Technology-Based Service Encounters Using Self-Service Technologies in the Healthcare Industry

Abstract

While there have been studies discussing the influence of technology-based services on the overall service efficiency and quality of organizations in various industries, very little effort has been devoted to investigating this issue in the healthcare industry. Hospital image is considered a crucial factor influencing patients' choice of hospitals, but few studies specifically examine its association with technology-based services. By consulting the model of the European Customer Satisfaction Index, a research model for evaluating the impact of the use of technology-based services on hospital image, patient satisfaction, and patient loyalty in the healthcare industry is developed and examined in this study using survey data collected from 738 patients at two medical centers with an online appointment system. The research results confirm the importance of providing quality technology-based services in enhancing hospital image, patient satisfaction, and patient loyalty. The implications of this research and suggestions for future work are also discussed.

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Technology-Based Service Encounters Using Self-
Service Technologies in the Healthcare Industry
Wei-Tsong Wang
a
, Shih-Yu Cheng
a
& Lin-Yo Huang
a
a
National Cheng Kung University, Tainan City, Taiwan
Accepted author version posted online: 07 Jun 2012.Published online: 27 Jan 2013.
To cite this article: Wei-Tsong Wang , Shih-Yu Cheng & Lin-Yo Huang (2013) Technology-Based Service Encounters Using Self-
Service Technologies in the Healthcare Industry, International Journal of Human-Computer Interaction, 29:3, 139-155, DOI:
10.1080/10447318.2012.695728
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Intl. Journal of Human–Computer Interaction, 29: 139–155, 2013
Copyright © Taylor & Francis Group, LLC
ISSN: 1044-7318 print / 1532-7590 online
DOI: 10.1080/10447318.2012.695728
Technology-Based Service Encounters Using Self-Service
Technologies in the Healthcare Industry
Wei-Tsong Wang, Shih-Yu Cheng, and Lin-Yo Huang
National Cheng Kung University, Tainan City, Taiwan
Although there have been studies discussing the influence of
technology-based services on the overall service efficiency and
quality of organizations in various industries, very little effort has
been devoted to investigating this issue in the healthcare indus-
try. Hospital image is considered to be a crucial factor influencing
patients’ choice of hospitals, but few studies specifically examine
its association with technology-based services. By consulting the
model of the European Customer Satisfaction Index, a research
model for evaluating the impact of the use of technology-based
services on hospital image, patient satisfaction, and patient loy-
alty in the healthcare industry is developed and examined in this
study using survey data collected from 738 patients at two medical
centers with an online appointment system. The research results
confirm the importance of providing quality, technology-based ser-
vices in enhancing hospital image, patient satisfaction, and patient
loyalty. The implications of this research and suggestions for future
work are also discussed.
1. INTRODUCTION
The management of healthcare institutions is particularly
challenging because of their unique features, which include
fluctuating demand, capital intensiveness, and the inability to
store service capacity for future conciliation of supply and
demand (Arrow, 1963; van Doren & Smith, 1987). However, the
use of information technologies (IT) has been one of the most
effective ways to enhance the performance of healthcare insti-
tutions, enabling t hem to develop and offer higher quality and
more reliable healthcare services (Favela, Tentori, & Gonzalez,
2010; Lindberg & Humphreys, 1995; Menachemi, Saunders,
Chukmaitov, Matthews, & Brooks, 2007; Randell Wilson, &
Fitzpatrick, 2010).
The importance of IT in enhancing operational effectiveness
and developing the competitive advantages of organizations has
drawn significant attention from researchers, and many stud-
ies have focused on examining the technical dimensions of
developing and implementing technology-based service tools,
Address correspondence to Wei-Tsong Wang, Department of
Industrial and Information Management, National Cheng Kung
University, 1st University Road, East District, Tainan City, Taiwan 701.
E-mail: wtwang@mail.ncku.edu.tw
such as online-appointment systems (OAS), electronic med-
ical records systems, and online medical diagnosis systems.
Nevertheless, very little attention has been paid to the soft
side of technology-based services (Chang & Chang, 2008),
which would provide us with more comprehensive answers
to the question of what kinds of services actually benefit
healthcare institutions and for what reasons. In addition, hos-
pital image has long been considered one of the crucial fac-
tors influencing patients’ choice of hospital (Gooding, 1995;
Heischmidt, Hekmat, & Gordon, 1993; K. H. Kim, Kim, Kim,
Kim, & Kang, 2008), and a favorable image can result in
increased patient numbers (Akinci, Esatoglu, Tengilimoglu,
& Parsons, 2004; Andaleeb, 2001). Consequently, establish-
ing a favorable hospital image has become a critical task
for hospital administrators. However, very few studies have
attempted to investigate the influence of technology-based ser-
vices on patient satisfaction and patient loyalty via hospital
image.
Therefore, by consulting the European Customer
Satisfaction Index (ECSI), this study has developed a model
consisting of four constructs, namely, technology-based service
encounters, hospital image, patient satisfaction, and patient
loyalty, and examined the structural relationships among them
by taking the OAS services of two research-based medical
centers in Taiwan as an example. The research results are
expected to advance our understanding of the relationships
among technology-based service encounters and the key
service marketing factors included in the proposed research
model.
The remaining part of this article is organized as follows.
First, a review of the literature concerning technology-based
service encounters, hospital image, patient satisfaction, and
patient loyalty is presented, followed by the introduction of the
associated research hypotheses. Second, the r ationale for the
proposed research model and the research method is introduced.
Third, the findings acquired by the use of structural equation
modeling (SEM) technique are presented along with a discus-
sion of their theoretical and practical implications. Finally, a
conclusion is drawn to summarize the contributions of this
study, the research limitations, and the corresponding future
research directions.
139
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140 W.-T. WANG ET AL.
2. LITERATURE REVIEW
2.1. Patient Loyalty
The conceptualization of patient loyalty has been a work-
in-progress in the healthcare service marketing literature over
the last three decades, which has considered loyalty to institu-
tion, service, brand, and physician. Among the different kinds
of patient loyalty, patient loyalty to a healthcare service institu-
tion, such as a hospital, is the preference to consistently return
to the same healthcare service provider for the same or differ-
ent healthcare services in the future (Chaska, 2006). In other
words, patient loyalty to a healthcare service institution involves
not only a favorable cognitive attitude and the patients’ confi-
dence in the healthcare services received from the healthcare
service institution, resulting from an earlier positive experience,
but also the actions undertaken to overcome any obstacles that
may prevent the patient from returning to the same institution
(Caruana & Fenech, 2005). Consequently, in this study patient
loyalty to a healthcare service institution is defined as a deeply
held, long-term commitment to reuse/repatronize and recom-
mend the preferred healthcare services from the same healthcare
service institution (e.g., a hospital), free from the effects of sit-
uational factors and marketing efforts, which have the potential
to result in switching behaviors (Chahal, 2010; Oliver, 1999).
From a pragmatic viewpoint, patient loyalty has been consid-
ered far more valuable than other factors, including patient satis-
faction and perceived value, and its benefits can be seen directly
in long-term increased patient volumes, revenue, and profitabil-
ity (Chaska, 2006; MacStravic, 1994). However, patient loyalty
to a particular healthcare service provider is neither absolute
nor permanent, and thus needs to be carefully managed and
maintained (MacStravic, 1987; Roberge, Beaulieu, Haddad,
Lebeau, & Pineault, 2001; Torres, Vasquez-Parraga, & Barra,
2009). A number of academics suggest that the development
of customer/patient loyalty to a particular organization can be
divided into four sequential stages, as follows: cognitive (pref-
erence for one organization over its competitors), affective (a
liking or positive attitude toward the organization has developed
as a result of previous experience), conative/intentional (strong
organization-specific commitment to repatronize is developed),
and action/behavioral (working to overcome any obstacles to
repatronize when there are specific healthcare needs to be
fulfilled; e.g., Caruana & Fenech, 2005; Oliver, 1999). This
implies that different loyalty management and marketing strate-
gies should be applied to patients at different stages in order to
increase or maintain their loyalty.
In line with the findings of researchers in the area of
corporate marketing (e.g., Day, 1969; Dick & Basu, 1994;
Rundle-Thiele & Mackay, 2001), prior healthcare/hospital ser-
vice marketing research indicates that the concept of loyalty
can be better comprehended by taking into consideration two
dimensions: psychological/attitudinal and behavioral (Chahal,
2008; Ehinger, 2010; Laura & Daniela, 2010; MacStravic, 1987,
1995; Torres et al., 2009). Behavioral loyalty refers to actual
repeated patronage over a given period, whereas psychological
loyalty can be observed in the forms of stated preference, com-
mitment, and patronage intention, regardless of actual patronage
behaviors (Chao, 2008; Odin, Odin, & Valette-Florence, 2001;
Torres et al., 2009). These two perspectives of loyalty are mea-
sured in the healthcare service marketing literature in terms of
factors that include repatronage, word-of-mouth recommenda-
tions, and attitude/feelings toward the healthcare services of the
hospital (e.g., Chahal, 2008; Lonial, Menezes, Tarim, Tatoglu,
& Zaim, 2010; Salgaonkar & Mekoth, 2004).
2.2. Technology-Based Service Encounters
The concept of service encounters is considered one of the
key components of service marketing. Such a service encounter
is defined as “a period of time during which a consumer directly
interacts with a service” (Shostack, 1985, p. 243). This defi-
nition is based on the fact that a consumer interacts with the
personnel, facilities, and other tangible elements of a com-
pany during a given period when he or she requests a s ervice
(Bitner, 1990; Bitner, Booms, & Mohr, 1994). Correspondingly,
consumer perceptions of a service encounter, as one of the
components of service quality (McAlexander, Kaldenberg, &
Koenig, 1994), refer to the consumer’s evaluation of his or her
personal interactions with a component of a service provider
during a service transaction (i.e., the process of service deliv-
ery). This indicates that a consumer’s evaluation of service
encounters is not completely identical to their overall eval-
uation of the service request. Failed service encounters can
result in dissatisfaction and reduced loyalty and trust, switching
behaviors, and ultimately financial losses (Bitner et al., 1994;
Keaveney, 1995; Tax & Brown, 1998). Evaluations of consumer
perceptions of a service encounter should thus be carried out,
specifically to enhance understanding of how consumers deter-
mine the perceived quality of a service and what they expect the
service provider to do (Winsted, 1997).
With the continued advances in IT, the use of self-service
technologies (SSTs), which allow users to produce and use
services without direct contact with the personnel of the ser-
vice providers (e.g., online banking, online automated hotel
checkout, and online package tracking), has become a common
way to achieve quality customer service in various indus-
tries (Erikssona & Nilssonb, 2007; Vlachos, Giaglis, Lee, &
Vrechopoulos, 2011). Some academics use the term technology-
based service encounters to represent the interactions between
customers and technology-based service platforms, which are
gradually becoming key to long-term business prosperity
(Meuter, Ostrom, Roundtree, & Bitner, 2000). It is thus impor-
tant for service marketing professionals to understand and
evaluate the interactions that occur among technology, cus-
tomers, the organization, and its employees from various per-
spectives (Parasuraman & Grewal, 2000). These perspectives
include investigations that relate technology-based services
encounters to technology adoption (e.g., Matthing, Kristensson,
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TECHNOLOGY-BASED SERVICE ENCOUNTERS 141
Gustafsson, & Parasuraman, 2006; Walker, Craig-Lees, Hecker,
& Francis, 2002), customer complaints (e.g., Snellman &
Vihtkari, 2003), corporate image (e.g., Heinonen, 2008), and
customer relationship management (Wang & Hsu, 2012).
A number of implications can be derived from the findings
of previous studies of technology-based services encounters.
The first is that the criteria used by customers to evaluate
technology-based services encounters, such as conformance
with customers’ specific needs, reliability, trustworthiness, and
convenience, share a great deal of similarity with those used
to evaluate traditional interpersonal service encounters (Hsieh,
2005). In addition, these criteria/factors must work in con-
cert rather than being mutually exclusive in order to provide
a favorable customer experience. Another implication is that
technology-based service encounters have a positive impact
on customer satisfaction, mostly because the process of a
technology-based service can be completed more easily and
quickly and fulfill the customers’ needs better than its traditional
alternatives (Erikssona & Nilssonb, 2007; Meuter et al., 2000).
The last implication is that customer perceptions of technology-
based service encounters tend to be the fundamental reference
based on which customers develop their perceptions and atti-
tudes toward the products, services, and company as a whole
(Heinonen, 2008; Wang & Hsu, 2012). This finding implies the
existence of significant relationships between technology-based
service encounters and the key service marketing elements
beyond customer expectations (e.g., quality-related measures)
and satisfaction, such as corporate image and customer loyalty.
Today’s highly competitive environment calls for improve-
ments in healthcare service quality and efficiency in order
for healthcare institutions to gain a competitive edge, and
the implementation of electronic healthcare systems/SSTs is
considered the most effective means to achieve this (Dorr,
Wilcox, Donnelly, Burns, & Clayton, 2005; Essen, 2009; Storni,
2010). However, research indicates that electronic healthcare
systems/SSTs are not a flawless panacea for achieving pre-
ferred customer/patient perceptions on healthcare services, and
their implementation needs to be carefully plotted based on
advanced knowledge of how customers/patients assess, use,
and interact with these services (i.e., technology-based service
encounters; Laura & Daniela, 2010; Rogers, Kirk, Gately, May,
& Finch, 2011).
Prior research indicates that immediate knowledge of
appointment times and convenient and time-saving ways of
making appointments are ranked the most important advan-
tages of the electronic healthcare systems/SSTs for patients,
both of which are achievable based on a successful OAS
(Van Schaik, Flynn, Van Wersch, Douglass, & Cann, 2004).
In addition, a high-quality OAS can better coordinate patient
appointments and referrals, and thus can eliminate unnecessary
back-office management efforts (Erdem, James, & Clow, 2004).
Consequently, the OAS of a hospital/healthcare institution has
been one of the most popular components in implementing elec-
tronic healthcare systems (Chang & Chang, 2008; Silvestre,
Sue, & Allen, 2009), as it has the potential to achieve qual-
ity customer service, which is beneficial for both the hospital
and its patients. Based on the preceding discussion, the con-
struct of OAS technology-based service encounters is defined as
patients’ evaluation of their personal interactions with the OAS
of their respective hospital during the process of requesting and
arranging healthcare services.
A number of studies have investigated the impact of the
implementation and performance of an OAS and other elec-
tronic health systems on patient perceptions of healthcare
institutions. For example, effective delivery of the booking
services enabled by OAS provides patients with a sense of
empowerment, as they have more control over the ways they
request healthcare services and thus are more likely to develop
positive perceptions of the related healthcare professionals or
healthcare institutions in general (Edenius & Westelius, 2004;
Silvestre et al., 2009). In addition, quality, technology-based
service delivery achieved with these systems can lead to a
closer relationship between patients and healthcare profes-
sionals because of the increased patient dependence on the
convenient healthcare-related services provided (Mukherjee &
McGinnis, 2007; Rogers et al., 2011). Furthermore, findings of
recent studies indicate that the provision of an OAS is one of
the most effective online tools used by hospitals to promote
patient satisfaction, which in turn helps prevent patient migra-
tion and eventually produces loyal patients (Friend, 2011; Laura
& Daniela, 2010). However, there are few empirical studies
that investigate the effects of technology-based service encoun-
ters using an OAS on patient satisfaction, hospital image, and
patient loyalty. This study is thus conducted in order to enrich
the understanding of the relationships among these healthcare
service marketing factors.
2.3. Hospital Image
Organizational image has long been considered a critical fac-
tor that differentiates the products/services of one organization
from those of others, and thus it can significantly influence cus-
tomers’ repatronage intentions (e.g., Keller, 1993, 1998; O’Cass
& Lim, 2001; Yoo, Donthu, & Lee, 2000). An organization’s
image is built up as a result of all prior experiences that oth-
ers have with it (Boulding, 1956). Keller (1993) argued that
the image of an organization/brand is composed of a set of
associations linked to it that consumers hold in their memories.
An organization’s image can thus be formed and changed by
not only customers’ interactions with the organization but also
the organization’s market communication or public relation pro-
grams (Gray & Balmer, 1998; Kirdar, 2007). With reference to
these arguments, hospital image is defined as the sum of the
beliefs, ideas, and impressions of patients and/or the general
public with regard to a hospital, which are developed based on
their past experience with the hospital (Kotler & Clarke, 1987).
Hospital image is thus not created by a hospital itself, but rather
by the patients and/or the public, representing their overall
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142 W.-T. WANG ET AL.
impressions and perceptions of the services, reputation, and
characteristics of the institution (Akinci et al., 2004). Hospital
image is multidimensional; is associated with various features
such as equipment and facilities, employee attitudes and behav-
ior, and communication styles; and is not absolute but relative
to that of competing hospitals (Javalgi, Whipple, McManamon,
& Edick, 1992).
In the context of the healthcare industry, many academics
argue that patients’ past experiences with a hospital, which are
fundamental with regard to the building of the hospital’s image,
have a significant influence on the tendency to select a particu-
lar hospital in the future (e.g., Gooding, 1995; Heischmidt et al.,
1993; K. H. Kim et al., 2008). Consequently, hospital image has
been recognized as one of the most important factors influenc-
ing the hospital-choice decisions of patients (Akinci et al., 2004;
Berkowitz & Flexner, 1981; Javalgi, Rao, & Thomas, 1991).
Javalgi et al. (1992) indicated that patient perceptions and atti-
tudes toward a particular hospital, which are based on the
conditions of market competition and the relative strengths and
positionings of competitors, are critical in forming the image
of a hospital. In addition, it is found that hospital image can
be developed based on patient evaluations of both the technical
quality (e.g., perceived competence of healthcare profession-
als) and functional quality (e.g., perception of technology-
based services encounters) of the hospital (Laohasirichaikul,
Chaipoopirutana, & Combs, 2011). Furthermore, some prior
studies indicate that the recommendations of friends, rela-
tives, or healthcare professionals have a significant influence
on hospital choice decisions (e.g., Berkowitz & Flexner, 1981;
Boscarino & Steiber, 1984). These arguments imply a signifi-
cant effect of hospital image on patients’ psychological loyalty
to the hospital. Finally, successful image management is criti-
cal to a hospital’s strategic management, because image, as a
vital strategic resource, can lead to strong public support, out-
side funding, and effective human resource recruitment, and
thus can improve the hospital’s competitiveness (Elbeck, 1988;
Gray & Balmer, 1998; Javalgi et al., 1992). The preceding
discussion indicates that the role of hospital image in hos-
pital management deserves to be investigated in conjunction
with other key service marketing factors, such as technology-
based service encounters, patient satisfaction, and patient
loyalty.
2.4. Patient Satisfaction
Customer satisfaction results from customers’ positive expe-
riences of using a product or service (K. H. Kim et al., 2008),
and it is considered an emotional response based on the expec-
tations held before the experience (Oliver, 1997; Soderlund,
2006). Consequently, various studies have noted that dissatis-
faction is determined by the discrepancy between the actual
outcome and the desired outcome of a transaction, as recog-
nized by consumers (E. W. Anderson & Sullivan, 1993; Spreng
& Mackoy, 1996).
Satisfaction can thus be defined as a perception resulting
from a comparison of an actual experience with expecta-
tions, given the s acrifices made by the customer (Edvardsson,
Johnson, Gustafsson, & Strandvik, 2000; Fornell, 1992;
Westbrook, 1981). Customers will become dissatisfied if their
expectations are not met, but they tend to have high repurchase
rates and loyalty when products or services exceed expectations
(H. H. Lin & Wang, 2006; K. H. Kim et al., 2008). These argu-
ments have been supported by various studies in the context of
healthcare services (e.g., Chang & Chang, 2008; John, 1992;
MacStravic, 1987; Moliner, 2009). In this study, OAS patient
satisfaction is defined as patients’ perceptions resulting from a
comparison of an actual experience with their expectations with
regard to the use of the OAS to request healthcare services.
3. RESEARCH METHOD
3.1. Development of the Research Model and Hypotheses
This study was conducted to investigate the relationships
among OAS technology-based service encounters, hospital
image, OAS patient satisfaction, and patient loyalty. Thus, with
reference to the ECSI model, a theoretical research model that
consists of these constructs and the relationships among them
was developed, as presented in Figure 1.
From a resource-based perspective, an organization’s intan-
gible resources, such as customer loyalty and organizational
image, are recognized as key drivers of superior organizational
performance that can be created and enhanced by its IT capa-
bility (Bharadwaj, 2000; Stone, Good, & Baker-Eveleth, 2007).
In addition, in constructing a framework for the relationships
among appraisal, emotional/affective responses, and coping
responses (see Bagozzi, 1992; Lazarus, 1991), a number of
researchers suggest that perceived quality (appraisal) influences
satisfaction (an affective response) and that satisfaction directly
influences behavioral intention (a coping response). Based on
this framework, it is reasonable to infer that with a high-quality
SST, such as a hospital’s OAS, patients can better coordinate
their appointments and referrals, and thus may recognize the
Patient
Loyalty
OAS Patient
Satisfaction
OAS
Technology-based
Service Encounters
Hospital
Image
H1b
H1a
H3
H2a
H2b
FIG. 1. The proposed research model. Note. OAS = online-appointment
systems.
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TECHNOLOGY-BASED SERVICE ENCOUNTERS 143
high quality of the hospital’s nonmedical services, be more sat-
isfied with how they are treated, and more likely to return to the
same hospital for healthcare services in the future, given that
their medical conditions are not critical (Erdem et al., 2004).
Thus, similar to various other healthcare service studies (e.g.,
Chahal & Bala, 2012; Laohasirichaikul et al., 2011; Laura &
Daniela, 2010), the research model in this study is developed
to link OAS technology-based service encounters (appraisal)
and hospital image and OAS patient satisfaction (i.e., affec-
tive responses) to patient loyalty (i.e., a performance indicator
as a coping response), as suggested by the appraisal-response
framework proposed by Bagozzi (1992).
Technology-based service encounters, patient satisfaction,
and hospital image. Healthcare service marketing studies
have illustrated the significant positive relationship between
service quality and patient satisfaction (e.g., Andre et al.,
2008; McAlexander et al., 1994; O’Connor, Shewchuk, &
Bowers, 1991; Woodside, Frey, & Daly, 1989; Woodside
& Shinn, 1988). Because technology-based service encoun-
ters are considered one of the critical components of service
quality (McAlexander et al., 1994), these studies imply that
the higher the patients’ evaluation of the OAS technology-
based service encounters, the greater the patient satisfaction,
which in turn can lead to a higher probability of repatron-
age. For example, a number of studies have indicated the
positive effects of the consumers’ positive evaluations of the
use of IT-based tools/systems, or SSTs, on patient satisfaction
through improved service efficiency, increased effectiveness of
data communications, and reduced frequency of errors (e.g.,
Babulak, 2006; Chang & Chang, 2008; Dorr et al., 2005).
Kerwin (2002) specifically emphasized the role of the Internet
in the improvement of the delivery and quality of healthcare to
patients, which in turn leads to increased patient satisfaction.
Menachemi et al. (2007) also argued that a hospital’s adoption
of IT can provide timely access to clinical information, enhance
clinical decision making and service quality, and thus result in
increased patient satisfaction.
With regard to the relationship between technology-based
service encounters and image, Javalgi et al. (1992) argued that
image is formed inferentially based on consumer experiences
with a hospital regarding cost, quality of care, and a number
of other factors, such as the evaluation of various periph-
eral services (e.g., the OAS). Timmor and Rymon (2007) also
emphasized the importance of considering the effects of the cus-
tomers’ reaction toward technology-based services on the image
of an organization, and thus on the customers’ satisfaction
with the services they receive. This implies the causal path of
technology-based service encounters, image, and satisfaction.
In addition, it is argued in the prior research that one of the most
important motivations for implementing technology-enabled
services is to create and project a favorable organizational image
in the process of service delivery (Akinci et al., 2004; Azzam
& Alramahi, 2010; Edenius & Westelius, 2004; Gaur & Abdul
Waheed, 2003; W. B. Lin, 2007).
Based on the prior statements, the following hypotheses are
developed:
H1a: OAS technology-based service encounters have a positive
effect on OAS patient satisfaction.
H1b: OAS technology-based service encounters have a positive
effect on hospital image.
Hospital image, patient s at i s f act i on, and patient loyalty.
The image of an organization has consistently been used as
a key determinant of customer satisfaction in the literature,
as it reflects customers’ fundamental impressions of the orga-
nization (Lee & Joshi, 2007; Timmor & Rymon, 2007). For
example, the ECSI model highlights image as one of the impor-
tant determinants of customer satisfaction and loyalty (Cassel
& Eklof, 2001; Martensen, Gronholdt, & Kristensen, 2000).
The importance of image in achieving customer satisfaction
increases in areas that are relatively more complex to evaluate,
such as the healthcare industry, in which customer perceptions
of the quality of the services they receive are dependent on both
complex technical concerns and dynamic conditions of market
competition, and this is also addressed in the ECSI model.
In the context of the healthcare industry, Laohasirichaikul
et al. (2011) indicated that if a healthcare institution can build a
good image, patients will be more satisfied and are more likely
to repatronize it (i.e., behavioral loyalty) and tell their positive
experience to others (i.e., psychological loyalty). In addition,
Purwanto (2010) argued that a poor hospital image can result
in decreased patient trust and thus can negatively affect patient
loyalty. Chahal and Bala (2012) also argued that a positive
image can result in individuality and differentiation that lead to
a high level of patient loyalty and empirically validate the direct
positive effect of hospital image on this. Furthermore, other
healthcare service marketing studies also argue that ensuring
patient satisfaction is one of the major objectives of pub-
lic image creating efforts (e.g., Elbeck, 1988; Kirdar, 2007).
Andaleeb (2001) also argued that when consumer needs are
better met by a healthcare service provider, consumers are
more likely to develop a favorable service-oriented image of
the provider, their level of satisfaction tends to be higher, and
they are more likely to seek further services from the same ser-
vice provider again. Thus, based on the previous discussion, the
following hypotheses are proposed:
H2a: Hospital image has a positive effect on patient satisfaction.
H2b: Hospital image has a positive effect on patient loyalty.
Patient satisfaction and patient loyalty. There is evidence
in the traditional marketing literature showing a strong, signif-
icant, and positive relationship between customer s atisfaction
and loyalty (e.g., E. W. Anderson & Sullivan, 1993; Fornell,
1992; Reichheld, 1993). For example, the well-known ECSI
model, which is composed of factors such as image, expec-
tations, perceived product quality, perceived service quality,
perceived value, customer satisfaction, and loyalty, specifically
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144 W.-T. WANG ET AL.
depicts the positive impact of image and customer satisfaction
on customer loyalty. Such relationships have also been empiri-
cally validated in the context of multiple industries in various
European countries (e.g., Cassel & Eklof, 2001; Martensen
et al., 2000). M. K. Kim, Park, and Jeong (2004) also argued
that organizations must put significant effort into improving ser-
vice quality and offering customer-oriented services to boost
customer satisfaction to enhance customer loyalty.
In the healthcare industry, MacStravic (1987) argued that
patient satisfaction tends to be the most important basis for the
building of patient loyalty. In addition, the findings of a series
of studies regarding the market orientation of hospitals indicate
that high patient satisfaction, as a result of high performance,
can increase repeat visits (i.e., behavioral loyalty), voluntary
promotion by experienced patients to others (i.e., psycholog-
ical loyalty), and the attraction of new patients (Chaska, 2006;
MacStravic, 1994; Moliner, 2009; Raju, Lonial, & Gupta, 1995;
Raju, Lonial, Gupta, & Ziegler, 2000). Correspondingly, there
have also been studies that empirically validate the positive
influence of patient satisfaction on both the behavioral and psy-
chological loyalty of patients (e.g., Caruana & Fenech, 2005;
Chahal, 2010; O’Connor et al., 1991). Furthermore, Laura and
Daniela (2010) found that the implementation of Internet-based
SSTs allows hospitals to better serve their patients and thus can
lead to a high level of patient satisfaction that, in turn, can pre-
vent patient migration and enhance patient loyalty. Thus, the
following hypothesis is proposed:
H3: OAS patient satisfaction has a positive effect on patient
loyalty.
3.2. Development of Instruments
To develop an effective survey, 22 items with a 7-point Likert
scale related to the four constructs of the proposed research
model were developed after consulting the existing literature,
and these were then refined based on the specific topic of
this study. These items were then further revised to improve
their effectiveness based on the r esults of evaluations by and
discussions with two professors and two medical specialists.
Finally, these items were pilot-tested with 31 clinical patients
to examine their internal consistency and reliability using
Cronbach’s alpha coefficient analysis. I n this method of anal-
ysis, if the overall Cronbach’s alpha coefficient of all the items
of a construct is greater than 0.7, the items are considered highly
reliable (Kannan & Tan, 2005). Based on the results and feed-
back from the pilot test, the questionnaire was further refined.
The final questionnaire consisted of 18 items to assess the four
constructs of the proposed research model. Items included in
the final revised questionnaire were considered highly reliable,
as the individual Cronbach’s alpha coefficients of the four con-
structs were all greater than 0.7 (0.78, 0.78, 0.72, and 0.80).
Items in the survey were measured using a 7-point Likert scale
ranging from 1 (strongly disagree)to7(strongly agree)(see
Appendix A).
Items for measuring OAS technology-based service encoun-
ters were adopted from Chang and Chang (2008) and Zhu,
Wymer, and Chen (2002), which were originally used to mea-
sure service encounters in the context of healthcare SSTs
and online banking services. Items for measuring hospital
image were adopted from Ciavolino and Dahlgaard (2007)
and Lemmink, Schuijf, and Streukens (2003) for measuring
corporate image based on four dimensions: ability to attract,
marketing communications, perceived quality of services, and
patient associations with the hospital. Items for measuring
OAS patient satisfaction were adopted from Chang and Chang
(2008) and H. H. Lin and Wang (2006), which were devel-
oped based on patient evaluations of the performance of the
OAS. Finally, patient loyalty, based primarily on H. H. Lin and
Wang (2006) and Sahadev and Purani (2008), was measured
by items focusing on two dimensions: behavioral loyalty (e.g.,
intention to return) and psychological loyalty (e.g., voluntary
word-of-mouth).
3.3. Data Collection Method
Data for this study were collected from two research-based
medical centers that provide OAS services. One was located in
northern Taiwan (TU-North Hospital) and the other was located
in southern Taiwan (VX-South Hospital). A brief description of
the OASs of these two hospitals is presented in Appendix B.
To use the data collected to examine the relationships among
the constructs presented, the participants were patients who had
used the OAS services. Hard copies of the survey were person-
ally distributed by the eight members of the research team to
the potential respondents in the two medical centers. To solicit
a pool of respondents who would be as similar as possible to
the general population at the patients of the two medical cen-
ters, only 50 questionnaires were randomly distributed every
hour in the following three periods: 9 a.m. to 12 p.m., 2 p.m. to
5 p.m., and 6 p.m. to 9 p.m. The data collection process lasted
for 3 days in each of the two medical centers. Finally, 900 ques-
tionnaires were distributed and returned, and 162 incomplete or
problematic questionnaires were later removed, giving a valid
return rate of 82%. The 738 valid questionnaires were then used
for the analysis.
3.4. Data Analysis Method
The SEM method was used for data analysis, with max-
imum likelihood estimation used to acquire estimates of the
model parameters. A two-phase approach for SEM analysis
(J. Anderson & Gerbing, 1988; Hair, Black, Babin, Anderson,
& Tatham, 2006) was adopted in this study. First, the measure-
ment model was estimated using confirmatory factor analysis to
examine the overall fit, validity, and reliability of the model.
Second, the hypotheses between constructs were examined
using the structural model.
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TECHNOLOGY-BASED SERVICE ENCOUNTERS 145
4. RESEARCH RESULTS
4.1. Demographics of the Sample
Table 1 presents the demographic profile of the sample
population, where 40.1% of the respondents were male and
59.9% were female. In addition, 74.5% of the respondents
were between 20 and 50 years old, which demonstrates that
the outpatients in the samples were not predominantly middle-
aged or elderly. In terms of education level, more than 90% of
the respondents received senior high school level education or
above, indicating that most of them were capable of learning
and using technology-based service systems.
4.2. Analysis of the Measurement Model
The reliability of the measures for each of the four con-
structs was first tested by examining their individual Cronbach’s
alpha coefficients. The results indicated that all the individual
Cronbach’s alpha coefficients met the recommended level of
0.7 or higher (0.92, 0.85, 0.83, and 0.88), and thus no changes
were made at this stage.
Using the software program LISREL 8.8, confirmatory fac-
tor analyses were used to assess the measurement model in
terms of goodness-of-fit, convergent validity and discriminant
validity.
Although various indices can evaluate the goodness-of-fit
of a measurement model, Hair et al. (2006) proposed that the
most important is the chi-square. However, because chi-square
is likely to increase in response to an increase in sample size
and/or number of observed variables, it is inappropriate to use
TABLE 1
Demographic Profile of the Respondents
Frequency %
Gender
Male 296 40.1
Female 442 59.9
Total 738 100.0
Age (by years)
Younger than 19 14 1.9
20–29 158 21.4
30–39 202 27.4
40–49 190 25.7
50–59 123 16.7
60 and older 51 6.9
Total 738 100.0
Education level
Under senior high school 45 6.1
Senior high school 253 34.3
College 387 52.4
Graduate and above 53 7.2
Total 738 100.0
it as the sole indicator of goodness-of-fit. Therefore, Hair et al.
suggested that researchers should also report at least one abso-
lute fit measure (e.g., root mean square residual, standardized
root mean residual, root mean square error of approximation,
goodness-of-fit, or adjusted goodness-of-fit) and at least one
incremental fit measure (e.g., comparative fit index or normed
fit index). Here we report eight fit indices indicating accept-
able model fit (see Table 2). The goodness-of-fit indices for
the hypothesized measurement model are also summarized in
Table 2.
The initial test of the measurement model indicated that
a number of the model fit indices did not pass their individ-
ual recommended levels, and thus the measurement model was
revised through item deletion. Five items that exhibited low fac-
tor loadings and squared multiple correlations were removed
(viz., items OAS-TB5, HI3, OAS-PS4, PL1, and PL4), and
data for the remaining 13 items were used for subsequent
analysis. As shown in Table 2, all model fit indices indicated
an adequate measurement model, and it was thus concluded
that the measurement model exhibited good fit (Hair et al.,
2006).
The psychometric properties of the measurement model were
then assessed in terms of its convergent and discriminant valid-
ity (Bogozzi & Yi, 1988; Fornell & Larcker, 1981; Hair et al.,
2006). There are three primary measures for evaluating the con-
vergent validity of a measurement model: (a) the factor loadings
of the indicators, which must be statistically significant and have
values greater than 0.6 (Bogozzi & Yi, 1988); (b) composite
TABLE 2
Goodness-of-Fit Indices for the Measurement Model
Fit Indices Criteria
a
Result/Value
χ
2
statistic Insignificant; however, a
significant p value can be
expected
186.21 (Significant)
χ
2
/df < 53.16(df = 59)
RMSEA < 0.07 (with CFI of
0.92 or higher)
0.05
RMSR < 0.05 0.04
SRMR < 0.08 (with CFI > 0.92) 0.03
GFI > 0.9 0.96
AGFI > 0.8 0.94
CFI > 0.92 0.99
Note. RMSEA = root mean square error of approximation;
RMSR = root mean square residual; SRMR = standardized root mean
residual; GFI = goodness-of-fit; AGFI = adjusted goodness-of-fit;
CFI = comparative fit index.
a
The criteria are valid when the sample size is greater than 250 and
the number of observed indicators for all the latent constructs is
between 12 and 29 (Bagozzi & Yi, 1988; Gefen, Straub, & Boudreau,
2000; Hair et al., 2006; Hu & Bentler, 1999; Marsh, Hau, & Wen,
2004; Wheaton, Muthen, Alwin, & Summers, 1977).
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146 W.-T. WANG ET AL.
reliability, with values greater than 0.6 (Bogozzi & Yi 1988;
Fornell & Larcker, 1981); and (c) average variance extracted
estimates, with values greater than 0.5 (Bogozzi & Yi, 1988,
Fornell & Larcker, 1981; Hair et al., 2006).
The factor loadings of each of the remaining items were
checked first. As shown in Table 3, all the factor loadings
(ranging from 0.75 to 0.91) of the remaining items were statis-
tically significant and larger than the more restrictive criterion
of 0.7 put forth by Hair et al. (2006). Consequently, it was con-
cluded that each item in the measurement model was strongly
related to its respective construct. In addition, all composite reli-
ability values (ranging from 0.82 to 0.92) were higher than 0.6,
indicating a reliable measurement model. The average variance
extracted values ranged from 0.61 to 0.76, which indicated that
each construct was strongly related to its respective indicators.
Overall, the measurement model exhibited adequate convergent
validity.
The reliability of the measures for each of the five constructs
after the process of item deletion was then checked. As shown
in Table 4, all the individual Cronbach’s alpha coefficients of
the four constructs were greater than the recommended level of
0.7 (Kannan & Tan, 2005). Consequently, no further changes
were made.
Finally, the discriminant validity of the measurement model
was checked. As shown in Table 5, the AVE estimate of each
construct was larger than its squared correlations to any other
construct. This indicated that the constructs were more strongly
related to their respective indicators than to other constructs
in the model, and thus they all possessed discriminant valid-
ity (Fornell & Larcker, 1981). Table 6 presents descriptive
statistics for each of the constructs in the proposed research
model, and they show that the patients generally had favorable
TABLE 4
Cronbach’s Alpha Coefficient of the Constructs After Item
Deletion
Construct Cronbach’s α Item No.
Items
Deleted
OAS
technology-based
service encounters
0.92 4 OAS-TB5
Hospital image 0.82 3 HI3
OAS patient
satisfaction
0.90 3 OAS-PS4
Patient loyalty 0.86 3 PL1, PL4
Note. Total number of items is 13. OAS = online-appointment
systems.
TABLE 5
Discriminant Validity of the Measurement Model
Construct TB HI PS PL
OAS-TB 0.73
HI 0.07 0.61
OAS-PS 0.59 0.24 0.76
PL 0.12 0.55 0.25 067
Note. Diagonals represent the average variance extracted, and the
other matrix entries represent the squared factor correlations. TB =
technology-based service encounters; HI = hospital image; PS =
patient satisfaction; PL = patient loyalty; OAS = online-appointment
systems.
TABLE 3
Convergent Validity of the Measurement Model
Construct Indicator
Factor
Loading
a
Composite
Reliability
Average Variance
Extracted
OAS technology-based
service encounters
OAS-TB1 0.81 0.92 0.73
OAS-TB2 0.85
OAS-TB3 0.86
OAS-TB4 0.90
Hospital image HI1 0.83 0.86 0.67
HI2 0.76
HI4 0.75
OAS patient satisfaction OAS-PS2 0.88 0.91 0.76
OAS-PS3 0.91
OAS-PS4 0.83
Patient loyalty PL2 0.83 0.82 0.61
PL3 0.86
PL5 0.76
Note. OAS = online-appointment systems.
a
All factor loadings of the individual items are statistically significant (p < .01).
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TECHNOLOGY-BASED SERVICE ENCOUNTERS 147
TABLE 6
Descriptive Statistics of the Constructs
Construct M SD
OAS technology-based service
encounters
5.94 0.97
Hospital image 5.46 0.99
OAS patient satisfaction 5.55 1.07
Patient loyalty 5.53 0.97
Note. Total number of items is 13. OAS = online-appointment
systems.
perceptions of the quality of the technology-based services pro-
vided by the hospital and were satisfied with their experience in
the hospital. In addition, they exhibited a high degree of loy-
alty toward the hospital and had a positive perception of its
image.
4.3. Analysis of the Structural Model
Before the structural model was used for hypotheses testing,
its goodness-of-fit was examined using the same fit indices as
above. Table 7 summarizes the goodness-of-fit indices for the
structural model, with all the model fit indices indicating an
adequate structural model, and thus it was concluded that the
structural model exhibited a good fit (Hair et al., 2006).
TABLE 7
Goodness-of-Fit Indices for the Structural Model
Fit Indices Criteria
a
Result/Value
χ
2
statistic Insignificant; however, a
significant p value can
be expected
189.40 (Significant)
χ
2
/df < 53.16(df = 60)
RMSEA < 0.07 (with CFI of
0.92 or higher)
0.05
RMSR < 0.05 0.04
SRMR < 0.08 (with CFI > 0.92) 0.03
GFI > 0.9 0.96
AGFI > 0.8 0.94
CFI > 0.92 0.99
Note. RMSEA = root mean square error of approximation; RMSR =
root mean square residual; SRMR = standardized root mean resid-
ual; GFI = goodness-of-fit; AGFI = adjusted goodness-of-fit; CFI =
comparative fit index.
a
The criteria are valid when the sample size is greater than 250 and
the number of observed indicators for all the latent constructs is
between 12 and 29 (Bagozzi & Yi, 1988; Gefen et al., 2000; Hair et al.,
2006; Hu & Bentler, 1999; Marsh et al., 2004; Wheaton et al., 1977).
Patient Loyalty
(R
2
= 0.56)
OAS Patient
Satisfaction
(R
2
= 0.68)
OAS
Technology-based
Service Encounters
Hospital Image
(R
2
= 0.07)
0.27*
0.69*
0.18*
0.31*
0.64*
Note: Path significance: * p<0.01
FIG. 2. Hypothesis testing results. Note. OAS = online-appointment systems.
Path significance:
∗
p < .01.
Given an adequate structural model, the hypotheses were
then examined (Hair et al., 2006). Figure 2 presents the stan-
dardized path coefficients (γ and β), their significance for
the structural model, and the coefficients of determinant (R
2
)
for each endogenous construct. The standardized path coef-
ficient indicates the strength of t he relationships between
the independent and dependent variables. The R
2
value indi-
cates the percentage of variance explained by the i ndependent
variables.
As expected, H1a and H1b were supported, indicating that
OAS technology-based service encounters had a positive influ-
ence on OAS patient satisfaction (γ = 0.69) and hospital image
(γ = 0.27) and accounted for 7% of the variance of hospi-
tal image. In addition, H2a and H2b were supported, showing
that hospital image had a significant positive influence on both
OAS patient satisfaction (β = 0.31) and patient loyalty (β
= 0.64). Because H1a was also supported, it was found that
OAS technology-based service encounters and hospital image
jointly accounted for 68% of the variance of OAS patient
satisfaction.
Finally, H3 was supported, indicating that OAS patient satis-
faction had a direct positive effect on patient loyalty. Because all
the other hypotheses were also confirmed, it can be inferred that
OAS technology-based service encounters and hospital image
had an indirect positive influence on patient loyalty through
OAS patient satisfaction. Altogether, these factors accounted for
56% of the variance of patient loyalty.
Table 8 summarizes the significant direct/indirect effects
between the variables in the research model. Analysis of the
structural model indicated that OAS technology-based ser-
vice encounters had a stronger total effect (0.70) on OAS
patient satisfaction than on hospital image (0.27). In addition,
OAS technology-based service encounters (total effect = 0.70),
in comparison to hospital image (total effect = 0.31), were
found to have a stronger effect on OAS patient satisfaction.
Furthermore, the analysis showed that OAS technology-based
service encounters and hospital image had significant indirect
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148 W.-T. WANG ET AL.
TABLE 8
Effects Between Variables in the Proposed Research Model
Hospital Image
OAS Patient
Satisfaction Patient Loyalty
Direct
Effects
Indirect
Effects
Direct
Effects
Indirect
Effects
Direct
Effects
Indirect
Effects
OAS technology-based Service
encounters
0.27 — 0.69 0.08 — 0.31
Total effect 0.27 0.77 0.31
Hospital image — — 0.31 — 0.64 0.06
Total effect — 0.31 0.70
OAS patient satisfaction — — — — 0.18 —
Total effect ——0.18
Coefficient of determinant (R
2
) .07 .68 .56
Note. OAS = online-appointment systems.
effects on patient loyalty through both OAS patient satisfac-
tion. Finally, hospital image (total effect = 0.70), in comparison
to OAS technology-based service encounters (total effect =
0.31) and OAS patient satisfaction (total effect = 0.18), had the
largest influence on patient loyalty.
5. RESEARCH IMPLICATIONS
5.1. Implications for Theory
The results of hypotheses testing have a number of impli-
cations for theory regarding OAS technology-based service
encounters, hospital image, patient satisfaction, and patient loy-
alty. To begin with, OAS technology-based service encounters
were found to have a direct positive influence on both OAS
patient satisfaction and hospital image (H1a and H1b), consis-
tent with the results of earlier studies (e.g., Akinci et al., 2004;
Berry, Seiders, & Grewal, 2002; Kerwin, 2002; Menachemi
et al., 2007). This result underlines the importance of providing
patients with quality, technology-based services, which enable
convenient service delivery and favorable human–computer
interactions, in increasing both satisfaction and hospital image.
This also implies the importance of the patients’ impressions
of the technology-based service provision processes, namely,
their perceptions of the technology-based service encounters, in
terms of influencing patients’ affective responses (e.g., hospi-
tal image and patient satisfaction) toward the healthcare service
institutions. This suggests that future researchers should pay
more attention to the significance of process-driven evaluations
(e.g., service encounters) of a service, instead of simply focus-
ing on outcome-driven evaluations (e.g., service quality), when
conducting similar research projects.
It was also found that OAS technology-based service
encounters made indirect positive contributions to OAS patient
satisfaction through hospital image (H1b and H2a). In addition,
consistent with prior studies (e.g., Akinci et al., 2004; Andaleeb,
2001; Chahal & Bala, 2012; Javalgi et al., 1992; Raju et al.,
2000), our findings confirm the positive impacts of hospital
image and OAS patient satisfaction on patient loyalty (H2b and
H3). The contribution of the validation of these four hypothe-
ses to future theorizing is threefold. First, the research results
suggest that the causal path moving along OAS technology-
based service encounters, hospital image, and OAS patient
satisfaction can significantly affect patient loyalty, as indicated
in the original ECSI model. This finding thus further high-
lights the importance of considering these service marketing
factors and the indicated causal paths simultaneously when con-
ducting technology-based service research in the context of the
healthcare service industry.
Second, the research results are consistent with those of pre-
vious ECSI-related studies by confirming the positive direct
influence of image on satisfaction and the important role
that hospital image plays in the indirect relationship between
technology-based service encounters and patient satisfaction.
This finding further verifies the importance of image as the
main driver of satisfaction, in comparison to quality-related
constructs (Cassel & Eklof, 2001; Martensen et al., 2000) in
the context of the healthcare service industry. Finally, hospital
image is found to have a greater influence on patient loyalty than
OAS technology-based service encounters and OAS patient sat-
isfaction, as suggested in the ECSI literature (e.g., Cassel &
Eklof, 2001; Martensen et al., 2000). Cassel and Eklof (2001)
found that the inclusion of image as a latent variable in the
variants of the ECSI model consistently and significantly adds
to the explanatory power of the ECSI model through various
paths, such as the one associated with loyalty, and our research
findings offer further support for this argument.
To conclude, based on the prior discussion, this study has
three main theoretical implications. First, this study applied
the concept of service encounters to the service marketing of
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TECHNOLOGY-BASED SERVICE ENCOUNTERS 149
healthcare institutions by considering and empirically exam-
ining the influence of the OAS technology-based service
encounters on the key service marketing constructs. The
research results indicate the significant direct and indirect influ-
ences of OAS technology-based service encounters on hospital
image, patient satisfaction, and patient loyalty, thus providing
researchers with support for the use of technology-based ser-
vice encounters in the context of the healthcare service industry.
Second, this study contributes to IT-based service research by
using the ECSI to develop a theoretical model that can sig-
nificantly interpret and predict satisfaction and loyalty, and
empirically validating its significance in the context of the
healthcare service industry. The direct and indirect relation-
ships among the constructs depicted in the proposed research
model were validated using statistically rigorous methods, and
OAS technology-based service encounters, hospital image, and
patient satisfaction were all found to be significant factors
in determining patient loyalty. The research results have thus
advanced our understanding of the relationships between the
technology-based service encounters and the key service mar-
keting constructs, and demonstrated the robustness of the ECSI
model in interpreting and predicting the performance of the
service marketing efforts made by the healthcare service pro-
fessionals. Finally, with reference to the ECSI model, this study
further demonstrated the significant explanatory power of image
with regard to consumers’ behavioral intention toward a specific
healthcare service institution and thus provided support for the
use of the construct of image in future studies.
5.2. Implications for Practice
With reference to the theoretical implications just presented,
the research results provide hospital administrators with a num-
ber of insights into the impact of designing and implementing
technology-based services. First, by empirically examining the
proposed research model, this study has verified the importance
of facilitating favorable patient-perceived technology-based ser-
vice encounters in order to lead to the formation of a favorable
hospital image and a high level of patient satisfaction and, in
turn, to enhance patient loyalty to a hospital. Accordingly, it is
recommended that hospitals put significant efforts into improv-
ing perceived nonmedical service quality by implementing and
appropriately delivering quality, technology-based services with
their limited resources.
Second, the research findings identified hospital image as the
most important factor influencing loyalty compared to service-
quality-related factors and satisfaction, given the condition that
the severity of the patients’ complaints is relatively low. This
implies that hospitals should promote a favorable public image
by providing patients with high-quality technology-based ser-
vices to make them feel satisfied with the nonmedical services
they receive, and thus develop a high level of loyalty. Although
the high availability and quality of technology-based services
can positively influence the feelings of patients that occur
during their interactions with the hospital, and can lead to a pos-
itive hospital image, the image of a hospital is developed relative
to those of competing hospitals, rather than being an absolute
judgment (Javalgi et al., 1992). Hospital administrators should
thus put more effort into consistently providing high-quality and
innovative technology-based services and corresponding ser-
vice delivery processes relative to their competitors. A viable
means to achieve this is to discover and take into consideration
patient needs by inviting patients to participate when develop-
ing various technology-based services (Hsieh, 2005; Matthing
et al., 2006).
Finally, it is found that the use of technology-based services,
particularly those t hat are considered SSTs (e.g., the OAS of
a hospital), is mostly initiated and controlled by the patients.
It is thus important for the administrators and IT professionals
of hospitals to design interfaces that enable patients to ini-
tiate a technology-based service easily (e.g., provision of an
eye-catching and highly accessible web portal) and to deter-
mine how they intend to utilize the service conveniently (i.e.,
offering flexibility in selecting how the service is delivered
enabled by highly interactive technology-based processes) in
order to achieve their specific goals. In addition, as indicated
in Appendix B, a hospital’s OAS tends to include more func-
tions than simply making appointments online. It would t hus be
helpful, in terms of creating positive patient experience with a
technology-based service, if hospital administrators and IT pro-
fessionals made efforts to categorize the functions included in
this service based on their individual objectives, such as increas-
ing satisfaction and loyalty, and then plan for the design and
provision of these functions and the associated interfaces in
accordance with these aims (Laura & Daniela, 2010). Sample
categories may include functions for promoting communica-
tions in order to enhance patient loyalty (e.g., automatic email
reminders to the patients regarding their upcoming appoint-
ments), functions for providing timely and reliable information
in order to facilitate patient satisfaction (e.g., real-time online
update on the progress of the appointments of a specific physi-
cian who is currently on duty), and functions for providing basic
service-request advice to improve hospital image (e.g., sugges-
tions regarding the physician/medical division with which a
patient may make an appointment based on their self-reported
complaints).
6. CONCLUSION
This study was motivated by the fact that although the impor-
tance of technology-based services in enhancing operational
effectiveness of healthcare institutions has drawn significant
attention from researchers, very few studies have attempted
to investigate the influence of technology-based services on
patient loyalty through its antecedents, such as hospital image
and patient satisfaction. The lack of such studies limits our
understanding of how patients determine the perceived quality
of the nonmedical services provided by hospitals, and of what
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150 W.-T. WANG ET AL.
they actually expect the hospitals to do from a service mar-
keting perspective. Therefore, in this study, a research model
for comprehending and explaining the direct and indirect rela-
tionships among OAS technology-based service encounters,
hospital image, OAS patient satisfaction, and patient loyalty
was developed with reference to the ECSI model. The proposed
research model was empirically examined using structural equa-
tion modeling, and the results provided considerable support for
the model. Specifically, all the research hypotheses were statisti-
cally supported, indicating the significance of technology-based
service encounters, hospital image, and patient satisfaction with
regard to a specific kind of SST, such as an OAS in this study,
in explaining the variance of patient loyalty.
As with all empirical research, this study has some lim-
itations. First, it aimed to investigate the dynamics of the
implementation of a specific type of technology-based service,
namely the OAS, at two research-oriented medical centers.
Consequently, the findings may not be generalized to the imple-
mentation of other types of technology-based services (e.g.,
online medical record query systems) in different contexts
(e.g., regional clinics). More research that aims to examine
the proposed theoretical model using a variety of samples and
technology-based services in similar and different contexts is
thus needed to further validate and refine the model. Second,
although the results of a literature review implied that loy-
alty might have a direct positive effect on image (e.g., K. H.
Kim et al., 2008), this relationship was not examined in this
study because of the statistical problems with regard to the
examination of a nonrecursive model (i.e., a structured model
with reciprocal relationships between one or more pairs of
latent constructs) using the SEM method. Future research that
uses a variety of statistical techniques to empirically exam-
ine the reciprocal relationship between loyalty and image in
the context of the healthcare industry ought to be conducted.
Furthermore, this study was conducted without considering the
means through which the patients access the OAS of a hospi-
tal. As the use of mobile devices has becoming more popular,
future research projects that investigate the influences of a hos-
pital’s technology-based services on the perceptions of patients
who use different devices to access these services, such as per-
sonal computers or smartphones, are worth conducting. Finally,
this study investigated the manner in which a specific kind of
technology-based service impacts the business of healthcare
institutions by incorporating variables adopted from the area
of service marketing, and the constructs in the proposed the-
oretical model were able to explain a significant amount of
the variance of patient loyalty (R
2
= 0.56). However, there is
still room for improvement to better understand how and why
the implementation of technology-based services influences the
business of service-oriented organizations in various contexts.
Future research that explicitly investigates the effects of other
service-marketing related variables, such as brand equity and
switching barriers (K. H. Kim et al., 2008; M. K. Kim et al.,
2004), and variables related to the evaluation of quality and
user satisfaction with regard to IT/IS, such as perceived ease
of use and perceived usefulness in the well-known technology
acceptance model (Davis, Bagozzi, & Warshaw, 1989), and the
user satisfaction determinants with regard to evaluating SSTs
(Meuter et al., 2000), is strongly encouraged.
ACKNOWLEDGMENTS
We thank the editor and anonymous reviewers for their
valuable feedback on this paper. We also thank the medical cen-
ters studied for their support and the survey respondents for
providing valuable data.
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ABOUT THE AUTHORS
Wei-Tsong Wang received his Ph.D. in Information Science
from the State University of New York at Albany. His
works have appeared in various journals, such as Journal
of Information Science and Online Information Review.His
current research interests include behaviors of e-commerce
consumers and user acceptance on information technology.
Shih-Yu Cheng is an assistant professor of the Department
of Industrial and Information Management at National Cheng
Kung University, Taiwan. She received her Ph.D. in Human
Resource Development at the University of Minnesota, Twin
Cities. Her current research interests include leadership
styles, knowledge sharing, and the improvement of employee
performance.
Lin-Yo Huang received her M.S. in Information and
Industrial Management from National Cheng Kung University,
Taiwan. Ms. Huang’s areas of interests include service man-
agement, medical management, and marketing. She is currently
working for a pharmaceutical company as a customer relation-
ship management professional.
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154 W.-T. WANG ET AL.
APPENDIX A
TABLE A1:
List of Survey Items by Construct
Item Question Reference
OAS Technology-based
service encounters
OAS-TB1 The online appointment system of this hospital is very useful to me. Chang & Chang, 2008; Zhu
et al., 2002OAS-TB2 Using the online appointment system of this hospital is timesaving
for me.
OAS-TB3 Using the online appointment system of this hospital makes it more
convenient to request medical services.
OAS-TB4 I can easily make an appointment through the online appointment
system of this hospital.
OAS-TB5 The online appointment system of this hospital can fulfill most of
my needs when making doctor’s appointments. (Discarded)
Hospital image
HI1 This hospital is my first choice when I need high-quality medical
services.
Ciavolino & Dahlgaard,
2007; Lemmink et al., 2003
HI2 I heard positive things about this hospital.
HI3 I experienced positive things in this hospital. (Discarded)
HI4 This hospital is known to offer excellent medical services.
OAS Patient satisfaction
OAS-PS 1 The online appointment system service meets my expectations. Chang & Chang 2008; Lin &
Wang, 2006OAS-PS 2 I received very satisfactory service from the online appointment
system service.
OAS-PS 3 The online appointment system service of this hospital is
successful.
OAS-PS 4 I am a loyal customer of this hospital. (Discarded)
Patient loyalty
PL1 I would consider this hospital when I need medical services in the
future. (Discarded)
Lin & Wang, 2006; Sahadev
& Purani, 2008
PL2 I would go to this hospital when I need medical services in the
future.
PL 3 I would recommend this hospital to someone who seeks high
quality medical services.
PL 4 I would say positive things about this hospital to other people.
(Discarded)
PL 5 My preference for this hospital would not change in the future.
Note. OAS = online-appointment systems.
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TECHNOLOGY-BASED SERVICE ENCOUNTERS 155
APPENDIX B
TABLE B1:
The OASs of the Two Participating Medical Centers
Main Feature of the OAS TU-North Hospital VX-South Hospital
Portal of the OAS A hyperlink i s provided in the standard
menu on the home page of the hospital’s
website.
A hyperlink is provided in the standard
functional menu on the home page of
the hospital’s website
Making medical appointments
online
An appointment can be made, reviewed,
revised, or canceled online up to
3 months prior to the date of the
appointment.
An appointment can be made, reviewed,
revised, or cancelled online up to
2 weeks prior to the date of the
appointment. An alternative system that
allows patients to make appointments
online via smartphones is also available.
Instructions for using the OAS It is available on the OAS’s main page. It is available on the OAS’s main page.
Key information announced on
the OAS
Information, including introductions and
the shifts of the physicians, the ad hoc
changes in the shifts of the physicians,
and instructions about alternative ways
to make appointments (e.g., telephone
voice recording system), is available.
Information, including introductions and
the shifts of the physicians, the ad hoc
changes in the shifts of the physicians,
and instructions about alternative ways
to make appointments (e.g., telephone
voice recording system), is available.
Online consulting function The interactive features and graphic
design of this function can help patients
determine the department or the
physician they should visit based on
their selection of predefined health
complaints on the web page.
This is not yet available on the OAS.
Query of personal medical
examination reports
This is not yet available on the OAS. It is available on the OAS. Patients are
required to install a special software
package and use a smart card reader for
identity verification before the medical
examination reports can be retrieved
from the OAS.
Real-time progress of the
appointments of the
physicians in practice
It is available on the OAS for all medical
departments. The progress report is
updated every 5 min.
It is available on the OAS for only
10 medical departments. The progress
report is updated every 5 min.
Note. OAS = online-appointment systems.
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