,
indicating precision in these estimates.
Effect sizes provide a quantitative measure of the importance of an effect. In the context of this study, effect sizes for indirect
effects are smaller compared to those for total effects, which is expected as indirect effects are typically more subtle than direct
effects.
The detailed analysis indicates that High-Performance Work Systems (HPWS) and Supervisor Support (SS) significantly impact
Nurse Service Quality (NSQ), both directly and indirectly, via Career Satisfaction (CS). The significant total effects highlight the
substantial influence of HPWS and SS on CS and NSQ in the context of the healthcare environment.
This data provides valuable insights for healthcare administrators looking to enhance service quality. By understanding the direct
and mediated effects of HPWS and SS on career satisfaction and nurse service quality, healthcare organizations can strategically
focus their resources on areas that yield significant improvements in nurse satisfaction and patient care outcomes.
According to Figure 3, Measurement Model Evaluation Result[12][49][40]
High-Performance Work Systems (HPWS) and Career Satisfaction (CS)
hich
is statistically significant (p<0.01). This finding suggests that when nurses perceive their work environment as high-performing
characterized by effective communication, opportunities for development, and a supportive cultureit substantially contributes to
their career satisfaction. The R2 value of 0.58 for CS indicates that the model's variables, particularly the HPWS, account for 58%
of the variability in career satisfaction among nurses.
Supervisor Support (SS) and Career Satisfaction (CS)
0.25, p<0.01), indicating that supportive behaviors from
supervisors, such as feedback, encouragement, and resources, correlate with higher levels of career satisfaction among nurses.
= -0.03, p = 0.32), suggesting that the influence of SS
on HPWS is not straightforward and may be more nuanced.
Career Satisfaction (CS) as a Mediator
CS serves as a mediator between HPWS and NSQ. While the direct path from HPWS to NSQ is not significant
).
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This finding highlights the complex mediating role of career satisfaction, which stems from high-performance work systems and
leads to enhanced nurse service quality.
Supervisor Support (SS) and Nurse Service Quality (NSQ)
ates
directly into higher service quality. SS also indirectly influences NSQ via CS (the interaction term is significant), underscoring
the multifaceted role of supervisory support.
The Moderating Effect of Supervisor Support
The moderation analysis reveals a pivotal role for supervisor support as it relates to career satisfaction and its impact on service
quality. When predicting NSQ, the interaction term SS*CS is significant, indicating that the strength of the relationship between
career satisfaction and service quality is dependent upon the level of supervisory support. This suggests that the more support
The model's R2 for NSQ is 0.31, meaning its factors account for 31% of the variance in nurse service quality
These insights are vital for healthcare administrators as they highlight the synergistic effect of combining high-performance work
systems with supervisory support to enhance career satisfaction, which in turn elevates the quality of service delivered by nurses.
Robust supervisory support must complement the effective implementation of HPWS to fully realize HPWS's potential for
improving service quality.
Discussion
According to Figure 2. View two graphs with low-high values of moderating variable and data points.
Low Supervisor Support (Low SS). In the left scatter plot, the relationship between HPWS and CS under conditions of low
supervisor support appears to follow a nonlinear, possibly quadratic pattern. The curvature suggests that at lower levels of HPWS,
increments in these systems might not significantly influence career satisfaction, or the effect may even be slightly negative.
However, as HPWS increases past a certain threshold, career satisfaction begins to rise considerably. This could indicate that
without the foundational support of supervisors, the benefits of HPWS on career satisfaction are not fully realized until a
substantial level of HPWS is present.
High Supervisor Support (High SS). The right scatter plot with high supervisor support shows a more pronounced curve,
implying a stronger and more positive relationship between HPWS and career satisfaction when supervisor support is high. This
plot suggests that even modest levels of HPWS in conjunction with high supervisor support can result in increased career
satisfaction. Furthermore, as HPWS levels rise, the rate of increase in career satisfaction also accelerates, indicating a potentially
exponential relationship.
Low Supervisor Support (Low SS). In the left plot, there is an interesting non-linear pattern between CS and NSQ under
conditions of low supervisor support. Initially, as CS increases, NSQ decreases, reaching a nadir, and then as CS continues to rise,
NSQ sharply increases. This U-shaped curve suggests that at low levels of supervisor support, moderate increases in career
satisfaction may not immediately translate into enhanced service quality. However, beyond a certain level of career satisfaction,
the quality of service provided by nurses improves significantly.
High Supervisor Support (High SS). The right plot shows a similar U-shaped relationship but with a noticeable shift upwards.
In this context, even with minimal CS, NSQ starts at a higher point compared to the "Low SS" scenario. As CS increases, there's
an initial dip in NSQ, but it quickly starts to rise as CS continues to grow, suggesting that high supervisor support buffers the
initial negative impact of low career satisfaction on service quality.
These visual patterns suggest that supervisor support might play a moderating role in the relationship between HPWS and career
satisfaction. In the context of low supervisor support, HPWS may need to reach a higher level to overcome the lack of support
and positively impact career satisfaction. Conversely, when supervisor support is high, the positive effects of HPWS on career
satisfaction are not only more immediate but also more substantial.
The difference in the relationship patterns across the two plots emphasizes the importance of considering supervisory support as a
key component in enhancing the efficacy of HPWS. The data suggest that healthcare organizations aiming to leverage HPWS to
boost career satisfaction may also need to ensure that their supervisory structures are supportive, as this could amplify the positive
effects of their work systems.
These patterns indicate that supervisor support may have a moderating influence on the impact of career satisfaction on nurse
service quality. Specifically, high levels of supervisor support could mitigate negative aspects of lower career satisfaction,
maintaining a baseline of service quality and enhancing the positive impact as career satisfaction grows.
The non-linear relationship here implies that interventions to improve nurse service quality may need to consider more than just
enhancing career satisfaction; they must also account for the level of supervisor support. This could mean that for healthcare
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organizations, bolstering supervisor support could be just as crucial as improving other aspects of nurses' work environment to
maintain and improve service quality.
Figure 2. View two graphs with low-high values of moderating variable and data points.
Implications
The study's findings on the moderating role of supervisor support in the context of high-performance work systems (HPWS) and
their effect on nurse service quality through career satisfaction have important implications for healthcare management and
policy.
Leadership Development. Training programs that enhance leadership qualities in supervisors could be a key strategy. Effective
leadership has the potential to amplify the benefits of HPWS on career satisfaction and, by extension, on service quality.
Strategic HRM. Human resources management (HRM) needs to consider both structural and relational elements to foster a
conducive work environment. This includes the development of HPWS that are complemented by supportive supervisory
practices.
Employee Engagement. Initiatives aimed at increasing career satisfaction among nurses should be a priority. Engaged
employees are more likely to provide high-quality care, and this study underlines the pathway through which engagement can be
achieved.
Organizational Culture. A supportive organizational culture is integral to the successful implementation of HPWS. The
moderating effect of supervisor support suggests that the cultural context shapes the success of these systems
Limitations and Future Research
This study is not without its limitations. The authors suggest directions for further research to deepen and expand understanding
of the relationships among HPWS, supervisor support, career satisfaction, and nurse service quality.
Cross-Sectional Design. The study's cross-sectional nature limits the ability to infer causality. Longitudinal designs would better
track the changes over time and the enduring effects of HPWS and supervisor support.
Sample Diversity. The research might have limitations concerning the diversity of the sample. Including a broader demographic
and different healthcare settings would enhance the generalizability of the findings.
Measurement of Constructs. The operationalization of constructs like career satisfaction and service quality may vary; thus, the
measurement tools should be examined for cross-context validity.
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Future Research
Expanding the Model. Future research could explore additional variables that might impact the primary relationships studied,
such as job autonomy, team dynamics, and organizational justice.
Interventional Studies. Interventional studies that implement specific changes in supervisor support and measure the subsequent
impact on HPWS effectiveness and service quality would provide a practical roadmap for improvements.
Cultural Contexts. Comparative studies across different cultural and healthcare contexts could reveal the extent to which cultural
factors influence the moderating role of supervisor support.
Quantitative and Qualitative Methods. Combining quantitative data with qualitative insights could enrich the understanding of
how and why supervisor support plays a critical role.
V. Conclusion
This study highlights the significant impact of High-Performance Work Systems (HPWS) and supervisory support on nurse
service quality through the mediating role of career satisfaction. Our findings demonstrate that effective implementation of
HPWS significantly enhances career satisfaction among nurses, which subsequently improves service quality. Moreover, the
addition of supervisory support not only directly contributes to service quality but also amplifies the positive effects of HPWS on
career satisfaction and indirectly on service quality. This dual pathway underscores the importance of supportive supervisory
practices alongside structured performance systems in healthcare settings.
Acknowledgment
The authors express their heartfelt gratitude to all those who contributed to the successful completion of this study. Special thanks
go to Dr. Wisnu Prajogo, MBA, for his invaluable expertise in Human Resources Management & Behavioral Management, which
significantly shaped the development of this research. His guidance and profound knowledge in these areas were instrumental in
framing the theoretical and practical applications explored in this study.
We also extend our appreciation to Dr. Yani Restiani Widjaja, M.M., whose insights into the management practices and academic
rigor have enriched our understanding and added substantial depth to our analysis.
Further gratitude is extended to the staff and management of Tjitrowardojo General Hospital, Purworejo, for their willingness to
participate in this study and for their invaluable contributions to the fieldwork. Their collaboration and openness greatly enriched
our research.
We also thank the academic staff of the Department of Management, ARS University, and STIE YKPN for their guidance and
insightful critiques during the development of this study. Our colleagues and research assistants who offered their time and
expertise throughout the study, and our families for their patience and encouragement, also deserve our heartfelt thanks.
Finally, we extend our appreciation to the reviewers and editors for their constructive feedback, which significantly improved the
manuscript.
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Appendix A
Figure 3. Measurement Model Evaluation Result[12][49][40]
Appendix B
Tabel 5. Latent Variables Coefficient (Average Variance Extracted)
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Appendix C
Tabel 6. Path Coefficient (Structural Model/Inner Model)[49]
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
CS
0.561
0.247
-0.032
NSQ
0.467
0.156
0.226
P values for total effects
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
CS
<0.001
<0.001
0.324
NSQ
<0.001
0.012
<0.001
Appendix D
Tabel 7. Linear and nonlinear (warped) relationships among latent variables[50][4]
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
HPWS
SS
CS
Warped
Warped
Warped
NSQ
Warped
Warped
Warped
SS*HPWS
SS*CS
Appendix E
Tabel 8. Indirect and total effects[51][49][46][52][51]
Indirect effects for paths with 2 segments
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
NSQ
0.087
0.038
-0.005
Number of paths with 2 segments
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
NSQ
1
1
1
P values of indirect effects for paths with 2 segments
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
NSQ
0.039
0.220
0.460
Standard errors of indirect effects for paths with 2 segments
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
NSQ
0.049
0.050
0.050
Effect sizes of indirect effects for paths with 2 segments
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
NSQ
0.044
0.021
0.001
Sums of indirect effects
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
NSQ
0.087
0.038
-0.005
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Number of paths for indirect effects
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
NSQ
1
1
1
P values for sums of indirect effects
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
NSQ
0.039
0.220
0.460
Standard errors for sums of indirect effects
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
NSQ
0.049
0.050
0.050
Effect sizes for sums of indirect effects
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
NSQ
0.044
0.021
0.001
Total effects
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
CS
0.561
0.247
-0.032
NSQ
0.087
0.506
0.156
-0.005
0.226
Number of paths for total effects
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
CS
1
1
1
NSQ
1
2
1
1
1
P values for total effects
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
CS
<0.001
<0.001
0.324
NSQ
0.039
<0.001
0.012
0.460
<0.001
Standard errors for total effects
HPWS
S
CS
NSQ
SS*HPWS
SS*CS
CS
0.063
0.067
0.070
NSQ
0.049
0.064
0.069
0.050
0.068
Effect sizes for total effects
HPWS
SS
CS
NSQ
SS*HPWS
SS*CS
CS
0.421
0.168
0.006
NSQ
0.044
0.280
0.075
0.001
0.061
