Behavioral Intention In Revisiting Hospital Under The Effect Of Expertise, Reputation And Service Quality.doc

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Behavioral Intention In Revisiting Hospital Under The Effect Of Expertise,
Reputation And Service Quality
Pham Bao Duy
International University, Vietnam National University HCMC, Vietnam
Nguyen Tan Loi
Eastern International University, Vietnam
Ho Nhut Quang
International University, Vietnam National University HCMC, Vietnam
Abstract
The well-noted extensive solution for strengthening the hospital’s obstacles from various perspectives are
seeking in Vietnam context. Considering the foothold of medical industry formulated by government, the
medical industry grants as sustainable fundamental development observed through FDI and governmental
equity with shaky restriction. As strikingly demands in healthcare services, private and public hospitals in
Vietnam, however, divulge the noticeable missing pieces are service quality, trust and satisfaction in arousing
rehash visitors whom reveal the disavowal through pursuing highly experience expectation in comprehensive
alternatives. Consequently, the deeply understanding in the rehash patients’ behavioral intention, especially,
with further with appealing unfamiliar one is envisaged vital pharmaceutical to intensify specifically
hospitals’ aspects. Methodically, quantitative research spread out the study in advance, the sample size
accounted at 316 processing in Explanatory Factors Analysis and deeply exploiting by Structural Equation
Modeling method. The outcomes spotlight the significant initial effect of independent dimensions in
reputation and expertise toward trust, service quality toward satisfaction as well as towards behavioral
intention in revisiting the hospital. Undoubtedly, there are some recommendations toughen up specifically
the retained problems for hospitals in Vietnam.
Keywords: Vietnam Healthcare, Service Quality, Expertise, Behavioral Intention in Revisiting, Hospital in
Vietnam, Sustainable Development.
1. Introduction
Healthcare has long been the initial service for human, which provides significant purposes in
examination, prediction, treatment and control the health. According to the former researches, it is
considered as the high level of association services (Hogg, Laing, & Newholm, 2004). In some emergency
situation, the hospital is not only considered as the place for health improvement and examination but also
become the crucial place for saving a life. Therefore, the high level in emotional vulnerable following with
the hazard is not deniable (Jadad, 1998).
According to General Statistic Office in Vietnam, there are 1,101 hospitals in 2012 and higher 36% compared
with the statistics in 2007. In addition, the number and scale of corporation and organization operating in medical
and health care field such as Hoan My, VinMec and TMMC has been increased in recent years. It
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provides the proof that the importance and attention of medical industry in Vietnam. However, with the
interesting figures demonstrated in the recent report, the fundamental right – basic healthcare would be not
considered as the right in developing countries included Vietnam where people from rural areas, even the
government spend hundreds of million dollars in medical industries which mostly focus on facilities’
expansion and improvement in suburban.
In other sides, according to Vietnam 2035 general report has been posted by World Bank and Ministry of
Planning and Investment in Vietnam, it shows directly that the proportion of GDP shares 6% for medical and
health care over 2 decades from 2015 to 2035. While GDP and the growth rate have been reported outstanding
positively increasing whereas the out-of-pocket spending in medical still appeared with 49%, it shows the
importance of medical fields among people even the total expenditure must be paid by their money
According to WHO, the bed occupancy rate should not be over 80% of the hospitals’ capacity. Vietnam,
however, witnessed a high occupancy rate especially in large cities. A research of “Study on Current Situation of
Overcrowding, Under-Crowding in Hospitals at Levels and Recommended Solutions for Improvement” of
Ministry of Health in 2011 demonstrates that central and provincial hospitals always in overcrowding situation. It
is in the ranged from 120-150% of the hospital beds used and some special cases over 200% in big central hospital
in Ho Chi Minh city and Ha Noi capital. According to the research of PricewaterhouseCoopers (Vietnam) Ltd.
Company called “The Vietnamese healthcare industry: moving to next level”, it mentioned the overload services
in large and popular hospitals in Vietnam while remote area hospital and regional polyclinic in suburban is lack of
patient. Moreover, it agreed that Vietnamese tends to approach national hospital instead of provincial hospital
due to the mindset in lack of quality in medical staff and medical equipment.
Following the article “The poor still miss out on healthcare in Vietnam” published in 2015 on Joint
Learning Network – The global community of health systems practitioners and policymakers in 27 countries
including Vietnam, it stated that “Health Care Fund for the Poor” is signed in 2002 under the Decision 139
signed by Prime Minister of Vietnam in order to support the healthcare service Vietnam especially people
live in communes. However, this program was not appreciated by World Bank, the research “Health
Insurance for the Poor: Initial Impacts of Vietnam’s Health Care Fund for the Poor” found that the reduction
of out-of-pocket spending has not happened
According to a recent survey by the Ministry of Health, every year around 40,000 Vietnamese go abroad
for health treatment purpose and spend $1 billion in 2010 and nearly $2 billion for total expenditures in early
2016. It raises the problem that whether hospitals in Vietnam still not meet the needs of domestic demand in
health care purpose even 6% of GDP in Vietnam spend for medical as mentioned above. In addition, in
Vietnam, the average people make the out-of-pocket payment for health care is almost accounted for 75%
the total spending in health care (Knowles et al. 2005). According to the research of Gludner and Rifkin in
1993, private healthcare provider appealing the demand of people who have intend in healthcare service as
the public service is deficient and imperfect, the case of Vietnam and Uganda.
2. Literature Reviews
2.1. Service Quality
Service quality is a measurement on the matching between services delivered and customer’s
expectation. Service quality must be delivered that match with customer expectation on a reliable basic
(Lewis and Booms, 1983)
Unfortunately, the evaluation for service quality in the scale that built-up through previous study for
healthcare industry has been on the rocks. Instead of the value comes from the result of health care, patients
do not completely evaluate the comprehensive problem in service quality through their perspective. In
addition, some sectors found many difficulties to determine whether it will be added on the service quality
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assessment or not such as the emergency affected to the probability in survival or vegetable existence, the
question is not able to figure out people who responsible to assess the evaluation. In addition, the lack of
skill also expertise to define the service whether it was conducted following the process or not (Newcome,
1997; Williams, 1994). As a result, hospitals take their advantages in the evaluation of patient with the
misleading in technical service quality aspect (Bowers et al., 1994; Ettinger, 1998; Donabedian, 1988), focus
on the interaction between patients and physicians and approach with potential customer with the
misleading of former evaluation.
According to Bowers et al. in 1994, they suggest that the scale of service quality in patient’s determinant
take important role in their satisfaction through SERVQUAL model. Before the Bower and his partners’
findings, a former study also used SERVQUAL in implication in the antecedent of service quality in
satisfying patient (Reidenbach and Sandifer-Smallwood, 1990). It is explained from another study in health
care service industry that “As a construct, customer satisfaction has been noted as a special form of
consumer attitude; it is a post-purchase phenomenon reflecting how much the consumer likes or dislikes the
service after experiencing it” (Woodside, AG. Frey, LL. and Daly, RT., 1989). It comes to the first hypothesis:
Hypothesis 1. A service quality of hospital is positively related to patient’s (customer’s) satisfaction
In health care research, SERVQUAL scale is the precursor model for evaluating the outcomes behavioral
intention comes from service quality (Reidenbach and Sandifer-Smallwood, 1990), and other variant model
with the same result, for example, Headley and Miller developed 6-dimensional based on primitive
SERVQUAL model in 1990. It can be seen obviously that the service quality is a significant dimension not
only satisfy customer but also attract customer in repurchasing service or product.
Hypothesis 5. A service quality of hospital is positively related to patient’s (customer’s) behavioral intention in
revisiting hospital.
2.2. Satisfaction
In 1980, Oliver built the definition that “In brief, customer satisfaction is a summary cognitive and affective
reaction to a service incident (or sometimes to a long-term service relationship). Satisfaction (or dissatisfaction)
results from experiencing a service quality encounter and comparing that encounter with what was expected”.
To analyze the level of satisfaction that customer measured based on service, product that provided by an
organization through figures based on questionnaires and feedback from the frontline staff. It could be the
positive judgments’ outcome from using a product or service from customer perspectives (Westbrook, 1980).
Related to the definition, it suggests that satisfaction is the emotional evaluation, it is a chain of individuals’
assessment rather than an individual perspective (Cronin and Taylor, 1994; Hunt, 1977). The scale of
satisfaction is defined from dissatisfying to satisfying where other arguments implicate that the customer
satisfaction assessment proves a comprehensive evaluation than the specific outcome of a transaction.
According to Singh and Sirdeshrnukh (2000), customer’s experiences is defined as the directly evaluation
on some cues which included satisfaction. Based on implicit and explicit cues, customer can gradually
formulate the trustworthiness with firm (Doney and Cannon 1997). If build up a strongly satisfaction from
customer, customer may have more confidence with the firm, which is the basic for increasing their trust on
service provider. Thus,
Hypothesis 4. A patient’s (customer’s) satisfaction is positively related to their trust in hospital.
Satisfaction is the factor that combine many antecedent elements, when customer’s satisfaction increased,
it leads to the last variable, repurchasing intention or it can be considered as sub-dimensions of customer
loyalty (Kitapci, Akdogan, & Dortyol, 2014). In medical industry, there are varied study that mentioned this
relationship which shows the impact of satisfaction on behavioral intention (Anderson and Sullivan, 1993;
Bitner, 1990; Reichheld, 1996; Woodside and Shinn, 1988; Woodside et al., 1989). Considering customer’s
satisfaction as the intermediate variable, majority of studies suggests that there were the indirect influences
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between the behavioral intention and service quality where using value and satisfaction as the mediate
factor (e.g., Anderson and Sullivan, 1993; Gotlieb, Grewal, and Brown, 1994; Patterson and Spreng, 1997;
Roest and Pieters, 1997; Taylor, 1997). Hence,
Hypothesis 6. A patient (customer’s) satisfaction is positively related to their behavioral intention in revisiting
hospital.
2.3. Trust
Trust comes from the belief of a party’s promise or sentence is reliable and the obligation that party need
to be fulfilled in vice versa for relationship purpose (Schurr and Ozanne, 1985). Based on the trust, the
interaction of a buyer’s perception future and service provider (seller) is anticipated (Doney and Cannon,
1997). It creates a long-term orientation of a relationship B2C in positive ways (Ganesan, 1994). The trust’s
advantages which create strong relationship in business has been researched in the literature review of
Morgan and Hunt in 1994. The individual experience is considered as the trustworthy source rather than the
referral from relatives or friends which is explained as the second-hand trust referral or the popular.
Building trust efforts is core value of all business in general and hospital service in specific, the results
from this long journey is the substantial development where customer loyalty and attraction are not
deniable. There are some evidences show the behavioral intention in repurchasing services, products are the
origin of trust (Morgan and Hunt, 1994; Chaudhuri and Holbrook, 2001). As trust shows confidence in
looking for new customer as the reliability and integrity has been prepared, it is the main component for
long-term relationship orientation as it moves the focus in present to continuity and future conditions
(Doney and Cannon, 1997; Ganesan, 1994). Therefore, it results in a hypothesis that:
Hypothesis 7. A patient (customer’s) trust is positively related to their behavioral intention in revisiting hospital.
2.4. Expertise
Knowledge and experience of service providers in the main services are two terms that typically measure
in expertise (Crosby el at., 1990). In Medicine and Surgery perspectives, the expertise requires a mastery in
relevant skills also the diversity of knowledge in many aspects. Unlike other fields, physicians require the
diverse knowledge such as biology, chemistry, physics as the basement and up-to-date their specialization
that they pursuit from the beginning. Besides, ethics, cognitive and motor must be consistent interpersonally
according to their leaning in behavior and responsibility. Moreover, clinicians require higher level in their
enormous knowledge not only in their specialization but also conduct the relevant field from pharmacist to
the surgeon. Considering medical diagnosis is the general skill of the physicians, the expertise of the doctors
is defined through the accuracy of medical diagnosis because the combination of higher experience and
knowledge are deeply and varied (Feltovich et al., 1984; Neufeld et al., 1981)
A study found that the source of credibility and trustworthiness is the results of individual’s perception
on level of expertise, it implicates a positively effects on trust (Busch and Wilson, 1976). In other words, the
level of experts creates the trust’s foundation. According to the research of Crosby, Evans and Cowles in
1990, trust signal was founded from the expertise’s perception of customer. It can be related to the
trustworthy company where the appearance of relationship between expertise and trust create positively
effects (Newell and Goldsmith, 2001). In specific of hospital service, the expertise is the undeniable role
which contribute to the decision and recommendation on customer’s health. The enhancements in trust are
depending in the major of the expertise which provide the skilled-set learned from the perennial experience
and qualifications or highly achievements in their professional career. Therefore,
Hypothesis 2. A worker’s expertise in hospital is positively related to patient’s (customer’s) trust.
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2.5. Reputation
The customer’s belief and trust that the firm is truthful and equitable is defined as firm reputation (Doney
and Cannon, 1997). In widely views, it is a general overview measurement of a corporate or a firm in level
whether it is “good” or “bad” (Weiss, Anderson, & MacInnis, 1999; Roberts & Dowling, 2002). In the sense of
reliability, reputation is defined as the collective opinions which evaluate positively the trustworthiness and
it results in the individual’s perspective in what they believed or positive said about the firm’s character
(Freeman, 1979). Hospital’s reputation could be directly affected by concrete financial problems, even the
professional pride is highly attracted by a motivating factor. In specific, the sponsor and investment from
corporate and individuals are founded as the huge amount to maintain the operation. Especially in human
health service sector, it is necessary to concentrate on the corporate reputation due to the dense of customer
relation which is the most problematic affected customer perspectives to the hospital (Chase, 1978).
In previous research, it implicated that the customer’s evaluation on reputation of a service provider will
positively impact on the acknowledgement on firm’s trustworthiness through information transference
process (Doney and Canon, 1997). A study of Devon Johnson and Kent Grayson in 2005 suggested that firm
reputation is the antecedent of both affective and cognitive trust, “customer who is not yet sufficiently
familiar with a service provider may extrapolate his/her opinions directly from the reputation of the firm”.
Hence, the hypothesis is built,
Hypothesis 3. A hospital’s reputation is positively related to patient’s (customer’s) trust.
2.6. Behavioral Intention
The decision that intend to perform in a specific way is considered as intention (Fishbein and Ajzen,
1975). A person who have their subjective perception ability that he or she will enjoy in a given behavior is
defined as behavioral intention (Committee on Communication for Behavior Change in the 21st Century,
2002). In other way, it can be the level that a person has built self-conscious intention to engage or not
engage with some specified future behavior, it is a signal about the customer future’s behaviors (Venkatesh
et.al., 2003; Lai and Chen, 2011)
Through previous research, it was definite to believe the important role of conceptual framework in
study. The model was prompted and changed by related empirical studies in a health care service provider
which can apply in Vietnam context.
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Service Quality
Expertise
H1(+)
H5(+)
Satisfaction
H6(+)
H4(+)
H2(+)
Trust H7(+)
H3(+)
Reputation
Figure 6: Conceptual Model
Behavioral Intention
3. Methodology
3.1. Research Method
Qualitative and quantitative methods are considered two main method in processing the research for
various purpose, especially in achieving knowledge from the study (Ritchie and O’Connor, 2004). The
quantitative research is supported from the statistics where it can retrieve from the primary or the secondary
data such as survey, questionnaires or previous data. Meanwhile, the qualitive research method based on the
evaluation of themes which is retrieved from the observation or interview. In other words, it is the
unmathematical method. In 2001, Soguno suggested that the objectives of the study were able to express
clearly which could bring back to the society the enhancement in general views in many aspects also the
effect of each other.
In this study, quantitative research was selected to go further. With the same goals that qualitative
research delivering, the goals focus on the solution, recommendation based on society problems, concerns or
the supporting in further research for other developing quantitative potential approach. Besides, the
quantitative methods delivered a deeper insight or different aspects the problem that the study concerns
which support for sociologist or the experts in various industry. In other words, it could not be rejected that
it provided the comprehensive conclusion and recommendation for social problems or concerns especially.
Meanwhile, it went further with other research that give a deeper knowledge in the phenomena following
the research of Strauss and Corbin; Lundahl and Skärvad in 1998 and 1999 respectively. It can be pointed out
the common collecting data in quantitative method such as deliver survey through paper form, online form,
telephone interview or face-to-face interview. Moreover, the collection can be assessed through email, pop-
ups website ads. In other words, there are various ways to conduct the data for quantitative research. On
other views, due to the various ways in collecting data, it tended to apply popular with a significant sample
size at the short period comparing to the qualitative research method.
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3.2. Subject
The data was collected through paper research in site location, no any online form is accepted on this
research due to integrity and reliability of the research also the characteristics of the dependent variables
which focus on the patients have already visited one of among four hospitals mentioned below. Vinmec
Central Park International Hospital which is accounted for 178 operating beds with the mission of
“Delivering first-class healthcare service”, Vinmec is considered as the hospital have the best hospital’s
service quality in Vietnam. Secondly, Hoan My hospital which is belonged to Hoan My Medical
Corporation. In Vietnam, Hoan My is leading the private healthcare network where 13 hospitals and 5 clinics
has been established since 1999, there are 808 doctors and 3918 full-time employees are working in among
these hospitals. Also, it accounted to 2399 beds are in operation and the limitation over 3407 beds. In Ho Chi
Minh city, Hoan My Hospital was allocated in Phu Nhuan district which serves daily over 2,000 patients
with the capacity up to 261 beds. Thirdly, HCMC University Medical Clinic which was one of the most
trustful destination of the patient where it serves over 2 million of patient annually. With 3 centers were
allocated in around the city, they proudly to be one of the largest operating beds capacity in Ho Chi Minh
city at over 1000 operating beds. Moreover, the hospital is well-known as the medical center having more
than 100 professor and vice professor involved in medical examination and treatment. Finally, 175 Military
Hospital which is considered as the big central hospital in Vietnam that apply modern technique in
healthcare process, 175 Military Hospital is highly appreciated from the respect of doctor whom most of
them begin their career in military where ethics and behaviors is strictly applied. In 2018, the hospital is
expected to upgrade the operating bed scales up to 1,500 after the newest building is delivered in operation.
There are 350 questionnaires had been delivered to the patients, and there are 316 valid respondents
accounted at 90.3%. The sample size included age range from 18 to over 55 where the outcome placed mostly
in the 26-35 age group. Over 316 valid respondents, 52.85% of them are female accounted for 167 people.
Patients in the final sample focused on the income level from 10 to 20 million VND at 42.72%.
3.3. Sample Size
According to the research of Gorsuch; Hatcher in 1983 and 1994 respectively, the ratio should be
allocated in 1:5 where 1 items is answered by 5 respondents in EFA analysis which is also tested in this
study for further validating. In other words, with 40 items, the samples size is required to approach at 200
units. Moreover, supporting from the research of Comfrey and Lee in 1992, he found that by assessing the
sample size higher than 300 units, it can result in the great research outcomes, meanwhile, the lower one is
not quite appreciated. Hence, it comes to the decision that the valid sample size must be 316. Some data was
eliminated due to the invalid data, the paper survey must be higher than the number mentioned above.
3.4. Measures
In scaled question, it cannot be denied that the Likert Scale is the most appropriate method to apply for
conducting the survey purpose. Which is raised and promoted through the research of Rensis Likert in 1932.
However, the Liker Scale provided the various of measurement scale from 2-10, it results in the complicated
decision to determine which scale is better for this research. Although, most researches apply 5-point scales
for assessing their data through questioner, there are some evidences show that 7-point measurement scales
which could provide stronger correlations between one another items in t-test outcomes (Lewis, 1993).
Applying the Theory Planned of Behavior (TPB) in 2002, Ajzen not only provided the instruction in applying
the behavioral intention item in the study but also point out that 7-point scales are preferable when
constructing the factors around behavioral intention. Besides, the demographic data giving the general
information for the receiver to have a comprehensive evaluation about the sample.
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Construct Sub-scale Item Measurement
SQCP1 There were many signboards in hospital
Service The process for taking queue number for health
Quality of The SQCP2 examination was quick and simple
Process I did not have to wait long for medical examination
Concerns SQCP3 from physician
(SQCP) SQCP4 The lab test was done in a prompt way
SQCP5 The payment procedure was quick and simple
Service
The hospital’s employee, nurses and doctors are
SQHC1 friendly
Quality of The
SQHC2 They are willing to help me as much as they could.
Hospital
Concerns SQHC3 They explained medication process well
(SQHC)
Service Quality
SQHC4 They were really cared about my health
Service The doctor gave an explanation sufficiently of my
Quality of The SQDC1 problem, lab test’s result and treatment process
Doctor The doctor was willing to answer many questions,
Concerns SQDC2 enough to understand everything
(SQDC) SQDC3 The doctor made me feel comfortable
The waiting areas for examination and treatment
Service
SQT1 were wide and pleasant
SQT2 It was easy to access amenities (e.g., canteen, ATM)
Quality of The
The parking lots were always available for
Tangible
stakeholders (e.g., hospital’s employee, nurses,
Concerns
SQT3 doctors, patients, relatives)
(SQT)
The hospital was equipped with the latest care
SQT4 equipment and facilities (e.g. lifts, air conditioners)
The physicians were knowledgeable and highly
EXP1 educated
The physician instructed and explained fully clear
EXP2 and understandable about concerns
The hospital applied latest research, techniques and
Expertise (Exp) EXP3 methods in medical examination and treatment.
The hospital’s employee and nurses were well-
EXP4 educated in responding any situation
The hospital’s employee, nurses and physicians
were well-known about his/ her responsibilities and
EXP5 obligations
REP1 The hospital was highly regarded in Vietnam
The hospital was known as the one of the most
REP2 capable hospital in Vietnam
REP
The hospital had positive posts and comments on
media (e.g. journals, news, television, scientific
REP3 conference, social networks)
My friends, families and relatives positively know
REP4 about this hospital
TRS1 I had no reason to doubt about physician’s advice.
TRS
I absolutely believed in the lab test and
TRS2 examination’s result.
I would feel a sense of improper result if I used
TRS3 treatment and examinations of other hospitals
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I feel the physician understand and respond caringly
TRS4 and specifically my condition.
I feel more comfortable and safe when I was taken
TRS5 care of nurses in this hospital
I satisfied about facilities and equipment in the
SAS1 hospital.
I satisfied about the hospital’s staff behavior. (e.g.
SAS
SAS2 doctors, nurses, employee)
SAS3 I satisfied about the hospital’s expertise.
I satisfied with my experience in treatment and
SAS4 examination that I received in the hospital.
SAS5 I satisfied with my decision to choose the hospital
I will recommend friends; family members and
BEI1 relatives should use service in this hospital
If I needed medical services in the future, I would
BEI2 consider this hospital as my first choice.
I will tell other people good things about this
BEI BEI3 hospital
I do not care about the distance between my home
and this hospital if I needed medical services in the
BEI4 future
Even price increased, I still choose this hospital as the
BEI5 primary medical services.
Table 16: Measurement Scales
3.5. Process of Data Analysis
In this study, AMOS (version 20.0) and SPSS (version 21.0) are two tool-kit has been applied for extracted
the raw data collected from the respondents through the survey on-site. Particularly, SPSS support in figure
out the descriptive statistics not only for demographic data but also for the scaled questions. For ensuring
the validity and reliability of the study also the model, this research is verified from SPSS to AMOS. In
specific, the reliability of this research would be tested through the Cronbach’s Alpha and Corrected Item –
Total Correlation. Before moving the model and data to AMOS, the testing in EFA is required for validation
purpose where it test the validation of items and group of items called factors in Promax methods, this
output was used as the default model purpose in Pattern Matrix Model Builder of AMOS’s plugin. The
default model is built to understand whether the validation and reliability is accepted on this first testing in
the AMOS – called CFA, also checking the application of model in population between factors including
observed and latent. After conducting the CFA, the model was formulated in SEM where the test is
conducted for checking the complicated relationship among unobserved and latent variables.
4. Data Analysis and Results
4.1. Sample Characteristics
Following the methodology, the survey has been delivered on-site among 4 hospitals mentioned above.
As online survey has not been used, the paper surveys have been transferred to Excel where allow writer to
analyze the data. Unfortunately, there are 316 among 350 surveys have been conducted is valid. Invalid
questionnaires are come from mostly people misunderstanding in scaling question also the routine in not
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following the instruction has been implemented before conducting. In short, 90.3% data is analyzed in
deeper testing.
FREQUENCY PROPORTION
VALID 316 90.3%
INVALID 34 9.7%
Table 17: Response Rate
It can be observed directly that the proportion of Female is higher than Male at nearly 52.85% while
another computed at 47.15%. Particularly, it can be seen from the pie chart that women tend to concern
about these problems rather than men. Continually, it can be seen that people in the two ages group: 26-35
and 36-45 accounted for more than 55% of the respondents. It means that people in those age quite concern
about their health rather than other age group. People with the higher age group is lower as they are
unwilling to conduct the test while their relatives are responsible to handle it. In general, about the income
level, two those income levels group below 10 million and 10 – 20 million accounted more than 83% where
half of them belongs to each other. It can be explained that the vary of income level approach this study.
Evaluation Criteria FREQUENCY PROPORTION
Gender
Male 149 47.15%
Female 167 52.85%
18 – 25 60 18.99%
26 – 35 91 28.80%
Age 36 – 45 88 27.85%
46 – 55 24 7.59%
Over 55 53 16.77%
Below 10 million 131 41.46%
Income Level 10 – 20 million 135 42.72%
Over 20 million 50 15.82%
Table 18: Demographic Analysis
4.2. Preliminary Analysis
It can be seen from any research that the reliability test takes an important role to evaluate whether the
research can be trustable, consistent and reliable or not. By considering through internal consistency measured by
the Cronbach’s Alpha which is formulated and implemented by Lee Cronbach’s in 1951. Basically, most of
researchers have the agreement that Cronbach’s Alpha index of the data must at least in the range between 0.6 and
0.7 which is examined as an acceptable point, also it could be pointed out through the study of Slater (1995),
Peterson (1994) also Nunnally (1978). Besides, the reliability test including another tool that support in the
increasing of the Cronbach’s Alpha and the reliability of the research. Based on the valued in Corrected Items –
Total Correlation, it can be seen clearly that whether factors affect inversely to the reliability test or not. According
to the research of DeVellis in 1991, the figure Corrected Item – Total Correlation have the point which is lower
than 0.3 would be removed in order to increase the reliability. In other meanings, Corrected Item – Total
Correlation tools would be a helpful tool to erase the item that downgrade the Cronbach’s Alpha of the factor for
increasing the reliability in purpose. According to the output, the corrected items – total correlation of all items in
9 variables have the lowest point at 0.565; therefore, it created a huge gap between the risk point and the research
when one item among these meet the corrected items – total correlation at 0.3. From the highest to the lowest point
with Cronbach’s Alpha, the Cronbach’s Alpha of Reputation, Trust, Satisfaction, Behavioral Intention, Service
Quality in Convenience Concern, Expertise, Service Quality in Health Care's Provider Concern, Service Quality in
Tangibles and Service Quality in Doctor’s Concern are
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0.920, 0.912, 0.913, 0.901, 0.898, 0.893, 0.891, 0.876, 0.871 and 0.833 in respectively. Above all, evaluation tools
including the Cronbach’s Alpha and Corrected Items – Total Correlation show that the need for removing
items to enhance the reliability and consistence of the research are not appeared.
[Table 4 in here]
Besides, factors analysis is a diagnostic tool that combine the diversity techniques in term of statistic for
estimating the level of population structure purpose which basically based on the variation of variables through
observation and defined the correlation among these. In this section, Principal Axis Factoring (PAF) is based on
the raw data and constructs new dimensional space for the accessible displays purpose while maintain variability
and reduce the weak connection into a fewer number of components (Fabrigar et al., 1999). The tables applying
PAF as the extraction methods in this stage especially in the Total Variance Explained table which focus on the last
row in Cumulative Extraction Sums of Squared Loadings which means how variables can explain the dependent
variable with the requirement over 50% while Eigenvalue represents for the variance explained by each variable,
according to Kaiser’s research in 1960, the additional evaluation tool which based on the Eigenvalue > 1. The rule
for Eigenvalue > 1 is consistently applied in many researches as Thompson and Daniel mentioned in their
research. According to previous studies, Kaiser-Meyer-Olkin and Bartlett’s Test would be analyzed in this section,
based on the output, the KMO ratio and Sig at 0.901 and 0.000 in respectively, the KMO coefficient is qualified
when it is over 0.6, meanwhile, the sig. at 0.00 < 0.05 which means that the items have a significantly correlation
among others. At this stage, “Promax” is applied as the rotation methods for analyzing confirmatory factor
analysis (CFA) rather than “Varimax” which is rather applicable for EFA analysis purpose. The default option in
SPSS - “Varimax”, however, has some problems in analysis due to the uncorrelation among dimensions which
cause the error or mislead figures in CFA step (Pett, Lackey, & Sullivan, 2003). In the pattern matrix which applied
extraction method as PAF and rotation method as “Promax”. Other requirements implement that factor loading
which illustrate the meaningful practicality of EFA in research, the item’s factor loading must be greater than 0.3
or the value would be refused.
[Table 5,6,7 in here respectively]
4.3. Confirmatory Factor Analysis (CFA)
631

Figure 2: Measurement Standardized Modelling
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4.3.1 Model Fit Checking
In CFA analysis for model fit purpose, several figures are considered. Firstly, it can be mentioned to Chi-
square/df (CMIN/df) or 2/df, this evaluation support for assessing the conceptual model in detail whether it fit
with the data sample that implemented before. If the ratio of the degree of freedom ratio under Chi-square is
placed in the range from 3 to 1, which would be considered as the adequate fit between the collected data and the
conceptual model (Carmines and McIver, 1981). By providing other updates, the ratio which is lower than 2 and
higher than 5 is highly recommended (Carmines and Hocevar, 1985). P-value as mentioned above as the Sig is not
allowed higher than 0.05 to prove the correlation among variables. In addition, standardized root means square
residual (SRMR) is the differences between the residual collection of data and the covariance model (Hooper et al.,
2008). According to the research of Byrne, Diamantopoulos and Siguaw in 1998 and 2000 respectively, the SRMR
ratio is allowed from 1.0 to 0 and the model would be affordable model is appreciated below 0.5.
Notwithstanding, the ratio as high as 0.8 would be acceptable (Hu and Bentler, 1999). Besides, the Root Mean
Squared Error of Approximation or RMSEA also aims to measure how well model optimal fit to the population
instead of sample sizes (Byrne, 1998). By collecting from the previous study, it can be founded that RMSEA
assessment varies the evaluation based on which output is belonged to the ranges. In 1996, MacCallum, Browne
and Sugawara suggested that the output lower than 0.01 is considered the excellent fit while close fit is the range
between 0.01 and 0.05. If any didn’t achieve higher than 0.08, it is considered as the good fit while output lower
than 0.10 is briefed as mediocre fit. Continuing with MacCallum and his partners’ research, other studies suggest
that it would be cut-off if RMSEA is higher than 0.10. PCLOSE or close-fitting model is set-up for retest the
RMSEA purpose, the assumption of null hypothesis is the RMSEA is not higher than 0.05. In other words, RMSEA
must be at least containing the output with close fit. Therefore, it called PCLOSE where it must be higher than 0.05
to prove that RMSEA is lower or equal 0.05. Non-normed Fit Index (NNFI) or known as Turkey-Lewis Index (TLI)
in the Amos application, this is an upgrade version of NFI based on the disadvantage of NFI where it implements
the model fit based on Chi-squared and df. The value of NNFI are recommended higher than 0.9 for model fitting
purpose (Hair et al, 1998). In addition, the Comparative Fit Index is considered as the variant of NNFI, unlike NFI
it was developed for applying the model fit with the small sample size without corruption (Tabachnick and Fidell,
2007). According to Hu and his partner in 1999, they recommended CFI indicating the good model fit if CFI is
higher or equal 0.95. Meanwhile, the index which is higher than 0.9 is also review as the acceptable model fit.
Likewise, GFI was created as an alternative for Chi-squared, based on the variances in model fit support for the
population covariances. It would be allowed for adjusting the GFI based on degrees of freedom and observed
variables. It was recommended the GFI are equal or higher than 0.95 is more appropriate for the model while 0.9
was considered as good and some cases can be extent for acceptable purpose with the index higher than 0.8 (Miles
and Shevlin, 1998) while AGFI required to be higher than 80% is good fit.
Measurement Thresholds Current Indices
Chi-square/DF (CMIN/DF) < 2 Good; < 5 Acceptable 1.450 (Good Result)
> 0.95: Great
CFI (Comparative Fit Index) 0.95 – 0.9: Good 0.961 (Great Result)
0.9 – 0.8: Sometimes Acceptable
> 0.95: Great
GFI (Goodness-of-Fit Index) 0.95 – 0.9: Good 0.864 (Acceptable Result)
TLI (Tucker Lewis Index) ≥ 0.9 0.957 (Good Result)
AGFI > 0.8 0.841 (Good Result)
SRMR < 0.09 0.0428 (Good Result)
< 0.01: excellent fit 0.038 (Close Fit)
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RMSEA (Root Mean Squared Error
of Approximation)
0.01 – 0.05: close fit
0.05 – 0.80: good fit
0.08 – 0.10: mediocre fit
>= 0.1: poor fit
Table 19: Model Fit Assessment
Checking the Standardized Regression Weight also extract the Average Variance Extracted (AVE) in
order to build-up the foundation support the convergent validity measurement afford with the theoretical
model also applying for the discriminant validity assessment in seeking the both AVEs while provide the r
squared in correlation where both AVEs in the construct is suggested to be higher than the squared of the
correlation estimate. In addition, evaluating the Standardized Regression Weight where it is preferred to be
higher than 0.5 for checking convergent validity purpose (Hair et al, 2006).
4.3.2. Reliability Checking
Besides, Composite Reliability (CR) is reevaluated for CFA model fits checking reliability purpose,
comparing to the Cronbach’s Alpha which is tool for assessing the reliability in EFA. From AMOS results, it
shows the estimation in r squared for computing the CR in the established formula that has been reminded
in Fornell and Larcker in 1981. The output of CR must be equivalent to the assessment criteria of Cronbach’s
Alpha where the ratio higher than 0.7 is preferable.
The formula below shows the instruction in calculating CR also the AVE followed the former research of
Joreskog in 1971 and Fornell and Larcker in 1981 respectively.
Composite Reliability (CR) Formula
Equation 1: Composite reliability (Joreskog 1971)
=
(∑ =1 λ)2
(∑ =1 λ)2 + ∑ =1(1 − λ2)
Where:
λ: is corresponding factor loadings on the Standardized Regression Weight
1- λ2: is the variance’s error in the ith indicator of construct.
p: is the number of indicators
Average Variance Extracted (AVE) Formula
Equation 2: Extracted variance–VE (Fornell& Larcker 1981)
∑ 2
= =1
∑ =1 2 + ∑ =1(1 − 2)
Where:
λ: is corresponding factor loadings on the Standardized Regression Weight
1- λ2: is the variance’s error in the ith indicator of construct.
p: is the number of indicators
The Average Variance Extracted (AVE) and the Composite Reliability (CR) has been extracted from the output
of AMOS assessment through the Standardized Regression Weight table. Based on the result of AVEs, there are 9
factors have variance extracted result over the assessment criteria (>0.5) where it ranged from 0.624 to 0.741.
Additionally, the second reliability assessment – composite reliability results in the good output which most of the
factors having CR over 0.8 while it is considered as preferable when CR larger or equal 0.5. As can be seen from
the evaluation, it cannot be denied that the model qualifying with the reliability test.
4.3.3. Convergent Validity Checking
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Estimate S.E. C.R. P Label
SAS1 <--- SAS 1
SAS2 <--- SAS 0.97 0.062 15.659 ***
SAS3 <--- SAS 0.978 0.061 16.09 ***
SAS4 <--- SAS 0.985 0.063 15.66 ***
SAS5 <--- SAS 0.956 0.063 15.092 ***
SQCP1 <--- SQCP 1
SQCP2 <--- SQCP 0.911 0.061 15.005 ***
SQCP3 <--- SQCP 0.886 0.061 14.588 ***
SQCP4 <--- SQCP 1.065 0.065 16.432 ***
SQCP5 <--- SQCP 0.82 0.058 14.081 ***
BEI1 <--- BEI 1
BEI2 <--- BEI 0.97 0.06 16.102 ***
BEI3 <--- BEI 0.992 0.061 16.293 ***
BEI4 <--- BEI 0.944 0.061 15.516 ***
BEI5 <--- BEI 0.977 0.061 16.071 ***
EXP1 <--- EXP 1
EXP2 <--- EXP 0.967 0.069 13.929 ***
EXP3 <--- EXP 0.927 0.067 13.794 ***
EXP4 <--- EXP 0.954 0.062 15.347 ***
EXP5 <--- EXP 1.038 0.065 15.878 ***
REP1 <--- REP 1
REP2 <--- REP 0.939 0.048 19.475 ***
REP3 <--- REP 1.045 0.049 21.321 ***
REP4 <--- REP 0.942 0.048 19.589 ***
TRS1 <--- TRS 1
TRS2 <--- TRS 0.942 0.056 16.841 ***
TRS3 <--- TRS 0.925 0.055 16.902 ***
TRS4 <--- TRS 1.01 0.058 17.402 ***
TRS5 <--- TRS 0.971 0.054 17.976 ***
SQHC1 <--- SQHC 1
SQHC2 <--- SQHC 0.973 0.064 15.125 ***
SQHC3 <--- SQHC 1.016 0.066 15.482 ***
SQHC4 <--- SQHC 0.934 0.064 14.67 ***
SQT1 <--- SQT 1
SQT2 <--- SQT 0.963 0.05 19.316 ***
SQT3 <--- SQT 0.909 0.048 19.125 ***
SQT4 <--- SQT 0.671 0.06 11.246 ***
SQDC1 <--- SQDC 1
SQDC2 <--- SQDC 1.035 0.078 13.301 ***
SQDC3 <--- SQDC 0.964 0.075 12.856 ***
Table 20: Regression Weights: (Group Number-Default model)
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Standardized Regression Weights Estimate
SAS1 <--- SAS 0.793
SAS2 <--- SAS 0.806
SAS3 <--- SAS 0.823
SAS4 <--- SAS 0.806
SAS5 <--- SAS 0.783
SQCP1 <--- SQCP 0.792
SQCP2 <--- SQCP 0.791
SQCP3 <--- SQCP 0.773
SQCP4 <--- SQCP 0.853
SQCP5 <--- SQCP 0.751
BEI1 <--- BEI 0.819
BEI2 <--- BEI 0.798
BEI3 <--- BEI 0.804
BEI4 <--- BEI 0.776
BEI5 <--- BEI 0.797
EXP1 <--- EXP 0.784
EXP2 <--- EXP 0.754
EXP3 <--- EXP 0.748
EXP4 <--- EXP 0.818
EXP5 <--- EXP 0.843
REP1 <--- REP 0.874
REP2 <--- REP 0.841
REP3 <--- REP 0.884
REP4 <--- REP 0.843
TRS1 <--- TRS 0.834
TRS2 <--- TRS 0.804
TRS3 <--- TRS 0.806
TRS4 <--- TRS 0.822
TRS5 <--- TRS 0.840
SQHC1 <--- SQHC 0.819
SQHC2 <--- SQHC 0.794
SQHC3 <--- SQHC 0.810
SQHC4 <--- SQHC 0.774
SQT1 <--- SQT 0.864
SQT2 <--- SQT 0.867
SQT3 <--- SQT 0.860
SQT4 <--- SQT 0.591
SQDC1 <--- SQDC 0.791
SQDC2 <--- SQDC 0.815
SQDC3 <--- SQDC 0.764
Table 21: Standardized Regression Weights (Group number 1-Default model)
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Considering data from the table 23 the p-value displays as the encryption *** which represents for the
number that lower than 0.01. Hence, it must be lower than the requirement at 0.05. In addition, by focusing
on the estimation of Standardized Regression Weights, all values are higher than the threshold at 0.5.
4.3.4. Discriminant Validity Checking
According to the research of Fornell and his partner – Larcker in 1981, the discriminant validity is assessed by computing the Average
Variance Extracted (AVE) of the variables in the construct, then comparing to the squared of correlation ration or r squared (r2), both AVEs of
these must be higher than the r squared, or root squared of AVEs greater than the involved correlation ratio.
Estimate Both AVEs >
SAS <--> SQCP 0.361 0.130 Valid
SAS <--> BEI 0.776 0.602 Valid
SAS <--> EXP 0.045 0.002 Valid
SAS <--> REP 0.464 0.215 Valid
SAS <--> TRS 0.614 0.377 Valid
SAS <--> SQHC -0.217 0.047 Valid
SAS <--> SQT 0.483 0.233 Valid
SAS <--> SQDC 0.042 0.002 Valid
SQCP <--> BEI 0.442 0.195 Valid
SQCP <--> EXP -0.080 0.006 Valid
SQCP <--> REP 0.289 0.084 Valid
SQCP <--> TRS 0.372 0.138 Valid
SQCP <--> SQHC -0.185 0.034 Valid
SQCP <--> SQT 0.277 0.077 Valid
SQCP <--> SQDC 0.239 0.057 Valid
BEI <--> EXP -0.065 0.004 Valid
BEI <--> REP 0.483 0.233 Valid
BEI <--> TRS 0.676 0.457 Valid
BEI <--> SQHC -0.067 0.004 Valid
BEI <--> SQT 0.478 0.228 Valid
BEI <--> SQDC 0.071 0.005 Valid
EXP <--> REP 0.021 0.000 Valid
EXP <--> TRS -0.015 0.000 Valid
EXP <--> SQHC -0.024 0.001 Valid
EXP <--> SQT 0.116 0.013 Valid
EXP <--> SQDC -0.079 0.006 Valid
REP <--> TRS 0.571 0.326 Valid
REP <--> SQHC -0.169 0.029 Valid
REP <--> SQT 0.439 0.193 Valid
REP <--> SQDC -0.045 0.002 Valid
TRS <--> SQHC -0.067 0.004 Valid
TRS <--> SQT 0.458 0.210 Valid
TRS <--> SQDC 0.197 0.039 Valid
SQHC <--> SQT -0.012 0.000 Valid
637

SQHC <--> SQDC
SQT <--> SQDC
-0.255
0.125
0.065
0.016
Valid
Valid
Table 22: Correlations (CFA)
By applying the criteria which AVEs > 2, it is founded that the outputs show the AVEs of both constructs is higher than 2 in any correlation. Hence, it can not be
deniable that the model is valid with discriminant validity assessment.
4.4. Structural Equation Modeling (SEM)
In the previous section, CFA was used in the AMOS application at the first step to evaluate the
correlation between the observed variables and latent variables. Then, it was upgraded in SEM with the
framework model where it is the extension of general linear model and allowed researcher. It is considered
as the powerful and argil technique to analyze the framework, also pointed out particularly the relationship
between unobserved and observed items.
The outcomes of first SEM have been showed in the diagram below.
Figure 3: First Structural Equation Model 4.4.1. First SEM
Model Fit
Similarly, to conduct the Model Fit assessment of CFA, the results illustrate straightforwardly comparing
to the criteria have been implemented in the CFA section. Chi-square over degree of freedom or CMIN/df at
1.499 where it demonstrates the great outcome where it need to be lower than 2. Goodness-of-Fit Index (GFI)
638

and Adjusted-Goodness-of-Fit Index (AGFI) reach at 0.858 and 0.837 respectively where it leads to a good
outcome following the thresholds. Assessing the Comparative Fit Index (CFI), the result produce positively
where it shows the great outcome at 0.956. In addition, TLI is considered as the confidently model fit which
reaches at 0.952. The close fit to the population is confirmed where the outcome after evaluating RMSEA
where it reaches at 0.040. SRMR is calculated through plugin at 0.0541 which is qualified in the requirement
of theory (0.09). To sum-up, the information will be provided in short below:
> 0.95: Great
> 0.95: Great
0.858 (Acceptable
GFI (Goodness-of-Fit Index) 0.95 – 0.9: Good
Result)
< 0.01: excellent fit
RMSEA
0.01 – 0.05: close fit
0.05 – 0.80: good fit 0.040 (Close Fit)
(Root Mean Squared Error of Approximation)
>= 0.1: poor fit
Table 23: Model Fit Assessment in first round
Correlation Observed Variables Testing
SQHC <--> SQT -0.014 0.076 -0.186 0.853
SQHC <--> SQDC -0.246 0.067 -3.693 ***
SQCP <--> SQHC -0.228 0.08 -2.842 0.004
REP <--> SQHC -0.234 0.091 -2.588 0.01
EXP <--> SQHC -0.031 0.078 -0.392 0.695
SQT <--> SQDC 0.141 0.074 1.916 0.055
SQCP <--> SQDC 0.277 0.079 3.517 ***
SQCP <--> SQT 0.396 0.095 4.184 ***
REP <--> SQT 0.745 0.115 6.497 ***
EXP <--> SQT 0.159 0.09 1.76 0.078
REP <--> SQDC -0.044 0.086 -0.514 0.607
EXP <--> SQDC -0.095 0.075 -1.258 0.208
SQCP <--> REP 0.513 0.112 4.568 ***
SQCP <--> EXP -0.126 0.093 -1.355 0.176
EXP <--> REP 0.036 0.105 0.345 0.73
Table 24: Covariances (Group number 1: Default model) (First Round)
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According to the figure above, p-value promote weak correlations among relationship between Service
Quality in Hospital Concerns and Service Quality in Tangible Concerns; Expertise and Service Quality in
Hospital Concerns; Service Quality in Tangible Concerns and Service Quality in Doctor Concerns; Expertise
and Service Quality in Tangible Concerns; Reputation and Service Quality in Doctor Concerns; Expertise
and Service Quality in Doctor Concerns; Service Quality in Process Concerns and Expertise; Expertise and
Reputation where p-value assessments are over 0.05. Hence, those correlations should be eliminated in the
second SEM.
Hypothesis Testing
According to the Business Statistic Textbook 7th Edition, p-value is defined as the assessment whether it is considered
as the statistically significant when the value is lower 0.05 (p<0.05). When the p-valued reaches over 0.05, the items’
relationship is a weak correlation. Particularly, if p-value is weaker than 0.05, it means that it can reject null hypothesis.
Meanwhile, it fails to reject the 0 or null hypothesis. Moreover, the relationship between 2 factors could be negative or
positive belonged to the estimate column while the value explains the ratio effect of item A to item B. Particularly, for the
factor A’s outcome increase by 1, the item B will be affect negatively or positively based on the figure in estimate column.
Regression Weight Standardized
Hypotheses Regression Weight Result
Estimate
P-
value
1: A service quality of hospital
SAS <- SQHC -0.239 *** -0.194 Support
SAS <- SQT 0.473 *** 0.444 Support
is positively related to patient’s
(customer’s SAS <- SQDC -0.151 0.055 -0.114 No Support
) satisfaction SAS <- SQCP 0.249 *** 0.242 Support
2: A worker’s expertise in
TRS <- EXP -0.490 0.330 -0.047 No Support
hospital is positively related to
patient’s (customer’s) trust.
3: A hospital’s reputation is
TRS <- REP 0.342 *** 0.382 Support
positively related to patient’s
(customer’s) trust.
4: A patient’s (customer’s)
satisfaction is positively related TRS <- SAS 0.460 *** 0.460 Support
to their trust in hospital.
5: A service quality of hospital
BEI <- SQDC -0.031 0.592 -0.025 No Support
BEI <- SQCP 0.150 0.001 0.155 Support
is positively related to patient’s
(customer’s) behavioral BEI <- SQT 0.048 0.343 0.048 No Support
intention in revisiting hospital. BEI <- SQHC 0.109 0.040 0.094 Support
6: A patient (customer’s)
satisfaction is positively related
BEI <- SAS 0.519 *** 0.552 Support
to their behavioral intention in
revisiting hospital.
7: A patient (customer’s) trust
is positively related to their
behavioral intention in revisiting BEI <- TRS 0.262 *** 0.279 Support
hospital.
Table 25: SEM Output in first round
The outcome from the figure above illustrates that excluding the correlation between Service Quality in
Doctor Concerns and Satisfaction; Expertise and Trust; Service Quality in Doctor Concerns and Behavioral
Intention; Service Quality in Tangibles Concern and Behavioral Intention, the remaining is considered as
640

statistical independence where p-value < 0.05 or the reliability level over 95%. Hence, among these variables
are not qualified at H-test will be eliminated in the following SEM. Thus, after the first SEM there are 4
correlations has been rejected. Moreover, 2 factors Expertise and Service Quality in Hospital Concerns are
eliminated out of SEM model due to the incorporation with other variables
4.4.2. Final SEM
Figure 4: Final Structural Equation Model
Model Fit
> 0.95: Great
> 0.95: Great
0.878 (Acceptable
GFI (Goodness-of-Fit Index) 0.95 – 0.9: Good
Result)
< 0.01: excellent fit
RMSEA
0.01 – 0.05: close fit
0.05 – 0.80: good fit 0.059 (Good Fit)
(Root Mean Squared Error of Approximation)
>= 0.1: poor fit
Table 26: Model Fit Assessment in final round
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Following the table, most output evaluations are appropriate with the criteria of model fit measurement
scale. the CMIN/df = 1.627 where it is lower than 2 is good; the CFI =0.958 would be affordable at the great
outcome; the TLI = 0.954 where the criteria equal or over 0.9; the SRMR = 0.059 belongs to good result
assessment where it is lower than 0.09 and the RMSEA = 0.059 where it proves that the model can apply in
good fit with the population. Besides, by considering GFI and AGFI, it still reaches at 0.878 (sometimes
acceptable) and 0.851 (good result) respectively after the last SEM evaluation. In short, the model fit is quite
good in apply to the population.
Correlation Observed Variables Testing
SQCP <--> SQHC -0.226 0.078 -2.894 0.004
SQCP <--> SQT 0.396 0.094 4.23 ***
REP <--> SQHC -0.233 0.083 -2.816 0.005
REP <--> SQT 0.749 0.114 6.587 ***
SQCP <--> REP 0.509 0.112 4.564 ***
Table 27: Covariances (Group number 1: Default model) (Final Round)
Following the outcome of this table above, the p-value of the correlations among observed factors were
below the standard requirement at 0.05. Hence, it cannot be denied that all these relationships show the
significance one another.
Hypothesis Testing
At this final section, the hypothesis is eliminated in term of the enhancement to approach the population
as the reliability test, also the problem occurred when assessing the sub-scale of service quality in doctor
concerns. By removing the following correlations in the first SEM, it can be seen that all of correlation
support for the theoretical model. Meanwhile, the significant negative effect on customer satisfaction from
service quality which is not correlated with the ground model delivered from beginning. In term of the
correlation that one item can be explained by other item in the hypothesis, it is well-noted that Customer
Satisfaction can be explained up to 55,2% of the Behavioral Intention in Revisiting the Hospital.
5. Conclusions and Recommendations
5.1. Conclusions and Recommendations
Rehash visitors are a basic advantage for any effective hospital business. The best method to retain rehash
visitors is to achieve high customer expectations or to give an administration that surpasses the customers'
expectations. Tragically, be that as it may, immaculate customer administration might be a for all intents and
purposes unattainable objective, on the grounds that the hospital business has such novel attributes as
concurrent higher customer steadfastness, though the poor reactions may provoke customers to switch
hospital. Along these lines, a viable exertion for benefit recuperation subsequent to encountering faulty
administration must be precisely arranged and done keeping in mind the end goal to build up a long-haul
association with the customers.
The consequences of this examination give valuable insights into the conduct of customer who have
encountered broken administration with the resulting follow-up activity of administration recuperation. The
examination results should illuminate the endeavors of any hospital administrator who seeks after to guarantee
that the visitors getting administration recuperation endeavors see an abnormal state of fulfillment. Some more
practical implications are proposed herewith. It is obviously clear frame our discoveries that the
642

majority of the sub-urban open hospitals are not having that level of administration which will fulfill their
patient. All through this investigation we have broken down administration quality over the accompanying
administration measurements and removed the distance between expected administration and saw benefit.
As research result, Trust is one of the most on customer conduct. For practical proposals, to fabricate
customers' trustworthiness, hospital's workers at all association point ought to give provoke benefit, ought
to dependably be prepared to encourage customers, ought to never be excessively caught up with, making it
impossible to react to customer's demand. Also, the conduct of workers ought to impart certainty among the
purchasers, ought to be reliably polite with the customers, and ought to have the information to answer
patient inquiries.
However, the research still exists some limitations.
5.2. Research Limitations
It can be clearly seen that the main drawback that the research faced is the area that the study is able to
approach. The questionnaires are intended to be the primary data where it can be collected most people who
used the four mentioned hospital which is Vinmec International Hospital, Hoan My International Hospital,
Military Hospital 175 and Ho Chi Minh City (HCMC) University Medical Center in Ho Chi Minh city.
Hence, the exclusion of other hospitals in Vietnam is appeared, it can be noted as the geographical barriers.
The study, however, can quite approach the population through the proved final model.
In the second line, as can be seen from the timetable, the time limitation also financial support are
drawbacks to conduct the sample size enough for directly applying in the population size. Particularly, the
outcomes of survey must be larger than the current proposed sample size. Further research should be
conducted in other big cities such as Ha Noi, Da Nang or Can Tho for diversifying the data also results
purposes which enhance the research to be more persuasive.
The vary of respondents in educational aspects also considered as one of the drawbacks. Comparing to
the demand, status and perspective of each person, I found that some people to the hospital after
consideration through their assessments, meanwhile, the demand in revisiting hospital seems to be
considered as the routine without considering any assessments.
643

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