Factors affecting customer loyalty of mobile RFID services in Korea.
Park, Yong-Jae ; Rim, Myung-Hwan ; Lee, Seung-Koog 等
Introduction
Radio frequency identification (RFID) is the process and physical
infrastructure by which a unique identifier within a predefined protocol
definition is transferred from a device to a reader via radio frequency
waves (Banks et al. 2007). In recent years, RFID has been applied in
wide-ranging areas, from supply chain management to service industries,
distribution logistics and manufacturing (Kim et al. 2008). The global
RFID market is expected to grow from US$ 5.63 billion in 2010 to US$
24.13 billion in 2021. The RFID reader market for cellular phones,
valued at US$ 0.18 billion in 2010, is forecasted to grow to US$ 1.6
billion in 2021 (Das, Harrop 2010). A mobile RFID service refers to the
service which retrieves the stored information in RFID tags attached to
various objects in the surrounding environments and transmits it over
wireless internet by using a built-in RFID reader in a mobile handset.
The service is currently tried in various fields. For example, it is
being used or under the pilot test in retail stores around the world
(Boeck et al. 2011); a mobile RFID based tour service system is
developed (Hsu, Liao 2011); a customer in a hotel can lock or unlock his
room with a mobile device equipped with RFID based near-field
communication technology (Sheivachman 2012). Hence, mobile RFID services
are now attracting related industries' attention as a new service
model of potentially high business value.
In USA, RFID technology is adopted in the federal government-led
u-health projects, and an increasingly wide array of services bundles,
which combine mobile communications, RFID and wireless internet with
other services, are becoming available. In Europe, Nokia and a few other
cell phone makers invented 13.55MHz-band handsets applying mobile RFID
technology, and are developing various business models based on the
technology (Park et al. 2008). In Japan, mobile phone prototypes with
capacity to read RFID tags have already been released, and a mobile RFID
reader has been commercially rolled out jointly with Hitachi (Hiroyuki
2007). In Korea, mobile RFID services are currently being piloted by its
two largest telecoms, SK Telecom and Korea Telecom, and the commercial
launch is scheduled for the near future. Mobile RFID services, currently
in preparation for commercial launch, are product information or
authentication services such as genuine ginseng verification, genuine
drug verification, safe taxi, food history, Korean premium beef
verification, u-museum, and touch book. More recently, Korean telecom
operators have started to design new service models such as McDonald
order service, allowing mobile users to order and pay with a RFID
tag-attached menu and the RFID reader embedded in the handset. Efforts
to expand the range of functions provided by existing mobile RFID
services are, therefore, actively underway.
The importance of customer loyalty as a value creation factor has
been much stressed in the business marketing field. By deriving factors
that affect customer loyalty, developing strategies to increase customer
loyalty are topics which attract continuous attention from researchers.
Customer loyalty is a matter that deserves serious attention from
providers of mobile RFID services because its extent can have a direct
influence on the business value of this new service. The practical
relevance of identifying factors that induce customer loyalty and
suggesting improvement strategies can hardly be overestimated. The
knowledge of factors affecting customer loyalty toward mobile RFID
services, ahead of its commercial roll-out, can help their providers
attract loyal customers and ultimately maximize their revenue from this
source. The existing literature on this topic is mostly limited to
establishing influence factors for customer loyalty and providing
strategies for increasing it. Factors that influence customer loyalty
can vary depending on whether each type of customer group being
favorable or unfavorable toward the offered products or service. The
analysis of how influencing factors differ according to the customer
type would make the providers of mobile RFID services recognize where
service improvement efforts should be focused.
The purpose of this study is to present telecom service managers
and providers the implications of how to enhance customer loyalty by
drawing the factors that influence customer loyalty toward mobile RFID
services. Furthermore, customers of mobile RFID services were divided
into two groups, favorably disposed and unfavorably disposed toward them
and proposed customer loyalty strategies that are specifically suited to
each of the two groups. To achieve these research goals, factors
affecting customer loyalty on the basis of the existing literature were
selected, a conceptual model was constructed and related hypotheses were
formulated. Next, a survey of mobile users with a mobile RFID service
experience was conducted. The data with structural equation model was
analyzed, and the hypotheses were tested. Finally, based on the results
of the analysis, we derived implications of practical importance for
telecommunications service managers and providers who seek to increase
the loyalty to the new services.
1. Theoretical background
1.1. The current status of mobile RFID services in Korea
The Korean government has recently included RFID services in the
list of new engines for economic growth and is currently working on a
plan to foster this technology field into a competitive industry. The
recent creation of the Mobile RFID Forum is one instance of this
government-level effort to ensure a good head start for this technology
service. The forum was established to create an innovative service model
which delivers a new value to mobile users, by linking RFID
infrastructure with the wireless internet infrastructure which is
currently used only to provide mobile communications with internet
access. The larger purposes of this forum include enhancing the quality
of life for Koreans and discovering new industry fields to increase
economic value-added and improve national competitiveness. Currently,
the Mobile RFID Forum is concentrating its efforts on timely discovery
and identification of mobile RFID-related technologies and development
of technical standards to enable the early take-off of this sector.
The test runs of various types of mobile RFID services have been
performed by SK Telecom and Korea Telecom, the two largest telecom
operators of Korea, since 2005. Efforts to discover and develop service
models with high revenue potential have been ongoing ever since. The
types of mobile RFID services currently in the pilot in Korea are listed
in Table 1.
1.2. Perceived quality in mobile RFID services
Perceived quality defines customers' intangible perceptions or
judgments of the overall quality or superiority of a product or service
(Ramaseshan, Tsao 2007). Perceived quality, known as a factor that
influences the value and satisfaction a customer perceives toward a
product or service, can have the following specific characteristics,
when applied to a mobile RFID service: The first is the quality of tag
recognition. As the mobile RFID service is an information retrieval
service gaining stored information from RFID tag on surrounding objects,
the accuracy of tag recognition, recognition rate, and distance are the
most important quality factors (Park et al. 2008).
Second, as the tag information obtained is transmitted over
wireless internet, the quality of connection to the wireless internet is
also an important determinant of quality (Park et al. 2008). Chae et al.
(2002), for instance, found that the quality of connection between RFID
system and wireless internet played an essential role in customers'
quality perception.
Third, ease of use, a quality factor believed to have important
influence on satisfaction of information system users (Rai et al. 2002;
DeLone, McLean 2004), is likely to also have an effect on perceived
quality of a mobile RFID service. Hong et al. (2006), in their study on
mobile data services, found empirical evidence that ease of use
decisively affected a potential user's intention to actually use
the service.
Finally, the service reading an RFID tag provides a user with the
relevant content over wireless internet. The content should be accurate,
easy to understand and up-to-date, so that their quality can be a
potentially important factor shaping perceived quality of the service
(Chae et al. 2002; DeLone, McLean 2004; Park et al. 2008). Prior studies
on perceived quality are listed in Table 2.
1.3. Switching cost and switching barriers in mobile RFID services
Porter (1998) defined switching cost as one-time cost facing the
buyer when switching from one supplier's product to another's.
Switching cost includes not only objectively measurable monetary cost
but also the psychological effect of becoming a customer of a new firm,
and the time and effort involved in buying a new brand (Klemperer 1995;
Kim et al. 2003; Aydin, Ozer 2005a). The mobile RFID service, a new
technology service with RFID, requires adaptation cost, and move-in
cost. The adaptation cost is related to the effort to adapt to a new
service, and the move-in cost incurs in this transition. In an empirical
study of switching cost, Kim et al. (2004) classified perceived cost in
mobile telecommunications services into three types: loss cost,
adaptation cost and move-in cost. They, meanwhile, defined loss cost as
perceived loss in social status and performance associated with
switching from an existing carrier to a new one. However, loss cost is
not applicable to the mobile RFID service because it results not from
the loss of social status and performance connected with the secession
from existing carrier but from switching to a new service. Thus, the
categories of perceived cost were limited to adaptation cost and move-in
cost.
Switching barriers may be defined as 'the consumer's
assessment of the resources and opportunities needed to perform the
switching act, or alternatively, the constraints that prevent the
switching act' (Bansal, Taylor 1999; Ranaweera, Prabhu 2003). Kim
et al. (2004) defined switching barriers as the 'economic and
psychological difficulty perceived by customer, when switching
carriers'. In the context of the mobile RFID service, switching
barriers were defined as constraints related to switching or subscribing
to the service, in other words, the perceived economic and psychological
difficulty associated with adopting a new service.
Shin and Kim (2008), meanwhile, reported, in their empirical study
on mobile services, the existence of a causal relationship between
switching cost and switching barriers in which the former influence the
latter. Noteworthy previous studies on switching cost and switching
barriers are shown in Table 3.
1.4. Perceived value, customer satisfaction and loyalty in mobile
RFID services
According to Zeithaml (1988), perceived value is assumed to involve
consumer's assessment of the ratio of perceived benefits to
perceived cost. And Yang and Peterson (2004) state that perceived value
results from an evaluation of the relative rewards and sacrifices
associated with the offering. Fornell et al. (1996) considered perceived
value a major determinant of customer satisfaction and defined it as the
perceived level of product quality relative to the price paid. They
analyzed causal relationships among perceived value, satisfaction, and
loyalty. Based on the study of Fornell et al. (1996), perceived value is
defined as the perceived level of a mobile RFID service quality relative
to the price paid.
In the business marketing field, the empirical researches that try
to unveil the antecedents and consequences of customer satisfaction have
been extensively handled by many scholars. Customer satisfaction,
according to Oliver (1997), is customer reaction to the state of
fulfillment, and customer judgment of the fulfilled state. As for Lin
and Wang (2006), they described customer satisfaction with regard to
m-commerce as the summary affective response or feeling of a customer in
relation to her/his experience with all aspects that were developed by
an m-service to market its products and services. They revealed that
there existed the causal relationship among perceived value, customer
satisfaction and loyalty. Rooted in Lin and Wang's definition,
customer satisfaction was redefined as the summary affective response or
feeling of a potential customer in relation to her/his experience with
all aspects developed by a mobile RFID service.
Customer loyalty constitutes an important area of research in
marketing literature, and can be a core objective for telecom operators.
Kim et al. (2004), in their empirical analysis which found that customer
loyalty is influenced by customer satisfaction, regarded customer
loyalty in telecommunications services as the combination of
customers' favorable attitude toward a service and intention to
re-purchase this service. Turel and Serenko (2006) stated that in the
mobile services context, loyalty is defined as a favorable attitude
toward a specific service provider that leads to a combination of
repurchase likelihood of additional services from the same provider and
tolerance to price increase. They also attested the existence of a
causal relationship among perceived value, customer satisfaction and
customer loyalty. On the basis of these preceding works, customer
loyalty in mobile RFID services was defined as a favorable attitude
toward the service resulting in the willingness to reuse it and
recommend it to others. Prior studies on perceived value, customer
satisfaction and loyalty are listed in Table 4.
2. Research model and hypotheses
To determine factors that affect customers' loyalty toward a
mobile RFID service, a research model based on the existing literature
was set (Fig. 1): The higher customers' perceived quality, the
greater their perceived value and satisfaction; the greater the
switching cost, the higher the switching barrier; the greater their
perceived value, satisfaction and switching barrier, the higher their
loyalty to a mobile RFID service (Kim et al. 2004). The influence of
perceived quality on perceived value and customer satisfaction has been
confirmed through numerous empirical studies. For example, Hellier et
al. (2003), in their investigation of factors affecting customers'
repurchase intention in insurance services, analyzed that perceived
value was effectively influenced by perceived quality. In a study on the
Turkish mobile telephone market, Aydin and Ozer (2005b) found that
perceived quality had a significant effect on customer satisfaction.
Turel and Serenko (2006) verified that concerning mobile services in
Canada, perceived quality affected perceived value and customer
satisfaction. Through an empirical analysis, Park et al. (2008) showed
that perceived quality factors impacted on customer satisfaction.
Drawing on the above-described existing literature, the following
hypotheses (H1 and H2) were formulated:
H1. Perceived quality has a positive effect on perceived value.
H1.1 The quality of RFID tag recognition has a positive effect on
perceived value.
H1.2 The quality of connection has a positive a positive effect on
perceived value.
H1.3 Ease of use has a positive a positive effect on perceived
value.
H1.4 Content quality has a positive effect on perceived value.
H2. Perceived quality has a positive effect on satisfaction.
H2.1 The quality of RFID tag recognition has a positive effect on
satisfaction.
H2.2 The quality of connection has a positive a positive effect on
satisfaction.
H2.3 Ease of use has a positive a positive effect on satisfaction.
H2.4 Content quality has a positive a positive effect on
satisfaction.
[FIGURE 1 OMITTED]
In empirical studies on the causal relationship between perceived
cost and switching barriers, Kim et al. (2004) found that switching
barriers in mobile telecommunications services were affected by
perceived switching cost. Shin and Kim (2008) reached similar results.
They proved that switching barriers were influenced by switching cost
perceived by customers in their empirical investigation of mobile
services. Based on these studies, the following hypotheses (H3) were
formulated:
H3. Switching cost has a positive effect on switching barriers.
H3.1 Adaptation cost has a positive effect on switching barriers.
H3.2 Move-in cost has a positive effect on switching barriers.
The existence of causal relationships among perceived value,
customer satisfaction and loyalty has been empirically verified in
various service sectors. In a study investigating the relationship of
perceived value, customer satisfaction and loyalty in online banking
services, Yang and Peterson (2004) advanced that perceived value
positively impacted on both customer satisfaction and loyalty, and that
customer satisfaction, in turn, influenced positively customer loyalty.
Lai (2004) reported that customers' satisfaction with short message
services (SMS) is affected by their perceived value. Lin and Wang (2006)
reached a similar conclusion in their study of factors affecting
customer loyalty in mobile commerce contexts. They stated that perceived
value influenced both customer satisfaction and loyalty, while
customers' satisfaction also affected their level of loyalty. In
the study on customer satisfaction with Canadian mobile services, Turel
and Serenko (2006) empirically verified the thesis that customer
satisfaction was influenced by perceived value, and customer loyalty was
positively affected by customer satisfaction. Based on the
above-discussed existing literature, three hypotheses (H4-H6) on
perceived value, customer satisfaction and loyalty were set up.
The causal relationship between switching barriers and customer
loyalty in the context of telecommunications services was explored by
Kim et al. (2004). They found that the higher the switching barriers,
the stronger the customer loyalty toward services or service providers:
In a situation where competitive services or service providers exist,
high switching barriers can increase customers' loyalty through the
customer lock-in effect. Hence, the hypothesis H7 was established on the
relationship between switching barriers and customer loyalty.
H4. Perceived value has a positive effect on customer satisfaction.
H5. Perceived value has a positive effect on customer loyalty.
H6. Satisfaction has a positive effect on customer loyalty.
H7. Switching barriers have a positive effect on customer loyalty.
3. Methodology
3.1. Data, sample and measurement of factors
While the research factors and items were chosen largely based on
prior studies, the measurement items were appropriately modified and
redefined to suit the purposes of this study. All survey items were
measured using a five-point scale ranging from "very low" to
"very high". The measurement items used in the survey were
tested through a preliminary survey, which was conducted with
technological experts and business professionals involved in the mobile
RFID field. After necessary modifications, 23 measurement items were
finally selected (Table 5).
Through online survey, data were collected from customers with some
experience in the use of mobile RFID pilot services. The mobile RFID
pilot service surveyed covers 16 services including genuine ginseng
verification and u-Portal provided by SK Telecom and Korea Telecom, as
shown in Table 1. Of a total of 350 responses returned, 317 were
retained, after discarding incomplete or otherwise invalid responses.
In the experience of mobile RFID service, the respondents underwent
average 5 or more pilot services; 43.6% of them tested the service 5 to
10 times, followed by 5 times or less (41.4%), 11 to 15 times (7.7%),
and 16 times or more (7.3%). In WTP (willingness to pay) for the service
charge, 2.5 US$ or less (63.8%) was predominant, followed by 2.5 US$ to
US$ 5 (21%), 5 US$ to 7.5 US$ (7.2%), 7.5 US$ to 10 US$ (4.6%), and 12
US$ or more (3.4%).
The socio-demographic characteristics of the sample were as
follows: male respondents (61.2%) largely exceeded women (38.8%) in
number. People aged 20 to 29 years (42.3%) and 30 to 39 years (31.2%)
represented the majority of the sample. By educational level, college
graduates and above accounted for an overwhelming majority of 74.4%, and
by employment status, 63.1% of the total respondents were employed. By
income, earners of 1,600 to 2,500 US$ a month made up the largest group
(43.9%).
3.2. Measurement and structural models
A confirmatory factor analysis (CFA) was performed to test the
adequacy of the measurement model. The adequacy of the measurement
models was evaluated on the criteria of overall fit with the data,
reliability, convergent validity, and discriminant validity (Chiou
2004).
Seven common model-fit measures were used to assess the measurement
model's overall goodness of fit: the ratio of [chi square] to
degrees-of-freedom (d.f.), adjusted goodness-of-fit index (AGFI),
normalized fit index (NFI), non-normalized fit index (NNFI), comparative
fit index (CFI), relative fit index (RFI) and root mean square error of
approximation (RMSEA)(Lin, Wang 2006). The reliability of the factors
was estimated through internal consistency. The internal consistency
(IC) of the measurement model was calculated with the Eq. (1) (Fornell,
Larcker 1981). An IC coefficient of 0.7 or higher is considered as a
satisfactory level of internal consistency.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (1)
[[lambda].sub.i]: The loading of each of the measurement items on
its corresponding factor, var([[epsilon].sub.i]) =
1-[[lambda].sup.2.sub.i].
Convergent validity of the factors was estimated with average
variance extracted (AVE). The AVE was calculated by Eq. (2) (Fornell,
Larcker 1981). The AVE was above the recommended 0.5 level, which meant
that more than one-half of the variances observed in the items were
accounted for by their hypothesized factors (Lin, Wang 2006).
Discriminant validity is the degree to which items differentiate between
factors, or measure different factors. To test discriminant validity,
the shared variances were compared to the factors with the AVE of the
individual factors (Fornell, Lacker 1981; Lin, Wang 2006).
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (2)
[[lambda].sub.i]: The loading of each of the measurement items on
its corresponding factor, var([[epsilon].sub.i]) =
1-[[lambda].sup.2.sub.i].
The proposed research model was tested by performing structural
equation model (SEM) with the maximum likelihood estimation method using
AMOS 5.0. The SEM is a most widely used technique for testing and
estimating causal relations between latent factors, especially, in
empirical studies of social science. A similar set of fit indices was
used to prove the structural model: the ratio of [chi square] to
degrees-of-freedom (d.f.), AGFI, NFI, NNFI, CFI, RFI and RMSEA.
3.3. Analysis of customer types
Gerpott et al. (2001) classified customers into four different
categories by analyzing a matrix between loyalty and satisfaction:
optimistic customers, impressed customers, disappointed customers,
pessimistic customers. In this study, these four classifications were
simplified into two categories (Fig. 2). One group, 'favorable
customers', including optimistic customers and impressed customers,
is characterized by a positive perception and attitude, and higher
loyalty to the mobile RFID services. The other group, 'unfavorable
customers, comprising disappointed customers and pessimistic customers,
is sceptical and not loyal to the services. A total of 317 samples were
used in the global model analysis. Among them 'favorable customer
group' whose loyalty for mobile RFID service is higher than 3 (the
medium value of 5-point Likert Scale) is 124, and 'unfavorable
customer group' whose loyalty is lower than 3 is 183. Here can be
seen an attempt to analyze how the factors affecting customers'
loyalty to mobile RFID services are different between these two groups.
[FIGURE 2 OMITTED]
4. Results
4.1. Results of measurement model testing
As shown in Table 6, all fit indices estimated for the measurement
model met their respective recommended values, and the model proved to
be valid.
Thus the testing of reliability, convergent validity, and
discriminant validity was proceeded. The results of these tests were as
follows (Table 7): all factors exceeded 0.7 (recommended value > 0.7)
in internal consistency, which attested to a good level of reliability.
AVE values for all factors were greater than 0.5 (recommended value >
0.5), which showed convergent validity. The discriminant validity
analysis also revealed (Table 8) that AVE square roots values of the
diagonal were greater than all other values, which proved its validity.
4.2. Results of structural model testing
To test the conceptual model and hypotheses suggested, the validity
of the structural model was evaluated. As shown in Table 9, all fit
indices for the structural model met the recommended value, which proved
to be valid. The results of structural model testing are presented in
the diagram in Fig. 3.
All hypotheses were accepted except H1.1, H1.4, H2.2, H2.3 and
H3.2. The quality of tag recognition (estimate = 0.464, p < 0.01) and
content quality (estimate = 0.360, p < 0.01) proved to have a direct
impact on the customer satisfaction. The quality of connection (estimate
= 0.713, p < 0.01) and ease of use (estimate = 0.267, p < 0.05),
meanwhile, were found to affect perceived value but have no direct
relation with satisfaction. These results will mean that connection
quality and ease of use would rather indirectly than directly influence
customer satisfaction by perceived value (estimate = 0.333, p < 0.01)
which has a direct effect on satisfaction; as perceived value (estimate
= 0.481, p < 0.01) and satisfaction (estimate = 0.265, p < 0.05)
affected the loyalty, it is significant to manage perceived quality
factors (i.e. quality of tag recognition, connection quality, ease of
use, and content quality) which is the antecedent of the two. In the
case of switching cost, while adaptation cost affects switching barriers
(estimate = 0.924, p < 0.01), move-in cost had no significant effect
on them; the higher the switching barriers, the stronger the loyalty
(estimate = 0.163, p < 0.05). Detailed results of hypothesis testing
are given in Table 10.
[FIGURE 3 OMITTED]
4.3. Results of difference analysis by customer type
In this study, customers were divided into two groups according to
their attitude toward mobile RFID services: favorable customers and
unfavorable customers. In order to analyze whether there is any
difference between these two groups, in terms of factors affecting the
loyalty, an analysis of structural equation model on two sub-structural
models was performed. As can be seen from Table 11, all fit indices for
two sub-structural models met the recommended value and structural
equation model proved to be valid.
The results of testing the two sub-structural models are
respectively shown in diagrams in Fig. 4. In the favorable customers
group, the quality of tag recognition proved to have a direct effect on
satisfaction (estimate = 0.288, p < 0.1), and connection quality had
no influence on either perceived value or satisfaction. Ease of use
(estimate = 0.396, p < 0.1) and content quality (estimate = 0.553, p
< 0.1), while they did impact on perceived value of a mobile RFID
service, appeared to have no significant effect on satisfaction,
suggesting that their influence on satisfaction may be indirect through
perceived value.
As for the unfavorable customers group, unlike with the favorable
customers group, tag recognition quality (estimate = 0.441, p < 0.01)
and content quality (estimate = 0.274, p < 0.05) had an influence on
satisfaction, but not on perceived value; connection quality (estimate =
0.634, p < 0.05) and ease of use (estimate = 0.277, p < 0.1) had a
significant effect on perceived value, but not on satisfaction,
indicating that their influence on satisfaction is indirect through
perceived value (estimate = 0.344, p < 0.01) which directly affects
satisfaction.
[FIGURE 4 OMITTED]
As for perceived value and satisfaction, they influenced
customers' loyalty in both favorable and unfavorable customers
groups, which recognized the importance of managing perceived quality
that affect these two factors.
Among favorable customers, switching cost had no influence on
switching barriers, nor did switching barriers affect the loyalty. This
suggest that favorable customers to mobile RFID services are generally
unaffected by switching cost or barriers to a new service. On the other
hand, among unfavorable customers, adaptation cost proved to have an
effect on switching barriers (estimate = 0.426, p < 0.01), and
switching barriers, in turn, impacted on the loyalty (estimate = 0.289,
p < 0.1),which indicate that managing adaptation cost, an influence
factor of switching barriers, is required.
Implications and conclusions
This study has attempted to find strategies to enhance the loyalty
of customers to mobile RFID services by identifying the factors that
affect their loyalty. The implications of the results of this empirical
analysis for telecommunications service managers and providers are as
follows: First, perceived value and satisfaction were the two influence
factors for customer loyalty to mobile RFID services. Among perceived
quality factors that are antecedents of perceived value and
satisfaction, tag recognition and content were direct influence factors
on customer satisfaction; connection quality and ease of use indirect
influence factors on it by means of the perceived value. This means that
in order to increase customers' perceived value and satisfaction
influencing their loyalty to the services, it may be necessary for
service providers to constantly strive for the improvement of perceived
quality factors: they must make special exertions to enhance the quality
of tag recognition, connection quality, ease of use, and content quality
and to step up R&D for both related hardware and software.
Second, switching barrier was affected by adaptation cost. This
implies that service providers must manage adaptation cost in order to
motivate customers to switch over to a new mobile RFID service. When
customers try to adopt or switch to a new service, they tend to be
reluctant to change on account of adaptation cost for learning and
searching information to become familiar with the service. Hence, the
policy to eliminate obstacles to learning and information search is
essential so as to promote and facilitate the use of mobile RFID
services.
Third, the analysis of each customer type revealed that, in the
case of favorable customers group, tag recognition quality directly
affected satisfaction, whereas ease of use and content quality
indirectly influenced it by the intermediary of perceived value.
Meanwhile, among unfavorable customers, both tag recognition quality and
content quality had a direct impact on their satisfaction. But the
quality of connection and ease of use indirectly affected satisfaction
by perceived value. Perceived quality factors which influence perceived
value and satisfaction, therefore, differed according to each customer
type. And switching cost had no influence on switching barriers among
favorable customers group, while adaptation cost appeared to have an
effect on switching barriers. In other words, depending on the type of
customers group, there existed a difference between switching cost and
switching barriers: Though no significant difference was found between
the two groups in terms of influence of perceived value and satisfaction
on customers' loyalty, there was a clear difference in terms of
influence of switching barriers on it.
These results suggest that differentiated service improvement
strategies must apply to each customer group. If mobile RFID service
providers target on favorable customers group with a positive attitude,
they must concentrate their efforts on improving three perceived quality
factors (i.e. tag recognition quality, ease of use and content quality).
On the other hand, if they want to win unfavorable customers group with
a negative attitude, they must not only improve four factors (i.e. tag
recognition quality, connection, ease of use and content quality), but
also manage adaptation cost.
The main significance of this study is that by determining factors
affecting customer loyalty in mobile RFID services, we offer practical
suggestions on what service improvement efforts are needed in order to
increase customer loyalty and, thereby, to create the business value of
this service model. However, as this study has analyzed the
determination of the factors which influence customer loyalty and the
correlation between them through the result of online survey, there
exists a limit to discuss specific factors related to mobile RFID
services at this time of a pilot service, and a deeper analysis is
required for the factors in the context of the service. In the future,
it is demanded to evaluate users' perception of these parameters in
multiple points of time as well as the empirical application of
different strategies to improve selected factors to examine their
influence on customers' loyalty.
Future research may also need to explore causal relationship
between customer loyalty and retention because we can measure and
identify the causal relationship between them when the migration of
customers over to the similar services occurs after the full commercial
launch of mobile RFID services. Furthermore, it is required to map out
service strategies for more subdivided target customers by analyzing the
differences among customers' personal characteristics such as
gender and age, etc. and among four customer groups (optimistic
customers, impressed customers, disappointed customers, and pessimistic
customers) suggested by Gerpott et al. (2001) in the future when mobile
RFID services spread.
Caption: Fig. 1. Research model
Caption: Fig. 2. Analysis of customer types
Caption: Fig. 3. Results of structural model testing
Caption: Fig. 4. Results of testing the sub-structural model
(favorable/unfavorable customers)
doi: 10.3846/20294913.2013.837413
Received 13 May 2011; accepted 15 April 2012
References
Aydin, S.; Ozer, G. 2005a. Customer loyalty and the effect of
switching cost as a moderator variable, Marketing Intelligence &
Planning 23(1): 89-103. http://dx.doi.org/10.1108/02634500510577492
Aydin, S.; Ozer, G. 2005b. National customer satisfaction indices:
an implementation in the Turkish mobile telephone market, Marketing
Intelligence & Planning 23(5): 486-504.
http://dx.doi.org/10.1108/02634500510612654
Banks, J.; Pachano, M.; Thompson, L.; Hanny, D. 2007. RFID applied.
John Wiley & Sons, Inc. 509 p.
http://dx.doi.org/10.1002/9780470168226
Bansal, H. S.; Taylor, S. F. 1999. The service provider switching
model (SPSM): a model of consumer switching behavior in the service
industry, Journal of Service Research 2(2): 200-218.
http://dx.doi.org/10.1177/109467059922007
Boeck, H.; Roy, J.; Durif, F.; Gregoire, M. 2011. The effect of
perceived intrusion on consumers' attitude towards using an
RFID-based marketing program, Procedia Computer Science 5: 841-848.
http://dx.doi.org/10.1016/j.procs.2011.07.116
Chae, M.; Kim, J.; Kim, H.; Ryu, H. 2002. Information quality for
mobile internet services: a theoretical model with empirical validation,
Electronic Markets 12(1): 38-46.
http://dx.doi.org/10.1080/101967802753433254
Chiou, J. S. 2004. The antecedents of consumers' loyalty
toward internet service providers, Information & Management 41(6):
685-695. http://dx.doi.org/10.1016/jim.2003.08.006
Das, R.; Harrop, P. 2010. RFID forecasts, players and opportunities
2011-2021. IDTechEX. 278 p.
DeLone, W. H.; McLean, E. R. 2004. Measuring e-commerce success:
applying the DeLone & McLean information systems Success model,
International Journal of Electronic Commerce 9(1): 31-47.
Fornell, C.; Johnson, M. D.; Anderson, E.; Cha, W. J.; Bryant, B.
E. 1996. The American customer satisfaction index: nature, purpose, and
findings, Journal of Marketing 60(4): 7-18.
http://dx.doi.org/10.2307/1251898
Fornell, C.; Larcker, D. F. 1981. Evaluating structural equation
models with unobservable variables and measurement error, Journal of
Marketing Research 18(1): 39-50. http://dx.doi.org/10.2307/3151312
Gerpott, T. J.; Rams, W.; Schindler, A. 2001. Customer retention,
loyalty, and satisfaction in the German mobile cellular
telecommunications market, Telecommunications Policy 25(4): 249-269.
http://dx.doi.org/10.1016/S0308-5961(00)00097-5
Hellier, P. K.; Geursen, G. M.; Carr, R. A.; Rickard, J. A. 2003.
Customer repurchase intention: a general structural equation model,
European Journal of Marketing 37(11-12): 1762-1800.
http://dx.doi.org/10.1108/03090560310495456
Hiroyuki, F. 2007. Mobile RFID in Japan case studies and issues, in
RFID/USN Korea 2007 International Conference, October 31-November 01,
2007, Korea.
Hong, S. J.; Tam, K. Y.; Kim, J. 2006. Mobile data service fuels
the desire for uniqueness, Communications of the ACM 49(9): 89-94.
http://dx.doi.org/10.1145/1151030.1151034
Hsu, H. H.; Liao, H. T. 2011. A mobile RFID-based tour system with
instant microblogging, Journal of Computer and System Sciences 77(4):
720-727. http://dx.doi.org/10.1016/j.jcss.2010.02.011
Joo, Y. G.; Sohn, S. Y. 2008. Structural equation model for
effective CRM of digital content industry, Expert System with
Applications 34(1): 63-71. http://dx.doi.org/10.1016/j.eswa.2006.08.016
Kim, J. S.; Choi, W.; Choi, G. Y.; Pyo, C. S.; Chae, J. S. 2008.
Shorted microstrip patch antenna using inductively coupled feed for UHF
RFID Tag, ETRI Journal 30(4): 600-602.
http://dx.doi.org/10.4218/etrij.08.0208.0074
Kim, M. J. 2007. KTF mobile RFID service, in RFID/USN KOREA
2007International Conference, October 31-November 01, 2007, Korea.
Kim, M.; Kliger, D.; Vale, B. 2003. Estimating switching cost: the
case of banking, Journal of Financial Intermediation 12(1): 25-56.
http://dx.doi.org/10.1016/S1042-9573(02)00005-0
Kim, M. K.; Park, M. C.; Jeong D. H. 2004. The effects of customer
satisfaction and switching barrier on customer loyalty in Korean mobile
telecommunication services, Telecommunications Policy 28(2): 145-159.
http://dx.doi.org/10.1016/j.telpol.2003.12.003
Klemperer, P. 1995. Competition when consumers have switching cost:
an overview with applications to industrial organization, macroeconomics
and international trade, Review of Economic Studies 62(4): 515-539.
http://dx.doi.org/10.2307/2298075
Lai, T. L. 2004. Service quality and perceived value's impact
on satisfaction, intention and usage of short message service,
Information Systems Frontiers 6(4): 353-368.
http://dx.doi.org/10.1023/B:ISFI.0000046377.32617.3d
Lin, H. H.; Wang, Y. S. 2006. An examination of the determinants of
customer loyalty in mobile commerce contexts, Information &
Management 43(3): 271-282. http://dx.doi.org/10.1016/j.im.2005.08.001
Negash, S.; Ryan, T.; Igbaria, M. 2003. Quality and effectiveness
in web-based customer support systems, Information & Management
40(8): 757-768. http://dx.doi.org/10.1016/S0378-7206(02)00101-5
Oliver, R. L. 1997. Satisfaction: a behavioral perspective on the
consumer. Boston: Irwin McGraw-Hill.
Park, Y. J.; Heo, P. S.; Rim, M. H. 2008. Measurement of a customer
satisfaction index for improvement of mobile RFID services in Korea,
ETRI Journal 30(5): 634-643.
http://dx.doi.org/10.4218/etrij.08.1308.0059
Porter, M. 1998. Competitive strategy: techniques for analyzing
industries and competitors. New York: Free Press. 432 p.
Rai, A.; Lang, S. S.; Welker, R. B. 2002. Assessing the validity of
IS success models: an empirical test and theoretical analysis,
Information Systems Research 13(1): 50-69.
http://dx.doi.org/10.1287/isre.13.1.50.96
Ranaweera, C.; Prabhu, J. 2003. The influence of satisfaction,
trust and switching barriers on customer retention in a continuous
purchasing setting, International Journal of Service Industry Management
14(4): 374-395. http://dx.doi.org/10.1108/09564230310489231
Ramaseshan, B.; Tsao, H. Y. 2007. Moderating effects of the brand
concept on the relationship between brand personality and perceived
quality, Journal of Brand Management 14(6): 458-466.
http://dx.doi.org/10.1057/palgrave.bm.2550090
Sheivachman, A. 2012. RFID, mobile drive lock trends in 2012, Hotel
Management 227(1): 28-29.
Shin, D. H.; Kim, W. Y. 2008. Forecasting customer switching
intention in mobile service: an exploratory study of predictive factors
in mobile number portability, Technological Forecasting & Social
Change 75(6): 854-874. http://dx.doi.org/10.1016/j.techfore.2007.05.001
Turel, O.; Serenko, A. 2006. Satisfaction with mobile services in
Canada: an empirical investigation, Telecommunications Policy 30(5-6):
314-331. http://dx.doi.org/10.1016/j.telpol.2005.10.003
Wang, Y. S.; Liao, Y. W. 2007. The conceptualization and
measurement of m-commerce user satisfaction, Computers in Human Behavior
23(1): 381-398. http://dx.doi.org/10.1016/jxhb.2004.10.017
Yang, Z.; Peterson, R. T. 2004. Customer perceived value,
satisfaction, and loyalty: the role of switching cost, Psychology &
Marketing 21(10): 799-822. http://dx.doi.org/10.1002/mar.20030
Yoon, J. H. 2007. Issues & experience of mobile RFID service
trial, in RFID/USN KOREA 2007 International Conference, October
31-November 01, 2007, Korea.
Yoon, J. H. 2010. Introduction of RFID smart project, in ICTForum
KOREA 2010 Conference.
Zeithaml, V. A. 1988. Consumer perceptions of price, quality, and
value: a means-end model and synthesis of evidence, Journal of Marketing
52(3): 2-22. http://dx.doi.org/10.2307/1251446
Yong-Jae PARK (a), Myung-Hwan RIM (b), Seung-Koog LEE (c)
(a) Korea Institute of Ceramic Engineering and Technology, 77
Digital-ro 10-gil, Geumcheon-gu Seoul, Republic of Korea
(b, c) ETRI, Technology Strategy Research Division, 218 Gajeong-ro
Yuseong-gu Daejeon, 305-700, Republic of Korea
Corresponding author Myung-Hwan Rim
E-mail: mhrim@etri.re.kr
Yong-Jae PARK. She has been working as a senior researcher of KICET
(Korea Institute of Ceramic Engineering and Technology) since 2012. She
received a PhD in Business Administration from Kyungpook National
University of Korea in 2006. She was a researcher in the ETRI
(Electronics and Telecommunications Research Institute), Korea from 2006
to 2012. Her research interests include technology planning and industry
policy in the field of information communications, RFID/USN, and
ceramics.
Myung-Hwan RIM. He has been working as a principal researcher of
ETRI (Electronics and Telecommunications Research Institute) after
graduating from Hanyang Graduate School in 1989. He received a PhD in
Economics from Hanyang University of Korea in 2005 and worked as a
visiting scholar at Stanford University of USA in 2006. Also, he is
currently the Vice President of the KITA (Korea Society of Information
Technology Applications). He has carried out techno-economic analysis
projects related to information technology for 25 years and published
over 70 papers about economic effects and technology policy. His
research interests include technology planning, R&D management, and
engineering economics in the field of information communications,
RFID/USN, and digital content.
Seung-Koog LEE. He has been working as a principal researcher of
ETRI (Electronics and Telecommunications Research Institute) after
graduating from Graduate School of Public Administration Seoul National
University in 1980. He received a PhD in Public Administration from
Chongju University of Korea in 1988. Also, he is currently a Prof. at
the Faculty of Management of Technology of the UST (University of
Science and Technology) and the Vice President of the KOTIS (Korea
Technology Innovation Society). He has carried out ICT policy projects
for 30 years. His research interests include ICT policy, technology
planning and R&D management in the field of information
communications.
Table 1. Trial services of mobile RFID in Korea
Providers Service Name Provide Service
Genuine Ginseng Product verification,
Verification products information
u-Portal Music, movie, game
downloads and news
feed
Genuine drug Verification Verification of a
drug's authenticity
by reading embedded
RFID tags
Safe Taxi Tracks taxi
passenger's travel
route
Food History Food information
including origin and
date of
SK production
Telecom Korean Premium Beef Beef information
Verification including place of
origin and date of
slaughtering
Touch Book Provides book
summaries and reader
reviews
McDonalds Touch Order Food order placing by
tagging the menu
U-Museum Download and E-mail
transmission of
exhibits information
u-PIFF Retrieves information
on Pusan
International Film
Festival
Wine information Service Provision of the
detailed data on wine
Indoor Navigation Service Guides users to their
desired destinations
using RFID
Shopping Guide Service A personalized
shopping guide
service, using
customer-provided
information on
shopping
Korea preferences and goals
Telecom Sending My Position Service Sends user location
information via MMS
to friends in
complicated
environments
U-Game Service Mission or event-
based gaming
assistance
Busan/Daegu City Tour Useful information
Service about touring city
using RFID
Note: The data taken from Yoon (2007, 2010) and Kim (2007) were
reedited for the purposes of this paper.
Table 2. Prior studies on quality factors
Researcher Tag Connection Ease Content
Recognition of use
Chae et al. (2002) [check] [check]
Rai et al. (2002) [check]
DeLone, McLean (2004) [check] [check]
Hong et al. (2006) [check]
Park et al. (2008) [check] [check] [check]
Table 3. Prior studies on switching cost and switching barriers
Researcher Switching Switching
Cost Barriers
Klemperer (1995) [check]
Porter (1998) [check]
Kim et al. (2003) [check]
Kim et al. (2004) [check] [check]
Aydin, Ozer (2005a) [check]
Shin, Kim (2008) [check] [check]
Table 4. Prior studies on perceived value, satisfaction and loyalty
of customer
Researcher Perceived Satisfaction Loyalty
Value
Zeithaml (1988) [check]
Fornell et al. (1996) [check] [check] [check]
Oliver (1997) [check]
Kim et al. (2004) [check] [check]
Yang, Peterson (2004) [check]
Lin, Wang (2006) [check] [check] [check]
Turel, Serenko (2006) [check] [check] [check]
Table 5. Factors and measurement items
Factors Measurement Items Related Studies
Tag Ta1 Accuracy of tag reading
recognition Ta2 Distance of tag reading Park et al.
Ta3 Degree rate of tag (2008)
recognition
Connection Cn1 The mobile RFID service Park et al.
system is stable, and (2008); Chae
errors are few and et al. (2002)
infrequent
Cn2 Quick response to RFID
tags and short download
time
Ease of use Ea1 The mobile RFID service Hong et al.
system is easy to use (2006); Wang,
Ea2 The mobile RFID service Liao (2007)
system is user-friendly
Co1 The content delivered Wang, Liao
is accurate (2007); Negash
et al. (2003)
Content Co2 The content delivered
is up to date
Co3 The content is clear
and understandable
Adaptation Ad1 Inconvenience of having Kim et al. (2004)
cost to learn to use a
mobile RFID service
Ad2 Need to search for
information on mobile
RFID services
Move-in Mo1 Cost of replacing a Kim et al. (2004)
cost mobile RFID device
Mo2 Cost of using mobile
internet
Switching Sw1 Economic loss Kim et al. (2004)
barrier associated with
switching mobile RFID
services
Sw2 Psychological burden Shin, Kim (2008)
associated with
switching mobile RFID
services
Perceived Pv1 Mobile RFID services Lin, Wang (2006)
value are very valuable
Pv2 Mobile RFID services
are good value for
money
Sa1 Overall satisfaction Park et al.
with the mobile RFID (2008); Fornell
service et al. (1996);
Satisfaction Sa2 Degree of expectancy
disconfirmation
Sa3 Performance versus Turel, Serenko
ideal mobile RFID
services
Loyalty Lo1 Intention to reuse the (2006); Joo,
mobile RFID service Sohn (2008)
Lo2 Intention to recommend
the mobile RFID service
Table 6. Results of fit indices for the measurement model
Fit index Recommended value Measurement model
[chi square]/ [less than or 2.71
d.f. equal to]3.00
AGFI [greater than or 0.89
equal to]0.80
NFI [greater than or 0.95
equal to]0.90
NNFI [greater than or 0.95
equal to]0.90
CFI [greater than or 0.97
equal to]0.90
RFI [greater than or 0.92
equal to]0.90
RMSEA [less than or 0.07
equal to]0.08
Table 7. Results of reliability and convergent validity of factors
Factor Internal AVE
Consistency
Tag Recognition (C1) 0.912 0.776
Connection (C2) 0.883 0.791
Ease of use (C3) 0.889 0.801
Content (C4) 0.920 0.793
Adaptation Cost (C5) 0.736 0.643
Move-in Cost (C6) 0.886 0.796
Perceived Value (C7) 0.903 0.824
Customer Satisfaction (C8) 0.904 0.758
Switching Barriers (C9) 0.788 0.703
Customer Loyalty (C10) 0.914 0.841
Table 8. Results of discriminant validity testing
Factors C1 C2 C3 C4 C5 C6 C7
C1 0.881
C2 0.556 0.889
C3 0.560 0.465 0.895
C4 0.534 0.547 0.568 0.891
C5 -0.343 -0.267 -0.287 -0.387 0.802
C6 0.197 0.117 0.291 0.262 -0.681 0.892
C7 0.666 0.730 0.599 0.619 -0.422 0.120 0.908
C8 0.788 0.770 0.788 0.750 -0.415 0.191 0.791
C9 -0.307 -0.278 -0.238 -0.251 0.798 0.785 -0.341
C10 0.200 0.274 0.381 0.390 -0.138 0.121 0.786
Factors C8 C9 C10
C1
C2
C3
C4
C5
C6
C7
C8 0.871
C9 -0.348 0.838
C10 0.748 0.728 0.917
Table 9. Results of fit indices for the structural model
Fit index Recommended value Measurement
model
[chi square]/ [less than or 2.35
d.f. equal to]3.00
AGFI [greater than or 0.82
equal to]0.80
NFI [greater than or 0.91
equal to]0.90
NNFI [greater than or 0.93
equal to]0.90
CFI [greater than or 0.95
equal to]0.90
RFI [greater than or 0.90
equal to]0.90
RMSEA [less than or 0.07
equal to]0.08
Table 10. Results of research hypothesis (H1-H7) testing
Path Hypothesis Estimate S.E. t value. Result
Tag Recognition H1.1 -0.138 0.191 -0.726 Reject
[right arrow]
Perceived
Value
Connection H1 H1.2 0.713 *** 0.247 2.882 Accept
[right arrow]
Perceived
Value
Ease of use H1.3 0.267 ** 0.115 2.329 Accept
[right arrow]
Perceived
Value
Content H1.4 0.156 0.162 0.965 Reject
[right arrow]
Perceived
Value
Tag Recognition H2.1 0.464 *** 0.123 3.782 Accept
[right arrow]
Satisfaction
Connection H2 H2.2 -0.132 0.165 -0.800 Reject
[right arrow]
Satisfaction
Ease of use H2.3 0.033 0.075 0.444 Reject
[right arrow]
Satisfaction
Content H2.4 0.360 *** 0.100 3.591 Accept
[right arrow]
Satisfaction
Adaptation Cost H3 H3.1 0.924 *** 0.165 5.604 Accept
[right arrow]
Switching
Barriers
Move-in Cost H3.2 0.144 0.085 1.626 Reject
[right arrow]
Switching
Barriers
Perceived Value H4 0.333 *** 0.062 5.358 Accept
[right arrow]
Satisfaction
Perceived Value H5 0.481 *** 0.113 4.269 Accept
[right arrow]
Loyalty
Satisfaction H6 0.265 ** 0.129 2.054 Accept
[right arrow]
Loyalty
Switching H7 0.163 ** 0.067 1.982 Accept
Barriers
[right arrow]
Loyalty
Table 11. Results of fit indices for two sub-structural models
Fit index Recommended value Subgroup Subgroup
1 model 2 model
[chi square]/ [less than or equal to] 3.00 2.40 2.45
d.f.
AGFI [greater than or equal to] 0.80 0.80 0.81
NFI [greater than or equal to] 0.90 0.90 0.91
NNFI [greater than or equal to] 0.90 0.92 0.90
CFI [greater than or equal to] 0.90 0.91 0.92
RFI [greater than or equal to] 0.90 0.90 0.91
RMSEA [less than or equal to] 0.08 0.06 0.07
Note: 'Subgroup 1 model' refers to the sub-structural model for
favourable customers group (n = 124), 'Subgroup 2 model' refers to
the sub-structural model for unfavourable customers group (n = 193).
