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The Service Industries Journal
ISSN: 0264-2069 (Print) 1743-9507 (Online) Journal homepage: http://www.tandfonline.com/loi/fsij20
The impact of high-performance work systems in the health-care industry: employee reactions, service quality, customer satisfaction, and customer loyalty
Sang M. Lee , DonHee Lee & Chang-Yuil Kang
To cite this article: Sang M. Lee , DonHee Lee & Chang-Yuil Kang (2012) The impact of high- performance work systems in the health-care industry: employee reactions, service quality, customer satisfaction, and customer loyalty, The Service Industries Journal, 32:1, 17-36, DOI: 10.1080/02642069.2010.545397
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Published online: 15 Dec 2011.
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The impact of high-performance work systems in the health-care industry: employee reactions, service quality, customer satisfaction,
and customer loyalty
Sang M. Lee a , DonHee Lee
a∗ and Chang-Yuil Kangb
a Management, University of Nebraska-Lincoln, CBA209, Lincoln, NE 68588, USA;
b Medical
Information, Hyechon University, 333 Boksu-dong, Seo-gu 302-715, Daejeon, Republic of Korea
(Received 16 June 2010; final version received 16 October 2010)
The purpose of this study is to empirically test the effects of high-performance work systems (HPWS) on employee attitude, service quality, customer satisfaction, and customer loyalty in health-care organisations. The proposed research model was tested using structural equation modelling for hypotheses, based on data collected from 196 pairs of employee – customer respondents in four selected hospitals with more than 500 beds. The results indicate that hospitals can improve customer satisfaction and loyalty through efficient operations, employee engagement, and service quality. One of the key findings of our study is that HPWS in health-care organisations influence employee reaction and service quality.
Keywords: high-performance work systems; employee reaction; service quality; customer satisfaction and loyalty; health care
Introduction
Health-care systems operate for the diagnosis and treatment of diseases, prevention of
illness, and promotion of healthy living habits. The leaders and managers of health-care
providers try to deliver quality care, reduce medical errors, ensure patient safety, reduce
medical costs for both patients and the hospital, and provide needed patient and customer
service. They also attempt to provide workers with appropriate information systems, new
medical equipment, job skills, and incentives to achieve organisational performance
(Kling, 1995). However, human resource (HR) is the core capability and the most valuable
resource that enables such efforts (Bowen & Ostroff, 2004; Delaney & Huselid, 1996;
Wright, Gardner, Moynihan, & Allen, 2005).
HR practices have been recognised as an important factor, which influences organis-
ational performance (Bowen & Ostroff, 2004; Delaney & Huselid, 1996), such as service
quality, customer satisfaction, and employee engagement (Boxall & Macky, 2007; Dean,
2004; Evans & Davis, 2005; Purcell & Hutchinson, 2007). Bowen and Ostroff (2004)
proposed a relationship between HR management (HRM) and organisational performance,
as effective HRM promotes a favourable organisational climate, which encourages positive
employee attitudes and behaviour for organisational performance. Meyer and Collier
(2001) also reported that HRM practices are positively related to customer satisfaction in
the health-care system. Consequently, employee attitude and service quality are critical
ISSN 0264-2069 print/ISSN 1743-9507 online
# 2012 Taylor & Francis
http://dx.doi.org/10.1080/02642069.2010.545397
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∗Corresponding author. Email: [email protected]
The Service Industries Journal
Vol. 32, No. 1, January 2012, 17 – 36
success factors (CSFs) for improving customer satisfaction. Thus, the accumulated
experience of employees is a valuable asset for the hospital in providing quality care.
High-performance work systems (HPWS) as HR systems represent an important
concept in the workplace (Boxall & Macky, 2007; Evans & Davis, 2005). Evans and
Davis (2005) define an HPWS as ‘an integrated system of HR practices that is internally
and externally consistent and includes selective staffing, self-managed teams, extensive
training, open communication, and performance-compensation’ (pp. 759 – 760). Harmon
et al. (2003) and Scotti, Driscoll, Harmon, and Behson (2007) reported a positive relation-
ship between HPWS and customer satisfaction in the health-care industry. The HPWS
affects employees’ perceptions of service quality (Scotti et al., 2007) for improving
customer satisfaction. Based on previous research, a customer satisfied with the high
quality of the service will be more likely to have intentions to engage in repeat consump-
tion (Hallowell, Schlesinger, & Zornitsky, 1996; Rust & Zahorik, 1993; Scotti et al.,
2007). While the relationship between customer satisfaction and customer loyalty has
not been widely studied (Dean, 2004; Scotti et al., 2007), Baker and Taylor (1997)
supported this relationship based on a study of outpatients in not-for-profit hospitals.
Thus, a health-care system as a HPWS, with its integrated HR practices, is a critical
factor for organisational performance in the medical service industry (e.g. service
quality, customer satisfaction, and customer loyalty). Also, a HPWS requires both high-
skilled and low-skilled employees for improving organisational performance in a
health-care system (Berg & Frost, 2005; Harley, Allen, & Sargent, 2007).
Previous research on customer satisfaction focused mainly on quality of care and
employee satisfaction in HPWS. However, there has been a paucity of studies on
employees’ reaction and attitudes to improve quality of care and service. Therefore, the
purpose of this study is to empirically test the effects of HPWS on employee attitude,
service quality, customer satisfaction, and customer loyalty in health-care organisations.
A research model is proposed that includes both exogenous and endogenous factors that
influence the attainment of customer loyalty, based on previous studies. Survey data are
collected and analysed from hospital employees and customers concerning disease treat-
ment and/or preventive care. The proposed research model is examined using the structural
equation modelling (SEM) and t-test approach. The rest of this paper is organised as follows:
the second section presents a review of previous studies and concepts relevant to this study;
the third section proposes a research model and hypotheses; the fourth section shows a
research methodology; the fifth section reports the result of the model; and the sixth
section presents the conclusion and limitation of the study.
Literature review
In recent years, health care has become a critical issue in the world, along with the increased
concerns for medical errors, patient safety, and increasing medical costs (Olden &
McCaughrin, 2007; Stock, McFadden, & Gowen, 2007; Tucker, 2004). The service
environment of the health-care industry is determined by not only new facility and technol-
ogy support, but also the performance of employees in the organisation. Various methods
and tools are used by medical administrators, researchers, and health-care policy makers in
an effort to find a better way to provide high quality of the service at reasonable costs.
According to Milstein and Colla (2009), the US health-care system is not competi-
tive around the world in terms of its performance or costs. The US spent approximately
$7500 a year per person in 2009 on health care by care sources including government
agencies, private employers, and individuals. Other advanced countries, such as
18 S.M. Lee et al.
Germany, spend only about one-third of what the Americans spend. The customer satisfac-
tion score on care quality measurements in the US is not higher than that in other advanced
countries (Milstein & Darling, 2009). The US residents have less access to health care, spend
more money to receive the health care they get, and have more negative outcomes than the
residents of most other developed countries. The results of a study by Milstein and Colla
(2009) show that US workers and employers receive 23% less value (e.g. cost and perform-
ance) from the health-care system than the average of five leading economic competitors (i.e.
Canada, Japan, Germany, the United Kingdom, and France) and 46% less value than the
average of emerging competitors (e.g. Brazil, India, and China).
High-performance work system
Organisations develop HR practices to enhance employees’ skills, knowledge, and motiv-
ation to improve organisations’ performance (Bowen & Ostroff, 2004; Delaney & Huselid,
1996; Wright et al., 2005). Profitable firms with effective HR practices can share their
profits with employees in the form of higher pays or incentives, offer more training and
development opportunities, invest in advanced technology and systems, and encourage
participation in collaboration.
As HPWS represent an HRM approach (Beltrán-Martı́n, Roca-Puig, Escrig-Tena, &
Bou-Llusar, 2008), HPWS are ‘interrelated and aligned set of core characteristics, includ-
ing involvement, empowerment, trust, goal alignment, training, teamwork, communi-
cations, and performance-based rewards’ (Scotti et al., 2007, p. 111). HPWS include
managerial practices that increase the empowerment of employees and enhance their
skills through the support of the organisation (Appelbaum, Bailey, Berg, & Kalleberg,
2000; Lawler, 2005). The goal of HPWS is to increase efficiency and effectiveness by
motivating and assisting employees to complete tasks successfully (Beltrán-Martı́n
et al., 2008; Whitener, 2001). HPWS emphasise customer-focused work, empowered
work environments, employee motivation, and flexible and adaptive systems (Gephardt
& Van Buren, 1996). HPWS are the result of an organisation’s willingness to support
its employees to work effectively in positive environments. Therefore, the concept of
HPWS can be applied to any workplace, such as the health-care organisation, that
strives to perform more effectively.
These reasons lead to the following definition of HPWS in the health-care system:
health-care systems as HPWS support and manage work processes at the individual,
department, or group level (e.g. physicians, nursing, technicians, and administration in
the workplace) to improve work performance. According to the studies by Huselid
(1995) and Delaney & Huselid (1996), successful implementation of HPWS may
include employee motivation (e.g. compensation) and employee skills (e.g. training).
In this study, the factors associated with HPWS are measured by employee perceptions
about organisational training and education efforts, communication, and compensation.
These measurements of HPWS in the health-care organisation are developed based on
the studies of Beltrán-Martı́n et al. (2008), Evans and Davis (2005), and Scotti et al. (2007).
Employee reactions
Hospitals usually provide care to patients in a high-contact environment. Thus, customers
have expectations of an intensive interpersonal relationship with employees for a rela-
tively short duration of time (Goldstein, 2003). Thus, employees’ attitudes and behaviour
during their care service are critical factors for customer satisfaction.
The Service Industries Journal 19
To improve employees’ attitudes and behaviour for their work outcome and organis-
ational performance, organisations support employees to develop positive relationships
with customers. Employee attitudes and behaviour are interrelated. Thus, when an
employee has a negative perspective about the job, a supervisor, or the organisation as
a whole, then the employee is less likely to care about his/her job performance.
However, if an employee is fully engaged and motivated, he/she will take pride in
doing quality work, and strives to find ways to improve organisational performance.
Therefore, organisations need to develop HR practices that promote positive employee
attitudes, emotion, and organisational loyalty.
Employee reactions are attitudes and the degree of engagement determined by
employees’ perceived organisational support, politics, compensation systems, structure,
work activities, and goal (Fedor, Maslyn, Farmer, & Bettenhausen, 2008; Michie &
West, 2004). Ferris, Adams, Kolodinsky, Hochwarter, and Ammeter (2002) suggested
that higher levels of perceived political behaviour in the organisation are associated with
positive employee reactions. Eisenberger, Armeli, Rexwinkel, Lrvch, and Rhoades
(2001) proposed a positive relationship between perceived support and employees’ attitude
towards organisational goals. The proper compensation and support for employee motiv-
ation will improve employee reactions with positive attitudes, which are significant contri-
butors to organisational performance. The improved employee reaction in this study was
measured in terms of the extent to which employees perceived that they and the hospital
together fulfilled their obligations. This study adapted measurement items of the improved
employee reaction suggested by Ferris et al. (2002).
Service quality
The increasing concern about health care indicates the emergence of a new type of health-
care consumers (Bohmer, 2001). The new generation of health-care consumers (e.g.
patients, family of patients, and potential consumers) demands improved quality of
service, increased satisfaction, medical error reduction, and prevention of diseases. The
improved quality of patients’ care should be a top priority for hospital leaders and
mangers. If hospital leaders take initiatives in creating and improving service quality of
health care, their employees will benefit and patients will receive a better quality of
care from the increased value of service (Porter & Teisberg, 2004).
Service quality is influenced by employee satisfaction (Hartline & Ferrell, 1996) and is
related to customer satisfaction (Babakus, Bienstock, & Scotter, 2004). Hartline and
Ferrell (1996) presented evidence that satisfaction felt by the first-line customer-contact
employees is associated with service quality (Ennis & Harrington, 2001). Satisfied
employees tend to be more engaged in providing quality services (Hallowell et al.,
1996; Yee, Yeung, & Cheng, 2008).
A large number of studies have measured service quality with the SERVQUAL model
of Parasuraman, Zeithaml, and Berry (1988). Health-care organisations have also used a
variety of measurement tools, including employee feedback for service quality (Ennis
& Harrington, 2001). Yoon, Choi, and Park (2007) suggested that employees as internal
customers should be able to assess service quality for better patient outcome, such as
short lengths of stay in the hospital, reduced hospital infection, and reduction in
medical errors. Therefore, service quality can be measured by employees who actually
provide service to the patients. In this study, service quality is measured by employees’
perception (not customers’) about the quality of service provided by the hospital, as
service quality can be improved by efforts of employees and the hospital. Also, we
20 S.M. Lee et al.
assume that HPWS operate and manage work processes for improving performance (e.g.
improving service quality). Therefore, employees’ perceptions of service quality (rather
than customers’ perceptions) seem to be appropriate for this research.
Customer satisfaction and customer loyalty
The health-care industry is different from other service industries because health-care
deals with the dimension of patients’ need for disease treatment and/or improved well-
being. Also, customers in the health-care system include a group of external customers
(i.e. patients, family members of patients, and potential customers) and internal customers
(i.e. employees and the employer). As patients rarely act alone in their purchasing
decisions about care providers’ service (e.g. hospitals and physicians), concerning
disease treatment and after-care service, all members of the customer group are important
for the hospital (Bohmer, 2001). In addition, patients may want to receive a specific treat-
ment, care, or information about a disease they have received from the media or heard
from their family members and friends. In this study, however, the customer is limited
to patients and their family members.
Customer satisfaction is defined as the pleasurable emotional state of customers’ feel-
ings based on their experience from the service received in an organisation (Anderson,
Fornell, & Lehmann, 1994; Fornell, 1992). It has been empirically demonstrated that cus-
tomer satisfaction affects business performance (Mittal & Kamakura, 2001; Nagar &
Rajan, 2005) and customer loyalty (Stank, Goldsby, & Vickery, 1999; Verhoef, 2003).
Highly satisfied customers are more likely to purchase goods and services offered by
the same organisation (Anderson et al., 1994; Gronholdt, Martense, & Kristensen,
2000). Customer satisfaction also plays a critical role in improving the reputation and
the image of the organisation through word of mouth (WOM) of satisfied customers.
Measurement of patient or customer satisfaction is important, especially in the health-
care industry (Friesner, Neufelder, Raisor, & Bozman, 2008). Hospitals strive to increase
patient satisfaction and secure a growing number of loyal customers. Since satisfied
patients tend to reuse (i.e. revisit or repurchase) the hospital service, repeat patients are
able to receive improved care as medical staff searches for better ways of treatment
and/or diagnosis for patients’ diseases. Herzlinger (2006) suggested that patients enjoy
sharing their experience and information on treatment with other people. Also, patients’
feelings about the hospital affect their level of satisfaction (Herzlinger, 2006). Positive
WOM from satisfied patients can create opportunities to attract potential customers to
use the hospital service, which results in improved performance of the hospital.
Customer satisfaction about health care can be measured by the perceived quality of
care (e.g. treatment, hospital selection, and satisfaction with the after-care treatment
and/or service) (Doyle & Ware, 1997; Parasuraman et al., 1988; Zifko, Georgette, &
Robert, 1997), communication (Parasuraman et al., 1988; Zifko et al., 1997), and trust
(Doyle & Ware, 1997; Parasuraman et al., 1988; Zifko et al., 1997).
Customer loyalty is defined as prospects of future repurchase or renewal by customers
for an organisation (Andreassen & Lindestad, 1997). Loyal customers help promote the
organisation and its offerings through favourable WOM and recommendations directly
or indirectly (Heskett, Sasser, & Schlesinger, 1997; Heskett, Thomas, Loveman, Sasser,
& Schlesinger, 1994). Thus, business performance of the organisation improves through
increased sales of products and services. Therefore, hospital mangers should understand
how customer loyalty is influenced by the quality of care provided, and strive to keep
current customers and attract new ones.
The Service Industries Journal 21
In the study, the measurements for customer satisfaction were modified based on the
previous studies of Parasuraman et al. (1988), Homburg and Stock (2004), Chung and Lee
(2005), Hay and Hill (2006), Friesner et al. (2008), and Yee et al. (2008). This study
adapted measurement items of the customer loyalty suggested by Chung and Lee
(2005) and Hay and Hill (2006).
Research model and hypotheses
Figure 1 shows the proposed research model describing how health-care HPWS affect
employee reaction, service quality, customer satisfaction, and customer loyalty. The pro-
posed research model is based on paired data of employees (i.e. care team members) and
their customers as patients and/or patients’ family members. To enhance the generalisabil-
ity of our findings, data were collected from randomly selected hospitals.
Organisations implementing HPWS practices invest significantly in HR so that their
employees are well trained and skilled to perform their work (Becker & Huselid, 1998;
Michie & West, 2004). In other words, HPWS help enhance employee skills and
improve organisational performance through employees’ attitudes and motivation.
A high level of positive employee reaction is expected when there exists a high level of
employee empowerment (Peccei & Rosenthal, 2001), which in turn leads to motivation
and positive response of employees. In summary, HPWS may affect employee reaction
(e.g. motivation and response) and service quality, such as the improved quality of care
in the health-care system. Therefore, the following hypotheses are proposed:
H1: HPWS will positively affect employee reaction. H2: HPWS will positively affect service quality.
Employee satisfaction is the result of an organisation’s support policy (Heskett et al.,
1997; Rodney & Bannister, 2001), which would lead to improved customer satisfaction. In
other words, higher quality of service leads to a higher level of customer satisfaction
through the care and/or treatment activities of health-care providers (Heskett et al., 1994).
The service quality is a CSF for improving customer satisfaction, as supported by a
number of service quality studies (Hallowell et al., 1996; Heskett et al., 1994; Zeithaml,
Berry, & Parasuraman, 1996). There is a positive relationship between customer satisfac-
tion and loyalty (Andreassen & Lindestad, 1997; Fornell, 1992). Satisfied customers are
more likely to have a positive attitude towards the organisation, provide favourable
WOM to potential customers, build customer loyalty, and improve performance (Hay &
Hill, 2006; Heskett et al., 1994). Customer satisfaction directly affects behavioural inten-
tion for repurchases (Andreassen & Lindestad, 1997; Oliver, 1997). Consequently,
employee reaction and service quality are CSFs for improving customer satisfaction. Sat-
isfied customers tend to be loyal customers for the organisation. Therefore, the following
hypotheses are proposed:
Figure 1. The proposed research model.
22 S.M. Lee et al.
H3: Employee reaction will positively affect customer satisfaction. H4: Service quality will positively affect customer satisfaction. H5: Customer satisfaction will positively affect customer loyalty.
This study was designed to collect data from two different types of hospitals (public
and private) in order to determine whether or not there is a difference in each of the
HPWS variables. While the work environment and patient treatment protocols are
similar in both types, the perceptions of employees regarding HPWS may be different
in public versus private hospitals. Therefore, the following hypothesis is proposed:
H6: There will be mean differences in HPWS variables between public and private hospitals.
Research methodology
Data collection
A survey questionnaire was developed to test the proposed model. The questionnaire was
developed in English first and then translated into Korean by the operations management
faculty in South Korea. The Korean version was translated back into English by the Amer-
ican operations management experts who are bilingual. The two English version question-
naires had no significant difference. An initial questionnaire for employees was tested in a
pilot survey involving 35 employees in five departments of a hospital in South Korea.
Another initial questionnaire for patients and/or patients’ families was tested in a pilot
survey involving 35 patients in one of the hospitals in South Korea. Participation in this
survey was totally voluntary.
In this study, we selected four hospitals for data collection: two private and two public
hospitals. Large hospitals (i.e. more than 500 beds) were selected for data collection, since
most HPWS are practised by large organisations (Appelbaum et al., 2000; Ramsay, Scho-
larios, & Harley, 2000). Scotti et al. (2007) examined not-for-profit hospitals, because they
focus on ‘linking work environment to customer satisfaction’ as HPWS. Thus, they
suggested that future research would need to compare, public and private, not-for-profit
and for-profit hospitals. The hospital type and the number of beds in the hospital are
two important factors since HPWS generally operate in large hospitals. In HPWS, there
was no difference between high-skilled and low-skilled tasks for improving organisational
performance (Harley et al., 2007). Therefore, two private and two public hospitals with
more than 500 beds, each hospital with five departments, were selected for this study.
To have about an equal number of participants in each hospital, data were collected
from a care team member and a patient or the patient’s family member as pairs. Three
hundred questionnaires (five hospitals × 60) were distributed to care team members (e.g. doctors, nurses, pharmacist, administrator, and technicians) who have frequent con-
tacts with patients. We contacted a manager of each department for data collection, and
then questionnaires were randomly distributed to care team members in each department.
We received 202 (67.3%) responses. Returned questionnaires with incomplete or missing
items were removed. The final sample of 196 (65.3%) valid returned questionnaires was
used for analyses.
A special process was used to collect data for this study. First, to collect data from the
participating patients: (1) we met with the patients immediately after their contact was
completed with a doctor, nurse, pharmacist, administrator, or technician, and we requested
their cooperation in responding to our survey questionnaire; (2) if the patient agreed to par-
ticipate, the questionnaire was filled out in about 15 – 20 min. Second, to collect data from
the participating employees: (1) we asked the employees, who agreed to participate in the
The Service Industries Journal 23
study, to fill out the questionnaire for each patient (they were requested to complete the
questionnaire on the same day of their contact with the patient); (2) the questionnaire
completed by a patient was matched with the one filled out by an employee to make a
paired set to minimise serious sampling bias.
The participating care team members’ characteristics are as follows: 53.6% of the
respondents were from private hospitals, while 46.4% were from public hospitals.
As shown in Table 1, the majority of the customer respondents had experience of receiving
medical treatment and/or diagnosis in the past in the participating hospital (65.8%), while
34.2% did not. It means that 34.2% of the participating customers were first-time visitors.
For the patients or patients’ family members, homemaker (33.7%) was most prevalent,
followed by student (12.8%), business person (12.2), and office worker (8.2%).
In this study, we asked two kinds of questions to measure employees’ attitudes and
feelings about their hospital and important factors for their job satisfaction using seven
Table 1. Characteristics of the respondents.
Employee respondents’ characteristics Customer respondents’ characteristics
Items Frequency (%) Items Frequency (%)
Gender Gender Male 72 (36.7) Male 88 (44.9) Female 124 (63.3) Female 108 (55.1)
Age Age Range 23 – 57 Range 20 – 67
Work experiences in this hospital (in years)
Occupations
≤3 49 (25.0) Homemaker 66 (33.7) 3, and ≤10 61 (31.1) Student 25 (12.8) 10, and ≤20 61 (31.1) Business person 24 (12.2) .20 16 (8.2) Office worker 19 (9.7) Missing 9 (4.6) Professional 16 (8.2) Total 196 (100.0) Sale and service 11 (5.6)
Position Administrator 7 (3.6) Physician 26 (13.3) Technician 5 (2.6) Nurse 51 (26.0) Manufacturing 2 (1.0) Medical technician 58 (29.6) Others 15 (7.6) Administrator 49 (25.0) Missing 6 (3.0) Pharmacist 12 (6.1) Total 196 (100.0) Total 196 (100.0)
Willingness to use or recommend their own hospital
Medical care experiences in the past at this hospital
Yes 163 (83.2) Yes 129 (65.8) No 33 (16.8)
Ranking of items for job satisfaction Salary 84 (42.9) Promotion 52 (26.5) No 67 (34.2) Working time 26 (13.3) Compensation 6 (3.1) Bonus 4 (2.0) Relationship 2 (1.0) Others 2 (1.0) Missing 20 (10.2)
24 S.M. Lee et al.
measurement items as ranking of items. Overall, employees were loyal to their respective
hospital, as 83.2% answered that if they or their family members need care, they would
visit their hospital for treatment, and that they were willing to recommend their hospital
to others. In ranking the seven items for job satisfaction, respondents seemed to consider
salary (42.9%) as the most important followed by promotion (26.5%), working schedule
(13.3%), and compensation (3.1%). Occupations of the employee respondents were as
follows: nurse, 26.0%; administrator, 25.0%; medical technician, 29.6%; physician,
13.3%; and pharmacist, 6.1%.
Variables
The questionnaire utilised five-point Likert scales to measure the main constructs. Scales
to measure each of the constructs were developed based on prior studies as much as poss-
ible. Some measures were modified to adapt to this research.
We acknowledge that the approach we used to obtain dyadic data might have brought
the systematic bias problem in the level of customer satisfaction and customer loyalty,
since customers were selected in the hospital. The customers might have thought that
they should provide favourable comments about the hospital. It would lead to a positive
bias. According to Homburg and Stock (2004), different constructs, which are measured
with data collected from different parts (i.e. two parts – employees and customers),
would lead to a generally higher level of the dependent variable (i.e. customer loyalty
in this study). However, a positive bias would not affect the strength and significance of
relationships between constructs, because the causal modelling approach used in this
study for data analysis was based on the covariance between the measurements
(Homburg & Stock, 2004).
As mentioned above, different constructs in the research model were measured with
the data obtained from both sides of the pair. HPWS, employee reaction, and service
quality were measured on the basis of the care team members’ responses, while customer
satisfaction and customer loyalty were based on data collected from the customers as
patients and/or patients’ family members. As shown in Table 2, the mean for each variable
ranged from 2.58 (CP1) to 3.95 (COMM3), and the standard deviation ranged from 0.62
(COMM1) to 1.17 (CP2).
This model consists of five major components: HPWS, employee reaction, service
quality, customer satisfaction, and customer loyalty. HPWS is a multidimensional
construct with the second-order latent variables in three dimensions: training and education,
communication, and compensation. The customer satisfaction is a multidimensional
construct with the second-order latent variables in three dimensions: quality, trust, and
communication. On the other hand, employee reaction, service quality, and customer
loyalty were assessed by measurement items.
Reliability represents the variance of measurement values resulting from repeat
measurement of the same concept. It is related to non-systematic error that can be
expressed as stability, consistency, predictability, and accuracy. Reliability was tested
based on Cronbach’s a values (Table 2). All of the coefficients for the constructs exceeded
the threshold value of 0.70 for exploratory constructs (Nunnally, 1978). In the reliability
test, Cronbach’s a for HPWS was the highest (0.89), and the employee reaction was
the lowest (0.77). All of the Cronbach’s a for the five latent variables were significant
at p , 0.05.
Validity refers to the accuracy of a measure. Confirmatory factor analysis (CFA) is a
way of testing how well measured variables represent the constructs. The results of CFA
The Service Industries Journal 25
Table 2. Items of measurements and results of reliability.
Component Variable (Likert-type five-point scale, 1 ¼ very bad; 5 ¼ very good) M SD Cronbach’s
a
HPWS Training and education (ED)
Opportunities for education and training (ED1) 3.18 0.88 0.888 Training for current and future skills (ED2) 2.99 0.94 Overall support for education and training (ED3) 3.32 0.81
Communication (CO) Employee suggestion systems (CO1) 3.08 0.90 Communication network (CO2) 2.97 0.83 Open communication with supervisors or colleagues (CO3) 3.04 0.88
Compensation (CP) Merit-based bonus (CP1) 2.58 1.05 Level of pay is higher than that of other hospitals of about the same size (CP2) 2.71 1.17 Equitable rewards system (CP3) 2.67 1.02
Employee reaction Employee reaction (ER) My obligation has been fulfilled (ER1) 3.23 1.15 0.766 Have fulfilled my obligation during my task (ER2) 3.28 0.92 My organisation has fulfilled all of its obligations (ER3) 3.49 0.93
Service quality Service quality (SQ) A good health-care environment for providing treatment/diagnosis (TRE) 3.37 0.86 0.845 Our patients trust our hospital (TRU) 3.25 1.01 We are very pleased with our services delivered to patients (RES) 3.53 0.96
Customer satisfaction (CS)
Quality Well satisfied with the degree of treatment (Q1) 3.44 0.85 0.883 Well satisfied with the degree of hospital selection (Q2) 3.44 0.82 The overall degree of satisfaction after treatment (Q3) 3.46 0.80
Trust Delivers on its promises to the patient (TR1) 3.54 0.68 Explanation of follow-up care opportunities (TR2) 3.70 0.70 Degree of belief patients develop in doctors (TR3) 3.65 0.66
Communication (COMM)
Understanding the degree of the patient’s requirements (COMM1) 3.79 0.62 Concerns for individual patients (COMM2) 3.70 0.71 Quickly responding to the patient’s complaints or concerns (COMM3) 3.95 0.69
Customer loyalty (CL) Customer loyalty(CL) Would you come to this hospital again? (LOY1) 2.99 0.97 0.848 Would you recommend this hospital to your friends? (LOY2) 3.12 0.86 Overall, are you satisfied with the hospital service rather than with other
hospital service? (LOY3) 3.10 0.91
2 6
S .M
. L
e e
e t
a l.
can provide evidence of the convergent and discriminant validity of theoretical constructs
(Brown, 2006). The statistics of CFA are shown in Table 3 and Figures 2 – 4. As shown in
Figure 4, there are measurement models: employee reaction, service quality, and customer
loyalty. The standardised factor loading and t-value for measurement variables on SEM
analysis using the M+5.1 program are presented in Table 3. All variables proposed in the study were statistically significant at the 0.05 level, with the range of the standardised
factor loading from 0.61 to 0.97.
Based on a recommendation by Brown (2006), the CFA method is used. The measure-
ment model has good fits in first order: x 2 (288) ¼ 510.148, p , 0.0000, comparative fit
index (CFI ¼ 0.925), Tucker – Lewis index (TLI ¼ 0.914), root mean square error of
approximation (RMSEA ¼ 0.063), and standardised root mean square residual (SRMR
¼ 0.049), and in second order: x 2 (308) ¼ 582.351, p , 0.000, CFI ¼ 0.913, TLI ¼
0.901, RMSEA ¼ 0.067, and SRMR ¼ 0.060.
The bottom part in Table 4 presents the square roots of average variances extracted
(AVE) for latent variables, while the off-diagonal elements are the correlation coefficients
between latent variables. For adequate discriminant validity, the square root of AVE of
any latent variable should be greater than the correlation between the particular latent
variable and other latent variables (Barclay, Thompson, & Higgins, 1995). The statistics
shown in Table 4 satisfy this requirement, lending evidence to the discriminant validity.
Also, the results of the correlation between each variable are shown in Table 4.
To use a second-order factor, Beltrán-Martı́n et al. (2008, p. 1025) suggested the fol-
lowing: ‘(a) each observed variable will have a nonzero loading on the factor, (b) error
Table 3. Fit indices for CFAs.
Measurement model x 2
df p-Value CFI TLI RMSEA SRMR
First-order CFAs 510.148 288 0.000 0.929 0.914 0.063 0.049 Second-order CFAs 582.351 308 0.000 0.913 0.901 0.067 0.060
Figure 2. The second-order CFAs of HPWS.
The Service Industries Journal 27
terms associated with each observed variable will be uncorrelated, (c) the first-order
factors will be correlated, and (d) covariation among the first-order factors and the obser-
vable variable will be explained fully by their regression onto the second-order factor’.
In this model, HPWS and customer satisfaction are intercorrelated latent variables that
are measured by the second-order factor method using SEM. To measure a second-order
factor, the first-order factors operate as dependent variables. It means that their variances
and covariances are no longer the estimated parameters in the model (Beltrán-Martı́n et al.,
2008). The HPWS second-order CFAs are indicated in Figure 2. These results provide an
Figure 4. Measurement models of CFAs of ER, SQ, and CL.
Figure 3. The second-order CFAs of customer satisfaction.
Table 4. Correlation matrix and AVE.
Components HPWS ER SQ CS CL
HPWS 1 ER 0.574∗∗ 1 SQ 0.522∗∗ 0.376∗∗ 1 CS 0.493∗∗ 0.452∗∗ 0.715∗∗ 1 CL 0.669∗∗ 0.440∗∗ 0.416∗∗ 0.390∗∗ 1 CR 0.804 0.542 0.658 0.847 0.697 AVE 0.925 0.777 0.852 0.941 0.873 Sqrt (AVE) 0.961 0.881 0.923 0.970 0.934
CR (critical ratio) ¼ ∑
(factor loading 2 )/( ∑
(factor loading 2 ) +
∑ (error)).
AVE ¼ ∑
(factor loading) 2 /( ∑
(factor loading) 2 +
∑ (error)).
∗∗p , 0.01. ∗∗∗p , 0.001.
28 S.M. Lee et al.
evidence of an internal fit among the HPWS dimensions. Also, customer satisfaction
second-order CFAs (Figure 3) indicate the existence of an internal fit among the customer
satisfaction dimensions.
As shown in Figure 2, single-headed arrows leading from the second-order factor
(HPWS) to its first-order factors (ED, CO, and CP) indicate the prediction of these dimen-
sions. Also, in Figure 3, arrows leading from customer satisfaction (CS) to each of its first-
order factors (quality, trust, and communication) show the prediction of these dimensions.
Consequently, fit statistics related with this model confirm the proposed structure of
HPWS and customer satisfaction constructs (Table 3 and Figures 2 and 3). The suggestions
of Beltrán-Martı́n et al. (2008) for using the second-order factor for HPWS and customer
satisfaction were satisfied (Figures 2 and 3). As shown in Figure 4, the standardised
regression weights of CFAs of ER, SQ, and CL were all greater than 0.6.
Results
SEM and t-test were used to test the hypotheses. M+5.1 and SPSS 17.0 program were chosen for this study. This section presents the results of hypothesis tests including the
standardised coefficient of each path in the model. The results of goodness-of-fit test for
the study model are summarised in Table 5. The value of chi-square (x 2 ) was 671.652,
degrees of freedom (df) 313, CFI 0.910, TLI 0.892, and p-value 0.000. Compared with
the recommended values for the goodness-of-fit tests, the values of CFI (0.910), SRMR
(0.077), RMSEA (0.068), x 2
(671.652), and p-value (0.000) were all satisfied, while
TLI (0.892) was not.
The results of significance tests for paths of the model are shown in Table 6 and
Figure 5. For H1 test, the standardised path coefficient between HPWS and employee reac-
tion was 0.701 and statistically significant at the 0.001 level. Thus, H1 was supported.
Table 5. The results of goodness-of-fit test.
Model x 2
df p-Value CFI TLI SRMR RMSEA
Model 671.652 313 0.000 0.910 0.892 0.077 0.068 Recommended value .0.9 .0.9 ,0.08 ,0.08
Table 6. The results of significance test of the model.
Path Path
coefficient SE t-
Value p-Value Hypothesis
HPWS � employee reaction 0.701 0.060 11.771 0.000∗∗∗ Supported H1 HPWS � service quality 0.676 0.054 12.537 0.000∗∗∗ Supported H2 Employee reaction � customer
satisfaction 0.187 0.066 2.879 0.004∗∗ Supported H3
Service quality � customer satisfaction
0.855 0.049 17.434 0.000∗∗∗ Supported H4
Customer satisfaction � customer loyalty
0.560 0.061 9.155 0.000∗∗∗ Supported H5
Mean differences of HPWS variables between public and private hospitals
0.000∗∗∗ Supported H6
∗∗p , 0.01. ∗∗∗p , 0.001.
The Service Industries Journal 29
Improving employee perceptions of HPWS components, such as perceptions of organis-
ational training and education, communications, and compensation, are associated with
higher levels of employee reaction effectively fulfilling work obligations by the employee
and the organisation.
For H2 test, the standardised path coefficient between HPWS and the service quality
was 0.676 and statistically significant at the 0.001 level. H2 was supported. High levels of
employee perceptions regarding HPWS components are associated with employee percep-
tions of service quality. It means that organisational support of employees for improving
their work is important for service quality in the health-care industry.
For H3 test, the standardised path coefficient between employee reaction and customer
satisfaction was 0.187 and statistically significant at the 0.01 level. H3 was supported.
Employee perceptions about effectively fulfilling work obligations by the employee and
the organisation are associated with customer satisfaction. According to previous
studies (Chung and Lee, 2005; Heskett et al., 1994, 1997; Rodney & Bannister, 2001),
employee satisfaction can increase customer satisfaction. Employee satisfaction positively
influences employee reaction through employee motivation (Fedor et al., 2008; Ferris
et al., 2002; Purcell & Hutchinson, 2007).
For H4 test, the standardised path coefficient between service quality and customer sat-
isfaction was 0.855 and statistically significant at the 0.001 level. Therefore, H4 was sup-
ported. This result is similar to the result reported by previous studies (Babakus et al.,
2004; Hartline & Ferrell, 1996). Employee perceptions of service quality are positively
associated with customer satisfaction.
For H5, the standardised path coefficient between customer satisfaction and customer
loyalty was 0.560 and statistically significant at the 0.001 level. Thus, H5 was supported.
Figure 5. The coefficients of the path analysis of the model.
Table 7. Mean differences of HPWS variables between public and private hospitals.
Variables of HPWS Hospital N Mean Mean
difference Standard deviation
Standard error mean
Education (ED) Public 91 3.4029 0.4504 0.71062 0.07449 Private 105 2.9524 0.67055 0.06544
Communication (CO)
Public 91 3.3114 0.5241 0.70238 0.07363 Private 105 2.7873 0.74410 0.07262
Compensation (CP) Public 91 3.1758 0.9758 0.83347 0.08737 Private 105 2.2000 0.84555 0.08252
30 S.M. Lee et al.
Table 8. Independent samples test of HPWS.
Variables of HPWS
Levene’s test for equality of variances t-Test for equality of means
F- value Significance
t- Value df
Significance (two-tailed) Mean difference Standard error difference
95% Confidence interval of the
difference
Lower Upper
ED Equal variances assumed 1.046 0.308 4.563 194 0.000 0.45055 0.09874 0.25580 0.64529 Equal variances not assumed 4.544 186.426 0.000 0.45055 0.09915 0.25494 0.64616
CO Equal variances assumed 0.708 0.401 5.047 194 0.000 0.52405 0.10384 0.31925 0.72886 Equal variances not assumed 5.068 192.568 0.000 0.52405 0.10341 0.32008 0.72802
CP Equal variances assumed 0.003 0.958 8.111 194 0.000 0.97582 0.12030 0.73856 1.21309 Equal variances not assumed 8.120 190.796 0.000 0.97582 0.12018 0.73878 1.21287
T h
e S
e rv
ic e
In d
u strie
s J o
u rn
a l
3 1
In most of the previous studies, a strong relationship between service quality and customer
satisfaction was reported. Also, customer satisfaction has a strong relationship with custo-
mer loyalty. This study has a similar result as previous studies (Anderson et al., 1994;
Gronholdt et al., 2000; Stank et al., 1999; Verhoef, 2003). Thus, it is evident that satisfied
customers are more likely to be repeat customers.
For H6, the t-test assessed whether the means of the two groups were statistically
different from each other as HPWS. The results of the t-tests are shown in Tables 7
and 8. The comparison between the public and private hospitals indicates that there
were significant differences in HPWS variables: education (t ¼ 4.544, p ¼ 0.000), com-
munication (t ¼ 5.068, p ¼ 0.000), and compensation (t ¼ 8.120, p ¼ 0.000). Therefore,
H6 was supported (Table 6). HPWS variable in the public hospitals scored higher than
those in the private hospitals.
As shown in Tables 7 and 8, employees of public hospitals may have more educational
opportunities to improve their work skills and other support from their hospitals. Data in
our study indicate that public hospitals showed higher levels of both communication and
compensation as well. Harley et al. (2007) reported that in HPWS, there was no difference
between high-skilled and low-skilled tasks for improving organisational performance.
Table 6 presents the result of significance test for the research model, as well as the
summary of the hypothesis test.
Conclusions
Today, the medical service industry is the focus of reform throughout the world. Medical
leaders and managers believe that improving quality of care, reducing medical error, and
cost containment can happen through better systems and/or technologies. Medical service,
however, is a system that can best be characterised as follows: by the people, for the
people, and of the people. It means that employees, especially medical staffs, are of critical
importance in the health-care system.
Our study proposed a research model to investigate how HPWS influence customer
satisfaction and customer loyalty for health-care organisations. The six hypotheses in
this model were tested using data collected from 196 pairs of respondents at four hospitals
in South Korea.
The results of the study shed new insights about how hospitals can improve their oper-
ations, customer satisfaction, and customer loyalty through HPWS. Perhaps, the most
notable findings of our study are that health-care HPWS are associated with improved
employee reaction (0.701) and improved service quality (0.676). This result seems reason-
able in that all of the efforts to improve customer satisfaction and customer loyalty are
related to perceptions and attitudes of medical staff. Thus, it is essential for hospitals to
improve employee reaction to organisational support for their work through training
and education, communication, and compensation.
The health-care industry is a labour-intensive industry. From the results (Table 1), we
noticed that salary and promotion were ranked first and second, respectively, as important
factors for job satisfaction among the seven items presented in the study. Therefore, organ-
isation leaders and managers should focus on effective HRM, especially providing proper
compensation to improve employees’ job satisfaction.
There are some limitations of our study. First, this study considered a customer and an
employee as one set for the data. Since the study has shown that HPWS has higher scores
in public hospitals than in private ones, differences between the two hospital groups were
not analysed for other variables. We believe that a limitation in variable measurements
32 S.M. Lee et al.
comes from the method of collecting data from both the patients and employees. Second,
the survey data used in this study were collected from four hospitals with more than 500
beds in South Korea. Since there are many small-sized hospitals, a comparative study of
small versus large in terms of HPWS might yield interesting results. Third, there was a
time lapse between contact and completion of the survey for both groups. Patients com-
pleted the questionnaire immediately after the contact with employees, while employees
were given more time to complete their questionnaire on the same day. Thus, the gener-
alisability of this study’s results may be limited.
Future research should consider our limitations mentioned above and also include
cross-cultural study using different sizes and types of hospitals (e.g. medical tourism
hospitals) in a wider range of countries. In addition, as organisational culture in each
department of a hospital might be different, such sub-culture of each department should
be incorporated in the future study. Also, to reduce potential bias, a systematic approach,
such as the person-by-person method, is needed. Future research might gain insight from
comparing hospitals that have been characterised as high – low-performance facilities
before data collection. The analysis of the data that are distributed and collected according
to these characteristics, including a longitudinal investigation, may provide outcomes that
can be more useful for strategic management in the health-care industry.
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